diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml
index 15ef03642..606d9d074 100644
--- a/.github/workflows/codeql-analysis.yml
+++ b/.github/workflows/codeql-analysis.yml
@@ -17,7 +17,7 @@ on:
jobs:
analyze:
name: Analyze
- runs-on: ubuntu-20.04
+ runs-on: ubuntu-24.04
strategy:
fail-fast: false
@@ -27,26 +27,17 @@ jobs:
language: ['cpp']
# Supported version for OpenCV
opencv: [
- 4.2.0
+ 4.10.0
]
compiler: [g++]
steps:
- name: Checkout repository
- uses: actions/checkout@v2
- with:
- # We must fetch at least the immediate parents so that if this is
- # a pull request then we can checkout the head.
- fetch-depth: 2
-
- # If this run was triggered by a pull request event, then checkout
- # the head of the pull request instead of the merge commit.
- - run: git checkout HEAD^2
- if: ${{ github.event_name == 'pull_request' }}
+ uses: actions/checkout@v3
# Initializes the CodeQL tools for scanning.
- name: Initialize CodeQL
- uses: github/codeql-action/init@v1
+ uses: github/codeql-action/init@v3
with:
languages: ${{ matrix.language }}
# If you wish to specify custom queries, you can do so here or in a config file.
@@ -66,7 +57,7 @@ jobs:
# Autobuild attempts to build any compiled languages (C/C++, C#, or Java).
# If this step fails, then you should remove it and run the build manually (see below)
- name: Autobuild
- uses: github/codeql-action/autobuild@v1
+ uses: github/codeql-action/autobuild@v3
# âšī¸ Command-line programs to run using the OS shell.
# đ https://git.io/JvXDl
@@ -80,4 +71,4 @@ jobs:
# make release
- name: Perform CodeQL Analysis
- uses: github/codeql-action/analyze@v1
+ uses: github/codeql-action/analyze@v3
diff --git a/.github/workflows/macos.yml b/.github/workflows/macos.yml
new file mode 100644
index 000000000..175aa01c6
--- /dev/null
+++ b/.github/workflows/macos.yml
@@ -0,0 +1,30 @@
+name: Macos C/C++ CI
+
+on:
+ push:
+ branches: [ "master" ]
+ pull_request:
+ branches: [ "master" ]
+
+jobs:
+ build-macos:
+ runs-on: macos-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Install dependencies
+ run: |
+ brew uninstall cmake
+ brew update
+ brew install opencv cmake
+
+ - name: Configure CMake
+ run: |
+ mkdir -p build
+ cd build
+ cmake ..
+
+ - name: Build
+ working-directory: build
+ run: make
+
diff --git a/.github/workflows/ubuntu.yml b/.github/workflows/ubuntu.yml
new file mode 100644
index 000000000..6e60e5663
--- /dev/null
+++ b/.github/workflows/ubuntu.yml
@@ -0,0 +1,29 @@
+name: Ubuntu C/C++ CI
+
+on:
+ push:
+ branches: [ "master" ]
+ pull_request:
+ branches: [ "master" ]
+
+jobs:
+ build-linux:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Install dependencies
+ run: |
+ sudo apt-get update
+ sudo apt-get install -y libopencv-dev cmake g++
+
+ - name: Configure CMake
+ run: |
+ mkdir -p build
+ cd build
+ cmake ..
+
+ - name: Build
+ working-directory: build
+ run: make
+
diff --git a/.gitignore b/.gitignore
index 5eff17020..c25a85a74 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,8 @@ build
CMakeLists.txt.user*
CMakeFiles
CMakeCache.txt
+*.engine
+*.onnx
+*.weights
+*.pt
+*.pth
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 5f7bfcbb2..000000000
--- a/.travis.yml
+++ /dev/null
@@ -1,74 +0,0 @@
-language:
- - cpp
-dist: trusty
-compiler:
- - g++
-
-matrix:
- include:
- - os: linux
- addons:
- apt:
- sources:
- - ubuntu-toolchain-r-test
- packages:
- - g++-7
- env:
- - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7"
-
-before_install:
- - eval "${MATRIX_EVAL}"
- - sudo apt-get update
- - sudo apt-get update -qq
-
-install:
-
-# OpenCV v3.0.0 install code (modified from orignal source: https://github.com/jayrambhia/Install-OpenCV)
-
- # OpenCV dependencies - Details available at: http://docs.opencv.org/trunk/doc/tutorials/introduction/linux_install/linux_install.html
- - sudo apt-get install -y --allow-unauthenticated build-essential
- - sudo apt-get install -y --allow-unauthenticated cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
- - sudo apt-get install -y --allow-unauthenticated python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
-
- # Download v3.0.0 .zip file and extract.
- - curl -sL https://github.com/opencv/opencv/archive/4.3.0.zip > opencv.zip
- - unzip opencv.zip
- - cd opencv-4.3.0
-
- # Create a new 'build' folder.
- - mkdir build
- - cd build
-
- # Set build instructions for Ubuntu distro.
- - cmake -D CMAKE_BUILD_TYPE=RELEASE \
- -D CMAKE_INSTALL_PREFIX=/usr/local \
- -D WITH_TBB=ON \
- -D WITH_V4L=ON \
- -D BUILD_JAVA=OFF -D BUILD_opencv_java_bindings_generator=OFF -D BUILD_opencv_js=OFF\
- -D INSTALL_C_EXAMPLES=OFF -D BUILD_EXAMPLES=OFF \
- -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D INSTALL_TESTS=OFF \
- -D WITH_QT=OFF \
- -D WITH_OPENGL=OFF \
- -D BUILD_opencv_apps=OFF \
- -D BUILD_opencv_python3=OFF -D BUILD_opencv_python_tests=OFF -D INSTALL_PYTHON_EXAMPLES=OFF -D BUILD_NEW_PYTHON_SUPPORT=OFF -D BUILD_opencv_python_bindings_generator=OFF \
- -D CPU_BASELINE=AVX2 -D CPU_DISPATCH=AVX2 ..
-
-
- # Run 'make' with four threads.
- - make -j4
-
- # Install to OS.
- - sudo make install
-
- # Add configuration to OpenCV to tell it where the library files are located on the file system (/usr/local/lib)
- - sudo sh -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf'
-
- - sudo ldconfig
- - echo "OpenCV installed."
-
- # We need to return to the repo "root" folder, so we can then 'cd' into the C++ project folder.
- - cd ../../
-
-script:
- - cmake .
- - make -j4
diff --git a/CMakeLists.txt b/CMakeLists.txt
index f3b192a0c..fed4b1aec 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,7 +1,10 @@
-cmake_minimum_required(VERSION 3.5)
+cmake_minimum_required(VERSION 3.9)
-project(MTTracking)
+project(MTTracking VERSION 1.1.0)
+set_property(GLOBAL PROPERTY USE_FOLDERS ON)
+
+unset(CMAKE_C_FLAGS CACHE)
unset(CMAKE_CXX_FLAGS CACHE)
unset(CMAKE_CXX_FLAGS_RELEASE CACHE)
# unset(CMAKE_CXX_FLAGS_DEBUG CACHE)
@@ -15,11 +18,12 @@ endif()
set(CMAKE_CXX_STANDARD 17)
if (CMAKE_COMPILER_IS_GNUCXX)
- set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Wextra -pedantic-errors" CACHE STRING COMPILE_FLAGS FORCE)
- set(CMAKE_CXX_FLAGS_RELEASE "-O3 -g -march=native -mtune=native -funroll-loops -Wall -DNDEBUG -DBOOST_DISABLE_ASSERTS" CACHE STRING COMPILE_FLAGS FORCE)
- set(CMAKE_CXX_FLAGS_DEBUG "-O0 -g -march=native -mtune=native -Wall -DDEBUG" CACHE STRING COMPILE_FLAGS FORCE)
+ set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fPIC" CACHE STRING COMPILE_FLAGS FORCE)
+ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Wextra -pedantic-errors -fPIC" CACHE STRING COMPILE_FLAGS FORCE)
+ set(CMAKE_CXX_FLAGS_RELEASE "-O3 -g -march=native -mtune=native -funroll-loops -DNDEBUG -DBOOST_DISABLE_ASSERTS" CACHE STRING COMPILE_FLAGS FORCE)
+ set(CMAKE_CXX_FLAGS_DEBUG "-O0 -g -march=native -mtune=native -DDEBUG" CACHE STRING COMPILE_FLAGS FORCE)
elseif (MSVC)
- set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc /W4 -DGTL_STATIC" CACHE STRING COMPILE_FLAGS FORCE)
+ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc /W4 /utf-8" CACHE STRING COMPILE_FLAGS FORCE)
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /MD /Ox /Ob2 /Oi /Ot /arch:AVX2 /fp:fast /DNDEBUG" CACHE STRING COMPILE_FLAGS FORCE)
# set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /MDd /Od /Ob0 /DDEBUG" CACHE STRING COMPILE_FLAGS FORCE)
@@ -40,21 +44,26 @@ endif(SILENT_WORK)
include(CheckIncludeFileCXX)
check_include_file_cxx(filesystem HAVE_FILESYSTEM)
if(HAVE_FILESYSTEM)
- add_definitions(-DHAVE_FILESYSTEM)
message("Founded filesystem header")
else(HAVE_FILESYSTEM)
+ add_definitions(-DHAVE_EXPERIMENTAL_FILESYSTEM)
message("Do not found filesystem header")
endif(HAVE_FILESYSTEM)
-
option(BUILD_EXAMPLES "Should compiled examples (motion detection, pedestrians, faces, DNNs etc)?" ON)
if (BUILD_EXAMPLES)
add_subdirectory(example)
endif(BUILD_EXAMPLES)
+
+option(USE_CLIP "Should be used RuCLIP|CLIP for objects classification?" OFF)
+if (USE_CLIP)
+ add_definitions(-DUSE_CLIP)
+endif(USE_CLIP)
+
option(BUILD_CARS_COUNTING "Should compiled Cars counting example?" OFF)
if (BUILD_CARS_COUNTING)
- add_subdirectory(cars_counting)
+ add_definitions(-DBUILD_CARS_COUNTING)
endif(BUILD_CARS_COUNTING)
option(BUILD_ASYNC_DETECTOR "Should compiled async example with low fps Detector?" OFF)
@@ -62,39 +71,57 @@ if (BUILD_ASYNC_DETECTOR)
add_subdirectory(async_detector)
endif(BUILD_ASYNC_DETECTOR)
-option(BUILD_YOLO_LIB "Should compiled standalone yolo_lib with original darknet?" OFF)
-if (BUILD_YOLO_LIB)
- add_subdirectory(src/Detector/darknet)
- add_definitions(-DBUILD_YOLO_LIB)
-
-if (MSVC)
- if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "4")
- set(BIT_SYSTEM x32)
- else()
- set(BIT_SYSTEM x64)
- endif()
-
- set(LIB_PTHREAD pthreadVC2)
-else()
- set(LIB_PTHREAD pthread)
+option(BUILD_ONNX_TENSORRT "Should compiled TensorRT binding for ONNX models?" OFF)
+if (BUILD_ONNX_TENSORRT)
+ add_subdirectory(src/Detector/tensorrt_onnx)
+ add_definitions(-DBUILD_ONNX_TENSORRT)
+endif(BUILD_ONNX_TENSORRT)
+
+option(MTRACKER_PYTHON "Build mtracking Python bindings?" OFF)
+if(MTRACKER_PYTHON)
+ set(NUMPY_INCLUDE_DIR "" CACHE FILEPATH "Path to numpy header if cmake can't find them.")
+ if (NOT ${NUMPY_INCLUDE_DIR} STREQUAL "")
+ message( " *** NUMPY_INCLUDE_DIR : ${NUMPY_INCLUDE_DIR}" )
+ if(NOT EXISTS ${NUMPY_INCLUDE_DIR}/numpy/ndarrayobject.h)
+ message(SEND_ERROR "Can't find numpy/ndarrayobject.h in ${NUMPY_INCLUDE_DIR}")
+ endif()
+ include_directories(${NUMPY_INCLUDE_DIR})
endif()
-if (MSVC)
- file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/src/Detector/darknet/3rdparty/dll/${BIT_SYSTEM}/pthreadVC2.dll DESTINATION ${CMAKE_BINARY_DIR}/Debug)
- file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/src/Detector/darknet/3rdparty/dll/${BIT_SYSTEM}/pthreadVC2.dll DESTINATION ${CMAKE_BINARY_DIR}/Release)
+ set(PYBIND11_LTO_CXX_FLAGS "")
+ set(PYBIND11_PYTHON_VERSION 3)
+ add_subdirectory(thirdparty/pybind11)
+endif(MTRACKER_PYTHON)
- file(GLOB CUDNN_DLL ${CUDNN_DIR}/bin/*.dll)
- file(COPY ${CUDNN_DLL} DESTINATION ${CMAKE_BINARY_DIR}/Release)
- file(COPY ${CUDNN_DLL} DESTINATION ${CMAKE_BINARY_DIR}/Debug)
-endif()
-
-endif(BUILD_YOLO_LIB)
-
-option(BUILD_YOLO_TENSORRT "Should compiled TensorRT binding for YOLO?" OFF)
-if (BUILD_YOLO_TENSORRT)
- add_subdirectory(src/Detector/tensorrt_yolo)
- add_definitions(-DBUILD_YOLO_TENSORRT)
-endif(BUILD_YOLO_TENSORRT)
add_subdirectory(thirdparty)
add_subdirectory(src)
+
+
+# Create CMake config files for distribution
+set(INCLUDE_INSTALL_DIR include/ )
+set(LIB_INSTALL_DIR lib/ )
+
+install(EXPORT MTTrackingExports
+ FILE ${PROJECT_NAME}Targets.cmake
+ NAMESPACE ${PROJECT_NAME}::
+ DESTINATION ${LIB_INSTALL_DIR}/${PROJECT_NAME}/cmake
+)
+
+include(CMakePackageConfigHelpers)
+
+set(CONFIG_FILENAME ${PROJECT_NAME}Config.cmake)
+
+configure_package_config_file(${CONFIG_FILENAME}.in
+ ${CMAKE_CURRENT_BINARY_DIR}/${CONFIG_FILENAME}
+ INSTALL_DESTINATION ${LIB_INSTALL_DIR}/${PROJECT_NAME}/cmake
+ PATH_VARS INCLUDE_INSTALL_DIR)
+
+write_basic_package_version_file(
+ ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake
+ VERSION ${PROJECT_VERSION}
+ COMPATIBILITY SameMajorVersion )
+
+install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${CONFIG_FILENAME}
+ ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake
+ DESTINATION ${LIB_INSTALL_DIR}/${PROJECT_NAME}/cmake )
diff --git a/LICENSE b/LICENSE
index f288702d2..261eeb9e9 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,674 +1,201 @@
- GNU GENERAL PUBLIC LICENSE
- Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc.
- Copyright (C)
-
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with this program. If not, see Copyright (C)
- This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
- This is free software, and you are welcome to redistribute it
- under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License. Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
- You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-.
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
diff --git a/MTTrackingConfig.cmake.in b/MTTrackingConfig.cmake.in
new file mode 100644
index 000000000..796457847
--- /dev/null
+++ b/MTTrackingConfig.cmake.in
@@ -0,0 +1,10 @@
+set(@PROJECT_NAME@_VERSION @PROJECT_VERSION@)
+
+@PACKAGE_INIT@
+
+include("${CMAKE_CURRENT_LIST_DIR}/@PROJECT_NAME@Targets.cmake")
+
+set_and_check(@PROJECT_NAME@_INCLUDE_DIR "@PACKAGE_INCLUDE_INSTALL_DIR@")
+set_and_check(@PROJECT_NAME@_LIB_DIR "@PACKAGE_INCLUDE_INSTALL_DIR@../lib")
+
+check_required_components(@PROJECT_NAME@)
diff --git a/README.md b/README.md
index 9320d9531..a7850596d 100644
--- a/README.md
+++ b/README.md
@@ -1,207 +1,143 @@
-
-[](https://github.com/Smorodov/Multitarget-tracker/actions?query=workflow%3ACodeQL)
-
-# Last changes
-
-* Object type changed from string to enum
-
-* The base opencv_dnn detector instead special MobileNetOpnCV and YoloOpenCV
-
-# New videos!
-
-* Vehicles speed calculation with YOLO v4 (Thanks [Sam Blake for great idea!](https://medium.com/hal24k-techblog/how-to-track-objects-in-the-real-world-with-tensorflow-sort-and-opencv-a64d9564ccb1))
-
-[](https://youtu.be/qOHYvDwpsO0)
-
-
-* First step to ADAS with YOLO v4
-
-[](https://youtu.be/5cgg5fy90Xg)
-
-# Multitarget (multiple objects) tracker
-
-#### 1. Objects detector can be created with function [CreateDetector](https://github.com/Smorodov/Multitarget-tracker/blob/master/src/Detector/BaseDetector.cpp) with different values of the detectorType:
-
-1.1. Based on background substraction: built-in Vibe (tracking::Motion_VIBE), SuBSENSE (tracking::Motion_SuBSENSE) and LOBSTER (tracking::Motion_LOBSTER); MOG2 (tracking::Motion_MOG2) from [opencv](https://github.com/opencv/opencv/blob/master/modules/video/include/opencv2/video/background_segm.hpp); MOG (tracking::Motion_MOG), GMG (tracking::Motion_GMG) and CNT (tracking::Motion_CNT) from [opencv_contrib](https://github.com/opencv/opencv_contrib/tree/master/modules/bgsegm). For foreground segmentation used contours from OpenCV with result as cv::RotatedRect
-
-1.2. Haar face detector from OpenCV (tracking::Face_HAAR)
-
-1.3. HOG pedestrian detector from OpenCV (tracking::Pedestrian_HOG) and C4 pedestrian detector from [sturkmen72](https://github.com/sturkmen72/C4-Real-time-pedestrian-detection) (tracking::Pedestrian_C4)
-
-1.4. Detector based on opencv_dnn (tracking::DNN_OCV) and pretrained models from [chuanqi305](https://github.com/chuanqi305/MobileNet-SSD) and [pjreddie](https://pjreddie.com/darknet/yolo/)
-
-1.5. YOLO detector (tracking::Yolo_Darknet) with darknet inference from [AlexeyAB](https://github.com/AlexeyAB/darknet) and pretrained models from [pjreddie](https://pjreddie.com/darknet/yolo/)
-
-1.6. YOLO detector (tracking::Yolo_TensorRT) with NVidia TensorRT inference from [enazoe](https://github.com/enazoe/yolo-tensorrt) and pretrained models from [pjreddie](https://pjreddie.com/darknet/yolo/)
-
-1.7. You can to use custom detector with bounding or rotated rectangle as output.
-
-#### 2. Matching or solve an [assignment problem](https://github.com/Smorodov/Multitarget-tracker/blob/master/src/Tracker/Ctracker.h):
-
-2.1. Hungrian algorithm (tracking::MatchHungrian) with cubic time O(N^3) where N is objects count
-
-2.2. Algorithm based on weighted bipartite graphs (tracking::MatchBipart) from [rdmpage](https://github.com/rdmpage/maximum-weighted-bipartite-matching) with time O(M * N^2) where N is objects count and M is connections count between detections on frame and tracking objects. It can be faster than Hungrian algorithm
-
-2.3. [Distance](https://github.com/Smorodov/Multitarget-tracker/blob/master/src/Tracker/Ctracker.h) from detections and objects: euclidean distance in pixels between centers (tracking::DistCenters), euclidean distance in pixels between rectangles (tracking::DistRects), Jaccard or IoU distance from 0 to 1 (tracking::DistJaccard)
-
-#### 3. [Smoothing trajectories and predict missed objects](https://github.com/Smorodov/Multitarget-tracker/blob/master/src/Tracker/Ctracker.h):
-
-3.1. Linear Kalman filter from OpenCV (tracking::KalmanLinear)
-
-3.2. Unscented Kalman filter from OpenCV (tracking::KalmanUnscented) with constant velocity or constant acceleration models
-
-3.3. [Kalman goal](https://github.com/Smorodov/Multitarget-tracker/blob/master/src/Tracker/Ctracker.h) is only coordinates (tracking::FilterCenter) or coordinates and size (tracking::FilterRect)
-
-3.4. Simple [Abandoned detector](https://github.com/Smorodov/Multitarget-tracker/blob/master/src/Tracker/Ctracker.h)
-
-3.5. [Line intersection](https://github.com/Smorodov/Multitarget-tracker/blob/master/cars_counting/CarsCounting.cpp) counting
-
-#### 4. [Advanced visual search](https://github.com/Smorodov/Multitarget-tracker/blob/master/src/Tracker/Ctracker.h) for objects if they have not been detected:
-
-4.1. No search (tracking::TrackNone)
-
-4.2. Built-in DAT (tracking::TrackDAT) from [foolwood](https://github.com/foolwood/DAT), STAPLE (tracking::TrackSTAPLE) from [xuduo35](https://github.com/xuduo35/STAPLE) or LDES (tracking::TrackLDES) from [yfji](https://github.com/yfji/LDESCpp); KCF (tracking::TrackKCF), MIL (tracking::TrackMIL), MedianFlow (tracking::TrackMedianFlow), GOTURN (tracking::TrackGOTURN), MOSSE (tracking::TrackMOSSE) or CSRT (tracking::TrackCSRT) from [opencv_contrib](https://github.com/opencv/opencv_contrib/tree/master/modules/tracking)
-
-With this option the tracking can work match slower but more accuracy.
-
-#### 5. Pipeline
-
-5.1. Syncronous [pipeline - SyncProcess](https://github.com/Smorodov/Multitarget-tracker/blob/master/example/VideoExample.h):
-- get frame from capture device;
-- decoding;
-- objects detection (1);
-- tracking (2-4);
-- show result.
-
-This pipeline is good if all algorithms are fast and works faster than time between two frames (40 ms for device with 25 fps). Or it can be used if we have only 1 core for all (no parallelization).
-
-5.2. Pipeline with [2 threads - AsyncProcess](https://github.com/Smorodov/Multitarget-tracker/blob/master/example/VideoExample.h):
-- 1th thread takes frame t and makes capture, decoding and objects detection;
-- 2th thread takes frame t-1, results from first thread and makes tracking and results presentation (this is the Main read).
-
-So we have a latency on 1 frame but on two free CPU cores we can increase performance on 2 times.
-
-5.3. Fully [acynchronous pipeline](https://github.com/Smorodov/Multitarget-tracker/tree/master/async_detector) can be used if the objects detector works with low fps and we have a free 2 CPU cores. In this case we use 4 threads:
-- 1th main thread is not busy and used for GUI and result presentation;
-- 2th thread makes capture and decoding, puts frames in threadsafe queue;
-- 3th thread is used for objects detection on the newest frame from the queue;
-- 4th thread is used for objects tracking: waits the frame with detection from 3th tread and used advanced visual search (4) in intermediate frames from queue until it ges a frame with detections.
-
-This pipeline can used with slow but accuracy DNN and track objects in intermediate frame in realtime without latency.
-
-Also you can read [Wiki in Russian](https://github.com/Smorodov/Multitarget-tracker/wiki).
-
-#### Demo Videos
-
-* Mouse tracking:
-
-[](https://www.youtube.com/watch?v=2fW5TmAtAXM)
-
-* Motion Detection and tracking:
-
-[](https://www.youtube.com/watch?v=GjN8jOy4kVw)
-
-* Multiple Faces tracking:
-
-[](https://www.youtube.com/watch?v=j67CFwFtciU)
-
-* Simple Abandoned detector:
-
-[](https://www.youtube.com/watch?v=fpkHRsFzspA)
-
-#### Tested Platforms
-1. Ubuntu Linux 18.04 with x86 processors
-2. Ubuntu Linux 18.04 with Nvidia Jetson Nano (YOLO + darknet on GPU works!)
-3. Windows 10 (x64 and x32 builds)
-
-#### Build
-1. Download project sources
-2. Install CMake
-3. Install OpenCV (https://github.com/opencv/opencv) and OpenCV contrib (https://github.com/opencv/opencv_contrib) repositories
-4. Configure project CmakeLists.txt, set OpenCV_DIR (-DOpenCV_DIR=/path/to/opencv/build).
-5. If opencv_contrib don't installed then disable options USE_OCV_BGFG=OFF, USE_OCV_KCF=OFF and USE_OCV_UKF=OFF
-6. If you want to use native darknet YOLO detector with CUDA + cuDNN then set BUILD_YOLO_LIB=ON (Install first CUDA and cuDNN libraries from Nvidia)
-7. If you want to use YOLO detector with TensorRT then set BUILD_YOLO_TENSORRT=ON (Install first TensorRT library from Nvidia)
-8. For building example with low fps detector (now native darknet YOLO detector) and Tracker worked on each frame: BUILD_ASYNC_DETECTOR=ON
-9. For building example with line crossing detection (cars counting): BUILD_CARS_COUNTING=ON
-10. Go to the build directory and run make
-
-**Full build:**
-
- git clone https://github.com/Smorodov/Multitarget-tracker.git
- cd Multitarget-tracker
- mkdir build
- cd build
- cmake . .. -DUSE_OCV_BGFG=ON -DUSE_OCV_KCF=ON -DUSE_OCV_UKF=ON -DBUILD_YOLO_LIB=ON -DBUILD_YOLO_TENSORRT=ON -DBUILD_ASYNC_DETECTOR=ON -DBUILD_CARS_COUNTING=ON
- make -j
-
-How to run cmake on Windows for Visual Studio 15 2017 Win64: [example](https://github.com/Smorodov/Multitarget-tracker/blob/master/data/cmake_vs2017.bat). You need to add directory with cmake.exe to PATH and change build params in cmake.bat
-
-
-**Usage:**
-
- Usage:
- ./MultitargetTracker [--example]= [--start_frame]= [--end_frame]= [--end_delay]= [--out]= [--show_logs]= [--gpu]= [--async]= [--res]= [--settings]=
- ./MultitargetTracker ../data/atrium.avi -e=1 -o=../data/atrium_motion.avi
- Press:
- * 'm' key for change mode: play|pause. When video is paused you can press any key for get next frame.
- * Press Esc to exit from video
-
- Params:
- 1. Movie file, for example ../data/atrium.avi
- 2. [Optional] Number of example: 0 - MouseTracking, 1 - MotionDetector, 2 - FaceDetector, 3 - PedestrianDetector, 4 - OpenCV dnn objects detector, 5 - Yolo Darknet detector, 6 - YOLO TensorRT Detector
- -e=0 or --example=1
- 3. [Optional] Frame number to start a video from this position
- -sf=0 or --start_frame==1500
- 4. [Optional] Play a video to this position (if 0 then played to the end of file)
- -ef=0 or --end_frame==200
- 5. [Optional] Delay in milliseconds after video ending
- -ed=0 or --end_delay=1000
- 6. [Optional] Name of result video file
- -o=out.avi or --out=result.mp4
- 7. [Optional] Show Trackers logs in terminal
- -sl=1 or --show_logs=0
- 8. [Optional] Use built-in OpenCL
- -g=1 or --gpu=0
- 9. [Optional] Use 2 threads for processing pipeline
- -a=1 or --async=0
- 10. [Optional] Path to the csv file with tracking result
- -r=res.csv or --res=res.csv
- 11. [Optional] Path to the ini file with tracker settings
- -s=settings.ini or --settings=settings.ini
-
-More details here: [How to run examples](https://github.com/Smorodov/Multitarget-tracker/wiki/Run-examples).
-
-#### Thirdparty libraries
-* OpenCV (and contrib): https://github.com/opencv/opencv and https://github.com/opencv/opencv_contrib
-* Vibe: https://github.com/BelBES/VIBE
-* SuBSENSE and LOBSTER: https://github.com/ethereon/subsense
-* GTL: https://github.com/rdmpage/graph-template-library
-* MWBM: https://github.com/rdmpage/maximum-weighted-bipartite-matching
-* Pedestrians detector: https://github.com/sturkmen72/C4-Real-time-pedestrian-detection
-* Non Maximum Suppression: https://github.com/Nuzhny007/Non-Maximum-Suppression
-* MobileNet SSD models: https://github.com/chuanqi305/MobileNet-SSD
-* YOLO v3 models: https://pjreddie.com/darknet/yolo/
-* Darknet inference and YOLO v4 models: https://github.com/AlexeyAB/darknet
-* NVidia TensorRT inference and YOLO v5 models: https://github.com/enazoe/yolo-tensorrt
-* GOTURN models: https://github.com/opencv/opencv_extra/tree/c4219d5eb3105ed8e634278fad312a1a8d2c182d/testdata/tracking
-* DAT tracker: https://github.com/foolwood/DAT
-* STAPLE tracker: https://github.com/xuduo35/STAPLE
-* LDES tracker: https://github.com/yfji/LDESCpp
-* Ini file parser: https://github.com/benhoyt/inih
-
-#### License
-GNU GPLv3: http://www.gnu.org/licenses/gpl-3.0.txt
-
-#### Project cititations
+# Multitarget Tracker
+
+[](https://github.com/Smorodov/Multitarget-tracker/actions/workflows/ubuntu.yml)
+[](https://github.com/Smorodov/Multitarget-tracker/actions/workflows/macos.yml)
+[](https://github.com/Smorodov/Multitarget-tracker/actions/workflows/codeql-analysis.yml)
+
+## Latest Features
+
+- Add new SOTA: YOLOv26, YOLOv26-obb and YOLOv26-seg models from [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)
+- Add RT-DETRv4 (API similar D-FINE) detection model [RT-DETRs/RT-DETRv4](https://github.com/RT-DETRs/RT-DETRv4)
+- Add D-FINE seg detection model [ArgoHA/D-FINE-seg](https://github.com/ArgoHA/D-FINE-seg)
+- Add ByteTrack MOT algorithm based on [Vertical-Beach/ByteTrack-cpp](https://github.com/Vertical-Beach/ByteTrack-cpp)
+- Big code cleanup from old style algorithms and detectors: some bgfg detectors, some VOT trackes, Face and Pedestrin detectors, Darknet based backend for old YOLO etc
+- YOLOv13 detector works with TensorRT! Export pre-trained PyTorch models [here (iMoonLab/yolov13)](https://github.com/iMoonLab/yolov13) to ONNX format and run Multitarget-tracker with `-e=3` example
+- Instance segmentation model from RF-DETR detector works with TensorRT! Export pre-trained PyTorch models [here (roboflow/rf-detr)](https://github.com/roboflow/rf-detr) to ONNX format and run Multitarget-tracker with `-e=3` example
+- New linear assignment algorithm - [Jonker-Volgenant / LAPJV algorithm](https://github.com/yongyanghz/LAPJV-algorithm-c) used in [scipy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html) as alternative for Hungarian allgorithm
+- D-FINE detector works with TensorRT! Export pre-trained PyTorch models [here (Peterande/D-FINE)](https://github.com/Peterande/D-FINE) to ONNX format and run Multitarget-tracker with `-e=3` example
+- RF-DETR detector works with TensorRT! Export pre-trained PyTorch models [here (roboflow/rf-detr)](https://github.com/roboflow/rf-detr) to ONNX format and run Multitarget-tracker with `-e=3` example
+- YOLOv12 detector works with TensorRT! Export pre-trained PyTorch models [here (sunsmarterjie/yolov12)](https://github.com/sunsmarterjie/yolov12) to ONNX format and run Multitarget-tracker with `-e=3` example
+
+## Demo Videos
+
+### Detection & Tracking
+
+[](https://youtu.be/oKy7jEKT83c)
+[](https://youtu.be/gTpWnkMF7Lg)
+[](https://youtu.be/Pb_HnejRpY4)
+[](https://youtu.be/1e6ur57Fhzs)
+[](https://youtu.be/PalIIAfgX88)
+
+## Documentation
+
+### Core Components
+
+#### 1. Object Detectors
+Available through `CreateDetector` function with different `detectorType`:
+1. **Background Subtraction**:
+ - Built-in: VIBE (`tracking::Motion_VIBE`), SuBSENSE (`tracking::Motion_SuBSENSE`), LOBSTER (`tracking::Motion_LOBSTER`)
+ - OpenCV: MOG2 (`tracking::Motion_MOG2`)
+ - OpenCV Contrib: MOG (`tracking::Motion_MOG`), GMG (`tracking::Motion_GMG`), CNT (`tracking::Motion_CNT`)
+ - Foreground segmentation uses OpenCV contours producing `cv::RotatedRect`
+2. **Deep Learning Models**:
+ - OpenCV DNN module (`tracking::DNN_OCV`)
+ - TensorRT-accelerated YOLO (`tracking::Yolo_TensorRT`)
+
+#### 2. Matching Algorithms
+For solving assignment problems:
+- **Hungarian Algorithm** (`tracking::MatchHungrian`) - O(NÂŗ) complexity
+- **LAPJV** (`tracking::MatchBipart`) - O(M*N²) complexity
+- **Distance Metrics**:
+ - Center distance (`tracking::DistCenters`)
+ - Bounding box distance (`tracking::DistRects`)
+ - Jaccard/IoU similarity (`tracking::DistJaccard`)
+
+#### 3. Trajectory Smoothing
+- Kalman filters: Linear (`tracking::KalmanLinear`) and Unscented (`tracking::KalmanUnscented`)
+- State models: Constant velocity and constant acceleration
+- Tracking modes: Position-only (`tracking::FilterCenter`) and position+size (`tracking::FilterRect`)
+- Specialized features: Abandoned object detection, line intersection counting
+
+#### 4. Visual Search
+When targets disappear:
+- KCF (`tracking::TrackKCF`)
+- CSRT (`tracking::TrackCSRT`)
+- DaSiamRPN (`tracking::TrackDaSiamRPN`)
+- Vit (`tracking::TrackVit`)
+- Nano (`tracking::TrackNano`)
+
+### Processing Pipelines
+1. **Synchronous** (`SyncProcess`): Single-threaded processing
+2. **Asynchronous (2 threads)** (`AsyncProcess`): Decouples detection and tracking
+3. **Fully Asynchronous (4 threads)**: For low-FPS deep learning detectors
+
+### Installation & Building
+```bash
+git clone https://github.com/Smorodov/Multitarget-tracker.git
+cd Multitarget-tracker
+mkdir build && cd build
+cmake . .. \
+ -DUSE_OCV_BGFG=ON \
+ -DUSE_OCV_KCF=ON \
+ -DUSE_OCV_UKF=ON \
+ -DBUILD_ONNX_TENSORRT=ON \
+ -DBUILD_ASYNC_DETECTOR=ON \
+ -DBUILD_CARS_COUNTING=ON
+make -j
+```
+
+### Usage Guide
+Basic command syntax:
+```bash
+./MultitargetTracker [--example=] [--start_frame=]
+ [--end_frame=] [--end_delay=] [--out=]
+ [--show_logs] [--gpu] [--async] [--res=]
+ [--settings=] [--batch_size=]
+```
+
+Example:
+```bash
+./MultitargetTracker ../data/atrium.avi -e=1 -o=../data/atrium_motion.avi
+```
+
+Keyboard Controls:
+- `m`: Toggle play/pause
+- Any key: Step forward when paused
+- `Esc`: Exit
+
+### Integration as Library
+```cpp
+#include
+
+std::unique_ptr m_tracker;
+TrackerSettings settings;
+settings.SetDistance(tracking::DistJaccard);
+m_tracker = BaseTracker::CreateTracker(settings);
+```
+
+### Third-party Dependencies
+
+- [OpenCV (and contrib)](https://github.com/opencv/opencv)
+- [Vibe](https://github.com/BelBES/VIBE)
+- [Non Maximum Suppression](https://github.com/Nuzhny007/Non-Maximum-Suppression)
+- [Ini file parser](https://github.com/benhoyt/inih)
+- [Circular Code](https://github.com/LiorKogan/Circular)
+
+### License
+[Apache 2.0 License](https://github.com/Smorodov/Multitarget-tracker/blob/master/LICENSE)
+
+#### Project citations
1. Jeroen PROVOOST "Camera gebaseerde analysevan de verkeersstromen aaneen kruispunt", 2014 ( https://iiw.kuleuven.be/onderzoek/eavise/mastertheses/provoost.pdf )
2. Roberto Ciano, Dimitrij Klesev "Autonome Roboterschwarme in geschlossenen Raumen", 2015 ( https://www.hs-furtwangen.de/fileadmin/user_upload/fak_IN/Dokumente/Forschung_InformatikJournal/informatikJournal_2016.pdf#page=18 )
3. Wenda Qin, Tian Zhang, Junhe Chen "Traffic Monitoring By Video: Vehicles Tracking and Vehicle Data Analysing", 2016 ( http://cs-people.bu.edu/wdqin/FinalProject/CS585%20FinalProjectReport.html )
4. Ipek BARIS "CLASSIFICATION AND TRACKING OF VEHICLES WITH HYBRID CAMERA SYSTEMS", 2016 ( http://cvrg.iyte.edu.tr/publications/IpekBaris_MScThesis.pdf )
5. Cheng-Ta Lee, Albert Y. Chen, Cheng-Yi Chang "In-building Coverage of Automated External Defibrillators Considering Pedestrian Flow", 2016 ( http://www.see.eng.osaka-u.ac.jp/seeit/icccbe2016/Proceedings/Full_Papers/092-132.pdf )
6. Roberto Ciano, Dimitrij Klesev "Autonome Roboterschwarme in geschlossenen Raumen" in "informatikJournal 2016/17", 2017 ( https://docplayer.org/124538994-2016-17-informatikjournal-2016-17-aktuelle-berichte-aus-forschung-und-lehre-der-fakultaet-informatik.html )
-7. Omid Noorshams "Automated systems to assess weights and activity in grouphoused mice", 2017 ( https://pdfs.semanticscholar.org/e5ff/f04b4200c149fb39d56f171ba7056ab798d3.pdf )
+7. Omid Noorshams "Automated systems to assess weights and activity in grouphoused mice", 2017 ( https://pdfs.semanticscholar.org/e5ff/f04b4200c149fb39d56f171ba7056ab798d3.pdf )
8. RADEK VOPÃLENSKÃ "DETECTION,TRACKING AND CLASSIFICATION OF VEHICLES", 2018 ( https://www.vutbr.cz/www_base/zav_prace_soubor_verejne.php?file_id=181063 )
9. MÃĄrk RÃĄtosi, Gyula Simon "Real-Time Localization and Tracking using Visible Light Communication", 2018 ( https://ieeexplore.ieee.org/abstract/document/8533800 )
-10. Thi Nha Ngo, Kung-Chin Wu, En-Cheng Yang, Ta-Te Lin "Areal-time imaging system for multiple honey bee tracking and activity monitoring", 2019 ( https://www.sciencedirect.com/science/article/pii/S0168169919301498 )
-11. ROS, http://docs.ros.org/lunar/api/costmap_converter/html/Ctracker_8cpp_source.html
+10. Thi Nha Ngo, Kung-Chin Wu, En-Cheng Yang, Ta-Te Lin "A real-time imaging system for multiple honey bee tracking and activity monitoring", 2019 ( https://www.sciencedirect.com/science/article/pii/S0168169919301498 )
+11. Tiago Miguel, Rodrigues de Almeida "Multi-Camera and Multi-Algorithm Architecture for VisualPerception onboard the ATLASCAR2", 2019 ( http://lars.mec.ua.pt/public/LAR%20Projects/Vision/2019_TiagoAlmeida/Thesis_Tiago_AlmeidaVF_26Jul2019.pdf )
+12. ROS, http://docs.ros.org/lunar/api/costmap_converter/html/Ctracker_8cpp_source.html
+13. Sangeeth Kochanthara, Yanja Dajsuren, Loek Cleophas, Mark van den Brand "Painting the Landscape of Automotive Software in GitHub", 2022 ( https://arxiv.org/abs/2203.08936 )
+14. Fesus, A., Kovari, B., Becsi, T., Leginusz, L. "Dynamic Prompt-Based Approach for Open Vocabulary Multi-Object Tracking", 2025 ( https://link.springer.com/chapter/10.1007/978-3-031-81799-1_25 )
diff --git a/TODO b/TODO
deleted file mode 100644
index 88bb8aace..000000000
--- a/TODO
+++ /dev/null
@@ -1,28 +0,0 @@
-Global data association for multi-object tracking using network flows:
-1. https://github.com/nwojke/mcf
-2. https://github.com/jutanke/cabbage
-3. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies
-4. http://openaccess.thecvf.com/content_cvpr_2017/papers/Tang_Multiple_People_Tracking_CVPR_2017_paper.pdf
-5. https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection
-6. https://arxiv.org/abs/1903.05625
-7. https://arxiv.org/abs/1907.03961
-8. http://www.cvlibs.net/projects/online_tracking/
-9. https://github.com/jwchoi384/Gaussian_YOLOv3
-10. muSSP: https://github.com/yu-lab-vt/muSSP
-11. https://github.com/ifzhang/FairMOT
-12. https://github.com/AndreaHor/LifT_Solver
-
-Deep SORT:
-1. https://github.com/humoncy/YOLOv3-SORT-ReID
-2. https://github.com/nwojke/deep_sort
-3. https://github.com/bitzy/DeepSort
-3. https://github.com/oylz/DS
-
-New:
-1. https://github.com/ceccocats/tkDNN
-2. dasiamrpn_tracker.py -> C++
-
-Tests:
-1. Quality tests
-2. Performance tests
-
diff --git a/async_detector/AsyncDetector.cpp b/async_detector/AsyncDetector.cpp
index 63bb60498..58854ed2e 100644
--- a/async_detector/AsyncDetector.cpp
+++ b/async_detector/AsyncDetector.cpp
@@ -1,69 +1,54 @@
#include "AsyncDetector.h"
-///
-/// \brief DrawFilledRect
-///
-void DrawFilledRect(cv::Mat& frame, const cv::Rect& rect, cv::Scalar cl, int alpha)
-{
- if (alpha)
- {
- const int alpha_1 = 255 - alpha;
- const int nchans = frame.channels();
- int color[3] = { cv::saturate_cast(cl[0]), cv::saturate_cast(cl[1]), cv::saturate_cast(cl[2]) };
- for (int y = rect.y; y < rect.y + rect.height; ++y)
- {
- uchar* ptr = frame.ptr(y) + nchans * rect.x;
- for (int x = rect.x; x < rect.x + rect.width; ++x)
- {
- for (int i = 0; i < nchans; ++i)
- {
- ptr[i] = cv::saturate_cast((alpha_1 * ptr[i] + alpha * color[i]) / 255);
- }
- ptr += nchans;
- }
- }
- }
- else
- {
- cv::rectangle(frame, rect, cl, cv::FILLED);
- }
-}
-
///
/// \brief AsyncDetector::AsyncDetector
/// \param parser
///
AsyncDetector::AsyncDetector(const cv::CommandLineParser& parser)
- :
- m_showLogs(true),
- m_fps(25),
- m_startFrame(0),
- m_endFrame(0),
- m_finishDelay(0)
{
m_inFile = parser.get(0);
m_outFile = parser.get("out");
- m_showLogs = parser.get("show_logs") != 0;
+ m_showLogsLevel = parser.get("show_logs");
m_startFrame = parser.get("start_frame");
m_endFrame = parser.get("end_frame");
m_finishDelay = parser.get("end_delay");
- m_colors.push_back(cv::Scalar(255, 0, 0));
- m_colors.push_back(cv::Scalar(0, 255, 0));
- m_colors.push_back(cv::Scalar(0, 0, 255));
- m_colors.push_back(cv::Scalar(255, 255, 0));
- m_colors.push_back(cv::Scalar(0, 255, 255));
- m_colors.push_back(cv::Scalar(255, 0, 255));
- m_colors.push_back(cv::Scalar(255, 127, 255));
- m_colors.push_back(cv::Scalar(127, 0, 255));
- m_colors.push_back(cv::Scalar(127, 0, 127));
-}
+ m_colors.emplace_back(255, 0, 0);
+ m_colors.emplace_back(0, 255, 0);
+ m_colors.emplace_back(0, 0, 255);
+ m_colors.emplace_back(255, 255, 0);
+ m_colors.emplace_back(0, 255, 255);
+ m_colors.emplace_back(255, 0, 255);
+ m_colors.emplace_back(255, 127, 255);
+ m_colors.emplace_back(127, 0, 255);
+ m_colors.emplace_back(127, 0, 127);
+
+ // Create loggers
+ m_consoleSink = std::make_shared();
+ m_consoleSink->set_level(spdlog::level::from_str(m_showLogsLevel));
+ m_consoleSink->set_pattern("[%^%l%$] %v");
+
+ auto currentTime = std::chrono::system_clock::now();
+ auto transformed = currentTime.time_since_epoch().count() / 1000000;
+ std::time_t tt = std::chrono::system_clock::to_time_t(currentTime);
+ char buffer[80];
+#ifdef WIN32
+ tm timeInfo;
+ localtime_s(&timeInfo, &tt);
+ strftime(buffer, 80, "%G%m%d_%H%M%S", &timeInfo);
+#else
+ auto timeInfo = localtime(&tt);
+ strftime(buffer, 80, "%G%m%d_%H%M%S", timeInfo);
+#endif
-///
-/// \brief AsyncDetector::~AsyncDetector
-///
-AsyncDetector::~AsyncDetector()
-{
+ size_t max_size = 1024 * 1024 * 5;
+ size_t max_files = 3;
+ m_fileSink = std::make_shared("logs/" + std::string(buffer) + std::to_string(transformed % 1000) + ".txt", max_size, max_files);
+ m_fileSink->set_level(spdlog::level::from_str(m_showLogsLevel));
+
+ m_logger = std::shared_ptr(new spdlog::logger("traffic", { m_consoleSink, m_fileSink }));
+ m_logger->set_level(spdlog::level::from_str(m_showLogsLevel));
+ m_logger->info("Start service");
}
///
@@ -76,7 +61,7 @@ void AsyncDetector::Process()
bool stopFlag = false;
- std::thread thCapture(CaptureThread, m_inFile, m_startFrame, &m_fps, &m_framesQue, &stopFlag);
+ std::thread thCapture(CaptureThread, m_inFile, m_startFrame, &m_framesCount, &m_fps, &m_framesQue, &stopFlag);
#ifndef SILENT_WORK
cv::namedWindow("Video", cv::WINDOW_NORMAL | cv::WINDOW_KEEPRATIO);
@@ -122,25 +107,25 @@ void AsyncDetector::Process()
if (k == 27)
break;
#else
- std::this_thread::sleep_for(std::chrono::milliseconds(1));
+ //std::this_thread::sleep_for(std::chrono::milliseconds(1));
#endif
++framesCounter;
if (m_endFrame && framesCounter > m_endFrame)
{
- std::cout << "Process: riched last " << m_endFrame << " frame" << std::endl;
+ m_logger->info("Process: riched last {} frame", m_endFrame);
break;
}
}
- std::cout << "Stopping threads..." << std::endl;
+ m_logger->info("Stopping threads...");
stopFlag = true;
m_framesQue.SetBreak(true);
if (thCapture.joinable())
thCapture.join();
- std::cout << "work time = " << (allTime / freq) << std::endl;
+ m_logger->info("work time = {}", allTime / freq);
#ifndef SILENT_WORK
cv::waitKey(m_finishDelay);
#endif
@@ -149,61 +134,50 @@ void AsyncDetector::Process()
///
/// \brief AsyncDetector::DrawTrack
/// \param frame
-/// \param resizeCoeff
/// \param track
/// \param drawTrajectory
/// \param isStatic
///
void AsyncDetector::DrawTrack(cv::Mat frame,
- int resizeCoeff,
const TrackingObject& track,
bool drawTrajectory)
{
- auto ResizeRect = [&](const cv::Rect& r) -> cv::Rect
- {
- return cv::Rect(resizeCoeff * r.x, resizeCoeff * r.y, resizeCoeff * r.width, resizeCoeff * r.height);
- };
- auto ResizePoint = [&](const cv::Point& pt) -> cv::Point
- {
- return cv::Point(resizeCoeff * pt.x, resizeCoeff * pt.y);
- };
-
if (track.m_isStatic)
{
#if (CV_VERSION_MAJOR >= 4)
- cv::rectangle(frame, ResizeRect(track.m_rrect.boundingRect()), cv::Scalar(255, 0, 255), 2, cv::LINE_AA);
+ cv::rectangle(frame, track.m_rrect.boundingRect(), cv::Scalar(255, 0, 255), 2, cv::LINE_AA);
#else
- cv::rectangle(frame, ResizeRect(track.m_rrect.boundingRect()), cv::Scalar(255, 0, 255), 2, CV_AA);
+ cv::rectangle(frame, track.m_rrect.boundingRect(), cv::Scalar(255, 0, 255), 2, CV_AA);
#endif
}
else
{
#if (CV_VERSION_MAJOR >= 4)
- cv::rectangle(frame, ResizeRect(track.m_rrect.boundingRect()), cv::Scalar(0, 255, 0), 1, cv::LINE_AA);
+ cv::rectangle(frame, track.m_rrect.boundingRect(), cv::Scalar(0, 255, 0), 1, cv::LINE_AA);
#else
- cv::rectangle(frame, ResizeRect(track.m_rrect.boundingRect()), cv::Scalar(0, 255, 0), 1, CV_AA);
+ cv::rectangle(frame, track.m_rrect.boundingRect(), cv::Scalar(0, 255, 0), 1, CV_AA);
#endif
}
if (drawTrajectory)
{
- cv::Scalar cl = m_colors[track.m_ID % m_colors.size()];
+ cv::Scalar cl = m_colors[track.m_ID.ID2Module(m_colors.size())];
for (size_t j = 0; j < track.m_trace.size() - 1; ++j)
{
const TrajectoryPoint& pt1 = track.m_trace.at(j);
const TrajectoryPoint& pt2 = track.m_trace.at(j + 1);
#if (CV_VERSION_MAJOR >= 4)
- cv::line(frame, ResizePoint(pt1.m_prediction), ResizePoint(pt2.m_prediction), cl, 1, cv::LINE_AA);
+ cv::line(frame, pt1.m_prediction, pt2.m_prediction, cl, 1, cv::LINE_AA);
#else
- cv::line(frame, ResizePoint(pt1.m_prediction), ResizePoint(pt2.m_prediction), cl, 1, CV_AA);
+ cv::line(frame, pt1.m_prediction, pt2.m_prediction, cl, 1, CV_AA);
#endif
if (pt2.m_hasRaw)
{
#if (CV_VERSION_MAJOR >= 4)
- cv::circle(frame, ResizePoint(pt2.m_prediction), 4, cl, 4, cv::LINE_AA);
+ cv::circle(frame, pt2.m_prediction, 4, cl, 4, cv::LINE_AA);
#else
- cv::circle(frame, ResizePoint(pt2.m_prediction), 4, cl, 4, CV_AA);
+ cv::circle(frame, pt2.m_prediction, 4, cl, 4, CV_AA);
#endif
}
}
@@ -216,20 +190,17 @@ void AsyncDetector::DrawTrack(cv::Mat frame,
///
void AsyncDetector::DrawData(frame_ptr frameInfo, int framesCounter, int currTime)
{
- if (m_showLogs)
- {
- std::cout << "Frame " << framesCounter << ": ";
- int id = frameInfo->m_inDetector.load();
- if (id != FrameInfo::StateNotProcessed && id != FrameInfo::StateSkipped)
- std::cout << "(" << id << ") detects = " << frameInfo->m_regions.size() << ", ";
- std::cout << "tracks = " << frameInfo->m_tracks.size() << ", time = " << currTime << std::endl;
- }
+ int id = frameInfo->m_inDetector.load();
+ if (id != FrameInfo::StateNotProcessed && id != FrameInfo::StateSkipped)
+ m_logger->info("Frame {0} ({1}): ({2}) detects= {3}, tracks = {4}, time = {5}", framesCounter, m_framesCount, id, frameInfo->m_regions.size(), frameInfo->m_tracks.size(), currTime);
+ else
+ m_logger->info("Frame {0} ({1}): tracks = {2}, time = {3}", framesCounter, m_framesCount, frameInfo->m_tracks.size(), currTime);
for (const auto& track : frameInfo->m_tracks)
{
if (track.m_isStatic)
{
- DrawTrack(frameInfo->m_frame, 1, track, true);
+ DrawTrack(frameInfo->m_frame, track, true);
}
else
{
@@ -239,32 +210,32 @@ void AsyncDetector::DrawData(frame_ptr frameInfo, int framesCounter, int currTim
{
//std::cout << TypeConverter::Type2Str(track.m_type) << " - " << track.m_rect << std::endl;
- DrawTrack(frameInfo->m_frame, 1, track, true);
+ DrawTrack(frameInfo->m_frame, track, true);
std::string label = TypeConverter::Type2Str(track.m_type);// +": " + std::to_string(track.m_confidence);
int baseLine = 0;
cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
cv::Rect brect = track.m_rrect.boundingRect();
- if (brect.x < 0)
- {
- brect.width = std::min(brect.width, frameInfo->m_frame.cols - 1);
- brect.x = 0;
- }
- else if (brect.x + brect.width >= frameInfo->m_frame.cols)
- {
- brect.x = std::max(0, frameInfo->m_frame.cols - brect.width - 1);
- brect.width = std::min(brect.width, frameInfo->m_frame.cols - 1);
- }
- if (brect.y - labelSize.height < 0)
- {
- brect.height = std::min(brect.height, frameInfo->m_frame.rows - 1);
- brect.y = labelSize.height;
- }
- else if (brect.y + brect.height >= frameInfo->m_frame.rows)
- {
- brect.y = std::max(0, frameInfo->m_frame.rows - brect.height - 1);
- brect.height = std::min(brect.height, frameInfo->m_frame.rows - 1);
+ if (brect.x < 0)
+ {
+ brect.width = std::min(brect.width, frameInfo->m_frame.cols - 1);
+ brect.x = 0;
+ }
+ else if (brect.x + brect.width >= frameInfo->m_frame.cols)
+ {
+ brect.x = std::max(0, frameInfo->m_frame.cols - brect.width - 1);
+ brect.width = std::min(brect.width, frameInfo->m_frame.cols - 1);
+ }
+ if (brect.y - labelSize.height < 0)
+ {
+ brect.height = std::min(brect.height, frameInfo->m_frame.rows - 1);
+ brect.y = labelSize.height;
+ }
+ else if (brect.y + brect.height >= frameInfo->m_frame.rows)
+ {
+ brect.y = std::max(0, frameInfo->m_frame.rows - brect.height - 1);
+ brect.height = std::min(brect.height, frameInfo->m_frame.rows - 1);
}
DrawFilledRect(frameInfo->m_frame, cv::Rect(cv::Point(brect.x, brect.y - labelSize.height), cv::Size(labelSize.width, labelSize.height + baseLine)), cv::Scalar(200, 200, 200), 150);
cv::putText(frameInfo->m_frame, label, brect.tl(), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
@@ -279,7 +250,7 @@ void AsyncDetector::DrawData(frame_ptr frameInfo, int framesCounter, int currTim
/// \param framesQue
/// \param stopFlag
///
-void AsyncDetector::CaptureThread(std::string fileName, int startFrame, float* fps, FramesQueue* framesQue, bool* stopFlag)
+void AsyncDetector::CaptureThread(std::string fileName, int startFrame, int* framesCount, float* fps, FramesQueue* framesQue, bool* stopFlag)
{
cv::VideoCapture capture;
if (fileName.size() == 1)
@@ -293,8 +264,11 @@ void AsyncDetector::CaptureThread(std::string fileName, int startFrame, float* f
std::cerr << "Can't open " << fileName << std::endl;
return;
}
+ *framesCount = cvRound(capture.get(cv::CAP_PROP_FRAME_COUNT));
capture.set(cv::CAP_PROP_POS_FRAMES, startFrame);
+ time_point_t startTimeStamp = std::chrono::system_clock::now();
+
*fps = std::max(1.f, (float)capture.get(cv::CAP_PROP_FPS));
int frameHeight = cvRound(capture.get(cv::CAP_PROP_FRAME_HEIGHT));
@@ -319,14 +293,12 @@ void AsyncDetector::CaptureThread(std::string fileName, int startFrame, float* f
detectorConfig.emplace("maxCropRatio", "3.0");
#if 1
- detectorConfig.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_person));
- detectorConfig.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_car));
- detectorConfig.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_bicycle));
- detectorConfig.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_motorbike));
- detectorConfig.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_bus));
- detectorConfig.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_truck));
- //detectorConfig.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_traffic light));
- //detectorConfig.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_stop sign));
+ detectorConfig.emplace("white_list", "person");
+ detectorConfig.emplace("white_list", "car");
+ detectorConfig.emplace("white_list", "bicycle");
+ detectorConfig.emplace("white_list", "motorbike");
+ detectorConfig.emplace("white_list", "bus");
+ detectorConfig.emplace("white_list", "truck");
#endif
// Tracker
@@ -348,13 +320,13 @@ void AsyncDetector::CaptureThread(std::string fileName, int startFrame, float* f
{
trackerSettings.m_minStaticTime = minStaticTime;
trackerSettings.m_maxStaticTime = 60;
- trackerSettings.m_maximumAllowedSkippedFrames = cvRound(trackerSettings.m_minStaticTime * (*fps)); // Maximum allowed skipped frames
- trackerSettings.m_maxTraceLength = 2 * trackerSettings.m_maximumAllowedSkippedFrames; // Maximum trace length
+ trackerSettings.m_maximumAllowedLostTime = trackerSettings.m_minStaticTime; // Maximum allowed lost time
+ trackerSettings.m_maxTraceLength = 2 * trackerSettings.m_maximumAllowedLostTime; // Maximum trace length
}
else
{
- trackerSettings.m_maximumAllowedSkippedFrames = cvRound(2 * (*fps)); // Maximum allowed skipped frames
- trackerSettings.m_maxTraceLength = cvRound(4 * (*fps)); // Maximum trace length
+ trackerSettings.m_maximumAllowedLostTime = 2.; // Maximum allowed lost time
+ trackerSettings.m_maxTraceLength = 4.; // Maximum trace length
}
// Capture the first frame
@@ -364,13 +336,14 @@ void AsyncDetector::CaptureThread(std::string fileName, int startFrame, float* f
++frameInd;
std::thread thDetection(DetectThread, detectorConfig, firstFrame, framesQue, stopFlag);
- std::thread thTracking(TrackingThread, trackerSettings, framesQue, stopFlag);
+ std::thread thTracking(TrackingThread, trackerSettings, framesQue, *fps, stopFlag);
// Capture frame
for (; !(*stopFlag);)
{
frame_ptr frameInfo(new FrameInfo(frameInd));
frameInfo->m_dt = cv::getTickCount();
+ frameInfo->m_frameTimeStamp = startTimeStamp + std::chrono::milliseconds(cvRound(frameInd * (1000.f / (*fps))));
capture >> frameInfo->m_frame;
if (frameInfo->m_frame.empty())
{
@@ -385,9 +358,9 @@ void AsyncDetector::CaptureThread(std::string fileName, int startFrame, float* f
framesQue->AddNewFrame(frameInfo, 15);
#if 1
- std::this_thread::sleep_for(std::chrono::milliseconds(1000 / cvRound(*fps) - 1));
+ //std::this_thread::sleep_for(std::chrono::milliseconds(1000 / cvRound(*fps) - 1));
#else
- std::this_thread::sleep_for(std::chrono::milliseconds(1));
+ //std::this_thread::sleep_for(std::chrono::milliseconds(1));
#endif
++frameInd;
@@ -411,8 +384,7 @@ void AsyncDetector::CaptureThread(std::string fileName, int startFrame, float* f
void AsyncDetector::DetectThread(const config_t& config, cv::Mat firstFrame, FramesQueue* framesQue, bool* stopFlag)
{
cv::UMat ufirst = firstFrame.getUMat(cv::ACCESS_READ);
- std::unique_ptr detector = std::unique_ptr(CreateDetector(tracking::Detectors::Yolo_Darknet, config, ufirst));
- detector->SetMinObjectSize(cv::Size(firstFrame.cols / 50, firstFrame.cols / 50));
+ std::unique_ptr detector = BaseDetector::CreateDetector(tracking::Detectors::ONNX_TensorRT, config, ufirst);
for (; !(*stopFlag);)
{
@@ -420,7 +392,6 @@ void AsyncDetector::DetectThread(const config_t& config, cv::Mat firstFrame, Fra
if (frameInfo)
{
detector->Detect(frameInfo->m_clFrame);
- //std::this_thread::sleep_for(std::chrono::milliseconds(500));
const regions_t& regions = detector->GetDetects();
frameInfo->m_regions.assign(regions.begin(), regions.end());
@@ -435,18 +406,18 @@ void AsyncDetector::DetectThread(const config_t& config, cv::Mat firstFrame, Fra
/// \brief AsyncDetector::TrackingThread
/// \param
///
-void AsyncDetector::TrackingThread(const TrackerSettings& settings, FramesQueue* framesQue, bool* stopFlag)
+void AsyncDetector::TrackingThread(const TrackerSettings& settings, FramesQueue* framesQue, float fps, bool* stopFlag)
{
- std::unique_ptr tracker = std::make_unique(settings);
+ std::unique_ptr tracker = BaseTracker::CreateTracker(settings, fps);
for (; !(*stopFlag);)
{
frame_ptr frameInfo = framesQue->GetFirstDetectedFrame();
if (frameInfo)
{
- tracker->Update(frameInfo->m_regions, frameInfo->m_clFrame, frameInfo->m_fps);
+ tracker->Update(frameInfo->m_regions, frameInfo->m_clFrame, frameInfo->m_frameTimeStamp);
- frameInfo->m_tracks = tracker->GetTracks();
+ tracker->GetTracks(frameInfo->m_tracks);
frameInfo->m_inTracker.store(FrameInfo::StateCompleted);
framesQue->Signal(frameInfo->m_dt);
}
diff --git a/async_detector/AsyncDetector.h b/async_detector/AsyncDetector.h
index d76928324..9a0a2a763 100644
--- a/async_detector/AsyncDetector.h
+++ b/async_detector/AsyncDetector.h
@@ -10,7 +10,13 @@
#include
#include "BaseDetector.h"
-#include "Ctracker.h"
+#include "BaseTracker.h"
+
+#include "spdlog/spdlog.h"
+#include "spdlog/async.h"
+#include "spdlog/sinks/stdout_color_sinks.h"
+#include "spdlog/sinks/basic_file_sink.h"
+#include "spdlog/sinks/rotating_file_sink.h"
// ----------------------------------------------------------------------
@@ -23,8 +29,8 @@ struct FrameInfo
cv::UMat m_clFrame;
regions_t m_regions;
std::vector m_tracks;
+ time_point_t m_frameTimeStamp;
int64 m_dt = 0;
- float m_fps = 0;
size_t m_frameInd = 0;
static constexpr int StateNotProcessed = 0;
@@ -52,28 +58,32 @@ class AsyncDetector
{
public:
AsyncDetector(const cv::CommandLineParser& parser);
- ~AsyncDetector();
+ ~AsyncDetector() = default;
void Process();
private:
- bool m_showLogs = false;
- float m_fps = 0;
+ std::string m_showLogsLevel = "info";
+ float m_fps = 25;
std::string m_inFile;
std::string m_outFile;
int m_startFrame = 0;
int m_endFrame = 0;
int m_finishDelay = 0;
+ int m_framesCount = 0;
std::vector m_colors;
FramesQueue m_framesQue;
void DrawData(frame_ptr frameInfo, int framesCounter, int currTime);
+ void DrawTrack(cv::Mat frame, const TrackingObject& track, bool drawTrajectory = true);
- void DrawTrack(cv::Mat frame, int resizeCoeff, const TrackingObject& track, bool drawTrajectory = true);
-
- static void CaptureThread(std::string fileName, int startFrame, float* fps, FramesQueue* framesQue, bool* stopFlag);
+ static void CaptureThread(std::string fileName, int startFrame, int* framesCount, float* fps, FramesQueue* framesQue, bool* stopFlag);
static void DetectThread(const config_t& config, cv::Mat firstFrame, FramesQueue* framesQue, bool* stopFlag);
- static void TrackingThread(const TrackerSettings& settings, FramesQueue* framesQue, bool* stopFlag);
+ static void TrackingThread(const TrackerSettings& settings, FramesQueue* framesQue, float fps, bool* stopFlag);
+
+ std::shared_ptr m_consoleSink;
+ std::shared_ptr m_fileSink;
+ std::shared_ptr m_logger;
};
diff --git a/async_detector/CMakeLists.txt b/async_detector/CMakeLists.txt
index d03a46051..2a552a621 100644
--- a/async_detector/CMakeLists.txt
+++ b/async_detector/CMakeLists.txt
@@ -2,50 +2,30 @@ cmake_minimum_required (VERSION 3.5)
project(AsyncDetector)
-set(SOURCES
- main.cpp
- AsyncDetector.cpp
-)
+set(SOURCES main.cpp
+ AsyncDetector.cpp)
-set(HEADERS
- AsyncDetector.h
- Queue.h
-)
+set(HEADERS AsyncDetector.h
+ Queue.h)
# ----------------------------------------------------------------------------
# дОйавĐģŅĐĩĐŧ include диŅĐĩĐēŅĐžŅии
# ----------------------------------------------------------------------------
-INCLUDE_DIRECTORIES(
- ${PROJECT_SOURCE_DIR}/../src
- ${PROJECT_SOURCE_DIR}/../src/common
+INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/../src
+ ${PROJECT_SOURCE_DIR}/../src/mtracking
${PROJECT_SOURCE_DIR}/../src/Detector
${PROJECT_SOURCE_DIR}/../src/Detector/vibe_src
${PROJECT_SOURCE_DIR}/../src/Detector/Subsense
${PROJECT_SOURCE_DIR}/../src/Tracker
${PROJECT_SOURCE_DIR}/../src/Tracker/HungarianAlg
-)
+ ${PROJECT_SOURCE_DIR}/../thirdparty/spdlog/include)
-set(LIBS
- ${OpenCV_LIBS}
- mtracking
- mdetection
-)
+set(LIBS ${OpenCV_LIBS}
+ mtracking
+ mdetection)
-if (BUILD_YOLO_LIB)
-if (MSVC)
- if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "4")
- set(BIT_SYSTEM x32)
- else()
- set(BIT_SYSTEM x64)
- endif()
-
- link_directories(${PROJECT_SOURCE_DIR}/../src/Detector/darknet/3rdparty/lib/${BIT_SYSTEM})
-endif(MSVC)
-
- add_definitions(-DBUILD_YOLO_LIB)
-endif(BUILD_YOLO_LIB)
-
ADD_EXECUTABLE(${PROJECT_NAME} ${SOURCES} ${HEADERS})
+TARGET_LINK_LIBRARIES(${PROJECT_NAME} PRIVATE ${LIBS})
-TARGET_LINK_LIBRARIES(${PROJECT_NAME} ${LIBS})
+set_target_properties(${PROJECT_NAME} PROPERTIES FOLDER "apps")
\ No newline at end of file
diff --git a/async_detector/Queue.h b/async_detector/Queue.h
index eb931979b..46aa468f0 100644
--- a/async_detector/Queue.h
+++ b/async_detector/Queue.h
@@ -221,7 +221,7 @@ class FramesQueue
}
if (!m_break.load())
{
- frame_ptr frameInfo = m_que.front();
+ frame_ptr frameInfo = std::move(m_que.front());
m_que.pop_front();
#if SHOW_QUE_LOG
QUE_LOG << "GetFirstProcessedFrame end: " << frameInfo->m_dt << ", frameInd " << frameInfo->m_frameInd << std::endl;
diff --git a/async_detector/main.cpp b/async_detector/main.cpp
index 09d1c2356..27a422b8e 100644
--- a/async_detector/main.cpp
+++ b/async_detector/main.cpp
@@ -18,13 +18,14 @@ static void Help()
const char* keys =
{
- "{ @1 |../data/atrium.avi | movie file | }"
- "{ sf start_frame |0 | Start a video from this position | }"
- "{ ef end_frame |0 | Play a video to this position (if 0 then played to the end of file) | }"
- "{ ed end_delay |0 | Delay in milliseconds after video ending | }"
- "{ o out | | Name of result video file | }"
- "{ sl show_logs |1 | Show Trackers logs | }"
- "{ g gpu |0 | Use OpenCL acceleration | }"
+ "{ @1 |../data/atrium.avi | movie file | }"
+ "{ sf start_frame |0 | Start a video from this position | }"
+ "{ ef end_frame |0 | Play a video to this position (if 0 then played to the end of file) | }"
+ "{ ed end_delay |0 | Delay in milliseconds after video ending | }"
+ "{ o out | | Name of result video file | }"
+ "{ show_logs |info | Show Trackers logs: trace, debug, info, warning, error, critical, off | }"
+ "{ g gpu |0 | Use OpenCL acceleration | }"
+ "{ contrast_adjustment |0 | Use contrast adjustment for frames before detection | }"
};
// ----------------------------------------------------------------------
diff --git a/cars_counting/CMakeLists.txt b/cars_counting/CMakeLists.txt
deleted file mode 100644
index 2b811ac89..000000000
--- a/cars_counting/CMakeLists.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-cmake_minimum_required (VERSION 3.5)
-
-project(CarsCounting)
-
-set(SOURCES
- main.cpp
- CarsCounting.cpp
-)
-
-set(HEADERS
- CarsCounting.h
-)
-
-# ----------------------------------------------------------------------------
-# дОйавĐģŅĐĩĐŧ include диŅĐĩĐēŅĐžŅии
-# ----------------------------------------------------------------------------
-INCLUDE_DIRECTORIES(
- ${PROJECT_SOURCE_DIR}/../src
- ${PROJECT_SOURCE_DIR}/../src/common
- ${PROJECT_SOURCE_DIR}/../src/Detector
- ${PROJECT_SOURCE_DIR}/../src/Detector/vibe_src
- ${PROJECT_SOURCE_DIR}/../src/Detector/Subsense
- ${PROJECT_SOURCE_DIR}/../src/Tracker
- ${PROJECT_SOURCE_DIR}/../src/Tracker/HungarianAlg
-)
-
-set(LIBS
- ${OpenCV_LIBS}
- mtracking
- mdetection
-)
-
-if (BUILD_YOLO_LIB)
-if (MSVC)
- if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "4")
- set(BIT_SYSTEM x32)
- else()
- set(BIT_SYSTEM x64)
- endif()
-
- link_directories(${PROJECT_SOURCE_DIR}/../src/Detector/darknet/3rdparty/lib/${BIT_SYSTEM})
-endif(MSVC)
-
- add_definitions(-DBUILD_YOLO_LIB)
-endif(BUILD_YOLO_LIB)
-
-ADD_EXECUTABLE(${PROJECT_NAME} ${SOURCES} ${HEADERS})
-
-
-TARGET_LINK_LIBRARIES(${PROJECT_NAME} ${LIBS})
diff --git a/cars_counting/CarsCounting.cpp b/cars_counting/CarsCounting.cpp
deleted file mode 100644
index 7de9a8b79..000000000
--- a/cars_counting/CarsCounting.cpp
+++ /dev/null
@@ -1,588 +0,0 @@
-#include "CarsCounting.h"
-
-///
-/// \brief CarsCounting::CarsCounting
-/// \param parser
-///
-CarsCounting::CarsCounting(const cv::CommandLineParser& parser)
- :
- m_showLogs(true),
- m_fps(25),
- m_isTrackerInitialized(false),
- m_startFrame(0),
- m_endFrame(0),
- m_finishDelay(0)
-{
-#ifdef _WIN32
- std::string pathToModel = "../../data/";
-#else
- std::string pathToModel = "../data/";
-#endif
-
-
- m_inFile = parser.get(0);
- m_outFile = parser.get("out");
- m_showLogs = parser.get("show_logs") != 0;
- m_startFrame = parser.get("start_frame");
- m_endFrame = parser.get("end_frame");
- m_finishDelay = parser.get("end_delay");
- m_drawHeatMap = parser.get("heat_map") != 0;
-
- m_weightsFile = parser.get("weights");
- m_configFile = parser.get("config");
- m_namesFile = parser.get("names");
- if (m_weightsFile.empty() && m_configFile.empty())
- {
- m_weightsFile = pathToModel + "yolov4.weights";
- m_configFile = pathToModel + "yolov4.cfg";
- }
- if (m_namesFile.empty())
- m_namesFile = pathToModel + "coco.names";
-
- std::map infMap;
- infMap.emplace("darknet", tracking::Detectors::Yolo_Darknet);
- infMap.emplace("ocvdnn", tracking::Detectors::DNN_OCV);
- std::string inference = parser.get("inference");
- auto infType = infMap.find(inference);
- if (infType != std::end(infMap))
- m_detectorType = infType->second;
- else
- m_detectorType = tracking::Detectors::Yolo_Darknet;
-
- std::cout << "Inference framework set " << inference << " used " << m_detectorType << ", weights: " << m_weightsFile << ", config: " << m_configFile << ", names: " << m_namesFile << std::endl;
-
- m_colors.push_back(cv::Scalar(255, 0, 0));
- m_colors.push_back(cv::Scalar(0, 255, 0));
- m_colors.push_back(cv::Scalar(0, 0, 255));
- m_colors.push_back(cv::Scalar(255, 255, 0));
- m_colors.push_back(cv::Scalar(0, 255, 255));
- m_colors.push_back(cv::Scalar(255, 0, 255));
- m_colors.push_back(cv::Scalar(255, 127, 255));
- m_colors.push_back(cv::Scalar(127, 0, 255));
- m_colors.push_back(cv::Scalar(127, 0, 127));
-}
-
-///
-/// \brief CarsCounting::~CarsCounting
-///
-CarsCounting::~CarsCounting()
-{
-
-}
-
-///
-/// \brief CarsCounting::Process
-///
-void CarsCounting::Process()
-{
- cv::VideoWriter writer;
-
-#ifndef SILENT_WORK
- cv::namedWindow("Video", cv::WINDOW_NORMAL | cv::WINDOW_KEEPRATIO);
-#endif
-
- int k = 0;
-
- double freq = cv::getTickFrequency();
-
- int64 allTime = 0;
-
- bool manualMode = false;
- int framesCounter = m_startFrame + 1;
-
- cv::VideoCapture capture;
- if (m_inFile.size() == 1)
- capture.open(atoi(m_inFile.c_str()));
- else
- capture.open(m_inFile);
-
- if (!capture.isOpened())
- {
- std::cerr << "Can't open " << m_inFile << std::endl;
- return;
- }
- capture.set(cv::CAP_PROP_POS_FRAMES, m_startFrame);
-
- m_fps = std::max(1.f, (float)capture.get(cv::CAP_PROP_FPS));
-
- cv::Mat colorFrame;
- capture >> colorFrame;
- if (colorFrame.empty())
- {
- std::cerr << "Frame is empty!" << std::endl;
- return;
- }
- if (!m_isTrackerInitialized)
- {
- cv::UMat uframe = colorFrame.getUMat(cv::ACCESS_READ);
- m_isTrackerInitialized = InitTracker(uframe);
- if (!m_isTrackerInitialized)
- {
- std::cerr << "Tracker initialize error!!!" << std::endl;
- return;
- }
- }
-
- for (;;)
- {
- capture >> colorFrame;
- if (colorFrame.empty())
- {
- std::cerr << "Frame is empty!" << std::endl;
- break;
- }
-
- int64 t1 = cv::getTickCount();
-
- cv::UMat uframe;
- if (!m_detector->CanGrayProcessing() || m_tracker->CanColorFrameToTrack())
- uframe = colorFrame.getUMat(cv::ACCESS_READ);
- else
- cv::cvtColor(colorFrame, uframe, cv::COLOR_BGR2GRAY);
-
- m_detector->Detect(uframe);
-
- const regions_t& regions = m_detector->GetDetects();
-
- m_tracker->Update(regions, uframe, m_fps);
-
- int64 t2 = cv::getTickCount();
-
- allTime += t2 - t1;
- int currTime = cvRound(1000 * (t2 - t1) / freq);
-
- DrawData(colorFrame, framesCounter, currTime);
-
-#ifndef SILENT_WORK
- cv::imshow("Video", colorFrame);
-
- int waitTime = manualMode ? 0 : 1;// std::max(1, cvRound(1000 / m_fps - currTime));
- k = cv::waitKey(waitTime);
- if (k == 'm' || k == 'M')
- manualMode = !manualMode;
- else if (k == 27)
- break;
-
-#else
- std::this_thread::sleep_for(std::chrono::milliseconds(1));
-#endif
-
- if (!m_outFile.empty() && !writer.isOpened())
- writer.open(m_outFile, cv::VideoWriter::fourcc('H', 'F', 'Y', 'U'), m_fps, colorFrame.size(), true);
- if (writer.isOpened())
- writer << colorFrame;
-
- ++framesCounter;
- if (m_endFrame && framesCounter > m_endFrame)
- {
- std::cout << "Process: riched last " << m_endFrame << " frame" << std::endl;
- break;
- }
- }
-
- std::cout << "work time = " << (allTime / freq) << std::endl;
-#ifndef SILENT_WORK
- cv::waitKey(m_finishDelay);
-#endif
-}
-
-///
-/// \brief CarsCounting::DrawTrack
-/// \param frame
-/// \param resizeCoeff
-/// \param track
-/// \param drawTrajectory
-///
-void CarsCounting::DrawTrack(cv::Mat frame,
- int resizeCoeff,
- const TrackingObject& track,
- bool drawTrajectory
- )
-{
- auto ResizeRect = [&](const cv::Rect& r) -> cv::Rect
- {
- return cv::Rect(resizeCoeff * r.x, resizeCoeff * r.y, resizeCoeff * r.width, resizeCoeff * r.height);
- };
- auto ResizePoint = [&](const cv::Point& pt) -> cv::Point
- {
- return cv::Point(resizeCoeff * pt.x, resizeCoeff * pt.y);
- };
-
- if (track.m_isStatic)
- {
-#if (CV_VERSION_MAJOR >= 4)
- cv::rectangle(frame, ResizeRect(track.m_rrect.boundingRect()), cv::Scalar(255, 0, 255), 2, cv::LINE_AA);
-#else
- cv::rectangle(frame, ResizeRect(track.m_rrect.boundingRect()), cv::Scalar(255, 0, 255), 2, CV_AA);
-#endif
- }
- else
- {
-#if (CV_VERSION_MAJOR >= 4)
- cv::rectangle(frame, ResizeRect(track.m_rrect.boundingRect()), cv::Scalar(0, 255, 0), 1, cv::LINE_AA);
-#else
- cv::rectangle(frame, ResizeRect(track.m_rrect.boundingRect()), cv::Scalar(0, 255, 0), 1, CV_AA);
-#endif
-
- if (!m_geoParams.Empty())
- {
- int traceSize = static_cast(track.m_trace.size());
- int period = std::min(2 * cvRound(m_fps), traceSize);
- const auto& from = m_geoParams.Pix2Geo(track.m_trace[traceSize - period]);
- const auto& to = m_geoParams.Pix2Geo(track.m_trace[traceSize - 1]);
- auto dist = DistanceInMeters(from, to);
-
- std::stringstream label;
- if (period >= cvRound(m_fps) / 4)
- {
- auto velocity = (3.6f * dist * m_fps) / period;
- //std::cout << TypeConverter::Type2Str(track.m_type) << ": distance " << std::fixed << std::setw(2) << std::setprecision(2) << dist << " on time " << (period / m_fps) << " with velocity " << velocity << " km/h: " << track.m_confidence << std::endl;
- if (velocity < 1.f || std::isnan(velocity))
- velocity = 0;
- //label << TypeConverter::Type2Str(track.m_type) << " " << std::fixed << std::setw(2) << std::setprecision(2) << velocity << " km/h";
- label << TypeConverter::Type2Str(track.m_type) << " " << cvRound(velocity) << " km/h";
-
- int baseLine = 0;
- double fontScale = 0.5;
- cv::Size labelSize = cv::getTextSize(label.str(), cv::FONT_HERSHEY_SIMPLEX, fontScale, 1, &baseLine);
-
- cv::Rect brect = track.m_rrect.boundingRect();
- if (brect.x < 0)
- {
- brect.width = std::min(brect.width, frame.cols - 1);
- brect.x = 0;
- }
- else if (brect.x + brect.width >= frame.cols)
- {
- brect.x = std::max(0, frame.cols - brect.width - 1);
- brect.width = std::min(brect.width, frame.cols - 1);
- }
- if (brect.y - labelSize.height < 0)
- {
- brect.height = std::min(brect.height, frame.rows - 1);
- brect.y = labelSize.height;
- }
- else if (brect.y + brect.height >= frame.rows)
- {
- brect.y = std::max(0, frame.rows - brect.height - 1);
- brect.height = std::min(brect.height, frame.rows - 1);
- }
- cv::rectangle(frame, cv::Rect(cv::Point(brect.x, brect.y - labelSize.height), cv::Size(labelSize.width, labelSize.height + baseLine)), cv::Scalar(200, 200, 200), cv::FILLED);
- cv::putText(frame, label.str(), brect.tl(), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(0, 0, 0));
-
- if (velocity > 3)
- AddToHeatMap(brect);
- }
- }
- }
-
- if (drawTrajectory)
- {
- cv::Scalar cl = m_colors[track.m_ID % m_colors.size()];
-
- for (size_t j = 0; j < track.m_trace.size() - 1; ++j)
- {
- const TrajectoryPoint& pt1 = track.m_trace.at(j);
- const TrajectoryPoint& pt2 = track.m_trace.at(j + 1);
-#if (CV_VERSION_MAJOR >= 4)
- cv::line(frame, ResizePoint(pt1.m_prediction), ResizePoint(pt2.m_prediction), cl, 1, cv::LINE_AA);
-#else
- cv::line(frame, ResizePoint(pt1.m_prediction), ResizePoint(pt2.m_prediction), cl, 1, CV_AA);
-#endif
- if (!pt2.m_hasRaw)
- {
-#if (CV_VERSION_MAJOR >= 4)
- cv::circle(frame, ResizePoint(pt2.m_prediction), 4, cl, 1, cv::LINE_AA);
-#else
- cv::circle(frame, ResizePoint(pt2.m_prediction), 4, cl, 1, CV_AA);
-#endif
- }
- }
- }
-}
-
-///
-/// \brief CarsCounting::InitTracker
-/// \param grayFrame
-///
-bool CarsCounting::InitTracker(cv::UMat frame)
-{
- bool res = true;
-
- if (m_drawHeatMap)
- {
- if (frame.channels() == 3)
- m_keyFrame = frame.getMat(cv::ACCESS_READ).clone();
- else
- cv::cvtColor(frame, m_keyFrame, cv::COLOR_GRAY2BGR);
- m_heatMap = cv::Mat(m_keyFrame.size(), CV_32FC1, cv::Scalar::all(0));
- }
-
- m_minObjWidth = frame.cols / 50;
-
- const int minStaticTime = 5;
-
- config_t config;
-
-#if 1
- switch (m_detectorType)
- {
- case tracking::Detectors::Yolo_Darknet:
- break;
-
- case tracking::Detectors::DNN_OCV:
-#if 1
- config.emplace("dnnTarget", "DNN_TARGET_CPU");
- config.emplace("dnnBackend", "DNN_BACKEND_OPENCV");
-#else
- config.emplace("dnnTarget", "DNN_TARGET_CUDA");
- config.emplace("dnnBackend", "DNN_BACKEND_CUDA");
-#endif
- break;
-
- default:
- break;
- }
-
- config.emplace("modelConfiguration", m_configFile);
- config.emplace("modelBinary", m_weightsFile);
- config.emplace("classNames", m_namesFile);
- config.emplace("confidenceThreshold", "0.5");
- config.emplace("nmsThreshold", "0.4");
- config.emplace("swapRB", "0");
- config.emplace("maxCropRatio", "-1");
-
- config.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_person));
- config.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_car));
- config.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_bicycle));
- config.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_motorbike));
- config.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_bus));
- config.emplace("white_list", std::to_string((objtype_t)ObjectTypes::obj_truck));
-
- m_detector = std::unique_ptr(CreateDetector(m_detectorType, config, frame));
-
-#else // Background subtraction
-
-#if 1
- config.emplace("history", std::to_string(cvRound(10 * minStaticTime * m_fps)));
- config.emplace("varThreshold", "16");
- config.emplace("detectShadows", "1");
- m_detector = std::unique_ptr(CreateDetector(tracking::Detectors::Motion_MOG2, config, frame));
-#else
- config.emplace("minPixelStability", "15");
- config.emplace("maxPixelStability", "900");
- config.emplace("useHistory", "1");
- config.emplace("isParallel", "1");
- m_detector = std::unique_ptr(CreateDetector(tracking::Detectors::Motion_CNT, config, m_useLocalTracking, frame));
-#endif
-
-#endif
-
- if (m_detector.get())
- m_detector->SetMinObjectSize(cv::Size(m_minObjWidth, m_minObjWidth));
- else
- res = false;
-
- if (res)
- {
- TrackerSettings settings;
- settings.SetDistance(tracking::DistJaccard);
- settings.m_kalmanType = tracking::KalmanLinear;
- settings.m_filterGoal = tracking::FilterCenter;
- settings.m_lostTrackType = tracking::TrackCSRT; // Use KCF tracker for collisions resolving. Used if m_filterGoal == tracking::FilterRect
- settings.m_matchType = tracking::MatchHungrian;
- settings.m_dt = 0.3f; // Delta time for Kalman filter
- settings.m_accelNoiseMag = 0.2f; // Accel noise magnitude for Kalman filter
- settings.m_distThres = 0.7f; // Distance threshold between region and object on two frames
- settings.m_minAreaRadiusPix = frame.rows / 20.f;
- settings.m_maximumAllowedSkippedFrames = cvRound(2 * m_fps); // Maximum allowed skipped frames
-
- settings.AddNearTypes(ObjectTypes::obj_car, ObjectTypes::obj_bus, false);
- settings.AddNearTypes(ObjectTypes::obj_car, ObjectTypes::obj_truck, false);
- settings.AddNearTypes(ObjectTypes::obj_person, ObjectTypes::obj_bicycle, true);
- settings.AddNearTypes(ObjectTypes::obj_person, ObjectTypes::obj_motorbike, true);
-
- settings.m_useAbandonedDetection = false;
- if (settings.m_useAbandonedDetection)
- {
- settings.m_minStaticTime = minStaticTime;
- settings.m_maxStaticTime = 60;
- settings.m_maximumAllowedSkippedFrames = cvRound(settings.m_minStaticTime * m_fps); // Maximum allowed skipped frames
- settings.m_maxTraceLength = 2 * settings.m_maximumAllowedSkippedFrames; // Maximum trace length
- }
- else
- {
- settings.m_maximumAllowedSkippedFrames = cvRound(10 * m_fps); // Maximum allowed skipped frames
- settings.m_maxTraceLength = cvRound(4 * m_fps); // Maximum trace length
- }
-
- m_tracker = std::make_unique(settings);
- }
-#if 0
-#if 0
- std::vector framePoints{ cv::Point(420, 348), cv::Point(509, 283), cv::Point(731, 281), cv::Point(840, 343) };
- std::vector geoPoints{ cv::Point2f(45.526646, 5.974535), cv::Point2f(45.527566, 5.973849), cv::Point2f(45.527904, 5.974135), cv::Point2f(45.526867, 5.974826) };
-#else
- std::vector framePoints{ cv::Point(1665, 746), cv::Point(246, 521), cv::Point(570, 282), cv::Point(1773, 378) };
- std::vector geoPoints{ cv::Point2f(30.258855, 60.006536), cv::Point2f(30.258051, 60.006855), cv::Point2f(30.258080, 60.007414), cv::Point2f(30.259066, 60.007064) };
-#endif
- m_geoParams.SetKeyPoints(framePoints, geoPoints);
-#endif
- return res;
-}
-
-///
-/// \brief CarsCounting::DrawData
-/// \param frame
-///
-void CarsCounting::DrawData(cv::Mat frame, int framesCounter, int currTime)
-{
- auto tracks = m_tracker->GetTracks();
-
- if (m_showLogs)
- {
- std::cout << "Frame " << framesCounter << ": tracks = " << tracks.size() << ", time = " << currTime << std::endl;
- }
-
- if (!m_geoParams.Empty())
- {
- std::vector points = m_geoParams.GetFramePoints();
- for (size_t i = 0; i < points.size(); ++i)
- {
- cv::line(frame, points[i % points.size()], points[(i + 1) % points.size()], cv::Scalar(255, 255, 255), 1, cv::LINE_AA);
- }
- }
-
- for (const auto& track : tracks)
- {
- if (track.m_isStatic)
- {
- DrawTrack(frame, 1, track, true);
- }
- else
- {
- if (track.IsRobust(cvRound(m_fps / 4), // Minimal trajectory size
- 0.8f, // Minimal ratio raw_trajectory_points / trajectory_lenght
- cv::Size2f(0.1f, 8.0f)) // Min and max ratio: width / height
- )
- {
- DrawTrack(frame, 1, track, true);
-
- CheckLinesIntersection(track, static_cast(frame.cols), static_cast(frame.rows));
- }
- }
- }
- //m_detector->CalcMotionMap(frame);
-
- for (const auto& rl : m_lines)
- {
- rl.Draw(frame);
- }
-
- cv::Mat heatMap = DrawHeatMap();
- if (!heatMap.empty())
- cv::imshow("Heat map", heatMap);
-}
-
-///
-/// \brief CarsCounting::AddLine
-/// \param newLine
-///
-void CarsCounting::AddLine(const RoadLine& newLine)
-{
- m_lines.push_back(newLine);
-}
-
-///
-/// \brief CarsCounting::GetLine
-/// \param lineUid
-/// \return
-///
-bool CarsCounting::GetLine(unsigned int lineUid, RoadLine& line)
-{
- for (const auto& rl : m_lines)
- {
- if (rl.m_uid == lineUid)
- {
- line = rl;
- return true;
- }
- }
- return false;
-}
-
-///
-/// \brief CarsCounting::RemoveLine
-/// \param lineUid
-/// \return
-///
-bool CarsCounting::RemoveLine(unsigned int lineUid)
-{
- for (auto it = std::begin(m_lines); it != std::end(m_lines);)
- {
- if (it->m_uid == lineUid)
- it = m_lines.erase(it);
- else
- ++it;
- }
- return false;
-}
-
-///
-/// \brief CarsCounting::CheckLinesIntersection
-/// \param track
-///
-void CarsCounting::CheckLinesIntersection(const TrackingObject& track, float xMax, float yMax)
-{
- auto Pti2f = [&](cv::Point pt) -> cv::Point2f
- {
- return cv::Point2f(pt.x / xMax, pt.y / yMax);
- };
-
- constexpr size_t minTrack = 5;
- if (track.m_trace.size() >= minTrack)
- {
- for (auto& rl : m_lines)
- {
- if (m_lastIntersections.find(track.m_ID) == m_lastIntersections.end())
- {
- if (rl.IsIntersect(Pti2f(track.m_trace[track.m_trace.size() - minTrack]), Pti2f(track.m_trace[track.m_trace.size() - 1])))
- m_lastIntersections.emplace(track.m_ID);
- }
- }
- }
-}
-
-///
-/// \brief CarsCounting::DrawHeatMap
-///
-cv::Mat CarsCounting::DrawHeatMap()
-{
- cv::Mat res;
- if (!m_heatMap.empty())
- {
- cv::normalize(m_heatMap, m_normHeatMap, 255, 0, cv::NORM_MINMAX, CV_8UC1);
- cv::applyColorMap(m_normHeatMap, m_colorMap, cv::COLORMAP_HOT);
- cv::bitwise_or(m_keyFrame, m_colorMap, res);
- }
- return res;
-}
-
-///
-/// \brief CarsCounting::AddToHeatMap
-///
-void CarsCounting::AddToHeatMap(const cv::Rect& rect)
-{
- if (m_heatMap.empty())
- return;
-
- constexpr float w = 0.001f;
- for (int y = 0; y < rect.height; ++y)
- {
- float* heatPtr = m_heatMap.ptr(rect.y + y) + rect.x;
- for (int x = 0; x < rect.width; ++x)
- {
- heatPtr[x] += w;
- }
- }
-}
diff --git a/cars_counting/main.cpp b/cars_counting/main.cpp
deleted file mode 100644
index 211f8ad6a..000000000
--- a/cars_counting/main.cpp
+++ /dev/null
@@ -1,59 +0,0 @@
-#include "CarsCounting.h"
-
-#include
-#include
-
-// ----------------------------------------------------------------------
-
-static void Help()
-{
- printf("\nExamples of the CarsCounting\n"
- "Usage: \n"
- " ./CarsCounting [--start_frame]= [--end_frame]= [--end_delay]= [--out]= [--show_logs]= \n\n"
- "Press:\n"
- "\'m\' key for change mode: play|pause. When video is paused you can press any key for get next frame. \n\n"
- "Press Esc to exit from video \n\n"
- );
-}
-
-const char* keys =
-{
- "{ @1 |../data/atrium.avi | movie file | }"
- "{ inf inference |darknet | Type of inference framework: darknet, ocvdnn | }"
- "{ w weights | | Weights of neural network: yolov4.weights | }"
- "{ c config | | Config file of neural network: yolov4.cfg | }"
- "{ n names | | File with classes names: coco.names | }"
- "{ sf start_frame |0 | Start a video from this position | }"
- "{ ef end_frame |0 | Play a video to this position (if 0 then played to the end of file) | }"
- "{ ed end_delay |0 | Delay in milliseconds after video ending | }"
- "{ o out | | Name of result video file | }"
- "{ sl show_logs |1 | Show Trackers logs | }"
- "{ g gpu |0 | Use OpenCL acceleration | }"
- "{ hm heat_map |0 | Draw heat map | }"
-};
-
-// ----------------------------------------------------------------------
-
-int main(int argc, char** argv)
-{
- Help();
-
- cv::CommandLineParser parser(argc, argv, keys);
-
- bool useOCL = parser.get("gpu") ? 1 : 0;
- cv::ocl::setUseOpenCL(useOCL);
- std::cout << (cv::ocl::useOpenCL() ? "OpenCL is enabled" : "OpenCL not used") << std::endl;
-
- CarsCounting cars_counting(parser);
-
- cars_counting.AddLine(RoadLine(cv::Point2f(0.1f, 0.7f), cv::Point2f(0.47f, 0.7f), 0));
- cars_counting.AddLine(RoadLine(cv::Point2f(0.52f, 0.6f), cv::Point2f(0.8f, 0.6f), 1));
-
- cars_counting.Process();
-
-#ifndef SILENT_WORK
- cv::destroyAllWindows();
-#endif
-
- return 0;
-}
diff --git a/data/MobileNetSSD_deploy.caffemodel b/data/MobileNetSSD_deploy.caffemodel
deleted file mode 100644
index 7104f06cd..000000000
Binary files a/data/MobileNetSSD_deploy.caffemodel and /dev/null differ
diff --git a/data/MobileNetSSD_deploy.prototxt b/data/MobileNetSSD_deploy.prototxt
deleted file mode 100644
index fdc812628..000000000
--- a/data/MobileNetSSD_deploy.prototxt
+++ /dev/null
@@ -1,1912 +0,0 @@
-name: "MobileNet-SSD"
-input: "data"
-input_shape {
- dim: 1
- dim: 3
- dim: 300
- dim: 300
-}
-layer {
- name: "conv0"
- type: "Convolution"
- bottom: "data"
- top: "conv0"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 32
- pad: 1
- kernel_size: 3
- stride: 2
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv0/relu"
- type: "ReLU"
- bottom: "conv0"
- top: "conv0"
-}
-layer {
- name: "conv1/dw"
- type: "Convolution"
- bottom: "conv0"
- top: "conv1/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 32
- pad: 1
- kernel_size: 3
- group: 32
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv1/dw/relu"
- type: "ReLU"
- bottom: "conv1/dw"
- top: "conv1/dw"
-}
-layer {
- name: "conv1"
- type: "Convolution"
- bottom: "conv1/dw"
- top: "conv1"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 64
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv1/relu"
- type: "ReLU"
- bottom: "conv1"
- top: "conv1"
-}
-layer {
- name: "conv2/dw"
- type: "Convolution"
- bottom: "conv1"
- top: "conv2/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 64
- pad: 1
- kernel_size: 3
- stride: 2
- group: 64
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv2/dw/relu"
- type: "ReLU"
- bottom: "conv2/dw"
- top: "conv2/dw"
-}
-layer {
- name: "conv2"
- type: "Convolution"
- bottom: "conv2/dw"
- top: "conv2"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv2/relu"
- type: "ReLU"
- bottom: "conv2"
- top: "conv2"
-}
-layer {
- name: "conv3/dw"
- type: "Convolution"
- bottom: "conv2"
- top: "conv3/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 128
- pad: 1
- kernel_size: 3
- group: 128
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv3/dw/relu"
- type: "ReLU"
- bottom: "conv3/dw"
- top: "conv3/dw"
-}
-layer {
- name: "conv3"
- type: "Convolution"
- bottom: "conv3/dw"
- top: "conv3"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv3/relu"
- type: "ReLU"
- bottom: "conv3"
- top: "conv3"
-}
-layer {
- name: "conv4/dw"
- type: "Convolution"
- bottom: "conv3"
- top: "conv4/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 128
- pad: 1
- kernel_size: 3
- stride: 2
- group: 128
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv4/dw/relu"
- type: "ReLU"
- bottom: "conv4/dw"
- top: "conv4/dw"
-}
-layer {
- name: "conv4"
- type: "Convolution"
- bottom: "conv4/dw"
- top: "conv4"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 256
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv4/relu"
- type: "ReLU"
- bottom: "conv4"
- top: "conv4"
-}
-layer {
- name: "conv5/dw"
- type: "Convolution"
- bottom: "conv4"
- top: "conv5/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- group: 256
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv5/dw/relu"
- type: "ReLU"
- bottom: "conv5/dw"
- top: "conv5/dw"
-}
-layer {
- name: "conv5"
- type: "Convolution"
- bottom: "conv5/dw"
- top: "conv5"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 256
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv5/relu"
- type: "ReLU"
- bottom: "conv5"
- top: "conv5"
-}
-layer {
- name: "conv6/dw"
- type: "Convolution"
- bottom: "conv5"
- top: "conv6/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 2
- group: 256
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv6/dw/relu"
- type: "ReLU"
- bottom: "conv6/dw"
- top: "conv6/dw"
-}
-layer {
- name: "conv6"
- type: "Convolution"
- bottom: "conv6/dw"
- top: "conv6"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv6/relu"
- type: "ReLU"
- bottom: "conv6"
- top: "conv6"
-}
-layer {
- name: "conv7/dw"
- type: "Convolution"
- bottom: "conv6"
- top: "conv7/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- group: 512
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv7/dw/relu"
- type: "ReLU"
- bottom: "conv7/dw"
- top: "conv7/dw"
-}
-layer {
- name: "conv7"
- type: "Convolution"
- bottom: "conv7/dw"
- top: "conv7"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv7/relu"
- type: "ReLU"
- bottom: "conv7"
- top: "conv7"
-}
-layer {
- name: "conv8/dw"
- type: "Convolution"
- bottom: "conv7"
- top: "conv8/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- group: 512
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv8/dw/relu"
- type: "ReLU"
- bottom: "conv8/dw"
- top: "conv8/dw"
-}
-layer {
- name: "conv8"
- type: "Convolution"
- bottom: "conv8/dw"
- top: "conv8"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv8/relu"
- type: "ReLU"
- bottom: "conv8"
- top: "conv8"
-}
-layer {
- name: "conv9/dw"
- type: "Convolution"
- bottom: "conv8"
- top: "conv9/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- group: 512
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv9/dw/relu"
- type: "ReLU"
- bottom: "conv9/dw"
- top: "conv9/dw"
-}
-layer {
- name: "conv9"
- type: "Convolution"
- bottom: "conv9/dw"
- top: "conv9"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv9/relu"
- type: "ReLU"
- bottom: "conv9"
- top: "conv9"
-}
-layer {
- name: "conv10/dw"
- type: "Convolution"
- bottom: "conv9"
- top: "conv10/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- group: 512
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv10/dw/relu"
- type: "ReLU"
- bottom: "conv10/dw"
- top: "conv10/dw"
-}
-layer {
- name: "conv10"
- type: "Convolution"
- bottom: "conv10/dw"
- top: "conv10"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv10/relu"
- type: "ReLU"
- bottom: "conv10"
- top: "conv10"
-}
-layer {
- name: "conv11/dw"
- type: "Convolution"
- bottom: "conv10"
- top: "conv11/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- group: 512
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv11/dw/relu"
- type: "ReLU"
- bottom: "conv11/dw"
- top: "conv11/dw"
-}
-layer {
- name: "conv11"
- type: "Convolution"
- bottom: "conv11/dw"
- top: "conv11"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv11/relu"
- type: "ReLU"
- bottom: "conv11"
- top: "conv11"
-}
-layer {
- name: "conv12/dw"
- type: "Convolution"
- bottom: "conv11"
- top: "conv12/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 2
- group: 512
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv12/dw/relu"
- type: "ReLU"
- bottom: "conv12/dw"
- top: "conv12/dw"
-}
-layer {
- name: "conv12"
- type: "Convolution"
- bottom: "conv12/dw"
- top: "conv12"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 1024
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv12/relu"
- type: "ReLU"
- bottom: "conv12"
- top: "conv12"
-}
-layer {
- name: "conv13/dw"
- type: "Convolution"
- bottom: "conv12"
- top: "conv13/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 1024
- pad: 1
- kernel_size: 3
- group: 1024
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv13/dw/relu"
- type: "ReLU"
- bottom: "conv13/dw"
- top: "conv13/dw"
-}
-layer {
- name: "conv13"
- type: "Convolution"
- bottom: "conv13/dw"
- top: "conv13"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 1024
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv13/relu"
- type: "ReLU"
- bottom: "conv13"
- top: "conv13"
-}
-layer {
- name: "conv14_1"
- type: "Convolution"
- bottom: "conv13"
- top: "conv14_1"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 256
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv14_1/relu"
- type: "ReLU"
- bottom: "conv14_1"
- top: "conv14_1"
-}
-layer {
- name: "conv14_2"
- type: "Convolution"
- bottom: "conv14_1"
- top: "conv14_2"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 2
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv14_2/relu"
- type: "ReLU"
- bottom: "conv14_2"
- top: "conv14_2"
-}
-layer {
- name: "conv15_1"
- type: "Convolution"
- bottom: "conv14_2"
- top: "conv15_1"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv15_1/relu"
- type: "ReLU"
- bottom: "conv15_1"
- top: "conv15_1"
-}
-layer {
- name: "conv15_2"
- type: "Convolution"
- bottom: "conv15_1"
- top: "conv15_2"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 2
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv15_2/relu"
- type: "ReLU"
- bottom: "conv15_2"
- top: "conv15_2"
-}
-layer {
- name: "conv16_1"
- type: "Convolution"
- bottom: "conv15_2"
- top: "conv16_1"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv16_1/relu"
- type: "ReLU"
- bottom: "conv16_1"
- top: "conv16_1"
-}
-layer {
- name: "conv16_2"
- type: "Convolution"
- bottom: "conv16_1"
- top: "conv16_2"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 2
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv16_2/relu"
- type: "ReLU"
- bottom: "conv16_2"
- top: "conv16_2"
-}
-layer {
- name: "conv17_1"
- type: "Convolution"
- bottom: "conv16_2"
- top: "conv17_1"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 64
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv17_1/relu"
- type: "ReLU"
- bottom: "conv17_1"
- top: "conv17_1"
-}
-layer {
- name: "conv17_2"
- type: "Convolution"
- bottom: "conv17_1"
- top: "conv17_2"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 128
- pad: 1
- kernel_size: 3
- stride: 2
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv17_2/relu"
- type: "ReLU"
- bottom: "conv17_2"
- top: "conv17_2"
-}
-layer {
- name: "conv11_mbox_loc"
- type: "Convolution"
- bottom: "conv11"
- top: "conv11_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 12
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv11_mbox_loc_perm"
- type: "Permute"
- bottom: "conv11_mbox_loc"
- top: "conv11_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv11_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv11_mbox_loc_perm"
- top: "conv11_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv11_mbox_conf"
- type: "Convolution"
- bottom: "conv11"
- top: "conv11_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 63
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv11_mbox_conf_perm"
- type: "Permute"
- bottom: "conv11_mbox_conf"
- top: "conv11_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv11_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv11_mbox_conf_perm"
- top: "conv11_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv11_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv11"
- bottom: "data"
- top: "conv11_mbox_priorbox"
- prior_box_param {
- min_size: 60.0
- aspect_ratio: 2.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "conv13_mbox_loc"
- type: "Convolution"
- bottom: "conv13"
- top: "conv13_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv13_mbox_loc_perm"
- type: "Permute"
- bottom: "conv13_mbox_loc"
- top: "conv13_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv13_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv13_mbox_loc_perm"
- top: "conv13_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv13_mbox_conf"
- type: "Convolution"
- bottom: "conv13"
- top: "conv13_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 126
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv13_mbox_conf_perm"
- type: "Permute"
- bottom: "conv13_mbox_conf"
- top: "conv13_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv13_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv13_mbox_conf_perm"
- top: "conv13_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv13_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv13"
- bottom: "data"
- top: "conv13_mbox_priorbox"
- prior_box_param {
- min_size: 105.0
- max_size: 150.0
- aspect_ratio: 2.0
- aspect_ratio: 3.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "conv14_2_mbox_loc"
- type: "Convolution"
- bottom: "conv14_2"
- top: "conv14_2_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv14_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv14_2_mbox_loc"
- top: "conv14_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv14_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv14_2_mbox_loc_perm"
- top: "conv14_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv14_2_mbox_conf"
- type: "Convolution"
- bottom: "conv14_2"
- top: "conv14_2_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 126
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv14_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv14_2_mbox_conf"
- top: "conv14_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv14_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv14_2_mbox_conf_perm"
- top: "conv14_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv14_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv14_2"
- bottom: "data"
- top: "conv14_2_mbox_priorbox"
- prior_box_param {
- min_size: 150.0
- max_size: 195.0
- aspect_ratio: 2.0
- aspect_ratio: 3.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "conv15_2_mbox_loc"
- type: "Convolution"
- bottom: "conv15_2"
- top: "conv15_2_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv15_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv15_2_mbox_loc"
- top: "conv15_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv15_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv15_2_mbox_loc_perm"
- top: "conv15_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv15_2_mbox_conf"
- type: "Convolution"
- bottom: "conv15_2"
- top: "conv15_2_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 126
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv15_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv15_2_mbox_conf"
- top: "conv15_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv15_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv15_2_mbox_conf_perm"
- top: "conv15_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv15_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv15_2"
- bottom: "data"
- top: "conv15_2_mbox_priorbox"
- prior_box_param {
- min_size: 195.0
- max_size: 240.0
- aspect_ratio: 2.0
- aspect_ratio: 3.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "conv16_2_mbox_loc"
- type: "Convolution"
- bottom: "conv16_2"
- top: "conv16_2_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv16_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv16_2_mbox_loc"
- top: "conv16_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv16_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv16_2_mbox_loc_perm"
- top: "conv16_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv16_2_mbox_conf"
- type: "Convolution"
- bottom: "conv16_2"
- top: "conv16_2_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 126
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv16_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv16_2_mbox_conf"
- top: "conv16_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv16_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv16_2_mbox_conf_perm"
- top: "conv16_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv16_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv16_2"
- bottom: "data"
- top: "conv16_2_mbox_priorbox"
- prior_box_param {
- min_size: 240.0
- max_size: 285.0
- aspect_ratio: 2.0
- aspect_ratio: 3.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "conv17_2_mbox_loc"
- type: "Convolution"
- bottom: "conv17_2"
- top: "conv17_2_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv17_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv17_2_mbox_loc"
- top: "conv17_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv17_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv17_2_mbox_loc_perm"
- top: "conv17_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv17_2_mbox_conf"
- type: "Convolution"
- bottom: "conv17_2"
- top: "conv17_2_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 126
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv17_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv17_2_mbox_conf"
- top: "conv17_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv17_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv17_2_mbox_conf_perm"
- top: "conv17_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv17_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv17_2"
- bottom: "data"
- top: "conv17_2_mbox_priorbox"
- prior_box_param {
- min_size: 285.0
- max_size: 300.0
- aspect_ratio: 2.0
- aspect_ratio: 3.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "mbox_loc"
- type: "Concat"
- bottom: "conv11_mbox_loc_flat"
- bottom: "conv13_mbox_loc_flat"
- bottom: "conv14_2_mbox_loc_flat"
- bottom: "conv15_2_mbox_loc_flat"
- bottom: "conv16_2_mbox_loc_flat"
- bottom: "conv17_2_mbox_loc_flat"
- top: "mbox_loc"
- concat_param {
- axis: 1
- }
-}
-layer {
- name: "mbox_conf"
- type: "Concat"
- bottom: "conv11_mbox_conf_flat"
- bottom: "conv13_mbox_conf_flat"
- bottom: "conv14_2_mbox_conf_flat"
- bottom: "conv15_2_mbox_conf_flat"
- bottom: "conv16_2_mbox_conf_flat"
- bottom: "conv17_2_mbox_conf_flat"
- top: "mbox_conf"
- concat_param {
- axis: 1
- }
-}
-layer {
- name: "mbox_priorbox"
- type: "Concat"
- bottom: "conv11_mbox_priorbox"
- bottom: "conv13_mbox_priorbox"
- bottom: "conv14_2_mbox_priorbox"
- bottom: "conv15_2_mbox_priorbox"
- bottom: "conv16_2_mbox_priorbox"
- bottom: "conv17_2_mbox_priorbox"
- top: "mbox_priorbox"
- concat_param {
- axis: 2
- }
-}
-layer {
- name: "mbox_conf_reshape"
- type: "Reshape"
- bottom: "mbox_conf"
- top: "mbox_conf_reshape"
- reshape_param {
- shape {
- dim: 0
- dim: -1
- dim: 21
- }
- }
-}
-layer {
- name: "mbox_conf_softmax"
- type: "Softmax"
- bottom: "mbox_conf_reshape"
- top: "mbox_conf_softmax"
- softmax_param {
- axis: 2
- }
-}
-layer {
- name: "mbox_conf_flatten"
- type: "Flatten"
- bottom: "mbox_conf_softmax"
- top: "mbox_conf_flatten"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "detection_out"
- type: "DetectionOutput"
- bottom: "mbox_loc"
- bottom: "mbox_conf_flatten"
- bottom: "mbox_priorbox"
- top: "detection_out"
- include {
- phase: TEST
- }
- detection_output_param {
- num_classes: 21
- share_location: true
- background_label_id: 0
- nms_param {
- nms_threshold: 0.45
- top_k: 100
- }
- code_type: CENTER_SIZE
- keep_top_k: 100
- confidence_threshold: 0.25
- }
-}
diff --git a/data/cmake_vs2017.bat b/data/cmake_vs2017.bat
index f5061fe1e..810a1c36d 100644
--- a/data/cmake_vs2017.bat
+++ b/data/cmake_vs2017.bat
@@ -11,9 +11,9 @@ cmake.exe . .. -G "Visual Studio 15 2017 Win64" ^
-DBUILD_ASYNC_DETECTOR=ON ^
-DBUILD_CARS_COUNTING=ON ^
-DBUILD_YOLO_LIB=ON ^
- -DCUDNN_INCLUDE_DIR=C:/cudnn-10.0-windows10-x64-v7.6.5.32/cuda/include ^
- -DCUDNN_LIBRARY=C:/cudnn-10.0-windows10-x64-v7.6.5.32/cuda/lib/x64/cudnn.lib ^
+ -DCUDNN_INCLUDE_DIR=C:/cudnn-11.1-windows-x64-v8.0.5.39/cuda/include ^
+ -DCUDNN_LIBRARY=C:/cudnn-11.1-windows-x64-v8.0.5.39/cuda/lib/x64/cudnn.lib ^
-DBUILD_YOLO_TENSORRT=ON ^
- -DTensorRT_LIBRARY=C:/TensorRT-5.1.5.0/lib/*.lib ^
- -DTensorRT_INCLUDE_DIR=C:/TensorRT-5.1.5.0/include
+ -DTensorRT_LIBRARY=C:/TensorRT-7.2.3.4/lib/*.lib ^
+ -DTensorRT_INCLUDE_DIR=C:/TensorRT-7.2.3.4/include
cmake.exe --build . -j 6 --config Release
diff --git a/data/cmake_vs2022.bat b/data/cmake_vs2022.bat
new file mode 100644
index 000000000..f554e4ba9
--- /dev/null
+++ b/data/cmake_vs2022.bat
@@ -0,0 +1,19 @@
+cd ..
+md build
+cd build
+cmake.exe . .. -G "Visual Studio 17 2022" -A "x64" ^
+ -DOpenCV_DIR=C:/work/libraries/opencv/opencv_64_2022 ^
+ -DUSE_OCV_BGFG=ON ^
+ -DUSE_OCV_KCF=ON ^
+ -DUSE_OCV_UKF=ON ^
+ -DSILENT_WORK=OFF ^
+ -DBUILD_EXAMPLES=ON ^
+ -DBUILD_ASYNC_DETECTOR=ON ^
+ -DBUILD_CARS_COUNTING=ON ^
+ -DBUILD_YOLO_LIB=ON ^
+ -DCUDNN_INCLUDE_DIR=C:/cuda/cudnn-windows-x86_64-8.6.0.163_cuda11/include ^
+ -DCUDNN_LIBRARY=C:/cuda/cudnn-windows-x86_64-8.6.0.163_cuda11/lib/x64/cudnn.lib ^
+ -DBUILD_YOLO_TENSORRT=ON ^
+ -DTensorRT_LIBRARY=C:/cuda/TensorRT-8.4.3.1/lib/*.lib ^
+ -DTensorRT_INCLUDE_DIR=C:/cuda/TensorRT-8.4.3.1/include
+cmake.exe --build . -j 6 --config Release
diff --git a/data/coco.data b/data/coco.data
deleted file mode 100644
index 30038417c..000000000
--- a/data/coco.data
+++ /dev/null
@@ -1,8 +0,0 @@
-classes= 80
-train = /home/pjreddie/data/coco/trainvalno5k.txt
-valid = coco_testdev
-#valid = data/coco_val_5k.list
-names = data/coco.names
-backup = /home/pjreddie/backup/
-eval=coco
-
diff --git a/data/coco.names b/data/coco/coco.names
similarity index 100%
rename from data/coco.names
rename to data/coco/coco.names
diff --git a/data/coco/coco_91.names b/data/coco/coco_91.names
new file mode 100644
index 000000000..c5b8f040d
--- /dev/null
+++ b/data/coco/coco_91.names
@@ -0,0 +1,91 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+trafficlight
+firehydrant
+streetsign
+stopsign
+parkingmeter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+hat
+backpack
+umbrella
+shoe
+eyeglasses
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sportsball
+kite
+baseballbat
+baseballglove
+skateboard
+surfboard
+tennisracket
+bottle
+plate
+wineglass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hotdog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+mirror
+diningtable
+window
+desk
+toilet
+door
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cellphone
+microwave
+oven
+toaster
+sink
+refrigerator
+blender
+book
+clock
+vase
+scissors
+teddybear
+hairdrier
+toothbrush
+hairbrush
diff --git a/data/coco/full.names b/data/coco/full.names
new file mode 100644
index 000000000..ca76c80b5
--- /dev/null
+++ b/data/coco/full.names
@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
diff --git a/data/coco/white_full.names b/data/coco/white_full.names
new file mode 100644
index 000000000..4e53804f6
--- /dev/null
+++ b/data/coco/white_full.names
@@ -0,0 +1,9 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
diff --git a/data/combined.txt.model b/data/combined.txt.model
deleted file mode 100644
index d76ef96cd..000000000
--- a/data/combined.txt.model
+++ /dev/null
@@ -1,6151 +0,0 @@
-solver_type L2R_L2LOSS_SVC
-nr_class 2
-label 1 -1
-nr_feature 6144
-bias 1
-w
--0.005003520347911358
-0.002069803013184546
-0.005174648715386205
-0.005770423962997794
--0.01448714985262205
--0.0367621344258923
--0.02794459664664868
-0.01101089457859012
-0.008666665134766542
--0.02012648943416123
-0.06575279828180118
-0.02424015059444452
--0.02078629004738948
--0.0174857471188182
-0.01500355425557434
-0.00630631014205099
-0.01284952839634534
--0.01440907292094286
-0.03312943265799884
--0.01037956465816266
-0.01091253397466537
--0.008546722578458163
--0.000137242882255898
--0.006039288632080092
-0.02785929565990963
-0.01875779505813649
-0.001523177575111411
-0.001975857592573251
-0.0355570028413526
-0.00222207838522583
-0.01676492228306924
--0.014860750409855
--0.0007474025127886761
-0.001263892979786343
-0.01315113524375824
--0.004714853506517448
--0.02282390016881265
--0.005469724342330157
--0.02510834369198174
-0.01764526328517421
--0.007660696427159833
-0.004981475262397511
--0.01690123403309758
-0.006578004626564695
--0.01275568672542528
--0.002289109263499082
--0.0003020207915855601
--0.0247449283333634
--0.004337326818695084
--0.02127970823354044
--0.00205813933788758
--0.00697602903805972
-0.0378438461173811
-0.0271264404242818
--0.01749792865161751
--0.02820862901581239
--0.02182719361448771
-0.03875054149879911
-0.02987474445180364
-0.008319553173496204
-0.01863953592520786
--0.01377758079542864
-0.02447160859508605
--0.006161226087019822
-0.002664889266725591
--0.004195764438625247
-0.0258392781694716
-0.03597235631195608
--0.02312395751737616
--0.0264391761526756
-0.008879937882809405
-0.01317848370247718
-0.01442150630026301
-0.01147596267241498
-0.008140992211158772
-0.005028185738844742
-0.006646845288083352
--0.03718286608957103
--0.003187494927532327
-0.02737291596187517
--0.001307662724359647
-0.01299300062055115
--0.0077356816468095
--0.02314412706978508
-0.007754396219211935
-0.00237656846180913
--0.001021865536112009
--0.01633714512603848
-0.03010638215034521
-0.001959273739762304
--0.01388451168609694
--0.02902924355619753
--0.006209452098316521
--0.04394066992596347
-0.005438391961759498
-0.0004430470439009493
-0.01839911411320389
-0.01312083096711165
--0.003596098504759031
-0.008205299317585775
-0.01158208655470198
-0.01143715477397159
--0.01337154284471177
-0.01332920143227819
--0.01079045366246222
--0.002444117903920587
-0.0004225568703275601
-0.008454014893288557
-0.01382371036393016
--0.0008277937384174614
--0.003095762379452143
-0.005920876257174643
--0.018435862770501
--0.01325746795130324
-0.01572515069785909
--0.03636954142028007
-0.03327623206298854
--0.008945717976554125
--0.01512813884792744
-0.002350702483139244
-0.01876030251118957
-0.01426424964277364
--0.0005273014953322304
--0.02554912327528925
-0.02471023092682494
-0.0005748998346132857
--0.001425466379099617
-0.001345201563785435
-0.009799302154985012
--0.001669371383296941
--0.03301632929190409
-0.001847080476171332
-0.0001326086229401944
--0.0209294907115855
--0.006623246612253285
-0.004417154127878315
-0.01134309840262906
--0.02975562097965451
-0.0137113098352971
--0.01336515888658423
--0.03056310050626673
--0.01643979100209925
--0.02140371227986727
--0.003964933677113916
--0.01220623609634657
-0.001639161831019996
--0.02070818633068418
-0.01311884189460462
-0.005854753472316884
-0.01308041798150547
-0.002198357793834611
--0.0007248654118830881
-0.04799990767843172
-0.003443565906509785
--0.0172558476936482
-0.0325125542177515
-0.0360486890477587
-0.01615102901872734
-0.02015870522725214
-0.0005302504072158193
--0.01491375362800119
--0.03574601936142056
--0.03892317418311501
--0.009719590114107404
--0.01384706348878529
--0.02032916461597326
--0.007949499630951016
-0.03510776808035251
--0.0007874502885196378
-0.006597192831704409
--0.00103788408425645
--0.03949375140812197
-0.01678745005331527
-0.009661148644304823
-0.0008120819316022267
--0.01360737543568321
-0.002153041211443719
--0.03278667140273392
--0.03419092177714698
-0.009065814936435299
-0.01915344068421205
--0.0166592764738116
--0.04608819472223481
-0.006810744023721218
-0.006806002439833975
--0.02293703473477056
--0.05606649769217235
-0.005418833639128474
-0.009339001173000717
-0.003557382462747092
--0.0003072634595633458
-0.01103428562528969
--0.001007442607148272
--0.01986254205070798
--0.003417074690970731
--0.02089191748796974
-0.01755733343424117
--0.01421880445163473
--0.007706256573348884
--0.0316305685923691
--0.02001652813974759
-0.02608050258069597
--0.03436624696618167
-0.02159538092131812
-0.00708380668103448
--0.003111134841378749
--0.004926318633533562
--0.001885235455617492
--0.01859751732172839
--0.01414900801814637
--0.02612153641307975
--0.009910123218352357
--0.006365778234346128
--0.01733177591856793
--0.006387543032471968
-0.008249748548657456
-0.01882365990003728
-0.004518595720905678
-0.004560404907278093
-0.02327821450917032
--0.002912346258285851
-0.02390941125930645
--0.03844018508339368
-0.0007930765426637359
-0.008025583670277952
-0.03943723601097654
-0.01167127417601206
-0.01772513051309598
-0.03317502203206862
-0.000893028963210232
--0.002394382684041357
-0.005016197319593135
--0.005279746775419308
--0.01388213471146204
-0.02621994621239625
-0.01302632594667858
-0.01185686121428527
-0.009362310203846862
-9.299257314664079e-05
-0.0004835098562204914
--0.002713304851430683
--0.01226718338014476
--0.009877020734323479
--0.0002919310650716624
-0.005194238050333014
--0.001662500000528359
--0.01011829167243831
-0.00773409574225973
--0.01235530121162729
--0.001547045998920955
--0.03075499845710892
-0.005740627724918379
-0.006992768992037683
-0.01175696745869032
--0.0007052100534328659
-0.0004624023950462225
-0.009228664121560546
-0.01924883493209572
--0.008364770638044009
-0.01567359364829157
-0.02287365117026793
-0.01211287886899546
-0.02567785299236859
-0.01503671634458731
-0.00189163873778995
-0.01758876251815896
-0.03250564528844271
-0.006482028534461236
-0.02290977956631286
-0.01272808235187591
-0.01443941206680375
-0.0002526422229615169
-0.003539317372202528
-0.01253998372839131
-0.02069401285759962
--0.002050895412099214
-0.01399534715079634
-0.01687501104736621
-0.002147809845668891
--3.142267451271448e-06
--0.03082919024973758
-0.00409833618300046
-0.01764242996451706
--0.01824280153221269
--0.006756723434835348
--0.0001450205760352501
--0.00533948906003771
--0.0266457012220864
-0.001344069557904624
--0.02128193239408986
--0.003659783389711483
--0.001852477576654513
--0.005583923348945666
--0.01929853851723307
-0.005032591258722955
-0.01916912290010374
-0.0308524055529778
-0.01373768182407962
-0.04879855877438867
-0.002400002027748995
-0.01194276900965131
-0.02472784420662469
-0.0152947015615006
-0.008229897751963602
--0.008467217513181489
--0.02719336608141725
-0.002982316793370578
--0.005297610306972945
--0.01178221269438317
-0.00291670535831723
-0.01514055354402407
-0.000843004470314921
-0.03418930834618266
--0.007025330524047327
--0.006446516301463833
-0.02243084941706928
-0.01535965071131457
--0.01781964505478506
--0.005839037552966624
--0.004932980815182085
-0.003013755445519076
--0.006938248188829542
--0.0162717211528717
--0.01798497036975532
--0.004645095001116832
-0.01089958322771388
--0.02477933155243444
-0.009662665748671747
--0.006078020463963898
--0.01345160543476964
--0.01660954500628031
-0.002251851517249183
--0.01416684620464115
--0.02017698268253854
--0.008873431760135699
--0.02034543497200495
-0.0006466372085425197
--0.04878086408083279
--0.0027483181373843
-0.01151986776061373
--0.0162265344855075
--0.003256801104442416
--0.0005921090590034859
--0.03103687301830104
-0.02003929866102026
-0.002993458840512375
--0.01774967334631447
--0.01129472265352868
-0.003989231828537564
--0.01614046959996082
--0.007969116104857841
--0.02862444942950542
-0.009149897923654771
--0.003472215813725276
-0.00824400324360478
--0.00514985193198036
-0.0300742921766338
-0.002550702409033196
-0.02522315171355281
-0.002239783524537956
--0.004458744012660044
--0.002685748035980243
--0.02003084152623159
--0.01394261241327988
--0.007585878529702793
--0.009753075559757908
-0.01003755003767885
--0.03275255100672173
-0.04517121808063266
-0.002434769598134361
-0.008076037975557804
--0.01300263749545634
-0.06302110617897295
-0.01687153647848769
--0.00930962876055192
--0.02893428004246289
-0.02770691707775359
--0.005809321332112434
--0.01989749364275967
--0.02537833095641314
-0.014181271454644
-0.004880702816569228
--0.002332279482607529
-2.360044861530883e-05
--0.0009735975942982823
--0.0006798436192469192
-0.01153117588693445
-0.01366220762121318
--0.01213616902933886
--0.03932064342835775
-0.01955031541064437
-0.02112332835847193
--0.01045117123023636
-0.0092375852883199
-0.005412830881953227
--0.009652129185600017
--0.03190723978774343
--0.004397134614096571
-0.0004653366920910375
-0.001801215448612087
-0.03659409852610278
-0.02164238473311287
-0.02952981655803447
-0.02760507272583731
-0.004868429239963265
-0.008575218199526917
-0.007607863558952699
-0.001140099589671244
-0.0359315056212372
-0.005974921143204162
--0.02207612606506396
-0.02224194803133473
--9.614643673055539e-05
--0.01235900634964228
-0.004810958582936387
-0.0005553201699475764
--0.04959617981761505
--0.02175683843999135
--0.002460682721387544
--0.04291025402634877
--0.007117533720152886
-0.009538461446059016
--0.02533512467883599
-0.0108719727858132
-0.01030006497856292
-0.00534026568387216
--0.02907301857171546
-0.009029428125775235
-0.003677969233221066
--0.00835826756718748
--0.006584188295177362
--0.01725879084872028
-0.01481922594341135
--0.007829060961651034
--0.02239608867235906
--0.007239649108550513
--0.02079908011132775
--0.0122402558111806
-0.02223178833145775
--0.02642044855104548
-0.02460513396431681
-0.01025987325311695
--0.007924876597065754
--0.008469098669326826
-0.01162427789506362
-0.00757125579340331
--0.006651687432548528
--2.011760012418087e-05
-0.007571021043648778
-0.004030482263946447
-0.03764152288361701
-0.01113734846155956
-0.00762682292082857
--0.03114581685183132
--0.01292423174770759
--0.006479712360404123
-0.02762814909895847
-0.06441575785681913
--0.01090172949626452
--0.007697231186210299
--0.004743469016488679
--0.005243778891252429
--0.008732594053048595
--0.01768043245652324
-0.005280437836049898
-0.0006756851338277547
--0.002040749662085092
-0.01905870498060573
--0.003946572948967477
--0.0008642364785054555
-0.001916326215645003
--0.01981899711425225
-0.02575605458110392
-0.003919275294279793
--0.03415197974394633
--0.02038051887277834
--0.01947517087181787
-0.00232395253007428
--0.005005491814130655
--0.01574473896058118
--0.02108647113005644
--0.0390933560020202
--0.03381029152027456
--0.01374129627045977
--0.02156975197338225
--0.01742708450368665
--0.02264858345077536
--0.004319312911059957
-0.003887998324021627
-0.0232337907043668
--0.01624569974857062
--0.01125570695150681
--0.003702716727484901
--0.01400482921065592
-0.007011594349336642
--0.007573526736130579
--0.001032097377261878
--0.001047323103030321
--0.01016048814324655
--0.003540457030422892
-0.02083373788634737
--0.004967312684087704
--0.01222224714773651
--0.01253554718983541
-0.03627487978079577
--0.009200758609774632
--0.007995646261622871
--0.00661199873420455
-0.006537820686641866
--0.016958763151676
--0.007092649599914428
--0.007604167826467551
--0.001179703955100132
--0.004741446528877903
-0.01117942094133146
--0.01394367513870646
-0.006387018723445646
--0.01684775476809702
--0.008020723392915732
-0.02508636717351514
-0.01386299878517946
-0.0006163434569274912
-0.0226836256010943
--0.01192388276706972
--0.0145837783431355
--0.005101115192672873
-0.001440175842190362
--0.005931771432837823
-0.00751238104121529
--0.01367162748473635
-0.05651837815750417
-0.01617179191117598
--0.007314792298486002
--0.01149634251780092
-0.02047849803547178
-0.001023744937027721
-0.01471597875686846
-0.022454024687387
-0.0005124787304554669
-0.01797356687499928
-0.02827670547412039
-0.003611171801694697
-0.0153327716641086
--0.01084053129238595
-0.01120063482298114
-0.01146654149954871
--0.02186676349862713
--0.00878975941847282
-0.008550492104857975
--0.02310384955313644
--0.02413534865119355
--0.008913855602135069
--0.01039153630834005
-0.01066052334187154
--0.03595931444715042
--0.0008432535929275898
--0.008362226615794064
-0.012899442247164
-0.02930312588308863
--0.0009787165783535431
-0.00892214047220974
--0.02385351670709739
-0.002751156663230699
--0.05194250425000137
-0.005817084444285272
--0.006364928017669904
-0.006554827842347569
--0.008228184021934926
-0.02026067920090452
-0.0215193956363785
--0.0008296911817124732
-0.01498914366173797
--0.01072137031975259
--0.01324846683708499
-0.02690443982661931
-0.01217516804592704
-0.009245736318703481
--0.008722076304629822
-0.02223697697300151
-0.0006550776533903614
-0.008829673087587061
--0.009493019881536384
-0.02427893935681946
-0.01138262875558655
-0.008640065790915905
-0.008527334397447163
--0.0098517077633191
--0.001196774832557466
-0.01245135534212849
--0.01246543959827981
--0.01467837007481979
-0.005603671578263095
-0.01812599547963147
--0.003369978597163579
-0.02449992214460639
--0.01115553459047753
-0.005743490644701667
--0.01878079907846746
-0.014432070644365
-0.01323665835919854
-0.0231617086915668
-0.007303629654081573
--0.01427799433969487
--0.00831587623286807
-0.01432656833254217
--0.05565068721077037
--0.01649008086536423
-0.003193506865209827
-0.007043880530607463
-0.001381125822896695
--0.01284515838845966
-0.006167126542678558
--0.04102781952506392
--0.01144135058687889
--0.0171272919308507
--0.02751586690268498
--0.008662432431961922
--0.006198087795207047
--0.02528455231929987
--0.007763599861541351
--0.02216969816341033
--0.008917916822090642
-0.01167172747087434
--0.00395815915473401
--0.01948004640889512
-0.0256515703053247
--0.04185080715448322
--0.007234759438274927
-0.03046349003474088
-0.004402390410109493
-0.0188800543541134
-0.006061779987044675
-0.005257153766757948
-0.01501098576484898
--0.003452513228169648
--0.009840069829909743
-0.01654368552238866
-0.01980687921490583
-0.01317655728945977
-0.005416029144507892
-0.005951078966694971
--0.0132302571559345
-0.008730753821912659
--0.00626315087461618
--0.01274903461132639
--0.01032682640154472
-0.004530181275655699
-0.002186481569415141
-0.001848766116629715
-0.01948645099304751
--0.00984287213593949
--0.0180245006576852
--0.03178474172238394
-0.01668474916730084
--0.03386605864417302
--0.0209899028109063
--0.01516451159349272
--0.01415197055902736
-0.004454583359133935
--0.002347227621329079
-0.007593957870811625
-0.01094008076835564
-0.002658616813757957
--0.01021824349388779
--0.01473303058960936
-0.01503165577939028
-0.001971848067470669
-0.005478605934674059
-0.03879781096731207
--0.01418999381390136
--0.02919782949085926
-0.002659307296285537
--0.004162860232794933
--0.02452273251202593
--0.02897421148975042
--0.03982240308212088
--0.01756816048193873
--0.02231481586771881
--0.01283711115525081
-0.009687819609816677
-0.0003102851276981148
-0.004850545105721929
--0.02300374335417232
--0.03357373683186745
--0.007839363000441121
--0.01276449801594297
-0.001456108171957251
-0.01902255297265176
-0.004446782695133483
-0.02133695901712592
--0.01976270669646911
--0.009685266451037178
-0.01117016334713575
-0.001370637539317458
--0.04889195949797102
--0.01341348733981518
-0.008660924479100355
--0.006907570434681563
--0.05135507268549426
--0.006779827440184504
-0.0008447743350087972
-0.0003712496640469208
--0.0003096522608354965
-0.01243270782590681
-0.01482536527371885
-0.02348964808340296
-0.005496272695601416
--0.0114049635202043
-0.01840097419337173
-0.006809039144939852
--0.0007937697922170802
-0.01955452591090784
--0.01876173414526512
-0.02710441349839449
--0.01917579124605914
-0.008709505240794215
--0.03285763002067066
-0.008752924583107179
--0.009943121157616173
-0.0154154708695081
--0.004067415511192045
--0.01662204632073492
-0.007713758102871562
--0.02813539294527369
--0.007732075358615671
--0.009669999234922904
-0.003334904816078541
-0.006649279863757005
-0.01590848455206553
-0.01644953937864884
--0.00417288889175121
--0.002969069162019801
-0.002023646471877018
-0.007582525973530554
--0.007257317751561066
-0.008973606018068452
-0.01348266443738668
-0.02305226829085775
-0.01666651816093993
-0.005733506299952687
-0.02159362222256716
-0.02116430086849335
-0.02082676800221974
-0.01677199759208592
--0.003676742192525374
-0.0133594044288274
--0.006966099325457501
--0.01086624358270451
--0.01750917256243403
-0.003772616822794145
-0.01445849007631814
-0.01008133637260704
--0.005569721859292821
-0.008549287445647404
-0.02281150273017499
--0.001416827691289391
--0.0150205897591585
-0.009187222437584734
-0.01236589259930272
-0.001630923087993068
--0.008152626816441411
-0.00252836853997357
--0.03222698840858164
--0.004781627978895055
--0.01246961236144454
-0.007181335714098646
-0.002028017078066252
--0.000931230815235418
--0.004526607560690233
--0.004996772754726691
--0.01279344662708906
-0.009563394319059554
-0.02632692231095652
--0.01028169608130237
-0.01228557262409598
--0.006016241022531138
--0.005900086155128476
-0.004077276432807645
--0.003347002165918217
--0.007247045554594302
--0.01728603566366559
--0.007859542706558123
-0.008038538291788092
--0.002645537288856739
-5.631471902796162e-05
-0.02961785542620612
--0.007282038702990256
-0.009838163956636713
--0.0004541778055609444
--0.018851081439879
--0.007379994526259774
-0.00434105343449142
--0.0261505533204982
--0.01014342479063603
--0.013312296220665
-0.02535063133725272
--0.008604934926263814
-0.004249021610531419
-0.03842678837784637
-0.01633589451662827
-0.02331866322491402
--0.01513965575352876
--0.0006038281169199562
-0.01997283243793812
--0.03909843620979078
--0.03575608791174428
--0.007581068743560676
-0.0134128227318906
--0.01239968046154283
--0.01261845051053615
-0.03049350370135833
--0.007226058769735589
--0.03017111294837905
-0.003929833351086065
-0.02064870228684692
-0.01667975044362359
--0.005677985457621018
-0.01203943098849442
--0.006498714389489587
--0.008899659825761869
--0.03245627621117831
-0.002287396896372641
--0.01747357375295201
--0.005263202761156655
--0.002032089753542359
--0.02790047025537045
-0.01059833973557582
-0.01282794753260645
--0.004232534790009283
-0.0009357382639767375
--0.03576428937547799
--0.00083780002237321
-0.004978412793922927
-0.01258086485260979
--0.02632671321711023
-0.002238249644627302
-0.009422383756532184
--0.03012115733417143
-0.02005045674263074
--0.02794243219462856
-0.007944221635426341
-0.04687920120304011
-0.01769916917983936
-0.01151348530964719
-0.004522737294149561
--0.02845976288069472
-0.0294433539432547
--0.0005098037823555218
-0.01334523409664714
--0.02082987004203069
--0.01235735391652316
--0.03065008463184635
-0.01668406439250987
-0.001161124906661016
--0.006925260487972621
-0.004003793255223161
-0.006773856522758004
-0.006686522031750745
--0.002928694763745174
--0.03515073814306881
-0.02232431930467069
--0.01810688808631062
-0.01101243690552882
-0.006149247655117805
-0.006563621638512924
-0.002742267343763326
--0.002934133638926621
-0.006218681022348667
--0.01299984475141811
--0.009197691693698922
--0.01707199630179087
-0.0003562807362521091
-0.00942523639502338
-0.00534091608854026
--0.003072840871387063
-0.005975332047230792
-0.003410723032808418
--0.004858688991411602
-0.008732599815901198
--0.006263204051530742
-0.01976643804093755
--0.004093722954093836
--0.02379095106889214
--0.009397992598695693
-0.01512927032265278
--0.01912868562825778
-0.0022241066256846
--0.02146993368663007
--0.0160001202402291
-0.008397155968020901
--0.002147075557762623
-0.0286505559232231
--0.006493694282837291
--0.00555377503013729
--0.03541788382866402
--0.00971100561622322
-0.0106989109655744
-0.003088634382949682
-0.01342848260381314
-0.01738877692590295
--0.0004090858687376276
-0.01080847082271384
-0.02624079697899995
--0.01006288148859815
--0.001314596113568419
-0.002427969831360457
-0.01759485420134566
-0.00335404119796532
--0.0006235249319439905
-0.01576131784070835
-0.01024487503860761
--0.01323614327231774
-0.01347837951322855
--0.008469883535618087
-0.0234011066639342
-0.0245682063330038
--0.004141336085151608
-0.007272684160143496
--0.003025842895219159
-0.01215033041189319
-0.009843505777525033
-0.003392946616741205
--0.0008123250732940032
-0.005735360954720714
--0.02214883681806597
-0.01152493861337968
-0.005657061300064797
-0.0214609622218948
-0.02191934252453447
--0.01181257471694092
--0.02185621762028104
--0.005980574085987012
--0.001250567183323252
--0.01352735566891516
--0.01324867112556336
--0.007003203601505609
-0.04801858977417573
--0.007267288733386458
--0.01192539853984659
-0.01126640007483809
-0.003475505967396253
--0.02071941632647525
--0.0005726632876119915
--0.02124756696850617
-0.009165408753565928
--0.01596268692181218
--0.005410651845821386
-0.03260214469903287
-0.01180725287964493
--0.0172347020873347
--0.02303862219300323
-0.007084016282822183
-0.009034675608347871
-0.02071271363538896
--0.009337486351723779
--0.002787646393651261
--0.007540650869381602
-0.001861903136428323
-0.008547987580841467
--0.006074118708461347
-0.004796385069469019
-0.036937313265285
--0.007751895864964408
--0.01396263115572428
--0.0116446885137104
-0.004208290438050806
--0.003508682408418307
--0.009086530015347198
-0.007974846659870112
-0.01356496285280434
-0.003528156114944038
--0.01425837980743495
--0.02639577591607816
--0.01147037072516825
-0.008995922258968891
--0.005476601754817025
-0.00701839078881001
--0.01197198408600142
-0.03583687709242954
--0.004955060770351197
--0.003485903071434123
--0.005966957995723868
--0.00239923975529647
--0.02938000831829269
-0.01724271618774327
-0.007881455062929622
--0.002376120846033699
--0.02201814907702552
--0.01516904893084525
-0.001345944917531528
--0.01581797713778328
-0.02007238195262654
--0.02152801725351697
--0.003499986557644242
-0.002047534776443752
--0.01703357104186727
--0.001862878534423749
--0.0006485279936755399
-0.0157709023893298
--0.02129401144776363
--7.010851507036585e-05
-0.009858520788405455
-0.002171557588504047
-0.01372494912786795
--0.01131839846222653
--0.002573610063288637
-0.004621947287358354
-0.004480559250421569
-0.008404828524134737
--0.005233266333193224
-0.01015328308671275
--0.02031568488211503
-0.01613797689501945
-0.01087596118627642
-0.001261085050951664
--0.004333542983386869
--0.001655390807485342
--0.02829844289512774
--0.02780752210416536
--0.01149875156822533
-0.009339410166709276
--0.004295733687255748
--0.001730982684304364
--0.0005518559355724889
--0.0006390047475683922
--0.006176003354337999
-0.001652431106243968
-0.009975680609600053
--0.02797716032439142
--0.02796078978464962
-0.01293655799452167
-0.009462661686558181
-0.001720462174180446
-0.00538565413143505
--0.03982530334741089
--0.0230692181112452
--0.02775607670932757
--0.01867121876882981
-0.006124590369981068
--0.0115123357938056
--0.003292444294299765
--0.04226045495291274
--0.04357481183566375
-0.02271855345833626
--0.00368781224889539
--0.02605808767965867
--0.0207169378045665
--0.01040852873734821
-0.002794919733733894
--0.01272359864278693
--0.01715868726144977
--0.0226767421324506
--0.01394224301366201
--0.03070126894664099
--0.02572050885216095
--0.002008218893069433
--0.004650434812568835
--0.019088732402207
--0.01858335187924904
-0.01147255610535523
-0.02778753112518982
-0.01532477851527851
-0.02947139164742346
-0.0006836624135609246
-0.02169082365026173
-0.007002409027876449
--0.02969237553403356
--0.004677918740103713
--0.03079164869444623
-0.005921347620402013
-0.01535746133649107
-0.02607031986513803
-0.0009086930421562581
--0.01339850803104417
-0.001333154985832634
-4.523194894381093e-05
--0.003980997228864974
-0.007425556043199984
--0.01715065179755247
--0.009572315371755902
-0.01939270169416925
--0.006169037978444524
-0.002081107773190845
-0.02022986357913467
--0.0296829271989799
--0.01265391942337506
-0.002324521523848259
--0.007833253639127683
--0.01065668480385865
--0.007275690202289063
--0.03829107984712437
--0.001788424778669945
-0.01740012438947449
--0.02592312340793472
--0.005363133735930519
-0.008910702131600497
-0.0268562611766555
-0.004729785523850201
--0.02132410325556956
-0.003506265723436317
--0.004407086210031078
--0.002573578308815765
-0.01639184693073134
--0.02121863359582059
-0.03216023953632659
--0.01966522325521147
--0.008384905317312516
-0.007667680613204453
-0.006738477741570359
-0.02110768068472282
-0.01416261736187769
--0.002783537962769521
-0.01350074401990306
--0.01238523711923566
--0.00836844333622231
--0.0001439869586081977
--0.01188376881311585
-0.01436928598772737
-0.003701623706305913
--0.0001570848046949758
--0.01819086682984613
-0.003876201604736075
--0.02628373458248906
-0.01532336312094816
--0.002162087531885517
--0.003681890038165341
--0.02158096626403967
--0.008089374395960726
-0.01871300583129416
-0.00171283457770458
-0.01472417724262834
--0.006057375363335498
-0.01507825626174323
--0.0285148420666857
--0.002616051751570292
-0.03035710440489543
-0.01991031087699378
-0.006601068137507364
-0.006701670317906971
-0.006709548563959679
--0.05549094661077508
-0.00846851981125136
--0.001310453723899519
-0.0093500523387716
-0.04403808006011854
-0.03026800906903658
-0.00354281819681558
-0.02073404252501665
-0.01229763550669773
-0.003145973733199772
--0.006398797982830115
-0.002429985726766671
-0.006928799740781565
-0.02301236623245129
--0.01999113303747349
-0.02447745467679782
-0.02432190174233914
-0.0006711037648610372
-0.0002625404952274126
-0.01644475727207527
-0.01381828021931978
--0.006573518789668793
--0.001156057956530029
--0.02613865519501167
-0.02361864292094831
-0.001538780202158474
--0.02099547098618208
-0.01180313448441145
-0.02124335357395203
-0.01069889427636538
--0.03134015728741897
-0.01124294269619394
-0.00526642481142349
-0.0009161121776108466
--0.01239099961886825
-0.0324917634048297
-0.004414170836474048
--0.02711929184169255
--0.0025413945878939
--0.009420688242076257
--0.01465679300880252
-0.002994704691665026
-0.01303448781022158
--0.008555980553149619
-0.03758171739578691
-0.01369238095434317
-0.03102939622740859
--0.02139151757909076
-0.01996929817050929
-0.006100769717538641
--0.01907228158195173
--0.004594001391666422
--0.01526195415261814
-6.404736927731995e-05
-0.007539458885203562
--0.003758019633489102
--0.0129643274399308
--0.0317117822959932
-0.01430740619264509
-0.01694960457866395
--0.006149883023033967
--0.006285779399859284
-0.01300576890431393
--0.001299501562367687
--0.00831666041744163
--0.03836364702808483
--0.0002810624539674921
-0.03089618347328809
-0.00912228057212104
-0.01045996785905204
-0.002976913329276479
--0.003029722063112611
-0.01883520752600152
-0.0001731651016554949
-0.00180922617372372
--0.007927668771411461
--0.01684815360985548
--0.009873785166053379
--0.03507024190894514
--0.01450769214639179
--0.001469580626923542
--0.004086785500224501
-0.009139807157084722
--0.003662090009687014
-0.01857811816775874
--0.05140866349543106
--0.00891822066339383
-0.009491031535568597
--0.005602467195330627
--0.0006649467196109227
--8.829420871987943e-05
-0.001953480971514434
-0.01842889028580623
-0.01055343116676067
-0.03310209584083586
-0.01316171414399559
-0.0006077435305859221
-0.0074151944193758
--0.01070742607748996
-0.04039232784923243
-0.04351448887082744
-0.01554534296542149
--0.002623518226829833
--0.01291027010589196
-0.02102299872240167
-0.003941404585195916
-0.01065377304111713
--0.002679951433724593
--0.002197120987992306
-0.0004573578802347325
--0.04940684894304934
--0.01257021805803177
--0.004517346375347697
-0.02969722084853378
-0.01062016921093883
--0.01679256910348336
-0.002506829076528198
--0.02793814340952067
-0.008155525009253166
--0.002999720645911231
--0.004507767137964646
--0.008437836693765133
-0.01350066833075329
-0.0009649577049395295
-0.004327225998526005
--0.03894084336514281
-0.03011161810407604
--0.005866895842685601
--0.02962246632526599
--0.01681067036895511
--0.005837722828876494
-0.001806177792797483
-0.02239029701557491
-0.01567381931622651
-0.0161521231265102
-0.005420737670518611
-0.01430669777088642
-0.0001729845813669936
--0.006186082840723674
--0.001544224393595961
--0.01365928360859867
-0.01762679295692892
--0.01484575650890804
-0.02162990788304155
--0.001737349184228652
--0.008102272154194312
-0.01417889801209776
--0.005233640200117716
--0.009979657204175272
-0.001468139079119028
-0.006999075277513353
--0.01070522731509243
-0.01776039103677174
--0.002902028748232633
-0.002690456055858746
--0.005389317738992409
--0.001106849239182241
--0.002280029606724315
--0.0041617960039521
--0.00271124534171149
--0.002274521205382005
--0.001199122806967336
--0.003381413462688697
--0.002069650024562991
--0.02045259891373353
--0.004691797159059354
--0.01208489563332363
-0.02952037745740787
-0.01863784978971038
--0.0008226739496181532
-0.0281790813136313
-0.01162368607078986
-0.01057071758529727
--0.01141344276211927
-0.02485476244422052
-0.04142366328399727
--0.01840940780746401
-0.007121708177212917
-0.01465907309643502
-0.001962801902275464
-0.01929200351396545
--0.01321126031683692
-0.03201451917980419
--0.004374109292591829
-0.01376705380192939
--0.01392858697730624
-0.02131172669155383
-0.01361921131946055
--0.0007643569560068617
--0.008327947797862457
-0.02493391528440564
--0.009019696476093448
--0.01193179010597067
--0.009205421559163807
--0.008873285819021167
-0.004123744671031855
-0.0151181856841698
--0.0257053897731305
-0.006408013558245591
-0.006055883490354082
-0.003394584847893433
-0.01048456725519591
--0.002901671959328282
-0.0177621789019933
--0.005446654006457716
-0.008041762327690672
--0.02755412078947034
-0.009040012365746202
-0.02055796391038681
--0.01432827622475474
--0.006200933929039688
--0.02595742122388938
-0.01195999481460946
-0.02078173343079194
--0.02717770899474817
-0.0121787165001857
--0.009760118634534383
--0.01693100754318937
--0.0191939037441852
-0.002165468622998385
-0.007951599055087752
-0.01197262017914202
-0.02903205101763904
-0.00510062691965784
-0.03955743110441621
--0.03802025548196284
-0.02341398939831249
--0.005006010168649757
--0.002596679966943346
-0.02580468541147135
--0.005439449416049159
-0.00242446870376706
--0.003027646069901763
--0.003832517352031034
--0.02071579284597203
-0.02475879872796235
--0.0001523846707009587
--0.01717768808928039
-0.0006527949369039565
-0.002764497900412905
-0.04319860048639278
--0.0003247530481430921
--0.004274968872881717
--0.0161417394934138
-0.01056885762068592
--0.002245443626152647
-0.02357311089687288
-0.01071281725118027
--0.01525025907003227
-0.02029888360897047
-0.001712451028408189
-0.001235863575940385
-0.009623287200969353
--0.01190860524543881
--0.0002081385004862613
--0.004649755010509842
-0.02583741107595951
--0.006368630882671673
--0.01131622678774626
--0.01795852580958378
--0.008555671738609756
--0.02929152273554363
--0.000709073223111646
-0.01646982830366987
-0.009003043399942207
--0.01250697082509796
--0.0143529999347808
-0.005058496428771095
-0.009903129116738418
--0.01387846206508754
-0.01033258842816794
--0.02241265100679323
--0.01049867657282457
--0.03472017120212344
-0.01140018640965411
-0.006353605583671204
-0.007044850867990847
--0.03334084130889706
--0.01815536714042336
--0.006597813653152828
--0.002944114564730585
--0.003151301080311825
--0.0175489836338453
--0.00292277431506027
--0.01269710773226217
--0.01817665904715475
-0.02891782468591024
-0.02363387193656099
--0.002334970686911417
-0.005075179967321053
--0.004293165263022737
-0.01338752888530809
--0.008189891283890605
--0.02888004328256247
-0.005976171943321576
--0.03054265593550055
-0.00409360046619447
-0.00700114704571883
-0.02040441627155631
--0.007255636770380255
-0.02471835355939004
--0.005770396316131142
--0.009481817421261014
--0.02537280598799997
--0.01616607340333887
--0.01166795471462989
--0.01030717362160463
--0.01020563128249139
-0.01204355715910457
-0.00107665412671168
--0.01105781610676676
--0.009829295582062216
--0.02579946510737528
--0.008813465255136351
-0.02744419233645272
-0.003154862143748513
-0.0306946417970386
--0.01923205519884618
--0.003731101453174634
-0.007690085875117345
--0.01173853945392214
--0.01176456165208266
-0.007089938013491295
--0.006279176213257642
-0.01609908110490792
--0.007092850346320017
-0.01161369460883531
--0.009005255962950274
--0.009507957296795625
-0.001804834127237599
--0.01478675733682424
--0.006397884495899582
--0.005405912722089408
-0.002908319350599251
--0.01114769937290839
--0.01938655970709622
-0.009852432980088754
--0.005054752670656166
-0.007016266710337288
--0.006703192086792324
-0.01871598645536511
-0.01492743141270434
--0.03745023957899398
--0.008485888380715899
--0.004038292982514992
--0.01198558971160972
--0.02375426326151019
--0.002903958307600663
-0.01133342954193736
--0.01683629094173543
--0.01223375044813632
--0.01739028583023601
-0.01037516025504453
--0.003107623465307698
-0.0002396046402810485
--0.02212865045010241
--0.004616187445806634
--0.02056273883767412
--0.01937964917474239
--0.01201589248589414
-0.008737530601627855
--0.01635095025773224
--0.003659369417039584
--0.01000491752906364
--0.01853179667369896
-0.01416680610967523
--0.00482065920008995
-0.004412067732489665
-0.005343602111942241
--0.004125881152733555
--0.01227852810373454
--0.01698938299633002
-0.0101925541242239
-0.007192541702351497
-0.003587128149857245
-0.004254068054333355
-0.02453396486988631
-0.003291990695359868
--0.01150989698542116
--0.007292218301004141
-0.006416084845809224
-0.004905692259397617
-0.005119232514710382
-0.003315245338359686
-0.007894083650786334
--0.003514580909909075
-0.008650577108185733
-0.005749784513878927
--0.004622953060973246
-0.01551305274611372
--0.00561225874185945
-0.01675692317561542
--0.00150730086269186
-0.0005249396477207156
--0.009371017454593199
-0.006201585857213717
--0.008948643361457943
--0.009399518899456512
-0.01320490256867866
-0.01140117538422683
--0.0217766939943769
-0.005361646334480745
-0.02220180489809371
-0.008501566039285405
-0.006953726085302888
-0.008704864669685175
--0.006441751769055778
--0.01381542177323393
--0.005944034331242305
--0.01218475348220682
-0.003922472310812214
--0.00602366271385793
--0.00213509669400386
--0.001033230842284614
-0.04120285166456205
--0.002188274964100489
--0.03098711780986696
--0.01049611767253735
-0.01124084396989755
--0.01111288004194584
--0.01014125674386878
-0.01778457458737278
-0.003281076382235856
-0.005805308666886533
--0.01742328898907234
-0.0009151763155163628
-0.01070431167493432
--0.01131637486733626
--0.006217205649200906
-0.000762881670301675
-0.01416269392415296
--0.009981211427369267
--0.01444880317085256
--0.01272026328173392
--0.02540926451129358
--0.0110940278000756
-0.01028752658432173
-0.01645240640409192
-0.02051555290889537
-0.00587454949398061
--0.003231574489585367
--0.03068125216141897
-0.01120657639672886
-0.008330686250254356
--0.009493475668337539
--0.01368142099305075
--0.01400800180051238
-0.01697814095090428
--0.01229647990724075
-0.01934460456855885
-0.001551965902217507
--0.009596304714087308
--0.01050409980890137
--0.01452079698399888
-0.01115842872397633
-0.01563242221630854
-0.007327640732617182
-0.00579267812636958
-0.001650448967289342
-0.009306936907413816
-0.007439166366189124
--0.01018925317033319
-0.00969163873057791
--0.01175712955749901
--0.007964891666828764
--0.006324480316764834
--0.002676458230663862
--0.01830728065071105
-0.0004147968191985238
--0.004296348619290511
--0.008092367990933611
--0.022057375682272
--0.0124513396451638
-0.0176467554648664
-0.01998212856241889
--0.01206368036008044
-0.001550206286200484
-0.007952215907272707
-0.003401219608152595
--0.01441786192752461
-0.02077645225973389
--0.03084454894573975
-0.0363784128960089
-0.02018201823968818
-0.01364249708937373
-0.00617436671021727
--0.002228029271523364
--0.03883719842159266
-0.01478723499798924
--0.01409578532458002
-0.009263019766992578
--0.02527446887922895
-0.001067351001919242
--0.003147110161307728
-0.0218906940497717
--0.03186871323025285
-0.00206437100102532
--0.0101308930174031
-0.0253970890795401
--0.02081592442687558
--0.0004463766730193994
--0.01028576381632534
-0.002770651697642995
--0.01424911978077413
--0.008075192534093672
--0.00623227572630522
--0.01474215367962464
--0.005506982026165508
--0.01674136864098932
-0.01020920282104112
--0.008875559230809248
--0.007370471144570866
-0.01253139647828153
-0.002695980001513695
--0.0006757054769491802
-0.004964574977104456
--0.01408464144738169
-4.952085915429198e-05
--0.009006168754575231
--0.002701926070958608
--0.03652006717782153
-0.00352810499820388
--0.03176354969543605
--0.006602237593244335
--0.008497205511120157
--0.02910878561275678
--0.005132216800594898
--0.01483773259105742
-0.001634658535703456
-0.00443287022948464
-0.01489563581253084
--0.003697830224897859
--0.01795687552894865
-0.01100850139522968
-0.01926404402098334
-0.01338736178114266
--0.001893031564882353
--0.003607267989229011
--0.009045911040115959
-0.0001338731318121252
--0.0005282693316574292
-0.001023639970315132
-0.01392749750217165
-0.01033420203439172
-0.005417066174624127
-0.0007537700818801907
-0.004432917114122274
--0.005334675763975693
--0.01565126773071325
--0.02731810662705819
-0.00291330405708042
-0.02421584151004475
--0.005249598455701276
--0.01226079962354958
-0.008546356057942133
--0.009246286439145173
--0.004109753261901383
--0.0024803664445579
--0.01844028177342592
--0.02206502169415311
--0.01501350500373293
--0.008289712471360374
-0.02888004605814559
--0.02365459934338274
-0.02534272022897188
-0.02061740623520734
-0.007184995361966554
--0.01013127271287851
--0.02192806487418398
-0.01498574666009387
--0.01514422347708445
-0.002152694875570596
--0.0005536661968098869
--0.02192714860170471
-0.01126830570741071
--0.01171952232304339
-0.009814866225505788
--0.001308025889672113
-0.02976895882458623
--0.02468174944609434
--0.0163768052140718
-0.00219581912761048
-0.02975168356536123
-0.02554194968222021
--0.004868350948090618
-0.004045195907803895
-0.01043056049576934
--0.000926534232991738
-0.004354649707789
-0.001226545839203135
-0.01560319963806674
-0.001905144611664917
--0.006326733869281486
-0.006569985477555002
-0.01157829013057778
-0.01957635534935027
--0.003321375280374849
-0.008416917690371759
--0.01003682501591048
--0.02776346586252576
--0.01152164576963457
--0.009744685466522959
-0.002071920485550573
--0.002979984701550322
--0.005479651753659253
--0.01364616209480277
-0.005988516978321402
--0.01538178215270365
-0.01758859567402726
--0.007290021252996312
--0.0106408859464586
-0.006720670206756741
-0.01431381187586868
-0.008090427295510915
-0.002720632529275261
--0.008710135586119407
-0.01636155692790863
-0.02049819960779948
-0.006206926831703229
--0.01508181677025995
-0.02031963757079138
--0.00648705750455322
--0.001608184223053293
-0.003556902509380737
--0.0082359740514686
-0.00212032554698236
-0.01652830061843327
--0.0002580693519802035
--0.0004127808258744676
-0.01613807090585076
--0.0246365408723604
-0.002117096890481068
--0.01063398364787518
-0.01759033003990421
--0.01240796838544492
-0.008783998410565892
--0.005568712386780652
-0.02764591914784978
--0.01108040913587555
-0.006797108011556103
--0.001327266061640904
-0.01205256256503283
-0.0139698404902154
-0.01516507736363942
--0.006898339858677156
-0.0212124928519463
--0.000148445693949533
--0.001895614077862456
-0.007632918467954866
--0.0004807396485253313
-0.0103066822307925
-0.003916236093863992
--0.009276712758796796
-0.00720428133283992
-0.0003994497827654003
--0.01667932575420029
-0.005148294190774466
--0.01406916688560416
--0.007982165164102308
--0.004712129143940626
--0.001515507636347674
-0.004540869651158856
-0.02274961027329835
-0.002268591438389791
--0.01079677399552864
--0.005959473329797133
-0.01319665351608812
--0.003658540995209761
--0.005158410699229659
--0.0008904921892527685
-0.01120458585360172
-0.009013221956260373
-0.002634157991232431
-0.0127132795061336
-0.004015897215154004
--0.001097823140122553
-0.009089997065789109
--0.005479713173394797
--0.01370333603244495
--0.006419213778338505
--0.006295421397127702
-0.00385608027579798
--0.01424023232732749
-0.0003384557981018936
--0.01006837056229665
--0.008717334337668801
-0.002936647617698697
-0.001239956879051464
--0.01746058354760543
--0.01871604598745298
--0.007320731364577541
-0.007779248230564683
--0.002467991322152353
--0.008672840961702172
-0.009631914420758723
-0.004896726634768131
--0.01576727848157885
--0.003573193571067725
--0.04192338953196287
--0.01527484967141659
-0.002072438515832884
-0.003424345102880843
-0.001994470215648774
-0.01466509385798341
--0.004415147584698019
--0.003145019022950545
-0.002095842840257558
-0.0133516797018277
-0.001493321829783797
-0.003069021280439011
-0.01714674764595616
-0.01353612282972051
--0.005082987813406201
--0.02672833333266446
--0.008488830239328666
-0.005640163781592418
-0.004109339175898203
-0.002481593591264732
-0.007660783872947382
--0.02313767904605801
--0.008787522946113163
-0.001583216750771078
--0.02147026299250385
--0.007796454695051316
--0.02037903170225247
--0.005047036581321928
--0.006620068624280741
--0.01242103455226167
-0.008387904339089664
-0.03371612071087385
-0.01176073241466659
-0.003636957329555025
--0.01972449564460617
--0.00322155193966083
--0.009339150369214354
-0.0005341079914695783
-0.005236183094495943
-0.005967084342315498
--0.002666981256748724
-0.01532543628646216
-0.010622684972155
--0.02175488913460132
-0.0009980325881792627
-0.005448783860387378
--0.01742789152095397
-0.04272284150790095
-0.005218702343931118
-0.01031743025332115
-0.0004207338245167571
-0.005953948859266752
-0.01116728542750235
--0.007658999606593614
--0.03799733527470842
--0.004217275268056678
--0.02327466359328111
--0.003540099153485855
--0.01355939863373944
--0.006081097456144878
--0.008182434877587901
-0.00288968943246931
--0.02879825862531147
-0.007406723912783224
--0.01397462016687308
--0.0198093785865078
-0.01383852211211272
-0.005208812763819525
-0.008619897198737169
-0.03086765013553723
--0.0006232975408835002
--0.009190095984739119
--0.001703885701654368
-0.007822859004768382
--0.01414732369915412
--0.007004530865133945
-0.01297750573911034
-0.002383433752758294
-0.01882511659263764
-0.02113437827974255
-0.001837737216603922
-0.03802063019626707
--0.004471401036174494
--0.02922211819090566
-0.001729673334381762
--0.006929139894733195
--0.004668969225044269
-0.0001286217071147185
--0.0002413885637529605
--0.003331467266312645
-0.004202900577206381
--0.02981849785327401
-0.008493475006931095
--0.01231716500203459
-0.0196896631964582
-0.002970411328770317
-0.01795934445082835
-0.01214209041458672
--0.01276243229978402
--0.0159461186524732
-0.007208098642611125
--0.03674408387931711
--0.003145622692913188
--0.01275860825691216
-0.008936599217483573
--0.0180704963035322
--0.02086921240022233
-0.002973712992810211
-0.01467132235696355
--0.02380029879126567
-0.00513482249627457
--0.02399715324972109
-0.01534672681064307
--0.003128532518688702
--0.007377468881844495
--0.0127545989370851
-0.005256913081541892
--0.004244761040882263
--0.004747073092273296
-0.01203554954684451
-0.006623621211960685
--0.04140957275759271
--0.0145725606288553
--0.01635225794479575
-0.01460143669157566
-0.009470800817233819
--0.02248572013527337
--0.02397058337937009
--0.0008486383658285704
-0.001024265886541806
-0.002143125438135531
--0.01062443874511401
--0.0009706296318111102
-0.004753473842249189
--0.0008420742830849998
--0.02022177760170625
--0.01931781893121642
--0.006529523714771198
--0.001884601106828844
--0.01943815748134014
-0.02886135044550207
--0.003305818953922596
--0.00272159641495474
--0.008870605242995076
-0.01362004369209151
-0.02043286039385004
--0.002175003186002545
--0.006954305622970268
--0.0005679007296741869
-0.00139035663462976
-0.005714924887406452
-0.001938363463719039
--0.0002415994783482895
-0.007000962088272354
-0.01123427563609114
-0.00287038214416711
--0.01798911025299659
-0.00370779670711086
--0.01084514546214282
--0.004012904287551436
-0.02873892138717055
-0.007137954627948636
--0.003694132171820698
--0.03835879195254621
--0.005902904884355925
-0.01205224112261373
--0.00974347336546042
-0.01001140888025003
-0.01432189225272032
--0.0139857508016259
-0.002438549314738905
-0.02809062366245842
-0.01235049867233432
-0.009663211759146596
-0.001410053869368667
-0.009973731580857582
-0.008742875282310604
-0.02041620113860615
--0.01064458245250706
-0.001514612074283982
-0.001365081156937389
-0.005933235598170941
--0.002691460823918697
-0.0370619414005915
--0.003854611648674897
--0.01553803661692626
-0.0007097145667704935
-0.004063903429233576
-0.02584296083319736
--0.004871916226947137
-0.01469468647295661
--0.0009054671156323789
-0.005135410684124127
--0.0113408257339239
--0.003796779329546391
-0.02440607481538242
-0.03401237765906474
-0.0103505746824679
-0.004260912786952374
-0.00577738114309503
--0.008000564086254861
--0.008689354599246638
--0.001704719191427385
-0.002522142952408068
-0.01252347922889033
-0.01037006316462945
--0.008133283249006716
-0.01849096318793482
-0.01279757683894496
--0.0002704084718143122
--0.01120057811504341
--0.01787424597016895
-0.01869016827400498
-0.01234369184454925
--0.002879643894441999
--0.01258183881033513
--0.002365820803913704
-0.002509934654712851
-0.001436991636734602
--0.02568024372509321
-0.00589528438123128
-0.01545851285764664
--0.00523896531027182
--0.03234347961174518
-0.007674725815789449
--0.009964328240874839
-0.007004386712421436
--0.01286763108334246
-0.0077721110681705
--0.004028054843890901
--0.002454777369002381
-0.009104579021518823
--0.01082336420502796
--0.0002165925288060717
--0.01844996898769538
--0.02792388284432292
--0.002975364873161067
--0.01037771469820443
-0.03359172335176542
--0.001311061554399674
-0.001743989928088901
--0.006306447835338422
--0.007961936756730558
-0.002065816620806474
-0.002647816307532489
--0.00276987187710808
--0.03278090589524882
--0.01490168637781322
--0.001515279675662869
--0.003049714925619508
--0.009284162714922474
-0.002878156169090059
-0.02084557097978784
-0.0003488664199960495
-0.0001635344928407963
--0.002844925069185727
-0.002069375412611047
--0.002465447579877238
--0.0002159478585181012
--0.007040160067226106
-0.01506935382181274
--0.02219040644910598
--0.004310026809657723
--0.001781670803243691
-0.003122239159630988
--0.005315849392103794
--0.006532599706156394
--0.001715932076123513
--0.002989108350223651
-0.02248270114473491
--0.006556181485085518
-0.004763862999797742
-0.02302189563103465
--0.02257955159853654
-0.004689299277101635
-0.0131821245905344
-0.003571745364035062
-0.01670166832866063
--0.0130637249930093
--0.008195505799059753
--0.007353624524849719
--0.01435806091140487
--0.005137334649216573
-0.02349783639382982
--0.006275925886844006
-0.01680073955923369
-0.008266812278978172
-0.004092963157955242
--0.01694838256425278
-0.03141164399251254
-0.01142088548669545
--0.01487351305868063
-0.001363795308014595
-0.005752476200893491
--0.01690086877198604
-0.003812042980651998
--0.01804164739435991
--0.02732359173193372
--0.0009347281646605042
-0.003713435883542896
-0.005064994812885675
--0.002106702516469806
--0.003349116128602967
-0.009566176231663646
--0.03354338041739061
--0.02090600631328638
-0.02128738093029466
--0.02786290924500091
-0.02347670925869287
-0.0176447655789603
-0.0005314377759297441
-0.003637824055762669
-0.00144811417567656
-0.02332289574525618
--0.001327568899650631
-0.0185338738268935
-0.01684697013622281
-0.00179317680439744
--0.01136914971660081
--0.008941787952923025
--0.01458483182229048
--0.0004492068077432991
-0.001495671322347571
--0.02059100197001574
-0.001083798881128133
--0.006659824070091525
-0.02488264676319637
--0.02021072513467227
--0.01989333499382044
--0.004279845029171672
--0.006086477171847801
-0.03113427268186063
-0.001172737935519379
-0.01874969540991632
-0.009014176341452485
-0.02444500859854239
--0.008700978998431089
--0.0115791402119921
--0.0108153142388073
--0.01912867813771941
-0.01018198323463959
--0.03393925330587132
--0.003924252520350437
--0.01034969958728167
-0.0009883829025064195
--0.01326250910790931
--0.005621689071267985
--0.009609510787098128
-0.007688016945296672
-0.004227157306997512
--0.02859549725762468
-0.01489192943066743
-0.02355293658754843
--0.02291419801680763
-0.01822321108532695
--0.01312570986555983
-0.01604906574506344
--0.0004595975531300679
-0.01899561490727598
--0.01462427248272488
-0.001494705441768974
-0.001905964683011385
--0.009799758105250618
-0.005547047278518652
-0.01382905931200318
-0.02092439496440041
-0.01936652207303139
-0.01550535030417885
-0.02674497747620155
-0.00576166763560173
--0.0115034015636466
--0.02112856402008893
--0.01482567728319138
-0.017082803519069
--0.01142404230521733
-0.004433560589238124
--0.004890778947616842
--0.03614331575040152
--0.001341824267321704
-0.001255218186316121
-0.01671012672273671
-0.01692900460602901
--0.02414695624971315
--0.005743197880363125
--0.0002696316502599029
--0.00450614971519081
-0.01831264206976355
--0.02556687993608042
--0.01770022394029044
-0.001507431986734305
--0.0004980745879944637
-0.01960154327174893
--0.004138551468099692
-0.001042239161567194
-4.909094144944038e-05
-0.006556915547313093
-0.01921241259466239
-0.01932421529795036
-0.001468164299560825
-0.01202916679468099
-0.01704819829638292
--0.0005507839172033823
-0.00381351811638083
-0.01118379326287371
-0.0315133145196759
-0.006386302086683933
-0.01407903384944601
--0.01081601079837569
--0.02129290823586634
--0.03664599377734008
--0.02667161218242034
--0.0007517421980233682
--0.02463666520626446
--0.006943864820063279
-0.007209044052348279
-0.0137080993207799
-0.01607735220295615
-0.01113602924837804
--0.006511092572930015
-0.003684141681970768
-0.001743380799568595
--0.007586067955615654
--0.009986408993960114
--0.01669451928853517
--0.02106918531968227
--0.008536992795825487
--0.004945685919596834
--0.01298872537500636
--0.02125024060177954
--0.006973566227285793
--0.01316817465346896
-0.003647933498417234
--0.01837289002544488
-0.001039968298474646
-0.02537865064065581
--0.01151045353743365
--0.008962012292139632
-0.02208625915567814
-0.02077239022858686
--0.01247060708682805
--0.007375083252304015
-0.01019843303946165
--0.009851862438267881
-0.000642808463346124
-0.0144460312764683
-0.005277248176850801
-0.001361616457871382
-0.01316088269705898
--0.00381459564002949
-0.01049048807091046
--0.003074273827120378
--0.01430489427954377
-0.001467040414201362
--0.005113496867326737
-0.0162397340962202
-0.02002709024923622
-0.01218471109199535
-0.01178019565942995
--0.01113540820875802
--0.003424481377394616
--0.0003671240081975226
-0.0004908632740117324
-0.002877624349510244
--0.00110181654274505
-0.003298956723275216
--0.0005889029162330185
--0.005032345698888414
-0.00191515320573489
--0.003198517958139457
--0.005703876267251325
-0.004371478694364967
--0.001911275604830361
--0.004036154421795037
-0.00535327169693817
--0.003423116494465768
--0.003803607899312536
-0.001550362546360488
-0.001621908303313092
--0.01070987768074877
-0.01142652274928564
-0.00988786219077363
--0.004655446689595956
--0.004913188418852635
--0.0120095913903339
--0.01335663139272611
--0.006026432003878769
-0.01131679237011094
--0.000444383493994854
--0.006418259543750929
--0.000392158564922129
--0.0172140138063681
-0.02020775528350636
-0.01470010924276834
-0.02792409533037354
-0.01528434207411887
-0.001330581468934865
--0.003442839349525545
-0.002911491826165634
-0.01689270901883589
--0.02864694838057828
-0.005697941117327089
-0.01725514154576722
--0.01994990488241509
--0.00692013997374557
-0.00944546517676562
--0.01652806061813834
-0.001746650279986182
-0.01190569412286772
-0.0006285132602118713
--0.004245878672251296
-0.003667277194884902
-0.01761684798198465
--0.004465864511546299
--0.00669851043596429
--0.02088558533532674
--0.005031008291812541
--0.020970298515525
-0.004675627562426433
--0.004281105090615566
--0.02024099186944913
--0.0209487674779752
--0.0005513734193942432
-0.02015265789923825
-0.01676482523689774
--0.01275346754330884
-0.02048546144781847
-0.009429682315738862
--0.008250894424783745
-0.02804283978831124
--0.02148751097779556
-0.006426382591364275
--0.005522955270290042
--0.0183250066012254
-0.01607746427600082
-0.01151173227955941
--0.02331610971392275
-0.02250211941681595
--0.01336649583204941
-0.0183500590129814
-0.02698680465312318
-0.00382174566798805
--0.003662191859213944
-0.0002390458208330781
--0.01652459740186936
--0.009364726479549161
--0.004274920298827001
-0.02534583897073507
-0.003265016781284869
-0.02497132380111935
-0.01612273873124122
-0.01095409046093517
--0.007928166644929851
--0.007249353817561233
-0.01260615720364627
--0.004932984372119232
-0.006796271592265942
--0.01100581095961108
--0.03936977704377553
--0.03020498529667122
--0.001125999161456072
--0.01506575858722887
--0.006704391195025735
--0.005842307861477143
-0.02658941391225035
-0.01624030078807339
-0.0002577229843212264
--0.01141418194098192
--0.009459311024563842
-0.0228155209868675
--0.01682692921879555
--0.004669168446513064
-0.002147956248980857
--0.009373288602506503
--0.0050570706205701
--0.01512786462362374
-0.002538098226317524
-0.02562269625152127
--0.006477718818795826
-0.03428520743774296
-0.003682234075161793
-0.004235193288878322
-0.007700114136755443
--0.01644923922407888
--0.01423582528584173
--0.0005338099463999129
-0.00301266958065962
-0.02277146963689098
--0.01397974234436716
--0.01431385347125994
-0.006079978950344602
-0.006532303026167034
-0.01361059425985696
--0.01296307116666921
-0.004676795868620871
-0.007564666580555421
--0.0146067307154655
-0.01352904425861247
--0.01632135002848036
--0.01149095548675444
--0.02965070117058712
--0.006913962784835835
-0.007919949437343057
-0.0002065301072555029
--0.01079563904850628
-0.01050195489500416
--0.01610434409957887
--0.01310679571637003
--0.02108069010253484
-0.007440627853076287
--0.001895800912861425
-0.003244746412517607
--0.00527074935348076
-0.02070222376186465
-0.03157770167961717
-0.01257504255850474
--0.03833464348626855
--0.01179891071944893
-0.00834370985776867
--0.0102066009217699
--0.01836784677466994
--0.007333155314888711
--0.01033989419356877
--0.01415856161195885
-0.04020795633287545
-0.01173107530763114
-0.01257040458696674
--0.01474852413533504
--0.01857237816250542
--0.01238911148251291
--0.01976794537252115
--0.02818178317146671
--0.004388787103571509
--0.01452629266449142
--0.03236570441782424
--0.0003941767401386946
-0.001719612835356063
--0.005894317606336506
--0.003810321954825299
-0.0005270276250813289
-0.007071992595765255
-0.01268028427941534
-0.01647945268154318
--0.01234377636327738
--0.007301472587575512
-0.0134270428797852
-0.01099398280772294
-0.01337798898201532
-0.02261132292152309
--0.01981982720803956
-0.00911311948697417
--0.00488981582279104
--0.02459900028421479
--0.01162682738004301
-0.01905702570511612
-0.00132393551515848
--0.01852679635813066
--0.01313961727286282
-0.001058958596005187
-0.01654553861510839
--0.0105108483148453
--0.003746959666241295
--0.0009723873728074475
-0.01762208224252926
-0.02584669267620573
-0.01335083561993331
--0.01082044217718478
-0.006492036234966164
-0.0006907878440447861
--0.009804570661036108
-0.01120854162861286
--0.001606561870553593
-0.02216596997103981
--0.009884699179391988
--0.01528922406773776
-0.008842022145278718
-0.01692303892803204
-0.02759571569424609
-0.0101765816686874
--0.01265316152996029
--0.0171890437074447
-0.009033905980518489
--0.004304614271996819
--0.00826650627476927
-0.006071479791476846
--0.01711988614818794
--0.03087169602841707
-0.01960013349083772
-0.02114130624773448
--0.003698143477789018
-0.01665571582999152
-0.006419193632957428
--0.01276808744431999
--0.01603943710194419
-0.01643459129875542
-0.003922889186219592
--0.01584155772301158
-0.02311905166087386
-0.0109385645740239
--0.005376403351910834
-0.009109733875422256
--0.0157785477878026
--0.007099614080983856
-0.01439959140438157
--0.002298661294363577
-0.02349746992508149
-0.003629503400692517
--0.01003649050670035
--0.01468978140989673
--0.009341402997386252
-0.01539382858813153
-0.007056184629012798
--0.02424052449330511
--0.01013438349156317
--0.0161106189463998
--0.008968145175644135
--0.008580799396574867
-0.007217677857083818
--0.001505020796593376
--0.009252048759125216
-0.0006322572879615543
--0.01362873965177152
--0.0006068447899512543
--0.02218425759213502
--0.03344566508308651
--0.01639386391818827
-0.001962087768299741
--0.03526650794905884
--0.02716860274747279
--7.252873791058854e-06
--0.03158285291418419
--0.001623340225867352
--0.01104157106194296
--0.005447367061230906
--0.02012298785820303
--0.01959336549695371
-0.01271556607725358
-0.007037157897222413
--0.002965922500334253
-0.00590758125794512
--0.01364028263341433
--0.02010108074600562
-0.009709222349498857
--0.01423346720587442
-0.009726284718385703
--0.01668415456711171
-0.006154823850582735
--0.02534602587405186
-0.01703970992267478
--0.0002098898215236721
-0.01301996193443654
-0.006127027443035371
-0.01244127461428899
--0.01432956237763387
-0.01395119683912871
--0.01568464085482351
-0.0001176104337549285
--0.02421691145033501
-0.000133025446500447
--0.01005712408048885
--0.01507405279576225
--0.001758876654180785
-0.007298171501762781
--0.003581791363545802
--0.02358688361607457
--0.002590266760278887
--0.01509102745872397
-0.03346706732342922
--0.01504549584126259
--0.001771791367793212
-0.002671613222433593
--0.0003709529221979855
-0.008728933944250549
-0.01391004449228192
-0.0075713781798582
-0.001815263478631578
--0.019966802603665
--0.01691576503225619
--0.01226879362884551
-0.02739827640152937
-0.008247124735032448
-0.02076070437183108
-0.0005848872978915303
--0.009779930206619349
-0.005675720692849909
--0.01951816439149086
--0.01056015423804004
--0.02534574544743919
--0.01925142490658781
-0.009922212784173618
--0.007176253680480551
-0.005451578879695868
-0.009918531347128976
--0.0006720105208877023
-0.0105588960696659
-0.02066111321455487
-0.008288703841042079
-0.005288264043898389
--0.01387705399840257
--0.0009930899310949576
--0.01056924902037246
--0.01238941323100972
-0.001870119050933335
-0.02686588041397876
--0.002065211177161746
--0.01278292221842857
--0.005142943319099935
-0.02377251988108977
--0.01159082718201512
--0.03379098646124078
-0.008318353679237245
-0.0114529071095676
--0.01645100703285619
--0.003181099628004045
--0.006916245828306857
--0.01278138467840095
-0.02146752707008293
-0.007600035285103075
-0.0526377486572026
--0.008154331199861043
--0.003390502996875069
-0.002062008044862195
--0.01517865902805408
--0.01389728504977737
-0.01137834139620722
-0.02036474884454893
-0.01386567122466357
-0.01310006832275501
--0.0004114574493389831
-0.01493565630573209
-0.00266181772025316
--0.003002376039593435
--0.02235784650481129
-0.01080210963443454
-0.02893646779230268
-0.02079502145671575
-0.01696665427415874
--0.00821198743310983
-0.03252951745743831
--0.0002119602316261224
--0.009151221812743944
-0.04070257868877229
-0.02967613461586078
--0.0009392779784078375
-0.008278877278034502
--0.003997352635048516
-0.003299601677460417
-0.002812284173243165
--0.01667055712361534
-0.01004118163730036
-0.003220893810245169
--0.02054611297250312
-0.005300592867177189
--0.0276434851611295
-0.01770331833063773
--0.01266179054227942
--0.02204107898268882
--0.002784037837270497
-0.004173796282149081
--0.008632584948110981
--0.001095515482764154
-0.02128074900090392
--0.001254958849304849
--0.004419810348378892
--0.005555478186945212
-0.01846776530131816
-0.001540673626872334
-0.002921914811082011
-0.005206986742777751
--0.002498625282395165
-0.01568001231641996
--0.02064791536508081
--0.009384359278438937
--0.01846263293103973
--0.004279961640969861
-0.0310155959502296
-0.02278584020863179
-0.004456847712152289
--0.0248668651197845
-0.006045076994333196
--0.01990997783962895
--0.01395455659290044
--0.006382026172753417
--0.003684809762670747
--0.01466019534038032
-0.01176884283707358
-0.01028352761420503
--0.01709943596191365
--0.004433992865450464
-0.007714383803794786
--0.03251271274410604
--0.02218674442464503
--0.0199451163926457
-0.01156248548342475
-0.003535659786422155
-0.00833831371179951
-0.0007474594260389695
-0.02810276146438476
-0.005727505750275822
--0.02458364128489382
--0.01062632241752211
-0.01141852620411286
-0.02165817996009011
--0.002370292424771842
--0.00243632094707594
-0.01249269639012427
-0.02516895808140605
--0.003309003623402556
-0.001559284947320723
--0.00194578392965551
--0.009251044517080588
--0.009362909904812894
--0.02417237177206746
--0.001254690935436669
--0.02288845194202567
--0.005734752371341872
--0.01427563410971964
--0.006363852293372386
--0.001562287672018273
--0.003412395608508961
-0.01296654411079786
--0.02383050116092834
-0.01944708601240925
-0.004642619424617453
--0.002601398101052739
--0.008971464113034624
-0.03676503580484968
-0.01423068213421022
--0.01773655798797057
-0.03871908578672768
-0.03964360236200849
-0.004463109524221587
--0.0002361359174218638
-0.005107668418078351
-0.003323590770881154
--0.005589388807401902
--0.02238946932039637
--0.003899800171947515
-0.01524521454188645
-0.01218266976100981
--0.01792749546122066
--0.01503548223806147
-0.002024560014768462
-0.01831192662299935
--0.005966062397026387
--0.009829197509904162
--0.004129005126445169
--0.0005858952377448667
-0.02452327675567211
-0.01329272263209953
--0.005371391037680929
-0.01818144228686881
-0.005330406867795841
-0.001644018928360227
--0.01326050890070832
-0.004065850555653653
--0.02741948362604604
-0.008848844008885852
-0.009685013981666823
-0.02299485400376819
--0.02062581336312209
-0.003570478582776307
-0.005109054188545755
-0.01525225932418012
-0.003300509886728114
--0.01039101723639359
-0.001478894165331377
--0.01944028958477118
--0.01697254376310092
--0.002141629122872058
-0.003301696199506949
--0.01434591013438162
--0.005107999497058222
-0.02256102406857514
-0.005657765061814224
-0.008780011300034423
--0.02241746509261875
--0.002272780186270637
--0.008657542563293784
-0.0021575973694357
-0.01336349975417421
--0.005981873037479388
-0.003790736331877263
-0.004140872679474091
--6.670914454933108e-05
--0.007315457382532402
-0.01215815364411283
-0.0008535743517559006
-0.0103060191291738
--0.003171904507291982
--0.001799830570586872
--0.01009750820301736
--0.003234315392605725
--0.01667326546333653
-0.001985844093464942
-0.0008755570258095779
--0.03324142561190395
--0.00513346728883581
-0.01349198768168489
--0.001884629120528639
--0.01067455799128833
--0.009052577826730036
--0.00963686447285496
--0.008426317920287769
-0.00222868039115213
-0.01425161561675471
-0.001152318369255607
-0.001019207509525064
--0.01823198609418282
--0.006527777887195718
--0.00230102788369858
-0.007513348468336135
--0.003576762697547948
--0.0006925910562061161
--0.006151036374173463
--0.02967808628872888
--0.01495870810410679
--0.02829215203691276
--0.01307077424018101
-0.004145194466820171
-0.01301554679763758
-0.01204314662106529
--0.007896881182351928
--0.002936518892835794
--0.01584137234858014
-0.03248689768717838
--0.01298404267246572
-0.0136470362798298
--0.003320805140709715
-0.01048840042848568
-0.007979589599270533
--0.00992977835896376
--0.005357187132347961
--0.01968606290952043
--0.01365581478873764
--0.01653047617420842
-0.008186062101038196
-0.001422792317902872
-0.003430948564170666
--0.006445923005789379
-0.02519639187680115
--0.00269855712628907
-0.01446977037462762
-0.002906802859013142
-0.01169415983924786
--0.01991106777674354
-0.02295310523935646
--0.005966728869365576
--0.009527416101862083
-0.02032987404184939
--0.02727924303001762
-0.03562935648936395
-0.01580005948870444
-0.02457018169518309
-0.01736100079211771
--0.0001498850191639035
-0.002718131521107321
-0.0002293909632381129
-0.01533726591844334
--0.0002921174741558058
--0.008789265837034863
--0.01214631799276147
--0.005771828433829178
-0.04187522792145641
--0.01012376786305625
-0.01861931501416082
--0.01438323107707888
--0.01342155661166992
--0.03357526386382455
--0.006826237914237056
--0.0006356986310774942
-0.007961888727519739
--0.01119856504631344
--0.01322733321158712
--0.004877102552970699
-0.00346087396311695
-0.03108907365246578
-0.01786536291866745
-0.009064996929934961
--0.01782528745427776
-0.02346156672659736
-0.01800870908899703
--0.0103503940354268
--0.001120251800766291
--0.0114589689458703
-0.04093179967132143
--0.002716911827795133
-0.0006807472810140623
--0.02841189917233079
--0.006436509718522184
-0.009020525383371787
-0.0112531976885015
--0.002530892831712752
--0.01497003470457219
--0.008304741511837474
-0.008732684668980653
-0.002339282367122902
-0.01081516002532559
-0.006101295963644212
--0.0006083981545125659
-0.0111925919478086
--0.01817106018404243
-0.006247508107651983
--0.0005560937016598665
-0.01593001697870242
--0.01399127181184955
-0.007103197798536229
-0.01590357633458265
--0.02817126311954438
--0.001907328250532535
--0.007421590158099848
--0.03286112748140223
-0.002060921833398145
--0.00550142042672082
--0.00807426328750442
--0.02792579795212676
-0.01743142669567604
--0.003818186494500163
--0.003613771667183527
--0.01449346711149582
--0.01230035645351027
--0.007890448296507955
--0.006916513211149906
-0.0005896081008539403
-0.006745808773798426
-0.0162682831645454
--0.003785729549068591
-0.008153100619575486
-0.006161850662829519
--0.004112142720619922
-0.0102169740638249
-0.03163672955152776
-0.01095923541816692
--0.01306363175868779
-0.01841526260512721
-0.02318835426168499
--0.008247187269686236
-0.01611268793272442
--0.009982654485706281
--0.01965346314290377
--0.04392394587071819
--0.01543615116627523
-0.00619010939060881
--0.01661978469937312
-0.002749501975780859
-0.01034152522149821
--0.01621423901008867
-0.002852204862551527
-0.001788157176179739
-0.01035698672559023
--0.02437197578331224
-0.007842483333716017
-0.009730880603382702
-0.00310695488466267
--0.007923273442411087
--0.01852459513463494
-0.001205385575680988
--0.006926671146404534
-0.01285900954481521
-0.006028116230868197
-0.01022213514487542
--0.01968123654387025
-0.007467184794166561
--0.001513862107688405
-9.275203202469809e-05
--0.02160593420112384
--0.003037808331528349
--0.00463388021826113
-0.0009072008471823982
-0.01209908437075692
--0.006763663457900014
--0.001432962532834532
--0.02637618313141538
-0.02145385516254548
-0.007823962878214012
-0.01297784112901031
--0.01681282127108752
--0.01081137084825162
--0.03057891160642245
-0.02703611698715908
--0.0004326449471321721
--0.0009016799908266695
-0.02096619150384676
-0.002862586767818165
--0.008249325788232904
-0.04259944984787371
-0.0128484749708137
-0.006286325770660806
--0.02675904010794916
-0.006881454635124842
-0.01763054342473057
-0.007041496630679498
--0.003859050967826861
-0.0003920201767116419
--0.01725842822724766
--0.004114684348636946
-0.00827885858990788
--0.01260824093519398
--0.007796541933741968
-0.002328399831708919
--0.0008823971650326642
-0.008185900051901292
--0.0004760619302936898
-0.006146148117364696
-0.020578570662521
--6.06603232904655e-06
--0.002161307033339793
--0.0007347499353844648
--0.01535776236513778
-0.02115795781593732
--0.004727285679441861
--3.076978446484957e-05
-0.01466515273507737
--0.007337214381672696
--0.005955293505640252
--0.009211815153339794
--0.001013830480485472
-0.003165817197710146
--0.02194133528525962
--0.0123339807218651
-0.0008037768341064717
--0.0181025967329025
-0.009644795261822022
-0.005900597676539191
--0.002868240527846985
-0.005740122848112462
--0.003141714655261093
--0.01328320993236815
--0.007019741866511205
-0.00131086464379034
-0.003449584783044305
--0.01108276947437757
-0.0004426494666975864
--0.01419997745867885
--0.001921288544665169
--0.00769478223964514
--0.004233784825981601
-0.01264627952885882
--0.0193665875821791
--0.004125892601370766
-0.01541290020980129
-0.008922721224138303
-0.0103141522715134
-0.002178373620125354
-0.01437701846676927
-0.03763652844020955
--0.005601920961801258
-0.002958666561788732
-0.00740438301661268
-0.003610226248119495
--0.007494497981039089
--0.0001244141391152115
-0.02262157542949992
-0.04267373566853217
-0.01047898258888232
--0.03100259525061034
--0.01330382121075038
--0.009646182802211604
--0.008507711599268539
-0.003047233632048879
-0.009104007732643219
-0.02704983410222673
-0.0139480306243001
-0.0008023522213313902
-0.01342774015015171
-0.04680392766486464
-0.001977258586009847
--0.01731072095950852
--0.0008673468987175747
-0.01024074315277151
-0.0220290881744565
--0.05461936562487078
--0.01671330146150685
--0.008816271755744957
-0.01875693996529748
--0.001970044567113991
-0.009553301991468191
-0.01405532688010382
-0.004772411399556049
--0.0300463536860414
-0.01515476181038647
-0.0161372997685075
-0.02163034003237584
-0.02347144630322073
--0.01676585880107048
-0.009572572885280394
--0.02296600424521639
--0.03113955917342857
--0.009365906472692302
--0.0002414886769392478
-0.01408412000351894
-0.007212212726331374
-0.02293836666086397
-0.004255111232987584
-0.02233744968861413
--0.01139204486677259
--0.003624043687740727
--0.0259464460035085
-0.0101768713611255
-0.008540364807789969
-0.006309910032365329
--0.004563537595672934
--0.04162241540206459
--0.001550925795703522
--0.01212730648130652
-0.001191433310164514
--0.006663741736751594
-0.01515059071752698
--0.007381250912364885
--0.002563720727718177
-0.02743224106869607
-0.01753686274227351
-0.001125710999688163
--0.02145905499244218
--0.01175833854148734
--0.003451049855824337
-0.001609729619164475
--7.249892792516692e-06
-0.01800621050569225
--0.004535380153641234
--0.02370092072119814
-0.004645685978706206
-0.01213633954531436
--0.02335332813744484
--0.001299381309864154
-0.00822616576228976
--0.009813973890719092
-0.01815210347300254
-0.01693559653205544
--0.001925541532559558
--0.02973922734869056
-0.02047630700354801
--0.006405079313002251
-0.007180420361159274
--0.01481911587333832
--0.02518710764752834
--0.007727895804268055
--0.002302905369232538
-0.01787154743983526
--0.005083889956903552
-0.0004807035131491862
--0.02367260047144851
--0.01148439108201316
--0.0004393562687057712
--0.02914766188081922
--0.01461389976664723
-0.001686048624952834
--0.003363138925213664
--0.02468060677073512
--0.002926810475046166
--0.003614934843680914
--0.02106436685202944
-0.01293251507906198
--0.005788114416657888
-0.007664502480287444
-0.006752197222882973
-0.01188513116288301
--0.00390192259270895
-0.02870095252493179
-0.002242517502101011
--0.0008154859464253555
-0.006271615538385315
--0.01364760442718507
--0.01422010075670395
-0.0001550506524105195
--0.01475633963484422
-0.0008581519030434652
--0.01655298564424954
--0.01291285653545685
--0.006600723488608474
-0.0006973498150875454
-0.001734889691953508
--0.02211454170288848
--0.007935948464175037
--0.009471362064735549
--0.00687801906152408
--0.002653541828557804
--0.02376741796206714
--0.001105150099200283
-0.005154064259733975
--0.0211389747315035
-0.01286504133121527
-0.006289622872198105
--0.002084011605783891
-0.02556985365021749
-0.01709252360864669
-0.00899275825243172
--0.009646607879679289
--0.021292184559867
--0.01125586041827413
--0.003274819727009699
--0.007649013867020772
--0.01888188905379376
-0.0002759683653543373
--0.008804193348626088
--0.01622478875444812
--0.01369160134034805
--0.01467412908456944
-0.009722947259323964
--0.02001782820039825
--0.003887019984632322
--0.04530632592307829
--0.007589493706426229
--0.004452622527134434
--0.01366356670992284
--0.0002516596596313096
--0.007881804552822187
--0.02320950635931417
--0.03198442479497952
-0.005096871985900889
-0.02774636879513193
--0.01742563267868617
-0.008826341668986208
-0.002801899089232056
--0.0121109875560514
-0.01281107657366471
-0.02722660206699005
--0.0002022642756702222
--0.003287871939784436
-0.003197845880977589
-0.007504731957759122
--0.01436268216965977
--0.01570014644383539
--0.00531481962300456
-0.007394798354655848
-0.007660398940911749
--0.002518748256518284
-0.002158094551403613
-0.001927191062784752
-0.01587267449450228
--0.02147868260069313
--0.01182043690984726
--0.000755724298548532
-0.01129132224356749
--0.007111148048983773
-0.0001758388475596971
-0.01152460459957085
--0.0006074430644429766
--0.006179619010560642
--0.01284124050767895
-0.01989345910197729
-0.01216053407628957
--0.008723770548105952
-0.003302631478968715
--0.005851925788608017
-0.004012092778994189
--0.03144821740774612
-0.01260273039001092
--0.0018103057931378
--0.003654124763568644
-0.02204958846307291
--0.005232378583426781
-0.01578578188444911
--0.001609639660602506
-0.005581637100705215
-0.01075980082732734
-0.004830613046387652
-0.002698585502203707
-0.0007461177046031344
--0.001466751889450543
-0.006153517336300792
-0.01329167176568332
-0.01450319940031771
-0.009234553114465877
-0.002810021747574173
--0.01697727141816338
-0.01123665760246998
-0.002124541022159736
-0.01390262972590621
--0.008649847934861591
-0.006162335713658087
--0.01941795756677263
--0.00580267354868882
-0.008216971894694714
-0.00442225946396887
-0.00133411786699842
-0.009998450828092406
-0.003232814125072982
--0.01004382385357007
--0.004005857570312589
--0.02379997202040748
-0.02119102728938049
-0.01622496974620829
-0.001558310490635856
-0.02363735930049632
--0.01517977909569882
-0.01442111699139319
-0.01054632306282705
--0.000900653503022452
--0.004289166731162807
--0.004706816699441552
--0.02273224442566374
--0.01347603156775597
--0.009755575988894637
--0.001127324985399928
--0.01684842293377311
--0.01588462186460805
--0.004123798643170593
--0.0186785359848796
--0.01132920220753525
--0.007452140775776656
-0.01731083234666636
-0.008636518802689895
--0.02666957377180495
--0.002043238853550058
-0.001232595802033744
-0.003183197424533352
--0.01780330883560586
--0.006888540169121596
-0.003640440225692836
-0.01249085235475565
-0.003573853789403236
-0.007649679457840198
-0.01694682211513468
--0.001076736228272598
-0.006806709232433727
--0.02516933522587362
-0.02571300823666357
-0.006469843965520342
--0.005217354467404563
--0.0007524223755534171
-0.01914259072937686
-0.0001635210972976692
--0.01886973417647719
--0.03283691601227646
--0.01021106291512091
--0.01039358306730022
--0.003844912336835711
-0.01505341621310096
--0.003733100581145783
--0.02908710666976869
--0.02079821849208126
--0.01128264392467088
-0.001484439146734521
-0.005376665337948546
--0.004552458380303557
-0.02505142806555408
--0.01791762140020132
--0.02816102674309326
-0.004765186734834283
--0.02377897226450733
-0.01124661755027366
--0.02291496848647871
--0.006378570573125721
--0.004212955056218151
--0.005172464100505938
-0.01951956329371419
-0.001067032040447101
--0.0212337355673045
-0.0104500378355902
--0.002750313408159631
-0.01838138546231393
-0.01103588447240928
-0.00294278862716998
--0.001496456938573537
--0.01235825304694326
--0.004784973133995887
--0.0145304553450953
--0.020140106162011
--0.00649299751854693
--0.0152953012393497
-0.004514143495064866
-0.0109016072304059
--0.03960501905355521
-0.01371169420850173
--0.001320239186767634
--0.01484213184204995
-0.0327029092061576
--0.0141335010090685
--0.0210443609576398
-0.007665197539278155
--0.01264553457679247
-0.02006843789864211
-0.005441667170899345
--0.01744461041429433
-1.212419434708521e-05
--0.01368567040520507
--0.009016701264038045
--0.003186756251476529
--0.02006309634706837
-0.007404114542599849
-0.002352436630585836
--0.0005338109721588705
-0.01167361284963523
-0.0421586983484245
-0.01003739211331729
--0.007773518035069656
--0.01152677320402499
--0.006671082036985199
--0.01759431528179283
-0.01360307666384341
--0.0140734535058952
-0.014236486843532
-0.009841414804657682
--0.003375232676774457
--0.01019207015367098
--0.005440720867744793
-0.02433834590656121
-0.00990296526148814
--0.01271482717545408
-0.005362931905873316
-0.01261851604816073
--0.009574888468903956
--0.01769048824441234
-0.01336024193461339
-0.004748150017263107
-0.01330626206249999
-0.002034255390311251
-0.004672331522973912
--0.02156332159651577
--0.0153669229298173
-0.01527014545682585
-0.02363608432652282
-0.001384455399370721
-0.0240363662792935
-0.02552393067600009
-0.02709365475569762
-0.02132684391082558
-0.004483419169985984
--0.01649861493134095
--0.0092342473152426
--0.01319064630755653
--0.003643793071693718
--0.0197475437691385
--0.003994801452147038
--0.002990360912840994
--0.0192888727616264
--0.001339941451199551
--0.01642196751266858
-0.01420953146836242
-0.008974932029288436
--0.02989795692915964
-0.008872894513016804
--0.006297590180550504
-0.02321590822087837
--0.01665498703114366
--0.005698120037312774
-0.007280931316962126
-0.003080904727680463
--0.00557740634035294
--0.00104246952627834
-0.005454012809754877
--0.01002492099601569
-0.01161941809066553
-0.02508609347111236
-0.005223515892492482
--0.00565139552149169
--0.01872817280569484
-0.009067802423220996
--0.006998996227259443
-0.008089984496130858
-0.02991334290998401
--0.03548166835152131
-0.01230134601422771
--0.01737064963703465
--0.02903886569650859
--0.0111393877203405
--0.02943279990176062
-0.03418693352944115
-0.003478428452752899
-0.004683991658266416
--0.008906813376685654
-0.006422704118738241
--0.009676341937862504
--0.02280210770554284
-0.005542359814264647
--0.01127976221935154
-0.02272517058639634
--0.01269946835307127
-0.01411583442236518
--0.0173203586910601
-0.003339116194443834
--0.01861329775144972
-0.02212966193136476
--0.02781329139154201
--0.0218128275638751
--0.003932053941037814
-0.008630890620953497
-0.01254089155308593
-0.006213317909976061
-0.01787667051215017
-0.02332843671447739
-0.01243436196543425
--0.01087863795629823
--0.01393953653606182
--0.009671820651194133
-5.478519393532387e-05
--0.02210024177452707
--0.001677496270470337
-0.0001319310649757755
--0.001108146910662826
--0.004201542780059761
--0.01429468455378747
-0.001875512661577212
--0.01976629591679434
-0.007199624538408438
--0.005513546013732728
-0.009493122547765961
--0.02931475960931559
--0.006557188016138213
--0.01624226463318285
-0.02012401505150916
-0.002224455263804069
--0.004055509930986369
-0.01669411745556907
--0.0008404923721063164
--0.00876071204872711
-0.02300731209100981
-0.02201575999693554
--0.008442528219364627
-0.01964005993379015
-0.0245259293798195
-0.02799786667924047
-0.04034048816718881
-0.002514677989239008
-0.003631306723336363
-0.01333130541788209
-0.005180309066577605
-0.006955112522422015
--0.002695423313747646
--0.0006143999353419546
--0.01183784297265191
--0.0221996880760404
--0.001661762667092851
-0.01620421957371512
-0.02058047856314743
-0.030121781673893
-0.01047755222995705
-0.004972500150327923
-0.006228687546233684
--1.831830180524466e-06
--0.004762067055073417
--0.001387774325787077
-0.02173331418537729
-0.01450610332824094
-0.01825236961214413
-0.01902660218284419
-0.01466500002943218
--0.01454984103556262
--0.02807121070706109
--0.008205880419726067
--0.02584023988698189
--0.005786006907455564
--0.006783572237874262
-0.018241250159699
-0.008978942917308512
-0.007576223407926604
--0.01600248188990005
--0.01105825845290509
-0.01612700074643601
-0.01125907293498918
--0.009110189134051008
-0.008700281467609835
--0.009502920889178647
-0.006117562896580875
--0.0006047785607954177
--0.02669981570177031
-0.02434079283924822
--0.005506521563884174
-0.02686315485546403
--0.0152260676896741
-0.006281130847753235
--0.01525627770873395
-0.002957363135672856
-0.008850973430756822
-0.029219269716664
--0.009433200414891815
-0.008761774574679807
--0.006912381117449397
--0.007585344647579727
-0.002493671630715789
--0.0008302299480667347
--0.01227293371713308
-0.02197281511938133
-0.008025919878023631
-0.02317062505097985
-0.01060004539960217
-0.02509688296574898
-0.01199354119820134
--0.007935417009972547
--0.01199306432812687
--0.00845302805208145
--0.0193208375784711
--0.01248185619191264
--0.0007155242533027175
-0.0007975345939231534
--0.02153598688329926
-0.002616140522354241
--0.002535882972465923
--0.00493178622533052
-0.01009581914072191
--0.01524790727401618
-0.02019304186943155
-0.00185997119107519
-0.03316813552620992
-0.01457439505917512
-0.004702630442664113
--0.01091187629139023
--0.03084893234114522
--0.009449966937694232
-0.008612008022292332
--0.01818182241816406
--0.000235980792110004
--0.005215899153847526
--0.003967029025564525
--0.009299318893942161
--0.002992213855126377
--0.008762814327358856
--0.01266358097640896
-0.01790981181088667
--0.02301842801614264
-0.00282181826052129
-0.007767915818340168
--0.002783168328430195
-0.007593271724295097
--0.007063224755874797
--0.007070914542405755
--0.01889520980484604
--0.0135606436224545
--0.006020044478371998
-0.002016663023160911
--0.0005593598848843508
-0.01715254605549726
-0.007520546776152653
--0.01048236268201505
-0.01662582819107709
--0.001240268176235298
-0.01369983726345297
--0.007764922224304761
-0.004756785630950981
--0.009559163166284973
--0.008521872296596622
--0.002001679010345239
-0.03532387456455704
-0.002980804622552097
--0.02234976350513048
-0.0007075110002783091
-0.0004313369817162948
-0.007654225103055262
-0.002241243007702808
--0.01120319555780888
-0.01644861239972022
--0.02280169648725505
--0.0004781473630653622
--0.01939394294887693
-0.02122874721007592
-0.009466348344938489
-0.01784227081438027
-0.006808143404756192
-0.003071810821036979
-0.0126904977064881
-0.0004121479581607766
-0.01098106728195575
-0.003284240020346807
--0.003065731711405691
-0.001013642973547149
--0.002350337867709835
--0.01158058981202177
-0.006006171849806169
-0.008467614363005631
--0.005834008160083576
-0.003572628868997989
-0.01067747594807222
-0.007703974237387842
-0.008326276449521991
--0.02139878594709162
-0.005913546278723272
--0.01015850345672509
--0.01228838108842892
-0.003964836139591737
--0.01131810730877458
-0.003350255335217148
-0.01378071730595013
-0.01582855502367351
-0.002270173149936549
--0.02753099762346006
--0.05458642059081979
-0.02096032105811956
--0.001335484491201703
-0.01423010597891592
--0.02489862457580549
-0.01558284862868952
-0.02820669000635973
-0.01158249832417735
--0.01860121434540492
--0.0006092550771201546
--0.01747960795757261
--0.0005409875725104656
-0.0136153622100675
--0.02373777414829764
--0.0008395239705694436
-0.00385574667561625
--0.008115589329708314
-0.001348610617313697
-0.001566003639002294
-0.01011761843041649
--0.0006608921203804334
--0.002575911857168541
-0.01815302480945557
--0.02072552125018492
--0.0262641385888572
--0.00442329267669539
-0.003623058058477043
-0.01736958407660402
-0.01185339056892922
-0.01491089713277747
-0.003033433952060501
--0.03566189430730117
--0.03240925406491323
--0.0239731766402716
--0.01742046142700509
--0.01467664260449347
-0.0008938419992220022
-0.01209376378045557
-0.02441103157422871
--0.01066050878697695
-0.006635337459409193
-0.001579031591803346
-0.0007379584919571555
-0.01442697709865477
--0.0009201054111061487
-0.006343522036326627
--0.008046016315949046
--0.006297029607812271
--0.0172546954889419
-0.006460912369517464
--0.002329618393333419
--0.007863998213912805
--0.02000387018598969
--0.01260559724438364
-0.01921327072230643
--0.01463553140805201
-0.002326277422815354
--0.02020158891088118
-0.01269230619097776
-0.02476304290947576
--0.00517040377964231
--0.002465953442932902
-0.001409118310045361
-0.01324025677522835
--0.03281199315770576
--0.0003423238073269405
--0.001246177163486365
--0.02748829694390831
--0.03015794723933947
--0.01924801816383726
-0.01222660916097782
--0.02055139124712056
--0.0008745854164056718
--0.005685244557756954
-0.003017674225079332
--0.01568443385868898
-0.01706211403336673
-0.01712415562152072
-0.01905680635399063
--0.002342804049826326
-0.002391042279268207
--0.01842818413682125
--0.02642391961824708
--0.001360081998066609
--0.01204509975338573
--0.01797442524509399
-0.00650415321361745
--0.008096629155731604
-0.001851714696659468
-0.007486337247737841
-0.01441049324431237
-0.00387699768868964
-0.006844537297344711
-0.02736233468481648
-0.005215242408168589
-0.001455948881156338
-0.01745843090745991
-0.000599199008415539
--0.02575695642395088
-0.009299947670920622
-0.003162467775286378
-0.02242416348286005
--0.001568095452381308
-0.0002971744930163637
--0.001554683234122159
-0.01059006605789259
--0.006310478860653798
--0.00161541665782159
--0.03068336323756268
--0.02630841528345237
-0.0126561169042398
-0.006227509667878234
--0.02257396240385347
--0.002011813484823319
-0.01180103911245726
-0.01581621539440026
--0.01823070604310181
--0.00362716938467956
-0.007187084995237632
--0.005141851659345853
-0.03045397549861853
--0.006370914531422539
-0.00143230184826514
-0.01771523209263294
-0.001877022396101989
-0.01812318513711229
--0.001567799541643825
--0.009594502271964017
--0.01065181315969197
--0.009723825357177508
-0.0068462349771462
-0.002674864853343341
-0.01675687399552197
--0.002999728454018915
-0.0003320632504074073
-0.0089567877274079
-0.006995749589962106
--0.01751345653549044
--0.0122589339318042
--0.02114035527388586
--0.0234242870460309
-0.005067914168254901
--0.002015454890077364
-0.001570858608644338
--0.01440096517550899
--0.0002569490148673067
-0.002590762395233006
-0.003113197084896254
-0.008888832357408378
--0.007309055974670796
-0.008546472979519104
--0.007487408644024259
-0.01468467143550598
-0.02721778477071241
--0.003169289676179141
--0.009660119064975006
-0.005542655758731487
-0.003041099659027408
--0.01387152297798735
-0.01154306930722966
--0.01624432639396148
--0.005179414522033523
--0.01296095317884778
-0.001035686504454403
--0.01458542986173656
--0.005672881234826287
-0.005871984424042715
-0.01222314070247131
-0.01065564032360763
-0.002873094970990704
--0.003544858241490187
--0.01986275069875775
-0.01770282214844496
-0.01518346418469071
-0.003598029123158551
--0.009484972074418648
--0.01980104566195168
--0.01864140429493547
--0.006366600431551619
--0.003590152107738395
--0.01108666093078196
--0.02363230447683965
--0.02810397154050185
-0.04358161260104806
-0.02965577143514629
-0.01631307634090015
-0.004265813328068092
--0.008949948667113976
--0.01540487984287483
--0.02324373613255554
--0.01294605666121041
--0.004757469994201534
--0.008748337048483219
--0.009029178788397516
--0.01940666705744033
-0.007194241069759199
-0.01336974079073218
--0.0173986693048557
--0.001462381680950147
-0.02418683813024882
-0.01007430439882672
-0.004293174649509116
-3.279015482495487e-05
-0.006536096835981952
-0.01661222391274818
--0.01218046237060773
--0.0008894670166556978
-0.02865334347744229
--0.002510828997796783
--0.02014045359714705
-0.0105902378128561
-0.006907431795473882
--0.001386094893788052
-0.0107862469842047
-0.02556772409882847
--0.01940556284588086
-0.01055544497566266
-0.01529272022451759
-0.01632223204583959
--0.003723701382305042
-0.005292373282768331
-0.01074647097007686
--0.001390334696726014
--0.005000550204930666
--0.0005861474952635248
-0.02356111316306296
-0.004516693101181987
-0.01189796409315707
--0.02393858303140722
--0.01005755066991063
--0.01592947074190488
--0.009134093320879519
-0.00399485919732284
--0.02000856405988809
--0.01631490561543362
--0.01711089392143549
-0.007982513069088572
--0.005608303054401505
--0.001226671687094277
-0.02425915836168496
-0.02189034170776282
-0.01369454436504142
-0.0002951253392119872
--0.006798025116779333
-0.007069106856879686
-0.003445530439520182
-0.02315414291550566
--0.03782637388154714
-0.02758922392078583
--0.004608301044793327
--0.01393077428919685
--0.02632151122798797
--0.01337028202518651
--0.01237240072137181
-0.007479449823903507
--0.01702435150545808
--0.03433728306255194
-0.002677277160745685
--0.00775994661867491
-0.007685157309950867
--0.01448886736735199
-0.03646831627829175
-0.02412834577327096
--0.00186146673470772
--0.01432617387416049
-0.004667873461486125
--0.01588261038629788
-0.01245513660661845
-0.01007868826382511
--0.005797379878496465
-0.007947415650676722
--0.01753389710271067
--0.0220967068975579
-0.01538763476728527
-0.009218232705024164
-0.0008123266863471926
--0.02827824033239022
--0.008422050289485233
-0.008962310576136234
-0.01472770980815467
--0.03380801125873714
--0.009181217601688781
--0.007142760118595578
--0.01070595596621364
--0.03629006350402003
-0.005854884624560524
--0.003434943149016071
--0.02495769520534488
-0.01806811216454718
--0.04743243101334736
-0.02633535657875922
--0.01054902999928291
-0.00187995928445413
--0.01367431870181536
-0.005509459995039222
--0.002760966171878608
-0.03524267084662847
-0.0001756655692120215
--0.01530821546171491
--0.002619454016890088
--0.01545706825884323
-0.00244194615771597
--0.02878119278148453
-0.0005359223490676475
-0.002632718091934924
--0.004484495000386859
--0.007731445899600694
-0.01714452039528974
-0.01311772616138143
--0.01239191064266938
--0.02193009895020458
-0.006765129257295823
--0.008511224096674316
-0.01258806145873188
-2.953615389715363e-05
-0.005391084544388754
--0.01324152141232201
--0.00851487749723602
-0.0002372532523585972
-0.01030696936019442
-0.001968084975612464
--0.009312935994278413
-0.011102823379852
-0.02196030089626533
--0.008711335438126414
-0.01209803597862764
-0.01430330899640266
-0.02233083074595976
-0.001386279969065225
--0.001458734387335184
-0.0133535942670621
--0.007584654867509442
-0.01773134799349694
-0.02446612947189461
-0.01506638421894652
--0.007371600543009406
--0.01342256893681092
-0.01985098440127005
--0.006546874618847636
--0.005446174244860969
--0.006875102331625598
-0.003389365849001732
-0.002199948611427924
-0.01459801647749705
-0.01810308200644735
-0.01117981661778893
--0.01268673001858681
--0.008473161743006311
--0.01374317576208295
-0.00163481134757355
-0.005278705326246254
-0.008968380644583222
--0.0009769150019168244
--0.007505584793028684
--0.01117219315462986
-0.01541792336983751
-0.02710125843852925
-0.02269429583280697
--0.008660284125027055
-0.00483634511207379
-0.008010010174108883
-0.004919172184792992
--0.005033643658121242
--0.009361103140145611
-0.01039952920978106
-0.0168728134498484
--0.002210214303858658
-0.001644286017232677
-0.0016473883694918
--0.02544694756751488
-0.0002627462135695244
--0.01250130016965456
--0.01509447931709746
--0.002253091948850594
-0.002679284372520129
--0.02236065939064861
--0.006587697464090558
-0.01956082453616368
-0.003462133301381847
--0.02279282515307312
-0.0008983647168733348
--0.00214573760881331
-0.01174230450208774
--0.005147334127329778
-0.01145394383815482
--0.0008223154777248628
--0.002003896027831352
--0.00429962105385158
--0.003645408838351193
-0.01003791709976675
-0.003755589830439826
--0.009057414797964661
-0.00108378385016625
--0.001485256100679439
-0.004725659052103081
--0.01725916348334064
--0.01465034209488935
-0.002365079669621194
--0.008163719172141724
--0.001075605502041322
--0.01354100851725906
--0.02337594152975484
--0.01339871784440387
--0.003773593071622333
--0.01505060873464348
--0.003026768159962125
--0.01258011873354536
--0.02214482680948268
--0.01574664087772463
-0.03217252443288242
--0.01298603111777673
--0.005925822839875057
--0.001644087676671262
--0.01903739968696957
-0.02133855878579407
--0.004167567535977313
--0.01629138495582334
--0.001457283029492609
--0.03232177514073072
--0.007571611519078271
--0.009901430500913444
--0.01316175359418615
--0.0008684598040789766
--0.005883452455566981
--0.006442656982238817
--0.01622374076624674
--0.004937040691055238
--0.006790018834121446
-0.005477333952238251
--0.01422204337088316
--0.0105162417604342
--0.01730872575917793
--0.001177343139364629
--0.01643161994344187
-0.02232543151929379
-0.004856239290661274
--0.004775805247646656
--0.01241966970821021
-0.001683854318048276
-0.01341142940483655
-0.01608338676543095
--0.01598837972712713
-0.006783714825382198
--0.01528601319027678
-0.0243970769979488
-0.03443117578194481
--0.009821883161118695
--0.005093588488883336
--0.008870239263593394
--0.006677370027695895
-0.009707389695374172
--0.01299853433879688
--0.01176897173115015
-0.01711791015402429
-0.005432066804389119
-0.0005383346873025084
-0.01867950508493068
-0.001138793808832343
--0.005788785401621202
-0.01779241122026541
--0.00426308758147475
-0.01206538862516047
-0.006870106993930029
--0.01377999600842015
--0.003745437525637571
-0.007999014320935204
-0.01050614247376416
-0.01843881599892026
--0.01717895313996071
-0.006596635273447197
-0.003579198577964199
--0.02543225406576396
--0.001557530080308451
-0.01674135968000739
-0.002447897979049682
-0.007542423975165838
--0.01611164034333274
-0.005487527810549575
--0.001741674390096337
-0.01203154859980562
-0.01594675409258664
-0.04287854779555862
--0.01031677214531021
-0.01525435020605757
--0.004472114677029778
--0.04314596815680957
--0.01878009034727854
-0.00842015633794331
--0.008560327238365216
-0.002417888044225988
-0.003141360129199047
--0.01327622919922524
-0.00240739148345492
-0.02110023806144208
-0.003981906198952117
--0.003498445488061459
-0.02833365247793092
--0.001051424494949802
--0.02430548692214859
-0.02727205402480347
--0.01205563256421332
--0.01131241017155675
-0.001442875242569484
-0.008945422549528429
-0.009785759157804294
--0.001988460840637024
-0.01321148050316419
-0.01086849952997028
-0.01556297420780715
-0.02806224765511247
-0.003984350793121086
--0.01035712191752304
--0.006517141308520551
--0.005397465057372651
--0.00713524924877088
-0.003705964327725333
-0.005001421080033161
--0.01339723674419992
--0.01515586829955307
--0.005718337347237418
-0.01100869280278039
--0.01053084979813007
-0.0004294393407962753
--0.01299248100449674
--0.02709292963770401
-0.02548358539644635
--0.02968152304922021
--0.0074779339971157
--0.004378392225775813
--0.00230931464809077
--0.02377674929568305
--0.007371895750751153
-0.01116199168577324
--0.006658593477453835
--0.02530835134582443
-0.02460182261648258
--0.02714180321875245
-0.01142929171019894
-0.02958991753091236
--0.003056752253358676
-0.03056121573801894
-0.009450681122954743
--0.003845920806718005
--0.01976281598517505
--0.01877940179195196
--0.009351956540127023
-0.00161613606744749
--0.01075110375017815
--0.002365227170694966
-0.006997163081335574
-0.02220070813143225
-0.003159546728129675
--0.02534661535995247
--0.005655320880152411
-0.001804959452269903
--0.03184139848659729
-0.01764925509598555
-0.003985458763753926
-0.009448410760337179
--0.01364018830925454
--0.001730736116933022
--0.01392567461441758
--0.009002500126537333
--0.007791264554680338
--0.002270507235609968
-0.004737090658872144
-0.01138363638236161
-0.00063287890843606
-0.008233619457178074
--0.02122013366262418
-0.01035269895196205
--0.003612956386870847
-0.005872440514353084
-0.02214910001406518
--0.004505210134343432
-0.007176145536187845
-0.03207861596905075
-0.03260694570918068
--0.02295940899589659
--0.0008044939896385729
--0.004902899406882458
--0.001803609086011628
--0.02547534369046859
-0.008339031455697046
-0.02896388500687931
--0.003480909616696143
--0.005124301767374085
-0.02158182571432071
--0.006246488857733734
--0.01051900619283845
-0.005503990374951596
--0.004782502640102333
--0.01363926271891486
-0.00216777907938039
-0.04326847417740708
--0.004524733702412898
--0.01456007053785066
--0.002449332959988761
--0.004036706496715323
--0.002183515300142529
-0.04280082678802109
-0.004082295245692164
-0.001265718213655355
-0.002079810562432237
-0.02691187644898443
-0.01944588618029608
-0.002007001024370076
--0.01697505232735191
--0.01989281786023239
--0.01109682663608693
--0.008696623144786482
--0.002413476953728136
--0.01258725869977341
--0.0007869444007620255
--0.02136730067890838
--0.0005813431717016943
--0.001041637439004884
-0.003695688302787591
-0.003799527982220035
-0.005864772640687918
--0.01250458247284068
-0.000882340298624268
-0.001590549631966471
--0.001807562204734891
--0.00242225688851789
-0.0161680297029911
--0.0003227593881921463
-0.009027993200080435
-0.002663507334251682
-0.01013530935833316
--0.001757586973210711
--0.005071872074905203
-0.009646760942940284
--0.005844214226176702
-0.02072028883714406
-0.004387147163223604
--0.001035153166440642
-0.009895163793190817
--0.01542652712175189
--0.00573718883872852
-0.003910540166469667
--0.01076148726055582
--0.005529488557247855
--0.005426325425868439
-0.00937907442924997
-0.002067897034571891
--0.004744997394110944
-0.002482888192322983
-0.01192812133997755
-0.007083367042630865
--0.02237825349496664
--0.01508654185335042
--0.004005457554853573
--0.02358368343891624
-0.01108524725337704
-0.006116881774818506
--0.009398793642311838
--0.01018379395836166
--0.000678896746564428
--0.001592775653576762
--0.01417088081526732
--0.01426928793113908
--0.003338173046620963
--0.01061186951088806
-0.01329776926039383
-0.01745906839500359
--0.01404291805352927
-0.01169437928079268
-0.001476824819444713
-0.03492567432903454
--0.008694675538107566
--0.005812747141314726
-0.006613300432141644
-0.01955078728853462
-0.03943271650417808
--0.02194225877669898
--0.01379058451679611
--0.002262888437192467
-0.01233424019896446
--0.01110852723794907
-0.008627369668247765
-0.0007782601368858096
--0.008054627048642932
--0.01774333590391123
--0.02643061485626071
-0.02592073180573467
--0.008426225971986683
--0.01297293591143285
-0.0004450720069415193
--0.008466169550768312
--0.0008731601680611137
--0.01938335746283689
-0.02876193742223214
--0.004524913700150957
--0.007260165219141731
--0.003262254581556876
--0.006295607198167523
--0.02871747077627873
--0.01710499229869638
--0.02109506399447368
--0.03614772513035996
--0.02329646209318838
-0.01153965983578244
--0.00158295408790223
-0.01470146677673169
-0.00794657514368333
--0.003101550423983673
-0.01651225477300172
-0.002014460695806933
--0.02098140841733181
--0.01973511613420898
-0.01476854480204782
--0.004416668526445178
--0.01543455572510525
-0.004693658975196252
-0.02717703528680578
-0.005441847157597294
-0.008941613860259212
--0.0001016496505559675
-0.02404397915330471
--0.01462995627876619
-0.01562843130123212
-0.006711945995703162
-0.01083955876779178
--0.002344214847598842
--0.01053824849008605
-0.01672011915740813
--0.007123418455373212
--0.01050276814996309
-0.01181375034352936
--0.003692388209212168
--0.006197181110515579
--0.004990649119706141
-0.02763406435365599
-0.008232879264365207
--7.635851920595113e-05
--0.01647957879344179
-0.004970544262147969
-0.02077511132500649
-0.02080228248128793
--0.02063231699555177
-0.03271939607800944
-0.01852565967928637
--0.01205664064847238
--0.002116742984091494
-0.01648815822592242
-0.006502062292723928
-0.004528820298251461
--0.02329025006564391
--0.002621260927044112
-0.008036775972731546
-0.003957689350239761
--0.01223085875126413
--0.004807480994937861
--0.00937288435718546
-0.009426976729854139
--0.004073623135211561
-0.01394869170258077
--0.003032907588016949
-0.01419920849776457
-0.003981445505857536
-0.03149207149963506
-0.02697533682541672
-0.0196505493889447
-0.003886927523359138
--0.0226550509588688
--0.001765852859099732
-0.02130096097811054
--0.01301965201122001
-0.006647781978185874
--0.004788550144350071
--0.02032918057752187
--0.02555113320794481
--0.006146132438767107
--0.03168984208800225
-0.01050926998081637
--0.01585244611803002
--0.01209930568144516
--0.01840396673303841
--0.01999030789127674
--0.007683178144557741
-0.005653841389420187
-0.00236062896936699
-0.005920526889319284
--0.004126061987838854
-0.008224602789655505
-0.003248361158424754
-0.009314744093099998
-0.02531155387347262
-0.001531360989604527
-0.004463419424068961
-0.01416045722933502
--0.006762527169707457
-0.008424776039206612
--0.01683109910378435
-0.03131674407955989
--0.0130605616982668
-0.009390660446481751
--0.003684966548829124
--0.03608880972776977
--0.03041779807728703
--0.0260813638574111
--0.01675681278014084
-0.02551667361835429
--3.307672422964662e-05
-0.04072899607500657
--0.004531213531825409
--0.003722029193317969
-0.0154853732717035
--0.009592785684314202
--0.006183420058898514
--0.01329990856859712
-0.01466750377679854
-0.02399693781801437
--0.0239621108032971
--0.004792716694845249
-0.01718289590602544
--0.01796587828695473
-0.00708122170691673
-0.006075358410119343
--0.01460363626244955
--0.02748912592714141
--0.03287920546697236
--0.002065223209649254
--0.004585338533037592
-0.01561895620428921
--0.01666166601362881
--0.008200336685154779
--0.01462885583440839
--0.01096387838293024
--0.004602835535997034
--0.001689067646043055
-0.009666802210291965
-0.03131502598052375
--0.02044321346140698
--0.001179923980752694
--0.01003304782932002
-0.03387629132001316
--0.009114524827415087
-0.02215591348240966
--0.003821432102033054
--0.00943066274026
-0.001087823722885975
-0.02175491923844605
-0.02227818411209811
-0.006847173497328751
-0.01838326704215108
--0.009626729277977619
--0.008736830558426214
--0.01289355891377251
-0.02107202375374004
-0.003024271435519285
--0.004782145792186998
--0.01090849840996144
-0.003576632097989232
--0.03271904084894355
--0.0313139365512832
-0.002646443621973686
--0.01755448948010632
-0.03310408083293312
--0.01471532685340964
--0.01941310925610028
--0.003047302499689319
-0.01367455474940252
--0.002630247304288531
--0.01889738361337345
-0.01092461422459755
-0.01378318728680117
--0.00653632733768279
-0.006088416912183969
-0.01632965665578229
-0.0009866326553690226
--0.02332315402704107
-0.01864112773492806
-0.007866838427917895
--0.003652488995947925
--0.00880963610108703
--0.00848970771541353
--0.003937148817801966
--0.0004248936383170823
-0.009166496794338596
-0.005525471080202236
-0.01163784259895482
-0.0003348339349303169
-0.007355973953225249
-0.0003971880607078939
--0.001721615327122501
--0.007567700675988665
--0.004511033218695818
--0.002928729319244283
-0.00236767622222564
--0.0009532957669714733
--0.002138588304012223
--0.003093597316895234
-0.003311886601406318
--0.001982453113180975
--0.000329968500908672
-0.0091042366072486
--0.003635810031537688
-0.003004991136891852
--0.001994148201600247
-0.01859402779087027
-0.01594096662220327
-0.006878030954645049
-0.004852622790295839
-0.01994901004475198
--0.01818286534983383
--0.006689212813708048
--0.002044798789832125
-0.03396037549765166
--0.000602469054994144
-0.003228718150871455
-0.005952119832391481
-0.006888000529981863
-0.00526410760350293
--0.006629374501131399
-0.01236076768832963
-0.00702823308362652
--0.00874362741435118
--0.005358545752747608
-0.03076653403219983
-0.0002369909265770311
-0.02838557326916515
--0.006665819007913285
-0.006317726449988297
-0.01960441897107203
-0.004993839926528477
--0.002889967825507399
-0.02230006788489563
--0.0009233096934082925
--0.01119466785607565
-0.00280794739247922
--0.01902413311459814
--0.02527669742720819
-0.002432645124033397
--0.01636941269283271
-0.0257974572286534
-0.01518472597594466
-0.01070374598739892
--0.009891866339335222
-0.04255417860420893
-0.02599083373080074
--0.0004157584544795538
--0.0128130626293219
-0.007653001052072896
--0.0150933395864396
-0.007619158139307343
--0.01913140679717263
-0.0009898521820700466
--0.008415772150386566
-0.004416067517567056
--0.008669761904759293
-0.023336874350295
-0.00575133334216248
-0.0310844029079565
--0.006442081159825758
--0.03252429226292493
-0.01055111653330694
--0.01180807811106285
-0.006522368417279286
-0.01511683051685856
--0.02091707201178297
--0.005873488721834425
-0.008505664137965467
--0.01946698883408938
--0.01176387850802354
--0.004836880973877548
--0.005845131634876481
-0.00165331302136902
--0.01412895836607164
--0.002181823201348984
-0.01145087120642928
--0.002202471684578441
-0.009869959778725517
--0.01769314022173792
-0.01251498185552966
-0.004251982453711679
--0.02344066626918898
-0.01990935720259963
-0.01467414885566977
-0.007499117997281543
-0.01115831595653478
-0.01125447223768158
--0.002132607272848243
--0.002151235416793215
--0.001386119560109006
--0.00435548160591074
-0.004902627347076239
-0.01662244889469332
-0.0182680604713647
--0.02257500506198227
-0.005955829773188407
--0.000448391655007923
--0.0246226618708224
-0.02595620539088966
-0.002626608588674199
--0.02991068494682555
--0.02110726292343404
--0.00447906804660547
--0.01446562822060928
--0.004355788986039104
--0.0161426678213275
--0.02482698838603063
--0.01531017461922219
-0.01234987262140467
--0.001575369163503677
--0.01393896567663532
-0.01579347583076785
--0.01195873615966028
-0.005356924621168618
-0.001391912030398659
--0.0004860039571481506
--0.002503961399176677
--0.002582375058873727
-0.0004347066589999512
--0.01575358655589022
-0.00837336221465078
-0.008463652655045579
--0.001022982430272171
-0.00520501696632679
--0.002986895043934904
-0.002267202740410702
--0.000432410593425114
-0.006481056569952309
--0.04800938712283203
-0.01219744499076798
--0.02286896996470082
--0.01295446358153783
--0.00544114002278997
--0.0001237358077866703
-0.002240164344840785
--0.0101150514317291
--0.0137522725123128
-0.004994765815622544
--0.01677559357707318
--0.01760740783336645
--0.003318770647864493
-0.006712631032641739
--0.004255388525951637
--0.004520004778856928
--0.02519599531229698
-0.00780982026567117
--0.01910927649965499
-0.004117824410622747
-0.008841719538471255
-0.00726362410036519
--0.004976155618507604
--0.03155080029538329
-0.009227914946593412
--0.01856140545211758
--0.02448406066441343
--0.004746170577440117
--0.001214472009549104
-0.0535408578276222
--0.008528038111435741
--0.03816144774023091
--0.01649557904580179
--0.02873313963071926
-0.01500359724942046
--0.01411336024963264
--0.005592538642348038
--0.009835830861223027
-0.03528496331221245
-0.02976552324709967
-0.01156566379187163
--0.0111850286336193
-0.001463332609201945
--0.009075223628426341
-0.0009346363071058567
--0.01234165908453529
-0.002968012104723452
--0.005533038806710751
--0.02688319393112949
--0.001325419476030691
-0.0330680224329899
-0.02629958558733903
--0.008662718845278909
--0.003625173293204199
-0.01425165352679195
-0.001228533672495057
--0.02565148184069205
--0.007080142837340462
-0.0004223911227202333
--0.003211110733183318
-0.008968612154802437
--0.01479846927038774
--0.008247930482367419
-0.002771963800329455
-0.01534408484507895
--0.01710026840391086
-0.01171027959991574
-0.02402212406967619
-0.00309137136966967
--0.002117538536186037
--0.00911457082057051
--0.009459703268057902
--0.00152315496863822
-0.01888945130102366
--0.01160647459368317
--0.01100378495199468
--0.02095568323355821
-0.006988652683639423
--0.01162141982520174
--0.01173569026504158
--0.007727105389662799
--0.008184212682712695
--0.0004380835180553959
-0.01326999051884771
-0.01728955878481326
--0.01452513473934265
--0.004537318772464566
-0.03443853622286151
--0.008004819950932249
-0.002580874765181795
--0.01721603326898202
--0.005988701343349047
-0.01356391900301972
--0.01899990136776278
--0.004660266012002088
--0.0001799670267742414
--0.008337724791892032
--0.02093198881690757
-0.00497643027829724
--0.004655166729874216
-0.008374116030766564
-0.01708520938317977
-0.00127074445223101
--0.007500741631608927
-0.01104679027628023
--0.0103847644255375
--0.004404265213231994
--0.005093248577204034
-0.01522986715527878
--0.001245939435174563
-0.000137615131160662
--0.000901876929547232
-0.01132340024071861
--0.004626502286594386
-0.01099961713290055
-0.01042898780140086
-0.01447321282904225
-0.01318539289919886
--0.0007025928114086782
-0.00592733002265691
--0.005005985573309787
--0.01841661831486491
--0.01090873985961376
-0.002457154252750759
--0.006182142241504851
--0.00593633374450929
--0.008204129405485656
--0.0005513062353826733
--0.0003262053347149774
--0.001484593273448234
-0.0002399278071498377
--0.004181326911945497
-0.02086970560028008
--0.001363182231838604
-0.01285457275618442
--0.01314161227296116
-0.005123954462881402
-0.01174799808671217
-0.008254679318684049
--0.0003914701295366052
--0.01448323127632071
-0.004590350196914486
-0.007429014750571237
--0.01791216865507756
-0.0155450637293779
--0.008386293179615549
-0.01389566374229496
--0.0005751971093523717
-0.001119223160355214
-0.006774709885436895
-0.004472819097868453
--0.02334784004813516
--0.02504836807289502
--0.009107378114486461
--0.002536504695090088
--0.02249429671421361
-0.02445384844701983
--0.009281353515978785
--0.01388931651242649
-0.005935502043677677
-0.01376845454522151
-0.005748472504045479
-0.01241346713199773
-0.007028821055044452
--0.0113789323718302
--0.01712669390800247
--0.02199995653215517
--0.002026079142969788
--0.006815008606571565
-0.03019077003408841
-0.005111824232890477
-0.0001240762395036108
-0.01747625822036971
-0.01709098435014246
-0.0006495272773432004
-0.02355623116398139
--0.01006215624589642
--0.0006836519330520807
--0.00664700668111633
-0.01335862796887182
--0.0071346556844671
--0.02733985624559693
--0.003758098950371275
-0.005455161445328613
-0.03297825037012105
--0.009420977780484646
--0.007229741488679471
--0.02141001566652911
-0.01145420506435428
--0.0224997140962453
--0.01104964908943273
-0.0027379471540166
-0.00170041668178301
--0.01090790133826591
-0.01409202623647622
--0.009751673412586452
--0.01223534940035067
-0.02305520640284937
--0.01524771581617245
--0.005324409813668551
-0.005939939252526662
--0.02414897280790787
--0.004958173675856237
-0.001229356921646565
--0.01438180073891094
-0.01628735103828915
-0.005323494255030514
--0.00390812841417758
-0.001609492620279846
--0.01996789587248918
-0.01219210112435778
--0.003871994096287004
--0.02889881872572414
-0.002482564292332902
--0.006975013135280087
--0.04634255691284854
--0.008395110552666082
-0.002831146147286676
--0.006162989723940918
--0.002421235527046069
-0.004394359287582313
-0.00433073776109897
-0.008412281327709688
-0.004734982682449173
--0.01660159032085854
-0.005488952525395874
--0.001791074044562602
--0.01946984491131826
--0.01652989235304309
--0.04273075468829583
-0.01055478124066546
--0.01166553623699939
-0.008995214246888105
--0.01706998461498297
-0.004376089678065407
--0.01543549035866707
-0.0008877688943729031
--0.01145316127346453
-0.002464920648666707
-0.001817785128128876
-0.03177326911342972
-0.00880651483187408
--0.006200743706968137
-0.01837332126698483
-0.00882725848335923
-0.004308607847147712
--0.00842877843306402
-0.02836981078349971
-0.00967868298921829
--0.006026203216130596
--0.002261321533324721
-0.004599563773545781
-0.01068700777622773
-0.008045841156592263
-0.01573259378650451
--0.005199937854318985
-0.008360536610402224
-0.002906926106021127
--0.007616712177906346
-0.001145348909207857
--0.005693724542258696
-0.003292062325396654
--0.01315248213028009
-0.02408815247506037
--0.009160904044120528
-0.01122126414768435
--0.02941265319724936
--0.01148169994412605
--0.02853228928863357
-0.000673452068606376
--0.01169284783012576
-0.005560227317419792
-0.005210142034995962
-0.01079073695426922
-0.001901969110087884
--0.005147450736950603
-0.02222156551038382
-0.01025434227612661
-0.002524536626112206
--0.005679254439708144
--0.02572794438290521
-0.01031382728177885
--0.01470150749362793
-0.03543497475775316
-0.005689245719326667
--0.01067330551251063
-0.0009548414424256995
--0.03095938558167714
--0.01635261658949197
--0.02308912696253445
-0.004674143378091358
--0.007621953397951528
--0.01230600455385311
-0.01936752839456654
-0.04728568573305469
--0.001895846479870579
-0.006036893799525046
--0.004638500528102567
-0.002640158175632735
--0.01767818562214534
-0.02752327226573516
-0.007193348964005182
-0.00489373436986091
--0.02426392040323166
--0.006217780241019454
-0.0008083012162578878
-0.01572494836014236
-0.0006097627854191757
-0.01936893065581738
--0.01218275403279498
-0.0165999979175938
-0.005329791590545539
-0.03533524460165303
--0.02272071365749606
-0.003316187937271407
-0.03236487003305031
-0.02143594909215428
--0.01218811504635551
-0.01877557477323152
-0.0042731381318537
--0.01955734781922343
-0.002925113006907796
--0.02323024950985484
--0.01345202502011725
--0.003304058746681566
--0.008684056156435452
--0.002163862574983402
--0.01643966156834209
-0.02586055388112574
-0.01407859308697153
--0.01337850116415801
-0.02324349341892464
--0.003671188223633858
--0.0103631765367267
--0.01555510748278809
-0.002404649550303531
--0.0163750566359102
-0.001903041260215059
-0.00722207833076819
-0.007501112406203057
-0.003803629459414235
-0.005714272791965208
--0.009968392639959861
-0.03006014022281655
--0.009070210931690463
--0.00814483621414277
-0.01770472018781694
-0.01367959534308426
-0.003604521091145355
-0.001271993741419705
-0.007133286519030656
--0.01315810057552531
--0.0155367824108035
--0.02106282389481737
--0.0244814282829759
-0.002209596062493842
--0.004584221608802652
--0.009438099820647092
-0.02583005464061603
--0.009909457090528598
-0.007113654579060055
-0.007227665055871958
--0.006484712434597325
--0.01189933289208502
-0.005590310231099386
-0.007397985380134291
-0.005407842712607397
--0.01920591786135048
--0.01171918300484283
-0.0007105742507445786
-0.006260336976728494
-0.006950419449576898
-0.002058232310930714
--0.0363614566676785
-0.00455735504751413
--0.006400843946043929
-0.01036767868636081
--0.0194957407002138
--0.007482184923385937
-0.008179379923398717
-0.01091666165169481
--0.01124579885012563
-0.006574801684584704
--0.001619829484791423
-0.003114017131408277
-0.01431117041012382
-0.03253441069271527
-0.006997404858053531
--0.01988037024675691
--0.001231390228950152
-0.001929071545877891
-0.005649390895935678
--0.02401916653708915
-0.009234650316530748
-0.01202399533808105
-0.0007777404826719666
--0.004244523168833211
-0.01542517568911507
--0.007607221462152986
-0.006415157018443484
-0.006113978962449352
-0.01767650506593157
-0.0280726424721713
-0.006039569056600746
-0.003089935519962547
--0.003097886759974776
-0.006985924919270068
--0.01964202528004993
--0.00404512804437
-0.004926470494239515
-0.002955021776779445
--0.01208045608229583
--0.0008870961228251745
-0.002872188482398446
-0.004583935690550723
--0.003858163788996736
--0.008541089535762022
-0.01232799719527246
-0.01198496114626416
--0.0376487804658333
--0.01239740283997382
--0.001438273787931985
-0.0130484721531434
-0.00392882918265642
-0.0004624613723549903
-0.00988422733329984
-0.01432758687115645
--0.01295527112561802
-0.01071001769917615
--0.01760074102594351
-0.01025089082363851
--0.02234255213784205
--0.02173085608298476
--0.00360001702717
-0.007860628749591106
--0.03032262509308284
--0.01032386846149099
-0.00864622049949451
--0.01486429206589223
--0.02464569526697027
--1.356897650041551e-05
--0.0249802268672495
-0.02073118207115261
-0.007877261053166643
-0.004734312137613272
-0.00281568530988095
--0.003626684377528272
-0.006663645507143537
--0.002417990464944067
--0.04259101424467835
--0.007744765423826181
-0.007762924695421096
--0.02871628090518566
-0.0004086141444095896
--0.005958034132690692
--0.005117810226449309
-0.00676070352035596
--0.007756069061809423
-0.004817211425157856
--0.008848955771684316
-0.002933174450045206
-0.007958756317302619
--0.00951207819635976
--0.01412364017349722
-0.004052790938513488
--0.006021536665858019
-0.01417334775672426
--0.005440701623951991
--0.01212959036890946
--0.01123115931482615
--0.02153961841801609
--0.0007934487767917474
--0.01056882475536789
-0.003049040040623498
-0.01078974910962729
--0.02259810865999345
-0.01180398841472394
-0.01907616534136599
--0.01337347782632039
-0.01352076439601524
-0.03841108368346392
--0.04055373316825232
-0.02833531234145752
-0.01652709019531644
--0.02033993480304984
-0.003655060252318861
-0.000786771673395233
--0.008266414290187582
-0.01362906475767354
--0.007772942977004248
--0.009794519653819026
--0.01081830980746562
-0.01778198066175413
--6.337367060934039e-05
--0.0003400641738031175
--0.006781242610329956
--0.004072983842581091
--0.001164080705672999
-0.006908706997562371
--0.004993150015659275
--0.002177302292586101
--0.0069359300816773
-0.008593972743989814
-0.02775783108677
-0.008631506504358042
-0.00413099363578831
--0.004145777660538971
--0.02612339911197775
-0.02261321343630619
--0.01097753142264771
--0.006255561837063394
--0.001702905788777154
--0.01414948821366367
-0.01492285574237289
--0.003126796773281403
-0.01731028146504614
--0.01007660250033313
-0.005940126251586111
-0.02830321696088971
--0.01657982983551986
--0.03820714029272418
--0.008129723342072553
--0.0171744786367911
-0.003839906906576135
--0.01716711541643853
--0.009369421885099248
-0.01876499888644826
--0.006730907102688891
--0.02222333069040138
-0.01602411831999552
-0.007773288259687514
-0.03012405961732158
-0.0199150608714509
-0.001048116846537224
-0.004190428790468613
-0.006068646508991573
--0.006948025164089164
-0.008448157587918515
-0.0005037567595575689
-0.01941094340009932
--0.01558262823935828
-0.02441847459204936
--0.01034157579081853
--0.01439937828896192
--0.02047233347858545
--0.01527119985291525
--0.02420322278461007
--0.003134143733260424
--0.01572790692753894
-0.0244147616010543
--0.005072830696996081
--0.01764259376087315
--0.002444796668407607
-0.02337880541947749
-0.008268183448937707
--0.0472520816506342
--0.006198419892204437
-0.01533382492127159
-0.03157494244398008
-0.02066852643023912
-0.0005212499234999194
--0.02832043108514444
--0.003024176325080968
-0.01826422943706914
-0.02325718918458554
--0.003337195695177221
-0.008751006585410372
--0.01109095382046884
--0.004918274026477905
-0.02318414903935491
--0.01577811276111911
--0.009481758579210874
--0.003271246810777286
-0.005759151212969622
--0.004913793396815016
-0.007348137944217296
--0.002091022978430097
-0.01574210259428629
--0.009943784653053926
-0.008028408046474443
-0.009954774769305598
-0.0282150308612504
-0.02308067747978576
-0.005954108039567689
-0.005619699463107813
--0.01249247942439769
-0.01290003048818869
--0.0003265508120333372
--0.01469157886755291
--0.006378406991335836
-0.008942414711110406
--0.01630974723025268
--0.01142928556562286
--0.006460002000714391
--0.01338443104625616
--0.005828552013528361
-0.01026697958827046
--0.02132481693647283
--0.007603400019158524
-0.005358107271568497
--0.01139980533626015
-0.008149538920396923
-0.0008710539203635368
-0.006156161791800552
--0.01961807017007714
--0.009556160215132187
-0.0002905488407600563
-0.002685614344771768
--0.00453110306326527
-0.001141345204497309
-0.02929899422818204
--0.000146685257744896
--0.01917343489512589
--0.01344873978246047
--0.002472150161016636
--0.004213550411267318
-0.02462283972800554
--0.01367361473040458
--0.008504043237079829
-0.01705807719727934
-0.00983585751175377
-0.01124340753305263
--0.001757970096786753
--0.0001334327687674705
-0.009646428776346975
-0.005015378952103714
-0.005265539624014826
-0.009735755869555203
-0.0026793006113741
-0.007716179608036353
-0.002667543106616402
-0.00196322262018457
--0.01755616568027096
--0.03105504367818732
--0.01852146545840607
--0.007892178156758462
--0.006344020793244809
-0.001736492655039864
-0.0005654292706995542
--0.01171120395627112
--0.01190598522173246
--0.009074451498291242
--0.007594742078175147
--0.008955932701465347
-0.002909371466195072
--0.02191025028141997
--0.02194379277787434
-0.000192683116072489
--0.009148711567351076
--0.001862945744971001
--0.002179597969434772
--0.01273896768328057
-0.008431554601103078
-0.00106024741333531
--0.005934655764505666
--0.002196371321038518
--0.0100894212552339
-0.02700045869582356
-0.007334900402754162
--0.009897847296833684
-0.00715976102733476
--0.01991733939380202
--0.007357571038926293
--0.001319399661219862
-0.04652879838361375
-0.009973162015194711
-0.009618742335764058
-0.03057005098961838
--0.02818024699769371
-0.003189392647853853
-0.009030429141936552
-0.00251109153778454
-0.001308665377700117
-0.01606308181099008
-0.01438997033976475
--0.01463184399033823
--0.0009535991470952108
--0.02583281586964435
--0.01412254762049034
-0.01390209474623605
-0.004950827196089225
-0.03497512079680302
--0.01123471153187777
--0.006679336715318984
-0.004201188760536267
-0.02052605940156248
--0.003058915191952805
--0.0001681188276387851
--0.005376602520693313
--0.005257042703876781
-0.01975915040983996
-0.003320443697274082
-0.004696593320418577
--0.0403446072861945
-0.0008715451809642304
-0.02230239133717548
-0.01365825094038805
-0.01799221302687173
-0.01512857695194226
--0.004925035054470859
--0.01665001713451024
-0.01754550906569217
--0.008585444873006637
--0.007689722259475283
-0.03039968227985917
-0.03850410626190468
--0.00519157452897538
--0.01204823750018192
-0.02725250362549569
--0.006654209129966883
--0.01412857624485538
-0.02578844478895474
-0.00996881675076023
--0.007549656552828025
-0.03038992015884526
-0.02038503412032708
-0.003779836143993172
--0.01123360077986335
--0.002467890119529495
-0.01531501741348034
--0.007316801130663386
--0.009613193308891771
-0.003157725172311934
-0.01483373140697417
-0.007592517011473918
--0.01704261468175717
-0.02214190083770232
--0.004406747703956672
--0.0207663346317325
--0.003782296682800211
--0.007051686339933606
--0.005914969893877447
-0.03373725520739661
-0.002425119865916307
--0.00309265302130277
--0.009216635366219115
-0.002746039378600673
--0.00888515005715354
--0.01054683990350574
-0.003287695026993249
--0.01217806469614054
-0.006502264552732562
-0.02714031616947617
-0.01828334470794161
--0.004619290241341466
--0.0006677405313025577
-0.01077791261163933
--0.03120473967703216
--0.03683242118198414
--0.01832041251490778
--0.0003497210665655151
-0.01020025716042055
--0.01524897691181655
--0.01005985079402065
--0.01900942416893886
-0.02834803241375867
-0.01551767911573025
--0.02833643852533742
-0.009895908852247073
-0.004952556944618854
--0.02180069944933652
-0.008523656075957786
--0.01223104310666341
-0.02357472449337427
--0.007848588429341781
-0.01007407812277661
--0.01049423319536491
--0.003692773467291717
--0.04100564678520459
-0.000269864666563532
-0.02038056679634657
-0.01114612403695811
--0.008490506292959653
-0.008225535377320379
-0.02192906253175392
-0.00240048131858734
--0.00402537600825677
-0.01547775852489192
-0.001991643746644618
--0.009955977451476189
--0.01752304483853321
--0.0004880264940159554
-0.00184148271379681
-0.0107713907643284
--0.02565781489879055
-0.003523787900757492
-0.03150256804863619
-0.01440471598178603
-0.01188344506108061
--0.007597927891781663
--0.005688475332986122
-0.008368961302056166
--0.03406343617413088
-0.007651773068034115
--0.002810614992210151
--0.01442355023193317
--0.00930942517908496
--0.01738687851762602
--0.01496750270809569
--0.001880131533996531
--0.02150940258425937
-0.01891878116566312
-0.02535369682026802
--0.008001460217392713
-0.01457954926397509
--0.005996851935756954
--0.02645879905527309
--0.01925532918353922
--0.03035450009468155
--0.004498623530743501
--0.03764008086239421
--0.004510430910161323
-0.002778516395608248
--0.004863356269083184
--0.002861916086107435
--0.03010558923798578
--0.009612790808077698
-0.006870374459038819
-0.007183679358267206
--0.01056143179122944
-0.009246213763820533
--0.03407414026039592
-0.006536109577288808
--0.03570880650006806
--0.00256972215619462
-0.02308572980782077
--0.02264905849994069
--0.05410349133434062
--0.03065995798434372
-0.031863893106199
--0.01789927994077019
--0.01578340952287412
-0.01612195199705951
--0.0161754669172831
-0.008967087146923434
-0.01951791168127714
--0.005550980727729709
-0.01593155394105586
--0.006737185408648629
-0.02658820277524416
-0.01964556236776288
-0.02800367959005816
-0.02928421563736574
--0.003406911554392224
--0.004683231814117317
-0.01469177006074971
-0.01043533264507262
--0.001326671671339284
--0.01501005052017033
--0.03712251598512212
--0.00579401557140454
--0.02453559300724241
--0.02285450929285504
--0.0006336748959054387
-0.01518813684715994
--0.01484761422123549
-0.01312734447579811
-0.002708230628160069
--0.02006481914527003
-0.0229095791354312
--0.01674121776424106
--0.007448030816575247
--0.0129143579538611
-0.005898341603338111
-0.03940992889730704
-0.01563042402664403
--0.0112737737131252
-0.01313133291807826
-0.03056694985152921
-0.01277810382730765
-0.006074553003625459
-0.002643228556582045
-0.001852565306432142
-0.009950528115110007
--0.00978732767477377
--0.01330989069333403
--0.001847937864769756
-0.004673887153457299
-0.006673689168090389
--0.007797684895687047
-0.003895373914808362
-0.000877769969073182
-0.003234935159984071
--0.002382328483122484
--0.006838504395495584
--0.007109499654775752
-0.004690532455906133
--0.01578540762506189
--0.0105186108454337
-0.006449627651086042
-0.0008659955369537049
--0.01593994288097261
--0.004364885651002412
--0.00297714642303416
--0.003276880160015473
--0.002913579875677131
--0.005293863394846808
-0.01500060536879348
-0.02718197426782389
-0.001973063604433861
--0.01110063579036806
-0.01856553495664489
-0.001107601409016411
-0.00561501627705944
--0.005546774648695785
--0.002920830040983198
--0.001072365539073448
--0.02868215724871776
-0.01108259439364273
--0.001038948672440086
--0.002551597250200969
-0.006789073246382256
--0.0106527188330943
--0.01393504545568204
--0.008254621084635002
--0.0007596866344118802
--0.01074174093348666
-0.002094179527578489
--0.006852628810115411
--0.005958139833308673
--0.01720135294300625
--0.001156409360436312
-0.005544355991337468
--0.002706345139482663
--0.004001522067199958
-0.03657233067842677
--0.003180665729650278
--0.00671291974076794
--0.01252689920645897
--0.04176006895435073
-0.00300204829262944
--0.0019631064042179
--0.002887174967372167
--0.009633091089705224
-0.03197655319102453
-0.004677648190332648
--0.00422058801337834
--0.02329711356613009
--0.0001658912860526463
-0.01047949595913793
--0.001330315136965086
-0.03675530086562152
-0.01582561317980396
--0.002708693519195159
--0.04822072866946137
-0.01280724733588027
-0.004492782277935439
--0.02846603272897769
--0.01550594501095104
-4.245254987837467e-05
--0.006435485714250382
-0.01236203449806062
--0.02945947057757245
--0.01467583504710125
--0.009194771191684775
--0.030940241609674
-0.005702229936330789
--0.02845484858111282
-0.01556843813261503
-0.005253243002647325
--0.01533881042846362
-0.004638977649766434
--0.02164714998971094
-0.00361133622408166
-0.0371863405304098
--0.007207325197702643
--0.008072944543663023
-0.03145071816656336
-0.004279937779200758
--0.02439676903785956
-0.01570624486136686
-0.02049964857755283
-0.008571127046193158
--0.01569139109953498
-0.007010297565927171
-0.0137918003747383
-0.004474221166233592
--0.01946445121864369
--0.008979531198974339
-0.006114188637525397
--0.01433565214411659
-0.01401985074914269
-0.02418435815121221
--0.04947458859574772
--0.008662373286325537
--0.007518488780997615
-0.01254010445474588
--0.009068525930721901
--0.015428080901077
-0.001245014469365529
-0.01340376860400185
-0.007408507975101764
-0.01224980159270087
-0.01221519533041613
--0.002450309316354523
--0.01403446778394327
--0.01501718422290021
-0.01382119355322259
-0.006925837551820885
-0.01422543346146635
-0.003126316130391671
--0.009759507585943447
--0.004515081040667552
-0.0184211583778267
--0.00301334658383286
-0.03526382100861304
--0.005763125629352025
-0.003859628102802385
-0.01640992715249558
--0.0132724185004416
--0.005429495624950713
--0.01610039127796882
-0.0190666544080012
--0.006008169545401953
--0.001640952182593106
-0.008678963120391098
--0.006560033202091975
--0.01178731303978556
-0.01678707458057755
--0.003742623816834697
-0.01025773572604616
--0.007378540838533612
--0.003727362841688868
--0.000837522225570645
-0.005631750173079518
--0.02295009761451031
--0.02060608942868164
--0.01459792328485058
--0.01602339266022188
-0.004820403169663087
-0.01327368680494862
--0.02007515477855184
--0.01433959294531711
-0.02069041124632693
-0.005073165997318196
--0.01869705326717852
--0.00697138840158981
--0.0332817989791832
-0.006186898695763961
-0.007756986993487383
--0.01343075855582001
--0.01331099248621712
-0.03630076040567996
-0.001750181901429801
-0.008236691580402281
--0.007424815882232308
-0.01302245024972943
-0.03309769642738106
--0.01093175213848803
--0.002247245997031039
--0.02008036255533713
--0.00885610589240567
-0.005350029110215819
--0.007763626370236366
-0.001855312284918393
--0.04054569097076473
--0.01931669526263645
-0.01037077748590742
--0.01378737972509295
--0.003298226780031409
--0.01077276358634363
-0.003565177543258448
--0.007346028800042844
--0.004967302033390996
-0.01162919611371515
--0.003618604735717639
--0.01771246381439291
--0.02162694884119417
--0.03058398165228755
-0.01129803453831104
-0.01988557807548826
-0.003090755698382868
--0.01503023188282204
-0.0139015637894172
--0.003170569976254531
-0.01004330018724429
--0.005514814437924848
--0.006909174112045331
-0.009132437683185673
--0.01626133377305174
--0.003395230429404601
-0.01706234056137755
--0.0009556272437728052
-0.01032593776344388
--0.0161055690697245
--0.003802496858683152
--0.0001045289663565352
-0.009260188578330154
--0.007087580885692222
-0.006637454797923579
--0.002473127019125103
-0.02403494801378056
--0.01140655705142183
-0.03183263598949247
-0.007399362812465869
-0.0175512050714865
--0.01006068316617398
--0.007405734287376196
--0.01282018810092489
--0.007026390622589841
-0.009910072367183956
-0.003474072157141561
-0.01442648729935228
-0.005078431989435122
-0.01707525876406475
--0.004732839122433889
-0.02889917475456872
-0.003105141177924763
--0.005230209002812439
--0.007474395437483116
-0.005351739605678192
--0.00791345102367071
--0.00787197521927511
--0.01117473676391647
--0.03604038065176966
--0.007511897661885605
-0.001634010057073694
-0.01377114410533289
-0.00980477295699818
-0.02397216916237251
-0.006303681208571039
-0.001305973472988622
-0.003068833736754394
-0.009712442755140544
-0.02313812611247533
-0.01847771290869842
--0.01240904162976993
-0.00866447519406865
--0.02296742859721455
-0.01726599170420226
-0.007680741808977268
--0.02359946657768762
--0.002506896760679496
--0.0014444060339337
--0.01188211223242937
-0.008462957566127537
--0.02036825267874936
-0.007048906669455735
-0.007320347017932488
--0.003888724139715328
--0.0002038557167446097
-0.01076458425422057
--0.008849398556298934
-0.01750518473606481
-0.00649076115189502
-0.01632116394140625
-0.001588517511907531
-0.005394235147066633
--0.008584940276567648
-0.01751061734991783
-0.002200734330235819
--0.006876940316865636
--0.007860281268000924
--0.01195695109695417
--0.02303525308255487
--0.02193756919565023
--0.004417098812208356
--0.01299878072111846
--0.01500916082433785
--0.002569339668940366
--0.0119437556285678
-0.02292838197667446
--0.01398311344776766
--0.002480835206852796
-0.0144114349141262
-0.007404394114182884
--0.007804194318305033
--0.003257282807043618
--0.01506450993692678
-0.01866899190302616
--0.0008034727598605041
-0.003208336163053487
-0.008976540539303772
--0.01259311946237895
--0.001366530593811902
--0.001725063596972607
-0.02144484948185182
-0.01384025574023827
--0.009909140743035153
-0.03315125027395317
-0.006904412816946368
-0.01382324888550336
-0.001598431960756535
--0.003804017504204842
-0.01900910121709114
-0.01261570802457964
--0.001364268621036571
--0.02693978151524247
--0.004580673028005613
-0.01316716943583306
-0.02459615893625969
-0.01361500452629209
-0.008176704201722099
-1.197955201233251e-05
--0.01117539339090682
--0.03311034229307786
-0.005231735134011704
-0.02224010929835316
--0.004402460390882429
-0.01270930673348074
--0.01055248764250602
-0.007862828278494667
--0.004655738824427762
--0.03145101100301825
--0.01631270519561777
--0.01235817303394284
-0.02039030310521683
-0.003747146981692299
--0.01773495066741079
-0.0158880806014404
-0.02355239702142845
--0.02519791103684815
--0.03996701995438993
--0.009147440906650548
--0.01201076937582297
--0.001745933908305584
--0.01015953270929641
-0.05411292102502311
-0.005879375847221719
-0.007251730069490883
-0.01237500663803921
--0.0004249924873270862
--0.01475861942556251
-0.01072882758323287
-0.007858024885138997
--0.01625218026698153
--0.01604772525044638
-0.00581368634557428
-0.00638483819377105
--0.0006513546862175954
-0.009131134521716044
-0.03521455157619021
-0.01066692724542432
--0.007809868984940659
--0.01380670346623858
-0.01696569213572393
-0.019291133231876
-0.02999733046749321
-0.007424388248721526
--0.01468518254145522
--0.01057420252769083
--0.001453259656950157
-0.01933253108711584
--0.03081772101281013
-0.01548993995195879
--0.005072038094306734
--0.01232566549368926
--0.02525342900595093
--0.01495885097694772
-0.002118847028197435
--0.005895042441752004
--0.002981069043224838
--0.02312955791798903
-0.01384561741944177
-0.007893081889197811
-0.003310096499544184
--0.01948649592910526
--0.02592984286373955
-0.007827077285211401
--0.008427002587605257
--0.03852505771688302
-0.03489698776233709
-0.02654639265967004
-0.03590686222294073
--0.001167594158782169
-0.004948762328436335
-0.01542619570998532
-0.005237409335841976
--0.004680062638277922
-0.03064082517211047
-0.02407422111065966
--0.01835936629463496
--0.01940408814121492
--0.0003015836736504799
--0.003279187286941378
-0.003460404511508226
--0.03006130330703631
--0.002221712135099534
-0.003245402796957847
--0.01010417721497479
--0.02210106881780647
-0.02139571462286946
--0.01172203886870713
-0.01603489720504438
--0.01158060220004198
-0.01795321187941191
-0.005762843557234979
-0.01244513045636123
-0.02100765127278477
-0.002256507965716916
--0.04498735438632368
-0.00406366452792569
--0.004715770210993587
--0.0163223762337607
--0.005126678270572817
-0.01882227980030095
-0.009301602762092702
-0.0365317617045287
-0.02083200977701033
--0.002695492681371857
-0.003197993533616333
--0.001472975051209063
--0.0003186004996362475
--0.00246748496713778
--0.007494180230948377
-0.01472725543282279
-0.03665734325216793
--0.01458367360933156
--0.02006216193689288
-0.01709203912042638
-0.007299759554254884
--0.02550838494588182
--0.009175745773407242
--0.002439928449770857
--0.009460724405402654
--0.02733190418472063
-0.001382641863765436
-0.04172133512368545
--0.01891337623770762
--0.01567365458450751
--0.03432054990161081
-0.01188376276679091
--0.03056358240640782
--0.002841202340091693
--0.0004848467491689695
-0.002707310166625807
--0.001994689711391858
-0.001979987282358538
--0.02218750955138959
-0.003053599109053886
--0.009304206656967136
--0.02381657953166434
-0.03351834306207179
--0.03080237455344828
-0.002712576360957537
--0.01866048178070419
--0.04275923139936443
--0.007897959145482122
-0.01116667018229131
--0.02290031686912631
--0.0365918939021931
--0.007672177373576339
--0.008362136036287748
--0.003078938324631922
-0.007039215751315215
--0.009018467205375623
--0.00470860635890057
-0.003602678919302242
--0.003086681927234068
-0.002502598254855494
-0.01520604009601336
-0.002918008994150385
-0.003850689193926608
--0.006880675210700436
--0.02072870478100336
--0.01345486105727304
-0.01669076439246846
-0.006398660888555329
-0.005579883521651698
-0.01870131934365079
--0.0208097178659284
--0.0009436042167345623
--0.02532025226035112
--0.001544584588500054
--0.04155546179234096
-0.004861309078836543
-0.01204093956078467
-0.04162541559719533
-0.02050737042312399
--0.001214160382287964
--0.003688134921549865
--0.004908708211978426
-0.01719240141975271
-0.01234398515654684
--0.002730021609324972
-0.01128233844293456
-0.02054598684784238
-0.004588128805327625
--0.009030848883505915
--0.005912720340995263
-0.02797786574909942
-0.0009488273136703182
-0.0007460614501613194
--0.008079922249812769
-0.007042786142229772
--0.006725859535732895
-0.002884405307200951
--0.01861324621635662
-0.005654274981213094
--0.006496547564795704
-0.009422114284617372
-0.003891733699865319
--0.01937898149158729
-0.009824713927864904
--0.003928011245137781
--0.004047471340990934
--0.003145561644031582
--0.005145018171695522
-0.009935977007277299
--0.006413503944716811
--0.003993981527416234
-0.0284827621070262
-0.0006976840598726484
--0.01890689118680361
--0.006379917738488249
-0.001106944144783874
--0.001821220227014897
--0.0004432979499690127
diff --git a/data/combined.txt.model_ b/data/combined.txt.model_
deleted file mode 100644
index 90edbb7a9..000000000
Binary files a/data/combined.txt.model_ and /dev/null differ
diff --git a/data/demo.py b/data/demo.py
new file mode 100644
index 000000000..7f467e45e
--- /dev/null
+++ b/data/demo.py
@@ -0,0 +1,103 @@
+import sys
+import glob
+import getopt
+import numpy as np
+import cv2 as cv
+import pymtracking as mt
+
+print("OpenCV Version: {}".format(cv.__version__))
+
+
+def draw_regions(img, regions, color):
+ for reg in regions:
+ brect = reg.brect
+ cv.rectangle(img, (brect.x, brect.y, brect.width, brect.height), color, 2)
+
+
+def draw_tracks(img, tracks, fps):
+ for track in tracks:
+ brect = track.GetBoundingRect()
+ if track.isStatic:
+ cv.rectangle(img, (brect.x, brect.y, brect.width, brect.height), (255, 0, 255), 2)
+ elif track.IsRobust(int(fps / 4), 0.7, (0.1, 10.), 3):
+ cv.rectangle(img, (brect.x, brect.y, brect.width, brect.height), (0, 255, 0), 2)
+ trajectory = track.GetTrajectory()
+ for i in range(0, len(trajectory) - 1):
+ cv.line(img, trajectory[i], trajectory[i+1], (0, 255, 0), 1)
+
+
+def main():
+ args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
+ try:
+ video_src = video_src[0]
+ except:
+ video_src = 0
+ args = dict(args)
+
+ cam = cv.VideoCapture(video_src)
+
+ _ret, img = cam.read()
+ print("cam.read res = ", _ret, ", im size = ", img.shape)
+
+ fps = cam.get(cv.CAP_PROP_FPS)
+ print(video_src, " fps = ", fps)
+
+ configBGFG = mt.KeyVal()
+ configBGFG.Add('useRotatedRect', '20')
+ configBGFG.Add('history', '1000')
+ configBGFG.Add("nmixtures", "3")
+ configBGFG.Add("backgroundRatio", "0.7")
+ configBGFG.Add("noiseSigma", "0")
+ print("configBGFG = ", configBGFG)
+ mdetector = mt.BaseDetector(mt.BaseDetector.Detectors.MOG, configBGFG, img)
+ print("CanGrayProcessing: ", mdetector.CanGrayProcessing())
+ mdetector.SetMinObjectSize((1, 1))
+
+ tracker_settings = mt.TrackerSettings()
+
+ tracker_settings.SetDistance(mt.MTracker.DistRects)
+ tracker_settings.kalmanType = mt.MTracker.KalmanLinear
+ tracker_settings.filterGoal = mt.MTracker.FilterCenter
+ tracker_settings.lostTrackType = mt.MTracker.TrackNone
+ tracker_settings.matchType = mt.MTracker.MatchHungrian
+ tracker_settings.useAcceleration = False
+ tracker_settings.dt = 0.5
+ tracker_settings.accelNoiseMag = 0.1
+ tracker_settings.distThres = 0.95
+ tracker_settings.minAreaRadiusPix = img.shape[0] / 5.
+ tracker_settings.minAreaRadiusK = 0.8
+ tracker_settings.useAbandonedDetection = False
+ tracker_settings.maximumAllowedSkippedFrames = int(2 * fps)
+ tracker_settings.maxTraceLength = int(2 * fps)
+
+ mtracker = mt.MTracker(tracker_settings)
+
+ while True:
+ _ret, img = cam.read()
+ if _ret:
+ print("cam.read res = ", _ret, ", im size = ", img.shape, ", fps = ", fps)
+ else:
+ break
+
+ mdetector.Detect(img)
+ regions = mdetector.GetDetects()
+ print("mdetector.Detect:", len(regions))
+
+ mtracker.Update(regions, img, fps)
+ tracks = mtracker.GetTracks()
+ print("mtracker.Update:", len(tracks))
+
+ vis = img.copy()
+ # draw_regions(vis, regions, (255, 0, 255))
+ draw_tracks(vis, tracks, fps)
+ cv.imshow('detect', vis)
+
+ if cv.waitKey(int(1000 / fps)) == 27:
+ break
+
+ print('Done')
+
+
+if __name__ == '__main__':
+ main()
+ cv.destroyAllWindows()
diff --git a/data/dota/DOTA_v1.0.names b/data/dota/DOTA_v1.0.names
new file mode 100644
index 000000000..adea76198
--- /dev/null
+++ b/data/dota/DOTA_v1.0.names
@@ -0,0 +1,15 @@
+plane
+ship
+storage_tank
+baseball_diamond
+tennis_court
+basketball_court
+ground_track_field
+harbor
+bridge
+large_vehicle
+small_vehicle
+helicopter
+roundabout
+soccer_ball_field
+swimming_pool
\ No newline at end of file
diff --git a/data/dota/DOTA_v1.5.names b/data/dota/DOTA_v1.5.names
new file mode 100644
index 000000000..4d18c4f11
--- /dev/null
+++ b/data/dota/DOTA_v1.5.names
@@ -0,0 +1,16 @@
+baseball_diamond
+basketball_court
+bridge
+container_crane
+ground_track_field
+harbor
+helicopter
+large_vehicle
+plane
+roundabout
+ship
+small_vehicle
+soccer_ball_field
+storage_tank
+swimming_pool
+tennis_court
diff --git a/data/haarcascade_frontalface_alt2.xml b/data/haarcascade_frontalface_alt2.xml
deleted file mode 100644
index b49cf5df3..000000000
--- a/data/haarcascade_frontalface_alt2.xml
+++ /dev/null
@@ -1,20719 +0,0 @@
-
-
-
-BOOST
- HAAR
- 20
- 20
-
- 109
-
- 0
- 20
-
- <_>
- 3
- 3.5069230198860168e-01
-
- <_>
-
- 0 1 0 4.3272329494357109e-03 -1 -2 1 1.3076160103082657e-02
-
- 3.8381900638341904e-02 8.9652568101882935e-01
- 2.6293140649795532e-01
- <_>
-
- 0 1 2 5.2434601821005344e-04 -1 -2 3 4.4573000632226467e-03
-
- 1.0216630250215530e-01 1.2384019792079926e-01
- 6.9103831052780151e-01
- <_>
-
- 1 0 4 -9.2708261217921972e-04 -1 -2 5 3.3989109215326607e-04
-
- 1.9536970555782318e-01 2.1014410257339478e-01
- 8.2586747407913208e-01
- <_>
- 9
- 3.4721779823303223e+00
-
- <_>
-
- 0 1 6 2.3025739938020706e-03 -1 -2 7 4.4174338690936565e-03
-
- 1.0183759778738022e-01 8.2190579175949097e-01
- 1.9565549492835999e-01
- <_>
-
- 0 1 8 2.2203210741281509e-02 -1 -2 9 -1.7283110355492681e-04
-
- 2.2054070234298706e-01 7.3263257741928101e-02
- 5.9314841032028198e-01
- <_>
-
- 0 1 10 4.3567270040512085e-03 -1 -2 11
- -2.6032889727503061e-03
-
- 1.8441149592399597e-01 4.0322139859199524e-01
- 8.0665212869644165e-01
- <_>
-
- 0 1 12 1.7309630056843162e-03 -1 -2 13
- -7.8146401792764664e-03
-
- 2.5483280420303345e-01 6.0570698976516724e-01
- 2.7790638804435730e-01
- <_>
-
- 0 1 14 -8.7343417108058929e-03 -1 -2 15
- 9.4522320432588458e-04
-
- 2.8899800777435303e-01 7.6165872812271118e-01
- 3.4956431388854980e-01
- <_>
-
- 1 0 16 4.9414858222007751e-02 -1 -2 17
- 4.4891750440001488e-03
-
- 8.1516528129577637e-01 2.8087830543518066e-01
- 6.0277748107910156e-01
- <_>
-
- 1 0 18 6.0313619673252106e-02 -1 -2 19
- -1.0762850288301706e-03
-
- 7.6075017452239990e-01 4.4440358877182007e-01
- 1.4373120665550232e-01
- <_>
-
- 1 0 20 -9.5083238556981087e-03 -1 -2 21
- 7.6601309701800346e-03
-
- 5.3181701898574829e-01 5.4110521078109741e-01
- 2.1806870400905609e-01
- <_>
-
- 1 0 22 7.6467678882181644e-03 -1 -2 23
- -8.4662932204082608e-04
-
- 1.1589600145816803e-01 2.3406790196895599e-01
- 5.9903818368911743e-01
- <_>
- 14
- 5.9844889640808105e+00
-
- <_>
-
- 1 0 24 -4.8506218008697033e-03 -1 -2 25
- -4.6141650527715683e-03
-
- 1.8054960668087006e-01 2.1778939664363861e-01
- 8.0182367563247681e-01
- <_>
-
- 0 1 26 -2.4301309604197741e-03 -1 -2 27
- 4.1787960799410939e-04
-
- 1.1413549631834030e-01 1.2030939757823944e-01
- 6.1085307598114014e-01
- <_>
-
- 0 1 28 1.0010929545387626e-03 -1 -2 29
- 1.0577100329101086e-03
-
- 2.0799599587917328e-01 3.3020541071891785e-01
- 7.5110942125320435e-01
- <_>
-
- 1 0 30 1.2376549420878291e-03 -1 -2 31
- 3.5315038985572755e-04
-
- 2.7682220935821533e-01 1.6682930290699005e-01
- 5.8294767141342163e-01
- <_>
-
- 0 1 32 -1.1953660286962986e-02 -1 -2 33
- 1.4182999730110168e-03
-
- 1.5087880194187164e-01 4.3912279605865479e-01
- 7.6465952396392822e-01
- <_>
-
- 1 0 34 3.4642980899661779e-03 -1 -2 35
- -1.4948950149118900e-02
-
- 2.6515561342239380e-01 2.2980530560016632e-01
- 5.4421657323837280e-01
- <_>
-
- 1 0 36 -1.0506849503144622e-03 -1 -2 37
- -4.0782918222248554e-03
-
- 3.6228439211845398e-01 2.6012599468231201e-01
- 7.2336578369140625e-01
- <_>
-
- 0 1 38 5.4242828628048301e-04 -1 -2 39
- -7.3204059153795242e-03
-
- 3.8496789336204529e-01 2.9655128717422485e-01
- 5.4803091287612915e-01
- <_>
-
- 0 1 40 1.1421289527788758e-03 -1 -2 41
- 1.1783400550484657e-03
-
- 4.1047701239585876e-01 7.2390240430831909e-01
- 2.7872839570045471e-01
- <_>
-
- 0 1 42 4.4077109545469284e-02 -1 -2 43
- 3.7900090683251619e-03
-
- 5.6405162811279297e-01 5.9475481510162354e-01
- 3.3120200037956238e-01
- <_>
-
- 0 1 44 -2.4291418958455324e-03 -1 -2 45
- 9.4262324273586273e-03
-
- 6.6032320261001587e-01 4.6806651353836060e-01
- 2.0643380284309387e-01
- <_>
-
- 0 1 46 8.0630257725715637e-03 -1 -2 47
- 5.2240812219679356e-03
-
- 5.2988511323928833e-01 5.2816027402877808e-01
- 1.9095499813556671e-01
- <_>
-
- 0 1 48 -7.0630568079650402e-03 -1 -2 49
- 5.6897541508078575e-03
-
- 1.3806459307670593e-01 5.4906368255615234e-01
- 1.2602810561656952e-01
- <_>
-
- 0 1 50 1.2472929665818810e-03 -1 -2 51
- 4.9543488770723343e-02
-
- 2.3726630210876465e-01 5.2401661872863770e-01
- 1.7692160606384277e-01
- <_>
- 19
- 8.5117864608764648e+00
-
- <_>
-
- 1 0 52 -4.9326149746775627e-03 -1 -2 53
- 2.7918140403926373e-05
-
- 1.9980649650096893e-01 2.2993800044059753e-01
- 7.3932111263275146e-01
- <_>
-
- 1 0 54 3.0876200180500746e-03 -1 -2 55
- 7.4669660534709692e-06
-
- 1.5338400006294250e-01 2.0368589460849762e-01
- 5.8549159765243530e-01
- <_>
-
- 0 1 56 1.8739729421213269e-03 -1 -2 57
- 9.3380251200869679e-04
-
- 2.0498959720134735e-01 3.2341998815536499e-01
- 7.3230141401290894e-01
- <_>
-
- 0 1 58 1.9151850137859583e-03 -1 -2 59
- -5.9683797881007195e-03
-
- 3.0451491475105286e-01 2.9321339726448059e-01
- 5.6212961673736572e-01
- <_>
-
- 0 1 60 -7.2115601506084204e-04 -1 -2 61
- -5.9663117863237858e-03
-
- 3.6580368876457214e-01 2.7121558785438538e-01
- 7.2263348102569580e-01
- <_>
-
- 0 1 62 3.0874179676175117e-02 -1 -2 63
- -1.1099710129201412e-02
-
- 4.4198378920555115e-01 3.6129769682884216e-01
- 5.2514511346817017e-01
- <_>
-
- 0 1 64 2.1164179779589176e-03 -1 -2 65
- -9.4317439943552017e-03
-
- 3.6286169290542603e-01 1.6010950505733490e-01
- 7.0522767305374146e-01
- <_>
-
- 0 1 66 -3.5266019403934479e-03 -1 -2 67
- -1.6907559474930167e-03
-
- 1.3012880086898804e-01 1.7863239347934723e-01
- 5.5215299129486084e-01
- <_>
-
- 0 1 68 4.6470930101349950e-04 -1 -2 69
- -1.0215570218861103e-02
-
- 3.4873831272125244e-01 2.6739910244941711e-01
- 6.6679191589355469e-01
- <_>
-
- 1 0 70 1.2634709710255265e-03 -1 -2 71
- -1.1875299736857414e-02
-
- 3.4378638863563538e-01 5.9953361749649048e-01
- 3.4977179765701294e-01
- <_>
-
- 0 1 72 -1.0732339695096016e-02 -1 -2 73
- 7.1836481802165508e-03
-
- 2.1504899859428406e-01 6.2714362144470215e-01
- 2.5195419788360596e-01
- <_>
-
- 0 1 74 -2.8340889140963554e-02 -1 -2 75
- -4.5813230099156499e-04
-
- 8.2411892712116241e-02 5.9100568294525146e-01
- 3.7052011489868164e-01
- <_>
-
- 1 0 76 4.2940340936183929e-03 -1 -2 77
- 1.0751079767942429e-02
-
- 1.5947279334068298e-01 5.9804809093475342e-01
- 2.8325080871582031e-01
- <_>
-
- 1 0 78 2.2465119138360023e-02 -1 -2 79
- -5.7988539338111877e-02
-
- 7.8770911693572998e-01 1.5557409822940826e-01
- 5.2396571636199951e-01
- <_>
-
- 1 0 80 7.2110891342163086e-03 -1 -2 81
- -4.8367571085691452e-02
-
- 6.6203659772872925e-01 1.4247199892997742e-01
- 4.4298338890075684e-01
- <_>
-
- 0 1 82 -1.4418059960007668e-02 -1 -2 83
- -2.3156389594078064e-02
-
- 1.5885409712791443e-01 2.3757989704608917e-01
- 5.2171349525451660e-01
- <_>
-
- 1 0 84 7.6985340565443039e-03 -1 -2 85
- -5.6248619221150875e-03
-
- 1.9417250156402588e-01 6.2784057855606079e-01
- 3.7460449337959290e-01
- <_>
-
- 1 0 86 -7.2936748620122671e-04 -1 -2 87
- 6.1783898854628205e-04
-
- 3.8409221172332764e-01 3.1064930558204651e-01
- 5.5378472805023193e-01
- <_>
-
- 1 0 88 -4.5803939428878948e-05 -1 -2 89
- -1.4719359569426160e-05
-
- 3.4444490075111389e-01 2.7295520901679993e-01
- 6.4289510250091553e-01
- <_>
- 19
- 8.4680156707763672e+00
-
- <_>
-
- 0 1 90 -1.3469370314851403e-03 -1 -2 91
- -2.4774789344519377e-03
-
- 1.6570860147476196e-01 2.2738510370254517e-01
- 6.9893497228622437e-01
- <_>
-
- 0 1 92 5.2632777951657772e-03 -1 -2 93
- 4.9075339920818806e-03
-
- 1.5120740234851837e-01 5.5644702911376953e-01
- 1.6054420173168182e-01
- <_>
-
- 0 1 94 -2.3254349362105131e-03 -1 -2 95
- -1.4665479538962245e-03
-
- 1.8802590668201447e-01 3.1224989891052246e-01
- 7.1653962135314941e-01
- <_>
-
- 1 0 96 -1.2311690300703049e-01 -1 -2 97
- 2.2108340635895729e-03
-
- 3.8595831394195557e-01 2.4552939832210541e-01
- 5.6957101821899414e-01
- <_>
-
- 0 1 98 2.0661531016230583e-03 -1 -2 99
- 3.6130280932411551e-04
-
- 2.7165201306343079e-01 2.2933620214462280e-01
- 7.2086298465728760e-01
- <_>
-
- 1 0 100 7.9957872629165649e-02 -1 -2 101
- 2.6064720004796982e-03
-
- 7.8336209058761597e-01 5.5452322959899902e-01
- 2.5506898760795593e-01
- <_>
-
- 1 0 102 6.5699010156095028e-03 -1 -2 103
- 1.6259610420092940e-03
-
- 1.8193900585174561e-01 3.5298758745193481e-01
- 6.5528190135955811e-01
- <_>
-
- 0 1 104 3.6204981151968241e-03 -1 -2 105
- -4.4391951523721218e-03
-
- 5.4623097181320190e-01 1.3598430156707764e-01
- 5.4158151149749756e-01
- <_>
-
- 0 1 106 -9.0540945529937744e-03 -1 -2 107
- -4.6067481162026525e-04
-
- 1.1151199787855148e-01 5.8467197418212891e-01
- 2.5983488559722900e-01
- <_>
-
- 0 1 108 -5.6621041148900986e-03 -1 -2 109
- 5.1165837794542313e-03
-
- 1.6105690598487854e-01 5.3766787052154541e-01
- 1.7394550144672394e-01
- <_>
-
- 0 1 110 -2.1362339612096548e-03 -1 -2 111
- -5.4809921421110630e-03
-
- 1.9020730257034302e-01 3.2720080018043518e-01
- 6.3648408651351929e-01
- <_>
-
- 0 1 112 -8.1061907112598419e-03 -1 -2 113
- 6.0048708692193031e-03
-
- 6.9148528575897217e-01 4.3273261189460754e-01
- 6.9638431072235107e-01
- <_>
-
- 0 1 114 -8.7028548121452332e-02 -1 -2 115
- -4.7809639945626259e-03
-
- 8.5941338539123535e-01 9.7394466400146484e-02
- 4.5870301127433777e-01
- <_>
-
- 0 1 116 -2.2166660055518150e-03 -1 -2 117
- 1.3642730191349983e-03
-
- 2.5546258687973022e-01 3.3190909028053284e-01
- 5.9641027450561523e-01
- <_>
-
- 0 1 118 -9.0077864006161690e-03 -1 -2 119
- -1.5494120307266712e-02
-
- 2.6665949821472168e-01 1.8481859564781189e-01
- 6.2459707260131836e-01
- <_>
-
- 1 0 120 -4.2165028862655163e-03 -1 -2 121
- 4.3249759823083878e-02
-
- 5.3799271583557129e-01 5.1830291748046875e-01
- 2.1704199910163879e-01
- <_>
-
- 1 0 122 2.8786511393263936e-04 -1 -2 123
- 1.2373150093480945e-03
-
- 2.6133841276168823e-01 2.7865320444107056e-01
- 5.9089881181716919e-01
- <_>
-
- 1 0 124 1.9528300035744905e-03 -1 -2 125
- -1.4947060262784362e-03
-
- 2.6128691434860229e-01 5.9154129028320312e-01
- 3.4557819366455078e-01
- <_>
-
- 1 0 126 3.5878680646419525e-03 -1 -2 127
- -2.5938691105693579e-03
-
- 1.5870520472526550e-01 1.2704110145568848e-01
- 5.9794288873672485e-01
- <_>
- 27
- 1.2578499794006348e+01
-
- <_>
-
- 0 1 128 3.5810680128633976e-03 -1 -2 129
- -2.8552350122481585e-03
-
- 1.9951049983501434e-01 7.3730701208114624e-01
- 2.9217371344566345e-01
- <_>
-
- 0 1 130 1.9758539274334908e-03 -1 -2 131
- 3.2583118882030249e-03
-
- 1.9564199447631836e-01 5.6920468807220459e-01
- 1.8390649557113647e-01
- <_>
-
- 0 1 132 2.3711679386906326e-04 -1 -2 133
- 2.5942500215023756e-03
-
- 2.1716670691967010e-01 2.7199891209602356e-01
- 7.1502441167831421e-01
- <_>
-
- 0 1 134 -2.5032449513673782e-02 -1 -2 135
- 6.3087949529290199e-03
-
- 1.8251839280128479e-01 5.6998378038406372e-01
- 3.5098528861999512e-01
- <_>
-
- 1 0 136 -3.2494920305907726e-03 -1 -2 137
- -1.4885730110108852e-02
-
- 4.0239268541336060e-01 3.6040958762168884e-01
- 7.2919952869415283e-01
- <_>
-
- 1 0 138 8.0623216927051544e-03 -1 -2 139
- 2.7405679225921631e-02
-
- 6.4914900064468384e-01 5.5189931392669678e-01
- 2.6596811413764954e-01
- <_>
-
- 1 0 140 3.4368600696325302e-02 -1 -2 141
- -2.7292970567941666e-02
-
- 6.7125129699707031e-01 1.6913780570030212e-01
- 4.3262779712677002e-01
- <_>
-
- 0 1 142 7.4452121043577790e-04 -1 -2 143
- 7.0336280623450875e-04
-
- 3.4051001071929932e-01 5.5167931318283081e-01
- 3.3113878965377808e-01
- <_>
-
- 0 1 144 -1.2275460362434387e-01 -1 -2 145
- 3.2559928949922323e-03
-
- 1.6753150522708893e-01 3.6157518625259399e-01
- 6.4207828044891357e-01
- <_>
-
- 0 1 146 -3.2090399414300919e-02 -1 -2 147
- 3.2957999501377344e-03
-
- 2.9210790991783142e-01 5.6130319833755493e-01
- 3.3578601479530334e-01
- <_>
-
- 0 1 148 -3.2273170072585344e-03 -1 -2 149
- 1.1171669466421008e-03
-
- 6.9706428050994873e-01 3.5411500930786133e-01
- 6.1440062522888184e-01
- <_>
-
- 1 0 150 -1.7279950901865959e-02 -1 -2 151
- 1.1741200461983681e-02
-
- 5.5371809005737305e-01 5.3419572114944458e-01
- 2.7571049332618713e-01
- <_>
-
- 1 0 152 4.6405228786170483e-03 -1 -2 153
- -1.6913030296564102e-02
-
- 2.4895210564136505e-01 1.7119289934635162e-01
- 5.5239528417587280e-01
- <_>
-
- 1 0 154 1.0060169734060764e-02 -1 -2 155
- -6.0715491417795420e-04
-
- 8.2734507322311401e-01 3.7793910503387451e-01
- 5.4762518405914307e-01
- <_>
-
- 1 0 156 -1.0865400545299053e-03 -1 -2 157
- 8.9362077414989471e-03
-
- 3.2965409755706787e-01 6.0628837347030640e-01
- 2.4342200160026550e-01
- <_>
-
- 1 0 158 -2.6372660067863762e-04 -1 -2 159
- 1.3110050000250340e-02
-
- 3.8140949606895447e-01 5.5176162719726562e-01
- 3.7268930673599243e-01
- <_>
-
- 0 1 160 -2.9806280508637428e-03 -1 -2 161
- -4.1619571857154369e-03
-
- 1.2296640127897263e-01 7.2522747516632080e-01
- 4.9734550714492798e-01
- <_>
-
- 0 1 162 3.3842328935861588e-02 -1 -2 163
- -1.2564560165628791e-03
-
- 5.3483128547668457e-01 5.8519148826599121e-01
- 4.3841668963432312e-01
- <_>
-
- 0 1 164 -1.9635230302810669e-02 -1 -2 165
- -9.9625496659427881e-04
-
- 2.2978340089321136e-01 6.2959378957748413e-01
- 4.1315990686416626e-01
- <_>
-
- 0 1 166 -2.3127110674977303e-02 -1 -2 167
- 2.3525709286332130e-02
-
- 1.6954590380191803e-01 5.1741302013397217e-01
- 5.9519391506910324e-02
- <_>
-
- 0 1 168 -1.9356520846486092e-02 -1 -2 169
- -4.1787112131714821e-03
-
- 1.3572479784488678e-01 2.9966288805007935e-01
- 5.7916951179504395e-01
- <_>
-
- 1 0 170 3.1488779932260513e-03 -1 -2 171
- 7.3972279205918312e-03
-
- 6.5925890207290649e-01 5.3071719408035278e-01
- 3.7951210141181946e-01
- <_>
-
- 0 1 172 7.1955118983169086e-06 -1 -2 173
- 4.7114409506320953e-02
-
- 3.1283149123191833e-01 5.5378931760787964e-01
- 1.0273090004920959e-01
- <_>
-
- 0 1 174 7.2878710925579071e-03 -1 -2 175
- -6.1887511983513832e-03
-
- 4.6608591079711914e-01 7.1588581800460815e-01
- 4.7244489192962646e-01
- <_>
-
- 1 0 176 2.9757320880889893e-03 -1 -2 177
- -1.8449809867888689e-03
-
- 5.9345688670873642e-02 7.0273017883300781e-01
- 4.7187310457229614e-01
- <_>
-
- 0 1 178 1.0239540279144421e-04 -1 -2 179
- 2.4277009069919586e-03
-
- 5.8947342634201050e-01 4.8623558878898621e-01
- 5.2475881576538086e-01
- <_>
-
- 0 1 180 -6.4751312136650085e-02 -1 -2 181
- 3.9380151429213583e-04
-
- 6.9174712896347046e-01 4.6696171164512634e-01
- 2.3824059963226318e-01
- <_>
- 31
- 1.4546750068664551e+01
-
- <_>
-
- 0 1 182 1.4397440245375037e-03 -1 -2 183
- -5.4068560712039471e-04
-
- 2.7734708786010742e-01 7.4271547794342041e-01
- 2.4797350168228149e-01
- <_>
-
- 1 0 184 -7.1237959673453588e-06 -1 -2 185
- -2.3661039303988218e-03
-
- 2.1995030343532562e-01 5.8899897336959839e-01
- 2.5957161188125610e-01
- <_>
-
- 0 1 186 1.7343269428238273e-03 -1 -2 187
- 1.5874590026214719e-03
-
- 1.8601259589195251e-01 4.1518709063529968e-01
- 7.1034741401672363e-01
- <_>
-
- 1 0 188 3.7285638973116875e-03 -1 -2 189
- -1.2883819639682770e-01
-
- 2.5279670953750610e-01 1.3930009305477142e-01
- 5.2545148134231567e-01
- <_>
-
- 1 0 190 7.9412180930376053e-03 -1 -2 191
- -1.2661729939281940e-02
-
- 2.4877290427684784e-01 2.7107000350952148e-01
- 6.6188377141952515e-01
- <_>
-
- 0 1 192 3.0146789868013002e-05 -1 -2 193
- -1.6330160200595856e-02
-
- 3.8128259778022766e-01 2.3264320194721222e-01
- 5.2630108594894409e-01
- <_>
-
- 0 1 194 1.4622770322603174e-05 -1 -2 195
- -2.0858660340309143e-02
-
- 4.2933320999145508e-01 1.6004039347171783e-01
- 6.7823147773742676e-01
- <_>
-
- 1 0 196 2.8194559272378683e-03 -1 -2 197
- 3.7899368908256292e-03
-
- 6.6792941093444824e-01 4.5877051353454590e-01
- 7.1762388944625854e-01
- <_>
-
- 1 0 198 3.5344641655683517e-02 -1 -2 199
- -1.1571600334718823e-03
-
- 1.8640750646591187e-01 5.5382597446441650e-01
- 3.1504508852958679e-01
- <_>
-
- 0 1 200 -5.8742752298712730e-03 -1 -2 201
- -1.5201780115603469e-05
-
- 2.8287911415100098e-01 5.8702242374420166e-01
- 3.7048238515853882e-01
- <_>
-
- 1 0 202 -2.2681879636365920e-04 -1 -2 203
- 3.7845689803361893e-03
-
- 4.2189309000968933e-01 6.6670012474060059e-01
- 2.4611820280551910e-01
- <_>
-
- 1 0 204 -8.5295992903411388e-05 -1 -2 205
- -4.4394891709089279e-02
-
- 3.5575878620147705e-01 1.6655470430850983e-01
- 5.2348488569259644e-01
- <_>
-
- 0 1 206 1.0126030538231134e-03 -1 -2 207
- -7.6327780261635780e-03
-
- 2.8846129775047302e-01 2.9693400859832764e-01
- 6.0801112651824951e-01
- <_>
-
- 0 1 208 4.0330411866307259e-03 -1 -2 209
- 1.3676689565181732e-01
-
- 4.5363900065422058e-01 5.1772642135620117e-01
- 1.4491820335388184e-01
- <_>
-
- 0 1 210 -5.0060478970408440e-03 -1 -2 211
- -1.2475839816033840e-02
-
- 7.6169097423553467e-01 2.1597060561180115e-01
- 5.4601877927780151e-01
- <_>
-
- 1 0 212 -9.4012258341535926e-04 -1 -2 213
- -1.2191980145871639e-02
-
- 3.9262959361076355e-01 3.4788811206817627e-01
- 5.5426627397537231e-01
- <_>
-
- 0 1 214 -5.4959481349214911e-04 -1 -2 215
- -2.1802430273965001e-04
-
- 6.0642760992050171e-01 5.6974071264266968e-01
- 1.7797139286994934e-01
- <_>
-
- 0 1 216 6.9115799851715565e-03 -1 -2 217
- -9.7631698008626699e-04
-
- 5.3793722391128540e-01 3.3278390765190125e-01
- 5.4615312814712524e-01
- <_>
-
- 0 1 218 -8.7870173156261444e-03 -1 -2 219
- -1.6761029837653041e-03
-
- 2.1161609888076782e-01 6.6358232498168945e-01
- 4.3658590316772461e-01
- <_>
-
- 1 0 220 -5.5694948881864548e-02 -1 -2 221
- -1.9844379276037216e-02
-
- 5.3874248266220093e-01 1.6028049588203430e-01
- 5.3304588794708252e-01
- <_>
-
- 0 1 222 -7.4751611100509763e-04 -1 -2 223
- 2.3032890632748604e-02
-
- 2.9174768924713135e-01 5.6081241369247437e-01
- 1.9979810714721680e-01
- <_>
-
- 1 0 224 -3.0700280331075191e-03 -1 -2 225
- -1.1636839481070638e-03
-
- 3.9383140206336975e-01 5.7574361562728882e-01
- 4.2394569516181946e-01
- <_>
-
- 1 0 226 2.2464339435100555e-01 -1 -2 227
- 1.4412109740078449e-03
-
- 7.6765531301498413e-01 5.3538662195205688e-01
- 2.5147768855094910e-01
- <_>
-
- 0 1 228 -3.0011249706149101e-02 -1 -2 229
- -5.3078960627317429e-02
-
- 2.3649039864540100e-01 2.3858639597892761e-01
- 5.4146647453308105e-01
- <_>
-
- 1 0 230 2.0800929050892591e-03 -1 -2 231
- -4.0738182142376900e-03
-
- 6.5116149187088013e-01 6.0304141044616699e-01
- 3.5877010226249695e-01
- <_>
-
- 1 0 232 -1.9529370591044426e-02 -1 -2 233
- -5.3309470415115356e-02
-
- 5.4235929250717163e-01 2.3609539866447449e-01
- 5.4017579555511475e-01
- <_>
-
- 0 1 234 -3.4849561750888824e-02 -1 -2 235
- -1.2658450007438660e-01
-
- 2.8369858860969543e-01 1.8135160207748413e-01
- 5.4210460186004639e-01
- <_>
-
- 0 1 236 7.3325118137290701e-06 -1 -2 237
- -1.1843870393931866e-02
-
- 3.9803659915924072e-01 2.6163849234580994e-01
- 5.2377301454544067e-01
- <_>
-
- 0 1 238 -4.8470678739249706e-03 -1 -2 239
- 8.1693977117538452e-03
-
- 2.4381080269813538e-01 5.3271460533142090e-01
- 8.1903767585754395e-01
- <_>
-
- 1 0 240 -6.4716790802776814e-03 -1 -2 241
- -1.5188479665084742e-05
-
- 4.6796938776969910e-01 5.5639117956161499e-01
- 4.3675860762596130e-01
- <_>
-
- 1 0 242 3.0696711037307978e-03 -1 -2 243
- -1.6296720423270017e-04
-
- 6.6643488407135010e-01 5.5946111679077148e-01
- 3.0427119135856628e-01
- <_>
- 39
- 1.8572250366210938e+01
-
- <_>
-
- 1 0 244 -9.8275858908891678e-03 -1 -2 245
- -4.1693858802318573e-03
-
- 2.1160189807415009e-01 6.9246852397918701e-01
- 3.0437770485877991e-01
- <_>
-
- 0 1 246 3.5341319744475186e-04 -1 -2 247
- 4.8054549843072891e-03
-
- 3.1832858920097351e-01 5.4565590620040894e-01
- 2.5222688913345337e-01
- <_>
-
- 0 1 248 2.1071180526632816e-04 -1 -2 249
- -2.8318869881331921e-03
-
- 2.9026180505752563e-01 3.1304559111595154e-01
- 6.8849372863769531e-01
- <_>
-
- 1 0 250 -7.5633679443853907e-06 -1 -2 251
- -8.2888139877468348e-04
-
- 2.9624658823013306e-01 3.0996260046958923e-01
- 5.7525151968002319e-01
- <_>
-
- 0 1 252 1.6209259629249573e-03 -1 -2 253
- 9.1338958591222763e-03
-
- 3.9931958913803101e-01 4.8273721337318420e-01
- 7.5378328561782837e-01
- <_>
-
- 0 1 254 -4.1212290525436401e-03 -1 -2 255
- -2.5447290390729904e-03
-
- 2.6169270277023315e-01 3.1087028980255127e-01
- 5.4912358522415161e-01
- <_>
-
- 0 1 256 -6.2652782071381807e-04 -1 -2 257
- -3.6596331483451650e-05
-
- 3.2396918535232544e-01 6.5174108743667603e-01
- 4.1789120435714722e-01
- <_>
-
- 1 0 258 1.3882719911634922e-02 -1 -2 259
- 1.0493700392544270e-03
-
- 6.7712038755416870e-01 4.1595110297203064e-01
- 5.6528919935226440e-01
- <_>
-
- 1 0 260 1.8215360119938850e-02 -1 -2 261
- -1.1334580369293690e-02
-
- 7.6896011829376221e-01 2.8733238577842712e-01
- 4.9889329075813293e-01
- <_>
-
- 1 0 262 -4.1097560897469521e-03 -1 -2 263
- 4.2612891411408782e-04
-
- 5.4630082845687866e-01 3.6312350630760193e-01
- 5.5125522613525391e-01
- <_>
-
- 1 0 264 6.0301548801362514e-03 -1 -2 265
- 3.3587709185667336e-04
-
- 1.1437670141458511e-01 2.8910788893699646e-01
- 5.4473417997360229e-01
- <_>
-
- 1 0 266 6.2279507983475924e-04 -1 -2 267
- -2.5837119668722153e-02
-
- 3.0234318971633911e-01 2.1670059859752655e-01
- 5.2781528234481812e-01
- <_>
-
- 1 0 268 2.1774910390377045e-02 -1 -2 269
- 1.7682299949228764e-03
-
- 3.2548341155052185e-01 5.2630507946014404e-01
- 7.5263291597366333e-01
- <_>
-
- 0 1 270 -1.3793810270726681e-02 -1 -2 271
- -5.0852829590439796e-03
-
- 7.4103301763534546e-01 6.8366098403930664e-01
- 4.5790711045265198e-01
- <_>
-
- 1 0 272 6.1795017682015896e-03 -1 -2 273
- 1.0030319914221764e-02
-
- 7.4499362707138062e-01 4.8607799410820007e-01
- 2.3614570498466492e-01
- <_>
-
- 0 1 274 -6.4201927743852139e-03 -1 -2 275
- -5.6961281225085258e-03
-
- 1.4673270285129547e-01 2.3478199541568756e-01
- 5.3233772516250610e-01
- <_>
-
- 0 1 276 -7.1498160250484943e-03 -1 -2 277
- 2.4450740311294794e-03
-
- 1.4770570397377014e-01 3.4985339641571045e-01
- 5.8035618066787720e-01
- <_>
-
- 1 0 278 -3.7503410130739212e-02 -1 -2 279
- 4.7799441381357610e-04
-
- 5.2595508098602295e-01 4.3628829717636108e-01
- 6.2089228630065918e-01
- <_>
-
- 0 1 280 -7.0806080475449562e-03 -1 -2 281
- 3.2818000763654709e-02
-
- 2.0394609868526459e-01 5.1983588933944702e-01
- 1.3711960613727570e-01
- <_>
-
- 1 0 282 6.5188988810405135e-04 -1 -2 283
- 4.6485587954521179e-03
-
- 6.3234299421310425e-01 4.7201630473136902e-01
- 6.5670871734619141e-01
- <_>
-
- 0 1 284 -1.9827929791063070e-03 -1 -2 285
- -1.6011310508474708e-03
-
- 6.0530602931976318e-01 5.0905191898345947e-01
- 3.1169331073760986e-01
- <_>
-
- 0 1 286 -3.0539939180016518e-03 -1 -2 287
- 4.3212040327489376e-04
-
- 3.4298041462898254e-01 3.8384029269218445e-01
- 5.7755982875823975e-01
- <_>
-
- 0 1 288 -2.7452120557427406e-02 -1 -2 289
- 9.3099439982324839e-04
-
- 2.1434690058231354e-01 5.9529662132263184e-01
- 3.7601581215858459e-01
- <_>
-
- 0 1 290 6.7144189961254597e-03 -1 -2 291
- -3.3701690845191479e-03
-
- 5.6926268339157104e-01 5.7843041419982910e-01
- 3.9742821455001831e-01
- <_>
-
- 0 1 292 -1.8903959542512894e-02 -1 -2 293
- -6.5850871615111828e-03
-
- 1.8188929557800293e-01 6.8491101264953613e-01
- 4.3515840172767639e-01
- <_>
-
- 1 0 294 5.8810501359403133e-03 -1 -2 295
- 8.0092082498595119e-04
-
- 2.7266609668731689e-01 4.2364311218261719e-01
- 5.8446758985519409e-01
- <_>
-
- 1 0 296 1.8510579830035567e-03 -1 -2 297
- 6.3273650594055653e-03
-
- 3.3713209629058838e-01 5.2702218294143677e-01
- 8.0536508560180664e-01
- <_>
-
- 0 1 298 -3.3820930402725935e-03 -1 -2 299
- -1.9292969955131412e-03
-
- 2.8660181164741516e-01 5.8889460563659668e-01
- 3.8957870006561279e-01
- <_>
-
- 1 0 300 1.4995220117270947e-02 -1 -2 301
- -2.6330750435590744e-02
-
- 2.1778169274330139e-01 1.7753170430660248e-01
- 5.6714701652526855e-01
- <_>
-
- 1 0 302 -4.1734222322702408e-03 -1 -2 303
- 2.7268350124359131e-02
-
- 4.6529620885848999e-01 4.7683110833168030e-01
- 5.6952387094497681e-01
- <_>
-
- 1 0 304 9.8880263976752758e-04 -1 -2 305
- -1.0528849670663476e-03
-
- 3.3974018692970276e-01 6.2500411272048950e-01
- 4.2884120345115662e-01
- <_>
-
- 0 1 306 5.2288072183728218e-03 -1 -2 307
- 3.0395459383726120e-02
-
- 5.3477621078491211e-01 4.1155189275741577e-01
- 5.6607538461685181e-01
- <_>
-
- 0 1 308 -7.9113930463790894e-02 -1 -2 309
- 1.8231669440865517e-02
-
- 7.8813230991363525e-01 3.6043399572372437e-01
- 5.5695050954818726e-01
- <_>
-
- 0 1 310 5.2288072183728218e-03 -1 -2 311
- 4.3922828626818955e-04
-
- 5.4166442155838013e-01 5.5071568489074707e-01
- 3.8822770118713379e-01
- <_>
-
- 0 1 312 -8.6501962505280972e-04 -1 -2 313
- 1.0326979681849480e-03
-
- 3.1858509778976440e-01 5.5783641338348389e-01
- 3.2192459702491760e-01
- <_>
-
- 0 1 314 -7.2997747920453548e-03 -1 -2 315
- -9.3629042385146022e-04
-
- 7.0732331275939941e-01 5.5580157041549683e-01
- 4.6138420701026917e-01
- <_>
-
- 0 1 316 -6.0483231209218502e-03 -1 -2 317
- 6.7529221996665001e-03
-
- 6.8692898750305176e-01 4.8703178763389587e-01
- 2.6503708958625793e-01
- <_>
-
- 0 1 318 5.3078029304742813e-02 -1 -2 319
- -1.0225810110569000e-03
-
- 5.2815151214599609e-01 6.0858821868896484e-01
- 4.3048679828643799e-01
- <_>
-
- 1 0 320 3.1270649284124374e-02 -1 -2 321
- -6.3522169366478920e-03
-
- 5.4458320140838623e-01 5.3283357620239258e-01
- 2.3643240332603455e-01
- <_>
- 45
- 2.1578119277954102e+01
-
- <_>
-
- 1 0 322 -6.2215630896389484e-03 -1 -2 323
- 2.1097389981150627e-03
-
- 2.6255810260772705e-01 1.5649929642677307e-01
- 6.7928832769393921e-01
- <_>
-
- 0 1 324 1.0845859535038471e-02 -1 -2 325
- 6.4230401767417789e-04
-
- 3.4858089685440063e-01 3.6982551217079163e-01
- 5.9216582775115967e-01
- <_>
-
- 1 0 326 7.3311722371727228e-04 -1 -2 327
- 1.0134200565516949e-03
-
- 3.0070841312408447e-01 3.6249229311943054e-01
- 7.0724260807037354e-01
- <_>
-
- 0 1 328 1.1093559674918652e-02 -1 -2 329
- -7.9127531498670578e-03
-
- 4.4167020916938782e-01 3.0287081003189087e-01
- 5.4173761606216431e-01
- <_>
-
- 0 1 330 1.2905309908092022e-02 -1 -2 331
- -4.2430912144482136e-03
-
- 4.3745040893554688e-01 4.4015899300575256e-01
- 7.5651907920837402e-01
- <_>
-
- 0 1 332 -2.1304309484548867e-04 -1 -2 333
- -2.2308640182018280e-03
-
- 2.3107869923114777e-01 3.5681959986686707e-01
- 5.7499992847442627e-01
- <_>
-
- 0 1 334 2.6400520000606775e-03 -1 -2 335
- 7.5101032853126526e-02
-
- 3.5936889052391052e-01 6.3635677099227905e-01
- 2.3270289599895477e-01
- <_>
-
- 0 1 336 -7.7012968249619007e-03 -1 -2 337
- 1.5588370151817799e-03
-
- 7.0746237039566040e-01 5.7002371549606323e-01
- 3.5904508829116821e-01
- <_>
-
- 0 1 338 -4.7687938786111772e-04 -1 -2 339
- 8.4234727546572685e-04
-
- 2.8054410219192505e-01 4.1254189610481262e-01
- 6.1779958009719849e-01
- <_>
-
- 1 0 340 -1.2825109995901585e-02 -1 -2 341
- -6.5156567143276334e-04
-
- 5.4030781984329224e-01 5.6336438655853271e-01
- 3.3565390110015869e-01
- <_>
-
- 0 1 342 -1.2006159871816635e-02 -1 -2 343
- 1.3213419588282704e-03
-
- 7.1095108985900879e-01 4.9038508534431458e-01
- 2.8245830535888672e-01
- <_>
-
- 0 1 344 -2.0307440310716629e-02 -1 -2 345
- 4.0180929936468601e-03
-
- 1.8913699686527252e-01 5.3779661655426025e-01
- 3.1194949150085449e-01
- <_>
-
- 1 0 346 4.5315311290323734e-03 -1 -2 347
- -4.4381739571690559e-03
-
- 7.2067582607269287e-01 1.8546679615974426e-01
- 4.9817329645156860e-01
- <_>
-
- 1 0 348 1.5692010056227446e-03 -1 -2 349
- -4.9516442231833935e-03
-
- 2.6382741332054138e-01 6.8710672855377197e-01
- 4.7146868705749512e-01
- <_>
-
- 0 1 350 -2.7429679408669472e-02 -1 -2 351
- 1.4181969454512000e-03
-
- 1.5482850372791290e-01 4.3768429756164551e-01
- 6.3273680210113525e-01
- <_>
-
- 0 1 352 -1.3078940100967884e-02 -1 -2 353
- -3.5092779435217381e-03
-
- 3.1668141484260559e-01 6.1997437477111816e-01
- 4.3796870112419128e-01
- <_>
-
- 1 0 354 1.8920730799436569e-02 -1 -2 355
- 2.1683350205421448e-03
-
- 1.4707140624523163e-01 5.8094590902328491e-01
- 3.4319490194320679e-01
- <_>
-
- 0 1 356 1.6401590546593070e-03 -1 -2 357
- 1.4005920093040913e-04
-
- 3.9594578742980957e-01 3.2400250434875488e-01
- 5.6466472148895264e-01
- <_>
-
- 1 0 358 -3.3137591090053320e-03 -1 -2 359
- -2.9459029901772738e-03
-
- 4.2745280265808105e-01 3.3416679501533508e-01
- 6.6279602050781250e-01
- <_>
-
- 0 1 360 1.3612229668069631e-04 -1 -2 361
- 6.0512032359838486e-04
-
- 4.0469279885292053e-01 5.4840582609176636e-01
- 3.5699409246444702e-01
- <_>
-
- 0 1 362 -1.7513990402221680e-02 -1 -2 363
- -1.8735030665993690e-02
-
- 1.8241509795188904e-01 7.9718202352523804e-01
- 5.0685691833496094e-01
- <_>
-
- 1 0 364 1.2065649963915348e-02 -1 -2 365
- -2.6544178836047649e-03
-
- 2.1670070290565491e-01 6.5841788053512573e-01
- 4.6282431483268738e-01
- <_>
-
- 1 0 366 1.4501289697363973e-03 -1 -2 367
- 1.0954019613564014e-02
-
- 2.0902520418167114e-01 5.1123052835464478e-01
- 7.7845758199691772e-01
- <_>
-
- 0 1 368 1.5771709382534027e-02 -1 -2 369
- -1.4252689667046070e-02
-
- 5.1323592662811279e-01 1.7424149811267853e-01
- 5.2671480178833008e-01
- <_>
-
- 0 1 370 3.0411860279855318e-05 -1 -2 371
- 2.3486299440264702e-02
-
- 3.4184479713439941e-01 5.6312650442123413e-01
- 2.0063939690589905e-01
- <_>
-
- 1 0 372 5.2205449901521206e-03 -1 -2 373
- -2.5812430307269096e-02
-
- 6.2496489286422729e-01 3.2032281160354614e-01
- 5.1993298530578613e-01
- <_>
-
- 0 1 374 -1.9526650430634618e-03 -1 -2 375
- -8.1470049917697906e-03
-
- 6.1407059431076050e-01 6.5928959846496582e-01
- 3.7111249566078186e-01
- <_>
-
- 1 0 376 3.2962448894977570e-03 -1 -2 377
- -1.3961310032755136e-03
-
- 2.9521119594573975e-01 3.3208039402961731e-01
- 5.5284148454666138e-01
- <_>
-
- 0 1 378 -4.1055441834032536e-03 -1 -2 379
- -1.0888779535889626e-02
-
- 1.7105500400066376e-01 3.3594349026679993e-01
- 5.6749051809310913e-01
- <_>
-
- 1 0 380 -7.6768421567976475e-03 -1 -2 381
- -9.7729787230491638e-03
-
- 4.7732418775558472e-01 8.0810451507568359e-01
- 4.8458281159400940e-01
- <_>
-
- 1 0 382 6.0439710505306721e-03 -1 -2 383
- -4.6134641161188483e-04
-
- 6.7840021848678589e-01 5.5146390199661255e-01
- 3.6423599720001221e-01
- <_>
-
- 1 0 384 5.7992361485958099e-02 -1 -2 385
- 5.9384980704635382e-04
-
- 1.2544350326061249e-01 4.4248789548873901e-01
- 5.7284617424011230e-01
- <_>
-
- 0 1 386 -6.2353480607271194e-03 -1 -2 387
- -1.2784929946064949e-02
-
- 2.8050419688224792e-01 1.9509120285511017e-01
- 5.6529247760772705e-01
- <_>
-
- 1 0 388 4.1973669431172311e-04 -1 -2 389
- 8.0646801507100463e-04
-
- 6.1664837598800659e-01 4.5265799760818481e-01
- 5.9444868564605713e-01
- <_>
-
- 1 0 390 -1.6339010326191783e-03 -1 -2 391
- -4.8299999907612801e-03
-
- 4.0869420766830444e-01 2.7935269474983215e-01
- 6.4449352025985718e-01
- <_>
-
- 1 0 392 -6.3992068171501160e-03 -1 -2 393
- 1.0819199681282043e-01
-
- 5.6716561317443848e-01 5.3118121623992920e-01
- 2.6143568754196167e-01
- <_>
-
- 1 0 394 6.5056560561060905e-04 -1 -2 395
- 2.0611250773072243e-02
-
- 2.9967740178108215e-01 4.4899430871009827e-01
- 6.8882799148559570e-01
- <_>
-
- 1 0 396 -2.5129050016403198e-02 -1 -2 397
- 1.7922939732670784e-03
-
- 5.1968640089035034e-01 3.4669959545135498e-01
- 5.5335879325866699e-01
- <_>
-
- 1 0 398 1.5626220265403390e-03 -1 -2 399
- -6.1898730928078294e-04
-
- 3.0814400315284729e-01 2.6938709616661072e-01
- 5.5444890260696411e-01
- <_>
-
- 0 1 400 4.8111421056091785e-03 -1 -2 401
- 2.2484229411929846e-03
-
- 5.5878478288650513e-01 4.6721130609512329e-01
- 6.0908252000808716e-01
- <_>
-
- 0 1 402 -3.0147239565849304e-02 -1 -2 403
- 2.7548679709434509e-01
-
- 9.0275919437408447e-01 4.7198349237442017e-01
- 2.1969200670719147e-01
- <_>
-
- 1 0 404 3.6894630175083876e-03 -1 -2 405
- 7.2957701049745083e-03
-
- 6.2730091810226440e-01 4.8392179608345032e-01
- 6.9090622663497925e-01
- <_>
-
- 0 1 406 -5.6211069226264954e-02 -1 -2 407
- -2.6478560175746679e-03
-
- 1.7384879291057587e-01 6.3041448593139648e-01
- 4.4743019342422485e-01
- <_>
-
- 1 0 408 -1.4534000074490905e-03 -1 -2 409
- 2.8540920466184616e-03
-
- 5.3025382757186890e-01 5.3383970260620117e-01
- 3.7968829274177551e-01
- <_>
-
- 1 0 410 5.8243022067472339e-04 -1 -2 411
- 9.2509482055902481e-04
-
- 3.2698369026184082e-01 4.5548120141029358e-01
- 6.3583481311798096e-01
- <_>
- 47
- 2.2585290908813477e+01
-
- <_>
-
- 0 1 412 1.9806440919637680e-02 -1 -2 413
- 7.0395611692219973e-04
-
- 2.8097251057624817e-01 3.1198260188102722e-01
- 7.0903062820434570e-01
- <_>
-
- 0 1 414 2.5563780218362808e-03 -1 -2 415
- 1.0824160417541862e-03
-
- 2.9819479584693909e-01 3.0205601453781128e-01
- 5.8088111877441406e-01
- <_>
-
- 1 0 416 -9.2893769033253193e-04 -1 -2 417
- -1.8009729683399200e-02
-
- 3.7381029129028320e-01 2.1631260216236115e-01
- 6.6192537546157837e-01
- <_>
-
- 1 0 418 2.3500190582126379e-03 -1 -2 419
- 8.1822491483762860e-04
-
- 2.9104039072990417e-01 5.5786228179931641e-01
- 3.3666279911994934e-01
- <_>
-
- 0 1 420 6.2095321482047439e-04 -1 -2 421
- 9.6780969761312008e-04
-
- 4.0724259614944458e-01 6.8595957756042480e-01
- 3.1054618954658508e-01
- <_>
-
- 1 0 422 4.8000211245380342e-04 -1 -2 423
- 9.0538640506565571e-05
-
- 3.3373329043388367e-01 3.3709588646888733e-01
- 5.4512107372283936e-01
- <_>
-
- 0 1 424 -4.3914798647165298e-02 -1 -2 425
- -5.6501338258385658e-03
-
- 2.6256701350212097e-01 6.0504627227783203e-01
- 3.2324150204658508e-01
- <_>
-
- 1 0 426 3.8661491125822067e-03 -1 -2 427
- -6.3069426687434316e-05
-
- 3.2626131176948547e-01 5.8173078298568726e-01
- 4.1643899679183960e-01
- <_>
-
- 1 0 428 5.2533738315105438e-02 -1 -2 429
- 1.3818660518154502e-03
-
- 7.0953989028930664e-01 5.2928757667541504e-01
- 2.5413888692855835e-01
- <_>
-
- 1 0 430 -8.9264067355543375e-04 -1 -2 431
- 8.5579507052898407e-02
-
- 4.0853410959243774e-01 5.2632361650466919e-01
- 3.0032029747962952e-01
- <_>
-
- 1 0 432 -1.8343339615967125e-04 -1 -2 433
- -9.7924815490841866e-03
-
- 4.0292051434516907e-01 3.5213199257850647e-01
- 6.6640049219131470e-01
- <_>
-
- 0 1 434 1.4428620226681232e-02 -1 -2 435
- -4.5687001198530197e-02
-
- 4.5935660600662231e-01 1.4747560024261475e-01
- 5.1786321401596069e-01
- <_>
-
- 0 1 436 -2.5763090234249830e-03 -1 -2 437
- -3.8301859050989151e-02
-
- 1.8372780084609985e-01 8.0826580524444580e-01
- 5.1666879653930664e-01
- <_>
-
- 0 1 438 2.8978290501981974e-03 -1 -2 439
- -2.5165060069411993e-03
-
- 4.7980138659477234e-01 3.3462959527969360e-01
- 5.4444491863250732e-01
- <_>
-
- 0 1 440 5.6281982688233256e-04 -1 -2 441
- 3.6684391088783741e-03
-
- 3.5890269279479980e-01 5.9831297397613525e-01
- 2.9839640855789185e-01
- <_>
-
- 1 0 442 2.1319789811968803e-03 -1 -2 443
- 7.6037310063838959e-03
-
- 6.1632239818572998e-01 5.2171301841735840e-01
- 2.0541590452194214e-01
- <_>
-
- 1 0 444 -1.1668079969240353e-04 -1 -2 445
- 3.1659509986639023e-03
-
- 3.4466689825057983e-01 5.5974847078323364e-01
- 2.6737868785858154e-01
- <_>
-
- 0 1 446 -2.2569499909877777e-02 -1 -2 447
- 2.7129601221531630e-04
-
- 6.9002681970596313e-01 4.4866389036178589e-01
- 5.5087852478027344e-01
- <_>
-
- 0 1 448 -1.5434459783136845e-02 -1 -2 449
- -8.4861656650900841e-03
-
- 2.0483230054378510e-01 1.2549529969692230e-01
- 5.0603562593460083e-01
- <_>
-
- 0 1 450 -1.1807470023632050e-01 -1 -2 451
- -1.2300079688429832e-03
-
- 6.7633062601089478e-02 5.6607007980346680e-01
- 4.2922011017799377e-01
- <_>
-
- 0 1 452 -7.0290351286530495e-03 -1 -2 453
- 8.9325206354260445e-03
-
- 7.1364039182662964e-01 4.3388760089874268e-01
- 7.0608752965927124e-01
- <_>
-
- 1 0 454 -4.7735981643199921e-02 -1 -2 455
- -4.4155579060316086e-02
-
- 5.2686852216720581e-01 2.5805801153182983e-01
- 5.4069608449935913e-01
- <_>
-
- 0 1 456 -2.5983480736613274e-02 -1 -2 457
- -4.7885831445455551e-03
-
- 1.9050540030002594e-01 2.5518929958343506e-01
- 5.3390771150588989e-01
- <_>
-
- 0 1 458 6.7423451691865921e-03 -1 -2 459
- 1.1654750443994999e-02
-
- 4.6933099627494812e-01 5.2619642019271851e-01
- 3.1454348564147949e-01
- <_>
-
- 0 1 460 -5.6982729583978653e-03 -1 -2 461
- -7.2983349673449993e-03
-
- 1.7568530142307281e-01 7.7747297286987305e-01
- 5.1242929697036743e-01
- <_>
-
- 0 1 462 7.9091778025031090e-03 -1 -2 463
- -1.5874979726504534e-04
-
- 5.2845597267150879e-01 3.8878020644187927e-01
- 5.5011737346649170e-01
- <_>
-
- 0 1 464 -6.2235877849161625e-03 -1 -2 465
- 1.3308860361576080e-03
-
- 2.4898290634155273e-01 4.2621460556983948e-01
- 5.9350621700286865e-01
- <_>
-
- 1 0 466 5.2055278792977333e-03 -1 -2 467
- 1.4065169729292393e-02
-
- 2.5452229380607605e-01 4.8519900441169739e-01
- 7.0214188098907471e-01
- <_>
-
- 0 1 468 -6.7384149879217148e-03 -1 -2 469
- 3.3406780567020178e-03
-
- 7.1432709693908691e-01 5.1757252216339111e-01
- 2.8086438775062561e-01
- <_>
-
- 1 0 470 -1.1880699545145035e-02 -1 -2 471
- 1.4226379571482539e-03
-
- 5.1732218265533447e-01 4.5028659701347351e-01
- 5.7956951856613159e-01
- <_>
-
- 1 0 472 2.9858129564672709e-03 -1 -2 473
- -2.0481580868363380e-03
-
- 1.9151160120964050e-01 6.5024322271347046e-01
- 4.5593151450157166e-01
- <_>
-
- 0 1 474 1.7122729914262891e-03 -1 -2 475
- -1.6980869695544243e-02
-
- 5.3762471675872803e-01 7.0562332868576050e-01
- 4.9146059155464172e-01
- <_>
-
- 0 1 476 -1.1290470138192177e-03 -1 -2 477
- 2.8620059601962566e-03
-
- 2.6787060499191284e-01 4.4108539819717407e-01
- 6.3683199882507324e-01
- <_>
-
- 0 1 478 -3.8065758999437094e-03 -1 -2 479
- 5.9090270660817623e-03
-
- 2.7635639905929565e-01 4.8673018813133240e-01
- 6.7287760972976685e-01
- <_>
-
- 0 1 480 1.1004370171576738e-03 -1 -2 481
- -2.3396299220621586e-03
-
- 4.0705141425132751e-01 2.6049488782882690e-01
- 6.1548602581024170e-01
- <_>
-
- 0 1 482 -3.6068160552531481e-03 -1 -2 483
- 4.0831189602613449e-02
-
- 5.7319998741149902e-01 4.9733769893646240e-01
- 7.3870068788528442e-01
- <_>
-
- 0 1 484 -7.1082250215113163e-03 -1 -2 485
- -9.3759730225428939e-04
-
- 6.9847512245178223e-01 2.6911678910255432e-01
- 4.7417798638343811e-01
- <_>
-
- 0 1 486 -1.6740820137783885e-03 -1 -2 487
- 8.8287703692913055e-02
-
- 3.5510140657424927e-01 5.2446138858795166e-01
- 2.0966500043869019e-01
- <_>
-
- 0 1 488 8.2009629113599658e-04 -1 -2 489
- -7.6624617213383317e-04
-
- 4.1310968995094299e-01 4.6202930808067322e-01
- 6.7754101753234863e-01
- <_>
-
- 1 0 490 6.5769668435677886e-04 -1 -2 491
- -2.1304790861904621e-03
-
- 5.6282752752304077e-01 5.5768597126007080e-01
- 4.5776501297950745e-01
- <_>
-
- 1 0 492 -3.7317050737328827e-04 -1 -2 493
- -1.1172230355441570e-02
-
- 4.9592560529708862e-01 5.6256359815597534e-01
- 2.0471079647541046e-01
- <_>
-
- 1 0 494 4.3435219675302505e-02 -1 -2 495
- 9.6736161503940821e-04
-
- 2.2421480715274811e-01 4.5333439111709595e-01
- 6.1999320983886719e-01
- <_>
-
- 0 1 496 -3.1452889088541269e-03 -1 -2 497
- 1.5233129961416125e-03
-
- 6.6627562046051025e-01 5.0079882144927979e-01
- 2.3849929869174957e-01
- <_>
-
- 1 0 498 2.0854279864579439e-03 -1 -2 499
- 3.6098200827836990e-02
-
- 3.7535008788108826e-01 5.1771712303161621e-01
- 1.6344930231571198e-01
- <_>
-
- 1 0 500 1.6179570229724050e-03 -1 -2 501
- -6.2132300809025764e-04
-
- 2.5873818993568420e-01 6.2995338439941406e-01
- 4.6587899327278137e-01
- <_>
-
- 1 0 502 7.1878539165481925e-04 -1 -2 503
- -3.9339520037174225e-02
-
- 3.3540761470794678e-01 2.1541289985179901e-01
- 5.2357137203216553e-01
- <_>
-
- 0 1 504 -1.0988829890266061e-03 -1 -2 505
- 2.1191420964896679e-03
-
- 6.4688968658447266e-01 2.8930890560150146e-01
- 5.2548158168792725e-01
- <_>
- 53
- 2.5609300613403320e+01
-
- <_>
-
- 0 1 506 5.2359891124069691e-03 -1 -2 507
- -2.2169889416545630e-03
-
- 3.2997110486030579e-01 7.0415931940078735e-01
- 3.2354658842086792e-01
- <_>
-
- 1 0 508 -8.2303592935204506e-03 -1 -2 509
- -8.2303592935204506e-03
-
- 4.9611708521842957e-01 7.1280431747436523e-01
- 4.9611708521842957e-01
- <_>
-
- 0 1 510 4.5343261444941163e-04 -1 -2 511
- -4.1777061414904892e-04
-
- 3.2084721326828003e-01 6.6139167547225952e-01
- 3.5513329505920410e-01
- <_>
-
- 0 1 512 2.7823769487440586e-03 -1 -2 513
- -6.0361868236213923e-05
-
- 3.7101349234580994e-01 5.7463937997817993e-01
- 3.8948801159858704e-01
- <_>
-
- 1 0 514 3.5061789676547050e-03 -1 -2 515
- 1.7013119941111654e-04
-
- 3.0541029572486877e-01 2.8855779767036438e-01
- 6.4877450466156006e-01
- <_>
-
- 1 0 516 -2.3378930054605007e-03 -1 -2 517
- -2.1369170863181353e-03
-
- 3.1744310259819031e-01 3.8209199905395508e-01
- 5.2328932285308838e-01
- <_>
-
- 0 1 518 1.0250400518998504e-03 -1 -2 519
- -4.4726220949087292e-05
-
- 3.6227950453758240e-01 6.5389591455459595e-01
- 4.0036809444427490e-01
- <_>
-
- 1 0 520 5.7102291611954570e-04 -1 -2 521
- 5.7743012439459562e-04
-
- 3.8931730389595032e-01 5.6145328283309937e-01
- 3.6876440048217773e-01
- <_>
-
- 1 0 522 7.9692091094329953e-04 -1 -2 523
- 3.5945948911830783e-04
-
- 6.4430278539657593e-01 3.3808529376983643e-01
- 5.8246481418609619e-01
- <_>
-
- 1 0 524 4.3973900028504431e-04 -1 -2 525
- -8.9061429025605321e-04
-
- 3.9387670159339905e-01 3.4279710054397583e-01
- 5.5156987905502319e-01
- <_>
-
- 1 0 526 5.4110242053866386e-03 -1 -2 527
- -8.5764907998964190e-04
-
- 3.8035380840301514e-01 6.4395052194595337e-01
- 4.1683459281921387e-01
- <_>
-
- 0 1 528 -2.2000649943947792e-02 -1 -2 529
- -7.8731682151556015e-03
-
- 6.6546010971069336e-01 4.1827228665351868e-01
- 5.6047242879867554e-01
- <_>
-
- 0 1 530 -2.7444459497928619e-02 -1 -2 531
- 1.9792269449681044e-03
-
- 6.5868628025054932e-01 3.2449120283126831e-01
- 4.8828700184822083e-01
- <_>
-
- 0 1 532 -5.6783691979944706e-03 -1 -2 533
- 1.5057219570735469e-05
-
- 2.2290790081024170e-01 4.1072851419448853e-01
- 5.7475912570953369e-01
- <_>
-
- 0 1 534 -5.4136710241436958e-03 -1 -2 535
- 5.3679239936172962e-03
-
- 2.0657970011234283e-01 4.9264231324195862e-01
- 7.1394848823547363e-01
- <_>
-
- 0 1 536 -3.1426660716533661e-03 -1 -2 537
- 1.0907390154898167e-02
-
- 6.7800867557525635e-01 5.2149301767349243e-01
- 1.1439959704875946e-01
- <_>
-
- 1 0 538 5.8436761610209942e-03 -1 -2 539
- 9.0507230197545141e-05
-
- 1.9375260174274445e-01 3.8125771284103394e-01
- 5.5141878128051758e-01
- <_>
-
- 0 1 540 -1.6345789656043053e-02 -1 -2 541
- 1.5987500082701445e-03
-
- 2.4740239977836609e-01 4.8177829384803772e-01
- 5.9230798482894897e-01
- <_>
-
- 0 1 542 -4.0257978253066540e-03 -1 -2 543
- -6.7750471644103527e-03
-
- 7.5082087516784668e-01 2.8798109292984009e-01
- 5.1996952295303345e-01
- <_>
-
- 0 1 544 -3.2470689620822668e-03 -1 -2 545
- 1.5409620245918632e-03
-
- 3.0449101328849792e-01 4.0634828805923462e-01
- 5.6765627861022949e-01
- <_>
-
- 0 1 546 -1.2858119793236256e-02 -1 -2 547
- -1.4824670506641269e-04
-
- 9.6717558801174164e-02 4.5378330349922180e-01
- 6.1153751611709595e-01
- <_>
-
- 1 0 548 -9.0210810303688049e-03 -1 -2 549
- -2.8795029968023300e-02
-
- 4.8077508807182312e-01 3.4037950634956360e-01
- 5.2555292844772339e-01
- <_>
-
- 1 0 550 9.0210810303688049e-03 -1 -2 551
- 7.4121179059147835e-03
-
- 7.5058358907699585e-01 5.4554468393325806e-01
- 3.2260689139366150e-01
- <_>
-
- 0 1 552 -3.7217529024928808e-03 -1 -2 553
- 1.9865889847278595e-01
-
- 2.3118489980697632e-01 5.2710479497909546e-01
- 1.4699299633502960e-01
- <_>
-
- 0 1 554 1.5208719560177997e-05 -1 -2 555
- -3.9089918136596680e-03
-
- 3.6781388521194458e-01 7.1319299936294556e-01
- 4.9938669800758362e-01
- <_>
-
- 0 1 556 2.5106288958340883e-03 -1 -2 557
- 2.3921660613268614e-04
-
- 5.3120541572570801e-01 4.6893781423568726e-01
- 5.7140219211578369e-01
- <_>
-
- 1 0 558 6.9443131797015667e-03 -1 -2 559
- 1.2065629707649350e-03
-
- 6.9487977027893066e-01 4.0045049786567688e-01
- 5.8748817443847656e-01
- <_>
-
- 0 1 560 2.5106288958340883e-03 -1 -2 561
- 1.7514040227979422e-03
-
- 5.3295719623565674e-01 5.5458492040634155e-01
- 3.4495818614959717e-01
- <_>
-
- 0 1 562 -4.1978210210800171e-03 -1 -2 563
- 1.3092850567772985e-03
-
- 1.2171830236911774e-01 5.3750497102737427e-01
- 3.4156250953674316e-01
- <_>
-
- 0 1 564 6.7396182566881180e-04 -1 -2 565
- -1.0530710220336914e-02
-
- 4.1951790452003479e-01 3.4607538580894470e-01
- 5.1558601856231689e-01
- <_>
-
- 0 1 566 -4.0672299265861511e-01 -1 -2 567
- -2.6314549148082733e-02
-
- 5.8065678924322128e-02 1.4734490215778351e-01
- 5.5593782663345337e-01
- <_>
-
- 1 0 568 2.2557149641215801e-03 -1 -2 569
- 1.2154860422015190e-02
-
- 5.4777151346206665e-01 4.2077910900115967e-01
- 5.6218808889389038e-01
- <_>
-
- 0 1 570 -1.8436539918184280e-02 -1 -2 571
- 5.3676147945225239e-04
-
- 6.4471471309661865e-01 2.7651271224021912e-01
- 4.8885959386825562e-01
- <_>
-
- 1 0 572 -2.6265541091561317e-03 -1 -2 573
- -5.1119807176291943e-04
-
- 5.2646911144256592e-01 5.7853102684020996e-01
- 4.2911028861999512e-01
- <_>
-
- 1 0 574 4.1454841266386211e-04 -1 -2 575
- -5.5028748465701938e-04
-
- 3.4554108977317810e-01 6.0269188880920410e-01
- 4.1438931226730347e-01
- <_>
-
- 0 1 576 -1.0347720235586166e-03 -1 -2 577
- -3.3966631162911654e-03
-
- 6.0952937602996826e-01 6.1082822084426880e-01
- 4.7077208757400513e-01
- <_>
-
- 1 0 578 3.1795909162610769e-03 -1 -2 579
- -1.6528950072824955e-04
-
- 3.2443669438362122e-01 3.8307571411132812e-01
- 5.7343262434005737e-01
- <_>
-
- 1 0 580 8.3725210279226303e-03 -1 -2 581
- -2.5799809955060482e-03
-
- 6.6109192371368408e-01 6.1393070220947266e-01
- 4.6861499547958374e-01
- <_>
-
- 1 0 582 9.0194388758391142e-04 -1 -2 583
- 3.6952210939489305e-04
-
- 3.5200220346450806e-01 2.5787541270256042e-01
- 5.4672420024871826e-01
- <_>
-
- 0 1 584 9.9746137857437134e-04 -1 -2 585
- -3.6688039544969797e-03
-
- 4.8201468586921692e-01 5.7101500034332275e-01
- 4.8319110274314880e-01
- <_>
-
- 0 1 586 -8.9501030743122101e-04 -1 -2 587
- 5.1904921419918537e-03
-
- 6.1336791515350342e-01 4.9285829067230225e-01
- 2.5813090801239014e-01
- <_>
-
- 0 1 588 4.2274440056644380e-04 -1 -2 589
- 8.5176713764667511e-03
-
- 4.4711241126060486e-01 5.1610249280929565e-01
- 3.3165338635444641e-01
- <_>
-
- 0 1 590 -3.6623608320951462e-02 -1 -2 591
- -4.1103712283074856e-03
-
- 9.2606216669082642e-02 8.5221147537231445e-01
- 5.1379078626632690e-01
- <_>
-
- 1 0 592 -6.6017331555485725e-03 -1 -2 593
- 2.5578640401363373e-02
-
- 5.4590600728988647e-01 5.2193528413772583e-01
- 1.9271859526634216e-01
- <_>
-
- 1 0 594 1.1447439901530743e-02 -1 -2 595
- 7.2427501436322927e-04
-
- 1.9160020351409912e-01 5.2315711975097656e-01
- 3.5353401303291321e-01
- <_>
-
- 1 0 596 9.7127500921487808e-03 -1 -2 597
- -1.1337569914758205e-02
-
- 6.4641010761260986e-01 7.3830378055572510e-01
- 4.9647438526153564e-01
- <_>
-
- 0 1 598 -8.1453882157802582e-03 -1 -2 599
- -8.5570756345987320e-03
-
- 3.6117058992385864e-01 3.4219071269035339e-01
- 5.9435117244720459e-01
- <_>
-
- 0 1 600 2.2993308957666159e-03 -1 -2 601
- 3.8430930580943823e-03
-
- 4.5501041412353516e-01 4.7168621420860291e-01
- 6.6561907529830933e-01
- <_>
-
- 1 0 602 -9.9116540513932705e-04 -1 -2 603
- 2.5496469810605049e-02
-
- 4.5927169919013977e-01 6.5634012222290039e-01
- 1.2588350474834442e-01
- <_>
-
- 1 0 604 -1.5748359262943268e-02 -1 -2 605
- -1.8046120181679726e-02
-
- 5.2395021915435791e-01 8.0158519744873047e-01
- 5.0079578161239624e-01
- <_>
-
- 1 0 606 1.0323390364646912e-02 -1 -2 607
- 1.6452240524813533e-03
-
- 2.2748200595378876e-01 4.3519461154937744e-01
- 5.8676278591156006e-01
- <_>
-
- 0 1 608 1.5881149098277092e-02 -1 -2 609
- 1.0586519725620747e-02
-
- 4.4650518894195557e-01 4.5444580912590027e-01
- 5.7071107625961304e-01
- <_>
-
- 0 1 610 -2.1531689912080765e-02 -1 -2 611
- 5.2480469457805157e-03
-
- 6.5276437997817993e-01 3.4447279572486877e-01
- 5.3246361017227173e-01
- <_>
- 67
- 3.2647129058837891e+01
-
- <_>
-
- 0 1 612 1.8219340126961470e-03 -1 -2 613
- 8.1313941627740860e-03
-
- 3.1087881326675415e-01 3.1332370638847351e-01
- 6.6458672285079956e-01
- <_>
-
- 0 1 614 1.7055979697033763e-03 -1 -2 615
- -7.4483548814896494e-05
-
- 2.6401311159133911e-01 5.6472051143646240e-01
- 3.4853729605674744e-01
- <_>
-
- 1 0 616 3.8342390325851738e-04 -1 -2 617
- 3.1868910882622004e-03
-
- 3.1406548619270325e-01 6.4891988039016724e-01
- 3.8877290487289429e-01
- <_>
-
- 1 0 618 1.6044320166110992e-01 -1 -2 619
- -6.7285560071468353e-03
-
- 7.2165298461914062e-01 1.6531379520893097e-01
- 5.1398259401321411e-01
- <_>
-
- 0 1 620 7.2638481469766703e-06 -1 -2 621
- 5.5551197146996856e-04
-
- 3.1406199932098389e-01 5.9936988353729248e-01
- 3.3173981308937073e-01
- <_>
-
- 0 1 622 -1.0822320356965065e-02 -1 -2 623
- -4.5834020711481571e-03
-
- 2.6529380679130554e-01 1.8495689332485199e-01
- 5.3139579296112061e-01
- <_>
-
- 1 0 624 -3.0205070506781340e-03 -1 -2 625
- 7.7864617109298706e-02
-
- 4.0400999784469604e-01 6.1581897735595703e-01
- 1.7864869534969330e-01
- <_>
-
- 0 1 626 2.6494380086660385e-02 -1 -2 627
- 3.6912109702825546e-02
-
- 4.5110899209976196e-01 4.5282199978828430e-01
- 5.9722828865051270e-01
- <_>
-
- 1 0 628 5.7857790961861610e-03 -1 -2 629
- 9.3849771656095982e-04
-
- 2.5338920950889587e-01 3.4104120731353760e-01
- 5.9236437082290649e-01
- <_>
-
- 0 1 630 -1.1003199964761734e-02 -1 -2 631
- -1.1737640015780926e-03
-
- 6.9580441713333130e-01 3.8510841131210327e-01
- 5.4081892967224121e-01
- <_>
-
- 0 1 632 -3.6596669815480709e-03 -1 -2 633
- -2.4822750128805637e-03
-
- 2.0093089342117310e-01 6.2953931093215942e-01
- 4.3950408697128296e-01
- <_>
-
- 0 1 634 -4.4606071896851063e-03 -1 -2 635
- -3.5969649907201529e-03
-
- 2.4052999913692474e-01 5.4501742124557495e-01
- 3.7823578715324402e-01
- <_>
-
- 0 1 636 -3.6222559865564108e-03 -1 -2 637
- 1.2059339787811041e-03
-
- 3.0338969826698303e-01 4.6337789297103882e-01
- 6.3359522819519043e-01
- <_>
-
- 1 0 638 4.3124938383698463e-03 -1 -2 639
- -4.4961250387132168e-03
-
- 6.5988260507583618e-01 6.6216969490051270e-01
- 4.7552469372749329e-01
- <_>
-
- 0 1 640 -1.3860689941793680e-03 -1 -2 641
- -5.1588460337370634e-04
-
- 2.8012010455131531e-01 3.8294890522956848e-01
- 5.6236267089843750e-01
- <_>
-
- 0 1 642 7.0330002927221358e-05 -1 -2 643
- -2.0976549421902746e-04
-
- 4.5363429188728333e-01 5.6081390380859375e-01
- 4.2657798528671265e-01
- <_>
-
- 1 0 644 1.3642259873449802e-03 -1 -2 645
- 1.5483660390600562e-03
-
- 2.6370918750762939e-01 4.1707509756088257e-01
- 5.9329879283905029e-01
- <_>
-
- 0 1 646 1.9179609417915344e-01 -1 -2 647
- -4.4776909053325653e-03
-
- 5.2567642927169800e-01 6.6326218843460083e-01
- 4.8925888538360596e-01
- <_>
-
- 0 1 648 -1.2649179995059967e-01 -1 -2 649
- 6.5253327193204314e-05
-
- 1.4997789263725281e-01 4.2333200573921204e-01
- 5.7560402154922485e-01
- <_>
-
- 0 1 650 4.1856421157717705e-03 -1 -2 651
- 2.7478230185806751e-04
-
- 5.2888268232345581e-01 4.5240178704261780e-01
- 5.6041252613067627e-01
- <_>
-
- 0 1 652 -2.2906810045242310e-03 -1 -2 653
- 1.6744500026106834e-03
-
- 5.5782741308212280e-01 3.3230578899383545e-01
- 5.5587881803512573e-01
- <_>
-
- 1 0 654 1.2349759927019477e-03 -1 -2 655
- -8.7158754467964172e-03
-
- 3.6539471149444580e-01 1.9245339930057526e-01
- 5.3136497735977173e-01
- <_>
-
- 1 0 656 4.6613621525466442e-03 -1 -2 657
- -8.5815992206335068e-03
-
- 2.0277309417724609e-01 7.6360601186752319e-01
- 5.1408261060714722e-01
- <_>
-
- 0 1 658 1.4352120459079742e-02 -1 -2 659
- -7.7948719263076782e-03
-
- 5.2529758214950562e-01 2.6329371333122253e-01
- 5.3286892175674438e-01
- <_>
-
- 0 1 660 -3.4155680332332850e-03 -1 -2 661
- -4.2639090679585934e-03
-
- 2.4160879850387573e-01 3.9365449547767639e-01
- 5.4787421226501465e-01
- <_>
-
- 0 1 662 8.7177697569131851e-03 -1 -2 663
- -3.2232629600912333e-03
-
- 4.7881990671157837e-01 3.6316120624542236e-01
- 5.2883160114288330e-01
- <_>
-
- 0 1 664 -4.2188368737697601e-02 -1 -2 665
- 1.9875749945640564e-02
-
- 6.9311392307281494e-01 4.5201000571250916e-01
- 6.8550550937652588e-01
- <_>
-
- 1 0 666 -3.1134510412812233e-02 -1 -2 667
- 5.7032387703657150e-03
-
- 5.3004240989685059e-01 5.6068921089172363e-01
- 4.2306229472160339e-01
- <_>
-
- 1 0 668 5.2733682096004486e-03 -1 -2 669
- -3.1231069006025791e-03
-
- 3.2472288608551025e-01 1.9856959581375122e-01
- 5.3498727083206177e-01
- <_>
-
- 0 1 670 4.6453849063254893e-04 -1 -2 671
- 3.0355889350175858e-02
-
- 4.2075088620185852e-01 5.1534587144851685e-01
- 3.1181010603904724e-01
- <_>
-
- 0 1 672 -4.2992769740521908e-03 -1 -2 673
- 1.9509199773892760e-04
-
- 3.2745069265365601e-01 5.9530782699584961e-01
- 4.2255210876464844e-01
- <_>
-
- 0 1 674 -7.7784480527043343e-03 -1 -2 675
- 1.6917599365115166e-02
-
- 7.2111797332763672e-01 4.9365919828414917e-01
- 7.0302772521972656e-01
- <_>
-
- 0 1 676 -5.1948569715023041e-02 -1 -2 677
- -5.4751220159232616e-03
-
- 1.4255349338054657e-01 6.0593318939208984e-01
- 4.3939951062202454e-01
- <_>
-
- 0 1 678 1.5210839592327829e-05 -1 -2 679
- 1.0235579684376717e-03
-
- 4.4888499379158020e-01 4.2565500736236572e-01
- 5.7954382896423340e-01
- <_>
-
- 0 1 680 -1.0427719826111570e-04 -1 -2 681
- 8.7853781878948212e-03
-
- 4.2460399866104126e-01 4.9580091238021851e-01
- 6.7594307661056519e-01
- <_>
-
- 0 1 682 3.4012699034065008e-03 -1 -2 683
- 5.8582378551363945e-04
-
- 5.4234808683395386e-01 3.6365428566932678e-01
- 5.4643487930297852e-01
- <_>
-
- 0 1 684 -2.2973360028117895e-03 -1 -2 685
- -1.4330189675092697e-02
-
- 2.5488188862800598e-01 6.5876567363739014e-01
- 4.5328021049499512e-01
- <_>
-
- 0 1 686 9.8565965890884399e-04 -1 -2 687
- -4.6640761196613312e-02
-
- 3.8227710127830505e-01 3.0773219466209412e-01
- 5.2441328763961792e-01
- <_>
-
- 0 1 688 -1.1907300353050232e-01 -1 -2 689
- 1.9333280622959137e-02
-
- 1.0338629782199860e-01 5.5547451972961426e-01
- 3.2213169336318970e-01
- <_>
-
- 0 1 690 3.1427849084138870e-02 -1 -2 691
- 2.0082130504306406e-04
-
- 4.6823790669441223e-01 5.3730702400207520e-01
- 3.8006669282913208e-01
- <_>
-
- 0 1 692 -6.2584900297224522e-03 -1 -2 693
- 8.2861045375466347e-03
-
- 1.7992070317268372e-01 5.0950688123703003e-01
- 7.5446051359176636e-01
- <_>
-
- 0 1 694 2.0529709290713072e-03 -1 -2 695
- 3.2524869311600924e-03
-
- 5.6286448240280151e-01 4.8016890883445740e-01
- 5.8021020889282227e-01
- <_>
-
- 0 1 696 -3.1884901225566864e-02 -1 -2 697
- 1.8379340181127191e-03
-
- 1.7427450418472290e-01 3.4665969014167786e-01
- 5.1071548461914062e-01
- <_>
-
- 1 0 698 -4.8512680223211646e-04 -1 -2 699
- -2.5407879147678614e-03
-
- 5.3260862827301025e-01 6.3427752256393433e-01
- 4.9926930665969849e-01
- <_>
-
- 0 1 700 -5.1559060811996460e-03 -1 -2 701
- -4.4968750327825546e-02
-
- 3.4334290027618408e-01 1.8681369721889496e-01
- 5.2154648303985596e-01
- <_>
-
- 1 0 702 5.8984281495213509e-03 -1 -2 703
- 3.2763120252639055e-03
-
- 6.2293052673339844e-01 4.9357721209526062e-01
- 7.2179448604583740e-01
- <_>
-
- 1 0 704 -1.0161520185647532e-04 -1 -2 705
- -1.6290300118271261e-04
-
- 5.0079762935638428e-01 6.0241490602493286e-01
- 2.3295080661773682e-01
- <_>
-
- 0 1 706 9.0541364625096321e-03 -1 -2 707
- 3.5398490726947784e-02
-
- 4.5104169845581055e-01 5.1419967412948608e-01
- 2.8602918982505798e-01
- <_>
-
- 0 1 708 5.6469351984560490e-03 -1 -2 709
- -2.4807190056890249e-03
-
- 4.7049251198768616e-01 4.1798511147499084e-01
- 6.7266470193862915e-01
- <_>
-
- 0 1 710 -4.1088787838816643e-03 -1 -2 711
- -2.0714469719678164e-03
-
- 5.8098018169403076e-01 6.0747838020324707e-01
- 4.5240598917007446e-01
- <_>
-
- 0 1 712 -2.8939060866832733e-03 -1 -2 713
- 1.3467279495671391e-03
-
- 3.3835199475288391e-01 5.6969100236892700e-01
- 3.9708450436592102e-01
- <_>
-
- 0 1 714 -9.0779133141040802e-02 -1 -2 715
- -8.3171762526035309e-02
-
- 1.5027019381523132e-01 7.5736707448959351e-01
- 4.9364370107650757e-01
- <_>
-
- 0 1 716 -1.4107000315561891e-03 -1 -2 717
- 5.5668760091066360e-02
-
- 3.3909329771995544e-01 5.0250971317291260e-01
- 7.4220830202102661e-01
- <_>
-
- 0 1 718 5.7701539248228073e-02 -1 -2 719
- -4.2503291368484497e-01
-
- 5.1973718404769897e-01 9.7346916794776917e-02
- 5.1857399940490723e-01
- <_>
-
- 0 1 720 -4.4380719191394746e-04 -1 -2 721
- 1.7924769781529903e-04
-
- 3.6493501067161560e-01 5.6192791461944580e-01
- 3.7602970004081726e-01
- <_>
-
- 1 0 722 5.0382469780743122e-03 -1 -2 723
- 1.5191170386970043e-02
-
- 6.3284450769424438e-01 4.9360820651054382e-01
- 7.4265247583389282e-01
- <_>
-
- 0 1 724 -1.2300389818847179e-02 -1 -2 725
- 1.5168030513450503e-03
-
- 1.3893499970436096e-01 5.0919622182846069e-01
- 3.4826481342315674e-01
- <_>
-
- 1 0 726 9.5754547510296106e-04 -1 -2 727
- -1.8962200731039047e-02
-
- 6.0363167524337769e-01 2.3191730678081512e-01
- 5.1166528463363647e-01
- <_>
-
- 0 1 728 -2.2272260859608650e-02 -1 -2 729
- -2.5145230814814568e-02
-
- 6.5550220012664795e-01 1.3260710239410400e-01
- 4.6740341186523438e-01
- <_>
-
- 0 1 730 1.9533900544047356e-02 -1 -2 731
- -1.1231349781155586e-03
-
- 5.1820272207260132e-01 6.3182431459426880e-01
- 4.8255190253257751e-01
- <_>
-
- 0 1 732 -1.4861139934509993e-03 -1 -2 733
- 3.5002888762392104e-04
-
- 2.9186710715293884e-01 5.6213712692260742e-01
- 4.2492130398750305e-01
- <_>
-
- 1 0 734 -1.1231349781155586e-03 -1 -2 735
- 1.0409739799797535e-02
-
- 4.8137450218200684e-01 5.1840060949325562e-01
- 2.0512230694293976e-01
- <_>
-
- 0 1 736 -8.7832562625408173e-02 -1 -2 737
- 1.6584879485890269e-03
-
- 1.1799219995737076e-01 4.9878111481666565e-01
- 6.9737559556961060e-01
- <_>
-
- 1 0 738 -2.3008750285953283e-03 -1 -2 739
- 3.3026169985532761e-02
-
- 5.3398311138153076e-01 5.0332891941070557e-01
- 6.8519067764282227e-01
- <_>
-
- 0 1 740 -1.3585069682449102e-03 -1 -2 741
- 7.8067491995170712e-04
-
- 3.0028221011161804e-01 4.5930838584899902e-01
- 6.4400452375411987e-01
- <_>
-
- 1 0 742 -1.8025759607553482e-02 -1 -2 743
- 1.2354910140857100e-03
-
- 5.3112912178039551e-01 4.7291061282157898e-01
- 5.7214611768722534e-01
- <_>
-
- 0 1 744 -9.2583027435466647e-04 -1 -2 745
- 8.0123997759073973e-04
-
- 3.6623328924179077e-01 5.3619897365570068e-01
- 3.0086329579353333e-01
- <_>
- 63
- 3.0672130584716797e+01
-
- <_>
-
- 0 1 746 2.4914839304983616e-03 -1 -2 747
- -5.0488598644733429e-02
-
- 3.4223890304565430e-01 7.7034580707550049e-01
- 4.5163908600807190e-01
- <_>
-
- 1 0 748 -7.7838351717218757e-04 -1 -2 749
- 2.3572890495415777e-04
-
- 3.2563421130180359e-01 3.4065559506416321e-01
- 5.8970272541046143e-01
- <_>
-
- 0 1 750 4.5575071126222610e-03 -1 -2 751
- 8.1241987645626068e-03
-
- 4.3065789341926575e-01 7.1495872735977173e-01
- 4.3456849455833435e-01
- <_>
-
- 0 1 752 -4.4612158671952784e-04 -1 -2 753
- -2.8972938889637589e-04
-
- 3.2959741353988647e-01 5.8456200361251831e-01
- 3.5266879200935364e-01
- <_>
-
- 0 1 754 7.1604831646254752e-06 -1 -2 755
- -3.8497708737850189e-04
-
- 4.0819549560546875e-01 4.2031130194664001e-01
- 6.6341269016265869e-01
- <_>
-
- 0 1 756 1.9489860278554261e-04 -1 -2 757
- -1.7083849757909775e-02
-
- 3.9424669742584229e-01 2.2940720617771149e-01
- 5.2389609813690186e-01
- <_>
-
- 0 1 758 8.3513697609305382e-04 -1 -2 759
- 7.5499608647078276e-04
-
- 3.0260318517684937e-01 6.0321962833404541e-01
- 3.4124588966369629e-01
- <_>
-
- 1 0 760 8.0216713249683380e-03 -1 -2 761
- -3.8930509239435196e-02
-
- 7.3062407970428467e-01 3.5993251204490662e-01
- 5.2343809604644775e-01
- <_>
-
- 1 0 762 -7.0348767621908337e-05 -1 -2 763
- -8.5350573062896729e-03
-
- 3.4937581419944763e-01 2.7461090683937073e-01
- 5.6265860795974731e-01
- <_>
-
- 0 1 764 1.0854450054466724e-02 -1 -2 765
- 4.5329501153901219e-04
-
- 5.2822262048721313e-01 4.5220491290092468e-01
- 6.0543018579483032e-01
- <_>
-
- 0 1 766 1.8117150466423482e-04 -1 -2 767
- 4.6641560038551688e-04
-
- 3.3068621158599854e-01 1.4550000429153442e-01
- 5.3849279880523682e-01
- <_>
-
- 1 0 768 -8.4854792803525925e-03 -1 -2 769
- -1.8934309482574463e-02
-
- 4.8141559958457947e-01 3.5637411475181580e-01
- 5.4051452875137329e-01
- <_>
-
- 1 0 770 4.9814549274742603e-03 -1 -2 771
- 3.4286780282855034e-03
-
- 6.9577431678771973e-01 5.0508928298950195e-01
- 2.3169949650764465e-01
- <_>
-
- 1 0 772 4.4203791185282171e-04 -1 -2 773
- 2.3822550429031253e-04
-
- 6.0185819864273071e-01 4.7550821304321289e-01
- 5.5852377414703369e-01
- <_>
-
- 0 1 774 -6.4261639490723610e-03 -1 -2 775
- 9.9637769162654877e-03
-
- 2.2824659943580627e-01 4.0405881404876709e-01
- 5.6501698493957520e-01
- <_>
-
- 0 1 776 1.3654050417244434e-02 -1 -2 777
- -9.9892877042293549e-03
-
- 5.2677392959594727e-01 6.7940497398376465e-01
- 4.7970339655876160e-01
- <_>
-
- 1 0 778 3.6558631807565689e-02 -1 -2 779
- 4.8999379941960797e-05
-
- 8.8425733149051666e-02 4.0207880735397339e-01
- 5.4573321342468262e-01
- <_>
-
- 0 1 780 1.3654050417244434e-02 -1 -2 781
- 1.8802779959514737e-03
-
- 5.2676129341125488e-01 4.8060521483421326e-01
- 6.3943648338317871e-01
- <_>
-
- 0 1 782 -1.3654050417244434e-02 -1 -2 783
- 1.2778700329363346e-03
-
- 1.7248100042343140e-01 4.4798240065574646e-01
- 6.3100087642669678e-01
- <_>
-
- 1 0 784 9.8843395244330168e-04 -1 -2 785
- 1.4511500012304168e-05
-
- 5.9481692314147949e-01 4.8541748523712158e-01
- 5.3093612194061279e-01
- <_>
-
- 0 1 786 -2.2775429533794522e-04 -1 -2 787
- -1.4753740280866623e-02
-
- 3.1836318969726562e-01 3.0849760770797729e-01
- 5.3520262241363525e-01
- <_>
-
- 0 1 788 -3.4148250706493855e-03 -1 -2 789
- 7.5806681998074055e-03
-
- 6.1153268814086914e-01 4.9516460299491882e-01
- 7.0613312721252441e-01
- <_>
-
- 1 0 790 -5.7734688743948936e-03 -1 -2 791
- 7.4033669079653919e-05
-
- 3.7542209029197693e-01 4.1155171394348145e-01
- 5.8894449472427368e-01
- <_>
-
- 0 1 792 -8.2278084009885788e-03 -1 -2 793
- 5.3380909375846386e-03
-
- 9.5610566437244415e-02 5.3005087375640869e-01
- 3.9618980884552002e-01
- <_>
-
- 0 1 794 -2.7049109339714050e-03 -1 -2 795
- 7.7341338619589806e-03
-
- 6.4818692207336426e-01 5.1104402542114258e-01
- 3.1215190887451172e-01
- <_>
-
- 0 1 796 1.0886609554290771e-02 -1 -2 797
- 1.1038660071790218e-02
-
- 4.8014289140701294e-01 5.4297101497650146e-01
- 4.1623631119728088e-01
- <_>
-
- 0 1 798 -1.0054199956357479e-02 -1 -2 799
- 7.7072880230844021e-03
-
- 7.3293352127075195e-01 5.3568720817565918e-01
- 3.4555470943450928e-01
- <_>
-
- 0 1 800 -5.8278098003938794e-04 -1 -2 801
- -2.5739220436662436e-03
-
- 3.6550220847129822e-01 3.7767601013183594e-01
- 5.3917747735977173e-01
- <_>
-
- 0 1 802 -7.0167761296033859e-03 -1 -2 803
- -1.7727289814502001e-03
-
- 4.0393048524856567e-01 6.9504439830780029e-01
- 4.9811169505119324e-01
- <_>
-
- 1 0 804 -1.6318289563059807e-02 -1 -2 805
- -1.1663000099360943e-02
-
- 5.2967327833175659e-01 5.8426398038864136e-01
- 4.7895029187202454e-01
- <_>
-
- 1 0 806 2.5881489273160696e-03 -1 -2 807
- -3.7328999023884535e-03
-
- 6.0921788215637207e-01 6.7217427492141724e-01
- 4.0668940544128418e-01
- <_>
-
- 0 1 808 -1.4355930034071207e-03 -1 -2 809
- 1.8340899841859937e-03
-
- 3.5850879549980164e-01 5.3711581230163574e-01
- 4.0335071086883545e-01
- <_>
-
- 1 0 810 1.2280289828777313e-01 -1 -2 811
- 5.0228700041770935e-02
-
- 1.5475720167160034e-01 5.4338437318801880e-01
- 8.4292672574520111e-02
- <_>
-
- 1 0 812 -2.1437000483274460e-02 -1 -2 813
- -3.1009620055556297e-02
-
- 4.8600539565086365e-01 1.8330100178718567e-01
- 5.2075541019439697e-01
- <_>
-
- 0 1 814 -1.2973720207810402e-02 -1 -2 815
- 1.5818020328879356e-03
-
- 7.0482409000396729e-01 4.1705870628356934e-01
- 5.8651638031005859e-01
- <_>
-
- 1 0 816 -9.7806248813867569e-03 -1 -2 817
- 1.1735740117728710e-03
-
- 5.3079181909561157e-01 5.5224531888961792e-01
- 3.5071650147438049e-01
- <_>
-
- 1 0 818 1.4651629608124495e-03 -1 -2 819
- 2.3532148916274309e-03
-
- 3.0426511168479919e-01 5.3393232822418213e-01
- 2.8062361478805542e-01
- <_>
-
- 0 1 820 -6.1809681355953217e-03 -1 -2 821
- 6.5688649192452431e-04
-
- 6.4101332426071167e-01 5.6208711862564087e-01
- 4.3903189897537231e-01
- <_>
-
- 1 0 822 2.6228010654449463e-02 -1 -2 823
- -1.7958110198378563e-02
-
- 6.4455568790435791e-01 2.0027139782905579e-01
- 4.6246650815010071e-01
- <_>
-
- 1 0 824 -7.6468721963465214e-03 -1 -2 825
- -2.7482809964567423e-03
-
- 5.2632009983062744e-01 5.8739811182022095e-01
- 4.8366001248359680e-01
- <_>
-
- 1 0 826 1.3851850293576717e-02 -1 -2 827
- 2.6369190309196711e-03
-
- 1.5661309659481049e-01 4.2701789736747742e-01
- 5.8066600561141968e-01
- <_>
-
- 0 1 828 -3.1513599678874016e-03 -1 -2 829
- -1.4788460248382762e-05
-
- 6.2158662080764771e-01 5.5766427516937256e-01
- 4.1220021247863770e-01
- <_>
-
- 0 1 830 -7.3676988482475281e-02 -1 -2 831
- -3.0912780202925205e-03
-
- 1.5367099642753601e-01 6.3442689180374146e-01
- 4.5074120163917542e-01
- <_>
-
- 0 1 832 7.9240966588258743e-03 -1 -2 833
- 8.5778040811419487e-03
-
- 5.4579752683639526e-01 5.4016572237014771e-01
- 3.8907998800277710e-01
- <_>
-
- 1 0 834 5.5403169244527817e-03 -1 -2 835
- -1.1886510037584230e-04
-
- 3.5556110739707947e-01 5.8367502689361572e-01
- 4.2743161320686340e-01
- <_>
-
- 0 1 836 -1.8408369272947311e-02 -1 -2 837
- -2.3490579333156347e-03
-
- 5.8604401350021362e-01 4.4989579916000366e-01
- 5.4981988668441772e-01
- <_>
-
- 1 0 838 -7.6157399453222752e-03 -1 -2 839
- -3.3190969843417406e-03
-
- 4.1009929776191711e-01 6.7013788223266602e-01
- 4.3530011177062988e-01
- <_>
-
- 1 0 840 -9.4642979092895985e-04 -1 -2 841
- 8.7858550250530243e-03
-
- 5.3911769390106201e-01 5.5040502548217773e-01
- 3.9909350872039795e-01
- <_>
-
- 1 0 842 1.6395459533669055e-04 -1 -2 843
- -2.3508940357714891e-03
-
- 3.5929331183433533e-01 4.0341728925704956e-01
- 5.8060771226882935e-01
- <_>
-
- 1 0 844 7.5449963333085179e-05 -1 -2 845
- 2.7018489316105843e-02
-
- 5.4123848676681519e-01 4.9449229240417480e-01
- 5.5894362926483154e-01
- <_>
-
- 1 0 846 8.4561208495870233e-04 -1 -2 847
- -1.1687109945341945e-03
-
- 5.8092182874679565e-01 4.7469571232795715e-01
- 2.8458958864212036e-01
- <_>
-
- 1 0 848 2.2897500544786453e-02 -1 -2 849
- 7.0879262685775757e-01
-
- 2.4144110083580017e-01 5.1957648992538452e-01
- 1.0300920158624649e-01
- <_>
-
- 1 0 850 3.7483830004930496e-02 -1 -2 851
- 1.2827500468119979e-03
-
- 1.8146389722824097e-01 4.2460718750953674e-01
- 5.7079732418060303e-01
- <_>
-
- 0 1 852 -5.1718312315642834e-03 -1 -2 853
- 2.7545939665287733e-03
-
- 6.1433231830596924e-01 5.2056711912155151e-01
- 4.2204418778419495e-01
- <_>
-
- 0 1 854 -3.6072919610887766e-03 -1 -2 855
- -2.5258748792111874e-04
-
- 3.1825920939445496e-01 5.7104682922363281e-01
- 4.2260938882827759e-01
- <_>
-
- 1 0 856 -7.0514748804271221e-03 -1 -2 857
- -5.4323761723935604e-03
-
- 5.1628297567367554e-01 2.6662889122962952e-01
- 5.2146798372268677e-01
- <_>
-
- 1 0 858 -1.4652940080850385e-05 -1 -2 859
- -1.8556920113041997e-03
-
- 3.9817610383033752e-01 3.3227631449699402e-01
- 5.7058340311050415e-01
- <_>
-
- 1 0 860 4.7609540633857250e-03 -1 -2 861
- 1.5676260227337480e-03
-
- 6.6365581750869751e-01 5.5055677890777588e-01
- 4.4206619262695312e-01
- <_>
-
- 1 0 862 5.4239919409155846e-03 -1 -2 863
- -6.4692399464547634e-03
-
- 5.9599381685256958e-01 5.3695940971374512e-01
- 3.7443399429321289e-01
- <_>
-
- 0 1 864 -7.8038539504632354e-04 -1 -2 865
- 4.5086450874805450e-02
-
- 4.1035950183868408e-01 5.1775068044662476e-01
- 1.8781000375747681e-01
- <_>
-
- 0 1 866 -5.1405387930572033e-03 -1 -2 867
- -2.1236129105091095e-02
-
- 2.3528920114040375e-01 1.7087510228157043e-01
- 5.4249739646911621e-01
- <_>
-
- 0 1 868 -2.3763340432196856e-03 -1 -2 869
- 5.4122589528560638e-02
-
- 5.8365309238433838e-01 5.1174330711364746e-01
- 1.8659310042858124e-01
- <_>
-
- 0 1 870 -5.3492980077862740e-04 -1 -2 871
- -5.8454048121348023e-04
-
- 5.1086932420730591e-01 4.7754910588264465e-01
- 2.4398539960384369e-01
- <_>
- 71
- 3.4677078247070312e+01
-
- <_>
-
- 0 1 872 3.0031939968466759e-03 -1 -2 873
- 6.9161207647994161e-04
-
- 3.3496499061584473e-01 4.5183679461479187e-01
- 7.2893542051315308e-01
- <_>
-
- 0 1 874 1.1212790384888649e-02 -1 -2 875
- -7.6108198845759034e-04
-
- 2.9508009552955627e-01 5.6690549850463867e-01
- 2.8308510780334473e-01
- <_>
-
- 0 1 876 1.1984579759882763e-04 -1 -2 877
- -1.9725349557120353e-04
-
- 4.0905779600143433e-01 6.9514942169189453e-01
- 4.6378681063652039e-01
- <_>
-
- 1 0 878 -5.5180420167744160e-03 -1 -2 879
- 1.2148249661549926e-03
-
- 3.1676751375198364e-01 3.3167061209678650e-01
- 5.3963977098464966e-01
- <_>
-
- 0 1 880 -4.2497441172599792e-03 -1 -2 881
- -9.4915721565485001e-03
-
- 2.6005738973617554e-01 7.4842947721481323e-01
- 5.0731921195983887e-01
- <_>
-
- 1 0 882 6.5378600265830755e-04 -1 -2 883
- -4.9741100519895554e-04
-
- 3.9520108699798584e-01 5.8802747726440430e-01
- 3.5521200299263000e-01
- <_>
-
- 0 1 884 -4.3079249560832977e-02 -1 -2 885
- -5.1999092102050781e-04
-
- 2.4348780512809753e-01 3.1955629587173462e-01
- 5.5854547023773193e-01
- <_>
-
- 1 0 886 -4.5451628975570202e-03 -1 -2 887
- -7.9610403627157211e-03
-
- 4.8452898859977722e-01 3.8011810183525085e-01
- 5.3585118055343628e-01
- <_>
-
- 1 0 888 -3.1919340835884213e-04 -1 -2 889
- -1.9223889335989952e-02
-
- 4.3563291430473328e-01 2.6130661368370056e-01
- 6.1554962396621704e-01
- <_>
-
- 0 1 890 -1.3076990144327283e-03 -1 -2 891
- 1.9825039431452751e-02
-
- 5.9420621395111084e-01 4.9454280734062195e-01
- 7.3848551511764526e-01
- <_>
-
- 0 1 892 -2.2013280540704727e-03 -1 -2 893
- -7.8596705570816994e-03
-
- 2.2144819796085358e-01 3.6009770631790161e-01
- 5.2985501289367676e-01
- <_>
-
- 1 0 894 1.4142199652269483e-03 -1 -2 895
- -1.1232759803533554e-02
-
- 5.7765662670135498e-01 6.9344568252563477e-01
- 4.8272070288658142e-01
- <_>
-
- 1 0 896 2.9746301006525755e-03 -1 -2 897
- 5.3283828310668468e-04
-
- 3.2166770100593567e-01 3.9625000953674316e-01
- 5.6803637742996216e-01
- <_>
-
- 1 0 898 1.0105259716510773e-02 -1 -2 899
- -1.1653699912130833e-02
-
- 7.5674182176589966e-01 6.5235567092895508e-01
- 5.0270539522171021e-01
- <_>
-
- 0 1 900 -7.0609981194138527e-03 -1 -2 901
- 2.2343141026794910e-03
-
- 2.5387701392173767e-01 4.3872770667076111e-01
- 6.1776322126388550e-01
- <_>
-
- 1 0 902 -2.9802279546856880e-02 -1 -2 903
- 1.1611840454861522e-03
-
- 5.2011400461196899e-01 4.6479099988937378e-01
- 6.1842548847198486e-01
- <_>
-
- 1 0 904 9.4824447296559811e-04 -1 -2 905
- 4.1284630424343050e-04
-
- 3.0409941077232361e-01 4.5188081264495850e-01
- 6.2457829713821411e-01
- <_>
-
- 0 1 906 -3.1203540042042732e-02 -1 -2 907
- 2.7652881108224392e-03
-
- 2.7889358997344971e-01 4.6985000371932983e-01
- 6.5024542808532715e-01
- <_>
-
- 1 0 908 2.5644779205322266e-02 -1 -2 909
- -7.5331530533730984e-03
-
- 1.8051710724830627e-01 3.2080689072608948e-01
- 5.5220228433609009e-01
- <_>
-
- 1 0 910 3.2047149725258350e-03 -1 -2 911
- -2.4282479716930538e-04
-
- 6.4369338750839233e-01 5.6767052412033081e-01
- 4.5091038942337036e-01
- <_>
-
- 0 1 912 -6.1979342717677355e-04 -1 -2 913
- -8.0101029016077518e-04
-
- 3.1221461296081543e-01 2.9651939868927002e-01
- 5.2304947376251221e-01
- <_>
-
- 1 0 914 -9.1816839994862676e-04 -1 -2 915
- 1.2239529751241207e-03
-
- 5.4647117853164673e-01 4.6185028553009033e-01
- 5.6795489788055420e-01
- <_>
-
- 0 1 916 -6.8743730662390590e-04 -1 -2 917
- -1.8252469599246979e-03
-
- 5.4308801889419556e-01 5.4336231946945190e-01
- 3.3852210640907288e-01
- <_>
-
- 1 0 918 -7.4570789001882076e-03 -1 -2 919
- 5.3775748237967491e-03
-
- 5.2655947208404541e-01 4.8572158813476562e-01
- 6.8151241540908813e-01
- <_>
-
- 1 0 920 3.7602309603244066e-03 -1 -2 921
- 8.7752222316339612e-04
-
- 2.8321608901023865e-01 3.9668309688568115e-01
- 5.5124807357788086e-01
- <_>
-
- 1 0 922 5.5084479972720146e-03 -1 -2 923
- -7.5949047459289432e-04
-
- 6.7846202850341797e-01 3.9065030217170715e-01
- 5.4572027921676636e-01
- <_>
-
- 1 0 924 1.6352660022675991e-03 -1 -2 925
- -1.2750849418807775e-04
-
- 3.6402040719985962e-01 5.8297240734100342e-01
- 4.1949799656867981e-01
- <_>
-
- 0 1 926 2.2067610174417496e-02 -1 -2 927
- -1.9203789532184601e-02
-
- 4.6067029237747192e-01 3.2614830136299133e-01
- 5.2360808849334717e-01
- <_>
-
- 0 1 928 -1.2998109683394432e-02 -1 -2 929
- -3.1332690268754959e-03
-
- 7.0221120119094849e-01 2.8704708814620972e-01
- 5.0764769315719604e-01
- <_>
-
- 1 0 930 -5.2937557920813560e-03 -1 -2 931
- 2.1857069805264473e-03
-
- 4.7095209360122681e-01 4.7082918882369995e-01
- 6.1698418855667114e-01
- <_>
-
- 0 1 932 -4.5750709250569344e-03 -1 -2 933
- -4.5152138918638229e-02
-
- 3.1142529845237732e-01 1.8514350056648254e-01
- 5.5048149824142456e-01
- <_>
-
- 1 0 934 -2.7783559635281563e-03 -1 -2 935
- -2.5752480141818523e-03
-
- 4.9373480677604675e-01 6.1529481410980225e-01
- 4.7354999184608459e-01
- <_>
-
- 1 0 936 1.1614130344241858e-03 -1 -2 937
- 2.3350189439952374e-03
-
- 6.5105718374252319e-01 4.0883418917655945e-01
- 5.6841522455215454e-01
- <_>
-
- 1 0 938 3.8499289657920599e-03 -1 -2 939
- 2.4529630318284035e-03
-
- 3.0258288979530334e-01 5.2325028181076050e-01
- 2.0176209509372711e-01
- <_>
-
- 1 0 940 3.6731390282511711e-03 -1 -2 941
- 2.1937100682407618e-03
-
- 6.4284259080886841e-01 4.3288651108741760e-01
- 6.4205098152160645e-01
- <_>
-
- 1 0 942 -6.4666871912777424e-03 -1 -2 943
- -5.7186251506209373e-03
-
- 5.2540659904479980e-01 2.4909840524196625e-01
- 5.2876192331314087e-01
- <_>
-
- 1 0 944 9.9941878579556942e-04 -1 -2 945
- -7.8276498243212700e-04
-
- 3.3297958970069885e-01 3.5983449220657349e-01
- 5.4983407258987427e-01
- <_>
-
- 0 1 946 4.3231188319623470e-03 -1 -2 947
- 4.0838290005922318e-03
-
- 4.8187050223350525e-01 5.2663302421569824e-01
- 3.1057891249656677e-01
- <_>
-
- 1 0 948 3.0515898833982646e-04 -1 -2 949
- 1.2640280183404684e-03
-
- 3.9952918887138367e-01 3.2284379005432129e-01
- 5.8192151784896851e-01
- <_>
-
- 0 1 950 -1.0152660310268402e-02 -1 -2 951
- -2.6863690000027418e-03
-
- 8.0260711908340454e-01 3.8756170868873596e-01
- 5.4665708541870117e-01
- <_>
-
- 1 0 952 -9.0515613555908203e-03 -1 -2 953
- -6.3204211182892323e-03
-
- 4.3720579147338867e-01 1.1265510320663452e-01
- 6.3954162597656250e-01
- <_>
-
- 0 1 954 2.6117300149053335e-03 -1 -2 955
- 1.4339019544422626e-02
-
- 5.4239892959594727e-01 4.9792730808258057e-01
- 6.0422360897064209e-01
- <_>
-
- 1 0 956 2.8452780097723007e-03 -1 -2 957
- 1.4783289771003183e-05
-
- 3.4910920262336731e-01 4.1950678825378418e-01
- 5.7759660482406616e-01
- <_>
-
- 0 1 958 8.1814555451273918e-03 -1 -2 959
- 6.6321990452706814e-03
-
- 4.8859870433807373e-01 5.4444682598114014e-01
- 4.4209951162338257e-01
- <_>
-
- 0 1 960 -2.2483461070805788e-03 -1 -2 961
- 1.2374560348689556e-02
-
- 6.6997921466827393e-01 4.4786059856414795e-01
- 6.5648937225341797e-01
- <_>
-
- 1 0 962 -6.6516688093543053e-03 -1 -2 963
- -8.5750613361597061e-03
-
- 5.5118787288665771e-01 4.0174451470375061e-01
- 5.4055362939834595e-01
- <_>
-
- 1 0 964 6.5078441984951496e-03 -1 -2 965
- 2.8675209730863571e-02
-
- 2.2943930327892303e-01 5.1779001951217651e-01
- 3.5677561163902283e-01
- <_>
-
- 0 1 966 7.0673860609531403e-03 -1 -2 967
- 1.2367829913273454e-03
-
- 5.5646997690200806e-01 3.6276981234550476e-01
- 5.5724138021469116e-01
- <_>
-
- 1 0 968 7.4818679131567478e-03 -1 -2 969
- 4.7109839506447315e-03
-
- 6.7849111557006836e-01 4.1212528944015503e-01
- 6.0722357034683228e-01
- <_>
-
- 1 0 970 -6.9405790418386459e-03 -1 -2 971
- 3.3302098512649536e-02
-
- 5.4597669839859009e-01 5.2767068147659302e-01
- 2.3749159276485443e-01
- <_>
-
- 1 0 972 3.6104630678892136e-02 -1 -2 973
- 1.9674649462103844e-02
-
- 7.2492793202400208e-02 4.6263459324836731e-01
- 8.2089632749557495e-01
- <_>
-
- 0 1 974 3.4766150638461113e-03 -1 -2 975
- 1.3987369602546096e-03
-
- 5.2087318897247314e-01 5.4844141006469727e-01
- 4.2300349473953247e-01
- <_>
-
- 1 0 976 4.0974249131977558e-03 -1 -2 977
- 2.6973790954798460e-03
-
- 2.7805531024932861e-01 5.4038310050964355e-01
- 3.7909889221191406e-01
- <_>
-
- 1 0 978 -5.6591699831187725e-03 -1 -2 979
- 3.9460969856008887e-04
-
- 4.7983360290527344e-01 3.7669500708580017e-01
- 5.4292291402816772e-01
- <_>
-
- 1 0 980 2.1750570740550756e-03 -1 -2 981
- 1.4614439569413662e-03
-
- 6.2071627378463745e-01 3.3579450845718384e-01
- 5.1426321268081665e-01
- <_>
-
- 1 0 982 -5.3006567759439349e-04 -1 -2 983
- 1.4869309961795807e-01
-
- 5.3446400165557861e-01 5.1596081256866455e-01
- 2.5618231296539307e-01
- <_>
-
- 1 0 984 -5.8816498494707048e-05 -1 -2 985
- -1.6275369562208652e-03
-
- 5.1230919361114502e-01 6.0176461935043335e-01
- 3.1093719601631165e-01
- <_>
-
- 0 1 986 -1.2881809845566750e-02 -1 -2 987
- 9.4982917653396726e-04
-
- 2.7122870087623596e-01 5.4424422979354858e-01
- 4.0288880467414856e-01
- <_>
-
- 1 0 988 -1.2315999716520309e-02 -1 -2 989
- 9.0286601334810257e-03
-
- 4.7360658645629883e-01 7.4514347314834595e-01
- 3.4879919886589050e-01
- <_>
-
- 0 1 990 -8.6876116693019867e-02 -1 -2 991
- -1.5107560102478601e-05
-
- 2.2903330624103546e-01 5.5178898572921753e-01
- 4.3931490182876587e-01
- <_>
-
- 0 1 992 -1.7457660287618637e-02 -1 -2 993
- -2.5219470262527466e-03
-
- 9.0167902410030365e-02 6.2335401773452759e-01
- 4.7894591093063354e-01
- <_>
-
- 0 1 994 1.0656520025804639e-03 -1 -2 995
- -4.2540300637483597e-03
-
- 5.4896962642669678e-01 5.5798089504241943e-01
- 4.3758779764175415e-01
- <_>
-
- 0 1 996 -9.0349102392792702e-03 -1 -2 997
- -1.5230999561026692e-03
-
- 3.5791561007499695e-01 5.6136602163314819e-01
- 3.9390438795089722e-01
- <_>
-
- 1 0 998 2.8441150207072496e-03 -1 -2 999
- -3.2824429217725992e-03
-
- 3.9015549421310425e-01 4.5286190509796143e-01
- 5.4413431882858276e-01
- <_>
-
- 1 0 1000 3.2161718991119415e-05 -1 -2 1001
- 3.0118400900391862e-05
-
- 5.8031117916107178e-01 3.3368501067161560e-01
- 5.5048561096191406e-01
- <_>
-
- 0 1 1002 -5.6150099262595177e-03 -1 -2 1003
- -1.7389209941029549e-02
-
- 6.1247891187667847e-01 8.7271630764007568e-02
- 5.2045881748199463e-01
- <_>
-
- 0 1 1004 -4.4361080654198304e-05 -1 -2 1005
- 1.0354899859521538e-04
-
- 3.9353290200233459e-01 5.9188538789749146e-01
- 4.1196140646934509e-01
- <_>
-
- 0 1 1006 1.5939630102366209e-03 -1 -2 1007
- 2.5440789759159088e-03
-
- 4.8396238684654236e-01 4.7873649001121521e-01
- 6.3606631755828857e-01
- <_>
-
- 0 1 1008 1.5083180187502876e-05 -1 -2 1009
- -9.9282202427275479e-05
-
- 4.2311170697212219e-01 4.2745891213417053e-01
- 6.0940480232238770e-01
- <_>
-
- 1 0 1010 5.5371708003804088e-04 -1 -2 1011
- 1.9186759600415826e-03
-
- 4.2719879746437073e-01 4.4971078634262085e-01
- 5.5491220951080322e-01
- <_>
-
- 1 0 1012 -5.0764222396537662e-04 -1 -2 1013
- 1.7236480489373207e-03
-
- 5.4771959781646729e-01 2.8829228878021240e-01
- 5.6151270866394043e-01
- <_>
- 75
- 3.6726501464843750e+01
-
- <_>
-
- 0 1 1014 1.3092169538140297e-02 -1 -2 1015
- 4.1446479735895991e-04
-
- 3.3388701081275940e-01 3.0993521213531494e-01
- 6.6774922609329224e-01
- <_>
-
- 0 1 1016 2.1835729479789734e-02 -1 -2 1017
- 4.8323940485715866e-02
-
- 4.3690490722656250e-01 4.3017241358757019e-01
- 6.1538851261138916e-01
- <_>
-
- 0 1 1018 1.6091950237751007e-03 -1 -2 1019
- 1.3469760306179523e-03
-
- 3.3873260021209717e-01 6.2487137317657471e-01
- 3.5941308736801147e-01
- <_>
-
- 0 1 1020 1.7729059618432075e-04 -1 -2 1021
- 3.6743620876222849e-04
-
- 3.8684248924255371e-01 4.4093450903892517e-01
- 5.4764741659164429e-01
- <_>
-
- 0 1 1022 -1.2352119665592909e-03 -1 -2 1023
- 1.1705530341714621e-03
-
- 3.2601711153984070e-01 4.1113489866256714e-01
- 6.0881638526916504e-01
- <_>
-
- 1 0 1024 -2.9695429475395940e-05 -1 -2 1025
- 2.7050738572143018e-04
-
- 4.2694228887557983e-01 4.3064668774604797e-01
- 5.8105140924453735e-01
- <_>
-
- 1 0 1026 -7.9626210208516568e-05 -1 -2 1027
- 3.3152441028505564e-04
-
- 3.6691430211067200e-01 4.6106639504432678e-01
- 6.2905901670455933e-01
- <_>
-
- 1 0 1028 -5.2305828779935837e-02 -1 -2 1029
- 2.6880469173192978e-02
-
- 5.3286898136138916e-01 5.2132612466812134e-01
- 3.2312199473381042e-01
- <_>
-
- 1 0 1030 -2.4203000066336244e-04 -1 -2 1031
- -1.6424639616161585e-03
-
- 3.5685700178146362e-01 3.4406611323356628e-01
- 5.6256049871444702e-01
- <_>
-
- 1 0 1032 -2.6830288697965443e-04 -1 -2 1033
- -2.2649629972875118e-03
-
- 4.5611730217933655e-01 5.3213518857955933e-01
- 3.6741548776626587e-01
- <_>
-
- 1 0 1034 1.5627209097146988e-02 -1 -2 1035
- 1.6211320459842682e-01
-
- 2.0293539762496948e-01 5.5630332231521606e-01
- 2.6188498735427856e-01
- <_>
-
- 0 1 1036 -3.7391691002994776e-03 -1 -2 1037
- -2.0878419745713472e-03
-
- 6.0621947050094604e-01 5.9507638216018677e-01
- 4.5451170206069946e-01
- <_>
-
- 1 0 1038 2.3334210272878408e-03 -1 -2 1039
- 6.5116386394947767e-05
-
- 6.4355242252349854e-01 3.5207340121269226e-01
- 5.1797789335250854e-01
- <_>
-
- 0 1 1040 7.4625718407332897e-03 -1 -2 1041
- -2.2032689303159714e-02
-
- 5.3266882896423340e-01 3.4919810295104980e-01
- 5.4292368888854980e-01
- <_>
-
- 0 1 1042 -8.3081610500812531e-03 -1 -2 1043
- -4.3259368976578116e-04
-
- 2.0840230584144592e-01 3.9652720093727112e-01
- 5.4254537820816040e-01
- <_>
-
- 1 0 1044 -3.2209228724241257e-02 -1 -2 1045
- -9.0424838708713651e-04
-
- 5.3064119815826416e-01 5.4503858089447021e-01
- 4.2566969990730286e-01
- <_>
-
- 1 0 1046 2.2727500181645155e-03 -1 -2 1047
- 5.9820008464157581e-03
-
- 5.9686112403869629e-01 4.7581401467323303e-01
- 3.1509441137313843e-01
- <_>
-
- 1 0 1048 -5.8856618124991655e-04 -1 -2 1049
- -8.8227191008627415e-04
-
- 4.8477488756179810e-01 5.4263162612915039e-01
- 4.3383410573005676e-01
- <_>
-
- 1 0 1050 -7.4473457061685622e-05 -1 -2 1051
- 3.9148979703895748e-04
-
- 4.2875099182128906e-01 6.3451850414276123e-01
- 4.1018518805503845e-01
- <_>
-
- 1 0 1052 -3.6939629353582859e-03 -1 -2 1053
- -1.1207849718630314e-02
-
- 4.8491048812866211e-01 4.1463369131088257e-01
- 5.4712641239166260e-01
- <_>
-
- 0 1 1054 -1.0337409563362598e-02 -1 -2 1055
- 3.6883640568703413e-03
-
- 2.8771838545799255e-01 5.1019018888473511e-01
- 7.2169512510299683e-01
- <_>
-
- 1 0 1056 -3.8984280545264482e-03 -1 -2 1057
- -5.9986729174852371e-03
-
- 5.2761822938919067e-01 6.6184598207473755e-01
- 4.8416310548782349e-01
- <_>
-
- 1 0 1058 4.5043681748211384e-03 -1 -2 1059
- 1.7799530178308487e-02
-
- 1.8741579353809357e-01 4.6169349551200867e-01
- 7.0889657735824585e-01
- <_>
-
- 0 1 1060 -1.8462570384144783e-02 -1 -2 1061
- 1.4931300029275008e-05
-
- 3.0019798874855042e-01 4.5618081092834473e-01
- 5.6107878684997559e-01
- <_>
-
- 0 1 1062 -8.6021229624748230e-02 -1 -2 1063
- -6.0818758356617764e-05
-
- 2.3417009413242340e-01 5.6722861528396606e-01
- 4.1999641060829163e-01
- <_>
-
- 1 0 1064 1.2670679716393352e-03 -1 -2 1065
- 1.3699879636988044e-03
-
- 6.2074822187423706e-01 5.3949588537216187e-01
- 3.8238629698753357e-01
- <_>
-
- 1 0 1066 3.3162781037390232e-03 -1 -2 1067
- -1.4532039640471339e-03
-
- 7.0616811513900757e-01 3.0655130743980408e-01
- 4.8273730278015137e-01
- <_>
-
- 1 0 1068 -7.1492061018943787e-02 -1 -2 1069
- 1.9857978913933039e-03
-
- 5.1931220293045044e-01 4.6424350142478943e-01
- 5.8076947927474976e-01
- <_>
-
- 1 0 1070 6.2516499310731888e-03 -1 -2 1071
- 2.7005500160157681e-03
-
- 2.9498139023780823e-01 4.5858868956565857e-01
- 6.0223537683486938e-01
- <_>
-
- 0 1 1072 1.1130389757454395e-02 -1 -2 1073
- 1.5092849731445312e-02
-
- 4.3578410148620605e-01 4.5615398883819580e-01
- 6.1190617084503174e-01
- <_>
-
- 0 1 1074 -2.7943300083279610e-02 -1 -2 1075
- 4.4036991312168539e-05
-
- 6.5371441841125488e-01 3.4747231006622314e-01
- 5.3369677066802979e-01
- <_>
-
- 0 1 1076 -1.2232770211994648e-02 -1 -2 1077
- -6.8591412855312228e-04
-
- 3.7316760420799255e-01 5.7172292470932007e-01
- 4.7933790087699890e-01
- <_>
-
- 0 1 1078 -3.8992990739643574e-03 -1 -2 1079
- 4.9113907152786851e-04
-
- 4.0564361214637756e-01 6.1740481853485107e-01
- 4.4717541337013245e-01
- <_>
-
- 1 0 1080 8.2117747515439987e-03 -1 -2 1081
- -4.5564480125904083e-02
-
- 6.1796981096267700e-01 2.2854949533939362e-01
- 5.2495658397674561e-01
- <_>
-
- 0 1 1082 -5.3631910122931004e-03 -1 -2 1083
- -1.2274970300495625e-02
-
- 1.7849500477313995e-01 7.2619527578353882e-01
- 4.5503988862037659e-01
- <_>
-
- 0 1 1084 5.4185991175472736e-03 -1 -2 1085
- 8.1846961984410882e-04
-
- 5.2529907226562500e-01 5.4452222585678101e-01
- 3.2722181081771851e-01
- <_>
-
- 1 0 1086 4.1358140297234058e-03 -1 -2 1087
- 3.9578010910190642e-04
-
- 7.0138317346572876e-01 4.9659439921379089e-01
- 3.2955980300903320e-01
- <_>
-
- 0 1 1088 4.6887691132724285e-03 -1 -2 1089
- -1.8255440518260002e-02
-
- 5.3626418113708496e-01 6.4961087703704834e-01
- 4.7571370005607605e-01
- <_>
-
- 0 1 1090 -6.2736468389630318e-03 -1 -2 1091
- 2.4320168886333704e-03
-
- 2.3437410593032837e-01 4.6201181411743164e-01
- 6.8984192609786987e-01
- <_>
-
- 0 1 1092 -4.9617629498243332e-02 -1 -2 1093
- 1.1701210169121623e-03
-
- 2.1007199585437775e-01 4.6215289831161499e-01
- 5.7971358299255371e-01
- <_>
-
- 0 1 1094 -4.5237291604280472e-02 -1 -2 1095
- 4.7563421539962292e-03
-
- 2.1182620525360107e-01 4.8846149444580078e-01
- 6.8724989891052246e-01
- <_>
-
- 1 0 1096 -1.4835969544947147e-02 -1 -2 1097
- 7.7436608262360096e-04
-
- 5.2751058340072632e-01 4.1723209619522095e-01
- 5.4911398887634277e-01
- <_>
-
- 1 0 1098 1.4835969544947147e-02 -1 -2 1099
- -8.0892542609944940e-04
-
- 2.1248769760131836e-01 5.4952150583267212e-01
- 4.2077958583831787e-01
- <_>
-
- 0 1 1100 7.7517668250948191e-04 -1 -2 1101
- -6.7618978209793568e-03
-
- 3.3219420909881592e-01 2.2129580378532410e-01
- 5.2326530218124390e-01
- <_>
-
- 0 1 1102 -4.0135860443115234e-02 -1 -2 1103
- -3.3651469275355339e-03
-
- 1.1017960309982300e-01 3.8101008534431458e-01
- 5.6172919273376465e-01
- <_>
-
- 1 0 1104 7.4713007779791951e-04 -1 -2 1105
- -4.2727389372885227e-03
-
- 5.7950568199157715e-01 6.3922691345214844e-01
- 4.7114381194114685e-01
- <_>
-
- 1 0 1106 3.6202510818839073e-03 -1 -2 1107
- 4.7307618660852313e-04
-
- 3.4098839759826660e-01 3.6593028903007507e-01
- 5.3881710767745972e-01
- <_>
-
- 1 0 1108 3.3094909042119980e-02 -1 -2 1109
- -1.1544119566679001e-02
-
- 7.1703857183456421e-01 6.3868182897567749e-01
- 4.6813040971755981e-01
- <_>
-
- 0 1 1110 -7.4234469793736935e-03 -1 -2 1111
- -4.2252950370311737e-03
-
- 3.2637009024620056e-01 5.7678192853927612e-01
- 4.3464180827140808e-01
- <_>
-
- 0 1 1112 1.8133109435439110e-02 -1 -2 1113
- 7.0903049781918526e-03
-
- 4.6978279948234558e-01 4.4373890757560730e-01
- 6.0616689920425415e-01
- <_>
-
- 0 1 1114 -1.3272940181195736e-02 -1 -2 1115
- 1.4632199599873275e-04
-
- 6.5585112571716309e-01 3.3763539791107178e-01
- 5.0916552543640137e-01
- <_>
-
- 0 1 1116 -3.5790191031992435e-03 -1 -2 1117
- -4.6997101162560284e-04
-
- 2.9478839039802551e-01 5.5569821596145630e-01
- 4.6654561161994934e-01
- <_>
-
- 0 1 1118 -4.8179440200328827e-02 -1 -2 1119
- -9.2581362696364522e-04
-
- 7.3383557796478271e-01 3.5438719391822815e-01
- 5.2851498126983643e-01
- <_>
-
- 0 1 1120 -1.4780730009078979e-02 -1 -2 1121
- -1.0027450323104858e-01
-
- 1.9444419443607330e-01 9.9049292504787445e-02
- 5.1398539543151855e-01
- <_>
-
- 0 1 1122 -9.3848101096227765e-04 -1 -2 1123
- -2.8861360624432564e-03
-
- 5.8271098136901855e-01 3.4414279460906982e-01
- 5.1488387584686279e-01
- <_>
-
- 1 0 1124 -4.3682761490345001e-02 -1 -2 1125
- 2.6115700602531433e-03
-
- 5.2079981565475464e-01 4.8355031013488770e-01
- 6.3222199678421021e-01
- <_>
-
- 1 0 1126 4.3682761490345001e-02 -1 -2 1127
- 1.7179530113935471e-03
-
- 1.3645380735397339e-01 4.5373201370239258e-01
- 6.0667508840560913e-01
- <_>
-
- 1 0 1128 -3.3964909613132477e-02 -1 -2 1129
- -1.0993590112775564e-03
-
- 4.9683749675750732e-01 5.8316808938980103e-01
- 4.6882399916648865e-01
- <_>
-
- 1 0 1130 5.4301079362630844e-02 -1 -2 1131
- 1.0993590112775564e-03
-
- 7.5682890415191650e-01 4.3301481008529663e-01
- 5.7684689760208130e-01
- <_>
-
- 1 0 1132 -1.4954120160837192e-05 -1 -2 1133
- 3.1415868550539017e-02
-
- 4.4432818889617920e-01 5.2744728326797485e-01
- 3.0378559231758118e-01
- <_>
-
- 1 0 1134 1.0831849649548531e-02 -1 -2 1135
- 8.6545711383223534e-04
-
- 3.5817208886146545e-01 5.9375840425491333e-01
- 4.2946299910545349e-01
- <_>
-
- 1 0 1136 2.2743160370737314e-03 -1 -2 1137
- 3.9340821094810963e-03
-
- 5.9545767307281494e-01 4.7922229766845703e-01
- 5.8561331033706665e-01
- <_>
-
- 1 0 1138 8.1451907753944397e-03 -1 -2 1139
- -5.2763288840651512e-03
-
- 3.5734778642654419e-01 4.0260228514671326e-01
- 5.7647430896759033e-01
- <_>
-
- 1 0 1140 -8.3787851035594940e-03 -1 -2 1141
- 1.5621910570189357e-03
-
- 4.9813330173492432e-01 4.7365880012512207e-01
- 5.5836081504821777e-01
- <_>
-
- 1 0 1142 3.2318739686161280e-03 -1 -2 1143
- 6.6804019734263420e-03
-
- 6.1674368381500244e-01 4.1314241290092468e-01
- 6.2806951999664307e-01
- <_>
-
- 0 1 1144 -3.3396480139344931e-03 -1 -2 1145
- -2.0933480560779572e-01
-
- 3.4463581442832947e-01 1.0386580228805542e-01
- 5.2044892311096191e-01
- <_>
-
- 1 0 1146 6.3805822283029556e-03 -1 -2 1147
- -6.0137799009680748e-03
-
- 2.1674020588397980e-01 6.7383992671966553e-01
- 4.8966509103775024e-01
- <_>
-
- 1 0 1148 -8.1756077706813812e-03 -1 -2 1149
- 6.3951779156923294e-04
-
- 5.1779150962829590e-01 4.8196458816528320e-01
- 5.4644381999969482e-01
- <_>
-
- 1 0 1150 1.0127760469913483e-03 -1 -2 1151
- 4.9784599104896188e-04
-
- 3.4235960245132446e-01 4.4884610176086426e-01
- 5.9126710891723633e-01
- <_>
-
- 1 0 1152 1.3596490316558629e-04 -1 -2 1153
- 1.3571660034358501e-02
-
- 5.5688631534576416e-01 5.1610678434371948e-01
- 1.7130009829998016e-01
- <_>
-
- 1 0 1154 3.0259079721872695e-05 -1 -2 1155
- -3.2625840976834297e-03
-
- 4.9162039160728455e-01 6.4046627283096313e-01
- 2.8590849041938782e-01
- <_>
-
- 1 0 1156 -1.9217010412830859e-04 -1 -2 1157
- 2.1993879228830338e-02
-
- 5.4592829942703247e-01 4.7157138586044312e-01
- 5.6900751590728760e-01
- <_>
-
- 1 0 1158 7.8907777788117528e-04 -1 -2 1159
- 5.0893891602754593e-04
-
- 3.2798269391059875e-01 4.3020078539848328e-01
- 5.6960451602935791e-01
- <_>
-
- 1 0 1160 1.1662710312521085e-04 -1 -2 1161
- 8.0604078248143196e-03
-
- 5.3872352838516235e-01 5.0214231014251709e-01
- 5.9653222560882568e-01
- <_>
-
- 1 0 1162 9.5925969071686268e-04 -1 -2 1163
- -1.9526129588484764e-02
-
- 3.4734940528869629e-01 6.4755451679229736e-01
- 4.6437820792198181e-01
- <_>
- 78
- 3.8236038208007812e+01
-
- <_>
-
- 0 1 1164 4.1242439299821854e-02 -1 -2 1165
- 1.5626709908246994e-02
-
- 3.3933150768280029e-01 5.1041001081466675e-01
- 7.7728152275085449e-01
- <_>
-
- 0 1 1166 2.9947189614176750e-04 -1 -2 1167
- -1.0037609608843923e-03
-
- 3.6646738648414612e-01 5.4056507349014282e-01
- 3.9262050390243530e-01
- <_>
-
- 0 1 1168 6.8128242855891585e-04 -1 -2 1169
- 1.3098999625071883e-04
-
- 4.2515191435813904e-01 4.1351449489593506e-01
- 6.9257462024688721e-01
- <_>
-
- 1 0 1170 3.1696720980107784e-03 -1 -2 1171
- -2.0587369799613953e-03
-
- 3.4558731317520142e-01 2.2341939806938171e-01
- 5.2861189842224121e-01
- <_>
-
- 1 0 1172 -4.6395038953050971e-04 -1 -2 1173
- 3.5089480224996805e-03
-
- 4.2065200209617615e-01 6.5029817819595337e-01
- 4.1175979375839233e-01
- <_>
-
- 1 0 1174 -2.3975980002433062e-03 -1 -2 1175
- 1.0901279747486115e-03
-
- 3.6733010411262512e-01 2.9062381386756897e-01
- 5.4451119899749756e-01
- <_>
-
- 0 1 1176 -1.6524370585102588e-04 -1 -2 1177
- -4.1602319106459618e-04
-
- 4.2335158586502075e-01 3.8863611221313477e-01
- 6.2691658735275269e-01
- <_>
-
- 0 1 1178 -2.3739910102449358e-04 -1 -2 1179
- 2.4739760905504227e-02
-
- 5.5244511365890503e-01 4.9600958824157715e-01
- 5.3734910488128662e-01
- <_>
-
- 0 1 1180 -1.5342839993536472e-02 -1 -2 1181
- 1.1540469713509083e-02
-
- 6.8494051694869995e-01 4.0372350811958313e-01
- 6.7869400978088379e-01
- <_>
-
- 1 0 1182 6.4230621792376041e-03 -1 -2 1183
- 1.2977809645235538e-02
-
- 3.8146761059761047e-01 5.5270588397979736e-01
- 3.7449559569358826e-01
- <_>
-
- 0 1 1184 1.1063399724662304e-03 -1 -2 1185
- 1.3743690215051174e-03
-
- 3.5209289193153381e-01 5.6419032812118530e-01
- 3.0750259757041931e-01
- <_>
-
- 0 1 1186 1.6233779489994049e-02 -1 -2 1187
- -8.1519351806491613e-04
-
- 4.8888280987739563e-01 5.4563212394714355e-01
- 4.7435501217842102e-01
- <_>
-
- 0 1 1188 -9.0782493352890015e-02 -1 -2 1189
- 1.1665210127830505e-02
-
- 2.9252481460571289e-01 4.6884548664093018e-01
- 6.2303477525711060e-01
- <_>
-
- 0 1 1190 -2.3286409676074982e-02 -1 -2 1191
- 2.1559339947998524e-03
-
- 6.8958431482315063e-01 5.3558021783828735e-01
- 3.4234660863876343e-01
- <_>
-
- 0 1 1192 -4.3167220428586006e-03 -1 -2 1193
- 1.5610599657520652e-03
-
- 5.9370762109756470e-01 4.7086599469184875e-01
- 2.7369970083236694e-01
- <_>
-
- 0 1 1194 1.4076639898121357e-02 -1 -2 1195
- 7.1018589660525322e-03
-
- 5.2871561050415039e-01 5.3361928462982178e-01
- 3.2248139381408691e-01
- <_>
-
- 0 1 1196 -4.8221647739410400e-03 -1 -2 1197
- -5.3852899000048637e-03
-
- 2.9839101433753967e-01 5.6239992380142212e-01
- 4.2959120869636536e-01
- <_>
-
- 1 0 1198 7.3483278974890709e-03 -1 -2 1199
- -3.5707519855350256e-03
-
- 6.8139612674713135e-01 5.8579689264297485e-01
- 4.6034291386604309e-01
- <_>
-
- 1 0 1200 2.3340100888162851e-03 -1 -2 1201
- 4.7432780265808105e-03
-
- 2.7448511123657227e-01 5.0475269556045532e-01
- 2.3627419769763947e-01
- <_>
-
- 0 1 1202 6.5055489540100098e-03 -1 -2 1203
- 1.2589249759912491e-02
-
- 5.2422481775283813e-01 4.8236909508705139e-01
- 6.7525368928909302e-01
- <_>
-
- 0 1 1204 -6.3358368352055550e-03 -1 -2 1205
- -5.7639651931822300e-03
-
- 1.7346349358558655e-01 6.3543808460235596e-01
- 4.5874750614166260e-01
- <_>
-
- 0 1 1206 1.3599749654531479e-03 -1 -2 1207
- 2.8404260054230690e-02
-
- 4.5803809165954590e-01 5.1763808727264404e-01
- 1.2043850123882294e-01
- <_>
-
- 0 1 1208 -9.2958156019449234e-03 -1 -2 1209
- -1.1800320353358984e-03
-
- 2.3379570245742798e-01 3.9028140902519226e-01
- 5.6529301404953003e-01
- <_>
-
- 0 1 1210 -2.0948140881955624e-03 -1 -2 1211
- 4.1679958812892437e-03
-
- 5.5120289325714111e-01 5.4559761285781860e-01
- 4.7989490628242493e-01
- <_>
-
- 1 0 1212 5.4458891972899437e-03 -1 -2 1213
- -1.2766510481014848e-03
-
- 6.1270868778228760e-01 5.3171318769454956e-01
- 3.8509321212768555e-01
- <_>
-
- 0 1 1214 5.9404270723462105e-04 -1 -2 1215
- 4.2309608310461044e-02
-
- 5.4464370012283325e-01 5.2346438169479370e-01
- 2.2130440175533295e-01
- <_>
-
- 0 1 1216 5.6189671158790588e-03 -1 -2 1217
- 7.2401198558509350e-03
-
- 4.9161979556083679e-01 1.4714759588241577e-01
- 4.8528939485549927e-01
- <_>
-
- 0 1 1218 -4.5610670931637287e-03 -1 -2 1219
- 4.5506159949582070e-05
-
- 2.7737739682197571e-01 4.6264618635177612e-01
- 5.7680791616439819e-01
- <_>
-
- 0 1 1220 -6.1903791502118111e-03 -1 -2 1221
- 8.1186462193727493e-04
-
- 1.6442899405956268e-01 4.7785910964012146e-01
- 6.2618649005889893e-01
- <_>
-
- 0 1 1222 1.3779809698462486e-02 -1 -2 1223
- 1.1290319962427020e-03
-
- 5.2573078870773315e-01 5.4980480670928955e-01
- 3.9831069111824036e-01
- <_>
-
- 0 1 1224 -1.0610350000206381e-04 -1 -2 1225
- 1.6695790691301227e-04
-
- 4.0335190296173096e-01 4.1493400931358337e-01
- 5.7953411340713501e-01
- <_>
-
- 1 0 1226 1.1290319962427020e-03 -1 -2 1227
- -1.2019349634647369e-01
-
- 3.9341148734092712e-01 7.3400482535362244e-02
- 5.2025860548019409e-01
- <_>
-
- 0 1 1228 -1.5230740420520306e-02 -1 -2 1229
- 3.5759829916059971e-03
-
- 3.7495058774948120e-01 5.0781500339508057e-01
- 6.6060662269592285e-01
- <_>
-
- 0 1 1230 1.3479460030794144e-02 -1 -2 1231
- -2.1162950433790684e-03
-
- 4.5477110147476196e-01 3.3110061287879944e-01
- 5.3842592239379883e-01
- <_>
-
- 0 1 1232 -1.7877709120512009e-02 -1 -2 1233
- 1.0931970318779349e-03
-
- 6.5132528543472290e-01 5.2647650241851807e-01
- 3.4569910168647766e-01
- <_>
-
- 0 1 1234 -3.0553159303963184e-03 -1 -2 1235
- 3.6365049891173840e-03
-
- 6.2686139345169067e-01 5.3992128372192383e-01
- 4.3453970551490784e-01
- <_>
-
- 0 1 1236 9.7896481747739017e-05 -1 -2 1237
- -3.2714448752813041e-04
-
- 3.8356059789657593e-01 3.3376678824424744e-01
- 5.5391657352447510e-01
- <_>
-
- 1 0 1238 4.3425030889920890e-04 -1 -2 1239
- 1.4005579985678196e-02
-
- 5.7882702350616455e-01 5.2750778198242188e-01
- 2.7011251449584961e-01
- <_>
-
- 0 1 1240 -9.2654931358993053e-04 -1 -2 1241
- 3.9504268206655979e-03
-
- 5.8522802591323853e-01 4.7283369302749634e-01
- 3.3139181137084961e-01
- <_>
-
- 1 0 1242 -5.8086868375539780e-04 -1 -2 1243
- -1.2018020264804363e-02
-
- 4.2588108777999878e-01 5.6097871065139771e-01
- 4.8951920866966248e-01
- <_>
-
- 0 1 1244 -1.4521540701389313e-01 -1 -2 1245
- -6.6049019806087017e-03
-
- 4.3894480913877487e-02 4.2291709780693054e-01
- 5.6162929534912109e-01
- <_>
-
- 1 0 1246 -3.4909751266241074e-02 -1 -2 1247
- 3.7478420417755842e-03
-
- 4.7881281375885010e-01 4.8002821207046509e-01
- 5.8013892173767090e-01
- <_>
-
- 1 0 1248 3.3038031309843063e-02 -1 -2 1249
- 3.6872599739581347e-03
-
- 7.0781761407852173e-01 4.4496241211891174e-01
- 5.9577310085296631e-01
- <_>
-
- 0 1 1250 -4.5311939902603626e-03 -1 -2 1251
- 4.1058510541915894e-03
-
- 4.1770470142364502e-01 5.3729480504989624e-01
- 3.7369269132614136e-01
- <_>
-
- 0 1 1252 -8.7599847465753555e-03 -1 -2 1253
- -2.3003309965133667e-02
-
- 6.6588079929351807e-01 2.6479220390319824e-01
- 5.1018178462982178e-01
- <_>
-
- 0 1 1254 5.3664818406105042e-03 -1 -2 1255
- 3.8971770554780960e-02
-
- 4.5486348867416382e-01 5.1570618152618408e-01
- 3.4364390373229980e-01
- <_>
-
- 0 1 1256 -2.7767190709710121e-02 -1 -2 1257
- -9.8894089460372925e-03
-
- 2.3543910682201385e-01 6.8877410888671875e-01
- 5.1110517978668213e-01
- <_>
-
- 0 1 1258 -3.2073140610009432e-03 -1 -2 1259
- -6.7484978353604674e-04
-
- 5.4388678073883057e-01 5.4511487483978271e-01
- 4.8313531279563904e-01
- <_>
-
- 0 1 1260 -5.1947520114481449e-03 -1 -2 1261
- -2.6169899501837790e-04
-
- 2.1134190261363983e-01 5.2736818790435791e-01
- 3.9925870299339294e-01
- <_>
-
- 0 1 1262 2.2421479225158691e-03 -1 -2 1263
- -1.2139769969508052e-03
-
- 4.6882608532905579e-01 5.5042350292205811e-01
- 4.3848711252212524e-01
- <_>
-
- 0 1 1264 -2.9469770379364491e-03 -1 -2 1265
- -3.9291830034926534e-04
-
- 3.8928470015525818e-01 6.0017228126525879e-01
- 4.5616629719734192e-01
- <_>
-
- 1 0 1266 6.2550729513168335e-01 -1 -2 1267
- 9.7744520753622055e-03
-
- 6.8125613033771515e-02 4.8130258917808533e-01
- 5.6206572055816650e-01
- <_>
-
- 1 0 1268 9.4378247857093811e-02 -1 -2 1269
- -1.9560910295695066e-03
-
- 6.6632293164730072e-02 3.5882329940795898e-01
- 5.2954071760177612e-01
- <_>
-
- 0 1 1270 9.0652769431471825e-03 -1 -2 1271
- 4.2138071148656309e-04
-
- 4.8226881027221680e-01 4.6703329682350159e-01
- 5.6831127405166626e-01
- <_>
-
- 1 0 1272 -4.4220191193744540e-04 -1 -2 1273
- -4.7313501127064228e-03
-
- 5.3607952594757080e-01 6.1372458934783936e-01
- 3.1880891323089600e-01
- <_>
-
- 0 1 1274 1.5395509544759989e-03 -1 -2 1275
- 2.4315000046044588e-03
-
- 4.4877201318740845e-01 4.8941668868064880e-01
- 6.7166537046432495e-01
- <_>
-
- 0 1 1276 -1.5581619925796986e-02 -1 -2 1277
- 1.0816920548677444e-03
-
- 3.3367419242858887e-01 4.7182199358940125e-01
- 5.9606271982192993e-01
- <_>
-
- 0 1 1278 -2.2197659127414227e-03 -1 -2 1279
- -9.3048671260476112e-04
-
- 3.5885548591613770e-01 6.2187129259109497e-01
- 4.8173001408576965e-01
- <_>
-
- 0 1 1280 -4.7418707981705666e-03 -1 -2 1281
- -6.2950369901955128e-03
-
- 2.5500270724296570e-01 6.7280787229537964e-01
- 5.0510638952255249e-01
- <_>
-
- 0 1 1282 3.5216049291193485e-03 -1 -2 1283
- -2.4289379362016916e-03
-
- 5.4019099473953247e-01 5.4194617271423340e-01
- 4.3471428751945496e-01
- <_>
-
- 0 1 1284 -2.5261470582336187e-03 -1 -2 1285
- -1.4817339833825827e-03
-
- 6.9706249237060547e-01 3.2634168863296509e-01
- 4.9178731441497803e-01
- <_>
-
- 0 1 1286 -2.2474530339241028e-01 -1 -2 1287
- 2.8342509176582098e-03
-
- 7.2937291115522385e-03 4.5792299509048462e-01
- 5.3798812627792358e-01
- <_>
-
- 0 1 1288 -2.0821610465645790e-02 -1 -2 1289
- 1.4896340144332498e-04
-
- 6.0240888595581055e-01 3.3361440896987915e-01
- 4.9628159403800964e-01
- <_>
-
- 0 1 1290 -3.3524499740451574e-03 -1 -2 1291
- -3.7279881536960602e-02
-
- 3.5587510466575623e-01 1.6985629498958588e-01
- 5.2089858055114746e-01
- <_>
-
- 1 0 1292 1.3896770542487502e-04 -1 -2 1293
- -3.1912620761431754e-04
-
- 5.5906862020492554e-01 5.8487337827682495e-01
- 3.7958368659019470e-01
- <_>
-
- 1 0 1294 5.4003461264073849e-04 -1 -2 1295
- 3.8956850767135620e-03
-
- 5.6702882051467896e-01 5.1826947927474976e-01
- 3.3277091383934021e-01
- <_>
-
- 1 0 1296 1.6084529925137758e-03 -1 -2 1297
- -5.7474587811157107e-04
-
- 5.4104858636856079e-01 6.0226422548294067e-01
- 3.6446440219879150e-01
- <_>
-
- 1 0 1298 1.3435039669275284e-02 -1 -2 1299
- 2.1368139423429966e-03
-
- 3.4412819147109985e-01 5.2924340963363647e-01
- 2.7470758557319641e-01
- <_>
-
- 1 0 1300 1.4157629571855068e-02 -1 -2 1301
- 5.3884391672909260e-03
-
- 8.0278682708740234e-01 5.2223151922225952e-01
- 3.5867279767990112e-01
- <_>
-
- 0 1 1302 8.8013410568237305e-03 -1 -2 1303
- 3.8858849438838661e-04
-
- 4.9003869295120239e-01 4.6810561418533325e-01
- 5.7219529151916504e-01
- <_>
-
- 0 1 1304 -2.2143588867038488e-03 -1 -2 1305
- -8.4642972797155380e-03
-
- 5.3888058662414551e-01 6.6755378246307373e-01
- 3.4484419226646423e-01
- <_>
-
- 1 0 1306 1.5044390223920345e-02 -1 -2 1307
- 7.6346402056515217e-03
-
- 9.2396140098571777e-01 4.8848968744277954e-01
- 6.3060528039932251e-01
- <_>
-
- 1 0 1308 3.3895121305249631e-04 -1 -2 1309
- 2.1157610171940178e-04
-
- 3.9974310994148254e-01 5.6639820337295532e-01
- 3.9729809761047363e-01
- <_>
-
- 1 0 1310 -2.7514949440956116e-02 -1 -2 1311
- 5.1603060215711594e-02
-
- 5.2010637521743774e-01 5.1407301425933838e-01
- 1.2451309710741043e-01
- <_>
-
- 1 0 1312 3.7510651163756847e-03 -1 -2 1313
- -2.1457639522850513e-03
-
- 3.8020950555801392e-01 3.3094480633735657e-01
- 5.4745388031005859e-01
- <_>
-
- 1 0 1314 -5.8178009930998087e-04 -1 -2 1315
- -9.3638541875407100e-04
-
- 4.8926019668579102e-01 5.9373992681503296e-01
- 4.6646690368652344e-01
- <_>
-
- 1 0 1316 4.1667491197586060e-02 -1 -2 1317
- -6.7763780243694782e-03
-
- 7.0213532447814941e-01 3.2227510213851929e-01
- 5.0683951377868652e-01
- <_>
-
- 1 0 1318 -2.9170580673962831e-03 -1 -2 1319
- 3.2789530814625323e-04
-
- 4.7177010774612427e-01 4.5093831419944763e-01
- 5.6511628627777100e-01
- <_>
- 91
- 4.4682968139648438e+01
-
- <_>
-
- 0 1 1320 1.1729800142347813e-02 -1 -2 1321
- 1.1712179984897375e-03
-
- 3.8052248954772949e-01 3.1400179862976074e-01
- 6.8581461906433105e-01
- <_>
-
- 1 0 1322 9.3555096536874771e-03 -1 -2 1323
- 1.6570610459893942e-03
-
- 6.8346732854843140e-01 2.9924729466438293e-01
- 5.4756778478622437e-01
- <_>
-
- 1 0 1324 -1.3387809740379453e-03 -1 -2 1325
- 1.7580550047568977e-04
-
- 2.9414069652557373e-01 3.8969779014587402e-01
- 5.8729708194732666e-01
- <_>
-
- 0 1 1326 -2.9473248869180679e-03 -1 -2 1327
- 8.3220899105072021e-03
-
- 3.5765719413757324e-01 5.2324008941650391e-01
- 3.2310879230499268e-01
- <_>
-
- 1 0 1328 7.4366689659655094e-03 -1 -2 1329
- -2.1322889369912446e-04
-
- 6.7156732082366943e-01 5.4705417156219482e-01
- 3.8633960485458374e-01
- <_>
-
- 0 1 1330 -7.8024631366133690e-03 -1 -2 1331
- 5.6611228501424193e-04
-
- 2.7714601159095764e-01 4.6891361474990845e-01
- 5.8519637584686279e-01
- <_>
-
- 0 1 1332 -9.2346500605344772e-03 -1 -2 1333
- -1.4676499631605111e-05
-
- 2.7043971419334412e-01 5.6225502490997314e-01
- 3.5793170332908630e-01
- <_>
-
- 0 1 1334 9.7007937729358673e-03 -1 -2 1335
- -3.5320650786161423e-03
-
- 4.1738718748092651e-01 4.1950130462646484e-01
- 5.5494689941406250e-01
- <_>
-
- 1 0 1336 2.1616410464048386e-02 -1 -2 1337
- 3.4567608963698149e-03
-
- 2.8573909401893616e-01 6.0245329141616821e-01
- 4.3775078654289246e-01
- <_>
-
- 0 1 1338 2.2914320230484009e-02 -1 -2 1339
- 3.4328910987824202e-03
-
- 4.6893501281738281e-01 4.6646049618721008e-01
- 5.7625621557235718e-01
- <_>
-
- 0 1 1340 -8.6510833352804184e-03 -1 -2 1341
- 1.4510039472952485e-03
-
- 6.3817399740219116e-01 3.7114879488945007e-01
- 5.5307507514953613e-01
- <_>
-
- 0 1 1342 7.8191719949245453e-03 -1 -2 1343
- 2.0798550394829363e-04
-
- 5.2643620967864990e-01 3.7305128574371338e-01
- 5.4457312822341919e-01
- <_>
-
- 0 1 1344 -3.9962218143045902e-03 -1 -2 1345
- -1.5010139577498194e-05
-
- 2.4381700158119202e-01 5.3246712684631348e-01
- 3.6829888820648193e-01
- <_>
-
- 0 1 1346 -4.2428788729012012e-03 -1 -2 1347
- 9.1374982148408890e-03
-
- 6.4814740419387817e-01 4.8961588740348816e-01
- 6.5588432550430298e-01
- <_>
-
- 1 0 1348 8.8254585862159729e-03 -1 -2 1349
- 9.4092212384566665e-04
-
- 3.6138701438903809e-01 5.5028957128524780e-01
- 3.6325180530548096e-01
- <_>
-
- 0 1 1350 -1.2503350153565407e-02 -1 -2 1351
- 8.6759645491838455e-03
-
- 2.2611320018768311e-01 4.9878901243209839e-01
- 6.8471962213516235e-01
- <_>
-
- 0 1 1352 -1.0416760109364986e-02 -1 -2 1353
- 2.7432460337877274e-03
-
- 2.4462990462779999e-01 3.5115250945091248e-01
- 5.3998267650604248e-01
- <_>
-
- 0 1 1354 -4.2385691776871681e-03 -1 -2 1355
- 1.8325870856642723e-02
-
- 6.8236732482910156e-01 4.8915800452232361e-01
- 7.1356189250946045e-01
- <_>
-
- 0 1 1356 -2.4334540590643883e-02 -1 -2 1357
- 4.6469361404888332e-04
-
- 3.5225218534469604e-01 4.0498688817024231e-01
- 5.5158257484436035e-01
- <_>
-
- 1 0 1358 3.4260009415447712e-03 -1 -2 1359
- -2.5827318895608187e-03
-
- 4.1267699003219604e-01 2.8994289040565491e-01
- 5.3864318132400513e-01
- <_>
-
- 1 0 1360 1.0545699624344707e-03 -1 -2 1361
- -9.1257691383361816e-04
-
- 3.7713441252708435e-01 5.8273869752883911e-01
- 4.2675569653511047e-01
- <_>
-
- 0 1 1362 2.6589010376483202e-03 -1 -2 1363
- 4.8598358407616615e-03
-
- 4.6881249547004700e-01 4.8539221286773682e-01
- 6.1636447906494141e-01
- <_>
-
- 1 0 1364 8.0638676881790161e-03 -1 -2 1365
- -7.5898370705544949e-03
-
- 1.7491950094699860e-01 6.8261897563934326e-01
- 4.8940700292587280e-01
- <_>
-
- 0 1 1366 3.6368070868775249e-04 -1 -2 1367
- 6.2594950199127197e-02
-
- 4.6145960688591003e-01 5.1830172538757324e-01
- 2.6866960525512695e-01
- <_>
-
- 0 1 1368 -4.9753207713365555e-03 -1 -2 1369
- -2.0880119409412146e-03
-
- 1.7584669589996338e-01 6.3693821430206299e-01
- 4.9300441145896912e-01
- <_>
-
- 1 0 1370 9.5644511748105288e-04 -1 -2 1371
- -3.1721461564302444e-02
-
- 4.1393989324569702e-01 6.0455572605133057e-01
- 4.8163640499114990e-01
- <_>
-
- 0 1 1372 1.2898689601570368e-03 -1 -2 1373
- 9.8405163735151291e-03
-
- 5.4508107900619507e-01 2.9240009188652039e-01
- 6.6996061801910400e-01
- <_>
-
- 1 0 1374 1.2237089686095715e-03 -1 -2 1375
- -8.4232585504651070e-03
-
- 6.2828367948532104e-01 5.9865701198577881e-01
- 4.8525801301002502e-01
- <_>
-
- 0 1 1376 -7.2726322105154395e-04 -1 -2 1377
- 4.6842931769788265e-03
-
- 3.3400490880012512e-01 5.1689237356185913e-01
- 2.6794800162315369e-01
- <_>
-
- 0 1 1378 -1.0379579616710544e-03 -1 -2 1379
- 9.1342730447649956e-03
-
- 5.9257918596267700e-01 5.4377281665802002e-01
- 4.3468001484870911e-01
- <_>
-
- 0 1 1380 1.4971119817346334e-03 -1 -2 1381
- 1.5762320253998041e-03
-
- 4.1295009851455688e-01 4.5228740572929382e-01
- 6.5562921762466431e-01
- <_>
-
- 0 1 1382 8.7496247142553329e-03 -1 -2 1383
- -8.5103599121794105e-04
-
- 4.5320340991020203e-01 3.7859839200973511e-01
- 5.4169750213623047e-01
- <_>
-
- 0 1 1384 -1.7325570806860924e-02 -1 -2 1385
- -8.3266440778970718e-03
-
- 6.8842482566833496e-01 3.0913260579109192e-01
- 5.2436548471450806e-01
- <_>
-
- 0 1 1386 1.5157909729168750e-05 -1 -2 1387
- 1.8041470320895314e-03
-
- 4.7657939791679382e-01 4.7253859043121338e-01
- 5.7165551185607910e-01
- <_>
-
- 1 0 1388 3.0691560823470354e-03 -1 -2 1389
- -5.2225510444259271e-05
-
- 2.1433599293231964e-01 5.6532102823257446e-01
- 4.3851110339164734e-01
- <_>
-
- 1 0 1390 1.0072169970953837e-04 -1 -2 1391
- 1.3573700562119484e-04
-
- 5.9247761964797974e-01 4.5734488964080811e-01
- 5.7693827152252197e-01
- <_>
-
- 1 0 1392 9.2137878527864814e-04 -1 -2 1393
- 3.0316581251099706e-04
-
- 5.9926092624664307e-01 3.6100810766220093e-01
- 5.0493258237838745e-01
- <_>
-
- 1 0 1394 3.9582479745149612e-02 -1 -2 1395
- 4.7519680112600327e-02
-
- 1.5384890139102936e-01 5.2161407470703125e-01
- 1.4283910393714905e-01
- <_>
-
- 1 0 1396 1.8871759995818138e-02 -1 -2 1397
- -3.9876459049992263e-04
-
- 2.8255069255828857e-01 4.0350168943405151e-01
- 5.4377931356430054e-01
- <_>
-
- 0 1 1398 4.6556600136682391e-04 -1 -2 1399
- 6.7090610973536968e-03
-
- 4.6689969301223755e-01 5.3313547372817993e-01
- 4.1365718841552734e-01
- <_>
-
- 0 1 1400 -1.8931160448119044e-03 -1 -2 1401
- -1.3056949712336063e-02
-
- 7.1551632881164551e-01 3.1178998947143555e-01
- 5.2084398269653320e-01
- <_>
-
- 1 0 1402 -1.9484119547996670e-04 -1 -2 1403
- 1.5093220099515747e-05
-
- 4.6376588940620422e-01 4.5616531372070312e-01
- 5.4452341794967651e-01
- <_>
-
- 1 0 1404 -7.1617960202274844e-06 -1 -2 1405
- 3.0164679628796875e-04
-
- 4.1931080818176270e-01 5.9662377834320068e-01
- 4.1005000472068787e-01
- <_>
-
- 0 1 1406 4.4195181690156460e-03 -1 -2 1407
- -7.3984181508421898e-03
-
- 4.8450559377670288e-01 6.2068462371826172e-01
- 4.9312090873718262e-01
- <_>
-
- 1 0 1408 -7.8031201846897602e-03 -1 -2 1409
- -1.0731429792940617e-02
-
- 5.2824628353118896e-01 9.1048341989517212e-01
- 3.4559220075607300e-01
- <_>
-
- 0 1 1410 1.4246780192479491e-03 -1 -2 1411
- -8.2717568147927523e-05
-
- 4.7085541486740112e-01 5.6516230106353760e-01
- 4.7310239076614380e-01
- <_>
-
- 1 0 1412 4.4803409837186337e-03 -1 -2 1413
- 3.0789140146225691e-03
-
- 6.1758869886398315e-01 5.1395332813262939e-01
- 3.4230878949165344e-01
- <_>
-
- 1 0 1414 -1.1310289846733212e-03 -1 -2 1415
- -1.0410690447315574e-03
-
- 4.9182820320129395e-01 5.9420871734619141e-01
- 4.9230429530143738e-01
- <_>
-
- 1 0 1416 1.1648540385067463e-03 -1 -2 1417
- 9.0057362103834748e-04
-
- 6.4052718877792358e-01 4.5043969154357910e-01
- 6.1920768022537231e-01
- <_>
-
- 0 1 1418 6.8781538866460323e-03 -1 -2 1419
- -3.5283900797367096e-02
-
- 5.3748130798339844e-01 2.2471010684967041e-01
- 5.2171707153320312e-01
- <_>
-
- 0 1 1420 -1.3320200378075242e-03 -1 -2 1421
- -2.3177571129053831e-03
-
- 2.5547030568122864e-01 3.7925159931182861e-01
- 5.2432268857955933e-01
- <_>
-
- 0 1 1422 2.1332940377760679e-04 -1 -2 1423
- 1.3467900454998016e-02
-
- 3.8603371381759644e-01 5.3806877136230469e-01
- 4.1783639788627625e-01
- <_>
-
- 0 1 1424 -1.2829169863834977e-03 -1 -2 1425
- 5.1571638323366642e-04
-
- 6.1336231231689453e-01 4.0285378694534302e-01
- 5.5368518829345703e-01
- <_>
-
- 0 1 1426 3.9254198782145977e-03 -1 -2 1427
- -3.3780589699745178e-02
-
- 5.2799212932586670e-01 2.3346750438213348e-01
- 5.1759117841720581e-01
- <_>
-
- 0 1 1428 -3.7853721529245377e-02 -1 -2 1429
- -4.0752900531515479e-04
-
- 1.0748530179262161e-01 5.3459298610687256e-01
- 4.1989380121231079e-01
- <_>
-
- 0 1 1430 -3.1193809118121862e-03 -1 -2 1431
- -1.5714969485998154e-02
-
- 3.8558250665664673e-01 3.3351901173591614e-01
- 5.2632021903991699e-01
- <_>
-
- 0 1 1432 -7.8525702701881528e-04 -1 -2 1433
- -2.8750501223839819e-04
-
- 5.8603972196578979e-01 5.4377847909927368e-01
- 3.7161049246788025e-01
- <_>
-
- 1 0 1434 2.8016859665513039e-02 -1 -2 1435
- -1.9018839811906219e-03
-
- 3.3307549357414246e-01 5.3665977716445923e-01
- 4.6937939524650574e-01
- <_>
-
- 1 0 1436 2.0647559314966202e-02 -1 -2 1437
- 4.3002571910619736e-03
-
- 1.0069560259580612e-01 4.8160359263420105e-01
- 6.2156772613525391e-01
- <_>
-
- 0 1 1438 1.3459140434861183e-02 -1 -2 1439
- -1.0320040397346020e-02
-
- 5.4619538784027100e-01 4.5784530043601990e-01
- 5.4193097352981567e-01
- <_>
-
- 1 0 1440 3.1990748643875122e-01 -1 -2 1441
- 9.2198798665776849e-04
-
- 2.0080469548702240e-01 5.1932811737060547e-01
- 3.9121940732002258e-01
- <_>
-
- 0 1 1442 4.1852539288811386e-04 -1 -2 1443
- 3.5891108564101160e-04
-
- 4.2997440695762634e-01 4.3445029854774475e-01
- 5.5319738388061523e-01
- <_>
-
- 0 1 1444 -2.0992439985275269e-01 -1 -2 1445
- -4.9328152090311050e-03
-
- 1.0757210105657578e-01 5.7627969980239868e-01
- 4.5746439695358276e-01
- <_>
-
- 1 0 1446 2.3409130517393351e-03 -1 -2 1447
- 4.7120270319283009e-03
-
- 7.4768078327178955e-01 5.2617651224136353e-01
- 4.5055508613586426e-01
- <_>
-
- 0 1 1448 2.8713190928101540e-02 -1 -2 1449
- -2.6156550738960505e-03
-
- 4.4071030616760254e-01 4.2442709207534790e-01
- 6.8929767608642578e-01
- <_>
-
- 0 1 1450 -1.3558969832956791e-02 -1 -2 1451
- -3.0331799644045532e-04
-
- 1.2522679567337036e-01 4.0777918696403503e-01
- 5.4428178071975708e-01
- <_>
-
- 0 1 1452 -5.5601762142032385e-04 -1 -2 1453
- 2.4025330785661936e-03
-
- 5.3780037164688110e-01 3.1665799021720886e-01
- 5.2857381105422974e-01
- <_>
-
- 1 0 1454 -3.4089901018887758e-03 -1 -2 1455
- 8.0019602319225669e-04
-
- 4.9052149057388306e-01 4.5227360725402832e-01
- 5.5806142091751099e-01
- <_>
-
- 1 0 1456 2.1901070140302181e-03 -1 -2 1457
- 3.3745369873940945e-03
-
- 6.6126817464828491e-01 5.1077651977539062e-01
- 3.3869299292564392e-01
- <_>
-
- 1 0 1458 8.0019602319225669e-04 -1 -2 1459
- 1.7346069216728210e-02
-
- 5.7075601816177368e-01 5.0160211324691772e-01
- 6.3064599037170410e-01
- <_>
-
- 0 1 1460 -1.9568449351936579e-03 -1 -2 1461
- -1.1229019612073898e-02
-
- 3.0178061127662659e-01 6.2938511371612549e-01
- 4.5204889774322510e-01
- <_>
-
- 0 1 1462 -2.6608388870954514e-03 -1 -2 1463
- -1.1615100316703320e-02
-
- 3.3440071344375610e-01 2.8253790736198425e-01
- 5.1509708166122437e-01
- <_>
-
- 0 1 1464 -9.5248602330684662e-02 -1 -2 1465
- 7.3701781220734119e-03
-
- 1.3982650637626648e-01 5.2939987182617188e-01
- 2.3317280411720276e-01
- <_>
-
- 1 0 1466 -1.4953900128602982e-02 -1 -2 1467
- 5.7038792874664068e-04
-
- 4.9404659867286682e-01 5.4665708541870117e-01
- 4.6267679333686829e-01
- <_>
-
- 1 0 1468 5.8516198769211769e-03 -1 -2 1469
- 2.1150549582671374e-04
-
- 6.2700408697128296e-01 5.5081409215927124e-01
- 4.0618729591369629e-01
- <_>
-
- 1 0 1470 -6.9679190346505493e-06 -1 -2 1471
- -7.9677387839183211e-04
-
- 4.0965679287910461e-01 5.6155568361282349e-01
- 4.6668860316276550e-01
- <_>
-
- 1 0 1472 1.9459480419754982e-02 -1 -2 1473
- -1.1160830035805702e-02
-
- 2.3114809393882751e-01 3.0870118737220764e-01
- 5.5146622657775879e-01
- <_>
-
- 1 0 1474 1.4056149870157242e-02 -1 -2 1475
- -3.2958350493572652e-04
-
- 7.0050561428070068e-01 5.7974857091903687e-01
- 4.6916508674621582e-01
- <_>
-
- 0 1 1476 -5.4636420682072639e-03 -1 -2 1477
- 5.8881669247057289e-05
-
- 5.9285950660705566e-01 3.7413978576660156e-01
- 5.1701688766479492e-01
- <_>
-
- 0 1 1478 6.6343429498374462e-03 -1 -2 1479
- 4.5263409614562988e-02
-
- 5.4149878025054932e-01 5.1803272962570190e-01
- 1.5296840667724609e-01
- <_>
-
- 0 1 1480 -8.0646127462387085e-03 -1 -2 1481
- 4.7389548853971064e-04
-
- 2.5154680013656616e-01 5.1219987869262695e-01
- 3.7259489297866821e-01
- <_>
-
- 1 0 1482 1.4877359717502259e-05 -1 -2 1483
- 2.4321159347891808e-02
-
- 5.5324357748031616e-01 4.9607661366462708e-01
- 5.9833151102066040e-01
- <_>
-
- 0 1 1484 6.9931396865285933e-05 -1 -2 1485
- 2.6287760119885206e-03
-
- 4.1639530658721924e-01 5.8801448345184326e-01
- 3.3996629714965820e-01
- <_>
-
- 1 0 1486 3.8190539926290512e-03 -1 -2 1487
- -2.5989150628447533e-02
-
- 7.8466212749481201e-01 3.2881140708923340e-01
- 5.1550877094268799e-01
- <_>
-
- 0 1 1488 1.2062400346621871e-03 -1 -2 1489
- -1.5557400183752179e-03
-
- 4.5960599184036255e-01 3.1269869208335876e-01
- 7.1833992004394531e-01
- <_>
-
- 1 0 1490 -2.2691930644214153e-03 -1 -2 1491
- 2.3287249496206641e-04
-
- 5.2740061283111572e-01 4.8786661028862000e-01
- 5.6151527166366577e-01
- <_>
-
- 1 0 1492 -5.5999699980020523e-03 -1 -2 1493
- -1.0496189817786217e-02
-
- 5.1608121395111084e-01 5.7016140222549438e-01
- 3.2048508524894714e-01
- <_>
-
- 0 1 1494 -1.4814930182183161e-05 -1 -2 1495
- -6.4287078566849232e-04
-
- 5.5388379096984863e-01 5.3494292497634888e-01
- 4.4721511006355286e-01
- <_>
-
- 0 1 1496 -1.8891949730459601e-04 -1 -2 1497
- -9.0413521975278854e-03
-
- 5.0128370523452759e-01 2.5629359483718872e-01
- 4.5033830404281616e-01
- <_>
-
- 1 0 1498 7.9534705728292465e-03 -1 -2 1499
- -2.7908999472856522e-03
-
- 2.6304998993873596e-01 5.7565087080001831e-01
- 4.8548638820648193e-01
- <_>
-
- 1 0 1500 3.2857100013643503e-03 -1 -2 1501
- 7.7063008211553097e-04
-
- 4.0847519040107727e-01 4.0733560919761658e-01
- 5.9202408790588379e-01
- <_>
- 97
- 4.7763450622558594e+01
-
- <_>
-
- 0 1 1502 6.3021942973136902e-02 -1 -2 1503
- -2.8374609537422657e-03
-
- 3.4193828701972961e-01 6.8295639753341675e-01
- 4.4045230746269226e-01
- <_>
-
- 0 1 1504 4.6461950987577438e-02 -1 -2 1505
- 2.9152540490031242e-02
-
- 4.3917450308799744e-01 4.6010631322860718e-01
- 6.3579368591308594e-01
- <_>
-
- 1 0 1506 -1.4000290320836939e-05 -1 -2 1507
- -1.2757079675793648e-03
-
- 3.7300100922584534e-01 3.0938240885734558e-01
- 5.9013700485229492e-01
- <_>
-
- 0 1 1508 1.3596529606729746e-03 -1 -2 1509
- 1.7991929780691862e-04
-
- 4.3375650048255920e-01 4.2175039649009705e-01
- 5.8468478918075562e-01
- <_>
-
- 1 0 1510 -1.4166639630275313e-05 -1 -2 1511
- 6.0252390539972112e-05
-
- 4.0846911072731018e-01 5.0872868299484253e-01
- 7.2771841287612915e-01
- <_>
-
- 1 0 1512 6.4320368692278862e-03 -1 -2 1513
- 4.6682319953106344e-04
-
- 2.9679030179977417e-01 4.1104629635810852e-01
- 5.5812197923660278e-01
- <_>
-
- 0 1 1514 5.7436279021203518e-03 -1 -2 1515
- 3.2019240316003561e-03
-
- 4.2873099446296692e-01 4.2661958932876587e-01
- 6.4440459012985229e-01
- <_>
-
- 1 0 1516 -5.7637941790744662e-04 -1 -2 1517
- -3.7901920732110739e-03
-
- 4.0848249197006226e-01 3.1819209456443787e-01
- 5.2306932210922241e-01
- <_>
-
- 1 0 1518 4.8914109356701374e-03 -1 -2 1519
- 4.6459292061626911e-03
-
- 3.5483568906784058e-01 5.6105977296829224e-01
- 2.6938489079475403e-01
- <_>
-
- 0 1 1520 -6.8799369037151337e-03 -1 -2 1521
- -1.8147470429539680e-02
-
- 6.2354081869125366e-01 2.8619819879531860e-01
- 5.2268481254577637e-01
- <_>
-
- 1 0 1522 1.1409220314817503e-04 -1 -2 1523
- -5.4334272863343358e-04
-
- 3.2578331232070923e-01 3.8829690217971802e-01
- 5.3411662578582764e-01
- <_>
-
- 0 1 1524 -2.7602489572018385e-03 -1 -2 1525
- -1.9730569329112768e-03
-
- 6.3539659976959229e-01 5.8807611465454102e-01
- 4.5930901169776917e-01
- <_>
-
- 1 0 1526 2.4565239436924458e-03 -1 -2 1527
- 1.9392010290175676e-04
-
- 3.1340101361274719e-01 5.2771317958831787e-01
- 3.6041069030761719e-01
- <_>
-
- 0 1 1528 7.8643016517162323e-02 -1 -2 1529
- 6.5276869572699070e-03
-
- 5.2903419733047485e-01 4.6544799208641052e-01
- 6.0449051856994629e-01
- <_>
-
- 0 1 1530 -7.8716799616813660e-02 -1 -2 1531
- 5.7298499159514904e-03
-
- 2.5411269068717957e-01 4.3669191002845764e-01
- 5.8228862285614014e-01
- <_>
-
- 1 0 1532 6.2386557692661881e-04 -1 -2 1533
- -8.5267230868339539e-02
-
- 5.4726922512054443e-01 1.4616079628467560e-01
- 5.1818108558654785e-01
- <_>
-
- 1 0 1534 4.0981110185384750e-02 -1 -2 1535
- 7.7135749161243439e-03
-
- 1.2701350450515747e-01 4.8326849937438965e-01
- 2.2235789895057678e-01
- <_>
-
- 0 1 1536 -6.8663940764963627e-03 -1 -2 1537
- 1.4559639617800713e-02
-
- 5.9189289808273315e-01 4.7615069150924683e-01
- 5.7272237539291382e-01
- <_>
-
- 0 1 1538 -1.0064310394227505e-02 -1 -2 1539
- 3.6274080630391836e-03
-
- 3.6367309093475342e-01 5.2717310190200806e-01
- 2.7405250072479248e-01
- <_>
-
- 0 1 1540 -2.3421540390700102e-03 -1 -2 1541
- -2.4686409160494804e-02
-
- 5.4977840185165405e-01 6.0598951578140259e-01
- 4.9603140354156494e-01
- <_>
-
- 1 0 1542 1.9456120207905769e-04 -1 -2 1543
- 3.1714211218059063e-04
-
- 3.7694650888442993e-01 4.0623620152473450e-01
- 5.6682151556015015e-01
- <_>
-
- 0 1 1544 2.0793990697711706e-03 -1 -2 1545
- 1.7982709687203169e-03
-
- 4.6186569333076477e-01 4.8675051331520081e-01
- 6.5184497833251953e-01
- <_>
-
- 0 1 1546 -2.2287059982772917e-04 -1 -2 1547
- 3.2623921288177371e-04
-
- 5.6775957345962524e-01 3.7107339501380920e-01
- 5.6766051054000854e-01
- <_>
-
- 0 1 1548 -6.6792681813240051e-02 -1 -2 1549
- -1.4869889710098505e-03
-
- 2.5115218758583069e-01 3.8867509365081787e-01
- 5.2622538805007935e-01
- <_>
-
- 0 1 1550 -5.0454870797693729e-03 -1 -2 1551
- -4.8297587782144547e-03
-
- 6.5574729442596436e-01 5.9341061115264893e-01
- 4.2859220504760742e-01
- <_>
-
- 1 0 1552 -1.0722599690780044e-03 -1 -2 1553
- 8.7901195511221886e-03
-
- 5.4260587692260742e-01 5.3513032197952271e-01
- 4.8342779278755188e-01
- <_>
-
- 0 1 1554 -7.1750381030142307e-03 -1 -2 1555
- 1.1251230025663972e-03
-
- 2.0671689510345459e-01 5.1122522354125977e-01
- 3.4687140583992004e-01
- <_>
-
- 0 1 1556 1.0634710080921650e-02 -1 -2 1557
- -1.1763219721615314e-02
-
- 4.4790080189704895e-01 6.2539017200469971e-01
- 4.9689871072769165e-01
- <_>
-
- 1 0 1558 9.2324063181877136e-02 -1 -2 1559
- 1.8991080578416586e-03
-
- 2.0313039422035217e-01 5.6187218427658081e-01
- 4.0465721487998962e-01
- <_>
-
- 1 0 1560 -1.0510340332984924e-02 -1 -2 1561
- -7.4531312566250563e-04
-
- 4.9432641267776489e-01 5.6134277582168579e-01
- 3.8453319668769836e-01
- <_>
-
- 1 0 1562 8.0041000619530678e-03 -1 -2 1563
- 5.8110528625547886e-03
-
- 7.7598422765731812e-01 4.6247330307960510e-01
- 6.2862771749496460e-01
- <_>
-
- 0 1 1564 -2.7918580919504166e-02 -1 -2 1565
- 2.1739399526268244e-03
-
- 2.4093140661716461e-01 5.3455048799514771e-01
- 3.5079580545425415e-01
- <_>
-
- 0 1 1566 -4.0639587678015232e-03 -1 -2 1567
- 6.0017139185220003e-04
-
- 6.6471010446548462e-01 4.9985098838806152e-01
- 3.0221650004386902e-01
- <_>
-
- 1 0 1568 1.9214770291000605e-03 -1 -2 1569
- -1.3860830105841160e-02
-
- 5.9191507101058960e-01 6.3517677783966064e-01
- 4.9933108687400818e-01
- <_>
-
- 1 0 1570 2.3006850853562355e-02 -1 -2 1571
- -1.3857929734513164e-03
-
- 1.9023360311985016e-01 5.2533692121505737e-01
- 3.9858600497245789e-01
- <_>
-
- 0 1 1572 1.2637410545721650e-03 -1 -2 1573
- -1.4675210230052471e-02
-
- 4.6661040186882019e-01 3.8231649994850159e-01
- 5.3266328573226929e-01
- <_>
-
- 0 1 1574 -2.9535070061683655e-03 -1 -2 1575
- -1.7189770005643368e-03
-
- 7.0636558532714844e-01 3.8134628534317017e-01
- 5.2467352151870728e-01
- <_>
-
- 1 0 1576 -4.2484089499339461e-04 -1 -2 1577
- -8.5248658433556557e-04
-
- 4.7916388511657715e-01 4.4912180304527283e-01
- 5.3709012269973755e-01
- <_>
-
- 1 0 1578 8.9034568518400192e-03 -1 -2 1579
- 1.4895649655954912e-05
-
- 2.0764739811420441e-01 4.4476351141929626e-01
- 5.6671631336212158e-01
- <_>
-
- 0 1 1580 -4.7091601300053298e-04 -1 -2 1581
- 4.3084810022264719e-04
-
- 5.4650712013244629e-01 5.4932618141174316e-01
- 4.5807081460952759e-01
- <_>
-
- 0 1 1582 -6.3893961487337947e-04 -1 -2 1583
- -7.3733746830839664e-05
-
- 5.5015718936920166e-01 5.0857907533645630e-01
- 3.3056980371475220e-01
- <_>
-
- 0 1 1584 -8.8991485536098480e-03 -1 -2 1585
- -1.0253350250422955e-02
-
- 4.2764690518379211e-01 1.1232180148363113e-01
- 5.1527231931686401e-01
- <_>
-
- 0 1 1586 -5.9637490659952164e-02 -1 -2 1587
- 2.1707199513912201e-02
-
- 7.3867720365524292e-01 4.9962919950485229e-01
- 1.3394139707088470e-01
- <_>
-
- 0 1 1588 9.9107045680284500e-03 -1 -2 1589
- -1.0998300276696682e-02
-
- 4.6790120005607605e-01 6.9286561012268066e-01
- 5.0120681524276733e-01
- <_>
-
- 1 0 1590 7.4608891736716032e-04 -1 -2 1591
- 2.9539171373471618e-04
-
- 5.8335822820663452e-01 3.8263911008834839e-01
- 5.5663508176803589e-01
- <_>
-
- 1 0 1592 5.0054129213094711e-02 -1 -2 1593
- -7.2330660186707973e-03
-
- 3.0027210712432861e-01 5.9080427885055542e-01
- 5.0008708238601685e-01
- <_>
-
- 0 1 1594 -2.6863380335271358e-03 -1 -2 1595
- -1.0195849463343620e-03
-
- 3.9750349521636963e-01 3.6976858973503113e-01
- 5.7561928033828735e-01
- <_>
-
- 0 1 1596 -2.0204920321702957e-02 -1 -2 1597
- 2.1340379025787115e-03
-
- 6.3752681016921997e-01 5.3632658720016479e-01
- 4.4331708550453186e-01
- <_>
-
- 0 1 1598 -1.8348889425396919e-03 -1 -2 1599
- -5.9489468112587929e-03
-
- 5.8289992809295654e-01 2.6806709170341492e-01
- 4.6428859233856201e-01
- <_>
-
- 0 1 1600 -2.3030120064504445e-04 -1 -2 1601
- 5.0581009127199650e-03
-
- 5.4753202199935913e-01 5.3208339214324951e-01
- 4.6464928984642029e-01
- <_>
-
- 0 1 1602 -5.1950011402368546e-04 -1 -2 1603
- -6.8620947422459722e-04
-
- 5.2327448129653931e-01 4.9350860714912415e-01
- 3.1031179428100586e-01
- <_>
-
- 0 1 1604 -7.4936267919838428e-03 -1 -2 1605
- -1.5682930126786232e-02
-
- 2.8830468654632568e-01 3.6403131484985352e-01
- 5.3687548637390137e-01
- <_>
-
- 0 1 1606 -3.2649750355631113e-03 -1 -2 1607
- 3.8463930832222104e-04
-
- 6.4686310291290283e-01 5.2596598863601685e-01
- 3.8314279913902283e-01
- <_>
-
- 1 0 1608 4.4492390006780624e-03 -1 -2 1609
- 2.3118320852518082e-02
-
- 2.0868189632892609e-01 4.9785330891609192e-01
- 5.9612572193145752e-01
- <_>
-
- 1 0 1610 2.0835159812122583e-03 -1 -2 1611
- 1.1513150529935956e-03
-
- 5.7464218139648438e-01 3.5868450999259949e-01
- 5.3634738922119141e-01
- <_>
-
- 1 0 1612 3.6104708909988403e-02 -1 -2 1613
- 3.6256198654882610e-04
-
- 2.8331369161605835e-01 5.4777222871780396e-01
- 4.1105321049690247e-01
- <_>
-
- 0 1 1614 -3.4635469783097506e-03 -1 -2 1615
- -2.8796829283237457e-03
-
- 5.9903860092163086e-01 5.7252532243728638e-01
- 4.1495120525360107e-01
- <_>
-
- 1 0 1616 -8.1119500100612640e-03 -1 -2 1617
- 4.5932079665362835e-03
-
- 5.3963518142700195e-01 5.3797042369842529e-01
- 3.8913029432296753e-01
- <_>
-
- 1 0 1618 7.0014740340411663e-03 -1 -2 1619
- 8.0169539432972670e-04
-
- 3.7146711349487305e-01 5.5295670032501221e-01
- 3.7558048963546753e-01
- <_>
-
- 1 0 1620 -8.6652329191565514e-03 -1 -2 1621
- -2.7315050829201937e-03
-
- 5.0257730484008789e-01 5.8503222465515137e-01
- 4.6175739169120789e-01
- <_>
-
- 1 0 1622 1.3301590224727988e-03 -1 -2 1623
- -4.2648240923881531e-03
-
- 5.9377008676528931e-01 5.6453680992126465e-01
- 3.9376249909400940e-01
- <_>
-
- 0 1 1624 6.3251499086618423e-03 -1 -2 1625
- -3.0753740575164557e-03
-
- 5.1821058988571167e-01 3.0074161291122437e-01
- 5.1964038610458374e-01
- <_>
-
- 0 1 1626 -7.3622138006612659e-04 -1 -2 1627
- 3.0082479497650638e-05
-
- 3.6975800991058350e-01 4.3275931477546692e-01
- 5.7158088684082031e-01
- <_>
-
- 0 1 1628 -3.8722730241715908e-03 -1 -2 1629
- 6.2879058532416821e-04
-
- 3.4737130999565125e-01 5.4382592439651489e-01
- 4.4539061188697815e-01
- <_>
-
- 1 0 1630 1.3411579420790076e-03 -1 -2 1631
- -8.3681922405958176e-03
-
- 6.5117138624191284e-01 1.4432950317859650e-01
- 4.8881998658180237e-01
- <_>
-
- 1 0 1632 9.3305751215666533e-04 -1 -2 1633
- -1.0746510233730078e-03
-
- 3.9511090517044067e-01 3.9102658629417419e-01
- 5.3495037555694580e-01
- <_>
-
- 0 1 1634 -1.8610050901770592e-02 -1 -2 1635
- 1.3651419430971146e-03
-
- 1.2757439911365509e-01 5.0382888317108154e-01
- 6.9513040781021118e-01
- <_>
-
- 0 1 1636 7.3744421824812889e-03 -1 -2 1637
- 8.4163323044776917e-03
-
- 5.2534431219100952e-01 5.0112438201904297e-01
- 7.3113328218460083e-01
- <_>
-
- 0 1 1638 5.1413988694548607e-03 -1 -2 1639
- 4.5847031287848949e-03
-
- 4.9535360932350159e-01 2.5355559587478638e-01
- 6.4624428749084473e-01
- <_>
-
- 1 0 1640 2.8565239161252975e-02 -1 -2 1641
- 4.3958800961263478e-04
-
- 2.3307220637798309e-01 4.7022441029548645e-01
- 5.5445492267608643e-01
- <_>
-
- 1 0 1642 3.1459458172321320e-02 -1 -2 1643
- 5.6011630222201347e-03
-
- 3.3689688891172409e-02 4.7871211171150208e-01
- 6.3383519649505615e-01
- <_>
-
- 0 1 1644 7.1835669223219156e-04 -1 -2 1645
- -5.5303089320659637e-03
-
- 5.4314869642257690e-01 4.1058328747749329e-01
- 5.4039907455444336e-01
- <_>
-
- 1 0 1646 1.4129279879853129e-03 -1 -2 1647
- 2.5530709535814822e-04
-
- 3.1055399775505066e-01 4.2544719576835632e-01
- 5.4471540451049805e-01
- <_>
-
- 1 0 1648 3.1966410460881889e-04 -1 -2 1649
- 5.0411392003297806e-03
-
- 6.1183619499206543e-01 5.2900421619415283e-01
- 4.2247870564460754e-01
- <_>
-
- 0 1 1650 7.7617880888283253e-03 -1 -2 1651
- 2.9374631121754646e-03
-
- 4.3153458833694458e-01 6.6292631626129150e-01
- 3.0289649963378906e-01
- <_>
-
- 1 0 1652 -1.6497720498591661e-03 -1 -2 1653
- -5.8834417723119259e-03
-
- 5.4918527603149414e-01 3.1885540485382080e-01
- 5.1842892169952393e-01
- <_>
-
- 1 0 1654 8.7459187489002943e-04 -1 -2 1655
- -1.5308779664337635e-02
-
- 3.3288308978080750e-01 3.9236080646514893e-01
- 5.2351391315460205e-01
- <_>
-
- 1 0 1656 3.2292451709508896e-02 -1 -2 1657
- -4.3842519517056644e-04
-
- 5.9776467084884644e-01 4.5416879653930664e-01
- 5.3694289922714233e-01
- <_>
-
- 1 0 1658 1.5429529594257474e-03 -1 -2 1659
- -2.4733028840273619e-03
-
- 6.3181412220001221e-01 3.4906330704689026e-01
- 4.7590249776840210e-01
- <_>
-
- 1 0 1660 2.0994939841330051e-03 -1 -2 1661
- -5.7541108690202236e-03
-
- 5.8871978521347046e-01 5.9613317251205444e-01
- 4.8419830203056335e-01
- <_>
-
- 0 1 1662 -1.0233130306005478e-02 -1 -2 1663
- 2.2554509341716766e-01
-
- 1.7054040729999542e-01 4.7793799638748169e-01
- 9.7879663109779358e-02
- <_>
-
- 1 0 1664 2.9666559770703316e-02 -1 -2 1665
- -2.8518449980765581e-03
-
- 5.8222240209579468e-01 5.4596269130706787e-01
- 4.6100661158561707e-01
- <_>
-
- 1 0 1666 9.7465328872203827e-04 -1 -2 1667
- 1.4044740055396687e-05
-
- 3.6703228950500488e-01 4.3023860454559326e-01
- 5.6917107105255127e-01
- <_>
-
- 0 1 1668 -1.7579430714249611e-02 -1 -2 1669
- -5.2381679415702820e-02
-
- 6.9173210859298706e-01 7.1100401878356934e-01
- 5.0601547956466675e-01
- <_>
-
- 0 1 1670 -1.1242110282182693e-02 -1 -2 1671
- -3.6728400737047195e-03
-
- 8.7691891193389893e-01 6.5191918611526489e-01
- 4.5460689067840576e-01
- <_>
-
- 0 1 1672 3.5082760732620955e-03 -1 -2 1673
- 6.1679710634052753e-03
-
- 5.3298658132553101e-01 5.2204591035842896e-01
- 2.9535189270973206e-01
- <_>
-
- 1 0 1674 -9.7009900491684675e-04 -1 -2 1675
- -1.0957010090351105e-02
-
- 5.0486332178115845e-01 5.8373582363128662e-01
- 3.0200859904289246e-01
- <_>
-
- 0 1 1676 -8.3272513002157211e-03 -1 -2 1677
- 2.9798380637657829e-05
-
- 3.1580638885498047e-01 4.3863898515701294e-01
- 5.4432111978530884e-01
- <_>
-
- 1 0 1678 2.8244039276614785e-04 -1 -2 1679
- -8.1364117795601487e-04
-
- 5.6253957748413086e-01 5.2811980247497559e-01
- 3.4014078974723816e-01
- <_>
-
- 1 0 1680 1.8008040497079492e-03 -1 -2 1681
- -6.9944779388606548e-03
-
- 3.4716591238975525e-01 4.4816970825195312e-01
- 5.3857702016830444e-01
- <_>
-
- 0 1 1682 4.5625398342963308e-05 -1 -2 1683
- -7.3189922841265798e-04
-
- 4.4925129413604736e-01 4.1673120856285095e-01
- 6.0211020708084106e-01
- <_>
-
- 0 1 1684 -2.9980219551362097e-04 -1 -2 1685
- -2.9060940505587496e-05
-
- 4.1484281420707703e-01 5.5920898914337158e-01
- 4.0732109546661377e-01
- <_>
-
- 0 1 1686 -5.9742690064013004e-04 -1 -2 1687
- 1.4831830048933625e-04
-
- 6.0889142751693726e-01 5.2983051538467407e-01
- 3.7619501352310181e-01
- <_>
-
- 1 0 1688 -2.9441029764711857e-03 -1 -2 1689
- 1.3741210103034973e-01
-
- 4.7160848975181580e-01 5.1013368368148804e-01
- 4.6746801584959030e-02
- <_>
-
- 0 1 1690 -8.8414177298545837e-02 -1 -2 1691
- 7.0610277354717255e-02
-
- 1.1818689852952957e-01 5.1190632581710815e-01
- 7.7784419059753418e-01
- <_>
-
- 0 1 1692 -7.7188978902995586e-03 -1 -2 1693
- 1.5115399844944477e-02
-
- 1.8741349875926971e-01 4.9800279736518860e-01
- 7.0058178901672363e-01
- <_>
-
- 0 1 1694 1.0671879863366485e-03 -1 -2 1695
- 7.0487911580130458e-04
-
- 4.4822388887405396e-01 6.2657529115676880e-01
- 4.4026550650596619e-01
- <_>
- 90
- 4.4251281738281250e+01
-
- <_>
-
- 1 0 1696 -9.8690733313560486e-02 -1 -2 1697
- 6.2373418360948563e-02
-
- 3.9994749426841736e-01 5.2477848529815674e-01
- 8.1935757398605347e-01
- <_>
-
- 0 1 1698 1.9496519817039371e-03 -1 -2 1699
- -8.9139147894456983e-04
-
- 3.5298168659210205e-01 5.8527278900146484e-01
- 3.2459780573844910e-01
- <_>
-
- 0 1 1700 -5.5150408297777176e-04 -1 -2 1701
- -1.1721949558705091e-03
-
- 3.8928169012069702e-01 4.3350520730018616e-01
- 6.5206241607666016e-01
- <_>
-
- 1 0 1702 -7.4480642797425389e-04 -1 -2 1703
- -2.6264840271323919e-03
-
- 4.0411350131034851e-01 5.6249821186065674e-01
- 3.9675250649452209e-01
- <_>
-
- 0 1 1704 -3.9712688885629177e-04 -1 -2 1705
- 3.5984949208796024e-03
-
- 3.8561120629310608e-01 5.9978890419006348e-01
- 4.2416140437126160e-01
- <_>
-
- 1 0 1706 5.3080618381500244e-03 -1 -2 1707
- 9.6319877775385976e-04
-
- 6.6601687669754028e-01 4.4813790917396545e-01
- 5.5834877490997314e-01
- <_>
-
- 0 1 1708 5.0776469288393855e-04 -1 -2 1709
- 3.6223160568624735e-03
-
- 3.5354590415954590e-01 3.4098070859909058e-01
- 5.4206877946853638e-01
- <_>
-
- 0 1 1710 -6.2061410397291183e-02 -1 -2 1711
- 6.4387189922854304e-04
-
- 1.9340839982032776e-01 4.0836268663406372e-01
- 5.4902219772338867e-01
- <_>
-
- 1 0 1712 2.6239909231662750e-02 -1 -2 1713
- 8.1940297968685627e-04
-
- 2.2857080399990082e-01 4.6486678719520569e-01
- 6.0173559188842773e-01
- <_>
-
- 1 0 1714 2.3833119485061616e-04 -1 -2 1715
- -1.5869759954512119e-03
-
- 3.5980388522148132e-01 4.2596510052680969e-01
- 5.4764348268508911e-01
- <_>
-
- 0 1 1716 -6.7263417877256870e-03 -1 -2 1717
- 1.1006110347807407e-02
-
- 6.5072381496429443e-01 5.1494097709655762e-01
- 3.3629849553108215e-01
- <_>
-
- 1 0 1718 7.1445819921791553e-03 -1 -2 1719
- -4.7233798541128635e-03
-
- 2.6729300618171692e-01 5.6521821022033691e-01
- 4.2981448769569397e-01
- <_>
-
- 1 0 1720 9.8437406122684479e-03 -1 -2 1721
- 1.5124640412977897e-05
-
- 1.1518859863281250e-01 4.3735980987548828e-01
- 5.6121289730072021e-01
- <_>
-
- 0 1 1722 3.9908871054649353e-02 -1 -2 1723
- 5.3903679363429546e-03
-
- 5.2046489715576172e-01 4.8134678602218628e-01
- 6.3612091541290283e-01
- <_>
-
- 0 1 1724 -3.9908871054649353e-02 -1 -2 1725
- 5.3903679363429546e-03
-
- 1.5068709850311279e-01 4.5816949009895325e-01
- 6.2002408504486084e-01
- <_>
-
- 1 0 1726 6.7005190066993237e-03 -1 -2 1727
- -1.2623789720237255e-02
-
- 3.4322351217269897e-01 3.0882269144058228e-01
- 5.2267378568649292e-01
- <_>
-
- 1 0 1728 1.1806610040366650e-02 -1 -2 1729
- -3.4257229417562485e-03
-
- 7.1879392862319946e-01 3.1208148598670959e-01
- 5.0658440589904785e-01
- <_>
-
- 0 1 1730 3.9385299896821380e-04 -1 -2 1731
- 3.4388188272714615e-02
-
- 4.7545841336250305e-01 5.2616578340530396e-01
- 3.3501741290092468e-01
- <_>
-
- 0 1 1732 -7.5009986758232117e-02 -1 -2 1733
- 4.9022492021322250e-04
-
- 1.7134809494018555e-01 4.7258019447326660e-01
- 5.9564691781997681e-01
- <_>
-
- 0 1 1734 -8.5525289177894592e-03 -1 -2 1735
- 1.3135520566720515e-04
-
- 6.5582227706909180e-01 4.8354008793830872e-01
- 5.5869138240814209e-01
- <_>
-
- 1 0 1736 4.7948658466339111e-03 -1 -2 1737
- 2.0124691072851419e-03
-
- 2.6457059383392334e-01 3.6579450964927673e-01
- 5.1247721910476685e-01
- <_>
-
- 0 1 1738 -1.1785479635000229e-01 -1 -2 1739
- 1.5575019642710686e-03
-
- 2.3856540024280548e-01 5.4904741048812866e-01
- 4.2747479677200317e-01
- <_>
-
- 0 1 1740 -1.5573759563267231e-02 -1 -2 1741
- -2.1854790393263102e-03
-
- 6.9389009475708008e-01 3.6459881067276001e-01
- 5.0925260782241821e-01
- <_>
-
- 0 1 1742 2.9272339306771755e-03 -1 -2 1743
- 6.4663668163120747e-03
-
- 4.6858081221580505e-01 4.9734100699424744e-01
- 7.7260971069335938e-01
- <_>
-
- 0 1 1744 -7.6140360906720161e-03 -1 -2 1745
- 4.1512572206556797e-03
-
- 6.8774658441543579e-01 4.7885251045227051e-01
- 6.9216579198837280e-01
- <_>
-
- 0 1 1746 2.7711640577763319e-03 -1 -2 1747
- -1.2836109846830368e-02
-
- 5.4818397760391235e-01 3.8001629710197449e-01
- 5.2044928073883057e-01
- <_>
-
- 0 1 1748 -2.4380050599575043e-03 -1 -2 1749
- 2.1713329479098320e-03
-
- 2.5824350118637085e-01 4.9611631035804749e-01
- 3.2152029871940613e-01
- <_>
-
- 1 0 1750 6.2800728483125567e-04 -1 -2 1751
- -9.7982389852404594e-03
-
- 5.4604238271713257e-01 6.0465437173843384e-01
- 4.9399220943450928e-01
- <_>
-
- 1 0 1752 7.3543828912079334e-03 -1 -2 1753
- -1.4665040187537670e-02
-
- 5.2910941839218140e-01 5.4461228847503662e-01
- 3.5673621296882629e-01
- <_>
-
- 0 1 1754 3.0244510620832443e-02 -1 -2 1755
- -5.6660208851099014e-02
-
- 5.5183291435241699e-01 6.9309788942337036e-01
- 5.0933879613876343e-01
- <_>
-
- 0 1 1756 -5.6967479176819324e-03 -1 -2 1757
- 3.0806770548224449e-02
-
- 3.2015261054039001e-01 4.9892461299896240e-01
- 2.2770540416240692e-01
- <_>
-
- 0 1 1758 2.2748769260942936e-03 -1 -2 1759
- 2.0436900667846203e-03
-
- 4.8109310865402222e-01 5.2838671207427979e-01
- 3.2559248805046082e-01
- <_>
-
- 0 1 1760 -8.6277956143021584e-03 -1 -2 1761
- 6.5113382879644632e-04
-
- 6.2665361166000366e-01 5.0971370935440063e-01
- 3.1919100880622864e-01
- <_>
-
- 0 1 1762 8.8188261725008488e-04 -1 -2 1763
- -1.4594909735023975e-02
-
- 4.5495858788490295e-01 2.6450389623641968e-01
- 5.1538681983947754e-01
- <_>
-
- 0 1 1764 -1.2304580304771662e-03 -1 -2 1765
- -2.1867299801670015e-04
-
- 6.1975848674774170e-01 5.4691988229751587e-01
- 4.2068558931350708e-01
- <_>
-
- 0 1 1766 -1.0909959673881531e-03 -1 -2 1767
- 3.5210378700867295e-04
-
- 4.1407600045204163e-01 5.4766088724136353e-01
- 4.1550210118293762e-01
- <_>
-
- 0 1 1768 -7.2563779540359974e-03 -1 -2 1769
- 1.4701850013807416e-03
-
- 7.1604692935943604e-01 5.2408081293106079e-01
- 3.7296628952026367e-01
- <_>
-
- 0 1 1770 1.1472719779703766e-04 -1 -2 1771
- 3.0506469774991274e-03
-
- 4.0337988734245300e-01 5.2639859914779663e-01
- 3.5600930452346802e-01
- <_>
-
- 0 1 1772 2.6269949739798903e-04 -1 -2 1773
- -3.6365550477057695e-03
-
- 4.5697999000549316e-01 3.0425709486007690e-01
- 5.8682537078857422e-01
- <_>
-
- 1 0 1774 -8.4893293678760529e-03 -1 -2 1775
- 5.8107408694922924e-03
-
- 4.9141570925712585e-01 4.9185299873352051e-01
- 6.2669628858566284e-01
- <_>
-
- 1 0 1776 7.5583951547741890e-04 -1 -2 1777
- -2.2017690353095531e-03
-
- 5.6332361698150635e-01 5.5539160966873169e-01
- 3.8276460766792297e-01
- <_>
-
- 0 1 1778 2.7908938936889172e-03 -1 -2 1779
- -1.8228569533675909e-03
-
- 5.4986977577209473e-01 4.3822830915451050e-01
- 5.4240328073501587e-01
- <_>
-
- 0 1 1780 -7.2495508939027786e-03 -1 -2 1781
- -6.8744522286579013e-04
-
- 2.8881219029426575e-01 3.4726551175117493e-01
- 5.0763708353042603e-01
- <_>
-
- 0 1 1782 2.5174440816044807e-03 -1 -2 1783
- -1.0151379741728306e-02
-
- 4.6612051129341125e-01 3.7447750568389893e-01
- 5.2940011024475098e-01
- <_>
-
- 1 0 1784 -4.1399952024221420e-03 -1 -2 1785
- -4.7078551724553108e-03
-
- 4.6604850888252258e-01 4.1750618815422058e-01
- 6.9163060188293457e-01
- <_>
-
- 1 0 1786 4.1981041431427002e-02 -1 -2 1787
- -1.4272999949753284e-02
-
- 2.0182150602340698e-01 7.5111979246139526e-01
- 5.0320839881896973e-01
- <_>
-
- 1 0 1788 4.0869521908462048e-03 -1 -2 1789
- 1.7606799956411123e-03
-
- 2.5045138597488403e-01 3.3014011383056641e-01
- 5.2183371782302856e-01
- <_>
-
- 0 1 1790 1.2550549581646919e-04 -1 -2 1791
- -2.9503209516406059e-03
-
- 4.6144428849220276e-01 4.6199500560760498e-01
- 5.2470302581787109e-01
- <_>
-
- 0 1 1792 -1.1312420247122645e-03 -1 -2 1793
- -1.6983180539682508e-03
-
- 6.3143682479858398e-01 3.4013068675994873e-01
- 5.0555270910263062e-01
- <_>
-
- 1 0 1794 -1.1457820422947407e-02 -1 -2 1795
- -8.4962565451860428e-03
-
- 4.9399960041046143e-01 2.9654508829116821e-01
- 5.1943677663803101e-01
- <_>
-
- 1 0 1796 1.1919089592993259e-02 -1 -2 1797
- 6.4416420646011829e-03
-
- 7.8869980573654175e-01 5.1069867610931396e-01
- 2.9671460390090942e-01
- <_>
-
- 0 1 1798 -8.7857811013236642e-04 -1 -2 1799
- -2.0312711130827665e-03
-
- 5.7143712043762207e-01 4.4812008738517761e-01
- 5.3849118947982788e-01
- <_>
-
- 0 1 1800 -1.5262430533766747e-03 -1 -2 1801
- 4.2860880494117737e-03
-
- 6.1935687065124512e-01 4.3398851156234741e-01
- 7.6972991228103638e-01
- <_>
-
- 1 0 1802 3.5010920837521553e-03 -1 -2 1803
- 1.2587670236825943e-02
-
- 3.1713891029357910e-01 5.2466988563537598e-01
- 4.2412081360816956e-01
- <_>
-
- 0 1 1804 2.6207490009255707e-04 -1 -2 1805
- 4.4701730075757951e-05
-
- 4.2318999767303467e-01 4.1741389036178589e-01
- 5.9196037054061890e-01
- <_>
-
- 0 1 1806 7.8084698179736733e-04 -1 -2 1807
- 8.8851212058216333e-04
-
- 4.2773890495300293e-01 3.7201610207557678e-01
- 5.2268189191818237e-01
- <_>
-
- 0 1 1808 2.3369069676846266e-03 -1 -2 1809
- 1.6688359901309013e-03
-
- 5.4780668020248413e-01 3.6286789178848267e-01
- 6.1500048637390137e-01
- <_>
-
- 0 1 1810 3.0844469438306987e-04 -1 -2 1811
- 3.4617560449987650e-03
-
- 4.7470751404762268e-01 4.5801380276679993e-01
- 5.5856817960739136e-01
- <_>
-
- 0 1 1812 1.8961310386657715e-02 -1 -2 1813
- 1.7347310483455658e-01
-
- 5.2988010644912720e-01 3.6983850598335266e-01
- 8.4986197948455811e-01
- <_>
-
- 1 0 1814 2.0020549709443003e-04 -1 -2 1815
- 1.0967060225084424e-03
-
- 5.5656617879867554e-01 4.7957131266593933e-01
- 6.2862598896026611e-01
- <_>
-
- 0 1 1816 1.5107099898159504e-04 -1 -2 1817
- -3.4463501069694757e-03
-
- 4.0524059534072876e-01 6.1730152368545532e-01
- 4.4142639636993408e-01
- <_>
-
- 1 0 1818 8.5176620632410049e-03 -1 -2 1819
- -3.5812109708786011e-02
-
- 3.5705709457397461e-01 3.1513288617134094e-01
- 5.2527028322219849e-01
- <_>
-
- 0 1 1820 -2.1155400201678276e-02 -1 -2 1821
- 8.9890940580517054e-04
-
- 6.1247211694717407e-01 5.1699757575988770e-01
- 3.5962718725204468e-01
- <_>
-
- 1 0 1822 -1.5613760333508253e-03 -1 -2 1823
- 6.7120860330760479e-04
-
- 4.9149879813194275e-01 4.5462110638618469e-01
- 5.3958117961883545e-01
- <_>
-
- 0 1 1824 -2.1597029641270638e-02 -1 -2 1825
- -2.4947229772806168e-02
-
- 1.9031339883804321e-01 6.9740772247314453e-01
- 4.9677160382270813e-01
- <_>
-
- 0 1 1826 1.8725979607552290e-03 -1 -2 1827
- 6.3912719488143921e-03
-
- 4.7489479184150696e-01 5.1801782846450806e-01
- 2.9243218898773193e-01
- <_>
-
- 0 1 1828 -9.1552399098873138e-03 -1 -2 1829
- 2.1715660113841295e-03
-
- 7.6658701896667480e-01 5.2155512571334839e-01
- 3.3657190203666687e-01
- <_>
-
- 1 0 1830 1.2330369791015983e-03 -1 -2 1831
- -4.0785901364870369e-04
-
- 6.2609577178955078e-01 4.5335099101066589e-01
- 5.3864890336990356e-01
- <_>
-
- 0 1 1832 4.6437609125860035e-04 -1 -2 1833
- -1.1600199650274590e-04
-
- 4.1034960746765137e-01 5.8303910493850708e-01
- 4.3041059374809265e-01
- <_>
-
- 0 1 1834 -1.2718720361590385e-02 -1 -2 1835
- 8.9431880041956902e-05
-
- 2.1325829625129700e-01 4.8728910088539124e-01
- 5.4589152336120605e-01
- <_>
-
- 0 1 1836 -3.3913689549081028e-04 -1 -2 1837
- -1.8026340752840042e-02
-
- 3.9743649959564209e-01 7.5685507059097290e-01
- 5.0456118583679199e-01
- <_>
-
- 1 0 1838 6.9179181009531021e-03 -1 -2 1839
- -1.1839679791592062e-04
-
- 3.9662998914718628e-01 4.1980829834938049e-01
- 5.4358041286468506e-01
- <_>
-
- 0 1 1840 -3.9474181830883026e-03 -1 -2 1841
- 6.0050919273635373e-05
-
- 6.3694578409194946e-01 5.2695667743682861e-01
- 3.8122430443763733e-01
- <_>
-
- 1 0 1842 9.1423643752932549e-03 -1 -2 1843
- 2.1305440168362111e-04
-
- 4.1567629575729370e-01 3.5235330462455750e-01
- 5.3494542837142944e-01
- <_>
-
- 1 0 1844 -2.0855850016232580e-04 -1 -2 1845
- 1.3130389852449298e-03
-
- 4.4033220410346985e-01 6.0581612586975098e-01
- 4.4682189822196960e-01
- <_>
-
- 1 0 1846 -2.9134768992662430e-03 -1 -2 1847
- 2.9645769391208887e-03
-
- 4.8257058858871460e-01 4.8359981179237366e-01
- 6.0392779111862183e-01
- <_>
-
- 1 0 1848 1.7772549763321877e-03 -1 -2 1849
- -7.7136349864304066e-03
-
- 6.8718272447586060e-01 2.8422209620475769e-01
- 5.1454281806945801e-01
- <_>
-
- 1 0 1850 5.1027478184551001e-04 -1 -2 1851
- 1.7460630042478442e-03
-
- 6.0244262218475342e-01 4.7566100955009460e-01
- 5.7211542129516602e-01
- <_>
-
- 1 0 1852 3.8068278809078038e-04 -1 -2 1853
- 2.8228890150785446e-03
-
- 4.9310690164566040e-01 3.3116981387138367e-01
- 6.2275981903076172e-01
- <_>
-
- 1 0 1854 -5.3000478073954582e-03 -1 -2 1855
- 4.4951299059903249e-05
-
- 5.2320927381515503e-01 3.9952319860458374e-01
- 5.3147977590560913e-01
- <_>
-
- 0 1 1856 3.2752458937466145e-03 -1 -2 1857
- -2.8162579983472824e-03
-
- 4.4816198945045471e-01 3.9079719781875610e-01
- 6.6716408729553223e-01
- <_>
-
- 0 1 1858 1.4112279750406742e-03 -1 -2 1859
- 8.3062034100294113e-03
-
- 5.3570109605789185e-01 4.7709658741950989e-01
- 5.5700999498367310e-01
- <_>
-
- 0 1 1860 2.2164839319884777e-03 -1 -2 1861
- -4.9868631176650524e-03
-
- 4.9471241235733032e-01 5.2413070201873779e-01
- 2.5126549601554871e-01
- <_>
-
- 1 0 1862 -3.6664260551333427e-03 -1 -2 1863
- -1.0581229813396931e-02
-
- 4.6195539832115173e-01 6.3017189502716064e-01
- 4.9730318784713745e-01
- <_>
-
- 1 0 1864 7.3366491124033928e-03 -1 -2 1865
- -3.9318940252996981e-04
-
- 2.8709700703620911e-01 4.2528051137924194e-01
- 5.5792468786239624e-01
- <_>
-
- 0 1 1866 -8.1375334411859512e-03 -1 -2 1867
- 2.4809150490909815e-03
-
- 5.7473158836364746e-01 5.2033740282058716e-01
- 3.9035668969154358e-01
- <_>
-
- 1 0 1868 8.8749779388308525e-04 -1 -2 1869
- -4.2194919660687447e-04
-
- 5.5343210697174072e-01 5.3380441665649414e-01
- 3.9258408546447754e-01
- <_>
-
- 0 1 1870 -7.9790111631155014e-03 -1 -2 1871
- 1.1439629597589374e-03
-
- 4.1443160176277161e-01 4.7013729810714722e-01
- 5.2817362546920776e-01
- <_>
-
- 1 0 1872 7.5542130507528782e-03 -1 -2 1873
- 1.0288399644196033e-03
-
- 2.5272560119628906e-01 5.6051462888717651e-01
- 4.2978560924530029e-01
- <_>
-
- 1 0 1874 -1.7234670231118798e-03 -1 -2 1875
- 5.7586699724197388e-01
-
- 4.8396828770637512e-01 5.1105028390884399e-01
- 8.0489329993724823e-02
- <_>
- 109
- 5.3755569458007812e+01
-
- <_>
-
- 0 1 1876 6.6640521399676800e-03 -1 -2 1877
- 8.9905522763729095e-03
-
- 3.8289201259613037e-01 4.8584291338920593e-01
- 7.3549592494964600e-01
- <_>
-
- 1 0 1878 5.7154200039803982e-03 -1 -2 1879
- 1.1257929727435112e-03
-
- 6.7232239246368408e-01 4.4295778870582581e-01
- 6.0707777738571167e-01
- <_>
-
- 1 0 1880 -9.1789010912179947e-04 -1 -2 1881
- -1.0492859873920679e-03
-
- 3.0763450264930725e-01 5.5936437845230103e-01
- 3.6510229110717773e-01
- <_>
-
- 0 1 1882 3.5453929740469903e-05 -1 -2 1883
- 2.9015709878876805e-04
-
- 4.2779681086540222e-01 4.5835450291633606e-01
- 5.2846831083297729e-01
- <_>
-
- 1 0 1884 1.6071660502348095e-04 -1 -2 1885
- -5.2961107576265931e-04
-
- 3.7981921434402466e-01 3.8504371047019958e-01
- 5.9396880865097046e-01
- <_>
-
- 0 1 1886 2.6682569296099246e-04 -1 -2 1887
- -1.3492540165316314e-04
-
- 4.1230249404907227e-01 5.7605999708175659e-01
- 4.2376458644866943e-01
- <_>
-
- 0 1 1888 -1.0841679759323597e-02 -1 -2 1889
- 1.2077829800546169e-02
-
- 3.9299210906028748e-01 5.7619231939315796e-01
- 2.7804449200630188e-01
- <_>
-
- 0 1 1890 2.2128869313746691e-03 -1 -2 1891
- -1.5266190283000469e-02
-
- 4.7945070266723633e-01 7.4055880308151245e-02
- 5.1535779237747192e-01
- <_>
-
- 1 0 1892 6.7929533543065190e-05 -1 -2 1893
- 1.7633590323384851e-04
-
- 5.8587378263473511e-01 3.5676109790802002e-01
- 5.5989629030227661e-01
- <_>
-
- 1 0 1894 8.1311381654813886e-04 -1 -2 1895
- 3.2630451023578644e-03
-
- 5.3468507528305054e-01 4.7825369238853455e-01
- 5.4567539691925049e-01
- <_>
-
- 0 1 1896 -3.9503918960690498e-03 -1 -2 1897
- -3.9864578866399825e-04
-
- 2.8318119049072266e-01 5.4852157831192017e-01
- 4.1596978902816772e-01
- <_>
-
- 0 1 1898 -1.1432520113885403e-02 -1 -2 1899
- 5.3339172154664993e-03
-
- 5.6391012668609619e-01 4.5969840884208679e-01
- 5.9312427043914795e-01
- <_>
-
- 1 0 1900 8.3193257451057434e-03 -1 -2 1901
- -4.2479918920435011e-04
-
- 3.2306200265884399e-01 3.7952938675880432e-01
- 5.4086112976074219e-01
- <_>
-
- 0 1 1902 -1.1189430207014084e-01 -1 -2 1903
- -7.5553781352937222e-03
-
- 1.1322979629039764e-01 6.3393700122833252e-01
- 4.8387709259986877e-01
- <_>
-
- 0 1 1904 -7.0337029173970222e-03 -1 -2 1905
- -1.4833680354058743e-02
-
- 5.6652551889419556e-01 6.7514181137084961e-01
- 4.1409450769424438e-01
- <_>
-
- 1 0 1906 8.7506724521517754e-03 -1 -2 1907
- 1.6645010327920318e-03
-
- 3.5612589120864868e-01 5.3472799062728882e-01
- 3.6497798562049866e-01
- <_>
-
- 1 0 1908 9.4900820404291153e-03 -1 -2 1909
- 1.1133110383525491e-03
-
- 2.7546560764312744e-01 4.2259928584098816e-01
- 5.6291788816452026e-01
- <_>
-
- 0 1 1910 9.4940755516290665e-03 -1 -2 1911
- -1.5396620146930218e-03
-
- 4.9060368537902832e-01 4.0070518851280212e-01
- 5.3807091712951660e-01
- <_>
-
- 1 0 1912 1.3434959948062897e-01 -1 -2 1913
- -9.4940755516290665e-03
-
- 2.2146719694137573e-01 7.3531562089920044e-01
- 5.0050330162048340e-01
- <_>
-
- 1 0 1914 2.0011790096759796e-02 -1 -2 1915
- -1.8875009845942259e-03
-
- 3.3279061317443848e-01 3.9152890443801880e-01
- 5.4018497467041016e-01
- <_>
-
- 1 0 1916 7.1842782199382782e-03 -1 -2 1917
- 1.6976969782263041e-03
-
- 7.1766048669815063e-01 4.5269781351089478e-01
- 6.0769128799438477e-01
- <_>
-
- 1 0 1918 4.9219978973269463e-03 -1 -2 1919
- 1.1803199537098408e-02
-
- 2.5698339939117432e-01 4.9996379017829895e-01
- 5.9582281112670898e-01
- <_>
-
- 0 1 1920 -9.7703449428081512e-03 -1 -2 1921
- 2.1174899302423000e-03
-
- 3.4590938687324524e-01 4.5151269435882568e-01
- 5.8297157287597656e-01
- <_>
-
- 0 1 1922 9.4801411032676697e-03 -1 -2 1923
- -2.6078789960592985e-03
-
- 4.8073920607566833e-01 3.4622168540954590e-01
- 5.2015948295593262e-01
- <_>
-
- 0 1 1924 -5.7252747938036919e-03 -1 -2 1925
- -8.2325618714094162e-03
-
- 6.5998530387878418e-01 2.8218281269073486e-01
- 5.1252847909927368e-01
- <_>
-
- 0 1 1926 8.9571950957179070e-04 -1 -2 1927
- -1.5021569561213255e-04
-
- 4.8838189244270325e-01 4.8299181461334229e-01
- 5.4287171363830566e-01
- <_>
-
- 0 1 1928 4.8489659093320370e-04 -1 -2 1929
- -9.6192650496959686e-02
-
- 4.4345989823341370e-01 2.2566360235214233e-01
- 5.9562277793884277e-01
- <_>
-
- 0 1 1930 -1.1053519556298852e-03 -1 -2 1931
- -1.0215040296316147e-01
-
- 4.5272240042686462e-01 2.8443491458892822e-01
- 5.1864528656005859e-01
- <_>
-
- 1 0 1932 3.0147889629006386e-03 -1 -2 1933
- 7.6131648384034634e-03
-
- 3.8089990615844727e-01 5.7186990976333618e-01
- 4.2625638842582703e-01
- <_>
-
- 1 0 1934 1.5197630273178220e-03 -1 -2 1935
- -1.4197279699146748e-02
-
- 5.9427189826965332e-01 7.7311038970947266e-01
- 4.9976539611816406e-01
- <_>
-
- 0 1 1936 -1.3818879611790180e-02 -1 -2 1937
- -5.0701329018920660e-04
-
- 6.6811382770538330e-01 3.3056080341339111e-01
- 4.7499749064445496e-01
- <_>
-
- 0 1 1938 -9.3537531793117523e-03 -1 -2 1939
- -9.4771059229969978e-03
-
- 2.8609329462051392e-01 6.1888831853866577e-01
- 4.8421001434326172e-01
- <_>
-
- 1 0 1940 1.6923650400713086e-03 -1 -2 1941
- 5.8652542065829039e-04
-
- 6.0702490806579590e-01 3.7826898694038391e-01
- 5.3681969642639160e-01
- <_>
-
- 0 1 1942 -2.5826620403677225e-03 -1 -2 1943
- -2.7307639829814434e-03
-
- 3.6902099847793579e-01 3.8571149110794067e-01
- 5.3181087970733643e-01
- <_>
-
- 1 0 1944 2.1871570497751236e-02 -1 -2 1945
- -1.5010299648565706e-05
-
- 2.3270089924335480e-01 5.5607229471206665e-01
- 4.3014100193977356e-01
- <_>
-
- 1 0 1946 5.3583700209856033e-03 -1 -2 1947
- 5.0057549960911274e-03
-
- 6.7676377296447754e-01 5.1949042081832886e-01
- 3.6128538846969604e-01
- <_>
-
- 0 1 1948 -1.9030070398002863e-03 -1 -2 1949
- -7.8506693243980408e-03
-
- 3.2378450036048889e-01 1.1948519945144653e-01
- 4.9917238950729370e-01
- <_>
-
- 1 0 1950 -2.7093670796602964e-03 -1 -2 1951
- 1.4138079714030027e-03
-
- 4.8549601435661316e-01 4.8723229765892029e-01
- 5.9035778045654297e-01
- <_>
-
- 1 0 1952 9.0300198644399643e-03 -1 -2 1953
- -9.7925681620836258e-04
-
- 6.5473157167434692e-01 5.8492732048034668e-01
- 4.5542308688163757e-01
- <_>
-
- 1 0 1954 1.3984439428895712e-03 -1 -2 1955
- 8.3372107474133372e-04
-
- 4.0646260976791382e-01 5.3995430469512939e-01
- 4.1528099775314331e-01
- <_>
-
- 1 0 1956 1.0551059618592262e-02 -1 -2 1957
- 8.8344102550763637e-05
-
- 1.7966809868812561e-01 4.2518630623817444e-01
- 5.4135227203369141e-01
- <_>
-
- 1 0 1958 -4.1022308170795441e-02 -1 -2 1959
- 7.5065628625452518e-03
-
- 5.2281248569488525e-01 4.8537430167198181e-01
- 6.0934442281723022e-01
- <_>
-
- 1 0 1960 4.1022308170795441e-02 -1 -2 1961
- -5.3961377125233412e-04
-
- 2.2050240635871887e-01 5.6927317380905151e-01
- 4.4687569141387939e-01
- <_>
-
- 0 1 1962 -6.8696036934852600e-02 -1 -2 1963
- -1.8447940237820148e-03
-
- 1.4833140373229980e-01 6.2112838029861450e-01
- 4.9666011333465576e-01
- <_>
-
- 0 1 1964 -6.0959919355809689e-03 -1 -2 1965
- -4.2068301700055599e-03
-
- 2.2946719825267792e-01 6.4070910215377808e-01
- 4.7485628724098206e-01
- <_>
-
- 1 0 1966 -7.1332789957523346e-04 -1 -2 1967
- 1.1756779998540878e-01
-
- 5.3549361228942871e-01 5.1369780302047729e-01
- 1.0595739819109440e-02
- <_>
-
- 0 1 1968 5.9354289987822995e-05 -1 -2 1969
- -6.3173691742122173e-03
-
- 3.7118038535118103e-01 1.7120739817619324e-01
- 5.0617581605911255e-01
- <_>
-
- 1 0 1970 1.4941499568521976e-02 -1 -2 1971
- -2.0789399277418852e-03
-
- 6.7291188240051270e-01 4.4106459617614746e-01
- 5.4440277814865112e-01
- <_>
-
- 0 1 1972 -7.0736219640821218e-04 -1 -2 1973
- -3.1247111037373543e-03
-
- 5.5689108371734619e-01 5.0238692760467529e-01
- 3.5624051094055176e-01
- <_>
-
- 1 0 1974 -7.8919378574937582e-04 -1 -2 1975
- 1.0179580189287663e-02
-
- 5.4567861557006836e-01 5.5451387166976929e-01
- 4.6223109960556030e-01
- <_>
-
- 1 0 1976 -2.7506109327077866e-03 -1 -2 1977
- 1.0601329617202282e-02
-
- 4.9425360560417175e-01 2.9612338542938232e-01
- 5.9643387794494629e-01
- <_>
-
- 0 1 1978 5.1466780714690685e-03 -1 -2 1979
- 7.6321147382259369e-02
-
- 5.4952287673950195e-01 5.1739591360092163e-01
- 2.9402169585227966e-01
- <_>
-
- 0 1 1980 -1.5027689514681697e-03 -1 -2 1981
- 1.2266670353710651e-02
-
- 3.1062999367713928e-01 4.6511501073837280e-01
- 6.8466138839721680e-01
- <_>
-
- 1 0 1982 -3.1118579208850861e-02 -1 -2 1983
- 2.8905589133501053e-02
-
- 5.2260571718215942e-01 5.1822441816329956e-01
- 2.7054280042648315e-01
- <_>
-
- 1 0 1984 4.7598380595445633e-02 -1 -2 1985
- 3.0808549374341965e-02
-
- 1.1095120012760162e-01 4.9386250972747803e-01
- 1.4041109383106232e-01
- <_>
-
- 1 0 1986 -2.1277810446918011e-04 -1 -2 1987
- 7.8969962894916534e-02
-
- 4.3923568725585938e-01 5.2165520191192627e-01
- 2.2941139340400696e-01
- <_>
-
- 0 1 1988 -1.0257950052618980e-02 -1 -2 1989
- 1.2604889925569296e-03
-
- 6.1766529083251953e-01 5.2362227439880371e-01
- 3.3289659023284912e-01
- <_>
-
- 1 0 1990 -3.3490460366010666e-02 -1 -2 1991
- -5.9202767442911863e-04
-
- 4.8661869764328003e-01 4.1164070367813110e-01
- 5.3956401348114014e-01
- <_>
-
- 1 0 1992 3.0320750738610514e-05 -1 -2 1993
- -5.4369680583477020e-04
-
- 5.6107360124588013e-01 5.6213891506195068e-01
- 3.4612038731575012e-01
- <_>
-
- 1 0 1994 -3.3490460366010666e-02 -1 -2 1995
- -5.9202767442911863e-04
-
- 4.8967620730400085e-01 4.3054041266441345e-01
- 5.3407138586044312e-01
- <_>
-
- 0 1 1996 2.0550889894366264e-03 -1 -2 1997
- -4.4353571720421314e-03
-
- 5.5449998378753662e-01 6.0385400056838989e-01
- 3.7465929985046387e-01
- <_>
-
- 1 0 1998 -8.4170423448085785e-02 -1 -2 1999
- 6.7419027909636497e-03
-
- 5.0073480606079102e-01 5.2980971336364746e-01
- 4.7161450982093811e-01
- <_>
-
- 1 0 2000 1.0278150439262390e-02 -1 -2 2001
- 5.8800862170755863e-03
-
- 6.2693750858306885e-01 5.1548278331756592e-01
- 3.8130408525466919e-01
- <_>
-
- 1 0 2002 -6.9679190346505493e-06 -1 -2 2003
- 8.2419527461752295e-04
-
- 4.4402399659156799e-01 4.6975341439247131e-01
- 5.4855042695999146e-01
- <_>
-
- 0 1 2004 -5.5268318392336369e-03 -1 -2 2005
- 9.6128671430051327e-04
-
- 5.5136048793792725e-01 3.6186391115188599e-01
- 5.8384567499160767e-01
- <_>
-
- 1 0 2006 2.4810510221868753e-03 -1 -2 2007
- -1.0480589699000120e-03
-
- 2.5232228636741638e-01 4.1172578930854797e-01
- 5.3929960727691650e-01
- <_>
-
- 0 1 2008 -6.1287907883524895e-03 -1 -2 2009
- 1.1682329932227731e-04
-
- 6.7263299226760864e-01 5.0411927700042725e-01
- 3.6077290773391724e-01
- <_>
-
- 0 1 2010 -3.9909478276968002e-02 -1 -2 2011
- 1.5859459526836872e-03
-
- 1.5637390315532684e-01 4.8919808864593506e-01
- 5.7798451185226440e-01
- <_>
-
- 0 1 2012 -2.2690229117870331e-02 -1 -2 2013
- 2.0916070789098740e-03
-
- 2.1868790686130524e-01 4.7715771198272705e-01
- 6.0992312431335449e-01
- <_>
-
- 0 1 2014 -2.4715419858694077e-02 -1 -2 2015
- -1.3419450260698795e-02
-
- 3.4639969468116760e-01 3.6306929588317871e-01
- 5.2521961927413940e-01
- <_>
-
- 0 1 2016 -6.0629472136497498e-03 -1 -2 2017
- -2.0921030081808567e-03
-
- 6.6663217544555664e-01 3.3995470404624939e-01
- 5.0356978178024292e-01
- <_>
-
- 0 1 2018 2.5961859151721001e-02 -1 -2 2019
- 1.7908669542521238e-04
-
- 5.0368028879165649e-01 5.4185307025909424e-01
- 4.3189769983291626e-01
- <_>
-
- 0 1 2020 -3.1546850223094225e-03 -1 -2 2021
- -1.1397759662941098e-03
-
- 7.2210252285003662e-01 3.3209729194641113e-01
- 5.0244337320327759e-01
- <_>
-
- 0 1 2022 -4.7840211540460587e-02 -1 -2 2023
- 4.1577088995836675e-04
-
- 1.9387650489807129e-01 4.8021888732910156e-01
- 5.7307147979736328e-01
- <_>
-
- 0 1 2024 -4.4247039477340877e-04 -1 -2 2025
- 1.4479350065812469e-03
-
- 4.2625150084495544e-01 5.7191711664199829e-01
- 4.0641531348228455e-01
- <_>
-
- 0 1 2026 1.5701510012149811e-02 -1 -2 2027
- 2.7805729769170284e-04
-
- 4.9957260489463806e-01 5.2892869710922241e-01
- 4.5817288756370544e-01
- <_>
-
- 0 1 2028 -2.9010509606450796e-03 -1 -2 2029
- 2.0830519497394562e-04
-
- 6.0121482610702515e-01 5.0579768419265747e-01
- 3.5994321107864380e-01
- <_>
-
- 1 0 2030 -5.1530029624700546e-02 -1 -2 2031
- 1.7163449956569821e-04
-
- 4.9917969107627869e-01 4.6754699945449829e-01
- 5.3747731447219849e-01
- <_>
-
- 1 0 2032 2.3614279925823212e-02 -1 -2 2033
- -5.6427798699587584e-04
-
- 6.5864789485931396e-01 3.8532960414886475e-01
- 5.1960402727127075e-01
- <_>
-
- 1 0 2034 6.6903959959745407e-03 -1 -2 2035
- -4.8789530992507935e-03
-
- 6.0042357444763184e-01 3.2932278513908386e-01
- 5.2452367544174194e-01
- <_>
-
- 0 1 2036 -6.8537332117557526e-03 -1 -2 2037
- 9.9893810693174601e-04
-
- 2.5659140944480896e-01 4.6154940128326416e-01
- 5.9424322843551636e-01
- <_>
-
- 0 1 2038 -1.3354700058698654e-04 -1 -2 2039
- 1.0165109997615218e-03
-
- 5.4873758554458618e-01 4.5783591270446777e-01
- 5.4269278049468994e-01
- <_>
-
- 1 0 2040 9.1216771397739649e-04 -1 -2 2041
- 1.0080259526148438e-03
-
- 3.9394611120223999e-01 4.0497899055480957e-01
- 5.5207037925720215e-01
- <_>
-
- 1 0 2042 -1.3102490629535168e-04 -1 -2 2043
- 5.5228749988600612e-04
-
- 4.8790889978408813e-01 4.8449438810348511e-01
- 5.5128258466720581e-01
- <_>
-
- 1 0 2044 -1.2130969844292849e-04 -1 -2 2045
- -1.5112989785848185e-05
-
- 4.3679711222648621e-01 6.4259552955627441e-01
- 4.8818269371986389e-01
- <_>
-
- 1 0 2046 -4.0125829400494695e-04 -1 -2 2047
- -6.5766851184889674e-04
-
- 5.3720992803573608e-01 5.8345532417297363e-01
- 4.8690780997276306e-01
- <_>
-
- 1 0 2048 6.2220421386882663e-04 -1 -2 2049
- 1.4663359615951777e-03
-
- 3.8246369361877441e-01 4.8134881258010864e-01
- 6.9667392969131470e-01
- <_>
-
- 0 1 2050 -4.9547709524631500e-02 -1 -2 2051
- 1.3017569435760379e-03
-
- 5.3927659988403320e-02 5.3374558687210083e-01
- 4.1607481241226196e-01
- <_>
-
- 0 1 2052 -4.4914530590176582e-03 -1 -2 2053
- 1.6592369647696614e-03
-
- 5.9974372386932373e-01 3.7271851301193237e-01
- 5.1156342029571533e-01
- <_>
-
- 0 1 2054 6.4695458859205246e-03 -1 -2 2055
- 4.9810269847512245e-03
-
- 5.2520352602005005e-01 5.2567178010940552e-01
- 3.9344060420989990e-01
- <_>
-
- 0 1 2056 -3.8536980748176575e-02 -1 -2 2057
- -2.8275650739669800e-01
-
- 2.0619249343872070e-01 6.1883211135864258e-02
- 4.9250578880310059e-01
- <_>
-
- 0 1 2058 -9.0301828458905220e-03 -1 -2 2059
- -4.3866269290447235e-02
-
- 3.1575900316238403e-01 2.0336820185184479e-01
- 5.1647698879241943e-01
- <_>
-
- 0 1 2060 -4.5701069757342339e-03 -1 -2 2061
- -2.3362410720437765e-03
-
- 6.6111832857131958e-01 2.8077891469001770e-01
- 4.9628761410713196e-01
- <_>
-
- 0 1 2062 5.3960331715643406e-03 -1 -2 2063
- -2.6297608856111765e-03
-
- 5.1463878154754639e-01 6.2844878435134888e-01
- 4.9555888772010803e-01
- <_>
-
- 0 1 2064 -3.8577478844672441e-03 -1 -2 2065
- 1.3963800156489015e-03
-
- 1.4867480099201202e-01 4.7013381123542786e-01
- 6.3209718465805054e-01
- <_>
-
- 1 0 2066 -8.8699469342827797e-03 -1 -2 2067
- -7.0626288652420044e-04
-
- 5.2868181467056274e-01 4.6483701467514038e-01
- 5.3332102298736572e-01
- <_>
-
- 0 1 2068 4.2645810171961784e-03 -1 -2 2069
- 6.1572100967168808e-02
-
- 5.0848782062530518e-01 3.6296251416206360e-01
- 8.7571567296981812e-01
- <_>
-
- 1 0 2070 -4.5381980016827583e-03 -1 -2 2071
- -4.0877899155020714e-03
-
- 4.8566961288452148e-01 4.5841160416603088e-01
- 5.4202407598495483e-01
- <_>
-
- 1 0 2072 6.4308601431548595e-03 -1 -2 2073
- 7.0455260574817657e-03
-
- 2.7073028683662415e-01 5.0574868917465210e-01
- 7.0265239477157593e-01
- <_>
-
- 1 0 2074 -2.3246440105140209e-03 -1 -2 2075
- 6.0276601288933307e-05
-
- 4.8272788524627686e-01 4.2472490668296814e-01
- 5.5087631940841675e-01
- <_>
-
- 1 0 2076 1.8084559589624405e-02 -1 -2 2077
- 8.4693520329892635e-04
-
- 8.1048011779785156e-01 5.1546192169189453e-01
- 3.5143798589706421e-01
- <_>
-
- 1 0 2078 -2.6931039988994598e-02 -1 -2 2079
- -4.2346641421318054e-03
-
- 4.8868888616561890e-01 4.6223780512809753e-01
- 5.3824782371520996e-01
- <_>
-
- 1 0 2080 2.6947110891342163e-02 -1 -2 2081
- 4.6446882188320160e-03
-
- 6.3665962219238281e-01 5.3685069084167480e-01
- 3.7654298543930054e-01
- <_>
-
- 0 1 2082 -6.9577661342918873e-03 -1 -2 2083
- 8.7609712500125170e-04
-
- 4.2346870899200439e-01 4.6724060177803040e-01
- 5.3506839275360107e-01
- <_>
-
- 1 0 2084 1.6103329835459590e-03 -1 -2 2085
- -1.2848590267822146e-03
-
- 5.7327628135681152e-01 5.4817992448806763e-01
- 3.7845930457115173e-01
- <_>
-
- 0 1 2086 1.0243539698421955e-02 -1 -2 2087
- 2.6889349101111293e-04
-
- 5.1559072732925415e-01 5.3531897068023682e-01
- 4.3871539831161499e-01
- <_>
-
- 0 1 2088 3.7903659977018833e-03 -1 -2 2089
- -2.9369680210947990e-02
-
- 5.0320029258728027e-01 5.8735388517379761e-01
- 2.2154450416564941e-01
- <_>
-
- 1 0 2090 6.0743088833987713e-03 -1 -2 2091
- -1.2710720300674438e-02
-
- 5.4170298576354980e-01 6.0565119981765747e-01
- 4.9851819872856140e-01
- <_>
-
- 0 1 2092 -5.9445449151098728e-03 -1 -2 2093
- -2.8927479870617390e-03
-
- 3.3520698547363281e-01 6.9292408227920532e-01
- 4.7782200574874878e-01
-
- <_>
-
- <_>
- 2 7 16 4 -1.
- <_>
- 2 9 16 2 2.
- <_>
-
- <_>
- 8 4 3 14 -1.
- <_>
- 8 11 3 7 2.
- <_>
-
- <_>
- 13 6 1 6 -1.
- <_>
- 13 9 1 3 2.
- <_>
-
- <_>
- 4 2 12 8 -1.
- <_>
- 8 2 4 8 3.
- <_>
-
- <_>
- 6 3 1 9 -1.
- <_>
- 6 6 1 3 3.
- <_>
-
- <_>
- 3 7 14 9 -1.
- <_>
- 3 10 14 3 3.
- <_>
-
- <_>
- 4 7 4 4 -1.
- <_>
- 4 9 4 2 2.
- <_>
-
- <_>
- 9 4 2 16 -1.
- <_>
- 9 12 2 8 2.
- <_>
-
- <_>
- 1 1 18 5 -1.
- <_>
- 7 1 6 5 3.
- <_>
-
- <_>
- 4 5 13 8 -1.
- <_>
- 4 9 13 4 2.
- <_>
-
- <_>
- 1 7 16 9 -1.
- <_>
- 1 10 16 3 3.
- <_>
-
- <_>
- 2 0 15 4 -1.
- <_>
- 2 2 15 2 2.
- <_>
-
- <_>
- 7 5 6 4 -1.
- <_>
- 9 5 2 4 3.
- <_>
-
- <_>
- 6 3 8 9 -1.
- <_>
- 6 6 8 3 3.
- <_>
-
- <_>
- 8 12 3 8 -1.
- <_>
- 8 16 3 4 2.
- <_>
-
- <_>
- 3 16 2 2 -1.
- <_>
- 3 17 2 1 2.
- <_>
-
- <_>
- 14 1 6 12 -1.
- <_>
- 14 1 3 12 2.
- <_>
-
- <_>
- 4 4 12 6 -1.
- <_>
- 8 4 4 6 3.
- <_>
-
- <_>
- 0 2 6 15 -1.
- <_>
- 3 2 3 15 2.
- <_>
-
- <_>
- 5 4 9 6 -1.
- <_>
- 5 6 9 2 3.
- <_>
-
- <_>
- 13 11 6 3 -1.
- <_>
- 13 12 6 1 3.
- <_>
-
- <_>
- 12 12 6 4 -1.
- <_>
- 12 14 6 2 2.
- <_>
-
- <_>
- 1 11 6 3 -1.
- <_>
- 1 12 6 1 3.
- <_>
-
- <_>
- 2 5 5 8 -1.
- <_>
- 2 9 5 4 2.
- <_>
-
- <_>
- 5 4 10 4 -1.
- <_>
- 5 6 10 2 2.
- <_>
-
- <_>
- 2 4 16 12 -1.
- <_>
- 2 8 16 4 3.
- <_>
-
- <_>
- 4 5 12 6 -1.
- <_>
- 8 5 4 6 3.
- <_>
-
- <_>
- 13 7 2 9 -1.
- <_>
- 13 10 2 3 3.
- <_>
-
- <_>
- 5 7 2 9 -1.
- <_>
- 5 10 2 3 3.
- <_>
-
- <_>
- 7 1 6 8 -1.
- <_>
- 9 1 2 8 3.
- <_>
-
- <_>
- 12 0 4 12 -1.
- <_>
- 14 0 2 6 2.
- <_>
- 12 6 2 6 2.
- <_>
-
- <_>
- 5 8 10 2 -1.
- <_>
- 5 9 10 1 2.
- <_>
-
- <_>
- 5 1 6 4 -1.
- <_>
- 7 1 2 4 3.
- <_>
-
- <_>
- 0 3 9 12 -1.
- <_>
- 3 3 3 12 3.
- <_>
-
- <_>
- 9 8 3 12 -1.
- <_>
- 9 12 3 4 3.
- <_>
-
- <_>
- 0 5 20 15 -1.
- <_>
- 0 10 20 5 3.
- <_>
-
- <_>
- 2 2 6 8 -1.
- <_>
- 2 2 3 4 2.
- <_>
- 5 6 3 4 2.
- <_>
-
- <_>
- 2 1 6 2 -1.
- <_>
- 2 2 6 1 2.
- <_>
-
- <_>
- 10 15 6 4 -1.
- <_>
- 13 15 3 2 2.
- <_>
- 10 17 3 2 2.
- <_>
-
- <_>
- 12 14 2 6 -1.
- <_>
- 12 16 2 2 3.
- <_>
-
- <_>
- 5 15 4 4 -1.
- <_>
- 5 15 2 2 2.
- <_>
- 7 17 2 2 2.
- <_>
-
- <_>
- 7 18 1 2 -1.
- <_>
- 7 19 1 1 2.
- <_>
-
- <_>
- 4 5 12 10 -1.
- <_>
- 10 5 6 5 2.
- <_>
- 4 10 6 5 2.
- <_>
-
- <_>
- 7 4 8 12 -1.
- <_>
- 11 4 4 6 2.
- <_>
- 7 10 4 6 2.
- <_>
-
- <_>
- 9 11 2 3 -1.
- <_>
- 9 12 2 1 3.
- <_>
-
- <_>
- 3 3 12 12 -1.
- <_>
- 3 3 6 6 2.
- <_>
- 9 9 6 6 2.
- <_>
-
- <_>
- 15 11 5 3 -1.
- <_>
- 15 12 5 1 3.
- <_>
-
- <_>
- 10 18 3 2 -1.
- <_>
- 11 18 1 2 3.
- <_>
-
- <_>
- 0 11 5 3 -1.
- <_>
- 0 12 5 1 3.
- <_>
-
- <_>
- 7 18 3 2 -1.
- <_>
- 8 18 1 2 3.
- <_>
-
- <_>
- 2 8 16 2 -1.
- <_>
- 2 9 16 1 2.
- <_>
-
- <_>
- 9 6 5 12 -1.
- <_>
- 9 12 5 6 2.
- <_>
-
- <_>
- 6 3 8 6 -1.
- <_>
- 6 6 8 3 2.
- <_>
-
- <_>
- 4 7 12 2 -1.
- <_>
- 8 7 4 2 3.
- <_>
-
- <_>
- 10 9 6 8 -1.
- <_>
- 10 13 6 4 2.
- <_>
-
- <_>
- 12 5 3 10 -1.
- <_>
- 12 10 3 5 2.
- <_>
-
- <_>
- 4 6 3 9 -1.
- <_>
- 4 9 3 3 3.
- <_>
-
- <_>
- 7 4 6 4 -1.
- <_>
- 9 4 2 4 3.
- <_>
-
- <_>
- 12 3 8 3 -1.
- <_>
- 12 3 4 3 2.
- <_>
-
- <_>
- 15 0 3 6 -1.
- <_>
- 15 3 3 3 2.
- <_>
-
- <_>
- 2 12 10 8 -1.
- <_>
- 2 12 5 4 2.
- <_>
- 7 16 5 4 2.
- <_>
-
- <_>
- 5 5 6 8 -1.
- <_>
- 5 9 6 4 2.
- <_>
-
- <_>
- 12 3 8 3 -1.
- <_>
- 12 3 4 3 2.
- <_>
-
- <_>
- 15 0 3 6 -1.
- <_>
- 15 3 3 3 2.
- <_>
-
- <_>
- 0 3 8 3 -1.
- <_>
- 4 3 4 3 2.
- <_>
-
- <_>
- 2 1 4 4 -1.
- <_>
- 2 3 4 2 2.
- <_>
-
- <_>
- 10 2 3 2 -1.
- <_>
- 11 2 1 2 3.
- <_>
-
- <_>
- 10 3 3 1 -1.
- <_>
- 11 3 1 1 3.
- <_>
-
- <_>
- 7 15 3 4 -1.
- <_>
- 7 17 3 2 2.
- <_>
-
- <_>
- 4 13 3 6 -1.
- <_>
- 4 15 3 2 3.
- <_>
-
- <_>
- 10 5 1 14 -1.
- <_>
- 10 12 1 7 2.
- <_>
-
- <_>
- 5 4 10 6 -1.
- <_>
- 5 6 10 2 3.
- <_>
-
- <_>
- 5 0 6 3 -1.
- <_>
- 7 0 2 3 3.
- <_>
-
- <_>
- 6 0 3 5 -1.
- <_>
- 7 0 1 5 3.
- <_>
-
- <_>
- 7 15 6 5 -1.
- <_>
- 9 15 2 5 3.
- <_>
-
- <_>
- 9 10 2 6 -1.
- <_>
- 9 12 2 2 3.
- <_>
-
- <_>
- 8 17 3 2 -1.
- <_>
- 9 17 1 2 3.
- <_>
-
- <_>
- 1 12 7 6 -1.
- <_>
- 1 14 7 2 3.
- <_>
-
- <_>
- 9 6 3 7 -1.
- <_>
- 10 6 1 7 3.
- <_>
-
- <_>
- 16 3 4 9 -1.
- <_>
- 16 6 4 3 3.
- <_>
-
- <_>
- 8 6 3 7 -1.
- <_>
- 9 6 1 7 3.
- <_>
-
- <_>
- 0 5 18 8 -1.
- <_>
- 0 5 9 4 2.
- <_>
- 9 9 9 4 2.
- <_>
-
- <_>
- 13 5 2 10 -1.
- <_>
- 13 10 2 5 2.
- <_>
-
- <_>
- 12 10 2 6 -1.
- <_>
- 12 13 2 3 2.
- <_>
-
- <_>
- 7 0 3 5 -1.
- <_>
- 8 0 1 5 3.
- <_>
-
- <_>
- 6 5 8 6 -1.
- <_>
- 6 7 8 2 3.
- <_>
-
- <_>
- 10 3 6 14 -1.
- <_>
- 13 3 3 7 2.
- <_>
- 10 10 3 7 2.
- <_>
-
- <_>
- 13 5 1 8 -1.
- <_>
- 13 9 1 4 2.
- <_>
-
- <_>
- 4 3 6 14 -1.
- <_>
- 4 3 3 7 2.
- <_>
- 7 10 3 7 2.
- <_>
-
- <_>
- 6 5 1 8 -1.
- <_>
- 6 9 1 4 2.
- <_>
-
- <_>
- 8 1 1 6 -1.
- <_>
- 8 3 1 2 3.
- <_>
-
- <_>
- 2 0 15 2 -1.
- <_>
- 2 1 15 1 2.
- <_>
-
- <_>
- 0 7 20 6 -1.
- <_>
- 0 9 20 2 3.
- <_>
-
- <_>
- 10 10 6 8 -1.
- <_>
- 10 14 6 4 2.
- <_>
-
- <_>
- 7 1 3 2 -1.
- <_>
- 8 1 1 2 3.
- <_>
-
- <_>
- 8 1 2 2 -1.
- <_>
- 9 1 1 2 2.
- <_>
-
- <_>
- 4 3 12 9 -1.
- <_>
- 4 6 12 3 3.
- <_>
-
- <_>
- 6 5 9 5 -1.
- <_>
- 9 5 3 5 3.
- <_>
-
- <_>
- 5 5 9 5 -1.
- <_>
- 8 5 3 5 3.
- <_>
-
- <_>
- 4 6 6 12 -1.
- <_>
- 4 10 6 4 3.
- <_>
-
- <_>
- 13 0 6 18 -1.
- <_>
- 13 0 3 18 2.
- <_>
-
- <_>
- 10 8 1 12 -1.
- <_>
- 10 12 1 4 3.
- <_>
-
- <_>
- 3 2 6 10 -1.
- <_>
- 3 2 3 5 2.
- <_>
- 6 7 3 5 2.
- <_>
-
- <_>
- 1 2 4 6 -1.
- <_>
- 3 2 2 6 2.
- <_>
-
- <_>
- 9 18 3 2 -1.
- <_>
- 10 18 1 2 3.
- <_>
-
- <_>
- 10 18 3 2 -1.
- <_>
- 11 18 1 2 3.
- <_>
-
- <_>
- 2 8 2 6 -1.
- <_>
- 2 10 2 2 3.
- <_>
-
- <_>
- 7 5 6 6 -1.
- <_>
- 7 7 6 2 3.
- <_>
-
- <_>
- 7 19 6 1 -1.
- <_>
- 9 19 2 1 3.
- <_>
-
- <_>
- 10 18 3 2 -1.
- <_>
- 11 18 1 2 3.
- <_>
-
- <_>
- 8 3 3 1 -1.
- <_>
- 9 3 1 1 3.
- <_>
-
- <_>
- 2 2 16 2 -1.
- <_>
- 2 2 8 1 2.
- <_>
- 10 3 8 1 2.
- <_>
-
- <_>
- 8 11 5 3 -1.
- <_>
- 8 12 5 1 3.
- <_>
-
- <_>
- 7 13 6 3 -1.
- <_>
- 7 14 6 1 3.
- <_>
-
- <_>
- 0 1 6 15 -1.
- <_>
- 2 1 2 15 3.
- <_>
-
- <_>
- 2 12 2 3 -1.
- <_>
- 2 13 2 1 3.
- <_>
-
- <_>
- 16 13 1 3 -1.
- <_>
- 16 14 1 1 3.
- <_>
-
- <_>
- 13 7 6 4 -1.
- <_>
- 16 7 3 2 2.
- <_>
- 13 9 3 2 2.
- <_>
-
- <_>
- 7 13 3 6 -1.
- <_>
- 7 16 3 3 2.
- <_>
-
- <_>
- 7 5 1 14 -1.
- <_>
- 7 12 1 7 2.
- <_>
-
- <_>
- 15 12 2 3 -1.
- <_>
- 15 13 2 1 3.
- <_>
-
- <_>
- 10 5 3 14 -1.
- <_>
- 10 12 3 7 2.
- <_>
-
- <_>
- 6 10 2 6 -1.
- <_>
- 6 13 2 3 2.
- <_>
-
- <_>
- 6 5 1 8 -1.
- <_>
- 6 9 1 4 2.
- <_>
-
- <_>
- 13 11 2 1 -1.
- <_>
- 13 11 1 1 2.
- <_>
-
- <_>
- 12 1 6 10 -1.
- <_>
- 15 1 3 5 2.
- <_>
- 12 6 3 5 2.
- <_>
-
- <_>
- 3 12 2 3 -1.
- <_>
- 3 13 2 1 3.
- <_>
-
- <_>
- 9 18 2 1 -1.
- <_>
- 10 18 1 1 2.
- <_>
-
- <_>
- 1 0 17 9 -1.
- <_>
- 1 3 17 3 3.
- <_>
-
- <_>
- 1 2 8 8 -1.
- <_>
- 1 2 4 4 2.
- <_>
- 5 6 4 4 2.
- <_>
-
- <_>
- 9 5 6 4 -1.
- <_>
- 9 5 3 4 2.
- <_>
-
- <_>
- 10 9 7 10 -1.
- <_>
- 10 14 7 5 2.
- <_>
-
- <_>
- 5 5 6 4 -1.
- <_>
- 8 5 3 4 2.
- <_>
-
- <_>
- 0 7 20 6 -1.
- <_>
- 0 9 20 2 3.
- <_>
-
- <_>
- 6 5 9 10 -1.
- <_>
- 6 10 9 5 2.
- <_>
-
- <_>
- 8 4 4 12 -1.
- <_>
- 8 10 4 6 2.
- <_>
-
- <_>
- 6 6 8 3 -1.
- <_>
- 6 7 8 1 3.
- <_>
-
- <_>
- 3 13 10 6 -1.
- <_>
- 3 13 5 3 2.
- <_>
- 8 16 5 3 2.
- <_>
-
- <_>
- 15 1 4 11 -1.
- <_>
- 15 1 2 11 2.
- <_>
-
- <_>
- 5 7 10 10 -1.
- <_>
- 10 7 5 5 2.
- <_>
- 5 12 5 5 2.
- <_>
-
- <_>
- 1 1 4 11 -1.
- <_>
- 3 1 2 11 2.
- <_>
-
- <_>
- 1 5 8 12 -1.
- <_>
- 1 11 8 6 2.
- <_>
-
- <_>
- 13 7 6 4 -1.
- <_>
- 16 7 3 2 2.
- <_>
- 13 9 3 2 2.
- <_>
-
- <_>
- 11 10 7 4 -1.
- <_>
- 11 12 7 2 2.
- <_>
-
- <_>
- 0 4 20 12 -1.
- <_>
- 0 4 10 6 2.
- <_>
- 10 10 10 6 2.
- <_>
-
- <_>
- 1 5 6 15 -1.
- <_>
- 1 10 6 5 3.
- <_>
-
- <_>
- 11 10 3 8 -1.
- <_>
- 11 14 3 4 2.
- <_>
-
- <_>
- 11 12 7 6 -1.
- <_>
- 11 14 7 2 3.
- <_>
-
- <_>
- 9 11 2 3 -1.
- <_>
- 9 12 2 1 3.
- <_>
-
- <_>
- 8 13 4 3 -1.
- <_>
- 8 14 4 1 3.
- <_>
-
- <_>
- 3 14 14 4 -1.
- <_>
- 10 14 7 2 2.
- <_>
- 3 16 7 2 2.
- <_>
-
- <_>
- 18 7 2 4 -1.
- <_>
- 18 9 2 2 2.
- <_>
-
- <_>
- 3 12 6 6 -1.
- <_>
- 3 14 6 2 3.
- <_>
-
- <_>
- 0 4 3 6 -1.
- <_>
- 0 6 3 2 3.
- <_>
-
- <_>
- 9 14 3 3 -1.
- <_>
- 9 15 3 1 3.
- <_>
-
- <_>
- 10 7 10 4 -1.
- <_>
- 15 7 5 2 2.
- <_>
- 10 9 5 2 2.
- <_>
-
- <_>
- 7 2 6 8 -1.
- <_>
- 7 6 6 4 2.
- <_>
-
- <_>
- 6 3 6 2 -1.
- <_>
- 8 3 2 2 3.
- <_>
-
- <_>
- 10 6 3 5 -1.
- <_>
- 11 6 1 5 3.
- <_>
-
- <_>
- 9 0 6 19 -1.
- <_>
- 11 0 2 19 3.
- <_>
-
- <_>
- 3 12 1 2 -1.
- <_>
- 3 13 1 1 2.
- <_>
-
- <_>
- 7 14 5 3 -1.
- <_>
- 7 15 5 1 3.
- <_>
-
- <_>
- 2 1 18 4 -1.
- <_>
- 11 1 9 2 2.
- <_>
- 2 3 9 2 2.
- <_>
-
- <_>
- 10 5 3 8 -1.
- <_>
- 11 5 1 8 3.
- <_>
-
- <_>
- 0 1 18 4 -1.
- <_>
- 0 1 9 2 2.
- <_>
- 9 3 9 2 2.
- <_>
-
- <_>
- 7 5 3 8 -1.
- <_>
- 8 5 1 8 3.
- <_>
-
- <_>
- 9 5 2 6 -1.
- <_>
- 9 7 2 2 3.
- <_>
-
- <_>
- 10 8 5 2 -1.
- <_>
- 10 9 5 1 2.
- <_>
-
- <_>
- 2 10 15 1 -1.
- <_>
- 7 10 5 1 3.
- <_>
-
- <_>
- 2 7 2 6 -1.
- <_>
- 2 9 2 2 3.
- <_>
-
- <_>
- 9 14 3 3 -1.
- <_>
- 9 15 3 1 3.
- <_>
-
- <_>
- 9 7 4 10 -1.
- <_>
- 9 12 4 5 2.
- <_>
-
- <_>
- 0 8 8 2 -1.
- <_>
- 0 8 4 1 2.
- <_>
- 4 9 4 1 2.
- <_>
-
- <_>
- 5 9 10 8 -1.
- <_>
- 5 9 5 4 2.
- <_>
- 10 13 5 4 2.
- <_>
-
- <_>
- 9 7 2 4 -1.
- <_>
- 9 7 1 4 2.
- <_>
-
- <_>
- 9 6 3 4 -1.
- <_>
- 10 6 1 4 3.
- <_>
-
- <_>
- 8 3 2 1 -1.
- <_>
- 9 3 1 1 2.
- <_>
-
- <_>
- 8 6 3 4 -1.
- <_>
- 9 6 1 4 3.
- <_>
-
- <_>
- 12 0 4 14 -1.
- <_>
- 14 0 2 7 2.
- <_>
- 12 7 2 7 2.
- <_>
-
- <_>
- 12 5 6 9 -1.
- <_>
- 12 5 3 9 2.
- <_>
-
- <_>
- 0 2 6 16 -1.
- <_>
- 3 2 3 16 2.
- <_>
-
- <_>
- 1 12 4 2 -1.
- <_>
- 1 13 4 1 2.
- <_>
-
- <_>
- 7 7 6 1 -1.
- <_>
- 9 7 2 1 3.
- <_>
-
- <_>
- 8 3 4 9 -1.
- <_>
- 8 6 4 3 3.
- <_>
-
- <_>
- 12 10 4 6 -1.
- <_>
- 12 13 4 3 2.
- <_>
-
- <_>
- 8 1 8 16 -1.
- <_>
- 12 1 4 8 2.
- <_>
- 8 9 4 8 2.
- <_>
-
- <_>
- 4 6 3 6 -1.
- <_>
- 4 9 3 3 2.
- <_>
-
- <_>
- 1 3 6 2 -1.
- <_>
- 4 3 3 2 2.
- <_>
-
- <_>
- 9 8 3 12 -1.
- <_>
- 9 12 3 4 3.
- <_>
-
- <_>
- 10 9 7 10 -1.
- <_>
- 10 14 7 5 2.
- <_>
-
- <_>
- 3 9 7 10 -1.
- <_>
- 3 14 7 5 2.
- <_>
-
- <_>
- 7 5 1 14 -1.
- <_>
- 7 12 1 7 2.
- <_>
-
- <_>
- 13 14 1 6 -1.
- <_>
- 13 16 1 2 3.
- <_>
-
- <_>
- 14 12 3 6 -1.
- <_>
- 14 14 3 2 3.
- <_>
-
- <_>
- 6 14 1 6 -1.
- <_>
- 6 16 1 2 3.
- <_>
-
- <_>
- 3 12 3 6 -1.
- <_>
- 3 14 3 2 3.
- <_>
-
- <_>
- 8 13 5 3 -1.
- <_>
- 8 14 5 1 3.
- <_>
-
- <_>
- 9 14 2 3 -1.
- <_>
- 9 15 2 1 3.
- <_>
-
- <_>
- 5 1 10 8 -1.
- <_>
- 5 1 5 4 2.
- <_>
- 10 5 5 4 2.
- <_>
-
- <_>
- 6 4 5 4 -1.
- <_>
- 6 6 5 2 2.
- <_>
-
- <_>
- 1 10 18 1 -1.
- <_>
- 7 10 6 1 3.
- <_>
-
- <_>
- 11 10 4 3 -1.
- <_>
- 11 10 2 3 2.
- <_>
-
- <_>
- 5 11 6 1 -1.
- <_>
- 7 11 2 1 3.
- <_>
-
- <_>
- 3 13 2 3 -1.
- <_>
- 3 14 2 1 3.
- <_>
-
- <_>
- 12 12 3 4 -1.
- <_>
- 12 14 3 2 2.
- <_>
-
- <_>
- 11 10 5 6 -1.
- <_>
- 11 12 5 2 3.
- <_>
-
- <_>
- 0 8 16 2 -1.
- <_>
- 0 9 16 1 2.
- <_>
-
- <_>
- 2 1 3 4 -1.
- <_>
- 2 3 3 2 2.
- <_>
-
- <_>
- 9 7 3 3 -1.
- <_>
- 10 7 1 3 3.
- <_>
-
- <_>
- 5 6 12 6 -1.
- <_>
- 9 6 4 6 3.
- <_>
-
- <_>
- 8 7 3 3 -1.
- <_>
- 9 7 1 3 3.
- <_>
-
- <_>
- 3 6 12 6 -1.
- <_>
- 7 6 4 6 3.
- <_>
-
- <_>
- 10 5 6 5 -1.
- <_>
- 12 5 2 5 3.
- <_>
-
- <_>
- 5 7 10 2 -1.
- <_>
- 5 7 5 2 2.
- <_>
-
- <_>
- 4 5 6 5 -1.
- <_>
- 6 5 2 5 3.
- <_>
-
- <_>
- 9 3 2 10 -1.
- <_>
- 9 8 2 5 2.
- <_>
-
- <_>
- 3 1 16 2 -1.
- <_>
- 11 1 8 1 2.
- <_>
- 3 2 8 1 2.
- <_>
-
- <_>
- 9 9 3 2 -1.
- <_>
- 9 10 3 1 2.
- <_>
-
- <_>
- 1 1 16 2 -1.
- <_>
- 1 1 8 1 2.
- <_>
- 9 2 8 1 2.
- <_>
-
- <_>
- 8 14 1 3 -1.
- <_>
- 8 15 1 1 3.
- <_>
-
- <_>
- 4 5 12 10 -1.
- <_>
- 10 5 6 5 2.
- <_>
- 4 10 6 5 2.
- <_>
-
- <_>
- 7 13 6 6 -1.
- <_>
- 10 13 3 3 2.
- <_>
- 7 16 3 3 2.
- <_>
-
- <_>
- 8 9 3 2 -1.
- <_>
- 8 10 3 1 2.
- <_>
-
- <_>
- 7 2 6 4 -1.
- <_>
- 9 2 2 4 3.
- <_>
-
- <_>
- 6 6 9 3 -1.
- <_>
- 6 7 9 1 3.
- <_>
-
- <_>
- 10 7 6 1 -1.
- <_>
- 12 7 2 1 3.
- <_>
-
- <_>
- 0 0 18 6 -1.
- <_>
- 6 0 6 6 3.
- <_>
-
- <_>
- 6 10 2 6 -1.
- <_>
- 6 13 2 3 2.
- <_>
-
- <_>
- 11 12 3 6 -1.
- <_>
- 11 15 3 3 2.
- <_>
-
- <_>
- 4 4 12 12 -1.
- <_>
- 10 4 6 6 2.
- <_>
- 4 10 6 6 2.
- <_>
-
- <_>
- 1 2 3 6 -1.
- <_>
- 2 2 1 6 3.
- <_>
-
- <_>
- 1 5 3 7 -1.
- <_>
- 2 5 1 7 3.
- <_>
-
- <_>
- 4 13 12 4 -1.
- <_>
- 10 13 6 2 2.
- <_>
- 4 15 6 2 2.
- <_>
-
- <_>
- 3 3 17 12 -1.
- <_>
- 3 9 17 6 2.
- <_>
-
- <_>
- 3 3 14 12 -1.
- <_>
- 3 3 7 6 2.
- <_>
- 10 9 7 6 2.
- <_>
-
- <_>
- 2 11 16 9 -1.
- <_>
- 2 14 16 3 3.
- <_>
-
- <_>
- 9 14 3 6 -1.
- <_>
- 9 17 3 3 2.
- <_>
-
- <_>
- 8 14 4 6 -1.
- <_>
- 10 14 2 3 2.
- <_>
- 8 17 2 3 2.
- <_>
-
- <_>
- 6 2 6 1 -1.
- <_>
- 8 2 2 1 3.
- <_>
-
- <_>
- 9 5 2 5 -1.
- <_>
- 10 5 1 5 2.
- <_>
-
- <_>
- 9 8 3 5 -1.
- <_>
- 10 8 1 5 3.
- <_>
-
- <_>
- 9 12 6 1 -1.
- <_>
- 9 12 3 1 2.
- <_>
-
- <_>
- 8 8 3 5 -1.
- <_>
- 9 8 1 5 3.
- <_>
-
- <_>
- 6 10 4 3 -1.
- <_>
- 8 10 2 3 2.
- <_>
-
- <_>
- 0 4 20 6 -1.
- <_>
- 0 6 20 2 3.
- <_>
-
- <_>
- 1 3 8 6 -1.
- <_>
- 1 3 4 3 2.
- <_>
- 5 6 4 3 2.
- <_>
-
- <_>
- 7 15 6 4 -1.
- <_>
- 7 17 6 2 2.
- <_>
-
- <_>
- 3 10 14 10 -1.
- <_>
- 3 15 14 5 2.
- <_>
-
- <_>
- 6 4 4 4 -1.
- <_>
- 8 4 2 4 2.
- <_>
-
- <_>
- 0 4 20 10 -1.
- <_>
- 0 9 20 5 2.
- <_>
-
- <_>
- 9 4 2 14 -1.
- <_>
- 9 11 2 7 2.
- <_>
-
- <_>
- 2 0 16 4 -1.
- <_>
- 2 2 16 2 2.
- <_>
-
- <_>
- 4 12 6 8 -1.
- <_>
- 4 12 3 4 2.
- <_>
- 7 16 3 4 2.
- <_>
-
- <_>
- 0 5 6 7 -1.
- <_>
- 3 5 3 7 2.
- <_>
-
- <_>
- 10 7 10 4 -1.
- <_>
- 15 7 5 2 2.
- <_>
- 10 9 5 2 2.
- <_>
-
- <_>
- 5 8 12 1 -1.
- <_>
- 9 8 4 1 3.
- <_>
-
- <_>
- 9 9 2 2 -1.
- <_>
- 9 10 2 1 2.
- <_>
-
- <_>
- 9 4 2 4 -1.
- <_>
- 9 6 2 2 2.
- <_>
-
- <_>
- 9 6 3 6 -1.
- <_>
- 10 6 1 6 3.
- <_>
-
- <_>
- 12 7 6 4 -1.
- <_>
- 15 7 3 2 2.
- <_>
- 12 9 3 2 2.
- <_>
-
- <_>
- 8 6 3 6 -1.
- <_>
- 9 6 1 6 3.
- <_>
-
- <_>
- 1 6 18 6 -1.
- <_>
- 1 6 9 3 2.
- <_>
- 10 9 9 3 2.
- <_>
-
- <_>
- 9 1 3 3 -1.
- <_>
- 10 1 1 3 3.
- <_>
-
- <_>
- 10 8 5 2 -1.
- <_>
- 10 9 5 1 2.
- <_>
-
- <_>
- 8 1 3 3 -1.
- <_>
- 9 1 1 3 3.
- <_>
-
- <_>
- 5 8 5 2 -1.
- <_>
- 5 9 5 1 2.
- <_>
-
- <_>
- 8 6 8 8 -1.
- <_>
- 12 6 4 4 2.
- <_>
- 8 10 4 4 2.
- <_>
-
- <_>
- 5 7 10 2 -1.
- <_>
- 5 7 5 2 2.
- <_>
-
- <_>
- 4 5 12 10 -1.
- <_>
- 4 5 6 5 2.
- <_>
- 10 10 6 5 2.
- <_>
-
- <_>
- 5 5 2 3 -1.
- <_>
- 5 6 2 1 3.
- <_>
-
- <_>
- 7 14 6 3 -1.
- <_>
- 7 15 6 1 3.
- <_>
-
- <_>
- 9 14 3 3 -1.
- <_>
- 9 15 3 1 3.
- <_>
-
- <_>
- 8 14 3 3 -1.
- <_>
- 8 15 3 1 3.
- <_>
-
- <_>
- 1 10 8 9 -1.
- <_>
- 1 13 8 3 3.
- <_>
-
- <_>
- 9 7 2 3 -1.
- <_>
- 9 8 2 1 3.
- <_>
-
- <_>
- 12 3 3 3 -1.
- <_>
- 13 3 1 3 3.
- <_>
-
- <_>
- 5 3 3 3 -1.
- <_>
- 6 3 1 3 3.
- <_>
-
- <_>
- 5 6 2 12 -1.
- <_>
- 5 10 2 4 3.
- <_>
-
- <_>
- 1 11 18 4 -1.
- <_>
- 10 11 9 2 2.
- <_>
- 1 13 9 2 2.
- <_>
-
- <_>
- 7 12 6 2 -1.
- <_>
- 7 13 6 1 2.
- <_>
-
- <_>
- 6 0 3 6 -1.
- <_>
- 7 0 1 6 3.
- <_>
-
- <_>
- 0 11 18 4 -1.
- <_>
- 0 11 9 2 2.
- <_>
- 9 13 9 2 2.
- <_>
-
- <_>
- 7 12 6 2 -1.
- <_>
- 7 13 6 1 2.
- <_>
-
- <_>
- 9 12 3 3 -1.
- <_>
- 9 13 3 1 3.
- <_>
-
- <_>
- 9 12 2 3 -1.
- <_>
- 9 13 2 1 3.
- <_>
-
- <_>
- 8 11 4 3 -1.
- <_>
- 8 12 4 1 3.
- <_>
-
- <_>
- 13 3 4 2 -1.
- <_>
- 13 4 4 1 2.
- <_>
-
- <_>
- 4 0 12 2 -1.
- <_>
- 4 1 12 1 2.
- <_>
-
- <_>
- 6 9 8 8 -1.
- <_>
- 6 9 4 4 2.
- <_>
- 10 13 4 4 2.
- <_>
-
- <_>
- 1 11 6 2 -1.
- <_>
- 1 12 6 1 2.
- <_>
-
- <_>
- 2 5 18 8 -1.
- <_>
- 11 5 9 4 2.
- <_>
- 2 9 9 4 2.
- <_>
-
- <_>
- 7 1 6 10 -1.
- <_>
- 7 6 6 5 2.
- <_>
-
- <_>
- 0 3 3 6 -1.
- <_>
- 0 5 3 2 3.
- <_>
-
- <_>
- 4 5 4 3 -1.
- <_>
- 4 6 4 1 3.
- <_>
-
- <_>
- 19 3 1 6 -1.
- <_>
- 19 5 1 2 3.
- <_>
-
- <_>
- 6 15 8 2 -1.
- <_>
- 6 16 8 1 2.
- <_>
-
- <_>
- 0 3 1 6 -1.
- <_>
- 0 5 1 2 3.
- <_>
-
- <_>
- 5 5 3 3 -1.
- <_>
- 5 6 3 1 3.
- <_>
-
- <_>
- 8 8 4 3 -1.
- <_>
- 8 9 4 1 3.
- <_>
-
- <_>
- 10 6 6 3 -1.
- <_>
- 12 6 2 3 3.
- <_>
-
- <_>
- 8 13 2 6 -1.
- <_>
- 8 16 2 3 2.
- <_>
-
- <_>
- 9 11 2 8 -1.
- <_>
- 9 15 2 4 2.
- <_>
-
- <_>
- 10 6 6 3 -1.
- <_>
- 12 6 2 3 3.
- <_>
-
- <_>
- 5 15 15 5 -1.
- <_>
- 10 15 5 5 3.
- <_>
-
- <_>
- 2 14 2 2 -1.
- <_>
- 2 15 2 1 2.
- <_>
-
- <_>
- 4 7 6 2 -1.
- <_>
- 6 7 2 2 3.
- <_>
-
- <_>
- 8 3 6 1 -1.
- <_>
- 10 3 2 1 3.
- <_>
-
- <_>
- 1 0 18 12 -1.
- <_>
- 7 0 6 12 3.
- <_>
-
- <_>
- 0 14 8 6 -1.
- <_>
- 4 14 4 6 2.
- <_>
-
- <_>
- 0 15 15 5 -1.
- <_>
- 5 15 5 5 3.
- <_>
-
- <_>
- 8 3 6 1 -1.
- <_>
- 10 3 2 1 3.
- <_>
-
- <_>
- 11 11 3 6 -1.
- <_>
- 11 14 3 3 2.
- <_>
-
- <_>
- 6 3 6 1 -1.
- <_>
- 8 3 2 1 3.
- <_>
-
- <_>
- 6 11 3 6 -1.
- <_>
- 6 14 3 3 2.
- <_>
-
- <_>
- 9 6 3 4 -1.
- <_>
- 10 6 1 4 3.
- <_>
-
- <_>
- 12 10 4 7 -1.
- <_>
- 12 10 2 7 2.
- <_>
-
- <_>
- 8 6 3 4 -1.
- <_>
- 9 6 1 4 3.
- <_>
-
- <_>
- 4 6 4 7 -1.
- <_>
- 6 6 2 7 2.
- <_>
-
- <_>
- 10 3 4 12 -1.
- <_>
- 10 3 2 12 2.
- <_>
-
- <_>
- 10 8 3 4 -1.
- <_>
- 11 8 1 4 3.
- <_>
-
- <_>
- 1 0 18 14 -1.
- <_>
- 7 0 6 14 3.
- <_>
-
- <_>
- 2 8 6 11 -1.
- <_>
- 5 8 3 11 2.
- <_>
-
- <_>
- 1 4 15 4 -1.
- <_>
- 1 6 15 2 2.
- <_>
-
- <_>
- 5 5 10 8 -1.
- <_>
- 5 9 10 4 2.
- <_>
-
- <_>
- 14 2 6 8 -1.
- <_>
- 14 2 3 8 2.
- <_>
-
- <_>
- 11 6 6 14 -1.
- <_>
- 14 6 3 7 2.
- <_>
- 11 13 3 7 2.
- <_>
-
- <_>
- 9 5 2 12 -1.
- <_>
- 9 11 2 6 2.
- <_>
-
- <_>
- 3 7 4 6 -1.
- <_>
- 3 9 4 2 3.
- <_>
-
- <_>
- 14 3 6 6 -1.
- <_>
- 14 3 3 6 2.
- <_>
-
- <_>
- 15 2 4 4 -1.
- <_>
- 15 4 4 2 2.
- <_>
-
- <_>
- 0 2 6 7 -1.
- <_>
- 3 2 3 7 2.
- <_>
-
- <_>
- 3 6 6 14 -1.
- <_>
- 3 6 3 7 2.
- <_>
- 6 13 3 7 2.
- <_>
-
- <_>
- 4 6 16 8 -1.
- <_>
- 4 10 16 4 2.
- <_>
-
- <_>
- 10 12 2 8 -1.
- <_>
- 10 16 2 4 2.
- <_>
-
- <_>
- 7 0 6 20 -1.
- <_>
- 9 0 2 20 3.
- <_>
-
- <_>
- 1 7 16 12 -1.
- <_>
- 1 7 8 6 2.
- <_>
- 9 13 8 6 2.
- <_>
-
- <_>
- 9 11 3 3 -1.
- <_>
- 9 12 3 1 3.
- <_>
-
- <_>
- 11 9 4 5 -1.
- <_>
- 11 9 2 5 2.
- <_>
-
- <_>
- 3 3 1 2 -1.
- <_>
- 3 4 1 1 2.
- <_>
-
- <_>
- 7 17 5 3 -1.
- <_>
- 7 18 5 1 3.
- <_>
-
- <_>
- 8 12 4 8 -1.
- <_>
- 10 12 2 4 2.
- <_>
- 8 16 2 4 2.
- <_>
-
- <_>
- 7 4 10 12 -1.
- <_>
- 12 4 5 6 2.
- <_>
- 7 10 5 6 2.
- <_>
-
- <_>
- 8 14 4 3 -1.
- <_>
- 8 15 4 1 3.
- <_>
-
- <_>
- 5 9 4 5 -1.
- <_>
- 7 9 2 5 2.
- <_>
-
- <_>
- 9 9 8 2 -1.
- <_>
- 9 9 4 2 2.
- <_>
-
- <_>
- 14 15 5 2 -1.
- <_>
- 14 16 5 1 2.
- <_>
-
- <_>
- 9 14 2 3 -1.
- <_>
- 9 15 2 1 3.
- <_>
-
- <_>
- 1 7 8 4 -1.
- <_>
- 1 7 4 2 2.
- <_>
- 5 9 4 2 2.
- <_>
-
- <_>
- 19 3 1 2 -1.
- <_>
- 19 4 1 1 2.
- <_>
-
- <_>
- 9 12 2 3 -1.
- <_>
- 9 13 2 1 3.
- <_>
-
- <_>
- 3 14 14 4 -1.
- <_>
- 3 14 7 2 2.
- <_>
- 10 16 7 2 2.
- <_>
-
- <_>
- 5 0 10 2 -1.
- <_>
- 5 1 10 1 2.
- <_>
-
- <_>
- 11 14 4 6 -1.
- <_>
- 11 16 4 2 3.
- <_>
-
- <_>
- 7 14 6 3 -1.
- <_>
- 7 15 6 1 3.
- <_>
-
- <_>
- 7 13 6 6 -1.
- <_>
- 7 13 3 3 2.
- <_>
- 10 16 3 3 2.
- <_>
-
- <_>
- 0 2 1 6 -1.
- <_>
- 0 4 1 2 3.
- <_>
-
- <_>
- 6 7 8 2 -1.
- <_>
- 6 8 8 1 2.
- <_>
-
- <_>
- 9 7 6 1 -1.
- <_>
- 9 7 3 1 2.
- <_>
-
- <_>
- 7 1 6 10 -1.
- <_>
- 7 6 6 5 2.
- <_>
-
- <_>
- 0 2 6 2 -1.
- <_>
- 0 3 6 1 2.
- <_>
-
- <_>
- 11 4 2 4 -1.
- <_>
- 11 4 1 4 2.
- <_>
-
- <_>
- 11 10 3 6 -1.
- <_>
- 11 13 3 3 2.
- <_>
-
- <_>
- 3 9 8 2 -1.
- <_>
- 7 9 4 2 2.
- <_>
-
- <_>
- 0 0 4 6 -1.
- <_>
- 2 0 2 6 2.
- <_>
-
- <_>
- 7 0 6 2 -1.
- <_>
- 9 0 2 2 3.
- <_>
-
- <_>
- 9 15 2 3 -1.
- <_>
- 9 16 2 1 3.
- <_>
-
- <_>
- 3 12 1 2 -1.
- <_>
- 3 13 1 1 2.
- <_>
-
- <_>
- 4 5 11 3 -1.
- <_>
- 4 6 11 1 3.
- <_>
-
- <_>
- 11 4 2 4 -1.
- <_>
- 11 4 1 4 2.
- <_>
-
- <_>
- 8 3 6 3 -1.
- <_>
- 10 3 2 3 3.
- <_>
-
- <_>
- 7 4 2 4 -1.
- <_>
- 8 4 1 4 2.
- <_>
-
- <_>
- 6 3 6 3 -1.
- <_>
- 8 3 2 3 3.
- <_>
-
- <_>
- 11 4 4 3 -1.
- <_>
- 11 5 4 1 3.
- <_>
-
- <_>
- 11 8 2 8 -1.
- <_>
- 11 12 2 4 2.
- <_>
-
- <_>
- 8 7 3 5 -1.
- <_>
- 9 7 1 5 3.
- <_>
-
- <_>
- 9 7 2 5 -1.
- <_>
- 10 7 1 5 2.
- <_>
-
- <_>
- 14 11 1 6 -1.
- <_>
- 14 13 1 2 3.
- <_>
-
- <_>
- 8 8 4 3 -1.
- <_>
- 8 9 4 1 3.
- <_>
-
- <_>
- 0 3 2 2 -1.
- <_>
- 0 4 2 1 2.
- <_>
-
- <_>
- 4 14 5 6 -1.
- <_>
- 4 16 5 2 3.
- <_>
-
- <_>
- 11 4 4 3 -1.
- <_>
- 11 5 4 1 3.
- <_>
-
- <_>
- 12 4 3 3 -1.
- <_>
- 12 5 3 1 3.
- <_>
-
- <_>
- 5 4 4 3 -1.
- <_>
- 5 5 4 1 3.
- <_>
-
- <_>
- 5 15 4 2 -1.
- <_>
- 7 15 2 2 2.
- <_>
-
- <_>
- 15 1 5 9 -1.
- <_>
- 15 4 5 3 3.
- <_>
-
- <_>
- 9 10 3 3 -1.
- <_>
- 9 11 3 1 3.
- <_>
-
- <_>
- 1 6 2 6 -1.
- <_>
- 1 8 2 2 3.
- <_>
-
- <_>
- 2 4 8 15 -1.
- <_>
- 2 9 8 5 3.
- <_>
-
- <_>
- 9 12 3 2 -1.
- <_>
- 9 13 3 1 2.
- <_>
-
- <_>
- 9 12 3 3 -1.
- <_>
- 9 13 3 1 3.
- <_>
-
- <_>
- 7 6 3 5 -1.
- <_>
- 8 6 1 5 3.
- <_>
-
- <_>
- 5 3 6 2 -1.
- <_>
- 7 3 2 2 3.
- <_>
-
- <_>
- 6 1 8 10 -1.
- <_>
- 10 1 4 5 2.
- <_>
- 6 6 4 5 2.
- <_>
-
- <_>
- 0 0 20 10 -1.
- <_>
- 10 0 10 5 2.
- <_>
- 0 5 10 5 2.
- <_>
-
- <_>
- 6 3 3 1 -1.
- <_>
- 7 3 1 1 3.
- <_>
-
- <_>
- 0 2 6 8 -1.
- <_>
- 2 2 2 8 3.
- <_>
-
- <_>
- 11 10 3 4 -1.
- <_>
- 11 12 3 2 2.
- <_>
-
- <_>
- 12 6 3 8 -1.
- <_>
- 12 10 3 4 2.
- <_>
-
- <_>
- 6 10 3 4 -1.
- <_>
- 6 12 3 2 2.
- <_>
-
- <_>
- 5 6 3 8 -1.
- <_>
- 5 10 3 4 2.
- <_>
-
- <_>
- 2 6 18 6 -1.
- <_>
- 11 6 9 3 2.
- <_>
- 2 9 9 3 2.
- <_>
-
- <_>
- 7 14 7 3 -1.
- <_>
- 7 15 7 1 3.
- <_>
-
- <_>
- 0 0 2 12 -1.
- <_>
- 1 0 1 12 2.
- <_>
-
- <_>
- 1 2 18 16 -1.
- <_>
- 1 10 18 8 2.
- <_>
-
- <_>
- 9 13 5 3 -1.
- <_>
- 9 14 5 1 3.
- <_>
-
- <_>
- 8 13 4 3 -1.
- <_>
- 8 14 4 1 3.
- <_>
-
- <_>
- 0 6 18 6 -1.
- <_>
- 0 6 9 3 2.
- <_>
- 9 9 9 3 2.
- <_>
-
- <_>
- 7 13 6 3 -1.
- <_>
- 7 14 6 1 3.
- <_>
-
- <_>
- 17 4 1 3 -1.
- <_>
- 17 5 1 1 3.
- <_>
-
- <_>
- 12 11 1 9 -1.
- <_>
- 12 14 1 3 3.
- <_>
-
- <_>
- 2 4 1 3 -1.
- <_>
- 2 5 1 1 3.
- <_>
-
- <_>
- 5 4 2 3 -1.
- <_>
- 5 5 2 1 3.
- <_>
-
- <_>
- 1 2 18 3 -1.
- <_>
- 7 2 6 3 3.
- <_>
-
- <_>
- 0 1 20 6 -1.
- <_>
- 0 3 20 2 3.
- <_>
-
- <_>
- 7 5 6 3 -1.
- <_>
- 9 5 2 3 3.
- <_>
-
- <_>
- 13 7 6 4 -1.
- <_>
- 16 7 3 2 2.
- <_>
- 13 9 3 2 2.
- <_>
-
- <_>
- 3 1 4 10 -1.
- <_>
- 3 1 2 5 2.
- <_>
- 5 6 2 5 2.
- <_>
-
- <_>
- 0 4 19 10 -1.
- <_>
- 0 9 19 5 2.
- <_>
-
- <_>
- 9 8 3 12 -1.
- <_>
- 9 12 3 4 3.
- <_>
-
- <_>
- 11 18 5 2 -1.
- <_>
- 11 19 5 1 2.
- <_>
-
- <_>
- 5 16 6 4 -1.
- <_>
- 5 16 3 2 2.
- <_>
- 8 18 3 2 2.
- <_>
-
- <_>
- 5 18 3 2 -1.
- <_>
- 5 19 3 1 2.
- <_>
-
- <_>
- 13 11 3 2 -1.
- <_>
- 13 12 3 1 2.
- <_>
-
- <_>
- 8 5 8 4 -1.
- <_>
- 8 5 4 4 2.
- <_>
-
- <_>
- 1 2 18 6 -1.
- <_>
- 1 2 9 3 2.
- <_>
- 10 5 9 3 2.
- <_>
-
- <_>
- 3 5 14 6 -1.
- <_>
- 3 7 14 2 3.
- <_>
-
- <_>
- 18 1 2 6 -1.
- <_>
- 18 3 2 2 3.
- <_>
-
- <_>
- 9 11 6 1 -1.
- <_>
- 11 11 2 1 3.
- <_>
-
- <_>
- 0 2 6 11 -1.
- <_>
- 3 2 3 11 2.
- <_>
-
- <_>
- 4 12 2 3 -1.
- <_>
- 4 13 2 1 3.
- <_>
-
- <_>
- 6 12 9 2 -1.
- <_>
- 9 12 3 2 3.
- <_>
-
- <_>
- 9 4 6 15 -1.
- <_>
- 9 4 3 15 2.
- <_>
-
- <_>
- 5 11 6 1 -1.
- <_>
- 7 11 2 1 3.
- <_>
-
- <_>
- 5 4 6 15 -1.
- <_>
- 8 4 3 15 2.
- <_>
-
- <_>
- 14 12 6 7 -1.
- <_>
- 14 12 3 7 2.
- <_>
-
- <_>
- 18 3 2 9 -1.
- <_>
- 18 6 2 3 3.
- <_>
-
- <_>
- 8 1 3 1 -1.
- <_>
- 9 1 1 1 3.
- <_>
-
- <_>
- 0 12 6 7 -1.
- <_>
- 3 12 3 7 2.
- <_>
-
- <_>
- 13 7 6 4 -1.
- <_>
- 16 7 3 2 2.
- <_>
- 13 9 3 2 2.
- <_>
-
- <_>
- 8 0 10 2 -1.
- <_>
- 8 1 10 1 2.
- <_>
-
- <_>
- 1 7 6 4 -1.
- <_>
- 1 7 3 2 2.
- <_>
- 4 9 3 2 2.
- <_>
-
- <_>
- 1 2 3 3 -1.
- <_>
- 1 3 3 1 3.
- <_>
-
- <_>
- 9 13 4 3 -1.
- <_>
- 9 14 4 1 3.
- <_>
-
- <_>
- 12 13 7 2 -1.
- <_>
- 12 14 7 1 2.
- <_>
-
- <_>
- 5 12 9 2 -1.
- <_>
- 8 12 3 2 3.
- <_>
-
- <_>
- 6 10 4 8 -1.
- <_>
- 6 14 4 4 2.
- <_>
-
- <_>
- 1 0 18 4 -1.
- <_>
- 7 0 6 4 3.
- <_>
-
- <_>
- 12 0 5 2 -1.
- <_>
- 12 1 5 1 2.
- <_>
-
- <_>
- 7 7 1 12 -1.
- <_>
- 7 13 1 6 2.
- <_>
-
- <_>
- 6 2 3 4 -1.
- <_>
- 7 2 1 4 3.
- <_>
-
- <_>
- 0 13 20 6 -1.
- <_>
- 0 15 20 2 3.
- <_>
-
- <_>
- 8 5 12 2 -1.
- <_>
- 14 5 6 1 2.
- <_>
- 8 6 6 1 2.
- <_>
-
- <_>
- 8 14 2 3 -1.
- <_>
- 8 15 2 1 3.
- <_>
-
- <_>
- 8 14 4 3 -1.
- <_>
- 8 15 4 1 3.
- <_>
-
- <_>
- 12 13 7 6 -1.
- <_>
- 12 15 7 2 3.
- <_>
-
- <_>
- 6 0 8 12 -1.
- <_>
- 10 0 4 6 2.
- <_>
- 6 6 4 6 2.
- <_>
-
- <_>
- 0 15 9 4 -1.
- <_>
- 0 17 9 2 2.
- <_>
-
- <_>
- 9 0 2 5 -1.
- <_>
- 10 0 1 5 2.
- <_>
-
- <_>
- 9 5 2 6 -1.
- <_>
- 9 5 1 6 2.
- <_>
-
- <_>
- 17 2 3 6 -1.
- <_>
- 17 4 3 2 3.
- <_>
-
- <_>
- 3 11 2 3 -1.
- <_>
- 3 12 2 1 3.
- <_>
-
- <_>
- 7 13 3 3 -1.
- <_>
- 7 14 3 1 3.
- <_>
-
- <_>
- 14 12 5 3 -1.
- <_>
- 14 13 5 1 3.
- <_>
-
- <_>
- 4 8 14 3 -1.
- <_>
- 4 9 14 1 3.
- <_>
-
- <_>
- 1 12 5 3 -1.
- <_>
- 1 13 5 1 3.
- <_>
-
- <_>
- 1 15 12 2 -1.
- <_>
- 1 15 6 1 2.
- <_>
- 7 16 6 1 2.
- <_>
-
- <_>
- 12 11 4 2 -1.
- <_>
- 12 12 4 1 2.
- <_>
-
- <_>
- 9 8 3 5 -1.
- <_>
- 10 8 1 5 3.
- <_>
-
- <_>
- 9 5 2 6 -1.
- <_>
- 10 5 1 6 2.
- <_>
-
- <_>
- 0 2 3 6 -1.
- <_>
- 0 4 3 2 3.
- <_>
-
- <_>
- 12 11 4 2 -1.
- <_>
- 12 12 4 1 2.
- <_>
-
- <_>
- 9 7 3 5 -1.
- <_>
- 10 7 1 5 3.
- <_>
-
- <_>
- 4 11 4 2 -1.
- <_>
- 4 12 4 1 2.
- <_>
-
- <_>
- 8 8 3 5 -1.
- <_>
- 9 8 1 5 3.
- <_>
-
- <_>
- 9 3 3 1 -1.
- <_>
- 10 3 1 1 3.
- <_>
-
- <_>
- 16 5 3 8 -1.
- <_>
- 17 5 1 8 3.
- <_>
-
- <_>
- 8 3 3 1 -1.
- <_>
- 9 3 1 1 3.
- <_>
-
- <_>
- 1 5 3 8 -1.
- <_>
- 2 5 1 8 3.
- <_>
-
- <_>
- 10 1 3 3 -1.
- <_>
- 11 1 1 3 3.
- <_>
-
- <_>
- 17 5 2 4 -1.
- <_>
- 17 5 1 4 2.
- <_>
-
- <_>
- 2 8 14 3 -1.
- <_>
- 2 9 14 1 3.
- <_>
-
- <_>
- 9 7 1 3 -1.
- <_>
- 9 8 1 1 3.
- <_>
-
- <_>
- 6 1 8 10 -1.
- <_>
- 6 6 8 5 2.
- <_>
-
- <_>
- 13 0 6 8 -1.
- <_>
- 16 0 3 4 2.
- <_>
- 13 4 3 4 2.
- <_>
-
- <_>
- 1 5 2 4 -1.
- <_>
- 2 5 1 4 2.
- <_>
-
- <_>
- 4 2 12 2 -1.
- <_>
- 4 3 12 1 2.
- <_>
-
- <_>
- 8 8 4 4 -1.
- <_>
- 8 10 4 2 2.
- <_>
-
- <_>
- 5 6 12 4 -1.
- <_>
- 9 6 4 4 3.
- <_>
-
- <_>
- 1 2 8 1 -1.
- <_>
- 5 2 4 1 2.
- <_>
-
- <_>
- 1 1 6 10 -1.
- <_>
- 3 1 2 10 3.
- <_>
-
- <_>
- 8 6 8 2 -1.
- <_>
- 8 6 4 2 2.
- <_>
-
- <_>
- 10 7 6 6 -1.
- <_>
- 12 7 2 6 3.
- <_>
-
- <_>
- 4 6 8 2 -1.
- <_>
- 8 6 4 2 2.
- <_>
-
- <_>
- 4 7 6 6 -1.
- <_>
- 6 7 2 6 3.
- <_>
-
- <_>
- 3 14 16 4 -1.
- <_>
- 3 16 16 2 2.
- <_>
-
- <_>
- 8 12 4 2 -1.
- <_>
- 8 13 4 1 2.
- <_>
-
- <_>
- 8 12 3 3 -1.
- <_>
- 8 13 3 1 3.
- <_>
-
- <_>
- 5 12 6 1 -1.
- <_>
- 8 12 3 1 2.
- <_>
-
- <_>
- 18 10 2 3 -1.
- <_>
- 18 11 2 1 3.
- <_>
-
- <_>
- 16 8 4 6 -1.
- <_>
- 16 10 4 2 3.
- <_>
-
- <_>
- 8 3 2 1 -1.
- <_>
- 9 3 1 1 2.
- <_>
-
- <_>
- 7 1 3 9 -1.
- <_>
- 8 1 1 9 3.
- <_>
-
- <_>
- 5 11 11 6 -1.
- <_>
- 5 14 11 3 2.
- <_>
-
- <_>
- 12 2 3 14 -1.
- <_>
- 12 9 3 7 2.
- <_>
-
- <_>
- 8 7 3 3 -1.
- <_>
- 9 7 1 3 3.
- <_>
-
- <_>
- 3 5 12 5 -1.
- <_>
- 7 5 4 5 3.
- <_>
-
- <_>
- 1 2 6 3 -1.
- <_>
- 4 2 3 3 2.
- <_>
-
- <_>
- 5 5 6 10 -1.
- <_>
- 5 5 3 5 2.
- <_>
- 8 10 3 5 2.
- <_>
-
- <_>
- 16 18 2 2 -1.
- <_>
- 16 18 1 2 2.
- <_>
-
- <_>
- 16 18 2 2 -1.
- <_>
- 16 18 1 2 2.
- <_>
-
- <_>
- 8 4 2 5 -1.
- <_>
- 9 4 1 5 2.
- <_>
-
- <_>
- 8 4 1 4 -1.
- <_>
- 8 6 1 2 2.
- <_>
-
- <_>
- 7 15 12 4 -1.
- <_>
- 13 15 6 2 2.
- <_>
- 7 17 6 2 2.
- <_>
-
- <_>
- 11 18 6 2 -1.
- <_>
- 11 19 6 1 2.
- <_>
-
- <_>
- 7 7 4 10 -1.
- <_>
- 7 12 4 5 2.
- <_>
-
- <_>
- 5 6 10 8 -1.
- <_>
- 5 10 10 4 2.
- <_>
-
- <_>
- 11 1 6 12 -1.
- <_>
- 14 1 3 6 2.
- <_>
- 11 7 3 6 2.
- <_>
-
- <_>
- 5 8 12 1 -1.
- <_>
- 9 8 4 1 3.
- <_>
-
- <_>
- 4 7 3 6 -1.
- <_>
- 4 9 3 2 3.
- <_>
-
- <_>
- 4 11 3 4 -1.
- <_>
- 4 13 3 2 2.
- <_>
-
- <_>
- 14 16 2 2 -1.
- <_>
- 14 17 2 1 2.
- <_>
-
- <_>
- 15 15 2 2 -1.
- <_>
- 15 16 2 1 2.
- <_>
-
- <_>
- 7 12 6 2 -1.
- <_>
- 7 13 6 1 2.
- <_>
-
- <_>
- 8 13 4 2 -1.
- <_>
- 8 14 4 1 2.
- <_>
-
- <_>
- 11 1 6 12 -1.
- <_>
- 14 1 3 6 2.
- <_>
- 11 7 3 6 2.
- <_>
-
- <_>
- 12 2 4 2 -1.
- <_>
- 12 3 4 1 2.
- <_>
-
- <_>
- 3 10 12 6 -1.
- <_>
- 3 10 6 3 2.
- <_>
- 9 13 6 3 2.
- <_>
-
- <_>
- 3 1 6 12 -1.
- <_>
- 3 1 3 6 2.
- <_>
- 6 7 3 6 2.
- <_>
-
- <_>
- 16 6 4 14 -1.
- <_>
- 18 6 2 7 2.
- <_>
- 16 13 2 7 2.
- <_>
-
- <_>
- 5 1 10 8 -1.
- <_>
- 10 1 5 4 2.
- <_>
- 5 5 5 4 2.
- <_>
-
- <_>
- 0 6 4 14 -1.
- <_>
- 0 6 2 7 2.
- <_>
- 2 13 2 7 2.
- <_>
-
- <_>
- 1 15 12 4 -1.
- <_>
- 1 15 6 2 2.
- <_>
- 7 17 6 2 2.
- <_>
-
- <_>
- 10 17 3 3 -1.
- <_>
- 11 17 1 3 3.
- <_>
-
- <_>
- 11 2 2 6 -1.
- <_>
- 12 2 1 3 2.
- <_>
- 11 5 1 3 2.
- <_>
-
- <_>
- 7 17 3 3 -1.
- <_>
- 8 17 1 3 3.
- <_>
-
- <_>
- 8 15 4 3 -1.
- <_>
- 8 16 4 1 3.
- <_>
-
- <_>
- 10 15 4 2 -1.
- <_>
- 12 15 2 1 2.
- <_>
- 10 16 2 1 2.
- <_>
-
- <_>
- 13 13 4 3 -1.
- <_>
- 13 14 4 1 3.
- <_>
-
- <_>
- 3 13 4 3 -1.
- <_>
- 3 14 4 1 3.
- <_>
-
- <_>
- 7 2 2 6 -1.
- <_>
- 7 2 1 3 2.
- <_>
- 8 5 1 3 2.
- <_>
-
- <_>
- 2 1 16 3 -1.
- <_>
- 2 2 16 1 3.
- <_>
-
- <_>
- 10 15 4 2 -1.
- <_>
- 12 15 2 1 2.
- <_>
- 10 16 2 1 2.
- <_>
-
- <_>
- 6 15 4 2 -1.
- <_>
- 6 15 2 1 2.
- <_>
- 8 16 2 1 2.
- <_>
-
- <_>
- 3 0 13 3 -1.
- <_>
- 3 1 13 1 3.
- <_>
-
- <_>
- 0 9 20 3 -1.
- <_>
- 0 10 20 1 3.
- <_>
-
- <_>
- 6 7 9 2 -1.
- <_>
- 6 8 9 1 2.
- <_>
-
- <_>
- 8 14 3 6 -1.
- <_>
- 9 14 1 6 3.
- <_>
-
- <_>
- 9 10 2 2 -1.
- <_>
- 9 11 2 1 2.
- <_>
-
- <_>
- 9 7 2 5 -1.
- <_>
- 9 7 1 5 2.
- <_>
-
- <_>
- 5 6 10 3 -1.
- <_>
- 5 6 5 3 2.
- <_>
-
- <_>
- 9 7 2 5 -1.
- <_>
- 10 7 1 5 2.
- <_>
-
- <_>
- 5 6 10 3 -1.
- <_>
- 10 6 5 3 2.
- <_>
-
- <_>
- 13 9 2 2 -1.
- <_>
- 13 9 1 2 2.
- <_>
-
- <_>
- 4 3 12 11 -1.
- <_>
- 8 3 4 11 3.
- <_>
-
- <_>
- 7 1 2 7 -1.
- <_>
- 8 1 1 7 2.
- <_>
-
- <_>
- 7 4 3 8 -1.
- <_>
- 8 4 1 8 3.
- <_>
-
- <_>
- 13 9 2 2 -1.
- <_>
- 13 9 1 2 2.
- <_>
-
- <_>
- 11 6 2 2 -1.
- <_>
- 12 6 1 1 2.
- <_>
- 11 7 1 1 2.
- <_>
-
- <_>
- 5 4 2 3 -1.
- <_>
- 5 5 2 1 3.
- <_>
-
- <_>
- 6 5 1 3 -1.
- <_>
- 6 6 1 1 3.
- <_>
-
- <_>
- 13 9 2 2 -1.
- <_>
- 13 9 1 2 2.
- <_>
-
- <_>
- 16 14 3 3 -1.
- <_>
- 16 15 3 1 3.
- <_>
-
- <_>
- 5 9 2 2 -1.
- <_>
- 6 9 1 2 2.
- <_>
-
- <_>
- 1 14 3 3 -1.
- <_>
- 1 15 3 1 3.
- <_>
-
- <_>
- 13 1 1 6 -1.
- <_>
- 13 3 1 2 3.
- <_>
-
- <_>
- 13 3 7 2 -1.
- <_>
- 13 4 7 1 2.
- <_>
-
- <_>
- 0 6 20 14 -1.
- <_>
- 0 13 20 7 2.
- <_>
-
- <_>
- 0 4 3 6 -1.
- <_>
- 0 6 3 2 3.
- <_>
-
- <_>
- 10 1 9 6 -1.
- <_>
- 10 3 9 2 3.
- <_>
-
- <_>
- 8 0 12 5 -1.
- <_>
- 8 0 6 5 2.
- <_>
-
- <_>
- 0 0 18 5 -1.
- <_>
- 6 0 6 5 3.
- <_>
-
- <_>
- 1 1 9 6 -1.
- <_>
- 1 3 9 2 3.
- <_>
-
- <_>
- 15 15 2 2 -1.
- <_>
- 15 16 2 1 2.
- <_>
-
- <_>
- 13 16 3 4 -1.
- <_>
- 13 18 3 2 2.
- <_>
-
- <_>
- 3 15 2 2 -1.
- <_>
- 3 16 2 1 2.
- <_>
-
- <_>
- 4 16 3 4 -1.
- <_>
- 4 18 3 2 2.
- <_>
-
- <_>
- 11 14 1 3 -1.
- <_>
- 11 15 1 1 3.
- <_>
-
- <_>
- 9 13 5 3 -1.
- <_>
- 9 14 5 1 3.
- <_>
-
- <_>
- 0 0 3 6 -1.
- <_>
- 0 2 3 2 3.
- <_>
-
- <_>
- 4 1 6 3 -1.
- <_>
- 6 1 2 3 3.
- <_>
-
- <_>
- 9 13 4 3 -1.
- <_>
- 9 14 4 1 3.
- <_>
-
- <_>
- 8 15 5 3 -1.
- <_>
- 8 16 5 1 3.
- <_>
-
- <_>
- 8 3 3 2 -1.
- <_>
- 9 3 1 2 3.
- <_>
-
- <_>
- 1 8 18 2 -1.
- <_>
- 1 9 18 1 2.
- <_>
-
- <_>
- 11 14 1 3 -1.
- <_>
- 11 15 1 1 3.
- <_>
-
- <_>
- 8 13 6 3 -1.
- <_>
- 8 14 6 1 3.
- <_>
-
- <_>
- 8 14 1 3 -1.
- <_>
- 8 15 1 1 3.
- <_>
-
- <_>
- 4 13 12 4 -1.
- <_>
- 4 13 6 2 2.
- <_>
- 10 15 6 2 2.
- <_>
-
- <_>
- 10 7 2 2 -1.
- <_>
- 10 7 1 2 2.
- <_>
-
- <_>
- 13 4 2 8 -1.
- <_>
- 14 4 1 4 2.
- <_>
- 13 8 1 4 2.
- <_>
-
- <_>
- 0 5 4 6 -1.
- <_>
- 0 7 4 2 3.
- <_>
-
- <_>
- 8 7 2 2 -1.
- <_>
- 9 7 1 2 2.
- <_>
-
- <_>
- 13 0 3 7 -1.
- <_>
- 14 0 1 7 3.
- <_>
-
- <_>
- 11 2 2 14 -1.
- <_>
- 11 2 1 14 2.
- <_>
-
- <_>
- 4 0 3 7 -1.
- <_>
- 5 0 1 7 3.
- <_>
-
- <_>
- 5 5 8 12 -1.
- <_>
- 5 5 4 6 2.
- <_>
- 9 11 4 6 2.
- <_>
-
- <_>
- 11 4 6 3 -1.
- <_>
- 11 5 6 1 3.
- <_>
-
- <_>
- 12 3 4 3 -1.
- <_>
- 12 4 4 1 3.
- <_>
-
- <_>
- 5 5 10 12 -1.
- <_>
- 5 5 5 6 2.
- <_>
- 10 11 5 6 2.
- <_>
-
- <_>
- 3 6 12 3 -1.
- <_>
- 9 6 6 3 2.
- <_>
-
- <_>
- 9 6 2 7 -1.
- <_>
- 9 6 1 7 2.
- <_>
-
- <_>
- 9 5 2 4 -1.
- <_>
- 9 5 1 4 2.
- <_>
-
- <_>
- 8 7 3 3 -1.
- <_>
- 9 7 1 3 3.
- <_>
-
- <_>
- 5 1 6 4 -1.
- <_>
- 7 1 2 4 3.
- <_>
-
- <_>
- 13 16 7 3 -1.
- <_>
- 13 17 7 1 3.
- <_>
-
- <_>
- 12 4 3 3 -1.
- <_>
- 12 5 3 1 3.
- <_>
-
- <_>
- 0 16 7 3 -1.
- <_>
- 0 17 7 1 3.
- <_>
-
- <_>
- 5 4 3 3 -1.
- <_>
- 5 5 3 1 3.
- <_>
-
- <_>
- 12 9 8 10 -1.
- <_>
- 12 9 4 10 2.
- <_>
-
- <_>
- 8 10 12 5 -1.
- <_>
- 12 10 4 5 3.
- <_>
-
- <_>
- 0 9 8 10 -1.
- <_>
- 4 9 4 10 2.
- <_>
-
- <_>
- 0 10 12 5 -1.
- <_>
- 4 10 4 5 3.
- <_>
-
- <_>
- 2 3 6 2 -1.
- <_>
- 5 3 3 2 2.
- <_>
-
- <_>
- 0 0 17 9 -1.
- <_>
- 0 3 17 3 3.
- <_>
-
- <_>
- 4 7 12 2 -1.
- <_>
- 8 7 4 2 3.
- <_>
-
- <_>
- 10 4 6 4 -1.
- <_>
- 12 4 2 4 3.
- <_>
-
- <_>
- 0 10 20 4 -1.
- <_>
- 0 12 20 2 2.
- <_>
-
- <_>
- 4 3 6 5 -1.
- <_>
- 6 3 2 5 3.
- <_>
-
- <_>
- 1 1 18 4 -1.
- <_>
- 7 1 6 4 3.
- <_>
-
- <_>
- 13 9 2 3 -1.
- <_>
- 13 9 1 3 2.
- <_>
-
- <_>
- 6 15 7 4 -1.
- <_>
- 6 17 7 2 2.
- <_>
-
- <_>
- 3 17 4 2 -1.
- <_>
- 3 18 4 1 2.
- <_>
-
- <_>
- 9 4 8 10 -1.
- <_>
- 9 9 8 5 2.
- <_>
-
- <_>
- 9 17 3 2 -1.
- <_>
- 10 17 1 2 3.
- <_>
-
- <_>
- 8 2 4 8 -1.
- <_>
- 8 6 4 4 2.
- <_>
-
- <_>
- 3 4 14 12 -1.
- <_>
- 3 4 7 6 2.
- <_>
- 10 10 7 6 2.
- <_>
-
- <_>
- 7 7 6 4 -1.
- <_>
- 9 7 2 4 3.
- <_>
-
- <_>
- 6 7 9 4 -1.
- <_>
- 6 9 9 2 2.
- <_>
-
- <_>
- 2 10 3 3 -1.
- <_>
- 2 11 3 1 3.
- <_>
-
- <_>
- 4 6 2 9 -1.
- <_>
- 4 9 2 3 3.
- <_>
-
- <_>
- 9 11 3 3 -1.
- <_>
- 9 12 3 1 3.
- <_>
-
- <_>
- 3 1 15 2 -1.
- <_>
- 3 2 15 1 2.
- <_>
-
- <_>
- 9 8 2 3 -1.
- <_>
- 9 9 2 1 3.
- <_>
-
- <_>
- 9 6 2 5 -1.
- <_>
- 10 6 1 5 2.
- <_>
-
- <_>
- 9 7 2 3 -1.
- <_>
- 9 8 2 1 3.
- <_>
-
- <_>
- 4 10 12 10 -1.
- <_>
- 4 15 12 5 2.
- <_>
-
- <_>
- 0 10 4 2 -1.
- <_>
- 0 11 4 1 2.
- <_>
-
- <_>
- 5 15 9 2 -1.
- <_>
- 5 16 9 1 2.
- <_>
-
- <_>
- 8 14 6 3 -1.
- <_>
- 8 15 6 1 3.
- <_>
-
- <_>
- 8 16 4 3 -1.
- <_>
- 8 17 4 1 3.
- <_>
-
- <_>
- 8 9 4 2 -1.
- <_>
- 8 10 4 1 2.
- <_>
-
- <_>
- 3 3 14 2 -1.
- <_>
- 3 4 14 1 2.
- <_>
-
- <_>
- 11 12 1 2 -1.
- <_>
- 11 13 1 1 2.
- <_>
-
- <_>
- 4 12 12 1 -1.
- <_>
- 8 12 4 1 3.
- <_>
-
- <_>
- 0 2 1 2 -1.
- <_>
- 0 3 1 1 2.
- <_>
-
- <_>
- 7 4 4 6 -1.
- <_>
- 9 4 2 6 2.
- <_>
-
- <_>
- 0 2 20 14 -1.
- <_>
- 10 2 10 7 2.
- <_>
- 0 9 10 7 2.
- <_>
-
- <_>
- 14 6 1 3 -1.
- <_>
- 14 7 1 1 3.
- <_>
-
- <_>
- 0 4 20 12 -1.
- <_>
- 0 4 10 6 2.
- <_>
- 10 10 10 6 2.
- <_>
-
- <_>
- 8 12 1 2 -1.
- <_>
- 8 13 1 1 2.
- <_>
-
- <_>
- 9 18 3 2 -1.
- <_>
- 10 18 1 2 3.
- <_>
-
- <_>
- 9 17 6 2 -1.
- <_>
- 11 17 2 2 3.
- <_>
-
- <_>
- 5 6 2 3 -1.
- <_>
- 5 7 2 1 3.
- <_>
-
- <_>
- 5 4 3 3 -1.
- <_>
- 5 5 3 1 3.
- <_>
-
- <_>
- 14 15 3 2 -1.
- <_>
- 14 16 3 1 2.
- <_>
-
- <_>
- 11 3 3 4 -1.
- <_>
- 12 3 1 4 3.
- <_>
-
- <_>
- 3 15 3 2 -1.
- <_>
- 3 16 3 1 2.
- <_>
-
- <_>
- 9 12 2 3 -1.
- <_>
- 9 13 2 1 3.
- <_>
-
- <_>
- 9 13 3 7 -1.
- <_>
- 10 13 1 7 3.
- <_>
-
- <_>
- 12 12 5 3 -1.
- <_>
- 12 13 5 1 3.
- <_>
-
- <_>
- 8 18 3 2 -1.
- <_>
- 9 18 1 2 3.
- <_>
-
- <_>
- 4 7 12 4 -1.
- <_>
- 4 7 6 2 2.
- <_>
- 10 9 6 2 2.
- <_>
-
- <_>
- 6 19 14 1 -1.
- <_>
- 6 19 7 1 2.
- <_>
-
- <_>
- 16 14 3 2 -1.
- <_>
- 16 15 3 1 2.
- <_>
-
- <_>
- 1 0 6 10 -1.
- <_>
- 1 0 3 5 2.
- <_>
- 4 5 3 5 2.
- <_>
-
- <_>
- 1 0 4 10 -1.
- <_>
- 1 0 2 5 2.
- <_>
- 3 5 2 5 2.
- <_>
-
- <_>
- 15 3 5 6 -1.
- <_>
- 15 5 5 2 3.
- <_>
-
- <_>
- 9 5 2 15 -1.
- <_>
- 9 10 2 5 3.
- <_>
-
- <_>
- 0 3 5 6 -1.
- <_>
- 0 5 5 2 3.
- <_>
-
- <_>
- 6 0 3 2 -1.
- <_>
- 7 0 1 2 3.
- <_>
-
- <_>
- 12 8 8 2 -1.
- <_>
- 16 8 4 1 2.
- <_>
- 12 9 4 1 2.
- <_>
-
- <_>
- 5 8 12 1 -1.
- <_>
- 9 8 4 1 3.
- <_>
-
- <_>
- 3 13 3 3 -1.
- <_>
- 3 14 3 1 3.
- <_>
-
- <_>
- 5 13 3 2 -1.
- <_>
- 5 14 3 1 2.
- <_>
-
- <_>
- 9 15 3 3 -1.
- <_>
- 9 16 3 1 3.
- <_>
-
- <_>
- 7 15 7 3 -1.
- <_>
- 7 16 7 1 3.
- <_>
-
- <_>
- 3 14 11 6 -1.
- <_>
- 3 16 11 2 3.
- <_>
-
- <_>
- 0 19 14 1 -1.
- <_>
- 7 19 7 1 2.
- <_>
-
- <_>
- 9 17 6 2 -1.
- <_>
- 11 17 2 2 3.
- <_>
-
- <_>
- 12 11 6 2 -1.
- <_>
- 14 11 2 2 3.
- <_>
-
- <_>
- 5 17 6 2 -1.
- <_>
- 7 17 2 2 3.
- <_>
-
- <_>
- 0 1 9 10 -1.
- <_>
- 3 1 3 10 3.
- <_>
-
- <_>
- 10 1 3 3 -1.
- <_>
- 11 1 1 3 3.
- <_>
-
- <_>
- 9 5 6 4 -1.
- <_>
- 9 5 3 4 2.
- <_>
-
- <_>
- 7 1 3 3 -1.
- <_>
- 8 1 1 3 3.
- <_>
-
- <_>
- 0 4 4 11 -1.
- <_>
- 2 4 2 11 2.
- <_>
-
- <_>
- 9 5 6 4 -1.
- <_>
- 9 5 3 4 2.
- <_>
-
- <_>
- 6 0 8 10 -1.
- <_>
- 10 0 4 5 2.
- <_>
- 6 5 4 5 2.
- <_>
-
- <_>
- 6 6 5 14 -1.
- <_>
- 6 13 5 7 2.
- <_>
-
- <_>
- 8 5 4 14 -1.
- <_>
- 8 12 4 7 2.
- <_>
-
- <_>
- 7 7 6 5 -1.
- <_>
- 9 7 2 5 3.
- <_>
-
- <_>
- 9 3 3 9 -1.
- <_>
- 9 6 3 3 3.
- <_>
-
- <_>
- 8 1 3 3 -1.
- <_>
- 9 1 1 3 3.
- <_>
-
- <_>
- 9 6 2 4 -1.
- <_>
- 10 6 1 4 2.
- <_>
-
- <_>
- 10 8 6 9 -1.
- <_>
- 10 8 3 9 2.
- <_>
-
- <_>
- 16 4 3 8 -1.
- <_>
- 17 4 1 8 3.
- <_>
-
- <_>
- 5 9 10 6 -1.
- <_>
- 5 9 5 3 2.
- <_>
- 10 12 5 3 2.
- <_>
-
- <_>
- 5 5 6 4 -1.
- <_>
- 8 5 3 4 2.
- <_>
-
- <_>
- 9 8 4 2 -1.
- <_>
- 9 9 4 1 2.
- <_>
-
- <_>
- 11 7 2 2 -1.
- <_>
- 11 7 1 2 2.
- <_>
-
- <_>
- 8 12 4 8 -1.
- <_>
- 8 12 2 4 2.
- <_>
- 10 16 2 4 2.
- <_>
-
- <_>
- 0 1 4 9 -1.
- <_>
- 0 4 4 3 3.
- <_>
-
- <_>
- 9 10 3 3 -1.
- <_>
- 9 11 3 1 3.
- <_>
-
- <_>
- 8 11 4 2 -1.
- <_>
- 8 12 4 1 2.
- <_>
-
- <_>
- 7 8 4 2 -1.
- <_>
- 7 9 4 1 2.
- <_>
-
- <_>
- 7 8 6 1 -1.
- <_>
- 9 8 2 1 3.
- <_>
-
- <_>
- 16 0 4 9 -1.
- <_>
- 16 0 2 9 2.
- <_>
-
- <_>
- 16 0 3 6 -1.
- <_>
- 16 3 3 3 2.
- <_>
-
- <_>
- 0 0 4 9 -1.
- <_>
- 2 0 2 9 2.
- <_>
-
- <_>
- 1 0 3 6 -1.
- <_>
- 1 3 3 3 2.
- <_>
-
- <_>
- 9 7 6 9 -1.
- <_>
- 11 7 2 9 3.
- <_>
-
- <_>
- 10 6 3 6 -1.
- <_>
- 11 6 1 6 3.
- <_>
-
- <_>
- 1 2 18 2 -1.
- <_>
- 1 2 9 1 2.
- <_>
- 10 3 9 1 2.
- <_>
-
- <_>
- 5 8 6 8 -1.
- <_>
- 7 8 2 8 3.
- <_>
-
- <_>
- 9 0 6 16 -1.
- <_>
- 11 0 2 16 3.
- <_>
-
- <_>
- 14 1 6 18 -1.
- <_>
- 17 1 3 9 2.
- <_>
- 14 10 3 9 2.
- <_>
-
- <_>
- 2 9 2 3 -1.
- <_>
- 2 10 2 1 3.
- <_>
-
- <_>
- 0 1 6 18 -1.
- <_>
- 0 1 3 9 2.
- <_>
- 3 10 3 9 2.
- <_>
-
- <_>
- 11 8 4 12 -1.
- <_>
- 11 8 2 12 2.
- <_>
-
- <_>
- 2 1 18 18 -1.
- <_>
- 2 10 18 9 2.
- <_>
-
- <_>
- 6 3 3 1 -1.
- <_>
- 7 3 1 1 3.
- <_>
-
- <_>
- 4 12 2 2 -1.
- <_>
- 4 13 2 1 2.
- <_>
-
- <_>
- 8 13 5 3 -1.
- <_>
- 8 14 5 1 3.
- <_>
-
- <_>
- 8 14 4 3 -1.
- <_>
- 8 15 4 1 3.
- <_>
-
- <_>
- 3 12 5 3 -1.
- <_>
- 3 13 5 1 3.
- <_>
-
- <_>
- 6 3 3 4 -1.
- <_>
- 7 3 1 4 3.
- <_>
-
- <_>
- 11 10 2 2 -1.
- <_>
- 12 10 1 1 2.
- <_>
- 11 11 1 1 2.
- <_>
-
- <_>
- 5 8 12 1 -1.
- <_>
- 9 8 4 1 3.
- <_>
-
- <_>
- 8 4 4 8 -1.
- <_>
- 10 4 2 8 2.
- <_>
-
- <_>
- 6 6 8 5 -1.
- <_>
- 10 6 4 5 2.
- <_>
-
- <_>
- 10 4 6 4 -1.
- <_>
- 12 4 2 4 3.
- <_>
-
- <_>
- 12 7 2 2 -1.
- <_>
- 13 7 1 1 2.
- <_>
- 12 8 1 1 2.
- <_>
-
- <_>
- 3 5 10 8 -1.
- <_>
- 3 9 10 4 2.
- <_>
-
- <_>
- 7 1 2 12 -1.
- <_>
- 7 7 2 6 2.
- <_>
-
- <_>
- 12 7 2 2 -1.
- <_>
- 13 7 1 1 2.
- <_>
- 12 8 1 1 2.
- <_>
-
- <_>
- 11 13 1 6 -1.
- <_>
- 11 16 1 3 2.
- <_>
-
- <_>
- 5 1 6 15 -1.
- <_>
- 7 1 2 15 3.
- <_>
-
- <_>
- 6 7 2 2 -1.
- <_>
- 6 7 1 1 2.
- <_>
- 7 8 1 1 2.
- <_>
-
- <_>
- 17 5 2 2 -1.
- <_>
- 17 6 2 1 2.
- <_>
-
- <_>
- 10 3 4 10 -1.
- <_>
- 12 3 2 5 2.
- <_>
- 10 8 2 5 2.
- <_>
-
- <_>
- 1 5 2 2 -1.
- <_>
- 1 6 2 1 2.
- <_>
-
- <_>
- 7 10 2 2 -1.
- <_>
- 7 10 1 1 2.
- <_>
- 8 11 1 1 2.
- <_>
-
- <_>
- 3 12 14 4 -1.
- <_>
- 10 12 7 2 2.
- <_>
- 3 14 7 2 2.
- <_>
-
- <_>
- 9 15 3 2 -1.
- <_>
- 9 16 3 1 2.
- <_>
-
- <_>
- 1 13 3 3 -1.
- <_>
- 1 14 3 1 3.
- <_>
-
- <_>
- 0 3 1 2 -1.
- <_>
- 0 4 1 1 2.
- <_>
-
- <_>
- 7 7 6 1 -1.
- <_>
- 9 7 2 1 3.
- <_>
-
- <_>
- 0 4 16 6 -1.
- <_>
- 0 6 16 2 3.
- <_>
-
- <_>
- 9 3 2 14 -1.
- <_>
- 9 10 2 7 2.
- <_>
-
- <_>
- 12 0 4 3 -1.
- <_>
- 12 0 2 3 2.
- <_>
-
- <_>
- 4 18 12 2 -1.
- <_>
- 8 18 4 2 3.
- <_>
-
- <_>
- 4 10 12 4 -1.
- <_>
- 8 10 4 4 3.
- <_>
-
- <_>
- 9 9 2 2 -1.
- <_>
- 9 10 2 1 2.
- <_>
-
- <_>
- 14 1 2 8 -1.
- <_>
- 15 1 1 4 2.
- <_>
- 14 5 1 4 2.
- <_>
-
- <_>
- 3 4 9 1 -1.
- <_>
- 6 4 3 1 3.
- <_>
-
- <_>
- 3 3 4 2 -1.
- <_>
- 3 4 4 1 2.
- <_>
-
- <_>
- 11 15 2 4 -1.
- <_>
- 11 17 2 2 2.
- <_>
-
- <_>
- 14 13 2 6 -1.
- <_>
- 14 15 2 2 3.
- <_>
-
- <_>
- 6 6 1 6 -1.
- <_>
- 6 9 1 3 2.
- <_>
-
- <_>
- 6 10 8 8 -1.
- <_>
- 6 14 8 4 2.
- <_>
-
- <_>
- 8 13 4 3 -1.
- <_>
- 8 14 4 1 3.
- <_>
-
- <_>
- 10 11 4 8 -1.
- <_>
- 10 15 4 4 2.
- <_>
-
- <_>
- 5 11 6 1 -1.
- <_>
- 7 11 2 1 3.
- <_>
-
- <_>
- 5 4 6 10 -1.
- <_>
- 8 4 3 10 2.
- <_>
-
- <_>
- 14 2 6 3 -1.
- <_>
- 14 3 6 1 3.
- <_>
-
- <_>
- 9 12 3 2 -1.
- <_>
- 9 13 3 1 2.
- <_>
-
- <_>
- 8 1 4 6 -1.
- <_>
- 8 3 4 2 3.
- <_>
-
- <_>
- 3 5 13 8 -1.
- <_>
- 3 9 13 4 2.
- <_>
-
- <_>
- 12 5 5 3 -1.
- <_>
- 12 6 5 1 3.
- <_>
-
- <_>
- 5 14 15 6 -1.
- <_>
- 5 16 15 2 3.
- <_>
-
- <_>
- 3 5 5 3 -1.
- <_>
- 3 6 5 1 3.
- <_>
-
- <_>
- 9 14 2 6 -1.
- <_>
- 9 14 1 3 2.
- <_>
- 10 17 1 3 2.
- <_>
-
- <_>
- 9 12 3 2 -1.
- <_>
- 9 13 3 1 2.
- <_>
-
- <_>
- 9 13 3 2 -1.
- <_>
- 9 14 3 1 2.
- <_>
-
- <_>
- 0 2 6 3 -1.
- <_>
- 0 3 6 1 3.
- <_>
-
- <_>
- 0 1 9 11 -1.
- <_>
- 3 1 3 11 3.
- <_>
-
- <_>
- 8 13 4 6 -1.
- <_>
- 10 13 2 3 2.
- <_>
- 8 16 2 3 2.
- <_>
-
- <_>
- 7 13 6 3 -1.
- <_>
- 7 14 6 1 3.
- <_>
-
- <_>
- 3 12 14 4 -1.
- <_>
- 3 12 7 2 2.
- <_>
- 10 14 7 2 2.
- <_>
-
- <_>
- 7 14 1 4 -1.
- <_>
- 7 16 1 2 2.
- <_>
-
- <_>
- 8 13 4 6 -1.
- <_>
- 10 13 2 3 2.
- <_>
- 8 16 2 3 2.
- <_>
-
- <_>
- 10 14 1 3 -1.
- <_>
- 10 15 1 1 3.
- <_>
-
- <_>
- 8 13 4 6 -1.
- <_>
- 8 13 2 3 2.
- <_>
- 10 16 2 3 2.
- <_>
-
- <_>
- 9 14 1 3 -1.
- <_>
- 9 15 1 1 3.
- <_>
-
- <_>
- 10 15 2 3 -1.
- <_>
- 10 16 2 1 3.
- <_>
-
- <_>
- 11 16 1 2 -1.
- <_>
- 11 17 1 1 2.
- <_>
-
- <_>
- 9 0 2 2 -1.
- <_>
- 9 1 2 1 2.
- <_>
-
- <_>
- 0 1 5 8 -1.
- <_>
- 0 5 5 4 2.
- <_>
-
- <_>
- 10 14 2 3 -1.
- <_>
- 10 15 2 1 3.
- <_>
-
- <_>
- 10 13 2 3 -1.
- <_>
- 10 14 2 1 3.
- <_>
-
- <_>
- 0 3 16 6 -1.
- <_>
- 0 6 16 3 2.
- <_>
-
- <_>
- 4 1 2 2 -1.
- <_>
- 5 1 1 2 2.
- <_>
-
- <_>
- 9 7 2 3 -1.
- <_>
- 9 8 2 1 3.
- <_>
-
- <_>
- 10 8 2 12 -1.
- <_>
- 10 12 2 4 3.
- <_>
-
- <_>
- 9 7 2 2 -1.
- <_>
- 10 7 1 2 2.
- <_>
-
- <_>
- 5 0 6 8 -1.
- <_>
- 7 0 2 8 3.
- <_>
-
- <_>
- 9 7 3 6 -1.
- <_>
- 10 7 1 6 3.
- <_>
-
- <_>
- 8 12 10 8 -1.
- <_>
- 8 16 10 4 2.
- <_>
-
- <_>
- 8 7 3 6 -1.
- <_>
- 9 7 1 6 3.
- <_>
-
- <_>
- 4 7 12 2 -1.
- <_>
- 10 7 6 2 2.
- <_>
-
- <_>
- 8 6 8 3 -1.
- <_>
- 8 6 4 3 2.
- <_>
-
- <_>
- 16 15 3 3 -1.
- <_>
- 16 16 3 1 3.
- <_>
-
- <_>
- 4 6 12 3 -1.
- <_>
- 10 6 6 3 2.
- <_>
-
- <_>
- 7 8 3 5 -1.
- <_>
- 8 8 1 5 3.
- <_>
-
- <_>
- 0 10 20 2 -1.
- <_>
- 10 10 10 1 2.
- <_>
- 0 11 10 1 2.
- <_>
-
- <_>
- 11 16 9 4 -1.
- <_>
- 14 16 3 4 3.
- <_>
-
- <_>
- 0 5 3 4 -1.
- <_>
- 1 5 1 4 3.
- <_>
-
- <_>
- 8 15 4 2 -1.
- <_>
- 8 15 2 1 2.
- <_>
- 10 16 2 1 2.
- <_>
-
- <_>
- 1 8 19 3 -1.
- <_>
- 1 9 19 1 3.
- <_>
-
- <_>
- 15 16 3 3 -1.
- <_>
- 15 17 3 1 3.
- <_>
-
- <_>
- 0 4 20 10 -1.
- <_>
- 0 4 10 5 2.
- <_>
- 10 9 10 5 2.
- <_>
-
- <_>
- 2 14 7 6 -1.
- <_>
- 2 16 7 2 3.
- <_>
-
- <_>
- 8 6 6 6 -1.
- <_>
- 10 6 2 6 3.
- <_>
-
- <_>
- 16 4 4 6 -1.
- <_>
- 16 6 4 2 3.
- <_>
-
- <_>
- 7 13 6 3 -1.
- <_>
- 7 14 6 1 3.
- <_>
-
- <_>
- 7 13 4 3 -1.
- <_>
- 7 14 4 1 3.
- <_>
-
- <_>
- 13 13 6 2 -1.
- <_>
- 13 14 6 1 2.
- <_>
-
- <_>
- 14 12 2 3 -1.
- <_>
- 14 13 2 1 3.
- <_>
-
- <_>
- 1 13 6 2 -1.
- <_>
- 1 14 6 1 2.
- <_>
-
- <_>
- 4 12 2 3 -1.
- <_>
- 4 13 2 1 3.
- <_>
-
- <_>
- 17 4 3 5 -1.
- <_>
- 18 4 1 5 3.
- <_>
-
- <_>
- 5 5 14 8 -1.
- <_>
- 12 5 7 4 2.
- <_>
- 5 9 7 4 2.
- <_>
-
- <_>
- 6 8 6 5 -1.
- <_>
- 8 8 2 5 3.
- <_>
-
- <_>
- 0 4 4 6 -1.
- <_>
- 0 6 4 2 3.
- <_>
-
- <_>
- 9 1 3 6 -1.
- <_>
- 10 1 1 6 3.
- <_>
-
- <_>
- 10 4 6 3 -1.
- <_>
- 10 5 6 1 3.
- <_>
-
- <_>
- 8 1 3 6 -1.
- <_>
- 9 1 1 6 3.
- <_>
-
- <_>
- 4 4 6 3 -1.
- <_>
- 4 5 6 1 3.
- <_>
-
- <_>
- 12 4 3 3 -1.
- <_>
- 12 5 3 1 3.
- <_>
-
- <_>
- 12 11 4 2 -1.
- <_>
- 12 12 4 1 2.
- <_>
-
- <_>
- 0 2 20 6 -1.
- <_>
- 0 2 10 3 2.
- <_>
- 10 5 10 3 2.
- <_>
-
- <_>
- 5 4 3 3 -1.
- <_>
- 5 5 3 1 3.
- <_>
-
- <_>
- 2 10 16 4 -1.
- <_>
- 10 10 8 2 2.
- <_>
- 2 12 8 2 2.
- <_>
-
- <_>
- 3 10 16 6 -1.
- <_>
- 11 10 8 3 2.
- <_>
- 3 13 8 3 2.
- <_>
-
- <_>
- 1 10 16 6 -1.
- <_>
- 1 10 8 3 2.
- <_>
- 9 13 8 3 2.
- <_>
-
- <_>
- 4 7 2 4 -1.
- <_>
- 5 7 1 4 2.
- <_>
-
- <_>
- 11 16 9 4 -1.
- <_>
- 14 16 3 4 3.
- <_>
-
- <_>
- 3 16 14 4 -1.
- <_>
- 10 16 7 2 2.
- <_>
- 3 18 7 2 2.
- <_>
-
- <_>
- 0 16 9 4 -1.
- <_>
- 3 16 3 4 3.
- <_>
-
- <_>
- 1 14 6 6 -1.
- <_>
- 1 14 3 3 2.
- <_>
- 4 17 3 3 2.
- <_>
-
- <_>
- 9 0 2 1 -1.
- <_>
- 9 0 1 1 2.
- <_>
-
- <_>
- 6 7 8 10 -1.
- <_>
- 10 7 4 5 2.
- <_>
- 6 12 4 5 2.
- <_>
-
- <_>
- 2 15 1 2 -1.
- <_>
- 2 16 1 1 2.
- <_>
-
- <_>
- 0 14 7 6 -1.
- <_>
- 0 16 7 2 3.
- <_>
-
- <_>
- 7 8 6 2 -1.
- <_>
- 7 9 6 1 2.
- <_>
-
- <_>
- 9 2 2 15 -1.
- <_>
- 9 7 2 5 3.
- <_>
-
- <_>
- 5 6 2 2 -1.
- <_>
- 5 7 2 1 2.
- <_>
-
- <_>
- 6 6 8 3 -1.
- <_>
- 6 7 8 1 3.
- <_>
-
- <_>
- 12 13 5 6 -1.
- <_>
- 12 15 5 2 3.
- <_>
-
- <_>
- 0 0 20 18 -1.
- <_>
- 0 9 20 9 2.
- <_>
-
- <_>
- 5 1 6 6 -1.
- <_>
- 7 1 2 6 3.
- <_>
-
- <_>
- 5 1 4 9 -1.
- <_>
- 7 1 2 9 2.
- <_>
-
- <_>
- 1 19 18 1 -1.
- <_>
- 7 19 6 1 3.
- <_>
-
- <_>
- 14 16 5 2 -1.
- <_>
- 14 17 5 1 2.
- <_>
-
- <_>
- 0 5 15 10 -1.
- <_>
- 0 10 15 5 2.
- <_>
-
- <_>
- 7 15 4 2 -1.
- <_>
- 7 15 2 1 2.
- <_>
- 9 16 2 1 2.
- <_>
-
- <_>
- 14 11 2 2 -1.
- <_>
- 14 12 2 1 2.
- <_>
-
- <_>
- 9 8 3 3 -1.
- <_>
- 9 9 3 1 3.
- <_>
-
- <_>
- 4 11 2 2 -1.
- <_>
- 4 12 2 1 2.
- <_>
-
- <_>
- 8 8 3 3 -1.
- <_>
- 8 9 3 1 3.
- <_>
-
- <_>
- 9 10 2 3 -1.
- <_>
- 9 11 2 1 3.
- <_>
-
- <_>
- 8 8 4 3 -1.
- <_>
- 8 9 4 1 3.
- <_>
-
- <_>
- 1 9 4 10 -1.
- <_>
- 1 9 2 5 2.
- <_>
- 3 14 2 5 2.
- <_>
-
- <_>
- 0 12 6 8 -1.
- <_>
- 2 12 2 8 3.
- <_>
-
- <_>
- 9 1 4 2 -1.
- <_>
- 11 1 2 1 2.
- <_>
- 9 2 2 1 2.
- <_>
-
- <_>
- 12 13 7 6 -1.
- <_>
- 12 15 7 2 3.
- <_>
-
- <_>
- 7 0 2 3 -1.
- <_>
- 7 1 2 1 3.
- <_>
-
- <_>
- 7 14 6 3 -1.
- <_>
- 9 14 2 3 3.
- <_>
-
- <_>
- 9 6 6 4 -1.
- <_>
- 11 6 2 4 3.
- <_>
-
- <_>
- 8 10 8 3 -1.
- <_>
- 8 10 4 3 2.
- <_>
-
- <_>
- 6 10 4 3 -1.
- <_>
- 8 10 2 3 2.
- <_>
-
- <_>
- 6 8 3 5 -1.
- <_>
- 7 8 1 5 3.
- <_>
-
- <_>
- 0 4 8 1 -1.
- <_>
- 4 4 4 1 2.
- <_>
-
- <_>
- 8 2 2 6 -1.
- <_>
- 8 2 1 3 2.
- <_>
- 9 5 1 3 2.
- <_>
-
- <_>
- 0 7 20 6 -1.
- <_>
- 0 9 20 2 3.
- <_>
-
- <_>
- 12 10 3 6 -1.
- <_>
- 12 13 3 3 2.
- <_>
-
- <_>
- 8 15 1 4 -1.
- <_>
- 8 17 1 2 2.
- <_>
-
- <_>
- 5 16 2 4 -1.
- <_>
- 5 18 2 2 2.
- <_>
-
- <_>
- 6 2 8 12 -1.
- <_>
- 6 6 8 4 3.
- <_>
-
- <_>
- 4 7 12 2 -1.
- <_>
- 8 7 4 2 3.
- <_>
-
- <_>
- 7 0 6 1 -1.
- <_>
- 9 0 2 1 3.
- <_>
-
- <_>
- 8 11 3 3 -1.
- <_>
- 8 12 3 1 3.
- <_>
-
- <_>
- 12 11 3 6 -1.
- <_>
- 12 14 3 3 2.
- <_>
-
- <_>
- 11 2 6 10 -1.
- <_>
- 14 2 3 5 2.
- <_>
- 11 7 3 5 2.
- <_>
-
- <_>
- 5 7 10 12 -1.
- <_>
- 5 7 5 6 2.
- <_>
- 10 13 5 6 2.
- <_>
-
- <_>
- 4 4 2 10 -1.
- <_>
- 4 9 2 5 2.
- <_>
-
- <_>
- 9 7 2 3 -1.
- <_>
- 9 7 1 3 2.
- <_>
-
- <_>
- 11 9 6 2 -1.
- <_>
- 11 9 3 2 2.
- <_>
-
- <_>
- 4 7 2 2 -1.
- <_>
- 5 7 1 2 2.
- <_>
-
- <_>
- 0 2 4 6 -1.
- <_>
- 0 4 4 2 3.
- <_>
-
- <_>
- 10 7 3 4 -1.
- <_>
- 11 7 1 4 3.
- <_>
-
- <_>
- 9 7 3 5 -1.
- <_>
- 10 7 1 5 3.
- <_>
-
- <_>
- 9 1 1 3 -1.
- <_>
- 9 2 1 1 3.
- <_>
-
- <_>
- 0 6 16 6 -1.
- <_>
- 0 6 8 3 2.
- <_>
- 8 9 8 3 2.
- <_>
-
- <_>
- 10 15 3 3 -1.
- <_>
- 10 16 3 1 3.
- <_>
-
- <_>
- 9 14 4 3 -1.
- <_>
- 9 15 4 1 3.
- <_>
-
- <_>
- 3 2 6 10 -1.
- <_>
- 3 2 3 5 2.
- <_>
- 6 7 3 5 2.
- <_>
-
- <_>
- 3 0 14 2 -1.
- <_>
- 3 1 14 1 2.
- <_>
-
- <_>
- 9 14 3 3 -1.
- <_>
- 9 15 3 1 3.
- <_>
-
- <_>
- 10 15 3 3 -1.
- <_>
- 10 16 3 1 3.
- <_>
-
- <_>
- 9 13 2 6 -1.
- <_>
- 9 16 2 3 2.
- <_>
-
- <_>
- 7 13 6 3 -1.
- <_>
- 7 14 6 1 3.
- <_>
-
- <_>
- 12 11 3 6 -1.
- <_>
- 12 14 3 3 2.
- <_>
-
- <_>
- 8 12 5 2 -1.
- <_>
- 8 13 5 1 2.
- <_>
-
- <_>
- 5 11 3 6 -1.
- <_>
- 5 14 3 3 2.
- <_>
-
- <_>
- 8 12 3 2 -1.
- <_>
- 8 13 3 1 2.
- <_>
-
- <_>
- 11 13 7 6 -1.
- <_>
- 11 15 7 2 3.
- <_>
-
- <_>
- 7 14 6 3 -1.
- <_>
- 7 15 6 1 3.
- <_>
-
- <_>
- 3 13 14 4 -1.
- <_>
- 3 13 7 2 2.
- <_>
- 10 15 7 2 2.
- <_>
-
- <_>
- 8 14 4 6 -1.
- <_>
- 8 14 2 3 2.
- <_>
- 10 17 2 3 2.
- <_>
-
- <_>
- 8 15 4 3 -1.
- <_>
- 8 16 4 1 3.
- <_>
-
- <_>
- 7 16 6 2 -1.
- <_>
- 9 16 2 2 3.
- <_>
-
- <_>
- 7 7 6 2 -1.
- <_>
- 7 8 6 1 2.
- <_>
-
- <_>
- 3 9 13 3 -1.
- <_>
- 3 10 13 1 3.
- <_>
-
- <_>
- 9 8 3 4 -1.
- <_>
- 9 10 3 2 2.
- <_>
-
- <_>
- 8 10 4 3 -1.
- <_>
- 8 11 4 1 3.
- <_>
-
- <_>
- 7 7 3 4 -1.
- <_>
- 8 7 1 4 3.
- <_>
-
- <_>
- 8 7 3 5 -1.
- <_>
- 9 7 1 5 3.
- <_>
-
- <_>
- 12 3 3 4 -1.
- <_>
- 13 3 1 4 3.
- <_>
-
- <_>
- 9 7 2 3 -1.
- <_>
- 9 7 1 3 2.
- <_>
-
- <_>
- 5 3 3 4 -1.
- <_>
- 6 3 1 4 3.
- <_>
-
- <_>
- 3 7 12 1 -1.
- <_>
- 7 7 4 1 3.
- <_>
-
- <_>
- 12 5 3 3 -1.
- <_>
- 12 6 3 1 3.
- <_>
-
- <_>
- 11 2 6 2 -1.
- <_>
- 11 3 6 1 2.
- <_>
-
- <_>
- 3 2 14 2 -1.
- <_>
- 3 2 7 1 2.
- <_>
- 10 3 7 1 2.
- <_>
-
- <_>
- 6 1 7 14 -1.
- <_>
- 6 8 7 7 2.
- <_>
-
- <_>
- 8 0 12 5 -1.
- <_>
- 8 0 6 5 2.
- <_>
-
- <_>
- 1 9 18 1 -1.
- <_>
- 7 9 6 1 3.
- <_>
-
- <_>
- 0 0 10 5 -1.
- <_>
- 5 0 5 5 2.
- <_>
-
- <_>
- 2 5 8 15 -1.
- <_>
- 2 10 8 5 3.
- <_>
-
- <_>
- 12 5 3 3 -1.
- <_>
- 12 6 3 1 3.
- <_>
-
- <_>
- 13 4 2 3 -1.
- <_>
- 13 5 2 1 3.
- <_>
-
- <_>
- 2 15 4 3 -1.
- <_>
- 2 16 4 1 3.
- <_>
-
- <_>
- 5 6 10 3 -1.
- <_>
- 10 6 5 3 2.
- <_>
-
- <_>
- 11 6 2 2 -1.
- <_>
- 12 6 1 1 2.
- <_>
- 11 7 1 1 2.
- <_>
-
- <_>
- 12 4 4 3 -1.
- <_>
- 12 5 4 1 3.
- <_>
-
- <_>
- 7 6 2 2 -1.
- <_>
- 7 6 1 1 2.
- <_>
- 8 7 1 1 2.
- <_>
-
- <_>
- 4 4 4 3 -1.
- <_>
- 4 5 4 1 3.
- <_>
-
- <_>
- 11 4 3 3 -1.
- <_>
- 12 4 1 3 3.
- <_>
-
- <_>
- 9 3 2 1 -1.
- <_>
- 9 3 1 1 2.
- <_>
-
- <_>
- 4 5 5 3 -1.
- <_>
- 4 6 5 1 3.
- <_>
-
- <_>
- 4 6 4 3 -1.
- <_>
- 4 7 4 1 3.
- <_>
-
- <_>
- 11 4 3 3 -1.
- <_>
- 12 4 1 3 3.
- <_>
-
- <_>
- 8 8 4 3 -1.
- <_>
- 8 9 4 1 3.
- <_>
-
- <_>
- 6 4 3 3 -1.
- <_>
- 7 4 1 3 3.
- <_>
-
- <_>
- 4 14 1 3 -1.
- <_>
- 4 15 1 1 3.
- <_>
-
- <_>
- 9 7 2 3 -1.
- <_>
- 9 7 1 3 2.
- <_>
-
- <_>
- 17 0 3 2 -1.
- <_>
- 17 1 3 1 2.
- <_>
-
- <_>
- 8 10 2 9 -1.
- <_>
- 8 13 2 3 3.
- <_>
-
- <_>
- 0 8 18 2 -1.
- <_>
- 0 9 18 1 2.
- <_>
-
- <_>
- 9 15 2 3 -1.
- <_>
- 9 16 2 1 3.
- <_>
-
- <_>
- 8 7 4 3 -1.
- <_>
- 8 8 4 1 3.
- <_>
-
- <_>
- 1 14 6 6 -1.
- <_>
- 1 14 3 3 2.
- <_>
- 4 17 3 3 2.
- <_>
-
- <_>
- 0 18 6 2 -1.
- <_>
- 0 19 6 1 2.
- <_>
-
- <_>
- 12 9 4 3 -1.
- <_>
- 12 9 2 3 2.
- <_>
-
- <_>
- 9 8 3 8 -1.
- <_>
- 10 8 1 8 3.
- <_>
-
- <_>
- 4 9 4 3 -1.
- <_>
- 6 9 2 3 2.
- <_>
-
- <_>
- 4 18 6 1 -1.
- <_>
- 6 18 2 1 3.
- <_>
-
- <_>
- 9 7 3 2 -1.
- <_>
- 10 7 1 2 3.
- <_>
-
- <_>
- 6 7 8 12 -1.
- <_>
- 10 7 4 6 2.
- <_>
- 6 13 4 6 2.
- <_>
-
- <_>
- 8 7 3 2 -1.
- <_>
- 9 7 1 2 3.
- <_>
-
- <_>
- 8 7 3 6 -1.
- <_>
- 9 7 1 6 3.
- <_>
-
- <_>
- 3 16 14 4 -1.
- <_>
- 10 16 7 2 2.
- <_>
- 3 18 7 2 2.
- <_>
-
- <_>
- 1 14 18 4 -1.
- <_>
- 10 14 9 2 2.
- <_>
- 1 16 9 2 2.
- <_>
-
- <_>
- 8 7 3 3 -1.
- <_>
- 8 8 3 1 3.
- <_>
-
- <_>
- 0 4 20 12 -1.
- <_>
- 0 4 10 6 2.
- <_>
- 10 10 10 6 2.
- <_>
-
- <_>
- 5 5 10 12 -1.
- <_>
- 10 5 5 6 2.
- <_>
- 5 11 5 6 2.
- <_>
-
- <_>
- 10 2 4 7 -1.
- <_>
- 10 2 2 7 2.
- <_>
-
- <_>
- 8 11 4 3 -1.
- <_>
- 8 12 4 1 3.
- <_>
-
- <_>
- 8 12 3 3 -1.
- <_>
- 8 13 3 1 3.
- <_>
-
- <_>
- 13 13 5 6 -1.
- <_>
- 13 15 5 2 3.
- <_>
-
- <_>
- 7 0 6 6 -1.
- <_>
- 9 0 2 6 3.
- <_>
-
- <_>
- 2 13 5 6 -1.
- <_>
- 2 15 5 2 3.
- <_>
-
- <_>
- 0 4 2 12 -1.
- <_>
- 0 4 1 6 2.
- <_>
- 1 10 1 6 2.
- <_>
-
- <_>
- 9 19 3 1 -1.
- <_>
- 10 19 1 1 3.
- <_>
-
- <_>
- 18 0 2 6 -1.
- <_>
- 18 2 2 2 3.
- <_>
-
- <_>
- 0 3 1 6 -1.
- <_>
- 0 5 1 2 3.
- <_>
-
- <_>
- 0 0 3 6 -1.
- <_>
- 0 2 3 2 3.
- <_>
-
- <_>
- 17 2 3 7 -1.
- <_>
- 18 2 1 7 3.
- <_>
-
- <_>
- 10 3 4 7 -1.
- <_>
- 10 3 2 7 2.
- <_>
-
- <_>
- 0 2 3 7 -1.
- <_>
- 1 2 1 7 3.
- <_>
-
- <_>
- 6 2 4 8 -1.
- <_>
- 8 2 2 8 2.
- <_>
-
- <_>
- 13 0 1 4 -1.
- <_>
- 13 2 1 2 2.
- <_>
-
- <_>
- 5 1 12 5 -1.
- <_>
- 9 1 4 5 3.
- <_>
-
- <_>
- 6 0 1 4 -1.
- <_>
- 6 2 1 2 2.
- <_>
-
- <_>
- 3 1 12 5 -1.
- <_>
- 7 1 4 5 3.
- <_>
-
- <_>
- 9 12 3 8 -1.
- <_>
- 10 12 1 8 3.
- <_>
-
- <_>
- 7 13 6 1 -1.
- <_>
- 9 13 2 1 3.
- <_>
-
- <_>
- 7 14 6 3 -1.
- <_>
- 7 15 6 1 3.
- <_>
-
- <_>
- 5 16 7 3 -1.
- <_>
- 5 17 7 1 3.
- <_>
-
- <_>
- 0 12 20 6 -1.
- <_>
- 0 14 20 2 3.
- <_>
-
- <_>
- 4 18 14 2 -1.
- <_>
- 4 19 14 1 2.
- <_>
-
- <_>
- 8 12 3 8 -1.
- <_>
- 9 12 1 8 3.
- <_>
-
- <_>
- 7 13 3 3 -1.
- <_>
- 7 14 3 1 3.
- <_>
-
- <_>
- 5 5 12 10 -1.
- <_>
- 11 5 6 5 2.
- <_>
- 5 10 6 5 2.
- <_>
-
- <_>
- 8 1 5 10 -1.
- <_>
- 8 6 5 5 2.
- <_>
-
- <_>
- 5 4 9 12 -1.
- <_>
- 5 10 9 6 2.
- <_>
-
- <_>
- 7 13 6 6 -1.
- <_>
- 7 15 6 2 3.
- <_>
-
- <_>
- 8 4 5 16 -1.
- <_>
- 8 12 5 8 2.
- <_>
-
- <_>
- 8 12 4 6 -1.
- <_>
- 8 15 4 3 2.
- <_>
-
- <_>
- 7 13 2 2 -1.
- <_>
- 7 13 1 1 2.
- <_>
- 8 14 1 1 2.
- <_>
-
- <_>
- 7 12 2 2 -1.
- <_>
- 7 12 1 1 2.
- <_>
- 8 13 1 1 2.
- <_>
-
- <_>
- 18 0 2 14 -1.
- <_>
- 18 0 1 14 2.
- <_>
-
- <_>
- 12 11 7 2 -1.
- <_>
- 12 12 7 1 2.
- <_>
-
- <_>
- 1 18 1 2 -1.
- <_>
- 1 19 1 1 2.
- <_>
-
- <_>
- 2 18 1 2 -1.
- <_>
- 2 19 1 1 2.
- <_>
-
- <_>
- 9 7 2 1 -1.
- <_>
- 9 7 1 1 2.
- <_>
-
- <_>
- 9 6 2 3 -1.
- <_>
- 9 6 1 3 2.
- <_>
-
- <_>
- 3 1 2 2 -1.
- <_>
- 4 1 1 2 2.
- <_>
-
- <_>
- 3 0 3 2 -1.
- <_>
- 3 1 3 1 2.
- <_>
-
- <_>
- 12 10 3 4 -1.
- <_>
- 12 12 3 2 2.
- <_>
-
- <_>
- 7 7 8 2 -1.
- <_>
- 7 8 8 1 2.
- <_>
-
- <_>
- 8 8 3 4 -1.
- <_>
- 8 10 3 2 2.
- <_>
-
- <_>
- 7 12 6 3 -1.
- <_>
- 7 13 6 1 3.
- <_>
-
- <_>
- 0 2 10 3 -1.
- <_>
- 5 2 5 3 2.
- <_>
-
- <_>
- 0 1 20 6 -1.
- <_>
- 0 3 20 2 3.
- <_>
-
- <_>
- 7 6 6 3 -1.
- <_>
- 9 6 2 3 3.
- <_>
-
- <_>
- 3 7 14 4 -1.
- <_>
- 3 9 14 2 2.
- <_>
-
- <_>
- 5 7 3 6 -1.
- <_>
- 5 9 3 2 3.
- <_>
-
- <_>
- 8 8 3 12 -1.
- <_>
- 8 12 3 4 3.
- <_>
-
- <_>
- 9 17 6 2 -1.
- <_>
- 12 17 3 1 2.
- <_>
- 9 18 3 1 2.
- <_>
-
- <_>
- 10 17 4 3 -1.
- <_>
- 10 18 4 1 3.
- <_>
-
- <_>
- 4 2 4 2 -1.
- <_>
- 4 3 4 1 2.
- <_>
-
- <_>
- 7 3 6 14 -1.
- <_>
- 9 3 2 14 3.
- <_>
-
- <_>
- 15 13 1 6 -1.
- <_>
- 15 16 1 3 2.
- <_>
-
- <_>
- 13 14 2 6 -1.
- <_>
- 13 16 2 2 3.
- <_>
-
- <_>
- 4 11 5 6 -1.
- <_>
- 4 14 5 3 2.
- <_>
-
- <_>
- 4 17 4 2 -1.
- <_>
- 6 17 2 2 2.
- <_>
-
- <_>
- 0 6 20 2 -1.
- <_>
- 0 6 10 2 2.
- <_>
-
- <_>
- 6 5 10 12 -1.
- <_>
- 11 5 5 6 2.
- <_>
- 6 11 5 6 2.
- <_>
-
- <_>
- 4 0 2 12 -1.
- <_>
- 4 0 1 6 2.
- <_>
- 5 6 1 6 2.
- <_>
-
- <_>
- 4 1 6 2 -1.
- <_>
- 6 1 2 2 3.
- <_>
-
- <_>
- 13 7 2 1 -1.
- <_>
- 13 7 1 1 2.
- <_>
-
- <_>
- 5 5 15 6 -1.
- <_>
- 5 7 15 2 3.
- <_>
-
- <_>
- 1 10 18 2 -1.
- <_>
- 1 10 9 1 2.
- <_>
- 10 11 9 1 2.
- <_>
-
- <_>
- 1 6 15 7 -1.
- <_>
- 6 6 5 7 3.
- <_>
-
- <_>
- 8 14 4 3 -1.
- <_>
- 8 15 4 1 3.
- <_>
-
- <_>
- 9 14 3 3 -1.
- <_>
- 9 15 3 1 3.
- <_>
-
- <_>
- 8 14 4 3 -1.
- <_>
- 8 15 4 1 3.
- <_>
-
- <_>
- 8 13 3 2 -1.
- <_>
- 8 14 3 1 2.
- <_>
-
- <_>
- 15 14 5 3 -1.
- <_>
- 15 15 5 1 3.
- <_>
-
- <_>
- 0 14 20 1 -1.
- <_>
- 0 14 10 1 2.
- <_>
-
- <_>
- 0 14 6 3 -1.
- <_>
- 0 15 6 1 3.
- <_>
-
- <_>
- 5 3 4 2 -1.
- <_>
- 5 4 4 1 2.
- <_>
-
- <_>
- 0 6 20 1 -1.
- <_>
- 0 6 10 1 2.
- <_>
-
- <_>
- 6 3 10 14 -1.
- <_>
- 11 3 5 7 2.
- <_>
- 6 10 5 7 2.
- <_>
-
- <_>
- 8 12 4 2 -1.
- <_>
- 8 13 4 1 2.
- <_>
-
- <_>
- 6 3 8 6 -1.
- <_>
- 6 3 4 3 2.
- <_>
- 10 6 4 3 2.
- <_>
-
- <_>
- 13 7 2 1 -1.
- <_>
- 13 7 1 1 2.
- <_>
-
- <_>
- 6 3 10 14 -1.
- <_>
- 11 3 5 7 2.
- <_>
- 6 10 5 7 2.
- <_>
-
- <_>
- 5 7 2 1 -1.
- <_>
- 6 7 1 1 2.
- <_>
-
- <_>
- 4 3 10 14 -1.
- <_>
- 4 3 5 7 2.
- <_>
- 9 10 5 7 2.
- <_>
-
- <_>
- 9 7 2 2 -1.
- <_>
- 9 7 1 2 2.
- <_>
-
- <_>
- 0 3 20 1 -1.
- <_>
- 0 3 10 1 2.
- <_>
-
- <_>
- 2 1 10 3 -1.
- <_>
- 2 2 10 1 3.
- <_>
-
- <_>
- 9 7 2 2 -1.
- <_>
- 10 7 1 2 2.
- <_>
-
- <_>
- 9 17 3 2 -1.
- <_>
- 10 17 1 2 3.
- <_>
-
- <_>
- 9 7 3 6 -1.
- <_>
- 10 7 1 6 3.
- <_>
-
- <_>
- 8 17 3 2 -1.
- <_>
- 9 17 1 2 3.
- <_>
-
- <_>
- 8 7 3 6 -1.
- <_>
- 9 7 1 6 3.
- <_>
-
- <_>
- 16 3 4 6 -1.
- <_>
- 16 5 4 2 3.
- <_>
-
- <_>
- 15 6 2 12 -1.
- <_>
- 16 6 1 6 2.
- <_>
- 15 12 1 6 2.
- <_>
-
- <_>
- 1 4 18 10 -1.
- <_>
- 1 4 9 5 2.
- <_>
- 10 9 9 5 2.
- <_>
-
- <_>
- 9 4 2 4 -1.
- <_>
- 9 6 2 2 2.
- <_>
-
- <_>
- 12 5 3 2 -1.
- <_>
- 12 6 3 1 2.
- <_>
-
- <_>
- 5 12 10 4 -1.
- <_>
- 5 14 10 2 2.
- <_>
-
- <_>
- 5 5 3 2 -1.
- <_>
- 5 6 3 1 2.
- <_>
-
- <_>
- 4 6 12 6 -1.
- <_>
- 8 6 4 6 3.
- <_>
-
- <_>
- 14 4 6 6 -1.
- <_>
- 14 6 6 2 3.
- <_>
-
- <_>
- 16 0 4 6 -1.
- <_>
- 18 0 2 3 2.
- <_>
- 16 3 2 3 2.
- <_>
-
- <_>
- 0 4 6 6 -1.
- <_>
- 0 6 6 2 3.
- <_>
-
- <_>
- 0 0 4 6 -1.
- <_>
- 0 0 2 3 2.
- <_>
- 2 3 2 3 2.
- <_>
-
- <_>
- 12 0 8 5 -1.
- <_>
- 12 0 4 5 2.
- <_>
-
- <_>
- 16 0 4 17 -1.
- <_>
- 16 0 2 17 2.
- <_>
-
- <_>
- 1 0 18 20 -1.
- <_>
- 7 0 6 20 3.
- <_>
-
- <_>
- 6 0 2 5 -1.
- <_>
- 7 0 1 5 2.
- <_>
-
- <_>
- 0 6 20 1 -1.
- <_>
- 0 6 10 1 2.
- <_>
-
- <_>
- 8 7 6 4 -1.
- <_>
- 10 7 2 4 3.
- <_>
-
- <_>
- 1 1 16 4 -1.
- <_>
- 1 1 8 2 2.
- <_>
- 9 3 8 2 2.
- <_>
-
- <_>
- 7 2 4 2 -1.
- <_>
- 7 2 2 1 2.
- <_>
- 9 3 2 1 2.
- <_>
-
- <_>
- 7 4 9 3 -1.
- <_>
- 7 5 9 1 3.
- <_>
-
- <_>
- 10 4 5 12 -1.
- <_>
- 10 10 5 6 2.
- <_>
-
- <_>
- 3 12 2 3 -1.
- <_>
- 3 13 2 1 3.
- <_>
-
- <_>
- 8 8 3 5 -1.
- <_>
- 9 8 1 5 3.
- <_>
-
- <_>
- 13 9 2 3 -1.
- <_>
- 13 9 1 3 2.
- <_>
-
- <_>
- 15 11 2 2 -1.
- <_>
- 15 12 2 1 2.
- <_>
-
- <_>
- 5 6 2 3 -1.
- <_>
- 5 7 2 1 3.
- <_>
-
- <_>
- 2 11 6 2 -1.
- <_>
- 2 12 6 1 2.
- <_>
-
- <_>
- 15 11 4 3 -1.
- <_>
- 15 12 4 1 3.
- <_>
-
- <_>
- 16 0 4 17 -1.
- <_>
- 16 0 2 17 2.
- <_>
-
- <_>
- 1 11 4 3 -1.
- <_>
- 1 12 4 1 3.
- <_>
-
- <_>
- 9 11 1 3 -1.
- <_>
- 9 12 1 1 3.
- <_>
-
- <_>
- 10 9 6 7 -1.
- <_>
- 10 9 3 7 2.
- <_>
-
- <_>
- 8 15 4 2 -1.
- <_>
- 8 16 4 1 2.
- <_>
-
- <_>
- 4 9 6 7 -1.
- <_>
- 7 9 3 7 2.
- <_>
-
- <_>
- 9 14 2 3 -1.
- <_>
- 9 15 2 1 3.
- <_>
-
- <_>
- 0 2 20 2 -1.
- <_>
- 10 2 10 1 2.
- <_>
- 0 3 10 1 2.
- <_>
-
- <_>
- 6 7 8 2 -1.
- <_>
- 6 8 8 1 2.
- <_>
-
- <_>
- 0 2 20 2 -1.
- <_>
- 0 2 10 1 2.
- <_>
- 10 3 10 1 2.
- <_>
-
- <_>
- 3 1 2 10 -1.
- <_>
- 3 1 1 5 2.
- <_>
- 4 6 1 5 2.
- <_>
-
- <_>
- 13 4 1 10 -1.
- <_>
- 13 9 1 5 2.
- <_>
-
- <_>
- 9 8 4 3 -1.
- <_>
- 9 9 4 1 3.
- <_>
-
- <_>
- 2 11 16 4 -1.
- <_>
- 2 11 8 2 2.
- <_>
- 10 13 8 2 2.
- <_>
-
- <_>
- 5 1 3 5 -1.
- <_>
- 6 1 1 5 3.
- <_>
-
- <_>
- 9 10 2 3 -1.
- <_>
- 9 11 2 1 3.
- <_>
-
- <_>
- 9 11 2 2 -1.
- <_>
- 9 12 2 1 2.
- <_>
-
- <_>
- 0 10 20 2 -1.
- <_>
- 0 11 20 1 2.
- <_>
-
- <_>
- 1 7 6 4 -1.
- <_>
- 1 7 3 2 2.
- <_>
- 4 9 3 2 2.
- <_>
-
- <_>
- 12 0 8 8 -1.
- <_>
- 16 0 4 4 2.
- <_>
- 12 4 4 4 2.
- <_>
-
- <_>
- 14 1 6 4 -1.
- <_>
- 16 1 2 4 3.
- <_>
-
- <_>
- 6 3 2 14 -1.
- <_>
- 6 10 2 7 2.
- <_>
-
- <_>
- 6 1 7 12 -1.
- <_>
- 6 7 7 6 2.
- <_>
-
- <_>
- 5 0 15 5 -1.
- <_>
- 10 0 5 5 3.
- <_>
-
- <_>
- 15 0 4 10 -1.
- <_>
- 15 0 2 10 2.
- <_>
-
- <_>
- 1 0 18 3 -1.
- <_>
- 7 0 6 3 3.
- <_>
-
- <_>
- 0 0 17 2 -1.
- <_>
- 0 1 17 1 2.
- <_>
-
- <_>
- 10 0 3 3 -1.
- <_>
- 11 0 1 3 3.
- <_>
-
- <_>
- 10 0 3 12 -1.
- <_>
- 11 0 1 12 3.
- <_>
-
- <_>
- 1 3 4 16 -1.
- <_>
- 1 3 2 8 2.
- <_>
- 3 11 2 8 2.
- <_>
-
- <_>
- 7 0 3 3 -1.
- <_>
- 8 0 1 3 3.
- <_>
-
- <_>
- 9 13 2 6 -1.
- <_>
- 9 16 2 3 2.
- <_>
-
- <_>
- 9 0 6 13 -1.
- <_>
- 11 0 2 13 3.
- <_>
-
- <_>
- 7 7 3 2 -1.
- <_>
- 8 7 1 2 3.
- <_>
-
- <_>
- 8 2 1 12 -1.
- <_>
- 8 6 1 4 3.
- <_>
-
- <_>
- 4 10 12 6 -1.
- <_>
- 10 10 6 3 2.
- <_>
- 4 13 6 3 2.
- <_>
-
- <_>
- 13 5 2 3 -1.
- <_>
- 13 6 2 1 3.
- <_>
-
- <_>
- 4 10 12 6 -1.
- <_>
- 4 10 6 3 2.
- <_>
- 10 13 6 3 2.
- <_>
-
- <_>
- 5 5 2 3 -1.
- <_>
- 5 6 2 1 3.
- <_>
-
- <_>
- 8 6 6 7 -1.
- <_>
- 10 6 2 7 3.
- <_>
-
- <_>
- 9 6 2 4 -1.
- <_>
- 9 6 1 4 2.
- <_>
-
- <_>
- 6 6 6 7 -1.
- <_>
- 8 6 2 7 3.
- <_>
-
- <_>
- 9 6 2 4 -1.
- <_>
- 10 6 1 4 2.
- <_>
-
- <_>
- 12 9 2 3 -1.
- <_>
- 12 9 1 3 2.
- <_>
-
- <_>
- 0 6 20 1 -1.
- <_>
- 0 6 10 1 2.
- <_>
-
- <_>
- 5 7 10 2 -1.
- <_>
- 10 7 5 2 2.
- <_>
-
- <_>
- 1 16 4 3 -1.
- <_>
- 1 17 4 1 3.
- <_>
-
- <_>
- 12 4 3 3 -1.
- <_>
- 12 5 3 1 3.
- <_>
-
- <_>
- 10 3 5 3 -1.
- <_>
- 10 4 5 1 3.
- <_>
-
- <_>
- 3 9 14 8 -1.
- <_>
- 3 9 7 4 2.
- <_>
- 10 13 7 4 2.
- <_>
-
- <_>
- 6 8 8 10 -1.
- <_>
- 6 8 4 5 2.
- <_>
- 10 13 4 5 2.
- <_>
-
- <_>
- 12 4 3 3 -1.
- <_>
- 12 5 3 1 3.
- <_>
-
- <_>
- 10 3 5 3 -1.
- <_>
- 10 4 5 1 3.
- <_>
-
- <_>
- 5 4 3 3 -1.
- <_>
- 5 5 3 1 3.
- <_>
-
- <_>
- 5 3 5 3 -1.
- <_>
- 5 4 5 1 3.
- <_>
-
- <_>
- 13 16 2 3 -1.
- <_>
- 13 17 2 1 3.
- <_>
-
- <_>
- 0 5 20 6 -1.
- <_>
- 0 7 20 2 3.
- <_>
-
- <_>
- 3 14 3 3 -1.
- <_>
- 3 15 3 1 3.
- <_>
-
- <_>
- 7 15 5 3 -1.
- <_>
- 7 16 5 1 3.
- <_>
-
- <_>
- 12 9 2 3 -1.
- <_>
- 12 9 1 3 2.
- <_>
-
- <_>
- 15 13 2 6 -1.
- <_>
- 15 13 1 6 2.
- <_>
-
- <_>
- 6 9 2 3 -1.
- <_>
- 7 9 1 3 2.
- <_>
-
- <_>
- 3 13 2 6 -1.
- <_>
- 4 13 1 6 2.
- <_>
-
- <_>
- 11 4 2 4 -1.
- <_>
- 11 4 1 4 2.
- <_>
-
- <_>
- 13 4 2 5 -1.
- <_>
- 13 4 1 5 2.
- <_>
-
- <_>
- 7 4 2 4 -1.
- <_>
- 8 4 1 4 2.
- <_>
-
- <_>
- 5 4 2 5 -1.
- <_>
- 6 4 1 5 2.
- <_>
-
- <_>
- 19 6 1 2 -1.
- <_>
- 19 7 1 1 2.
- <_>
-
- <_>
- 12 7 8 13 -1.
- <_>
- 12 7 4 13 2.
- <_>
-
- <_>
- 0 6 1 2 -1.
- <_>
- 0 7 1 1 2.
- <_>
-
- <_>
- 6 15 4 3 -1.
- <_>
- 6 16 4 1 3.
- <_>
-
- <_>
- 11 8 2 2 -1.
- <_>
- 11 9 2 1 2.
- <_>
-
- <_>
- 11 7 2 4 -1.
- <_>
- 11 7 1 4 2.
- <_>
-
- <_>
- 4 13 2 3 -1.
- <_>
- 4 14 2 1 3.
- <_>
-
- <_>
- 0 17 18 3 -1.
- <_>
- 6 17 6 3 3.
- <_>
-
- <_>
- 1 0 18 5 -1.
- <_>
- 7 0 6 5 3.
- <_>
-
- <_>
- 5 7 3 4 -1.
- <_>
- 5 9 3 2 2.
- <_>
-
- <_>
- 10 6 2 2 -1.
- <_>
- 10 6 1 2 2.
- <_>
-
- <_>
- 6 4 14 4 -1.
- <_>
- 13 4 7 2 2.
- <_>
- 6 6 7 2 2.
- <_>
-
- <_>
- 5 16 6 4 -1.
- <_>
- 5 16 3 2 2.
- <_>
- 8 18 3 2 2.
- <_>
-
- <_>
- 7 15 2 4 -1.
- <_>
- 7 17 2 2 2.
- <_>
-
- <_>
- 8 5 5 14 -1.
- <_>
- 8 12 5 7 2.
- <_>
-
- <_>
- 9 9 2 2 -1.
- <_>
- 9 10 2 1 2.
- <_>
-
- <_>
- 7 5 3 7 -1.
- <_>
- 8 5 1 7 3.
- <_>
-
- <_>
- 0 0 3 9 -1.
- <_>
- 0 3 3 3 3.
- <_>
-
- <_>
- 8 6 8 8 -1.
- <_>
- 12 6 4 4 2.
- <_>
- 8 10 4 4 2.
- <_>
-
- <_>
- 4 8 13 2 -1.
- <_>
- 4 9 13 1 2.
- <_>
-
- <_>
- 4 3 6 1 -1.
- <_>
- 6 3 2 1 3.
- <_>
-
- <_>
- 9 1 2 6 -1.
- <_>
- 9 3 2 2 3.
- <_>
-
- <_>
- 10 5 6 4 -1.
- <_>
- 12 5 2 4 3.
- <_>
-
- <_>
- 9 5 2 12 -1.
- <_>
- 9 9 2 4 3.
- <_>
-
- <_>
- 8 14 4 3 -1.
- <_>
- 8 15 4 1 3.
- <_>
-
- <_>
- 8 12 4 3 -1.
- <_>
- 8 13 4 1 3.
- <_>
-
- <_>
- 10 3 6 7 -1.
- <_>
- 12 3 2 7 3.
- <_>
-
- <_>
- 3 10 16 6 -1.
- <_>
- 3 12 16 2 3.
- <_>
-
- <_>
- 5 5 3 10 -1.
- <_>
- 5 10 3 5 2.
- <_>
-
- <_>
- 6 10 3 6 -1.
- <_>
- 6 13 3 3 2.
- <_>
-
- <_>
- 17 2 2 12 -1.
- <_>
- 17 2 1 12 2.
- <_>
-
- <_>
- 16 6 2 14 -1.
- <_>
- 16 13 2 7 2.
- <_>
-
- <_>
- 3 11 12 9 -1.
- <_>
- 3 14 12 3 3.
- <_>
-
- <_>
- 0 2 4 12 -1.
- <_>
- 2 2 2 12 2.
- <_>
-
- <_>
- 18 0 2 18 -1.
- <_>
- 18 0 1 18 2.
- <_>
-
- <_>
- 16 12 3 2 -1.
- <_>
- 16 13 3 1 2.
- <_>
-
- <_>
- 0 2 2 15 -1.
- <_>
- 1 2 1 15 2.
- <_>
-
- <_>
- 1 10 2 4 -1.
- <_>
- 1 12 2 2 2.
- <_>
-
- <_>
- 11 1 2 18 -1.
- <_>
- 11 1 1 18 2.
- <_>
-
- <_>
- 3 2 14 2 -1.
- <_>
- 10 2 7 1 2.
- <_>
- 3 3 7 1 2.
- <_>
-
- <_>
- 7 1 2 18 -1.
- <_>
- 8 1 1 18 2.
- <_>
-
- <_>
- 6 1 8 12 -1.
- <_>
- 6 7 8 6 2.
- <_>
-
- <_>
- 8 14 4 3 -1.
- <_>
- 8 15 4 1 3.
- <_>
-
- <_>
- 7 14 6 3 -1.
- <_>
- 7 15 6 1 3.
- <_>
-
- <_>
- 0 13 5 2 -1.
- <_>
- 0 14 5 1 2.
- <_>
-
- <_>
- 9 0 2 6 -1.
- <_>
- 9 0 1 3 2.
- <_>
- 10 3 1 3 2.
- <_>
-
- <_>
- 9 0 2 6 -1.
- <_>
- 10 0 1 3 2.
- <_>
- 9 3 1 3 2.
- <_>
-
- <_>
- 9 7 3 6 -1.
- <_>
- 10 7 1 6 3.
- <_>
-
- <_>
- 9 0 2 6 -1.
- <_>
- 9 0 1 3 2.
- <_>
- 10 3 1 3 2.
- <_>
-
- <_>
- 8 7 3 6 -1.
- <_>
- 9 7 1 6 3.
- <_>
-
- <_>
- 9 6 2 6 -1.
- <_>
- 9 6 1 6 2.
- <_>
-
- <_>
- 9 4 4 3 -1.
- <_>
- 9 4 2 3 2.
- <_>
-
- <_>
- 0 4 4 3 -1.
- <_>
- 0 5 4 1 3.
- <_>
-
- <_>
- 8 7 4 2 -1.
- <_>
- 8 8 4 1 2.
- <_>
-
- <_>
- 10 6 6 3 -1.
- <_>
- 12 6 2 3 3.
- <_>
-
- <_>
- 9 6 3 12 -1.
- <_>
- 9 10 3 4 3.
- <_>
-
- <_>
- 5 4 2 3 -1.
- <_>
- 5 5 2 1 3.
- <_>
-
- <_>
- 5 6 1 3 -1.
- <_>
- 5 7 1 1 3.
- <_>
-
- <_>
- 9 17 3 2 -1.
- <_>
- 10 17 1 2 3.
- <_>
-
- <_>
- 0 7 20 2 -1.
- <_>
- 0 8 20 1 2.
- <_>
-
- <_>
- 4 3 6 7 -1.
- <_>
- 6 3 2 7 3.
- <_>
-
- <_>
- 5 10 6 10 -1.
- <_>
- 5 10 3 5 2.
- <_>
- 8 15 3 5 2.
- <_>
-
- <_>
- 9 17 3 2 -1.
- <_>
- 10 17 1 2 3.
- <_>
-
- <_>
- 9 10 2 2 -1.
- <_>
- 9 11 2 1 2.
- <_>
-
- <_>
- 8 17 3 2 -1.
- <_>
- 9 17 1 2 3.
- <_>
-
- <_>
- 5 6 1 3 -1.
- <_>
- 5 7 1 1 3.
- <_>
-
- <_>
- 0 1 20 2 -1.
- <_>
- 10 1 10 1 2.
- <_>
- 0 2 10 1 2.
- <_>
-
- <_>
- 14 2 6 9 -1.
- <_>
- 14 5 6 3 3.
- <_>
-
- <_>
- 5 3 3 2 -1.
- <_>
- 5 4 3 1 2.
- <_>
-
- <_>
- 5 4 4 2 -1.
- <_>
- 7 4 2 2 2.
- <_>
-
- <_>
- 14 2 6 9 -1.
- <_>
- 14 5 6 3 3.
- <_>
-
- <_>
- 0 12 20 6 -1.
- <_>
- 0 14 20 2 3.
- <_>
-
- <_>
- 2 2 16 4 -1.
- <_>
- 2 2 8 2 2.
- <_>
- 10 4 8 2 2.
- <_>
-
- <_>
- 7 12 5 3 -1.
- <_>
- 7 13 5 1 3.
- <_>
-
- <_>
- 14 9 6 10 -1.
- <_>
- 14 9 3 10 2.
- <_>
-
- <_>
- 16 6 3 2 -1.
- <_>
- 16 7 3 1 2.
- <_>
-
- <_>
- 0 9 6 10 -1.
- <_>
- 3 9 3 10 2.
- <_>
-
- <_>
- 0 16 5 2 -1.
- <_>
- 0 17 5 1 2.
- <_>
-
- <_>
- 9 12 2 3 -1.
- <_>
- 9 13 2 1 3.
- <_>
-
- <_>
- 9 7 2 12 -1.
- <_>
- 9 11 2 4 3.
- <_>
-
- <_>
- 3 2 6 2 -1.
- <_>
- 5 2 2 2 3.
- <_>
-
- <_>
- 4 1 1 2 -1.
- <_>
- 4 2 1 1 2.
- <_>
-
- <_>
- 11 15 1 2 -1.
- <_>
- 11 16 1 1 2.
- <_>
-
- <_>
- 3 1 16 2 -1.
- <_>
- 11 1 8 1 2.
- <_>
- 3 2 8 1 2.
- <_>
-
- <_>
- 3 6 2 2 -1.
- <_>
- 3 6 1 1 2.
- <_>
- 4 7 1 1 2.
- <_>
-
- <_>
- 5 11 10 6 -1.
- <_>
- 5 11 5 3 2.
- <_>
- 10 14 5 3 2.
- <_>
-
- <_>
- 10 11 4 6 -1.
- <_>
- 10 14 4 3 2.
- <_>
-
- <_>
- 14 9 6 11 -1.
- <_>
- 16 9 2 11 3.
- <_>
-
- <_>
- 0 9 6 11 -1.
- <_>
- 2 9 2 11 3.
- <_>
-
- <_>
- 2 11 16 6 -1.
- <_>
- 2 11 8 3 2.
- <_>
- 10 14 8 3 2.
- <_>
-
- <_>
- 12 0 8 10 -1.
- <_>
- 16 0 4 5 2.
- <_>
- 12 5 4 5 2.
- <_>
-
- <_>
- 14 2 6 4 -1.
- <_>
- 16 2 2 4 3.
- <_>
-
- <_>
- 0 0 8 10 -1.
- <_>
- 0 0 4 5 2.
- <_>
- 4 5 4 5 2.
- <_>
-
- <_>
- 0 2 6 4 -1.
- <_>
- 2 2 2 4 3.
- <_>
-
- <_>
- 4 9 15 2 -1.
- <_>
- 9 9 5 2 3.
- <_>
-
- <_>
- 12 3 4 8 -1.
- <_>
- 14 3 2 4 2.
- <_>
- 12 7 2 4 2.
- <_>
-
- <_>
- 9 2 2 9 -1.
- <_>
- 10 2 1 9 2.
- <_>
-
- <_>
- 0 2 20 1 -1.
- <_>
- 10 2 10 1 2.
- <_>
-
- <_>
- 16 1 4 5 -1.
- <_>
- 16 1 2 5 2.
- <_>
-
- <_>
- 16 0 4 6 -1.
- <_>
- 16 3 4 3 2.
- <_>
-
- <_>
- 4 3 6 4 -1.
- <_>
- 6 3 2 4 3.
- <_>
-
- <_>
- 0 0 18 5 -1.
- <_>
- 6 0 6 5 3.
- <_>
-
- <_>
- 6 2 12 14 -1.
- <_>
- 12 2 6 7 2.
- <_>
- 6 9 6 7 2.
- <_>
-
- <_>
- 11 8 3 5 -1.
- <_>
- 12 8 1 5 3.
- <_>
-
- <_>
- 5 12 2 2 -1.
- <_>
- 5 13 2 1 2.
- <_>
-
- <_>
- 5 10 4 3 -1.
- <_>
- 7 10 2 3 2.
- <_>
-
- <_>
- 4 9 15 2 -1.
- <_>
- 9 9 5 2 3.
- <_>
-
- <_>
- 10 7 6 2 -1.
- <_>
- 12 7 2 2 3.
- <_>
-
- <_>
- 1 9 15 2 -1.
- <_>
- 6 9 5 2 3.
- <_>
-
- <_>
- 5 0 2 10 -1.
- <_>
- 5 0 1 5 2.
- <_>
- 6 5 1 5 2.
- <_>
-
- <_>
- 0 0 20 14 -1.
- <_>
- 0 7 20 7 2.
- <_>
-
- <_>
- 12 7 8 4 -1.
- <_>
- 12 7 4 4 2.
- <_>
-
- <_>
- 0 7 8 4 -1.
- <_>
- 4 7 4 4 2.
- <_>
-
- <_>
- 8 1 3 3 -1.
- <_>
- 9 1 1 3 3.
- <_>
-
- <_>
- 9 7 3 4 -1.
- <_>
- 10 7 1 4 3.
- <_>
-
- <_>
- 9 9 3 1 -1.
- <_>
- 10 9 1 1 3.
- <_>
-
- <_>
- 8 9 3 2 -1.
- <_>
- 8 10 3 1 2.
- <_>
-
- <_>
- 8 4 2 8 -1.
- <_>
- 8 4 1 4 2.
- <_>
- 9 8 1 4 2.
- <_>
-
- <_>
- 5 8 12 3 -1.
- <_>
- 5 9 12 1 3.
- <_>
-
- <_>
- 11 14 1 3 -1.
- <_>
- 11 15 1 1 3.
- <_>
-
- <_>
- 6 10 3 6 -1.
- <_>
- 6 12 3 2 3.
- <_>
-
- <_>
- 4 17 8 3 -1.
- <_>
- 4 18 8 1 3.
- <_>
-
- <_>
- 17 6 2 3 -1.
- <_>
- 17 7 2 1 3.
- <_>
-
- <_>
- 9 12 2 2 -1.
- <_>
- 10 12 1 1 2.
- <_>
- 9 13 1 1 2.
- <_>
-
- <_>
- 9 13 2 4 -1.
- <_>
- 9 13 1 2 2.
- <_>
- 10 15 1 2 2.
- <_>
-
- <_>
- 9 11 2 3 -1.
- <_>
- 9 12 2 1 3.
- <_>
-
- <_>
- 5 5 12 10 -1.
- <_>
- 11 5 6 5 2.
- <_>
- 5 10 6 5 2.
- <_>
-
- <_>
- 6 3 12 12 -1.
- <_>
- 12 3 6 6 2.
- <_>
- 6 9 6 6 2.
- <_>
-
- <_>
- 5 7 2 2 -1.
- <_>
- 5 7 1 1 2.
- <_>
- 6 8 1 1 2.
- <_>
-
- <_>
- 4 3 3 2 -1.
- <_>
- 5 3 1 2 3.
- <_>
-
- <_>
- 6 2 12 14 -1.
- <_>
- 12 2 6 7 2.
- <_>
- 6 9 6 7 2.
- <_>
-
- <_>
- 5 2 12 3 -1.
- <_>
- 9 2 4 3 3.
- <_>
-
- <_>
- 1 1 18 17 -1.
- <_>
- 7 1 6 17 3.
- <_>
-
- <_>
- 0 9 10 1 -1.
- <_>
- 5 9 5 1 2.
- <_>
-
- <_>
- 16 8 4 3 -1.
- <_>
- 16 9 4 1 3.
- <_>
-
- <_>
- 7 13 6 6 -1.
- <_>
- 7 16 6 3 2.
- <_>
-
- <_>
- 6 14 1 6 -1.
- <_>
- 6 16 1 2 3.
- <_>
-
- <_>
- 6 17 4 2 -1.
- <_>
- 6 18 4 1 2.
- <_>
-
- <_>
- 10 18 6 2 -1.
- <_>
- 13 18 3 1 2.
- <_>
- 10 19 3 1 2.
- <_>
-
- <_>
- 16 8 1 3 -1.
- <_>
- 16 9 1 1 3.
- <_>
-
- <_>
- 8 13 4 3 -1.
- <_>
- 8 14 4 1 3.
- <_>
-
- <_>
- 9 15 1 2 -1.
- <_>
- 9 16 1 1 2.
- <_>
-
- <_>
- 13 0 3 12 -1.
- <_>
- 14 0 1 12 3.
- <_>
-
- <_>
- 15 11 1 3 -1.
- <_>
- 15 12 1 1 3.
- <_>
-
- <_>
- 8 15 3 3 -1.
- <_>
- 8 16 3 1 3.
- <_>
-
- <_>
- 4 0 3 12 -1.
- <_>
- 5 0 1 12 3.
- <_>
-
- <_>
- 9 7 3 3 -1.
- <_>
- 10 7 1 3 3.
- <_>
-
- <_>
- 9 9 3 1 -1.
- <_>
- 10 9 1 1 3.
- <_>
-
- <_>
- 2 2 12 14 -1.
- <_>
- 2 2 6 7 2.
- <_>
- 8 9 6 7 2.
- <_>
-
- <_>
- 4 2 12 3 -1.
- <_>
- 8 2 4 3 3.
- <_>
-
- <_>
- 18 18 2 2 -1.
- <_>
- 18 18 1 2 2.
- <_>
-
- <_>
- 17 2 3 8 -1.
- <_>
- 18 2 1 8 3.
- <_>
-
- <_>
- 0 18 2 2 -1.
- <_>
- 1 18 1 2 2.
- <_>
-
- <_>
- 6 11 2 6 -1.
- <_>
- 6 14 2 3 2.
- <_>
-
- <_>
- 13 10 5 6 -1.
- <_>
- 13 12 5 2 3.
- <_>
-
- <_>
- 5 8 15 3 -1.
- <_>
- 5 9 15 1 3.
- <_>
-
- <_>
- 2 10 5 6 -1.
- <_>
- 2 12 5 2 3.
- <_>
-
- <_>
- 0 8 15 3 -1.
- <_>
- 0 9 15 1 3.
- <_>
-
- <_>
- 16 2 3 1 -1.
- <_>
- 17 2 1 1 3.
- <_>
-
- <_>
- 17 4 3 2 -1.
- <_>
- 18 4 1 2 3.
- <_>
-
- <_>
- 0 8 8 12 -1.
- <_>
- 0 8 4 6 2.
- <_>
- 4 14 4 6 2.
- <_>
-
- <_>
- 1 7 8 6 -1.
- <_>
- 1 7 4 3 2.
- <_>
- 5 10 4 3 2.
- <_>
-
- <_>
- 14 1 6 2 -1.
- <_>
- 16 1 2 2 3.
- <_>
-
- <_>
- 15 0 4 4 -1.
- <_>
- 17 0 2 2 2.
- <_>
- 15 2 2 2 2.
- <_>
-
- <_>
- 1 1 4 11 -1.
- <_>
- 3 1 2 11 2.
- <_>
-
- <_>
- 5 5 1 8 -1.
- <_>
- 5 9 1 4 2.
- <_>
-
- <_>
- 7 7 6 1 -1.
- <_>
- 9 7 2 1 3.
- <_>
-
- <_>
- 4 7 12 2 -1.
- <_>
- 8 7 4 2 3.
- <_>
-
- <_>
- 8 4 4 4 -1.
- <_>
- 8 6 4 2 2.
- <_>
-
- <_>
- 2 4 9 1 -1.
- <_>
- 5 4 3 1 3.
- <_>
-
- <_>
- 9 12 2 8 -1.
- <_>
- 9 16 2 4 2.
- <_>
-
- <_>
- 3 8 14 12 -1.
- <_>
- 3 14 14 6 2.
- <_>
-
- <_>
- 6 13 7 3 -1.
- <_>
- 6 14 7 1 3.
- <_>
-
- <_>
- 5 9 6 3 -1.
- <_>
- 7 9 2 3 3.
- <_>
-
- <_>
- 12 1 6 3 -1.
- <_>
- 12 2 6 1 3.
- <_>
-
- <_>
- 8 12 6 2 -1.
- <_>
- 8 13 6 1 2.
- <_>
-
- <_>
- 0 2 18 2 -1.
- <_>
- 0 2 9 1 2.
- <_>
- 9 3 9 1 2.
- <_>
-
- <_>
- 6 10 3 6 -1.
- <_>
- 6 13 3 3 2.
- <_>
-
- <_>
- 14 0 6 6 -1.
- <_>
- 14 0 3 6 2.
- <_>
-
- <_>
- 15 0 5 8 -1.
- <_>
- 15 4 5 4 2.
- <_>
-
- <_>
- 7 16 6 4 -1.
- <_>
- 9 16 2 4 3.
- <_>
-
- <_>
- 2 11 14 4 -1.
- <_>
- 2 11 7 2 2.
- <_>
- 9 13 7 2 2.
- <_>
-
- <_>
- 14 10 6 10 -1.
- <_>
- 14 10 3 10 2.
- <_>
-
- <_>
- 9 8 10 12 -1.
- <_>
- 14 8 5 6 2.
- <_>
- 9 14 5 6 2.
- <_>
-
- <_>
- 0 10 6 10 -1.
- <_>
- 3 10 3 10 2.
- <_>
-
- <_>
- 1 8 10 12 -1.
- <_>
- 1 8 5 6 2.
- <_>
- 6 14 5 6 2.
- <_>
-
- <_>
- 9 3 6 1 -1.
- <_>
- 11 3 2 1 3.
- <_>
-
- <_>
- 7 4 6 3 -1.
- <_>
- 9 4 2 3 3.
- <_>
-
- <_>
- 5 3 6 1 -1.
- <_>
- 7 3 2 1 3.
- <_>
-
- <_>
- 4 5 6 3 -1.
- <_>
- 6 5 2 3 3.
- <_>
-
- <_>
- 9 16 3 3 -1.
- <_>
- 9 17 3 1 3.
- <_>
-
- <_>
- 8 14 6 3 -1.
- <_>
- 8 15 6 1 3.
- <_>
-
- <_>
- 6 0 8 12 -1.
- <_>
- 6 0 4 6 2.
- <_>
- 10 6 4 6 2.
- <_>
-
- <_>
- 4 12 2 3 -1.
- <_>
- 4 13 2 1 3.
- <_>
-
- <_>
- 12 16 6 3 -1.
- <_>
- 12 17 6 1 3.
- <_>
-
- <_>
- 7 12 7 2 -1.
- <_>
- 7 13 7 1 2.
- <_>
-
- <_>
- 2 16 6 3 -1.
- <_>
- 2 17 6 1 3.
- <_>
-
- <_>
- 0 7 16 6 -1.
- <_>
- 0 10 16 3 2.
- <_>
-
- <_>
- 9 7 3 3 -1.
- <_>
- 10 7 1 3 3.
- <_>
-
- <_>
- 9 7 3 5 -1.
- <_>
- 10 7 1 5 3.
- <_>
-
- <_>
- 0 5 20 10 -1.
- <_>
- 0 5 10 5 2.
- <_>
- 10 10 10 5 2.
- <_>
-
- <_>
- 3 1 4 2 -1.
- <_>
- 5 1 2 2 2.
- <_>
-
- <_>
- 7 6 8 10 -1.
- <_>
- 11 6 4 5 2.
- <_>
- 7 11 4 5 2.
- <_>
-
- <_>
- 17 6 3 2 -1.
- <_>
- 17 7 3 1 2.
- <_>
-
- <_>
- 5 6 8 10 -1.
- <_>
- 5 6 4 5 2.
- <_>
- 9 11 4 5 2.
- <_>
-
- <_>
- 5 12 10 6 -1.
- <_>
- 5 14 10 2 3.
- <_>
-
- <_>
- 9 7 3 3 -1.
- <_>
- 10 7 1 3 3.
- <_>
-
- <_>
- 10 3 2 6 -1.
- <_>
- 11 3 1 3 2.
- <_>
- 10 6 1 3 2.
- <_>
-
- <_>
- 0 4 3 3 -1.
- <_>
- 0 5 3 1 3.
- <_>
-
- <_>
- 3 16 8 4 -1.
- <_>
- 3 16 4 2 2.
- <_>
- 7 18 4 2 2.
- <_>
-
- <_>
- 8 13 5 2 -1.
- <_>
- 8 14 5 1 2.
- <_>
-
- <_>
- 8 7 4 12 -1.
- <_>
- 8 11 4 4 3.
- <_>
-
- <_>
- 5 9 2 2 -1.
- <_>
- 6 9 1 2 2.
- <_>
-
- <_>
- 9 15 2 3 -1.
- <_>
- 9 16 2 1 3.
- <_>
-
- <_>
- 13 9 2 3 -1.
- <_>
- 13 9 1 3 2.
- <_>
-
- <_>
- 14 0 6 17 -1.
- <_>
- 16 0 2 17 3.
- <_>
-
- <_>
- 5 10 2 2 -1.
- <_>
- 6 10 1 2 2.
- <_>
-
- <_>
- 2 9 9 1 -1.
- <_>
- 5 9 3 1 3.
- <_>
-
- <_>
- 9 11 2 3 -1.
- <_>
- 9 12 2 1 3.
- <_>
-
- <_>
- 7 11 6 3 -1.
- <_>
- 7 12 6 1 3.
- <_>
-
- <_>
- 0 6 3 2 -1.
- <_>
- 0 7 3 1 2.
- <_>
-
- <_>
- 7 0 6 1 -1.
- <_>
- 9 0 2 1 3.
- <_>
-
- <_>
- 9 16 3 3 -1.
- <_>
- 9 17 3 1 3.
- <_>
-
- <_>
- 2 13 17 6 -1.
- <_>
- 2 16 17 3 2.
- <_>
-
- <_>
- 1 3 3 7 -1.
- <_>
- 2 3 1 7 3.
- <_>
-
- <_>
- 1 1 6 4 -1.
- <_>
- 3 1 2 4 3.
- <_>
-
- <_>
- 14 1 6 5 -1.
- <_>
- 14 1 3 5 2.
- <_>
-
- <_>
- 13 2 3 2 -1.
- <_>
- 13 3 3 1 2.
- <_>
-
- <_>
- 0 1 6 5 -1.
- <_>
- 3 1 3 5 2.
- <_>
-
- <_>
- 2 3 2 6 -1.
- <_>
- 2 5 2 2 3.
- <_>
-
- <_>
- 9 10 3 2 -1.
- <_>
- 9 11 3 1 2.
- <_>
-
- <_>
- 8 13 4 3 -1.
- <_>
- 8 14 4 1 3.
- <_>
-
- <_>
- 6 3 3 1 -1.
- <_>
- 7 3 1 1 3.
- <_>
-
- <_>
- 8 2 3 12 -1.
- <_>
- 8 6 3 4 3.
- <_>
-
- <_>
- 11 12 1 2 -1.
- <_>
- 11 13 1 1 2.
- <_>
-
- <_>
- 11 12 2 2 -1.
- <_>
- 12 12 1 1 2.
- <_>
- 11 13 1 1 2.
- <_>
-
- <_>
- 5 5 2 2 -1.
- <_>
- 5 6 2 1 2.
- <_>
-
- <_>
- 5 4 1 3 -1.
- <_>
- 5 5 1 1 3.
- <_>
-
- <_>
- 3 11 16 4 -1.
- <_>
- 11 11 8 2 2.
- <_>
- 3 13 8 2 2.
- <_>
-
- <_>
- 0 10 20 3 -1.
- <_>
- 0 11 20 1 3.
- <_>
-
- <_>
- 1 11 16 4 -1.
- <_>
- 1 11 8 2 2.
- <_>
- 9 13 8 2 2.
- <_>
-
- <_>
- 4 2 4 2 -1.
- <_>
- 4 3 4 1 2.
- <_>
-
- <_>
- 12 6 2 2 -1.
- <_>
- 13 6 1 1 2.
- <_>
- 12 7 1 1 2.
- <_>
-
- <_>
- 12 11 6 6 -1.
- <_>
- 12 13 6 2 3.
- <_>
-
- <_>
- 6 6 2 2 -1.
- <_>
- 6 6 1 1 2.
- <_>
- 7 7 1 1 2.
- <_>
-
- <_>
- 6 4 4 16 -1.
- <_>
- 8 4 2 16 2.
- <_>
-
- <_>
- 11 18 3 2 -1.
- <_>
- 11 19 3 1 2.
- <_>
-
- <_>
- 9 17 6 2 -1.
- <_>
- 12 17 3 1 2.
- <_>
- 9 18 3 1 2.
- <_>
-
- <_>
- 2 13 5 2 -1.
- <_>
- 2 14 5 1 2.
- <_>
-
- <_>
- 3 15 2 2 -1.
- <_>
- 3 16 2 1 2.
- <_>
-
- <_>
- 9 7 3 3 -1.
- <_>
- 10 7 1 3 3.
- <_>
-
- <_>
- 9 6 2 6 -1.
- <_>
- 9 6 1 6 2.
- <_>
-
- <_>
- 1 14 7 6 -1.
- <_>
- 1 16 7 2 3.
- <_>
-
- <_>
- 8 1 2 11 -1.
- <_>
- 9 1 1 11 2.
- <_>
-
- <_>
- 9 7 2 4 -1.
- <_>
- 9 7 1 4 2.
- <_>
-
- <_>
- 11 10 2 1 -1.
- <_>
- 11 10 1 1 2.
- <_>
-
- <_>
- 0 3 3 9 -1.
- <_>
- 1 3 1 9 3.
- <_>
-
- <_>
- 0 3 3 6 -1.
- <_>
- 0 5 3 2 3.
- <_>
-
- <_>
- 11 15 2 2 -1.
- <_>
- 12 15 1 1 2.
- <_>
- 11 16 1 1 2.
- <_>
-
- <_>
- 11 14 2 2 -1.
- <_>
- 12 14 1 1 2.
- <_>
- 11 15 1 1 2.
- <_>
-
- <_>
- 7 15 2 2 -1.
- <_>
- 7 15 1 1 2.
- <_>
- 8 16 1 1 2.
- <_>
-
- <_>
- 7 14 2 2 -1.
- <_>
- 7 14 1 1 2.
- <_>
- 8 15 1 1 2.
- <_>
-
- <_>
- 8 13 4 6 -1.
- <_>
- 10 13 2 3 2.
- <_>
- 8 16 2 3 2.
- <_>
-
- <_>
- 2 14 16 4 -1.
- <_>
- 10 14 8 2 2.
- <_>
- 2 16 8 2 2.
- <_>
-
- <_>
- 9 8 2 2 -1.
- <_>
- 9 9 2 1 2.
- <_>
-
- <_>
- 7 7 5 3 -1.
- <_>
- 7 8 5 1 3.
- <_>
-
- <_>
- 7 5 6 2 -1.
- <_>
- 9 5 2 2 3.
- <_>
-
- <_>
- 9 1 6 18 -1.
- <_>
- 11 1 2 18 3.
- <_>
-
- <_>
- 8 6 3 4 -1.
- <_>
- 9 6 1 4 3.
- <_>
-
- <_>
- 8 5 2 4 -1.
- <_>
- 8 5 1 2 2.
- <_>
- 9 7 1 2 2.
- <_>
-
- <_>
- 9 13 2 6 -1.
- <_>
- 10 13 1 3 2.
- <_>
- 9 16 1 3 2.
- <_>
-
- <_>
- 11 0 3 18 -1.
- <_>
- 12 0 1 18 3.
- <_>
-
- <_>
- 6 0 3 18 -1.
- <_>
- 7 0 1 18 3.
- <_>
-
- <_>
- 5 15 4 2 -1.
- <_>
- 7 15 2 2 2.
- <_>
-
- <_>
- 1 9 18 1 -1.
- <_>
- 7 9 6 1 3.
- <_>
-
- <_>
- 0 0 20 3 -1.
- <_>
- 0 1 20 1 3.
- <_>
-
- <_>
- 9 6 2 4 -1.
- <_>
- 10 6 1 4 2.
- <_>
-
- <_>
- 6 10 6 2 -1.
- <_>
- 8 10 2 2 3.
- <_>
-
- <_>
- 0 7 20 1 -1.
- <_>
- 0 7 10 1 2.
- <_>
-
- <_>
- 11 3 5 4 -1.
- <_>
- 11 5 5 2 2.
- <_>
-
- <_>
- 5 7 10 1 -1.
- <_>
- 10 7 5 1 2.
- <_>
-
- <_>
- 8 10 3 3 -1.
- <_>
- 8 11 3 1 3.
- <_>
-
- <_>
- 2 0 16 8 -1.
- <_>
- 10 0 8 4 2.
- <_>
- 2 4 8 4 2.
- <_>
-
- <_>
- 11 0 9 10 -1.
- <_>
- 11 5 9 5 2.
- <_>
-
- <_>
- 0 2 8 18 -1.
- <_>
- 4 2 4 18 2.
- <_>
-
- <_>
- 0 0 2 6 -1.
- <_>
- 0 2 2 2 3.
- <_>
-
- <_>
- 6 0 9 2 -1.
- <_>
- 6 1 9 1 2.
- <_>
-
- <_>
- 4 1 12 2 -1.
- <_>
- 4 2 12 1 2.
- <_>
-
- <_>
- 2 1 16 14 -1.
- <_>
- 2 8 16 7 2.
- <_>
-
- <_>
- 5 1 8 12 -1.
- <_>
- 5 7 8 6 2.
- <_>
-
- <_>
- 9 11 2 2 -1.
- <_>
- 9 12 2 1 2.
- <_>
-
- <_>
- 9 10 5 6 -1.
- <_>
- 9 12 5 2 3.
- <_>
-
- <_>
- 3 0 13 8 -1.
- <_>
- 3 4 13 4 2.
- <_>
-
- <_>
- 6 7 5 8 -1.
- <_>
- 6 11 5 4 2.
- <_>
-
- <_>
- 9 5 2 3 -1.
- <_>
- 9 6 2 1 3.
- <_>
-
- <_>
- 6 8 8 3 -1.
- <_>
- 6 9 8 1 3.
- <_>
-
- <_>
- 2 2 7 6 -1.
- <_>
- 2 5 7 3 2.
- <_>
-
- <_>
- 2 1 14 4 -1.
- <_>
- 2 1 7 2 2.
- <_>
- 9 3 7 2 2.
- <_>
-
- <_>
- 11 14 1 3 -1.
- <_>
- 11 15 1 1 3.
- <_>
-
- <_>
- 6 15 8 2 -1.
- <_>
- 6 16 8 1 2.
- <_>
-
- <_>
- 8 14 1 3 -1.
- <_>
- 8 15 1 1 3.
- <_>
-
- <_>
- 8 11 2 8 -1.
- <_>
- 8 15 2 4 2.
- <_>
-
- <_>
- 6 15 8 2 -1.
- <_>
- 6 16 8 1 2.
- <_>
-
- <_>
- 7 16 8 3 -1.
- <_>
- 7 17 8 1 3.
- <_>
-
- <_>
- 0 16 2 2 -1.
- <_>
- 0 17 2 1 2.
- <_>
-
- <_>
- 1 16 8 4 -1.
- <_>
- 1 16 4 2 2.
- <_>
- 5 18 4 2 2.
- <_>
-
- <_>
- 2 9 16 3 -1.
- <_>
- 2 10 16 1 3.
- <_>
-
- <_>
- 13 11 2 4 -1.
- <_>
- 13 11 1 4 2.
- <_>
-
- <_>
- 0 13 16 6 -1.
- <_>
- 0 15 16 2 3.
- <_>
-
- <_>
- 5 11 2 4 -1.
- <_>
- 6 11 1 4 2.
- <_>
-
- <_>
- 18 2 2 18 -1.
- <_>
- 19 2 1 9 2.
- <_>
- 18 11 1 9 2.
- <_>
-
- <_>
- 19 7 1 9 -1.
- <_>
- 19 10 1 3 3.
- <_>
-
- <_>
- 0 2 2 18 -1.
- <_>
- 0 2 1 9 2.
- <_>
- 1 11 1 9 2.
- <_>
-
- <_>
- 0 7 1 9 -1.
- <_>
- 0 10 1 3 3.
- <_>
-
- <_>
- 14 12 2 2 -1.
- <_>
- 14 13 2 1 2.
- <_>
-
- <_>
- 11 14 2 3 -1.
- <_>
- 11 15 2 1 3.
- <_>
-
- <_>
- 7 8 6 2 -1.
- <_>
- 7 9 6 1 2.
- <_>
-
- <_>
- 7 12 4 6 -1.
- <_>
- 7 12 2 3 2.
- <_>
- 9 15 2 3 2.
- <_>
-
- <_>
- 8 13 5 3 -1.
- <_>
- 8 14 5 1 3.
- <_>
-
- <_>
- 12 14 2 2 -1.
- <_>
- 13 14 1 1 2.
- <_>
- 12 15 1 1 2.
- <_>
-
- <_>
- 7 13 6 3 -1.
- <_>
- 7 14 6 1 3.
- <_>
-
- <_>
- 7 13 5 2 -1.
- <_>
- 7 14 5 1 2.
- <_>
-
- <_>
- 2 10 16 4 -1.
- <_>
- 10 10 8 2 2.
- <_>
- 2 12 8 2 2.
- <_>
-
- <_>
- 7 0 6 6 -1.
- <_>
- 9 0 2 6 3.
- <_>
-
- <_>
- 7 1 6 3 -1.
- <_>
- 7 2 6 1 3.
- <_>
-
- <_>
- 0 12 6 2 -1.
- <_>
- 0 13 6 1 2.
- <_>
-
- <_>
- 6 3 11 2 -1.
- <_>
- 6 4 11 1 2.
- <_>
-
- <_>
- 12 0 8 6 -1.
- <_>
- 16 0 4 3 2.
- <_>
- 12 3 4 3 2.
- <_>
-
- <_>
- 8 12 1 2 -1.
- <_>
- 8 13 1 1 2.
- <_>
-
- <_>
- 8 8 1 12 -1.
- <_>
- 8 12 1 4 3.
- <_>
-
- <_>
- 11 11 2 2 -1.
- <_>
- 12 11 1 1 2.
- <_>
- 11 12 1 1 2.
- <_>
-
- <_>
- 12 7 3 13 -1.
- <_>
- 13 7 1 13 3.
- <_>
-
- <_>
- 7 11 2 2 -1.
- <_>
- 7 11 1 1 2.
- <_>
- 8 12 1 1 2.
- <_>
-
- <_>
- 3 13 1 3 -1.
- <_>
- 3 14 1 1 3.
- <_>
-
- <_>
- 10 18 3 2 -1.
- <_>
- 11 18 1 2 3.
- <_>
-
- <_>
- 11 11 2 1 -1.
- <_>
- 11 11 1 1 2.
- <_>
-
- <_>
- 1 10 5 9 -1.
- <_>
- 1 13 5 3 3.
- <_>
-
- <_>
- 4 8 6 4 -1.
- <_>
- 6 8 2 4 3.
- <_>
-
- <_>
- 13 12 1 4 -1.
- <_>
- 13 14 1 2 2.
- <_>
-
- <_>
- 11 3 4 14 -1.
- <_>
- 13 3 2 7 2.
- <_>
- 11 10 2 7 2.
- <_>
-
- <_>
- 6 12 1 4 -1.
- <_>
- 6 14 1 2 2.
- <_>
-
- <_>
- 5 3 4 14 -1.
- <_>
- 5 3 2 7 2.
- <_>
- 7 10 2 7 2.
- <_>
-
- <_>
- 10 18 3 2 -1.
- <_>
- 11 18 1 2 3.
- <_>
-
- <_>
- 9 12 3 3 -1.
- <_>
- 9 13 3 1 3.
- <_>
-
- <_>
- 2 2 12 6 -1.
- <_>
- 2 2 6 3 2.
- <_>
- 8 5 6 3 2.
- <_>
-
- <_>
- 6 6 6 2 -1.
- <_>
- 9 6 3 2 2.
- <_>
-
- <_>
- 1 0 18 12 -1.
- <_>
- 7 0 6 12 3.
- <_>
-
- <_>
- 5 7 6 4 -1.
- <_>
- 5 7 3 2 2.
- <_>
- 8 9 3 2 2.
- <_>
-
- <_>
- 5 7 10 4 -1.
- <_>
- 5 9 10 2 2.
- <_>
-
- <_>
- 7 7 6 4 -1.
- <_>
- 9 7 2 4 3.
- <_>
-
- <_>
- 9 5 2 2 -1.
- <_>
- 9 6 2 1 2.
- <_>
-
- <_>
- 9 9 2 2 -1.
- <_>
- 9 10 2 1 2.
- <_>
-
- <_>
- 6 17 8 3 -1.
- <_>
- 6 18 8 1 3.
- <_>
-
- <_>
- 9 17 6 2 -1.
- <_>
- 12 17 3 1 2.
- <_>
- 9 18 3 1 2.
- <_>
-
- <_>
- 4 12 2 2 -1.
- <_>
- 4 13 2 1 2.
- <_>
-
- <_>
- 3 12 9 2 -1.
- <_>
- 3 13 9 1 2.
- <_>
-
- <_>
- 8 3 6 1 -1.
- <_>
- 10 3 2 1 3.
- <_>
-
- <_>
- 9 3 4 6 -1.
- <_>
- 11 3 2 3 2.
- <_>
- 9 6 2 3 2.
- <_>
-
- <_>
- 0 3 6 5 -1.
- <_>
- 3 3 3 5 2.
- <_>
-
- <_>
- 2 0 2 18 -1.
- <_>
- 2 6 2 6 3.
- <_>
-
- <_>
- 14 2 4 9 -1.
- <_>
- 14 5 4 3 3.
- <_>
-
- <_>
- 10 18 3 2 -1.
- <_>
- 11 18 1 2 3.
- <_>
-
- <_>
- 2 2 4 9 -1.
- <_>
- 2 5 4 3 3.
- <_>
-
- <_>
- 7 18 3 2 -1.
- <_>
- 8 18 1 2 3.
- <_>
-
- <_>
- 10 14 3 3 -1.
- <_>
- 10 15 3 1 3.
- <_>
-
- <_>
- 10 12 2 6 -1.
- <_>
- 10 15 2 3 2.
- <_>
-
- <_>
- 7 5 3 6 -1.
- <_>
- 7 7 3 2 3.
- <_>
-
- <_>
- 3 3 6 2 -1.
- <_>
- 3 4 6 1 2.
- <_>
-
- <_>
- 8 4 7 3 -1.
- <_>
- 8 5 7 1 3.
- <_>
-
- <_>
- 13 6 2 3 -1.
- <_>
- 13 7 2 1 3.
- <_>
-
- <_>
- 8 8 2 12 -1.
- <_>
- 8 12 2 4 3.
- <_>
-
- <_>
- 5 4 8 14 -1.
- <_>
- 5 4 4 7 2.
- <_>
- 9 11 4 7 2.
- <_>
-
- <_>
- 0 1 20 8 -1.
- <_>
- 10 1 10 4 2.
- <_>
- 0 5 10 4 2.
- <_>
-
- <_>
- 4 0 12 2 -1.
- <_>
- 4 1 12 1 2.
- <_>
-
- <_>
- 0 1 20 8 -1.
- <_>
- 0 1 10 4 2.
- <_>
- 10 5 10 4 2.
- <_>
-
- <_>
- 4 0 12 2 -1.
- <_>
- 4 1 12 1 2.
- <_>
-
- <_>
- 9 5 6 3 -1.
- <_>
- 9 5 3 3 2.
- <_>
-
- <_>
- 8 13 10 6 -1.
- <_>
- 8 15 10 2 3.
- <_>
-
- <_>
- 5 5 6 3 -1.
- <_>
- 8 5 3 3 2.
- <_>
-
- <_>
- 6 3 6 1 -1.
- <_>
- 8 3 2 1 3.
- <_>
-
- <_>
- 11 18 9 2 -1.
- <_>
- 14 18 3 2 3.
- <_>
-
- <_>
- 13 11 6 7 -1.
- <_>
- 13 11 3 7 2.
- <_>
-
- <_>
- 4 6 12 10 -1.
- <_>
- 4 6 6 5 2.
- <_>
- 10 11 6 5 2.
- <_>
-
- <_>
- 8 17 3 3 -1.
- <_>
- 9 17 1 3 3.
- <_>
-
- <_>
- 11 18 9 2 -1.
- <_>
- 14 18 3 2 3.
- <_>
-
- <_>
- 13 11 6 8 -1.
- <_>
- 13 11 3 8 2.
- <_>
-
- <_>
- 4 16 2 2 -1.
- <_>
- 4 17 2 1 2.
- <_>
-
- <_>
- 7 15 4 4 -1.
- <_>
- 7 17 4 2 2.
- <_>
-
- <_>
- 12 4 3 3 -1.
- <_>
- 12 5 3 1 3.
- <_>
-
- <_>
- 13 6 2 3 -1.
- <_>
- 13 7 2 1 3.
- <_>
-
- <_>
- 5 11 6 1 -1.
- <_>
- 7 11 2 1 3.
- <_>
-
- <_>
- 7 10 3 1 -1.
- <_>
- 8 10 1 1 3.
- <_>
-
- <_>
- 0 12 20 4 -1.
- <_>
- 0 14 20 2 2.
- <_>
-
- <_>
- 10 2 3 2 -1.
- <_>
- 10 3 3 1 2.
- <_>
-
- <_>
- 5 4 3 3 -1.
- <_>
- 5 5 3 1 3.
- <_>
-
- <_>
- 5 5 4 3 -1.
- <_>
- 5 6 4 1 3.
- <_>
-
- <_>
- 8 8 4 3 -1.
- <_>
- 8 9 4 1 3.
- <_>
-
- <_>
- 10 4 2 12 -1.
- <_>
- 10 8 2 4 3.
- <_>
-
- <_>
- 0 3 4 3 -1.
- <_>
- 0 4 4 1 3.
- <_>
-
- <_>
- 1 3 2 3 -1.
- <_>
- 1 4 2 1 3.
- <_>
-
- <_>
- 16 1 4 11 -1.
- <_>
- 16 1 2 11 2.
- <_>
-
- <_>
- 18 2 2 16 -1.
- <_>
- 19 2 1 8 2.
- <_>
- 18 10 1 8 2.
- <_>
-
- <_>
- 1 8 6 12 -1.
- <_>
- 3 8 2 12 3.
- <_>
-
- <_>
- 7 2 6 2 -1.
- <_>
- 7 2 3 1 2.
- <_>
- 10 3 3 1 2.
- <_>
-
- <_>
- 12 4 8 2 -1.
- <_>
- 16 4 4 1 2.
- <_>
- 12 5 4 1 2.
- <_>
-
- <_>
- 10 6 6 2 -1.
- <_>
- 12 6 2 2 3.
- <_>
-
- <_>
- 0 4 8 2 -1.
- <_>
- 0 4 4 1 2.
- <_>
- 4 5 4 1 2.
- <_>
-
- <_>
- 1 3 3 5 -1.
- <_>
- 2 3 1 5 3.
- <_>
-
- <_>
- 16 3 4 6 -1.
- <_>
- 16 5 4 2 3.
- <_>
-
- <_>
- 8 6 4 3 -1.
- <_>
- 8 7 4 1 3.
- <_>
-
- <_>
- 8 14 1 3 -1.
- <_>
- 8 15 1 1 3.
- <_>
-
- <_>
- 4 11 1 2 -1.
- <_>
- 4 12 1 1 2.
- <_>
-
- <_>
- 8 14 6 3 -1.
- <_>
- 8 15 6 1 3.
- <_>
-
- <_>
- 7 15 7 3 -1.
- <_>
- 7 16 7 1 3.
- <_>
-
- <_>
- 9 12 2 8 -1.
- <_>
- 9 16 2 4 2.
- <_>
-
- <_>
- 4 6 6 2 -1.
- <_>
- 6 6 2 2 3.
- <_>
-
- <_>
- 12 7 4 2 -1.
- <_>
- 12 8 4 1 2.
- <_>
-
- <_>
- 5 3 13 10 -1.
- <_>
- 5 8 13 5 2.
- <_>
-
- <_>
- 4 7 4 2 -1.
- <_>
- 4 8 4 1 2.
- <_>
-
- <_>
- 0 8 16 2 -1.
- <_>
- 0 8 8 1 2.
- <_>
- 8 9 8 1 2.
- <_>
-
- <_>
- 11 8 2 5 -1.
- <_>
- 11 8 1 5 2.
- <_>
-
- <_>
- 10 0 6 13 -1.
- <_>
- 10 0 3 13 2.
- <_>
-
- <_>
- 1 6 4 2 -1.
- <_>
- 1 7 4 1 2.
- <_>
-
- <_>
- 4 3 2 1 -1.
- <_>
- 5 3 1 1 2.
- <_>
-
- <_>
- 11 8 2 5 -1.
- <_>
- 11 8 1 5 2.
- <_>
-
- <_>
- 12 10 4 8 -1.
- <_>
- 12 10 2 8 2.
- <_>
-
- <_>
- 7 8 2 5 -1.
- <_>
- 8 8 1 5 2.
- <_>
-
- <_>
- 4 10 4 8 -1.
- <_>
- 6 10 2 8 2.
- <_>
-
- <_>
- 6 7 9 12 -1.
- <_>
- 9 7 3 12 3.
- <_>
-
- <_>
- 11 13 2 3 -1.
- <_>
- 11 13 1 3 2.
- <_>
-
- <_>
- 7 10 6 10 -1.
- <_>
- 10 10 3 10 2.
- <_>
-
- <_>
- 8 11 4 8 -1.
- <_>
- 8 11 2 4 2.
- <_>
- 10 15 2 4 2.
- <_>
-
- <_>
- 16 1 4 11 -1.
- <_>
- 16 1 2 11 2.
- <_>
-
- <_>
- 18 2 2 4 -1.
- <_>
- 18 2 1 4 2.
- <_>
-
- <_>
- 5 6 6 2 -1.
- <_>
- 5 6 3 1 2.
- <_>
- 8 7 3 1 2.
- <_>
-
- <_>
- 5 4 1 3 -1.
- <_>
- 5 5 1 1 3.
- <_>
-
- <_>
- 11 1 4 14 -1.
- <_>
- 11 1 2 14 2.
- <_>
-
- <_>
- 4 2 12 3 -1.
- <_>
- 8 2 4 3 3.
- <_>
-
- <_>
- 5 1 4 14 -1.
- <_>
- 7 1 2 14 2.
- <_>
-
- <_>
- 7 3 6 2 -1.
- <_>
- 9 3 2 2 3.
- <_>
-
- <_>
- 2 0 18 4 -1.
- <_>
- 8 0 6 4 3.
- <_>
-
- <_>
- 9 5 2 10 -1.
- <_>
- 9 10 2 5 2.
- <_>
-
- <_>
- 8 6 3 4 -1.
- <_>
- 9 6 1 4 3.
- <_>
-
- <_>
- 5 5 9 11 -1.
- <_>
- 8 5 3 11 3.
- <_>
-
- <_>
- 10 6 3 5 -1.
- <_>
- 11 6 1 5 3.
- <_>
-
- <_>
- 8 9 6 5 -1.
- <_>
- 8 9 3 5 2.
- <_>
-
- <_>
- 7 6 3 5 -1.
- <_>
- 8 6 1 5 3.
- <_>
-
- <_>
- 6 10 6 3 -1.
- <_>
- 9 10 3 3 2.
- <_>
-
- <_>
- 10 0 3 7 -1.
- <_>
- 11 0 1 7 3.
- <_>
-
- <_>
- 0 3 20 12 -1.
- <_>
- 0 9 20 6 2.
- <_>
-
- <_>
- 9 7 2 2 -1.
- <_>
- 10 7 1 2 2.
- <_>
-
- <_>
- 5 9 4 1 -1.
- <_>
- 7 9 2 1 2.
- <_>
-
- <_>
- 13 13 3 2 -1.
- <_>
- 13 14 3 1 2.
- <_>
-
- <_>
- 16 9 4 6 -1.
- <_>
- 16 9 2 6 2.
- <_>
-
- <_>
- 7 15 6 3 -1.
- <_>
- 7 16 6 1 3.
- <_>
-
- <_>
- 6 16 7 3 -1.
- <_>
- 6 17 7 1 3.
- <_>
-
- <_>
- 11 14 9 6 -1.
- <_>
- 11 16 9 2 3.
- <_>
-
- <_>
- 19 14 1 3 -1.
- <_>
- 19 15 1 1 3.
- <_>
-
- <_>
- 0 9 6 6 -1.
- <_>
- 3 9 3 6 2.
- <_>
-
- <_>
- 0 19 9 1 -1.
- <_>
- 3 19 3 1 3.
- <_>
-
- <_>
- 11 14 9 6 -1.
- <_>
- 11 16 9 2 3.
- <_>
-
- <_>
- 12 12 6 6 -1.
- <_>
- 12 14 6 2 3.
- <_>
-
- <_>
- 1 14 8 6 -1.
- <_>
- 1 16 8 2 3.
- <_>
-
- <_>
- 8 1 3 2 -1.
- <_>
- 9 1 1 2 3.
- <_>
-
- <_>
- 18 2 2 4 -1.
- <_>
- 18 2 1 4 2.
- <_>
-
- <_>
- 14 0 6 3 -1.
- <_>
- 16 0 2 3 3.
- <_>
-
- <_>
- 0 2 2 4 -1.
- <_>
- 1 2 1 4 2.
- <_>
-
- <_>
- 0 0 6 3 -1.
- <_>
- 2 0 2 3 3.
- <_>
-
- <_>
- 9 0 3 2 -1.
- <_>
- 10 0 1 2 3.
- <_>
-
- <_>
- 12 1 2 2 -1.
- <_>
- 12 1 1 2 2.
- <_>
-
- <_>
- 8 0 3 2 -1.
- <_>
- 9 0 1 2 3.
- <_>
-
- <_>
- 6 1 2 2 -1.
- <_>
- 7 1 1 2 2.
- <_>
-
- <_>
- 10 8 2 3 -1.
- <_>
- 10 9 2 1 3.
- <_>
-
- <_>
- 13 15 6 2 -1.
- <_>
- 13 16 6 1 2.
- <_>
-
- <_>
- 8 12 2 2 -1.
- <_>
- 8 12 1 1 2.
- <_>
- 9 13 1 1 2.
- <_>
-
- <_>
- 8 15 3 5 -1.
- <_>
- 9 15 1 5 3.
- <_>
-
- <_>
- 8 6 4 12 -1.
- <_>
- 8 12 4 6 2.
- <_>
-
- <_>
- 7 6 7 8 -1.
- <_>
- 7 10 7 4 2.
- <_>
-
- <_>
- 0 11 8 2 -1.
- <_>
- 0 12 8 1 2.
- <_>
-
- <_>
- 8 11 2 2 -1.
- <_>
- 8 11 1 1 2.
- <_>
- 9 12 1 1 2.
- <_>
-
- <_>
- 7 7 12 1 -1.
- <_>
- 11 7 4 1 3.
- <_>
-
- <_>
- 10 8 3 2 -1.
- <_>
- 11 8 1 2 3.
- <_>
-
- <_>
- 1 7 12 1 -1.
- <_>
- 5 7 4 1 3.
- <_>
-
- <_>
- 6 5 8 2 -1.
- <_>
- 6 5 4 1 2.
- <_>
- 10 6 4 1 2.
- <_>
-
- <_>
- 9 10 3 10 -1.
- <_>
- 10 10 1 10 3.
- <_>
-
- <_>
- 16 0 2 4 -1.
- <_>
- 16 0 1 4 2.
- <_>
-
- <_>
- 8 10 3 10 -1.
- <_>
- 9 10 1 10 3.
- <_>
-
- <_>
- 9 10 2 3 -1.
- <_>
- 9 11 2 1 3.
- <_>
-
- <_>
- 8 9 4 2 -1.
- <_>
- 10 9 2 1 2.
- <_>
- 8 10 2 1 2.
- <_>
-
- <_>
- 12 14 7 6 -1.
- <_>
- 12 16 7 2 3.
- <_>
-
- <_>
- 6 1 3 1 -1.
- <_>
- 7 1 1 1 3.
- <_>
-
- <_>
- 2 0 2 4 -1.
- <_>
- 3 0 1 4 2.
- <_>
-
- <_>
- 11 11 2 2 -1.
- <_>
- 12 11 1 1 2.
- <_>
- 11 12 1 1 2.
- <_>
-
- <_>
- 12 12 6 6 -1.
- <_>
- 12 14 6 2 3.
- <_>
-
- <_>
- 1 0 6 10 -1.
- <_>
- 1 0 3 5 2.
- <_>
- 4 5 3 5 2.
- <_>
-
- <_>
- 3 0 2 9 -1.
- <_>
- 3 3 2 3 3.
- <_>
-
- <_>
- 14 13 3 2 -1.
- <_>
- 14 14 3 1 2.
- <_>
-
- <_>
- 15 2 3 2 -1.
- <_>
- 15 3 3 1 2.
- <_>
-
- <_>
- 2 13 5 2 -1.
- <_>
- 2 14 5 1 2.
- <_>
-
- <_>
- 3 4 12 10 -1.
- <_>
- 3 4 6 5 2.
- <_>
- 9 9 6 5 2.
- <_>
-
- <_>
- 5 1 14 6 -1.
- <_>
- 5 3 14 2 3.
- <_>
-
- <_>
- 15 3 3 2 -1.
- <_>
- 15 4 3 1 2.
- <_>
-
- <_>
- 7 11 2 2 -1.
- <_>
- 7 11 1 1 2.
- <_>
- 8 12 1 1 2.
- <_>
-
- <_>
- 2 14 6 6 -1.
- <_>
- 2 16 6 2 3.
- <_>
-
- <_>
- 6 13 8 3 -1.
- <_>
- 6 14 8 1 3.
- <_>
-
- <_>
- 1 19 18 1 -1.
- <_>
- 7 19 6 1 3.
- <_>
-
- <_>
- 8 12 1 6 -1.
- <_>
- 8 15 1 3 2.
- <_>
-
- <_>
- 0 0 14 15 -1.
- <_>
- 0 5 14 5 3.
- <_>
-
- <_>
- 3 0 16 8 -1.
- <_>
- 3 4 16 4 2.
- <_>
-
- <_>
- 6 1 8 12 -1.
- <_>
- 6 7 8 6 2.
- <_>
-
- <_>
- 5 3 3 3 -1.
- <_>
- 6 3 1 3 3.
- <_>
-
- <_>
- 5 1 3 4 -1.
- <_>
- 6 1 1 4 3.
- <_>
-
- <_>
- 15 14 4 6 -1.
- <_>
- 17 14 2 3 2.
- <_>
- 15 17 2 3 2.
- <_>
-
- <_>
- 12 11 6 8 -1.
- <_>
- 15 11 3 4 2.
- <_>
- 12 15 3 4 2.
- <_>
-
- <_>
- 8 7 2 4 -1.
- <_>
- 9 7 1 4 2.
- <_>
-
- <_>
- 6 11 3 1 -1.
- <_>
- 7 11 1 1 3.
- <_>
-
- <_>
- 12 3 2 14 -1.
- <_>
- 12 3 1 14 2.
- <_>
-
- <_>
- 12 11 6 2 -1.
- <_>
- 15 11 3 1 2.
- <_>
- 12 12 3 1 2.
- <_>
-
- <_>
- 0 2 5 2 -1.
- <_>
- 0 3 5 1 2.
- <_>
-
- <_>
- 0 0 15 1 -1.
- <_>
- 5 0 5 1 3.
- <_>
-
- <_>
- 12 11 6 2 -1.
- <_>
- 15 11 3 1 2.
- <_>
- 12 12 3 1 2.
- <_>
-
- <_>
- 10 5 2 2 -1.
- <_>
- 10 5 1 2 2.
- <_>
-
- <_>
- 9 7 2 2 -1.
- <_>
- 10 7 1 2 2.
- <_>
-
- <_>
- 9 0 2 10 -1.
- <_>
- 9 0 1 5 2.
- <_>
- 10 5 1 5 2.
- <_>
-
- <_>
- 18 14 2 2 -1.
- <_>
- 18 15 2 1 2.
- <_>
-
- <_>
- 13 11 4 9 -1.
- <_>
- 13 14 4 3 3.
- <_>
-
- <_>
- 8 13 2 2 -1.
- <_>
- 8 13 1 1 2.
- <_>
- 9 14 1 1 2.
- <_>
-
- <_>
- 7 8 4 3 -1.
- <_>
- 7 9 4 1 3.
- <_>
-
- <_>
- 8 9 4 2 -1.
- <_>
- 8 10 4 1 2.
- <_>
-
- <_>
- 13 12 4 2 -1.
- <_>
- 13 13 4 1 2.
- <_>
-
- <_>
- 6 14 2 2 -1.
- <_>
- 6 14 1 1 2.
- <_>
- 7 15 1 1 2.
- <_>
-
- <_>
- 0 14 2 2 -1.
- <_>
- 0 15 2 1 2.
- <_>
-
- <_>
- 7 13 6 3 -1.
- <_>
- 7 14 6 1 3.
- <_>
-
- <_>
- 7 9 10 6 -1.
- <_>
- 7 11 10 2 3.
- <_>
-
- <_>
- 2 9 12 4 -1.
- <_>
- 6 9 4 4 3.
- <_>
-
- <_>
- 7 9 6 11 -1.
- <_>
- 10 9 3 11 2.
- <_>
-
- <_>
- 9 7 2 3 -1.
- <_>
- 9 8 2 1 3.
- <_>
-
- <_>
- 9 14 4 3 -1.
- <_>
- 9 15 4 1 3.
- <_>
-
- <_>
- 2 3 3 17 -1.
- <_>
- 3 3 1 17 3.
- <_>
-
- <_>
- 0 11 6 3 -1.
- <_>
- 0 12 6 1 3.
- <_>
-
- <_>
- 4 3 11 9 -1.
- <_>
- 4 6 11 3 3.
- <_>
-
- <_>
- 0 2 6 11 -1.
- <_>
- 3 2 3 11 2.
- <_>
-
- <_>
- 13 0 4 5 -1.
- <_>
- 13 0 2 5 2.
- <_>
-
- <_>
- 9 7 6 4 -1.
- <_>
- 12 7 3 2 2.
- <_>
- 9 9 3 2 2.
- <_>
-
- <_>
- 5 7 8 2 -1.
- <_>
- 9 7 4 2 2.
- <_>
-
- <_>
- 1 8 15 1 -1.
- <_>
- 6 8 5 1 3.
- <_>
-
- <_>
- 4 12 12 2 -1.
- <_>
- 8 12 4 2 3.
- <_>
-
- <_>
- 13 0 4 10 -1.
- <_>
- 15 0 2 5 2.
- <_>
- 13 5 2 5 2.
- <_>
-
- <_>
- 9 9 2 2 -1.
- <_>
- 9 10 2 1 2.
- <_>
-
- <_>
- 3 9 6 2 -1.
- <_>
- 6 9 3 2 2.
- <_>
-
- <_>
- 8 17 4 3 -1.
- <_>
- 8 18 4 1 3.
- <_>
-
- <_>
- 8 3 9 2 -1.
- <_>
- 11 3 3 2 3.
- <_>
-
- <_>
- 3 3 9 2 -1.
- <_>
- 6 3 3 2 3.
- <_>
-
- <_>
- 5 0 9 14 -1.
- <_>
- 8 0 3 14 3.
- <_>
-
- <_>
- 7 3 7 10 -1.
- <_>
- 7 8 7 5 2.
- <_>
-
- <_>
- 4 8 13 3 -1.
- <_>
- 4 9 13 1 3.
- <_>
-
- <_>
- 3 12 14 4 -1.
- <_>
- 3 12 7 2 2.
- <_>
- 10 14 7 2 2.
- <_>
-
- <_>
- 8 12 4 2 -1.
- <_>
- 8 13 4 1 2.
- <_>
-
- <_>
- 6 10 9 8 -1.
- <_>
- 6 14 9 4 2.
- <_>
-
- <_>
- 9 12 2 8 -1.
- <_>
- 9 16 2 4 2.
- <_>
-
- <_>
- 8 12 3 3 -1.
- <_>
- 8 13 3 1 3.
- <_>
-
- <_>
- 5 5 4 10 -1.
- <_>
- 7 5 2 10 2.
- <_>
-
- <_>
- 14 15 3 3 -1.
- <_>
- 14 16 3 1 3.
- <_>
-
- <_>
- 4 6 13 3 -1.
- <_>
- 4 7 13 1 3.
- <_>
-
- <_>
- 3 15 3 3 -1.
- <_>
- 3 16 3 1 3.
- <_>
-
- <_>
- 3 9 4 2 -1.
- <_>
- 3 9 2 1 2.
- <_>
- 5 10 2 1 2.
- <_>
-
- <_>
- 0 11 20 4 -1.
- <_>
- 10 11 10 2 2.
- <_>
- 0 13 10 2 2.
- <_>
-
- <_>
- 8 15 4 3 -1.
- <_>
- 8 16 4 1 3.
- <_>
-
- <_>
- 0 11 20 4 -1.
- <_>
- 0 11 10 2 2.
- <_>
- 10 13 10 2 2.
- <_>
-
- <_>
- 8 15 4 3 -1.
- <_>
- 8 16 4 1 3.
- <_>
-
- <_>
- 10 13 1 6 -1.
- <_>
- 10 16 1 3 2.
- <_>
-
- <_>
- 2 1 18 2 -1.
- <_>
- 11 1 9 1 2.
- <_>
- 2 2 9 1 2.
- <_>
-
- <_>
- 8 14 3 3 -1.
- <_>
- 8 15 3 1 3.
- <_>
-
- <_>
- 4 1 6 1 -1.
- <_>
- 6 1 2 1 3.
- <_>
-
- <_>
- 11 13 1 3 -1.
- <_>
- 11 14 1 1 3.
- <_>
-
- <_>
- 13 5 2 12 -1.
- <_>
- 13 11 2 6 2.
- <_>
-
- <_>
- 1 14 18 6 -1.
- <_>
- 1 16 18 2 3.
- <_>
-
- <_>
- 8 13 1 3 -1.
- <_>
- 8 14 1 1 3.
- <_>
-
- <_>
- 7 13 6 3 -1.
- <_>
- 7 14 6 1 3.
- <_>
-
- <_>
- 9 10 3 2 -1.
- <_>
- 9 11 3 1 2.
- <_>
-
- <_>
- 5 1 3 3 -1.
- <_>
- 6 1 1 3 3.
- <_>
-
- <_>
- 5 5 6 5 -1.
- <_>
- 8 5 3 5 2.
- <_>
-
- <_>
- 7 5 6 14 -1.
- <_>
- 7 12 6 7 2.
- <_>
-
- <_>
- 7 16 6 2 -1.
- <_>
- 9 16 2 2 3.
- <_>
-
- <_>
- 0 2 2 12 -1.
- <_>
- 1 2 1 12 2.
- <_>
-
- <_>
- 1 0 5 3 -1.
- <_>
- 1 1 5 1 3.
- <_>
-
- <_>
- 12 4 3 3 -1.
- <_>
- 12 5 3 1 3.
- <_>
-
- <_>
- 12 6 3 3 -1.
- <_>
- 12 7 3 1 3.
- <_>
-
- <_>
- 5 4 3 3 -1.
- <_>
- 5 5 3 1 3.
- <_>
-
- <_>
- 5 6 3 3 -1.
- <_>
- 5 7 3 1 3.
- <_>
-
- <_>
- 8 12 4 8 -1.
- <_>
- 10 12 2 4 2.
- <_>
- 8 16 2 4 2.
- <_>
-
- <_>
- 2 17 18 2 -1.
- <_>
- 11 17 9 1 2.
- <_>
- 2 18 9 1 2.
- <_>
-
- <_>
- 9 3 2 2 -1.
- <_>
- 9 4 2 1 2.
- <_>
-
- <_>
- 8 5 4 6 -1.
- <_>
- 8 7 4 2 3.
- <_>
-
- <_>
- 9 0 8 6 -1.
- <_>
- 9 2 8 2 3.
- <_>
-
- <_>
- 1 0 18 4 -1.
- <_>
- 7 0 6 4 3.
- <_>
-
- <_>
- 0 0 4 8 -1.
- <_>
- 2 0 2 8 2.
- <_>
-
- <_>
- 0 4 6 9 -1.
- <_>
- 2 4 2 9 3.
- <_>
-
- <_>
- 1 4 18 2 -1.
- <_>
- 7 4 6 2 3.
- <_>
-
- <_>
- 8 16 12 4 -1.
- <_>
- 14 16 6 2 2.
- <_>
- 8 18 6 2 2.
- <_>
-
- <_>
- 0 0 18 2 -1.
- <_>
- 0 0 9 1 2.
- <_>
- 9 1 9 1 2.
- <_>
-
- <_>
- 3 0 3 18 -1.
- <_>
- 4 0 1 18 3.
- <_>
-
- <_>
- 14 9 4 7 -1.
- <_>
- 14 9 2 7 2.
- <_>
-
- <_>
- 15 14 2 2 -1.
- <_>
- 15 15 2 1 2.
- <_>
-
- <_>
- 2 9 4 7 -1.
- <_>
- 4 9 2 7 2.
- <_>
-
- <_>
- 3 14 2 2 -1.
- <_>
- 3 15 2 1 2.
- <_>
-
- <_>
- 11 0 6 6 -1.
- <_>
- 11 2 6 2 3.
- <_>
-
- <_>
- 14 0 2 6 -1.
- <_>
- 15 0 1 3 2.
- <_>
- 14 3 1 3 2.
- <_>
-
- <_>
- 7 11 2 2 -1.
- <_>
- 7 11 1 1 2.
- <_>
- 8 12 1 1 2.
- <_>
-
- <_>
- 7 10 2 2 -1.
- <_>
- 8 10 1 2 2.
- <_>
-
- <_>
- 9 14 2 6 -1.
- <_>
- 9 17 2 3 2.
- <_>
-
- <_>
- 12 18 4 2 -1.
- <_>
- 12 19 4 1 2.
- <_>
-
- <_>
- 8 17 4 3 -1.
- <_>
- 8 18 4 1 3.
- <_>
-
- <_>
- 2 18 8 2 -1.
- <_>
- 2 19 8 1 2.
- <_>
-
- <_>
- 2 9 16 3 -1.
- <_>
- 2 10 16 1 3.
- <_>
-
- <_>
- 9 9 2 2 -1.
- <_>
- 9 10 2 1 2.
- <_>
-
- <_>
- 5 14 2 4 -1.
- <_>
- 5 14 1 2 2.
- <_>
- 6 16 1 2 2.
- <_>
-
- <_>
- 8 9 4 2 -1.
- <_>
- 8 9 2 1 2.
- <_>
- 10 10 2 1 2.
- <_>
-
- <_>
- 9 5 2 5 -1.
- <_>
- 9 5 1 5 2.
- <_>
-
- <_>
- 9 9 3 2 -1.
- <_>
- 10 9 1 2 3.
- <_>
-
- <_>
- 8 9 3 2 -1.
- <_>
- 9 9 1 2 3.
- <_>
-
- <_>
- 8 8 3 6 -1.
- <_>
- 9 8 1 6 3.
- <_>
-
- <_>
- 8 12 4 8 -1.
- <_>
- 10 12 2 4 2.
- <_>
- 8 16 2 4 2.
- <_>
-
- <_>
- 2 17 16 2 -1.
- <_>
- 10 17 8 1 2.
- <_>
- 2 18 8 1 2.
- <_>
-
- <_>
- 8 12 3 8 -1.
- <_>
- 9 12 1 8 3.
- <_>
-
- <_>
- 3 10 1 3 -1.
- <_>
- 3 11 1 1 3.
- <_>
-
- <_>
- 9 14 10 6 -1.
- <_>
- 14 14 5 3 2.
- <_>
- 9 17 5 3 2.
- <_>
-
- <_>
- 14 13 3 6 -1.
- <_>
- 14 15 3 2 3.
- <_>
-
- <_>
- 1 19 18 1 -1.
- <_>
- 7 19 6 1 3.
- <_>
-
- <_>
- 2 10 15 2 -1.
- <_>
- 7 10 5 2 3.
- <_>
-
- <_>
- 4 17 16 3 -1.
- <_>
- 4 18 16 1 3.
- <_>
-
- <_>
- 8 6 4 9 -1.
- <_>
- 8 9 4 3 3.
- <_>
-
- <_>
- 9 16 2 4 -1.
- <_>
- 9 16 1 2 2.
- <_>
- 10 18 1 2 2.
- <_>
-
- <_>
- 5 5 10 8 -1.
- <_>
- 5 9 10 4 2.
- <_>
-
- <_>
- 13 1 4 2 -1.
- <_>
- 13 1 2 2 2.
- <_>
-
- <_>
- 14 0 3 6 -1.
- <_>
- 14 2 3 2 3.
- <_>
-
- <_>
- 6 7 2 2 -1.
- <_>
- 6 7 1 1 2.
- <_>
- 7 8 1 1 2.
- <_>
-
- <_>
- 7 1 6 1 -1.
- <_>
- 9 1 2 1 3.
- <_>
-
- <_>
- 9 11 3 3 -1.
- <_>
- 9 12 3 1 3.
- <_>
-
- <_>
- 12 9 3 3 -1.
- <_>
- 13 9 1 3 3.
- <_>
-
- <_>
- 8 11 3 3 -1.
- <_>
- 8 12 3 1 3.
- <_>
-
- <_>
- 5 9 3 3 -1.
- <_>
- 6 9 1 3 3.
- <_>
-
- <_>
- 10 11 1 3 -1.
- <_>
- 10 12 1 1 3.
- <_>
-
- <_>
- 7 9 6 4 -1.
- <_>
- 10 9 3 2 2.
- <_>
- 7 11 3 2 2.
- <_>
-
- <_>
- 4 7 2 2 -1.
- <_>
- 4 7 1 1 2.
- <_>
- 5 8 1 1 2.
- <_>
-
- <_>
- 5 7 3 1 -1.
- <_>
- 6 7 1 1 3.
- <_>
-
- <_>
- 18 3 2 3 -1.
- <_>
- 18 4 2 1 3.
- <_>
-
- <_>
- 13 1 4 2 -1.
- <_>
- 13 1 2 2 2.
- <_>
-
- <_>
- 3 1 4 2 -1.
- <_>
- 5 1 2 2 2.
- <_>
-
- <_>
- 3 0 5 2 -1.
- <_>
- 3 1 5 1 2.
- <_>
-
- <_>
- 14 7 6 4 -1.
- <_>
- 17 7 3 2 2.
- <_>
- 14 9 3 2 2.
- <_>
-
- <_>
- 4 8 16 2 -1.
- <_>
- 4 9 16 1 2.
- <_>
-
- <_>
- 2 11 5 6 -1.
- <_>
- 2 13 5 2 3.
- <_>
-
- <_>
- 5 16 2 4 -1.
- <_>
- 5 16 1 2 2.
- <_>
- 6 18 1 2 2.
- <_>
-
- <_>
- 15 6 2 12 -1.
- <_>
- 16 6 1 6 2.
- <_>
- 15 12 1 6 2.
- <_>
-
- <_>
- 13 3 6 16 -1.
- <_>
- 15 3 2 16 3.
- <_>
-
- <_>
- 4 5 12 12 -1.
- <_>
- 4 5 6 6 2.
- <_>
- 10 11 6 6 2.
- <_>
-
- <_>
- 5 1 10 13 -1.
- <_>
- 10 1 5 13 2.
- <_>
-
- <_>
- 11 5 2 2 -1.
- <_>
- 12 5 1 1 2.
- <_>
- 11 6 1 1 2.
- <_>
-
- <_>
- 13 5 1 3 -1.
- <_>
- 13 6 1 1 3.
- <_>
-
- <_>
- 7 4 2 4 -1.
- <_>
- 7 4 1 2 2.
- <_>
- 8 6 1 2 2.
- <_>
-
- <_>
- 7 5 6 4 -1.
- <_>
- 10 5 3 4 2.
- <_>
-
- <_>
- 12 4 4 6 -1.
- <_>
- 14 4 2 3 2.
- <_>
- 12 7 2 3 2.
- <_>
-
- <_>
- 12 11 7 6 -1.
- <_>
- 12 13 7 2 3.
- <_>
-
- <_>
- 5 6 6 6 -1.
- <_>
- 7 6 2 6 3.
- <_>
-
- <_>
- 9 8 2 2 -1.
- <_>
- 9 9 2 1 2.
- <_>
-
- <_>
- 15 6 2 2 -1.
- <_>
- 16 6 1 1 2.
- <_>
- 15 7 1 1 2.
- <_>
-
- <_>
- 14 7 4 4 -1.
- <_>
- 16 7 2 2 2.
- <_>
- 14 9 2 2 2.
- <_>
-
- <_>
- 5 5 6 2 -1.
- <_>
- 7 5 2 2 3.
- <_>
-
- <_>
- 1 19 18 1 -1.
- <_>
- 7 19 6 1 3.
- <_>
-
- <_>
- 12 3 3 3 -1.
- <_>
- 12 4 3 1 3.
- <_>
-
- <_>
- 16 0 2 3 -1.
- <_>
- 16 1 2 1 3.
- <_>
-
- <_>
- 5 3 3 3 -1.
- <_>
- 5 4 3 1 3.
- <_>
-
- <_>
- 2 0 2 3 -1.
- <_>
- 2 1 2 1 3.
- <_>
-
- <_>
- 15 6 2 2 -1.
- <_>
- 16 6 1 1 2.
- <_>
- 15 7 1 1 2.
- <_>
-
- <_>
- 10 13 1 6 -1.
- <_>
- 10 16 1 3 2.
- <_>
-
- <_>
- 0 7 10 2 -1.
- <_>
- 0 7 5 1 2.
- <_>
- 5 8 5 1 2.
- <_>
-
- <_>
- 3 10 6 2 -1.
- <_>
- 3 11 6 1 2.
- <_>
-
- <_>
- 12 18 4 2 -1.
- <_>
- 12 19 4 1 2.
- <_>
-
- <_>
- 12 18 2 2 -1.
- <_>
- 13 18 1 1 2.
- <_>
- 12 19 1 1 2.
- <_>
-
- <_>
- 6 19 2 1 -1.
- <_>
- 7 19 1 1 2.
- <_>
-
- <_>
- 0 4 2 16 -1.
- <_>
- 0 4 1 8 2.
- <_>
- 1 12 1 8 2.
- <_>
-
- <_>
- 16 1 4 9 -1.
- <_>
- 16 4 4 3 3.
- <_>
-
- <_>
- 10 2 1 2 -1.
- <_>
- 10 3 1 1 2.
- <_>
-
- <_>
- 4 14 4 6 -1.
- <_>
- 4 14 2 3 2.
- <_>
- 6 17 2 3 2.
- <_>
-
- <_>
- 4 15 1 4 -1.
- <_>
- 4 17 1 2 2.
- <_>
-
- <_>
- 0 2 20 4 -1.
- <_>
- 10 2 10 2 2.
- <_>
- 0 4 10 2 2.
- <_>
-
- <_>
- 14 5 2 8 -1.
- <_>
- 14 9 2 4 2.
- <_>
-
- <_>
- 5 12 4 5 -1.
- <_>
- 7 12 2 5 2.
- <_>
-
- <_>
- 0 13 9 6 -1.
- <_>
- 0 15 9 2 3.
- <_>
-
- <_>
- 9 14 11 3 -1.
- <_>
- 9 15 11 1 3.
- <_>
-
- <_>
- 7 14 7 3 -1.
- <_>
- 7 15 7 1 3.
- <_>
-
- <_>
- 3 6 2 2 -1.
- <_>
- 3 6 1 1 2.
- <_>
- 4 7 1 1 2.
- <_>
-
- <_>
- 6 7 2 7 -1.
- <_>
- 7 7 1 7 2.
- <_>
-
- <_>
- 14 5 1 3 -1.
- <_>
- 14 6 1 1 3.
- <_>
-
- <_>
- 13 4 4 3 -1.
- <_>
- 13 5 4 1 3.
- <_>
-
- <_>
- 2 7 4 4 -1.
- <_>
- 2 7 2 2 2.
- <_>
- 4 9 2 2 2.
- <_>
-
- <_>
- 2 9 13 6 -1.
- <_>
- 2 12 13 3 2.
- <_>
-
- <_>
- 10 1 3 4 -1.
- <_>
- 11 1 1 4 3.
- <_>
-
- <_>
- 9 8 5 2 -1.
- <_>
- 9 9 5 1 2.
- <_>
-
- <_>
- 0 14 11 3 -1.
- <_>
- 0 15 11 1 3.
- <_>
-
- <_>
- 8 11 2 8 -1.
- <_>
- 8 15 2 4 2.
- <_>
-
- <_>
- 5 11 10 6 -1.
- <_>
- 5 14 10 3 2.
- <_>
-
- <_>
- 5 13 15 5 -1.
- <_>
- 10 13 5 5 3.
- <_>
-
- <_>
- 8 10 1 10 -1.
- <_>
- 8 15 1 5 2.
- <_>
-
- <_>
- 4 14 6 2 -1.
- <_>
- 6 14 2 2 3.
- <_>
-
- <_>
- 7 14 7 3 -1.
- <_>
- 7 15 7 1 3.
- <_>
-
- <_>
- 7 16 9 3 -1.
- <_>
- 7 17 9 1 3.
- <_>
-
- <_>
- 8 7 3 3 -1.
- <_>
- 8 8 3 1 3.
- <_>
-
- <_>
- 3 5 1 6 -1.
- <_>
- 3 8 1 3 2.
- <_>
-
- <_>
- 6 5 11 2 -1.
- <_>
- 6 6 11 1 2.
- <_>
-
- <_>
- 9 0 3 2 -1.
- <_>
- 10 0 1 2 3.
- <_>
-
- <_>
- 5 5 1 3 -1.
- <_>
- 5 6 1 1 3.
- <_>
-
- <_>
- 8 7 3 2 -1.
- <_>
- 9 7 1 2 3.
- <_>
-
- <_>
- 5 2 10 6 -1.
- <_>
- 10 2 5 3 2.
- <_>
- 5 5 5 3 2.
- <_>
-
- <_>
- 8 4 6 4 -1.
- <_>
- 8 4 3 4 2.
- <_>
-
- <_>
- 8 16 3 4 -1.
- <_>
- 9 16 1 4 3.
- <_>
-
- <_>
- 9 13 2 6 -1.
- <_>
- 9 13 1 3 2.
- <_>
- 10 16 1 3 2.
- <_>
-
- <_>
- 9 8 3 1 -1.
- <_>
- 10 8 1 1 3.
- <_>
-
- <_>
- 2 5 18 15 -1.
- <_>
- 2 10 18 5 3.
- <_>
-
- <_>
- 1 3 6 2 -1.
- <_>
- 4 3 3 2 2.
- <_>
-
- <_>
- 7 6 6 2 -1.
- <_>
- 9 6 2 2 3.
- <_>
-
- <_>
- 8 17 4 3 -1.
- <_>
- 8 18 4 1 3.
- <_>
-
- <_>
- 10 13 2 3 -1.
- <_>
- 10 14 2 1 3.
- <_>
-
- <_>
- 0 10 20 4 -1.
- <_>
- 0 12 20 2 2.
- <_>
-
- <_>
- 5 7 6 4 -1.
- <_>
- 5 7 3 2 2.
- <_>
- 8 9 3 2 2.
- <_>
-
- <_>
- 11 12 1 2 -1.
- <_>
- 11 13 1 1 2.
- <_>
-
- <_>
- 10 10 2 3 -1.
- <_>
- 10 11 2 1 3.
- <_>
-
- <_>
- 9 5 2 2 -1.
- <_>
- 9 6 2 1 2.
- <_>
-
- <_>
- 4 4 1 10 -1.
- <_>
- 4 9 1 5 2.
- <_>
-
- <_>
- 11 18 4 2 -1.
- <_>
- 11 18 2 2 2.
- <_>
-
- <_>
- 12 18 3 2 -1.
- <_>
- 12 19 3 1 2.
- <_>
-
- <_>
- 0 6 16 6 -1.
- <_>
- 0 6 8 3 2.
- <_>
- 8 9 8 3 2.
- <_>
-
- <_>
- 7 6 4 12 -1.
- <_>
- 7 12 4 6 2.
- <_>
-
- <_>
- 11 18 4 2 -1.
- <_>
- 11 18 2 2 2.
- <_>
-
- <_>
- 12 18 3 2 -1.
- <_>
- 12 19 3 1 2.
- <_>
-
- <_>
- 8 12 1 2 -1.
- <_>
- 8 13 1 1 2.
- <_>
-
- <_>
- 8 13 1 3 -1.
- <_>
- 8 14 1 1 3.
- <_>
-
- <_>
- 11 18 4 2 -1.
- <_>
- 11 18 2 2 2.
- <_>
-
- <_>
- 14 12 4 6 -1.
- <_>
- 14 12 2 6 2.
- <_>
-
- <_>
- 6 0 3 4 -1.
- <_>
- 7 0 1 4 3.
- <_>
-
- <_>
- 4 0 2 8 -1.
- <_>
- 4 0 1 4 2.
- <_>
- 5 4 1 4 2.
- <_>
-
- <_>
- 11 17 9 3 -1.
- <_>
- 14 17 3 3 3.
- <_>
-
- <_>
- 16 2 4 5 -1.
- <_>
- 16 2 2 5 2.
- <_>
-
- <_>
- 0 2 5 9 -1.
- <_>
- 0 5 5 3 3.
- <_>
-
- <_>
- 7 2 3 2 -1.
- <_>
- 8 2 1 2 3.
- <_>
-
- <_>
- 11 17 9 3 -1.
- <_>
- 14 17 3 3 3.
- <_>
-
- <_>
- 16 2 4 5 -1.
- <_>
- 16 2 2 5 2.
- <_>
-
- <_>
- 0 17 9 3 -1.
- <_>
- 3 17 3 3 3.
- <_>
-
- <_>
- 0 2 4 5 -1.
- <_>
- 2 2 2 5 2.
- <_>
-
- <_>
- 5 11 10 9 -1.
- <_>
- 5 14 10 3 3.
- <_>
-
- <_>
- 9 6 3 3 -1.
- <_>
- 9 7 3 1 3.
- <_>
-
- <_>
- 3 17 5 3 -1.
- <_>
- 3 18 5 1 3.
- <_>
-
- <_>
- 7 5 4 7 -1.
- <_>
- 9 5 2 7 2.
- <_>
-
- <_>
- 9 8 2 5 -1.
- <_>
- 9 8 1 5 2.
- <_>
-
- <_>
- 2 2 18 2 -1.
- <_>
- 2 3 18 1 2.
- <_>
-
- <_>
- 2 8 15 6 -1.
- <_>
- 7 8 5 6 3.
- <_>
-
- <_>
- 9 8 2 5 -1.
- <_>
- 10 8 1 5 2.
- <_>
-
- <_>
- 12 10 4 6 -1.
- <_>
- 12 12 4 2 3.
- <_>
-
- <_>
- 14 3 6 2 -1.
- <_>
- 14 4 6 1 2.
- <_>
-
- <_>
- 5 5 2 3 -1.
- <_>
- 5 6 2 1 3.
- <_>
-
- <_>
- 4 6 3 3 -1.
- <_>
- 4 7 3 1 3.
- <_>
-
- <_>
- 14 12 3 3 -1.
- <_>
- 14 13 3 1 3.
- <_>
-
- <_>
- 6 12 11 3 -1.
- <_>
- 6 13 11 1 3.
- <_>
-
- <_>
- 1 2 3 6 -1.
- <_>
- 1 4 3 2 3.
- <_>
-
- <_>
- 1 0 4 7 -1.
- <_>
- 3 0 2 7 2.
- <_>
-
- <_>
- 9 8 3 4 -1.
- <_>
- 10 8 1 4 3.
- <_>
-
- <_>
- 10 9 2 2 -1.
- <_>
- 10 10 2 1 2.
- <_>
-
- <_>
- 8 8 3 4 -1.
- <_>
- 9 8 1 4 3.
- <_>
-
- <_>
- 4 4 10 10 -1.
- <_>
- 4 9 10 5 2.
- <_>
-
- <_>
- 9 10 3 2 -1.
- <_>
- 10 10 1 2 3.
- <_>
-
- <_>
- 9 10 3 2 -1.
- <_>
- 9 11 3 1 2.
- <_>
-
- <_>
- 8 10 3 2 -1.
- <_>
- 9 10 1 2 3.
- <_>
-
- <_>
- 2 4 14 12 -1.
- <_>
- 2 4 7 6 2.
- <_>
- 9 10 7 6 2.
- <_>
-
- <_>
- 10 12 1 6 -1.
- <_>
- 10 15 1 3 2.
- <_>
-
- <_>
- 7 3 8 16 -1.
- <_>
- 11 3 4 8 2.
- <_>
- 7 11 4 8 2.
- <_>
-
- <_>
- 5 6 8 10 -1.
- <_>
- 5 6 4 5 2.
- <_>
- 9 11 4 5 2.
- <_>
-
- <_>
- 6 2 8 8 -1.
- <_>
- 6 2 4 4 2.
- <_>
- 10 6 4 4 2.
- <_>
-
- <_>
- 10 5 4 2 -1.
- <_>
- 12 5 2 1 2.
- <_>
- 10 6 2 1 2.
- <_>
-
- <_>
- 12 4 3 3 -1.
- <_>
- 12 5 3 1 3.
- <_>
-
- <_>
- 4 19 12 1 -1.
- <_>
- 8 19 4 1 3.
- <_>
-
- <_>
- 8 2 3 1 -1.
- <_>
- 9 2 1 1 3.
- <_>
-
- <_>
- 13 17 4 3 -1.
- <_>
- 13 18 4 1 3.
- <_>
-
- <_>
- 7 14 6 3 -1.
- <_>
- 7 15 6 1 3.
- <_>
-
- <_>
- 9 14 2 3 -1.
- <_>
- 9 15 2 1 3.
- <_>
-
- <_>
- 7 15 6 3 -1.
- <_>
- 7 16 6 1 3.
- <_>
-
- <_>
- 10 18 3 2 -1.
- <_>
- 11 18 1 2 3.
- <_>
-
- <_>
- 14 12 2 3 -1.
- <_>
- 14 13 2 1 3.
- <_>
-
- <_>
- 4 10 4 6 -1.
- <_>
- 4 12 4 2 3.
- <_>
-
- <_>
- 4 13 3 2 -1.
- <_>
- 4 14 3 1 2.
- <_>
-
- <_>
- 9 16 2 3 -1.
- <_>
- 9 17 2 1 3.
- <_>
-
- <_>
- 10 18 3 2 -1.
- <_>
- 11 18 1 2 3.
- <_>
-
- <_>
- 7 18 3 2 -1.
- <_>
- 8 18 1 2 3.
- <_>
-
- <_>
- 1 10 4 2 -1.
- <_>
- 1 11 4 1 2.
- <_>
-
- <_>
- 12 4 6 3 -1.
- <_>
- 12 5 6 1 3.
- <_>
-
- <_>
- 14 4 1 3 -1.
- <_>
- 14 5 1 1 3.
- <_>
-
- <_>
- 2 4 6 3 -1.
- <_>
- 2 5 6 1 3.
- <_>
-
- <_>
- 5 4 1 3 -1.
- <_>
- 5 5 1 1 3.
- <_>
-
- <_>
- 14 12 3 3 -1.
- <_>
- 14 13 3 1 3.
- <_>
-
- <_>
- 15 12 2 3 -1.
- <_>
- 15 13 2 1 3.
- <_>
-
- <_>
- 3 16 4 3 -1.
- <_>
- 3 17 4 1 3.
- <_>
-
- <_>
- 8 0 4 2 -1.
- <_>
- 8 1 4 1 2.
- <_>
-
- <_>
- 0 0 20 1 -1.
- <_>
- 0 0 10 1 2.
- <_>
-
- <_>
- 9 7 3 4 -1.
- <_>
- 10 7 1 4 3.
- <_>
-
- <_>
- 0 0 20 1 -1.
- <_>
- 10 0 10 1 2.
- <_>
-
- <_>
- 8 7 3 4 -1.
- <_>
- 9 7 1 4 3.
- <_>
-
- <_>
- 1 6 19 3 -1.
- <_>
- 1 7 19 1 3.
- <_>
-
- <_>
- 12 7 4 2 -1.
- <_>
- 12 8 4 1 2.
- <_>
-
- <_>
- 7 8 3 3 -1.
- <_>
- 7 9 3 1 3.
- <_>
-
- <_>
- 7 7 3 3 -1.
- <_>
- 8 7 1 3 3.
- <_>
-
- <_>
- 2 9 16 3 -1.
- <_>
- 2 10 16 1 3.
- <_>
-
- <_>
- 9 4 2 12 -1.
- <_>
- 9 8 2 4 3.
- <_>
-
- <_>
- 7 3 2 5 -1.
- <_>
- 8 3 1 5 2.
- <_>
-
- <_>
- 9 7 2 3 -1.
- <_>
- 9 8 2 1 3.
- <_>
-
- <_>
- 9 14 4 3 -1.
- <_>
- 9 15 4 1 3.
- <_>
-
- <_>
- 7 8 6 4 -1.
- <_>
- 10 8 3 2 2.
- <_>
- 7 10 3 2 2.
- <_>
-
- <_>
- 9 7 2 2 -1.
- <_>
- 10 7 1 2 2.
- <_>
-
- <_>
- 5 5 6 6 -1.
- <_>
- 7 5 2 6 3.
- <_>
-
- <_>
- 9 1 3 6 -1.
- <_>
- 10 1 1 6 3.
- <_>
-
- <_>
- 4 5 12 2 -1.
- <_>
- 8 5 4 2 3.
- <_>
-
- <_>
- 4 2 6 4 -1.
- <_>
- 6 2 2 4 3.
- <_>
-
- <_>
- 4 7 8 2 -1.
- <_>
- 4 8 8 1 2.
- <_>
-
- <_>
- 3 6 14 6 -1.
- <_>
- 10 6 7 3 2.
- <_>
- 3 9 7 3 2.
- <_>
-
- <_>
- 3 6 14 3 -1.
- <_>
- 3 6 7 3 2.
- <_>
-
- <_>
- 0 5 2 2 -1.
- <_>
- 0 6 2 1 2.
- <_>
-
- <_>
- 8 13 4 3 -1.
- <_>
- 8 14 4 1 3.
- <_>
-
- <_>
- 13 0 3 20 -1.
- <_>
- 14 0 1 20 3.
- <_>
-
- <_>
- 10 8 10 3 -1.
- <_>
- 10 9 10 1 3.
- <_>
-
- <_>
- 4 0 3 20 -1.
- <_>
- 5 0 1 20 3.
- <_>
-
- <_>
- 0 8 10 3 -1.
- <_>
- 0 9 10 1 3.
- <_>
-
- <_>
- 12 5 3 4 -1.
- <_>
- 13 5 1 4 3.
- <_>
-
- <_>
- 6 7 12 4 -1.
- <_>
- 10 7 4 4 3.
- <_>
-
- <_>
- 1 14 6 6 -1.
- <_>
- 1 14 3 3 2.
- <_>
- 4 17 3 3 2.
- <_>
-
- <_>
- 1 17 6 2 -1.
- <_>
- 1 18 6 1 2.
- <_>
-
- <_>
- 14 8 6 12 -1.
- <_>
- 17 8 3 6 2.
- <_>
- 14 14 3 6 2.
- <_>
-
- <_>
- 18 5 2 2 -1.
- <_>
- 18 6 2 1 2.
- <_>
-
- <_>
- 3 16 4 2 -1.
- <_>
- 3 16 2 1 2.
- <_>
- 5 17 2 1 2.
- <_>
-
- <_>
- 2 16 6 2 -1.
- <_>
- 4 16 2 2 3.
- <_>
-
- <_>
- 14 8 6 12 -1.
- <_>
- 17 8 3 6 2.
- <_>
- 14 14 3 6 2.
- <_>
-
- <_>
- 18 5 2 2 -1.
- <_>
- 18 6 2 1 2.
- <_>
-
- <_>
- 5 16 9 2 -1.
- <_>
- 8 16 3 2 3.
- <_>
-
- <_>
- 3 14 6 6 -1.
- <_>
- 3 14 3 3 2.
- <_>
- 6 17 3 3 2.
- <_>
-
- <_>
- 14 8 6 12 -1.
- <_>
- 17 8 3 6 2.
- <_>
- 14 14 3 6 2.
- <_>
-
- <_>
- 11 7 2 12 -1.
- <_>
- 11 11 2 4 3.
- <_>
-
- <_>
- 0 8 6 12 -1.
- <_>
- 0 8 3 6 2.
- <_>
- 3 14 3 6 2.
- <_>
-
- <_>
- 7 7 2 12 -1.
- <_>
- 7 11 2 4 3.
- <_>
-
- <_>
- 14 12 1 2 -1.
- <_>
- 14 13 1 1 2.
- <_>
-
- <_>
- 12 13 8 1 -1.
- <_>
- 12 13 4 1 2.
- <_>
-
- <_>
- 0 3 16 6 -1.
- <_>
- 0 6 16 3 2.
- <_>
-
- <_>
- 1 4 8 2 -1.
- <_>
- 1 4 4 1 2.
- <_>
- 5 5 4 1 2.
- <_>
-
- <_>
- 14 12 1 2 -1.
- <_>
- 14 13 1 1 2.
- <_>
-
- <_>
- 15 12 2 3 -1.
- <_>
- 15 13 2 1 3.
- <_>
-
- <_>
- 8 16 3 3 -1.
- <_>
- 8 17 3 1 3.
- <_>
-
- <_>
- 5 12 1 2 -1.
- <_>
- 5 13 1 1 2.
- <_>
-
- <_>
- 13 4 3 15 -1.
- <_>
- 14 4 1 15 3.
- <_>
-
- <_>
- 17 3 2 6 -1.
- <_>
- 18 3 1 3 2.
- <_>
- 17 6 1 3 2.
- <_>
-
- <_>
- 4 4 3 15 -1.
- <_>
- 5 4 1 15 3.
- <_>
-
- <_>
- 1 3 2 6 -1.
- <_>
- 1 3 1 3 2.
- <_>
- 2 6 1 3 2.
- <_>
-
- <_>
- 7 15 12 4 -1.
- <_>
- 7 17 12 2 2.
- <_>
-
- <_>
- 1 0 19 3 -1.
- <_>
- 1 1 19 1 3.
- <_>
-
- <_>
- 3 17 10 2 -1.
- <_>
- 3 17 5 1 2.
- <_>
- 8 18 5 1 2.
- <_>
-
- <_>
- 2 5 10 15 -1.
- <_>
- 2 10 10 5 3.
- <_>
-
- <_>
- 13 8 3 4 -1.
- <_>
- 13 10 3 2 2.
- <_>
-
- <_>
- 19 13 1 2 -1.
- <_>
- 19 14 1 1 2.
- <_>
-
- <_>
- 4 8 3 4 -1.
- <_>
- 4 10 3 2 2.
- <_>
-
- <_>
- 0 13 1 2 -1.
- <_>
- 0 14 1 1 2.
- <_>
-
- <_>
- 12 7 2 12 -1.
- <_>
- 12 13 2 6 2.
- <_>
-
- <_>
- 14 7 2 2 -1.
- <_>
- 15 7 1 1 2.
- <_>
- 14 8 1 1 2.
- <_>
-
- <_>
- 5 3 8 2 -1.
- <_>
- 5 4 8 1 2.
- <_>
-
- <_>
- 0 2 2 6 -1.
- <_>
- 0 4 2 2 3.
- <_>
-
- <_>
- 18 2 2 12 -1.
- <_>
- 19 2 1 6 2.
- <_>
- 18 8 1 6 2.
- <_>
-
- <_>
- 18 1 1 2 -1.
- <_>
- 18 2 1 1 2.
- <_>
-
- <_>
- 0 2 2 12 -1.
- <_>
- 0 2 1 6 2.
- <_>
- 1 8 1 6 2.
- <_>
-
- <_>
- 1 1 1 2 -1.
- <_>
- 1 2 1 1 2.
- <_>
-
- <_>
- 16 4 4 14 -1.
- <_>
- 18 4 2 7 2.
- <_>
- 16 11 2 7 2.
- <_>
-
- <_>
- 10 14 1 6 -1.
- <_>
- 10 17 1 3 2.
- <_>
-
- <_>
- 0 4 4 14 -1.
- <_>
- 0 4 2 7 2.
- <_>
- 2 11 2 7 2.
- <_>
-
- <_>
- 9 14 1 6 -1.
- <_>
- 9 17 1 3 2.
- <_>
-
- <_>
- 9 14 4 3 -1.
- <_>
- 9 15 4 1 3.
- <_>
-
- <_>
- 4 7 12 2 -1.
- <_>
- 8 7 4 2 3.
- <_>
-
- <_>
- 0 8 4 3 -1.
- <_>
- 0 9 4 1 3.
- <_>
-
- <_>
- 4 7 2 2 -1.
- <_>
- 4 7 1 1 2.
- <_>
- 5 8 1 1 2.
- <_>
-
- <_>
- 13 7 2 1 -1.
- <_>
- 13 7 1 1 2.
- <_>
-
- <_>
- 11 4 4 5 -1.
- <_>
- 11 4 2 5 2.
- <_>
-
- <_>
- 4 8 3 3 -1.
- <_>
- 5 8 1 3 3.
- <_>
-
- <_>
- 0 3 8 1 -1.
- <_>
- 4 3 4 1 2.
- <_>
-
- <_>
- 13 7 2 1 -1.
- <_>
- 13 7 1 1 2.
- <_>
-
- <_>
- 14 7 3 2 -1.
- <_>
- 15 7 1 2 3.
- <_>
-
- <_>
- 5 7 2 1 -1.
- <_>
- 6 7 1 1 2.
- <_>
-
- <_>
- 3 7 3 2 -1.
- <_>
- 4 7 1 2 3.
- <_>
-
- <_>
- 18 5 2 2 -1.
- <_>
- 18 6 2 1 2.
- <_>
-
- <_>
- 12 14 2 2 -1.
- <_>
- 13 14 1 1 2.
- <_>
- 12 15 1 1 2.
- <_>
-
- <_>
- 0 5 2 2 -1.
- <_>
- 0 6 2 1 2.
- <_>
-
- <_>
- 6 14 2 2 -1.
- <_>
- 6 14 1 1 2.
- <_>
- 7 15 1 1 2.
- <_>
-
- <_>
- 7 12 6 5 -1.
- <_>
- 9 12 2 5 3.
- <_>
-
- <_>
- 12 17 5 2 -1.
- <_>
- 12 18 5 1 2.
- <_>
-
- <_>
- 1 11 6 3 -1.
- <_>
- 4 11 3 3 2.
- <_>
-
- <_>
- 1 9 6 3 -1.
- <_>
- 4 9 3 3 2.
- <_>
-
- <_>
- 12 7 2 12 -1.
- <_>
- 12 13 2 6 2.
- <_>
-
- <_>
- 8 7 5 3 -1.
- <_>
- 8 8 5 1 3.
- <_>
-
- <_>
- 6 7 2 12 -1.
- <_>
- 6 13 2 6 2.
- <_>
-
- <_>
- 1 2 9 18 -1.
- <_>
- 4 2 3 18 3.
- <_>
-
- <_>
- 12 17 5 2 -1.
- <_>
- 12 18 5 1 2.
- <_>
-
- <_>
- 4 7 12 2 -1.
- <_>
- 4 7 6 2 2.
- <_>
-
- <_>
- 6 7 6 1 -1.
- <_>
- 8 7 2 1 3.
- <_>
-
- <_>
- 7 3 3 2 -1.
- <_>
- 8 3 1 2 3.
- <_>
-
- <_>
- 9 4 3 1 -1.
- <_>
- 10 4 1 1 3.
- <_>
-
- <_>
- 11 11 3 1 -1.
- <_>
- 12 11 1 1 3.
- <_>
-
- <_>
- 8 4 3 1 -1.
- <_>
- 9 4 1 1 3.
- <_>
-
- <_>
- 6 11 3 1 -1.
- <_>
- 7 11 1 1 3.
- <_>
-
- <_>
- 12 13 6 6 -1.
- <_>
- 12 15 6 2 3.
- <_>
-
- <_>
- 14 13 1 6 -1.
- <_>
- 14 15 1 2 3.
- <_>
-
- <_>
- 2 13 6 6 -1.
- <_>
- 2 15 6 2 3.
- <_>
-
- <_>
- 1 5 18 1 -1.
- <_>
- 7 5 6 1 3.
- <_>
-
- <_>
- 4 7 12 2 -1.
- <_>
- 10 7 6 1 2.
- <_>
- 4 8 6 1 2.
- <_>
-
- <_>
- 6 1 8 10 -1.
- <_>
- 10 1 4 5 2.
- <_>
- 6 6 4 5 2.
- <_>
-
- <_>
- 3 13 4 3 -1.
- <_>
- 3 14 4 1 3.
- <_>
-
- <_>
- 6 13 4 3 -1.
- <_>
- 6 14 4 1 3.
- <_>
-
- <_>
- 9 14 4 3 -1.
- <_>
- 9 15 4 1 3.
- <_>
-
- <_>
- 12 9 2 3 -1.
- <_>
- 12 10 2 1 3.
- <_>
-
- <_>
- 7 14 4 3 -1.
- <_>
- 7 15 4 1 3.
- <_>
-
- <_>
- 9 0 2 1 -1.
- <_>
- 10 0 1 1 2.
- <_>
-
- <_>
- 5 0 10 5 -1.
- <_>
- 5 0 5 5 2.
- <_>
-
- <_>
- 6 6 8 7 -1.
- <_>
- 6 6 4 7 2.
- <_>
-
- <_>
- 5 0 10 5 -1.
- <_>
- 10 0 5 5 2.
- <_>
-
- <_>
- 6 6 8 7 -1.
- <_>
- 10 6 4 7 2.
- <_>
-
- <_>
- 5 9 10 8 -1.
- <_>
- 10 9 5 4 2.
- <_>
- 5 13 5 4 2.
- <_>
-
- <_>
- 10 0 4 10 -1.
- <_>
- 12 0 2 5 2.
- <_>
- 10 5 2 5 2.
- <_>
-
- <_>
- 1 4 8 3 -1.
- <_>
- 1 5 8 1 3.
- <_>
-
- <_>
- 4 4 8 3 -1.
- <_>
- 4 5 8 1 3.
- <_>
-
- <_>
- 9 7 4 3 -1.
- <_>
- 9 8 4 1 3.
- <_>
-
- <_>
- 12 8 3 12 -1.
- <_>
- 12 14 3 6 2.
- <_>
-
- <_>
- 7 7 4 3 -1.
- <_>
- 7 8 4 1 3.
- <_>
-
- <_>
- 5 8 3 12 -1.
- <_>
- 5 14 3 6 2.
- <_>
-
- <_>
- 10 0 7 6 -1.
- <_>
- 10 2 7 2 3.
- <_>
-
- <_>
- 2 1 18 1 -1.
- <_>
- 8 1 6 1 3.
- <_>
-
- <_>
- 5 0 3 8 -1.
- <_>
- 6 0 1 8 3.
- <_>
-
- <_>
- 4 7 4 2 -1.
- <_>
- 4 8 4 1 2.
diff --git a/data/map/aire_0.dat b/data/map/aire_0.dat
new file mode 100644
index 000000000..08d5457c3
--- /dev/null
+++ b/data/map/aire_0.dat
@@ -0,0 +1,6 @@
+2
+5.97270011901855,45.5285783467813
+5.976382791996,45.5285783467813
+5.976382791996,45.5258330159471
+5.97270011901855,45.5258330159471
+(SASPlanet)
diff --git a/data/map/aire_0.jpg b/data/map/aire_0.jpg
new file mode 100644
index 000000000..6224da23a
Binary files /dev/null and b/data/map/aire_0.jpg differ
diff --git a/data/map/geo_bind.ini b/data/map/geo_bind.ini
new file mode 100644
index 000000000..d3b448e1b
--- /dev/null
+++ b/data/map/geo_bind.ini
@@ -0,0 +1,57 @@
+[points]
+
+# Points pair select manual: from maps.google [lat, lon] <-> [x, y] in pixels from imge editor (gimp, paint etc)
+# Pixel coordinates are in [0, 1] - independed from frame resolution
+lat0 = 45.526646
+lon0 = 5.974535
+px_x0 = 0.328125
+px_y0 = 0.483333333333333
+
+lat1 = 45.527566
+lon1 = 5.973849
+px_x1 = 0.39765625
+px_y1 = 0.393055555555556
+
+lat2 = 45.527904
+lon2 = 5.974135
+px_x2 = 0.57109375
+px_y2 = 0.390277777777778
+
+lat3 = 45.526867
+lon3 = 5.974826
+px_x3 = 0.65625
+px_y3 = 0.476388888888889
+
+
+[lines]
+
+# Line coordinates are in [0, 1] - independed from frame resolution
+line0_x0 = 0.1
+line0_y0 = 0.7
+line0_x1 = 0.47
+line0_y1 = 0.7
+
+line1_x0 = 0.52
+line1_y0 = 0.6
+line1_x1 = 0.8
+line1_y1 = 0.6
+
+
+[map]
+
+# optional: map exported from SASPlanet with *.dat file in Merkator projection
+
+file = map\aire_0.jpg
+
+left_top_lat = 45.5285783467813
+left_top_lon = 5.97270011901855
+
+right_top_lat = 45.5285783467813
+right_top_lon = 5.976382791996
+
+right_bottom_lat = 45.5258330159471
+right_bottom_lon = 5.976382791996
+
+left_bottom_lat = 45.5258330159471
+left_bottom_lon = 5.97270011901855
+
diff --git a/data/map/manual/france_bind.jpg b/data/map/manual/france_bind.jpg
new file mode 100644
index 000000000..bbeca503d
Binary files /dev/null and b/data/map/manual/france_bind.jpg differ
diff --git a/data/map/manual/paint1.png b/data/map/manual/paint1.png
new file mode 100644
index 000000000..d7d95172c
Binary files /dev/null and b/data/map/manual/paint1.png differ
diff --git a/data/map/manual/photo_2023-08-10_09-44-05.jpg b/data/map/manual/photo_2023-08-10_09-44-05.jpg
new file mode 100644
index 000000000..1801574bb
Binary files /dev/null and b/data/map/manual/photo_2023-08-10_09-44-05.jpg differ
diff --git a/data/map/manual/text_editor1.png b/data/map/manual/text_editor1.png
new file mode 100644
index 000000000..86186616e
Binary files /dev/null and b/data/map/manual/text_editor1.png differ
diff --git a/data/map/run_cars.bat b/data/map/run_cars.bat
new file mode 100644
index 000000000..cb696bd85
--- /dev/null
+++ b/data/map/run_cars.bat
@@ -0,0 +1,3 @@
+
+MultitargetTracker.exe map/Relaxing_highway_traffic.mp4 -e=7 --geo_bind=map/geo_bind.ini --settings=settings.ini
+
diff --git a/data/map/run_cars.sh b/data/map/run_cars.sh
new file mode 100644
index 000000000..f48fbe52a
--- /dev/null
+++ b/data/map/run_cars.sh
@@ -0,0 +1,2 @@
+./MultitargetTracker map/Relaxing_highway_traffic.mp4 -e=7 --geo_bind=map/geo_bind.ini --settings=settings.ini
+
diff --git a/data/reid/osnet_x0_25_msmt17.onnx b/data/reid/osnet_x0_25_msmt17.onnx
new file mode 100644
index 000000000..43a689e71
Binary files /dev/null and b/data/reid/osnet_x0_25_msmt17.onnx differ
diff --git a/data/settings.ini b/data/settings.ini
index 3deeff20c..d91395d5d 100644
--- a/data/settings.ini
+++ b/data/settings.ini
@@ -1,3 +1,55 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 6
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = ../../data/yolov4.weights
+nn_config = ../../data/yolov4.cfg
+class_names = ../../data/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = -1
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV4
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precison = FP32
+
+
[tracking]
#-----------------------------
@@ -69,12 +121,12 @@ min_area_radius_pix = -1
min_area_radius_k = 0.8
#-----------------------------
-# If the object do not assignment more than this frames then it will be removed
-max_skip_frames = 50
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
#-----------------------------
# The maximum trajectory length
-max_trace_len = 50
+max_trace_len = 2
#-----------------------------
# Detection abandoned objects
@@ -84,4 +136,4 @@ min_static_time = 5
# After this time (in seconds) the abandoned object will be removed
max_static_time = 25
# Speed in pixels. If speed of object is more that this value than object is non static
-max_speed_for_static = 10
\ No newline at end of file
+max_speed_for_static = 10
diff --git a/data/settings_coco.ini b/data/settings_coco.ini
new file mode 100644
index 000000000..b016a5554
--- /dev/null
+++ b/data/settings_coco.ini
@@ -0,0 +1,154 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+# nn_weights = data/coco/yolov7.onnx
+# nn_config = data/coco/yolov7.onnx
+
+# nn_weights = data/coco/yolov6s.onnx
+# nn_config = data/coco/yolov6s.onnx
+
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/yolov4.weights
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/yolov4.cfg
+
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco.names
+
+#-----------------------------
+confidence_threshold = 0.2
+
+max_crop_ratio = -1
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV4
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP32
+
+#-----------------------------
+# Detect only set of types, ";"
+white_list =
+
+#-----------------------------
+# For TensorRT optimization, bytes
+video_memory = 0;
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_deimv2.ini b/data/settings_deimv2.ini
new file mode 100644
index 000000000..e691d9ac1
--- /dev/null
+++ b/data/settings_deimv2.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/deimv2_dinov3_m_coco.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/deimv2_dinov3_m_coco.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.1
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = DFINE
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_dfine.ini b/data/settings_dfine.ini
new file mode 100644
index 000000000..0ad660cf1
--- /dev/null
+++ b/data/settings_dfine.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/dfine_m_obj2coco.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/dfine_m_obj2coco.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = DFINE
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_dfine_seg.ini b/data/settings_dfine_seg.ini
new file mode 100644
index 000000000..e82c504ae
--- /dev/null
+++ b/data/settings_dfine_seg.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/dfine_seg_s_coco.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/dfine_seg_s_coco.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = DFINE_IS
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_rfdetr.ini b/data/settings_rfdetr.ini
new file mode 100644
index 000000000..10037acdd
--- /dev/null
+++ b/data/settings_rfdetr.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/rfdetr_sim_coco.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/rfdetr_sim_coco.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco_91.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = RFDETR
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_rfdetr_seg.ini b/data/settings_rfdetr_seg.ini
new file mode 100644
index 000000000..f1cc5da66
--- /dev/null
+++ b/data/settings_rfdetr_seg.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/rfdetr_seg_coco.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/rfdetr_seg_coco.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco_91.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = RFDETR_IS
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_rtdetrv4.ini b/data/settings_rtdetrv4.ini
new file mode 100644
index 000000000..4734cf9fb
--- /dev/null
+++ b/data/settings_rtdetrv4.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/RTv4-M-hgnet.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/RTv4-M-hgnet.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = DFINE
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov10.ini b/data/settings_yolov10.ini
new file mode 100644
index 000000000..a7db951b1
--- /dev/null
+++ b/data/settings_yolov10.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov10s.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov10s.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.3
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV10
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov11_obb.ini b/data/settings_yolov11_obb.ini
new file mode 100644
index 000000000..36641f010
--- /dev/null
+++ b/data/settings_yolov11_obb.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolo11s-obb.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolo11s-obb.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/dota/DOTA_v1.0.names
+
+#-----------------------------
+confidence_threshold = 0.3
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV11_OBB
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov11_seg.ini b/data/settings_yolov11_seg.ini
new file mode 100644
index 000000000..a296bb651
--- /dev/null
+++ b/data/settings_yolov11_seg.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo11s-seg.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo11s-seg.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.3
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV11Mask
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov11m.ini b/data/settings_yolov11m.ini
new file mode 100644
index 000000000..e2d29ee39
--- /dev/null
+++ b/data/settings_yolov11m.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo11m.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo11m.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV11
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov11x.ini b/data/settings_yolov11x.ini
new file mode 100644
index 000000000..d8043afa7
--- /dev/null
+++ b/data/settings_yolov11x.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo11x.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo11x.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV11
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov11x_obb.ini b/data/settings_yolov11x_obb.ini
new file mode 100644
index 000000000..bb65f3b5b
--- /dev/null
+++ b/data/settings_yolov11x_obb.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolo11x-obb.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolo11x-obb.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/dota/DOTA_v1.0.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 1
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV11_OBB
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov12m.ini b/data/settings_yolov12m.ini
new file mode 100644
index 000000000..ca6d381b7
--- /dev/null
+++ b/data/settings_yolov12m.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_INFERENCE_ENGINE
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov12m.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov12m.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV12
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov12x.ini b/data/settings_yolov12x.ini
new file mode 100644
index 000000000..6f1e7be6a
--- /dev/null
+++ b/data/settings_yolov12x.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov12x.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov12x.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV12
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov13s.ini b/data/settings_yolov13s.ini
new file mode 100644
index 000000000..cfd75f5ba
--- /dev/null
+++ b/data/settings_yolov13s.ini
@@ -0,0 +1,148 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov13s.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov13s.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV13
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# UniversalTracker = 0
+# ByteTrack = 1
+tracker_type = 1
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackCSRT = 2
+# TrackDaSiamRPN = 3
+# TrackNano = 4
+# TrackVit = 5
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchLAPJV = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
+
+#-----------------------------
+# Settings only for m_tracker = tracking::ByteTrack
+bytetrack_track_buffer = 30
+bytetrack_track_thresh = 0.5
+bytetrack_high_thresh = 0.5
+bytetrack_match_thresh = 0.8
diff --git a/data/settings_yolov26m.ini b/data/settings_yolov26m.ini
new file mode 100644
index 000000000..23dd24f48
--- /dev/null
+++ b/data/settings_yolov26m.ini
@@ -0,0 +1,142 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_INFERENCE_ENGINE
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo26m.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo26m.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV26
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+# FP8
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov26m_obb.ini b/data/settings_yolov26m_obb.ini
new file mode 100644
index 000000000..d31e8425b
--- /dev/null
+++ b/data/settings_yolov26m_obb.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolo26m-obb.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolo26m-obb.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/dota/DOTA_v1.0.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 1
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV26_OBB
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov26m_seg.ini b/data/settings_yolov26m_seg.ini
new file mode 100644
index 000000000..3a4ed1d05
--- /dev/null
+++ b/data/settings_yolov26m_seg.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo26m-seg.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo26m-seg.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.3
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV26Mask
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov5.ini b/data/settings_yolov5.ini
new file mode 100644
index 000000000..85cc89cc7
--- /dev/null
+++ b/data/settings_yolov5.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov5m.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov5m.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 1.5
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV5
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov6.ini b/data/settings_yolov6.ini
new file mode 100644
index 000000000..140e07d8e
--- /dev/null
+++ b/data/settings_yolov6.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov6m.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov6m.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 1.5
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV6
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov7.ini b/data/settings_yolov7.ini
new file mode 100644
index 000000000..201ab70b7
--- /dev/null
+++ b/data/settings_yolov7.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov7_b1.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov7_b1.onnx
+#nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov7_b2.onnx
+#nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov7_b2.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 1
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV7
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov7mask.ini b/data/settings_yolov7mask.ini
new file mode 100644
index 000000000..bc6ca37c1
--- /dev/null
+++ b/data/settings_yolov7mask.ini
@@ -0,0 +1,146 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov7-seg_orig.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov7-seg_orig.onnx
+#nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov7_b1.onnx
+#nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov7_b1.onnx
+#nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov7_b2.onnx
+#nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov7_b2.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV7Mask
+#net_type = YOLOV7
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov8.ini b/data/settings_yolov8.ini
new file mode 100644
index 000000000..6be1c1db1
--- /dev/null
+++ b/data/settings_yolov8.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov8m.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov8m.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 1.5
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV8
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov8mask.ini b/data/settings_yolov8mask.ini
new file mode 100644
index 000000000..edec20eba
--- /dev/null
+++ b/data/settings_yolov8mask.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov8s-seg.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov8s-seg.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV8Mask
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov8x_obb.ini b/data/settings_yolov8x_obb.ini
new file mode 100644
index 000000000..05ebf94f6
--- /dev/null
+++ b/data/settings_yolov8x_obb.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolov8x-obb.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolov8x-obb.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/dota/DOTA_v1.0.names
+
+#-----------------------------
+confidence_threshold = 0.5
+
+max_crop_ratio = 1
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV8_OBB
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov9.ini b/data/settings_yolov9.ini
new file mode 100644
index 000000000..ec1382e5f
--- /dev/null
+++ b/data/settings_yolov9.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CUDA_FP16
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_CUDA
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov9-e.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov9-e.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.3
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV9
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/settings_yolov9_dota.ini b/data/settings_yolov9_dota.ini
new file mode 100644
index 000000000..6597ccf31
--- /dev/null
+++ b/data/settings_yolov9_dota.ini
@@ -0,0 +1,141 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 6
+# tensorrt = 5
+detector_backend = 5
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CUDA_FP16
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_CUDA
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolov9_DOTA.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/dota/yolov9_DOTA.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/dota/DOTA_v1.5.names
+
+#-----------------------------
+confidence_threshold = 0.3
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3
+# YOLOV4
+# YOLOV5
+net_type = YOLOV9
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0 // Euclidean distance between centers, pixels
+# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
+# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this seconds then it will be removed
+max_lost_time = 2
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 2
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10
diff --git a/data/tiny-yolo.cfg b/data/tiny-yolo.cfg
deleted file mode 100644
index 9a4a184f1..000000000
--- a/data/tiny-yolo.cfg
+++ /dev/null
@@ -1,139 +0,0 @@
-[net]
-# Training
-# batch=64
-# subdivisions=2
-# Testing
-batch=1
-subdivisions=1
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=425
-activation=linear
-
-[region]
-anchors = 0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
-bias_match=1
-classes=80
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=0
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/data/tiny-yolo.weights b/data/tiny-yolo.weights
deleted file mode 100644
index 0bb96d616..000000000
Binary files a/data/tiny-yolo.weights and /dev/null differ
diff --git a/data/voc.names b/data/voc.names
deleted file mode 100644
index 8420ab35e..000000000
--- a/data/voc.names
+++ /dev/null
@@ -1,20 +0,0 @@
-aeroplane
-bicycle
-bird
-boat
-bottle
-bus
-car
-cat
-chair
-cow
-diningtable
-dog
-horse
-motorbike
-person
-pottedplant
-sheep
-sofa
-train
-tvmonitor
diff --git a/data/yolov3-tiny.cfg b/data/yolov3-tiny.cfg
deleted file mode 100644
index 42c0fcf91..000000000
--- a/data/yolov3-tiny.cfg
+++ /dev/null
@@ -1,182 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=2
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 8
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-[yolo]
-mask = 1,2,3
-anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
diff --git a/data/yolov3-tiny.weights b/data/yolov3-tiny.weights
deleted file mode 100644
index aad7e6c80..000000000
Binary files a/data/yolov3-tiny.weights and /dev/null differ
diff --git a/data/yolov3.cfg b/data/yolov3.cfg
deleted file mode 100644
index 938ffff23..000000000
--- a/data/yolov3.cfg
+++ /dev/null
@@ -1,789 +0,0 @@
-[net]
-# Testing
-# batch=1
-# subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=608
-height=608
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-######################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 61
-
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 36
-
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
diff --git a/data/yolov4.cfg b/data/yolov4.cfg
deleted file mode 100644
index 47b9db61a..000000000
--- a/data/yolov4.cfg
+++ /dev/null
@@ -1,1155 +0,0 @@
-[net]
-batch=64
-subdivisions=8
-# Training
-#width=512
-#height=512
-width=608
-height=608
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.00261
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-#cutmix=1
-mosaic=1
-
-#:104x104 54:52x52 85:26x26 104:13x13 for 416
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-7
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-10
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=mish
-
-##########################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 85
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 54
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-##########################
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-scale_x_y = 1.2
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=leaky
-
-[route]
-layers = -1, -16
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-scale_x_y = 1.1
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=leaky
-
-[route]
-layers = -1, -37
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-scale_x_y = 1.05
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
diff --git a/demo.py b/demo.py
new file mode 100644
index 000000000..7f467e45e
--- /dev/null
+++ b/demo.py
@@ -0,0 +1,103 @@
+import sys
+import glob
+import getopt
+import numpy as np
+import cv2 as cv
+import pymtracking as mt
+
+print("OpenCV Version: {}".format(cv.__version__))
+
+
+def draw_regions(img, regions, color):
+ for reg in regions:
+ brect = reg.brect
+ cv.rectangle(img, (brect.x, brect.y, brect.width, brect.height), color, 2)
+
+
+def draw_tracks(img, tracks, fps):
+ for track in tracks:
+ brect = track.GetBoundingRect()
+ if track.isStatic:
+ cv.rectangle(img, (brect.x, brect.y, brect.width, brect.height), (255, 0, 255), 2)
+ elif track.IsRobust(int(fps / 4), 0.7, (0.1, 10.), 3):
+ cv.rectangle(img, (brect.x, brect.y, brect.width, brect.height), (0, 255, 0), 2)
+ trajectory = track.GetTrajectory()
+ for i in range(0, len(trajectory) - 1):
+ cv.line(img, trajectory[i], trajectory[i+1], (0, 255, 0), 1)
+
+
+def main():
+ args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
+ try:
+ video_src = video_src[0]
+ except:
+ video_src = 0
+ args = dict(args)
+
+ cam = cv.VideoCapture(video_src)
+
+ _ret, img = cam.read()
+ print("cam.read res = ", _ret, ", im size = ", img.shape)
+
+ fps = cam.get(cv.CAP_PROP_FPS)
+ print(video_src, " fps = ", fps)
+
+ configBGFG = mt.KeyVal()
+ configBGFG.Add('useRotatedRect', '20')
+ configBGFG.Add('history', '1000')
+ configBGFG.Add("nmixtures", "3")
+ configBGFG.Add("backgroundRatio", "0.7")
+ configBGFG.Add("noiseSigma", "0")
+ print("configBGFG = ", configBGFG)
+ mdetector = mt.BaseDetector(mt.BaseDetector.Detectors.MOG, configBGFG, img)
+ print("CanGrayProcessing: ", mdetector.CanGrayProcessing())
+ mdetector.SetMinObjectSize((1, 1))
+
+ tracker_settings = mt.TrackerSettings()
+
+ tracker_settings.SetDistance(mt.MTracker.DistRects)
+ tracker_settings.kalmanType = mt.MTracker.KalmanLinear
+ tracker_settings.filterGoal = mt.MTracker.FilterCenter
+ tracker_settings.lostTrackType = mt.MTracker.TrackNone
+ tracker_settings.matchType = mt.MTracker.MatchHungrian
+ tracker_settings.useAcceleration = False
+ tracker_settings.dt = 0.5
+ tracker_settings.accelNoiseMag = 0.1
+ tracker_settings.distThres = 0.95
+ tracker_settings.minAreaRadiusPix = img.shape[0] / 5.
+ tracker_settings.minAreaRadiusK = 0.8
+ tracker_settings.useAbandonedDetection = False
+ tracker_settings.maximumAllowedSkippedFrames = int(2 * fps)
+ tracker_settings.maxTraceLength = int(2 * fps)
+
+ mtracker = mt.MTracker(tracker_settings)
+
+ while True:
+ _ret, img = cam.read()
+ if _ret:
+ print("cam.read res = ", _ret, ", im size = ", img.shape, ", fps = ", fps)
+ else:
+ break
+
+ mdetector.Detect(img)
+ regions = mdetector.GetDetects()
+ print("mdetector.Detect:", len(regions))
+
+ mtracker.Update(regions, img, fps)
+ tracks = mtracker.GetTracks()
+ print("mtracker.Update:", len(tracks))
+
+ vis = img.copy()
+ # draw_regions(vis, regions, (255, 0, 255))
+ draw_tracks(vis, tracks, fps)
+ cv.imshow('detect', vis)
+
+ if cv.waitKey(int(1000 / fps)) == 27:
+ break
+
+ print('Done')
+
+
+if __name__ == '__main__':
+ main()
+ cv.destroyAllWindows()
diff --git a/example/CMakeLists.txt b/example/CMakeLists.txt
index 74c6c0708..163680cb1 100644
--- a/example/CMakeLists.txt
+++ b/example/CMakeLists.txt
@@ -2,56 +2,53 @@ cmake_minimum_required (VERSION 3.5)
project(MultitargetTracker)
-set(SOURCES
- main.cpp
- VideoExample.cpp
-)
+set(SOURCES main.cpp
+ VideoExample.cpp)
-set(HEADERS
- MouseExample.h
- VideoExample.h
- examples.h
-)
+set(HEADERS MouseExample.h
+ VideoExample.h
+ examples.h
+ MotionDetectorExample.h
+ FileLogger.h)
+
+if (BUILD_CARS_COUNTING)
+ set(SOURCES ${SOURCES} CarsCounting.cpp)
+ set(HEADERS ${HEADERS} CarsCounting.h)
+endif(BUILD_CARS_COUNTING)
# ----------------------------------------------------------------------------
# дОйавĐģŅĐĩĐŧ include диŅĐĩĐēŅĐžŅии
# ----------------------------------------------------------------------------
-INCLUDE_DIRECTORIES(
- ${PROJECT_SOURCE_DIR}/../src
- ${PROJECT_SOURCE_DIR}/../src/common
+INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/../src
+ ${PROJECT_SOURCE_DIR}/../src/mtracking
${PROJECT_SOURCE_DIR}/../src/Detector
${PROJECT_SOURCE_DIR}/../src/Detector/vibe_src
${PROJECT_SOURCE_DIR}/../src/Detector/Subsense
${PROJECT_SOURCE_DIR}/../src/Tracker
${PROJECT_SOURCE_DIR}/../src/Tracker/HungarianAlg
${PROJECT_SOURCE_DIR}/../thirdparty
-)
-
-set(LIBS
- ${OpenCV_LIBS}
- mtracking
- mdetection
- inih
-)
+ ${PROJECT_SOURCE_DIR}/../thirdparty/spdlog/include)
-if (BUILD_YOLO_LIB)
- if (MSVC)
- if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "4")
- set(BIT_SYSTEM x32)
- else()
- set(BIT_SYSTEM x64)
- endif()
+set(LIBS ${OpenCV_LIBS}
+ mtracking
+ mdetection
+ inih)
- link_directories(${PROJECT_SOURCE_DIR}/../src/Detector/darknet/3rdparty/lib/${BIT_SYSTEM})
- endif(MSVC)
+if (BUILD_ONNX_TENSORRT)
+ add_definitions(-DBUILD_ONNX_TENSORRT)
+endif(BUILD_ONNX_TENSORRT)
- add_definitions(-DBUILD_YOLO_LIB)
-endif(BUILD_YOLO_LIB)
+if (BUILD_CARS_COUNTING)
+ add_definitions(-DBUILD_CARS_COUNTING)
+endif(BUILD_CARS_COUNTING)
-if (BUILD_YOLO_TENSORRT)
- add_definitions(-DBUILD_YOLO_TENSORRT)
-endif(BUILD_YOLO_TENSORRT)
+if (USE_CLIP)
+ add_definitions(-DUSE_CLIP)
+ set(LIBS ${LIBS} ruclip)
+endif(USE_CLIP)
ADD_EXECUTABLE(${PROJECT_NAME} ${SOURCES} ${HEADERS})
-TARGET_LINK_LIBRARIES(${PROJECT_NAME} ${LIBS})
+TARGET_LINK_LIBRARIES(${PROJECT_NAME} PRIVATE ${LIBS})
+
+set_target_properties(${PROJECT_NAME} PROPERTIES FOLDER "apps")
\ No newline at end of file
diff --git a/example/CarsCounting.cpp b/example/CarsCounting.cpp
new file mode 100644
index 000000000..5ff8aca56
--- /dev/null
+++ b/example/CarsCounting.cpp
@@ -0,0 +1,472 @@
+#include "CarsCounting.h"
+#include
+
+///
+/// \brief CarsCounting::CarsCounting
+/// \param parser
+///
+CarsCounting::CarsCounting(const cv::CommandLineParser& parser)
+ : VideoExample(parser)
+{
+#ifdef _WIN32
+ std::string pathToModel = "../../data/";
+#else
+ std::string pathToModel = "../data/";
+#endif
+
+ m_drawHeatMap = parser.get("heat_map") != 0;
+
+ std::string settingsFile = parser.get("settings");
+ m_trackerSettingsLoaded = ParseTrackerSettings(settingsFile, m_trackerSettings);
+
+ m_logger->info("Inference loaded ({0}) from {1}: used {2} backend, weights: {3}, config: {4}, names: {5}",
+ m_trackerSettingsLoaded, settingsFile, m_trackerSettings.m_detectorBackend, m_trackerSettings.m_nnWeights, m_trackerSettings.m_nnConfig, m_trackerSettings.m_classNames);
+
+ m_geoBindFile = parser.get("geo_bind");
+}
+
+///
+/// \brief CarsCounting::DrawTrack
+/// \param frame
+/// \param track
+/// \param drawTrajectory
+/// \param framesCounters
+///
+void CarsCounting::DrawTrack(cv::Mat frame, const TrackingObject& track, bool drawTrajectory, int framesCounter, const std::string& /*userLabel*/)
+{
+ cv::Rect brect = track.m_rrect.boundingRect();
+
+ m_resultsLog.AddTrack(framesCounter, track.m_ID, brect, track.m_type, track.m_confidence);
+ m_resultsLog.AddRobustTrack(track.m_ID);
+
+ if (track.m_isStatic)
+ {
+#if (CV_VERSION_MAJOR >= 4)
+ cv::rectangle(frame, brect, cv::Scalar(255, 0, 255), 2, cv::LINE_AA);
+#else
+ cv::rectangle(frame, brect, cv::Scalar(255, 0, 255), 2, CV_AA);
+#endif
+ }
+ else
+ {
+#if (CV_VERSION_MAJOR >= 4)
+ cv::rectangle(frame, brect, cv::Scalar(0, 255, 0), 1, cv::LINE_AA);
+#else
+ cv::rectangle(frame, brect, cv::Scalar(0, 255, 0), 1, CV_AA);
+#endif
+
+ if (!m_geoParams.Empty())
+ {
+ int traceSize = static_cast(track.m_trace.size());
+ int period = std::min(2 * cvRound(m_fps), traceSize);
+ const auto& from = m_geoParams.Pix2Geo(track.m_trace[traceSize - period]);
+ const auto& to = m_geoParams.Pix2Geo(track.m_trace[traceSize - 1]);
+ auto dist = DistanceInMeters(from, to);
+
+ std::stringstream label;
+ if (period >= cvRound(m_fps) / 4)
+ {
+ auto velocity = (3.6f * dist * m_fps) / period;
+ //std::cout << TypeConverter::Type2Str(track.m_type) << ": distance " << std::fixed << std::setw(2) << std::setprecision(2) << dist << " on time " << (period / m_fps) << " with velocity " << velocity << " km/h: " << track.m_confidence << std::endl;
+ if (velocity < 1.f || std::isnan(velocity))
+ velocity = 0;
+ //label << TypeConverter::Type2Str(track.m_type) << " " << std::fixed << std::setw(2) << std::setprecision(2) << velocity << " km/h";
+ label << TypeConverter::Type2Str(track.m_type) << " " << cvRound(velocity) << " km/h";
+
+ int baseLine = 0;
+ double fontScale = (frame.cols < 2000) ? 0.5 : 1.;
+ cv::Size labelSize = cv::getTextSize(label.str(), cv::FONT_HERSHEY_TRIPLEX, fontScale, 1, &baseLine);
+
+ if (brect.x < 0)
+ {
+ brect.width = std::min(brect.width, frame.cols - 1);
+ brect.x = 0;
+ }
+ else if (brect.x + brect.width >= frame.cols)
+ {
+ brect.x = std::max(0, frame.cols - brect.width - 1);
+ brect.width = std::min(brect.width, frame.cols - 1);
+ }
+ if (brect.y - labelSize.height < 0)
+ {
+ brect.height = std::min(brect.height, frame.rows - 1);
+ brect.y = labelSize.height;
+ }
+ else if (brect.y + brect.height >= frame.rows)
+ {
+ brect.y = std::max(0, frame.rows - brect.height - 1);
+ brect.height = std::min(brect.height, frame.rows - 1);
+ }
+ cv::rectangle(frame, cv::Rect(cv::Point(brect.x, brect.y - labelSize.height), cv::Size(labelSize.width, labelSize.height + baseLine)), cv::Scalar(200, 200, 200), cv::FILLED);
+ cv::putText(frame, label.str(), brect.tl(), cv::FONT_HERSHEY_TRIPLEX, fontScale, cv::Scalar(0, 0, 0));
+
+ if (velocity > 3)
+ AddToHeatMap(brect);
+ }
+ }
+ }
+
+ if (drawTrajectory)
+ {
+ cv::Scalar cl = m_colors[track.m_ID.ID2Module(m_colors.size())];
+
+ for (size_t j = 0; j < track.m_trace.size() - 1; ++j)
+ {
+ const TrajectoryPoint& pt1 = track.m_trace.at(j);
+ const TrajectoryPoint& pt2 = track.m_trace.at(j + 1);
+#if (CV_VERSION_MAJOR >= 4)
+ cv::line(frame, pt1.m_prediction, pt2.m_prediction, cl, 1, cv::LINE_AA);
+#else
+ cv::line(frame, pt1.m_prediction, pt2.m_prediction, cl, 1, CV_AA);
+#endif
+ if (!pt2.m_hasRaw)
+ {
+#if (CV_VERSION_MAJOR >= 4)
+ cv::circle(frame, pt2.m_prediction, 4, cl, 1, cv::LINE_AA);
+#else
+ cv::circle(frame, pt2.m_prediction, 4, cl, 1, CV_AA);
+#endif
+ }
+ }
+ }
+}
+
+///
+/// \brief CarsCounting::InitDetector
+/// \param frame
+///
+bool CarsCounting::InitDetector(cv::UMat frame)
+{
+ if (!m_trackerSettingsLoaded)
+ return false;
+
+ config_t config;
+
+ config.emplace("modelConfiguration", m_trackerSettings.m_nnConfig);
+ config.emplace("modelBinary", m_trackerSettings.m_nnWeights);
+ config.emplace("confidenceThreshold", std::to_string(m_trackerSettings.m_confidenceThreshold));
+ config.emplace("classNames", m_trackerSettings.m_classNames);
+ config.emplace("maxCropRatio", std::to_string(m_trackerSettings.m_maxCropRatio));
+ config.emplace("maxBatch", std::to_string(m_trackerSettings.m_maxBatch));
+ config.emplace("gpuId", std::to_string(m_trackerSettings.m_gpuId));
+ config.emplace("net_type", m_trackerSettings.m_netType);
+ config.emplace("inference_precision", m_trackerSettings.m_inferencePrecision);
+ config.emplace("video_memory", std::to_string(m_trackerSettings.m_maxVideoMemory));
+ config.emplace("dnnTarget", m_trackerSettings.m_dnnTarget);
+ config.emplace("dnnBackend", m_trackerSettings.m_dnnBackend);
+ config.emplace("inWidth", std::to_string(m_trackerSettings.m_inputSize.width));
+ config.emplace("inHeight", std::to_string(m_trackerSettings.m_inputSize.height));
+
+ for (auto wname : m_trackerSettings.m_whiteList)
+ {
+ config.emplace("white_list", wname);
+ }
+
+ m_detector = BaseDetector::CreateDetector((tracking::Detectors)m_trackerSettings.m_detectorBackend, config, frame);
+
+ return m_detector.operator bool();
+}
+
+///
+/// \brief CarsCounting::InitTracker
+/// \param grayFrame
+///
+bool CarsCounting::InitTracker(cv::UMat frame)
+{
+ if (!m_trackerSettingsLoaded)
+ return false;
+
+ if (m_drawHeatMap)
+ {
+ if (frame.channels() == 3)
+ m_keyFrame = frame.getMat(cv::ACCESS_READ).clone();
+ else
+ cv::cvtColor(frame, m_keyFrame, cv::COLOR_GRAY2BGR);
+ m_heatMap = cv::Mat(m_keyFrame.size(), CV_32FC1, cv::Scalar::all(0));
+ }
+
+ const int minStaticTime = 5;
+
+ TrackerSettings settings;
+ settings.SetDistance(tracking::DistJaccard);
+ settings.m_kalmanType = tracking::KalmanLinear;
+ settings.m_filterGoal = tracking::FilterCenter;
+ settings.m_lostTrackType = tracking::TrackCSRT; // Use KCF tracker for collisions resolving. Used if m_filterGoal == tracking::FilterRect
+ settings.m_matchType = tracking::MatchHungrian;
+ settings.m_dt = 0.3f; // Delta time for Kalman filter
+ settings.m_accelNoiseMag = 0.2f; // Accel noise magnitude for Kalman filter
+ settings.m_distThres = 0.7f; // Distance threshold between region and object on two frames
+ settings.m_minAreaRadiusPix = frame.rows / 20.f;
+ settings.m_maximumAllowedLostTime = 2.; // Maximum allowed lost time
+
+ settings.AddNearTypes(TypeConverter::Str2Type("car"), TypeConverter::Str2Type("bus"), false);
+ settings.AddNearTypes(TypeConverter::Str2Type("car"), TypeConverter::Str2Type("truck"), false);
+ settings.AddNearTypes(TypeConverter::Str2Type("person"), TypeConverter::Str2Type("bicycle"), true);
+ settings.AddNearTypes(TypeConverter::Str2Type("person"), TypeConverter::Str2Type("motorbike"), true);
+
+ settings.m_useAbandonedDetection = false;
+ if (settings.m_useAbandonedDetection)
+ {
+ settings.m_minStaticTime = minStaticTime;
+ settings.m_maxStaticTime = 60;
+ settings.m_maximumAllowedLostTime = settings.m_minStaticTime; // Maximum allowed lost time
+ settings.m_maxTraceLength = 2 * settings.m_maximumAllowedLostTime; // Maximum trace length
+ }
+ else
+ {
+ settings.m_maximumAllowedLostTime = 10.; // Maximum allowed lost time
+ settings.m_maxTraceLength = 4.; // Maximum trace length
+ }
+
+ m_tracker = BaseTracker::CreateTracker(settings, m_fps);
+
+ ReadGeobindings(frame.size());
+ return true;
+}
+
+///
+/// \brief CarsCounting::DrawData
+/// \param frame
+///
+void CarsCounting::DrawData(cv::Mat frame, const std::vector& tracks, int framesCounter, int currTime)
+{
+ m_logger->info("Frame {0} ({1}): tracks = {2}, time = {3}", framesCounter, m_framesCount, tracks.size(), currTime);
+
+#if 1 // Debug output
+ if (!m_geoParams.Empty())
+ {
+ std::vector points = m_geoParams.GetFramePoints();
+ for (size_t i = 0; i < points.size(); ++i)
+ {
+ cv::line(frame, points[i % points.size()], points[(i + 1) % points.size()], cv::Scalar(255, 255, 255), 1, cv::LINE_AA);
+ }
+ }
+#endif
+
+ for (const auto& track : tracks)
+ {
+ if (track.m_isStatic)
+ {
+ DrawTrack(frame, track, true, framesCounter);
+ }
+ else
+ {
+ if (track.IsRobust(cvRound(m_fps / 4), // Minimal trajectory size
+ 0.8f, // Minimal ratio raw_trajectory_points / trajectory_lenght
+ cv::Size2f(0.1f, 8.0f)) // Min and max ratio: width / height
+ )
+ {
+ DrawTrack(frame, track, true, framesCounter);
+
+ CheckLinesIntersection(track, static_cast(frame.cols), static_cast(frame.rows));
+ }
+ }
+ }
+ //m_detector->CalcMotionMap(frame);
+
+ if (!m_geoParams.Empty())
+ {
+ cv::Mat geoMap = m_geoParams.DrawTracksOnMap(tracks);
+ if (!geoMap.empty())
+ {
+#ifndef SILENT_WORK
+ cv::namedWindow("Geo map", cv::WINDOW_NORMAL);
+ cv::imshow("Geo map", geoMap);
+#endif
+ if (true)
+ {
+ double k = 0.25;
+ cv::Size mapPreview(cvRound(frame.cols * k), cvRound(((frame.cols * k) / geoMap.cols) * geoMap.rows));
+ cv::resize(geoMap, frame(cv::Rect(frame.cols - mapPreview.width - 1, frame.rows - mapPreview.height - 1, mapPreview.width, mapPreview.height)), mapPreview, 0, 0, cv::INTER_CUBIC);
+ }
+ }
+ }
+
+ for (const auto& rl : m_lines)
+ {
+ rl.Draw(frame);
+ }
+
+ cv::Mat heatMap = DrawHeatMap();
+#ifndef SILENT_WORK
+ if (!heatMap.empty())
+ cv::imshow("Heat map", heatMap);
+#endif
+}
+
+///
+/// \brief CarsCounting::AddLine
+/// \param newLine
+///
+void CarsCounting::AddLine(const RoadLine& newLine)
+{
+ m_lines.push_back(newLine);
+}
+
+///
+/// \brief CarsCounting::GetLine
+/// \param lineUid
+/// \return
+///
+bool CarsCounting::GetLine(unsigned int lineUid, RoadLine& line)
+{
+ for (const auto& rl : m_lines)
+ {
+ if (rl.m_uid == lineUid)
+ {
+ line = rl;
+ return true;
+ }
+ }
+ return false;
+}
+
+///
+/// \brief CarsCounting::RemoveLine
+/// \param lineUid
+/// \return
+///
+bool CarsCounting::RemoveLine(unsigned int lineUid)
+{
+ for (auto it = std::begin(m_lines); it != std::end(m_lines);)
+ {
+ if (it->m_uid == lineUid)
+ it = m_lines.erase(it);
+ else
+ ++it;
+ }
+ return false;
+}
+
+///
+/// \brief CarsCounting::CheckLinesIntersection
+/// \param track
+///
+void CarsCounting::CheckLinesIntersection(const TrackingObject& track, float xMax, float yMax)
+{
+ auto Pti2f = [&](cv::Point pt)
+ {
+ return cv::Point2f(pt.x / xMax, pt.y / yMax);
+ };
+
+ constexpr size_t minTrack = 5;
+ if (track.m_trace.size() >= minTrack)
+ {
+ for (auto& rl : m_lines)
+ {
+ rl.IsIntersect(track.m_ID, Pti2f(track.m_trace[track.m_trace.size() - minTrack]), Pti2f(track.m_trace[track.m_trace.size() - 1]));
+ }
+ }
+}
+
+///
+/// \brief CarsCounting::DrawHeatMap
+///
+cv::Mat CarsCounting::DrawHeatMap()
+{
+ cv::Mat res;
+ if (!m_heatMap.empty())
+ {
+ cv::normalize(m_heatMap, m_normHeatMap, 255, 0, cv::NORM_MINMAX, CV_8UC1);
+ cv::applyColorMap(m_normHeatMap, m_colorMap, cv::COLORMAP_HOT);
+ cv::bitwise_or(m_keyFrame, m_colorMap, res);
+ }
+ return res;
+}
+
+///
+/// \brief CarsCounting::AddToHeatMap
+///
+void CarsCounting::AddToHeatMap(const cv::Rect& rect)
+{
+ if (m_heatMap.empty())
+ return;
+
+ constexpr float w = 0.001f;
+ for (int y = 0; y < rect.height; ++y)
+ {
+ float* heatPtr = m_heatMap.ptr(rect.y + y) + rect.x;
+ for (int x = 0; x < rect.width; ++x)
+ {
+ heatPtr[x] += w;
+ }
+ }
+}
+
+///
+/// \brief CarsCounting::ReadGeobindings
+///
+bool CarsCounting::ReadGeobindings(cv::Size frameSize)
+{
+ bool res = true;
+ INIReader reader(m_geoBindFile);
+
+ int parseError = reader.ParseError();
+ if (parseError < 0)
+ {
+ m_logger->critical("GeoBindFile file {} does not exist!", m_geoBindFile);
+ res = false;
+ }
+ else if (parseError > 0)
+ {
+ m_logger->critical("GeoBindFile file {0} parse error in line: {1}", m_geoBindFile, parseError);
+ res = false;
+ }
+ if (!res)
+ return res;
+
+ // Read frame-map bindings
+ std::vector geoPoints;
+ std::vector framePoints;
+ for (size_t i = 0;; ++i)
+ {
+ cv::Point2d geoPoint;
+ std::string lat = "lat" + std::to_string(i);
+ std::string lon = "lon" + std::to_string(i);
+ std::string px_x = "px_x" + std::to_string(i);
+ std::string px_y = "px_y" + std::to_string(i);
+ if (reader.HasValue("points", lat) && reader.HasValue("points", lon) && reader.HasValue("points", px_x) && reader.HasValue("points", px_y))
+ {
+ geoPoints.emplace_back(reader.GetReal("points", lat, 0), reader.GetReal("points", lon, 0));
+ framePoints.emplace_back(cvRound(reader.GetReal("points", px_x, 0) * frameSize.width), cvRound(reader.GetReal("points", px_y, 0) * frameSize.height));
+ }
+ else
+ {
+ break;
+ }
+ }
+ res = m_geoParams.SetKeyPoints(framePoints, geoPoints);
+
+ // Read map image
+ std::string mapFile = reader.GetString("map", "file", "");
+ std::vector mapGeoCorners;
+ mapGeoCorners.emplace_back(reader.GetReal("map", "left_top_lat", 0), reader.GetReal("map", "left_top_lon", 0));
+ mapGeoCorners.emplace_back(reader.GetReal("map", "right_top_lat", 0), reader.GetReal("map", "right_top_lon", 0));
+ mapGeoCorners.emplace_back(reader.GetReal("map", "right_bottom_lat", 0), reader.GetReal("map", "right_bottom_lon", 0));
+ mapGeoCorners.emplace_back(reader.GetReal("map", "left_bottom_lat", 0), reader.GetReal("map", "left_bottom_lon", 0));
+ m_geoParams.SetMapParams(mapFile, mapGeoCorners);
+
+ // Read lines
+ m_logger->info("Read lines:");
+ for (size_t i = 0;; ++i)
+ {
+ std::string line = "line" + std::to_string(i);
+ std::string x0 = line + "_x0";
+ std::string y0 = line + "_y0";
+ std::string x1 = line + "_x1";
+ std::string y1 = line + "_y1";
+ if (reader.HasValue("lines", x0) && reader.HasValue("lines", y0) && reader.HasValue("lines", x1) && reader.HasValue("lines", y1))
+ {
+ cv::Point2f p0(static_cast(reader.GetReal("lines", x0, 0)), static_cast(reader.GetReal("lines", y0, 0)));
+ cv::Point2f p1(static_cast(reader.GetReal("lines", x1, 0)), static_cast(reader.GetReal("lines", y1, 0)));
+ m_logger->info("Line {0}: ({1}, {2}) - ({3}, 4)", i, p0.x, p0.y, p1.x, p1.y);
+ AddLine(RoadLine(p0, p1, static_cast(i)));
+ }
+ else
+ {
+ break;
+ }
+ }
+
+ return res;
+}
diff --git a/cars_counting/CarsCounting.h b/example/CarsCounting.h
similarity index 63%
rename from cars_counting/CarsCounting.h
rename to example/CarsCounting.h
index 898a582ab..929672869 100644
--- a/cars_counting/CarsCounting.h
+++ b/example/CarsCounting.h
@@ -1,11 +1,7 @@
#pragma once
-#include "BaseDetector.h"
-
-#include "Ctracker.h"
-#include
-#include
-#include
+#include
+#include "VideoExample.h"
///
constexpr double DEG_TO_RAD = 0.017453292519943295769236907684886;
@@ -34,6 +30,34 @@ T DistanceInMeters(const cv::Point_& from, const cv::Point_& to)
return EarthRadius * Haversine(from, to);
}
+// Fix cv::getPerspectiveTransform for double
+template
+cv::Mat GetPerspectiveTransform(const std::vector>& src, const std::vector>& dst, int solveMethod)
+{
+ cv::Mat M(3, 3, CV_64F), X(8, 1, CV_64F, M.ptr());
+ double a[8][8], b[8];
+ cv::Mat A(8, 8, CV_64F, a), B(8, 1, CV_64F, b);
+
+ for (int i = 0; i < 4; ++i)
+ {
+ a[i][0] = a[i + 4][3] = src[i].x;
+ a[i][1] = a[i + 4][4] = src[i].y;
+ a[i][2] = a[i + 4][5] = 1;
+ a[i][3] = a[i][4] = a[i][5] = a[i + 4][0] = a[i + 4][1] = a[i + 4][2] = 0;
+ a[i][6] = -src[i].x * dst[i].x;
+ a[i][7] = -src[i].y * dst[i].x;
+ a[i + 4][6] = -src[i].x * dst[i].y;
+ a[i + 4][7] = -src[i].y * dst[i].y;
+ b[i] = dst[i].x;
+ b[i + 4] = dst[i].y;
+ }
+
+ cv::solve(A, B, X, solveMethod);
+ M.ptr()[8] = 1.;
+
+ return M;
+}
+
///
/// \brief The GeoParams class
///
@@ -63,7 +87,7 @@ class GeoParams
std::vector> tmpPix;
tmpPix.reserve(m_framePoints.size());
- for (auto pix : m_framePoints)
+ for (const auto& pix : m_framePoints)
{
tmpPix.emplace_back(static_cast(pix.x), static_cast(pix.y));
}
@@ -75,8 +99,8 @@ class GeoParams
}
std::cout << std::endl;
#endif
- cv::Mat toGeo = cv::getPerspectiveTransform(tmpPix, m_geoPoints);
- cv::Mat toPix = cv::getPerspectiveTransform(m_geoPoints, tmpPix);
+ cv::Mat toGeo = GetPerspectiveTransform(tmpPix, m_geoPoints, (int)cv::DECOMP_LU);
+ cv::Mat toPix = GetPerspectiveTransform(m_geoPoints, tmpPix, (int)cv::DECOMP_LU);
m_toGeo = toGeo;
m_toPix = toPix;
//std::cout << "To Geo: " << m_toGeo << std::endl;
@@ -85,6 +109,18 @@ class GeoParams
return res;
}
+ ///
+ bool SetMapParams(const std::string& mapfile, const std::vector>& mapGeoCorners)
+ {
+ m_map = cv::imread(mapfile);
+ m_mapGeoCorners.assign(std::begin(mapGeoCorners), std::end(mapGeoCorners));
+
+ if (!m_map.empty())
+ m_p2mParams.Init(m_mapGeoCorners, m_map.cols, m_map.rows);
+
+ return !m_map.empty();
+ }
+
///
cv::Point Geo2Pix(const cv::Point_& geo) const
{
@@ -113,12 +149,85 @@ class GeoParams
return m_framePoints.size() != m_geoPoints.size() || m_framePoints.size() < 4;
}
+ ///
+ cv::Mat DrawTracksOnMap(const std::vector& tracks)
+ {
+ if (m_map.empty())
+ return cv::Mat();
+
+ cv::Mat map = m_map.clone();
+
+#if 1 // Debug output
+ // Draw bindings points
+ for (size_t i = 0; i < m_geoPoints.size(); ++i)
+ {
+ cv::Point_ gp1(m_geoPoints[i % m_geoPoints.size()]);
+ cv::Point p1(m_p2mParams.Geo2Pix(gp1));
+
+ cv::Point_ gp2(m_geoPoints[(i + 1) % m_geoPoints.size()]);
+ cv::Point p2(m_p2mParams.Geo2Pix(gp2));
+
+ cv::line(map, p1, p2, cv::Scalar(255, 255, 255), 2, cv::LINE_AA);
+
+ //std::cout << p1 << " - " << p2 << std::endl;
+
+ std::stringstream label;
+ label << std::fixed << std::setw(5) << std::setprecision(5) << "[" << m_geoPoints[i].x << ", " << m_geoPoints[i].y << "]";
+ int baseLine = 0;
+ double fontScale = (map.cols < 1000 && map.rows < 1000) ? 0.5 : 1.;
+ cv::Size labelSize = cv::getTextSize(label.str(), cv::FONT_HERSHEY_TRIPLEX, fontScale, 1, &baseLine);
+ cv::putText(map, label.str(), p1, cv::FONT_HERSHEY_TRIPLEX, fontScale, cv::Scalar(255, 255, 255));
+ }
+#endif
+
+ // Draw tracks
+ for (const auto& track : tracks)
+ {
+ if (track.m_lastRobust)
+ {
+ auto geoCenter = Pix2Geo(track.m_rrect.center);
+ auto center = m_p2mParams.Geo2Pix(geoCenter);
+ //std::cout << "Convert: " << track.m_rrect.center << " -> " << geoCenter << " -> " << center << std::endl;
+ if (center.x > 0 && center.x < map.cols && center.y > 0 && center.y < map.rows)
+ cv::ellipse(map, center, cv::Size(5, 5), 0, 0, 360, cv::Scalar(255, 0, 255), cv::FILLED, 8);
+ }
+ }
+
+ return map;
+ }
+
private:
std::vector m_framePoints;
std::vector> m_geoPoints;
cv::Matx m_toGeo;
cv::Matx m_toPix;
+
+ cv::Mat m_map;
+ std::vector> m_mapGeoCorners;
+
+ ///
+ struct Pix2MapParams
+ {
+ cv::Matx m_toPix;
+
+ ///
+ cv::Point Geo2Pix(const cv::Point_& geo) const
+ {
+ cv::Vec g(geo.x, geo.y, 1);
+ auto p = m_toPix * g;
+ return cv::Point(cvRound(p[0] / p[2]), cvRound(p[1] / p[2]));
+ }
+
+ ///
+ void Init(const std::vector>& geoCorners, int mapWidth, int mapHeight)
+ {
+ std::vector> frameCorners { cv::Point_(0, 0), cv::Point_(mapWidth, 0), cv::Point_(mapWidth, mapHeight), cv::Point_(0, mapHeight) };
+ cv::Mat toPix = GetPerspectiveTransform(geoCorners, frameCorners, (int)cv::DECOMP_LU);
+ m_toPix = toPix;
+ }
+ };
+ Pix2MapParams m_p2mParams;
};
///
@@ -130,9 +239,7 @@ class RoadLine
///
/// \brief RoadLine
///
- RoadLine()
- {
- }
+ RoadLine() = default;
RoadLine(const cv::Point2f& pt1, const cv::Point2f& pt2, unsigned int uid)
:
m_pt1(pt1), m_pt2(pt2), m_uid(uid)
@@ -163,7 +270,7 @@ class RoadLine
///
void Draw(cv::Mat frame) const
{
- auto Ptf2i = [&](cv::Point2f pt) -> cv::Point
+ auto Ptf2i = [&](cv::Point2f pt)
{
return cv::Point(cvRound(frame.cols * pt.x), cvRound(frame.rows * pt.y));
};
@@ -187,13 +294,19 @@ class RoadLine
/// \param pt2
/// \return
///
- int IsIntersect(cv::Point2f pt1, cv::Point2f pt2)
+ int IsIntersect(track_id_t objID, cv::Point2f pt1, cv::Point2f pt2)
{
- bool isIntersect = CheckIntersection(pt1, pt2);
int direction = 0;
+ if (m_lastIntersections.find(objID) != m_lastIntersections.end())
+ return direction;
+
+ bool isIntersect = CheckIntersection(pt1, pt2);
+
if (isIntersect)
{
+ m_lastIntersections.emplace(objID);
+
cv::Point2f pt;
if ((m_pt1.x <= m_pt2.x) && (m_pt1.y > m_pt2.y))
{
@@ -241,6 +354,8 @@ class RoadLine
private:
+ std::unordered_set m_lastIntersections;
+
///
/// \brief CheckIntersection
/// \param pt1
@@ -330,62 +445,34 @@ class RoadLine
///
/// \brief The CarsCounting class
///
-class CarsCounting
+class CarsCounting final : public VideoExample
{
public:
CarsCounting(const cv::CommandLineParser& parser);
- virtual ~CarsCounting();
-
- void Process();
// Lines API
void AddLine(const RoadLine& newLine);
bool GetLine(unsigned int lineUid, RoadLine& line);
bool RemoveLine(unsigned int lineUid);
-protected:
-
- std::string m_weightsFile;
- std::string m_configFile;
- std::string m_namesFile;
- tracking::Detectors m_detectorType = tracking::Detectors::Yolo_Darknet;
-
- std::unique_ptr m_detector;
- std::unique_ptr m_tracker;
+private:
bool m_drawHeatMap = false;
- bool m_showLogs = false;
- float m_fps = 0;
-
- virtual bool InitTracker(cv::UMat frame);
-
- virtual void DrawData(cv::Mat frame, int framesCounter, int currTime);
-
- void DrawTrack(cv::Mat frame,
- int resizeCoeff,
- const TrackingObject& track,
- bool drawTrajectory = true);
-
-private:
-
- bool m_isTrackerInitialized = false;
- std::string m_inFile;
- std::string m_outFile;
- int m_startFrame = 0;
- int m_endFrame = 0;
- int m_finishDelay = 0;
- std::vector m_colors;
+ bool InitDetector(cv::UMat frame) override;
+ bool InitTracker(cv::UMat frame) override;
- int m_minObjWidth = 10;
+ void DrawData(cv::Mat frame, const std::vector& tracks, int framesCounter, int currTime) override;
+ void DrawTrack(cv::Mat frame, const TrackingObject& track, bool drawTrajectory, int framesCounter, const std::string& userLabel = "") override;
// Road lines
std::deque m_lines;
void CheckLinesIntersection(const TrackingObject& track, float xMax, float yMax);
- std::set m_lastIntersections;
// Binding frame coordinates to geographical coordinates
- GeoParams m_geoParams;
+ GeoParams m_geoParams;
+ std::string m_geoBindFile;
+ bool ReadGeobindings(cv::Size frameSize);
// Heat map for visualization long term detections
cv::Mat m_keyFrame;
diff --git a/example/FileLogger.h b/example/FileLogger.h
new file mode 100644
index 000000000..3b8109c2c
--- /dev/null
+++ b/example/FileLogger.h
@@ -0,0 +1,166 @@
+#pragma once
+#include
+#include
+#include
+
+#include
+
+#include "object_types.h"
+
+///
+/// \brief The ResultsLog class
+///
+class ResultsLog
+{
+public:
+ ///
+ ResultsLog(const std::string& fileName, int writeEachNFrame)
+ : m_fileName(fileName), m_writeEachNFrame(writeEachNFrame)
+ {
+ }
+
+ ///
+ ~ResultsLog()
+ {
+ WriteAll(true);
+ }
+
+ ///
+ bool Open()
+ {
+ m_resCSV.close();
+ if (m_fileName.size() > 5)
+ {
+ m_resCSV.open(m_fileName);
+ return m_resCSV.is_open();
+ }
+ return false;
+ }
+
+ ///
+ bool AddTrack(int framesCounter, track_id_t trackID, const cv::Rect& brect, objtype_t type, float confidence)
+ {
+ if (m_resCSV.is_open())
+ {
+ auto frame = m_frames.find(framesCounter);
+ if (frame == std::end(m_frames))
+ {
+ DetectsOnFrame tmpFrame;
+ tmpFrame.m_detects.emplace_back(trackID, brect, type, confidence);
+ m_frames.emplace(framesCounter, tmpFrame);
+ }
+ else
+ {
+ frame->second.m_detects.emplace_back(trackID, brect, type, confidence);
+ }
+ return true;
+ }
+ return false;
+ }
+
+ ///
+ void AddRobustTrack(track_id_t trackID)
+ {
+ m_robustIDs.insert(trackID);
+ }
+
+ ///
+ void Flush()
+ {
+ WriteAll(true);
+ m_frames.clear();
+ }
+
+private:
+ std::string m_fileName;
+ std::ofstream m_resCSV;
+
+ ///
+ struct Detection
+ {
+ cv::Rect m_rect;
+ objtype_t m_type;
+ float m_conf = 0.f;
+ track_id_t m_trackID = 0;
+
+ Detection(track_id_t trackID, const cv::Rect& brect, objtype_t type, float confidence)
+ {
+ m_type = type;
+ m_rect = brect;
+ m_conf = confidence;
+ m_trackID = trackID;
+ }
+ };
+
+ ///
+ struct DetectsOnFrame
+ {
+ std::vector m_detects;
+ };
+ std::map m_frames;
+ std::unordered_set m_robustIDs;
+ int m_writeEachNFrame = 1;
+
+ ///
+ void WriteAll(bool byFrames)
+ {
+ if (byFrames)
+ {
+#if 1
+ char delim = ',';
+ for (const auto& frame : m_frames)
+ {
+ if (frame.first % m_writeEachNFrame == 0)
+ {
+ for (const auto& detect : frame.second.m_detects)
+ {
+ if (m_robustIDs.find(detect.m_trackID) != std::end(m_robustIDs))
+ {
+ m_resCSV << frame.first << delim << TypeConverter::Type2Str(detect.m_type) << delim << detect.m_rect.x << delim << detect.m_rect.y << delim <<
+ detect.m_rect.width << delim << detect.m_rect.height << delim <<
+ detect.m_conf << delim << std::endl;
+ }
+ }
+ }
+ }
+#else
+ char delim = ' ';
+ for (const auto& frame : m_frames)
+ {
+ for (const auto& detect : frame.second.m_detects)
+ {
+ if (m_robustIDs.find(detect.m_trackID) != std::end(m_robustIDs))
+ {
+ m_resCSV << frame.first << delim << TypeConverter::Type2Str(detect.m_type) << delim << detect.m_rect.x << delim << detect.m_rect.y << delim <<
+ (detect.m_rect.x + detect.m_rect.width) << delim << (detect.m_rect.y + detect.m_rect.height) << delim <<
+ detect.m_conf << delim << detect.m_trackID << std::endl;
+ }
+ }
+ }
+#endif
+ }
+ else
+ {
+ char delim = ',';
+ for (auto id : m_robustIDs)
+ {
+ for (const auto& frame : m_frames)
+ {
+ if (frame.first % m_writeEachNFrame == 0)
+ {
+ for (const auto& detect : frame.second.m_detects)
+ {
+ if (detect.m_trackID == id)
+ {
+ m_resCSV << frame.first << delim << id.ID2Str() << delim << detect.m_rect.x << delim << detect.m_rect.y << delim <<
+ detect.m_rect.width << delim << detect.m_rect.height << delim <<
+ detect.m_conf << ",-1,-1,-1," << std::endl;
+ break;
+ }
+ }
+ }
+ }
+ }
+ }
+ }
+};
diff --git a/example/MotionDetectorExample.h b/example/MotionDetectorExample.h
new file mode 100644
index 000000000..7319c4079
--- /dev/null
+++ b/example/MotionDetectorExample.h
@@ -0,0 +1,227 @@
+#pragma once
+
+#include
+#include
+#include
+
+#include "VideoExample.h"
+
+#ifdef USE_CLIP
+#include "ruclip/ClipAPI.h"
+#endif // USE_CLIP
+
+///
+/// \brief The MotionDetectorExample class
+///
+class MotionDetectorExample final : public VideoExample
+{
+public:
+ MotionDetectorExample(const cv::CommandLineParser& parser)
+ : VideoExample(parser)
+ {
+#ifdef USE_CLIP
+ std::string clipModel = "C:/work/clip/ruclip_/CLIP/data/ruclip-vit-large-patch14-336";
+ std::string bpeModel = "C:/work/clip/ruclip_/CLIP/data/ruclip-vit-large-patch14-336/bpe.model";
+ m_clip.Init(clipModel, bpeModel, 336, 0, { "pedestrian", "person", "suv", "pickup", "car", "truck", "bus" });
+#endif // USE_CLIP
+
+ m_logger->info("MotionDetectorExample");
+ }
+
+protected:
+ ///
+ /// \brief InitDetector
+ /// \param frame
+ /// \return
+ ///
+ bool InitDetector(cv::UMat frame) override
+ {
+ m_logger->info("MotionDetectorExample::InitDetector");
+
+ m_minObjWidth = 2;
+
+ config_t config;
+ config.emplace("useRotatedRect", "0");
+
+ tracking::Detectors detectorType = tracking::Detectors::Motion_MOG;
+
+ switch (detectorType)
+ {
+ case tracking::Detectors::Motion_VIBE:
+ config.emplace("samples", "20");
+ config.emplace("pixelNeighbor", "1");
+ config.emplace("distanceThreshold", "20");
+ config.emplace("matchingThreshold", "3");
+ config.emplace("updateFactor", "16");
+ break;
+ case tracking::Detectors::Motion_MOG:
+ config.emplace("history", std::to_string(cvRound(5000 * m_fps)));
+ config.emplace("nmixtures", "3");
+ config.emplace("backgroundRatio", "0.7");
+ config.emplace("noiseSigma", "0");
+ break;
+ case tracking::Detectors::Motion_GMG:
+ config.emplace("initializationFrames", "50");
+ config.emplace("decisionThreshold", "0.7");
+ break;
+ case tracking::Detectors::Motion_CNT:
+ config.emplace("minPixelStability", "15");
+ config.emplace("maxPixelStability", std::to_string(cvRound(20 * m_minStaticTime * m_fps)));
+ config.emplace("useHistory", "1");
+ config.emplace("isParallel", "1");
+ break;
+ case tracking::Detectors::Motion_MOG2:
+ config.emplace("history", std::to_string(cvRound(20 * m_minStaticTime * m_fps)));
+ config.emplace("varThreshold", "10");
+ config.emplace("detectShadows", "1");
+ break;
+ }
+ m_detector = BaseDetector::CreateDetector(detectorType, config, frame);
+
+ if (m_detector.get())
+ {
+ m_detector->SetMinObjectSize(cv::Size(m_minObjWidth, m_minObjWidth));
+ return true;
+ }
+ return false;
+ }
+ ///
+ /// \brief InitTracker
+ /// \param frame
+ /// \return
+ ///
+ bool InitTracker(cv::UMat frame) override
+ {
+ m_logger->info("MotionDetectorExample::InitTracker");
+
+ if (!m_trackerSettingsLoaded)
+ {
+ m_trackerSettings.SetDistance(tracking::DistCenters);
+ m_trackerSettings.m_kalmanType = tracking::KalmanLinear;
+ m_trackerSettings.m_filterGoal = tracking::FilterCenter;
+ m_trackerSettings.m_lostTrackType = tracking::TrackNone; // Use visual objects tracker for collisions resolving. Used if m_filterGoal == tracking::FilterRect
+ m_trackerSettings.m_matchType = tracking::MatchHungrian;
+ m_trackerSettings.m_useAcceleration = false; // Use constant acceleration motion model
+ m_trackerSettings.m_dt = m_trackerSettings.m_useAcceleration ? 0.05f : 0.3f; // Delta time for Kalman filter
+ m_trackerSettings.m_accelNoiseMag = 0.1f; // Accel noise magnitude for Kalman filter
+ m_trackerSettings.m_distThres = 0.95f; // Distance threshold between region and object on two frames
+#if 1
+ m_trackerSettings.m_minAreaRadiusPix = frame.rows / 5.f;
+#else
+ m_trackerSettings.m_minAreaRadiusPix = -1.f;
+#endif
+ m_trackerSettings.m_minAreaRadiusK = 0.8f;
+
+ m_trackerSettings.m_useAbandonedDetection = false;
+ if (m_trackerSettings.m_useAbandonedDetection)
+ {
+ m_trackerSettings.m_minStaticTime = m_minStaticTime;
+ m_trackerSettings.m_maxStaticTime = 10;
+ m_trackerSettings.m_maximumAllowedLostTime = m_trackerSettings.m_minStaticTime; // Maximum allowed lost time
+ m_trackerSettings.m_maxTraceLength = 2 * m_trackerSettings.m_maximumAllowedLostTime; // Maximum trace length
+ }
+ else
+ {
+ m_trackerSettings.m_maximumAllowedLostTime = 2.; // Maximum allowed lost time
+ m_trackerSettings.m_maxTraceLength = 2.; // Maximum trace length
+ }
+ }
+
+ m_tracker = BaseTracker::CreateTracker(m_trackerSettings, m_fps);
+ return true;
+ }
+
+ ///
+ /// \brief DrawData
+ /// \param frame
+ /// \param tracks
+ /// \param framesCounter
+ /// \param currTime
+ ///
+ void DrawData(cv::Mat frame, const std::vector& tracks, int framesCounter, int currTime) override
+ {
+ m_logger->info("Frame {0} ({1}): tracks = {2}, time = {3}", framesCounter, m_framesCount, tracks.size(), currTime);
+
+#ifdef USE_CLIP
+ std::vector clipResult;
+ std::vector clipRects;
+ clipRects.reserve(tracks.size());
+ for (const auto& track : tracks)
+ {
+ clipRects.emplace_back(track.GetBoundingRect());
+ }
+ m_clip.ProcessFrame(frame, clipRects, clipResult);
+#endif // USE_CLIP
+
+ for (size_t i = 0; i < tracks.size(); ++i)
+ {
+ const auto& track = tracks[i];
+ if (track.m_isStatic)
+ {
+#ifdef USE_CLIP
+ DrawTrack(frame, track, false, framesCounter, clipResult[i].m_label + ", " + std::to_string(clipResult[i].m_conf));
+#else
+ DrawTrack(frame, track, false, framesCounter);
+#endif //USE_CLIP
+ std::string label = "abandoned " + track.m_ID.ID2Str();
+ int baseLine = 0;
+ cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_TRIPLEX, 0.5, 1, &baseLine);
+
+ cv::Rect brect = track.m_rrect.boundingRect();
+ if (brect.x < 0)
+ {
+ brect.width = std::min(brect.width, frame.cols - 1);
+ brect.x = 0;
+ }
+ else if (brect.x + brect.width >= frame.cols)
+ {
+ brect.x = std::max(0, frame.cols - brect.width - 1);
+ brect.width = std::min(brect.width, frame.cols - 1);
+ }
+ if (brect.y - labelSize.height < 0)
+ {
+ brect.height = std::min(brect.height, frame.rows - 1);
+ brect.y = labelSize.height;
+ }
+ else if (brect.y + brect.height >= frame.rows)
+ {
+ brect.y = std::max(0, frame.rows - brect.height - 1);
+ brect.height = std::min(brect.height, frame.rows - 1);
+ }
+ DrawFilledRect(frame, cv::Rect(cv::Point(brect.x, brect.y - labelSize.height), cv::Size(labelSize.width, labelSize.height + baseLine)), cv::Scalar(255, 0, 255), 150);
+ cv::putText(frame, label, brect.tl(), cv::FONT_HERSHEY_TRIPLEX, 0.5, cv::Scalar(0, 0, 0));
+ }
+ else
+ {
+ auto velocity = sqrt(sqr(track.m_velocity[0]) + sqr(track.m_velocity[1]));
+ if (track.IsRobust(4, // Minimal trajectory size
+ 0.3f, // Minimal ratio raw_trajectory_points / trajectory_lenght
+ cv::Size2f(0.2f, 5.0f), // Min and max ratio: width / height
+ 2))
+ //velocity > 30 // Velocity more than 30 pixels per second
+ {
+ //track_t mean = 0;
+ //track_t stddev = 0;
+ //TrackingObject::LSParams lsParams;
+ //if (track.LeastSquares2(20, mean, stddev, lsParams) && mean > stddev)
+ {
+#ifdef USE_CLIP
+ DrawTrack(frame, track, true, framesCounter, clipResult[i].m_label + ", " + std::to_string(clipResult[i].m_conf));
+#else
+ DrawTrack(frame, track, true, framesCounter);
+#endif //USE_CLIP
+ }
+ }
+ }
+ }
+ m_detector->CalcMotionMap(frame);
+ }
+
+private:
+ int m_minObjWidth = 8;
+ int m_minStaticTime = 5;
+
+#ifdef USE_CLIP
+ ClassificationCLIP m_clip;
+#endif // USE_CLIP
+};
diff --git a/example/MouseExample.h b/example/MouseExample.h
index 1ec385148..e58fd5a1e 100644
--- a/example/MouseExample.h
+++ b/example/MouseExample.h
@@ -1,6 +1,6 @@
#pragma once
-#include "Ctracker.h"
+#include "BaseTracker.h"
#include
#include
@@ -37,10 +37,8 @@ void MouseTracking(cv::CommandLineParser parser)
cv::Mat frame = cv::Mat(800, 800, CV_8UC3);
- if (!writer.isOpened())
- {
- writer.open(outFile, cv::VideoWriter::fourcc('P', 'I', 'M', '1'), 20, frame.size(), true);
- }
+ if (!outFile.empty() && !writer.isOpened())
+ writer.open(outFile, cv::VideoWriter::fourcc('h', '2', '6', '4'), 20, frame.size(), true);
// Set mouse callback
cv::Point2f pointXY;
@@ -56,10 +54,10 @@ void MouseTracking(cv::CommandLineParser parser)
settings.m_accelNoiseMag = 0.5f;
settings.m_distThres = 0.8f;
settings.m_minAreaRadiusPix = frame.rows / 20.f;
- settings.m_maximumAllowedSkippedFrames = 25;
- settings.m_maxTraceLength = 25;
+ settings.m_maximumAllowedLostTime = 1.;
+ settings.m_maxTraceLength = 1.;
- CTracker tracker(settings);
+ std::unique_ptr tracker = BaseTracker::CreateTracker(settings, 30.f);
track_t alpha = 0;
cv::RNG rng;
@@ -73,10 +71,10 @@ void MouseTracking(cv::CommandLineParser parser)
// Append circulating around mouse cv::Points (frequently intersecting)
std::vector pts;
- pts.push_back(Point_t(Xmeasured + 100.0f*sin(-alpha), Ymeasured + 100.0f*cos(-alpha)));
- pts.push_back(Point_t(Xmeasured + 100.0f*sin(alpha), Ymeasured + 100.0f*cos(alpha)));
- pts.push_back(Point_t(Xmeasured + 100.0f*sin(alpha / 2.0f), Ymeasured + 100.0f*cos(alpha / 2.0f)));
- pts.push_back(Point_t(Xmeasured + 100.0f*sin(alpha / 3.0f), Ymeasured + 100.0f*cos(alpha / 1.0f)));
+ pts.emplace_back(Xmeasured + 100.0f*sin(-alpha), Ymeasured + 100.0f*cos(-alpha));
+ pts.emplace_back(Xmeasured + 100.0f*sin(alpha), Ymeasured + 100.0f*cos(alpha));
+ pts.emplace_back(Xmeasured + 100.0f*sin(alpha / 2.0f), Ymeasured + 100.0f*cos(alpha / 2.0f));
+ pts.emplace_back(Xmeasured + 100.0f*sin(alpha / 3.0f), Ymeasured + 100.0f*cos(alpha / 1.0f));
alpha += 0.05f;
regions_t regions;
@@ -85,7 +83,6 @@ void MouseTracking(cv::CommandLineParser parser)
regions.push_back(CRegion(cv::Rect(cvRound(p.x), cvRound(p.y), 1, 1)));
}
-
for (size_t i = 0; i < pts.size(); i++)
{
#if (CV_VERSION_MAJOR >= 4)
@@ -95,9 +92,10 @@ void MouseTracking(cv::CommandLineParser parser)
#endif
}
- tracker.Update(regions, cv::UMat(), 100);
+ tracker->Update(regions, cv::UMat(), std::chrono::system_clock::now());
- auto tracks = tracker.GetTracks();
+ std::vector tracks;
+ tracker->GetTracks(tracks);
std::cout << tracks.size() << std::endl;
for (size_t i = 0; i < tracks.size(); i++)
@@ -118,9 +116,7 @@ void MouseTracking(cv::CommandLineParser parser)
}
if (writer.isOpened())
- {
writer << frame;
- }
#ifndef SILENT_WORK
cv::imshow("Video", frame);
diff --git a/example/VideoExample.cpp b/example/VideoExample.cpp
index 6cf8d2fce..4c41e0032 100644
--- a/example/VideoExample.cpp
+++ b/example/VideoExample.cpp
@@ -1,8 +1,5 @@
#include
#include
-#include
-
-#include
#include "VideoExample.h"
@@ -11,78 +8,75 @@
/// \param parser
///
VideoExample::VideoExample(const cv::CommandLineParser& parser)
- : m_resultsLog(parser.get("res"))
+ : m_resultsLog(parser.get("log_res"), parser.get("write_n_frame")),
+ m_cvatAnnotationsGenerator(parser.get("cvat_res"))
{
m_inFile = parser.get(0);
m_outFile = parser.get("out");
- m_showLogs = parser.get("show_logs") != 0;
+ m_showLogsLevel = parser.get("show_logs");
m_startFrame = parser.get("start_frame");
m_endFrame = parser.get("end_frame");
m_finishDelay = parser.get("end_delay");
-
- m_colors.push_back(cv::Scalar(255, 0, 0));
- m_colors.push_back(cv::Scalar(0, 255, 0));
- m_colors.push_back(cv::Scalar(0, 0, 255));
- m_colors.push_back(cv::Scalar(255, 255, 0));
- m_colors.push_back(cv::Scalar(0, 255, 255));
- m_colors.push_back(cv::Scalar(255, 0, 255));
- m_colors.push_back(cv::Scalar(255, 127, 255));
- m_colors.push_back(cv::Scalar(127, 0, 255));
- m_colors.push_back(cv::Scalar(127, 0, 127));
+ m_batchSize = std::max(1, parser.get("batch_size"));
+ m_useContrastAdjustment = parser.get("contrast_adjustment") != 0;
+
+ m_colors.emplace_back(255, 0, 0);
+ m_colors.emplace_back(0, 255, 0);
+ m_colors.emplace_back(0, 0, 255);
+ m_colors.emplace_back(255, 255, 0);
+ m_colors.emplace_back(0, 255, 255);
+ m_colors.emplace_back(255, 0, 255);
+ m_colors.emplace_back(255, 127, 255);
+ m_colors.emplace_back(127, 0, 255);
+ m_colors.emplace_back(127, 0, 127);
m_resultsLog.Open();
- std::string settingsFile = parser.get("settings");
- m_trackerSettingsLoaded = ParseTrackerSettings(settingsFile);
-}
+ // Create loggers
+ m_consoleSink = std::make_shared();
+ m_consoleSink->set_level(spdlog::level::from_str(m_showLogsLevel));
+ m_consoleSink->set_pattern("[%^%l%$] %v");
+
+ auto currentTime = std::chrono::system_clock::now();
+ auto transformed = currentTime.time_since_epoch().count() / 1000000;
+ std::time_t tt = std::chrono::system_clock::to_time_t(currentTime);
+ char buffer[80];
+#ifdef WIN32
+ tm timeInfo;
+ localtime_s(&timeInfo, &tt);
+ strftime(buffer, 80, "%G%m%d_%H%M%S", &timeInfo);
+#else
+ auto timeInfo = localtime(&tt);
+ strftime(buffer, 80, "%G%m%d_%H%M%S", timeInfo);
+#endif
-///
-/// \brief VideoExample::ParseTrackerSettings
-///
-bool VideoExample::ParseTrackerSettings(const std::string& settingsFile)
-{
- INIReader reader(settingsFile);
+ size_t max_size = 1024 * 1024 * 5;
+ size_t max_files = 3;
+ m_fileSink = std::make_shared("logs/" + std::string(buffer) + std::to_string(transformed % 1000) + ".txt", max_size, max_files);
+ m_fileSink->set_level(spdlog::level::from_str(m_showLogsLevel));
+
+ m_logger = std::shared_ptr(new spdlog::logger("traffic", { m_consoleSink, m_fileSink }));
+ m_logger->set_level(spdlog::level::from_str(m_showLogsLevel));
+ m_logger->info("Start service");
+
+ std::string settingsFile = parser.get("settings");
+ m_trackerSettingsLoaded = ParseTrackerSettings(settingsFile, m_trackerSettings);
- if (reader.ParseError() >= 0)
+ if (m_batchSize > 1)
{
- m_trackerSettings = TrackerSettings();
-
- auto distType = reader.GetInteger("tracking", "distance_type", -1);
- if (distType >=0 && distType < (int)tracking::DistsCount)
- m_trackerSettings.SetDistance((tracking::DistType)distType);
-
- auto kalmanType = reader.GetInteger("tracking", "kalman_type", -1);
- if (kalmanType >=0 && kalmanType < (int)tracking::KalmanCount)
- m_trackerSettings.m_kalmanType = (tracking::KalmanType)kalmanType;
-
- auto filterGoal = reader.GetInteger("tracking", "filter_goal", -1);
- if (filterGoal >=0 && filterGoal < (int)tracking::FiltersCount)
- m_trackerSettings.m_filterGoal = (tracking::FilterGoal)filterGoal;
-
- auto lostTrackType = reader.GetInteger("tracking", "lost_track_type", -1);
- if (lostTrackType >=0 && lostTrackType < (int)tracking::SingleTracksCount)
- m_trackerSettings.m_lostTrackType = (tracking::LostTrackType)lostTrackType;
-
- auto matchType = reader.GetInteger("tracking", "match_type", -1);
- if (matchType >=0 && matchType < (int)tracking::MatchCount)
- m_trackerSettings.m_matchType = (tracking::MatchType)matchType;
-
- m_trackerSettings.m_useAcceleration = reader.GetInteger("tracking", "use_aceleration", 0) != 0; // Use constant acceleration motion model
- m_trackerSettings.m_dt = static_cast(reader.GetReal("tracking", "delta_time", 0.4)); // Delta time for Kalman filter
- m_trackerSettings.m_accelNoiseMag = static_cast(reader.GetReal("tracking", "accel_noise", 0.2)); // Accel noise magnitude for Kalman filter
- m_trackerSettings.m_distThres = static_cast(reader.GetReal("tracking", "dist_thresh", 0.8)); // Distance threshold between region and object on two frames
- m_trackerSettings.m_minAreaRadiusPix = static_cast(reader.GetReal("tracking", "min_area_radius_pix", -1.));
- m_trackerSettings.m_minAreaRadiusK = static_cast(reader.GetReal("tracking", "min_area_radius_k", 0.8));
- m_trackerSettings.m_maximumAllowedSkippedFrames = reader.GetInteger("tracking", "max_skip_frames", 50); // Maximum allowed skipped frames
- m_trackerSettings.m_maxTraceLength = reader.GetInteger("tracking", "max_trace_len", 50); // Maximum trace length
- m_trackerSettings.m_useAbandonedDetection = reader.GetInteger("tracking", "detect_abandoned", 0) != 0;
- m_trackerSettings.m_minStaticTime = reader.GetInteger("tracking", "min_static_time", 5);
- m_trackerSettings.m_maxStaticTime = reader.GetInteger("tracking", "max_static_time", 25);
- m_trackerSettings.m_maxSpeedForStatic = reader.GetInteger("tracking", "max_speed_for_static", 10);
-
- return true;
+ m_frameInfo[0].SetBatchSize(m_batchSize);
+ m_frameInfo[1].SetBatchSize(m_batchSize);
}
- return false;
+ for (auto& fr : m_frameInfo[0].m_frames)
+ {
+ fr.SetUseAdjust(m_useContrastAdjustment);
+ }
+ for (auto& fr : m_frameInfo[1].m_frames)
+ {
+ fr.SetUseAdjust(m_useContrastAdjustment);
+ }
+
+ m_startTimeStamp = currentTime;
}
///
@@ -97,8 +91,6 @@ void VideoExample::SyncProcess()
bool manualMode = false;
#endif
- cv::Mat frame;
-
double freq = cv::getTickFrequency();
int64 allTime = 0;
@@ -107,27 +99,101 @@ void VideoExample::SyncProcess()
cv::VideoCapture capture;
if (!OpenCapture(capture))
{
- std::cerr << "Can't open " << m_inFile << std::endl;
+ m_logger->critical("Can't open {}", m_inFile);
return;
}
+#if 0
+ // Write preview
+ cv::Mat prFrame;
+ capture >> prFrame;
+ cv::Mat textFrame(prFrame.size(), CV_8UC3);
+ textFrame = cv::Scalar(0, 0, 0);
+ std::string label{ "Original video" };
+ int baseLine = 0;
+ double fontScale = (textFrame.cols < 1920) ? 2.0 : 3.0;
+ int thickness = 2;
+ int lineType = cv::LINE_AA;
+ int fontFace = cv::FONT_HERSHEY_TRIPLEX;
+ cv::Size labelSize = cv::getTextSize(label, fontFace, fontScale, thickness, &baseLine);
+ cv::putText(textFrame, label, cv::Point(textFrame.cols / 2 - labelSize.width / 2, textFrame.rows / 2 - labelSize.height / 2), fontFace, fontScale, cv::Scalar(255, 255, 255), thickness, lineType);
+ for (size_t fi = 0; fi < cvRound(2 * m_fps); ++fi)
+ {
+ WriteFrame(writer, textFrame);
+ }
+ WriteFrame(writer, prFrame);
+ for (;;)
+ {
+ capture >> prFrame;
+ if (prFrame.empty())
+ break;
+ WriteFrame(writer, prFrame);
+ }
+ textFrame = cv::Scalar(0, 0, 0);
+ label = "Detection result";
+ labelSize = cv::getTextSize(label, fontFace, fontScale, thickness, &baseLine);
+ cv::putText(textFrame, label, cv::Point(textFrame.cols / 2 - labelSize.width / 2, textFrame.rows / 2 - labelSize.height / 2), fontFace, fontScale, cv::Scalar(255, 255, 255), thickness, lineType);
+ for (size_t fi = 0; fi < cvRound(2 * m_fps); ++fi)
+ {
+ WriteFrame(writer, textFrame);
+ }
+ capture.release();
+ OpenCapture(capture);
+#endif
+
+ FrameInfo frameInfo(m_batchSize);
+ frameInfo.m_frames.resize(frameInfo.m_batchSize);
+ frameInfo.m_frameInds.resize(frameInfo.m_batchSize);
+ frameInfo.m_frameTimeStamps.resize(frameInfo.m_batchSize);
+
+ for (auto& fr : frameInfo.m_frames)
+ {
+ fr.SetUseAdjust(m_useContrastAdjustment);
+ }
+
int64 startLoopTime = cv::getTickCount();
+ //double fps = capture.get(cv::CAP_PROP_FPS);
+ //double readPeriodSeconds = 2.;
+ //int readPeriodFrames = cvRound(readPeriodSeconds * fps);
+
for (;;)
{
- capture >> frame;
- if (frame.empty())
- break;
+ //int currFramesPos = cvRound(capture.get(cv::CAP_PROP_POS_FRAMES));
+
+ size_t i = 0;
+ for (; i < m_batchSize; ++i)
+ {
+ capture >> frameInfo.m_frames[i].GetMatBGRWrite();
+ if (frameInfo.m_frames[i].empty())
+ break;
+ frameInfo.m_frameInds[i] = framesCounter;
+ frameInfo.m_frameTimeStamps[i] = GetNextTimeStamp(framesCounter);
+ frameInfo.m_frames[i].AdjustMatBGR();
+
+ ++framesCounter;
+ if (m_endFrame && framesCounter > m_endFrame)
+ {
+ m_logger->info("Process: riched last {} frame", m_endFrame);
+ break;
+ }
+
+ m_logger->debug("VideoExample::SyncProcess: Capture {0} frame", framesCounter);
+ }
+ if (i < m_batchSize)
+ break;
+
+ //capture.set(cv::CAP_PROP_POS_FRAMES, currFramesPos + readPeriodFrames);
if (!m_isDetectorInitialized || !m_isTrackerInitialized)
{
- cv::UMat ufirst = frame.getUMat(cv::ACCESS_READ);
+ cv::UMat ufirst = frameInfo.m_frames[0].GetUMatBGR();
if (!m_isDetectorInitialized)
{
m_isDetectorInitialized = InitDetector(ufirst);
if (!m_isDetectorInitialized)
{
- std::cerr << "CaptureAndDetect: Detector initialize error!!!" << std::endl;
+ m_logger->critical("CaptureAndDetect: Detector initialize error!!!");
break;
}
}
@@ -136,7 +202,7 @@ void VideoExample::SyncProcess()
m_isTrackerInitialized = InitTracker(ufirst);
if (!m_isTrackerInitialized)
{
- std::cerr << "CaptureAndDetect: Tracker initialize error!!!" << std::endl;
+ m_logger->critical("CaptureAndDetect: Tracker initialize error!!!");
break;
}
}
@@ -144,42 +210,41 @@ void VideoExample::SyncProcess()
int64 t1 = cv::getTickCount();
- regions_t regions;
- Detection(frame, regions);
- Tracking(frame, regions);
+ Detection(frameInfo);
+ Tracking(frameInfo);
int64 t2 = cv::getTickCount();
allTime += t2 - t1;
int currTime = cvRound(1000 * (t2 - t1) / freq);
- DrawData(frame, framesCounter, currTime);
+ for (i = 0; i < m_batchSize; ++i)
+ {
+ DrawData(frameInfo.m_frames[i].GetMatBGR(), frameInfo.m_tracks[i], frameInfo.m_frameInds[i], currTime);
#ifndef SILENT_WORK
- cv::imshow("Video", frame);
-
- int waitTime = manualMode ? 0 : 1;// std::max(1, cvRound(1000 / m_fps - currTime));
- int k = cv::waitKey(waitTime);
- if (k == 27)
- break;
- else if (k == 'm' || k == 'M')
- manualMode = !manualMode;
+ cv::imshow("Video", frameInfo.m_frames[i].GetMatBGR());
+
+ int waitTime = manualMode ? 0 : 1;// std::max(1, cvRound(1000 / m_fps - currTime));
+ int k = cv::waitKey(waitTime);
+ if (k == 27)
+ break;
+ else if (k == 'm' || k == 'M')
+ manualMode = !manualMode;
#else
- std::this_thread::sleep_for(std::chrono::milliseconds(1));
+ //std::this_thread::sleep_for(std::chrono::milliseconds(1));
#endif
- WriteFrame(writer, frame);
-
- ++framesCounter;
- if (m_endFrame && framesCounter > m_endFrame)
- {
- std::cout << "Process: riched last " << m_endFrame << " frame" << std::endl;
- break;
- }
+ WriteFrame(writer, frameInfo.m_frames[i].GetMatBGR());
+ }
+ if (framesCounter % 100 == 0)
+ m_resultsLog.Flush();
}
+ m_cvatAnnotationsGenerator.Save(m_inFile, m_framesCount, m_frameSize);
+
int64 stopLoopTime = cv::getTickCount();
- std::cout << "algorithms time = " << (allTime / freq) << ", work time = " << ((stopLoopTime - startLoopTime) / freq) << std::endl;
+ m_logger->info("algorithms time = {0}, work time = {1}", allTime / freq, (stopLoopTime - startLoopTime) / freq);
#ifndef SILENT_WORK
cv::waitKey(m_finishDelay);
#endif
@@ -203,30 +268,28 @@ void VideoExample::AsyncProcess()
double freq = cv::getTickFrequency();
- int framesCounter = m_startFrame + 1;
-
int64 allTime = 0;
int64 startLoopTime = cv::getTickCount();
- size_t processCounter = 0;
+ size_t processCounter = 0;
for (; !stopCapture.load(); )
{
FrameInfo& frameInfo = m_frameInfo[processCounter % 2];
+ m_logger->debug("--- waiting tracking from {0} ind = {1}", processCounter % 2, processCounter);
{
std::unique_lock lock(frameInfo.m_mutex);
- if (!frameInfo.m_cond.wait_for(lock, std::chrono::milliseconds(m_captureTimeOut), [&frameInfo]{ return frameInfo.m_captured; }))
+ if (!frameInfo.m_cond.wait_for(lock, std::chrono::milliseconds(m_captureTimeOut), [&frameInfo] { return frameInfo.m_captured.load(); }))
{
- std::cout << "Wait frame timeout!" << std::endl;
+ m_logger->info("--- Wait frame timeout!");
break;
}
}
-
+ m_logger->debug("--- tracking from {} in progress...", processCounter % 2);
if (!m_isTrackerInitialized)
{
- cv::UMat ufirst = frameInfo.m_frame.getUMat(cv::ACCESS_READ);
- m_isTrackerInitialized = InitTracker(ufirst);
+ m_isTrackerInitialized = InitTracker(frameInfo.m_frames[0].GetUMatBGR());
if (!m_isTrackerInitialized)
{
- std::cerr << "CaptureAndDetect: Tracker initialize error!!!" << std::endl;
+ m_logger->critical("--- AsyncProcess: Tracker initialize error!!!");
frameInfo.m_cond.notify_one();
break;
}
@@ -234,58 +297,62 @@ void VideoExample::AsyncProcess()
int64 t1 = cv::getTickCount();
- Tracking(frameInfo.m_frame, frameInfo.m_regions);
+ Tracking(frameInfo);
int64 t2 = cv::getTickCount();
allTime += t2 - t1 + frameInfo.m_dt;
int currTime = cvRound(1000 * (t2 - t1 + frameInfo.m_dt) / freq);
- //std::cout << "Frame " << framesCounter << ": td = " << (1000 * frameInfo.m_dt / freq) << ", tt = " << (1000 * (t2 - t1) / freq) << std::endl;
+ m_logger->debug("--- Frame {0}: td = {1}, tt = {2}", frameInfo.m_frameInds[0], 1000 * frameInfo.m_dt / freq, 1000 * (t2 - t1) / freq);
- DrawData(frameInfo.m_frame, framesCounter, currTime);
+ int key = 0;
+ for (size_t i = 0; i < m_batchSize; ++i)
+ {
+ DrawData(frameInfo.m_frames[i].GetMatBGR(), frameInfo.m_tracks[i], frameInfo.m_frameInds[i], currTime);
- WriteFrame(writer, frameInfo.m_frame);
+ WriteFrame(writer, frameInfo.m_frames[i].GetMatBGR());
- int k = 0;
#ifndef SILENT_WORK
- cv::imshow("Video", frameInfo.m_frame);
-
- int waitTime = manualMode ? 0 : 1;// std::max(1, cvRound(1000 / m_fps - currTime));
- k = cv::waitKey(waitTime);
- if (k == 'm' || k == 'M')
- {
- manualMode = !manualMode;
- }
+ cv::imshow("Video", frameInfo.m_frames[i].GetMatBGR());
+
+ int waitTime = manualMode ? 0 : 1;// std::max(1, cvRound(1000 / m_fps - currTime));
+ key = cv::waitKey(waitTime);
+ if (key == 'm' || key == 'M')
+ manualMode = !manualMode;
+ else
+ break;
#else
- std::this_thread::sleep_for(std::chrono::milliseconds(1));
+ //std::this_thread::sleep_for(std::chrono::milliseconds(1));
#endif
-
- {
- std::unique_lock lock(frameInfo.m_mutex);
- frameInfo.m_captured = false;
}
+
+ {
+ std::unique_lock lock(frameInfo.m_mutex);
+ m_logger->debug("--- tracking m_captured {0} - captured still {1}", processCounter % 2, frameInfo.m_captured.load());
+ assert(frameInfo.m_captured.load());
+ frameInfo.m_captured = false;
+ }
frameInfo.m_cond.notify_one();
- if (k == 27)
+ if (key == 27)
break;
- ++framesCounter;
- if (m_endFrame && framesCounter > m_endFrame)
- {
- std::cout << "Process: riched last " << m_endFrame << " frame" << std::endl;
- break;
- }
- ++processCounter;
+ ++processCounter;
+
+ if (processCounter % 100 == 0)
+ m_resultsLog.Flush();
}
stopCapture = true;
if (thCapDet.joinable())
thCapDet.join();
+ m_cvatAnnotationsGenerator.Save(m_inFile, m_framesCount, m_frameSize);
+
int64 stopLoopTime = cv::getTickCount();
- std::cout << "algorithms time = " << (allTime / freq) << ", work time = " << ((stopLoopTime - startLoopTime) / freq) << std::endl;
+ m_logger->info("--- algorithms time = {0}, work time = {1}", allTime / freq, (stopLoopTime - startLoopTime) / freq);
#ifndef SILENT_WORK
cv::waitKey(m_finishDelay);
@@ -302,87 +369,133 @@ void VideoExample::CaptureAndDetect(VideoExample* thisPtr, std::atomic& st
cv::VideoCapture capture;
if (!thisPtr->OpenCapture(capture))
{
- std::cerr << "Can't open " << thisPtr->m_inFile << std::endl;
+ thisPtr->m_logger->critical("+++ Can't open {}", thisPtr->m_inFile);
stopCapture = true;
return;
}
- int trackingTimeOut = thisPtr->m_trackingTimeOut;
- size_t processCounter = 0;
+ int framesCounter = 0;
+
+ const auto localEndFrame = thisPtr->m_endFrame;
+ auto localIsDetectorInitialized = thisPtr->m_isDetectorInitialized;
+ auto localTrackingTimeOut = thisPtr->m_trackingTimeOut;
+ size_t processCounter = 0;
for (; !stopCapture.load();)
{
FrameInfo& frameInfo = thisPtr->m_frameInfo[processCounter % 2];
-
+ thisPtr->m_logger->debug("+++ waiting capture to {0}, ind = {1}", processCounter % 2, processCounter);
{
std::unique_lock lock(frameInfo.m_mutex);
- if (!frameInfo.m_cond.wait_for(lock, std::chrono::milliseconds(trackingTimeOut), [&frameInfo]{ return !frameInfo.m_captured; }))
+ if (!frameInfo.m_cond.wait_for(lock, std::chrono::milliseconds(localTrackingTimeOut), [&frameInfo] { return !frameInfo.m_captured.load(); }))
{
- std::cout << "Wait tracking timeout!" << std::endl;
+ thisPtr->m_logger->info("+++ Wait tracking timeout!");
frameInfo.m_cond.notify_one();
break;
}
}
+ thisPtr->m_logger->debug("+++ capture to {0} in progress...", processCounter % 2);
+ if (frameInfo.m_frames.size() < frameInfo.m_batchSize)
+ {
+ frameInfo.m_frames.resize(frameInfo.m_batchSize);
+ frameInfo.m_frameInds.resize(frameInfo.m_batchSize);
+ frameInfo.m_frameTimeStamps.resize(frameInfo.m_batchSize);
+ }
- capture >> frameInfo.m_frame;
- if (frameInfo.m_frame.empty())
- {
- std::cerr << "CaptureAndDetect: frame is empty!" << std::endl;
- frameInfo.m_cond.notify_one();
- break;
+ cv::Mat frame;
+ size_t i = 0;
+ for (; i < frameInfo.m_batchSize; ++i)
+ {
+ capture >> frame;
+ if (frame.empty())
+ {
+ thisPtr->m_logger->error("+++ CaptureAndDetect: frame is empty!");
+ frameInfo.m_cond.notify_one();
+ break;
+ }
+ frameInfo.m_frames[i].GetMatBGRWrite() = frame;
+ frameInfo.m_frames[i].AdjustMatBGR();
+ frameInfo.m_frameInds[i] = framesCounter;
+ frameInfo.m_frameTimeStamps[i] = thisPtr->GetNextTimeStamp(framesCounter);
+ ++framesCounter;
+
+ if (localEndFrame && framesCounter > localEndFrame)
+ {
+ thisPtr->m_logger->info("+++ Process: riched last {} frame", localEndFrame);
+ break;
+ }
}
+ if (i < frameInfo.m_batchSize)
+ break;
- if (!thisPtr->m_isDetectorInitialized)
+ if (!localIsDetectorInitialized)
{
- cv::UMat ufirst = frameInfo.m_frame.getUMat(cv::ACCESS_READ);
- thisPtr->m_isDetectorInitialized = thisPtr->InitDetector(ufirst);
+ thisPtr->m_isDetectorInitialized = thisPtr->InitDetector(frameInfo.m_frames[0].GetUMatBGR());
+ localIsDetectorInitialized = thisPtr->m_isDetectorInitialized;
if (!thisPtr->m_isDetectorInitialized)
{
- std::cerr << "CaptureAndDetect: Detector initialize error!!!" << std::endl;
+ thisPtr->m_logger->critical("+++ CaptureAndDetect: Detector initialize error!!!");
frameInfo.m_cond.notify_one();
break;
}
}
int64 t1 = cv::getTickCount();
- thisPtr->Detection(frameInfo.m_frame, frameInfo.m_regions);
+ thisPtr->Detection(frameInfo);
int64 t2 = cv::getTickCount();
frameInfo.m_dt = t2 - t1;
- {
- std::unique_lock lock(frameInfo.m_mutex);
- frameInfo.m_captured = true;
- }
- frameInfo.m_cond.notify_one();
+ {
+ std::unique_lock lock(frameInfo.m_mutex);
+ thisPtr->m_logger->debug("+++ capture m_captured {0} - captured still {1}", processCounter % 2, frameInfo.m_captured.load());
+ assert(!frameInfo.m_captured.load());
+ frameInfo.m_captured = true;
+ }
+ frameInfo.m_cond.notify_one();
++processCounter;
}
stopCapture = true;
}
+///
+/// \brief VideoExample::GetNextTimeStamp
+/// \param framesCounter
+/// \return
+///
+time_point_t VideoExample::GetNextTimeStamp(int framesCounter) const
+{
+ if (m_useArchieveTime)
+ return m_startTimeStamp + std::chrono::milliseconds(cvRound(framesCounter * (1000.f / m_fps)));
+ else
+ return std::chrono::system_clock::now();
+}
+
///
/// \brief VideoExample::Detection
/// \param frame
/// \param regions
///
-void VideoExample::Detection(cv::Mat frame, regions_t& regions)
+void VideoExample::Detection(FrameInfo& frame)
{
- cv::UMat uframe;
- if (!m_detector->CanGrayProcessing())
- uframe = frame.getUMat(cv::ACCESS_READ);
- else
- cv::cvtColor(frame, uframe, cv::COLOR_BGR2GRAY);
-
- for (const auto& track : m_tracks)
+ if (m_trackerSettings.m_useAbandonedDetection)
{
- if (track.m_isStatic)
- m_detector->ResetModel(uframe, track.m_rrect.boundingRect());
+ for (const auto& track : m_tracks)
+ {
+ if (track.m_isStatic)
+ m_detector->ResetModel(frame.m_frames[0].GetUMatBGR(), track.m_rrect.boundingRect());
+ }
}
- m_detector->Detect(uframe);
-
- const regions_t& regs = m_detector->GetDetects();
-
- regions.assign(std::begin(regs), std::end(regs));
+ std::vector frames;
+ for (size_t i = 0; i < frame.m_frames.size(); ++i)
+ {
+ if (m_detector->CanGrayProcessing())
+ frames.emplace_back(frame.m_frames[i].GetUMatGray());
+ else
+ frames.emplace_back(frame.m_frames[i].GetUMatBGR());
+ }
+ frame.CleanRegions();
+ m_detector->Detect(frames, frame.m_regions);
}
///
@@ -390,42 +503,43 @@ void VideoExample::Detection(cv::Mat frame, regions_t& regions)
/// \param frame
/// \param regions
///
-void VideoExample::Tracking(cv::Mat frame, const regions_t& regions)
+void VideoExample::Tracking(FrameInfo& frame)
{
- cv::UMat uframe;
- if (m_tracker->CanColorFrameToTrack())
- uframe = frame.getUMat(cv::ACCESS_READ);
- else
- cv::cvtColor(frame, uframe, cv::COLOR_BGR2GRAY);
+ assert(frame.m_regions.size() == frame.m_frames.size());
+
+ frame.CleanTracks();
+ for (size_t i = 0; i < frame.m_frames.size(); ++i)
+ {
+ m_tracker->Update(frame.m_regions[i], frame.m_frames[i].GetUMatBGR(), frame.m_frameTimeStamps[i]);
+ m_tracker->GetTracks(frame.m_tracks[i]);
- m_tracker->Update(regions, uframe, m_fps);
+ m_cvatAnnotationsGenerator.NewDetects(frame.m_frameInds[i], frame.m_tracks[i], 0);
+ }
+ if (m_trackerSettings.m_useAbandonedDetection)
+ m_tracker->GetTracks(m_tracks);
}
///
/// \brief VideoExample::DrawTrack
/// \param frame
-/// \param resizeCoeff
/// \param track
/// \param drawTrajectory
///
void VideoExample::DrawTrack(cv::Mat frame,
- int resizeCoeff,
const TrackingObject& track,
bool drawTrajectory,
- int framesCounter)
+ int framesCounter,
+ const std::string& userLabel)
{
- auto ResizePoint = [resizeCoeff](const cv::Point& pt) -> cv::Point
- {
- return cv::Point(resizeCoeff * pt.x, resizeCoeff * pt.y);
- };
-
cv::Scalar color = track.m_isStatic ? cv::Scalar(255, 0, 255) : cv::Scalar(0, 255, 0);
cv::Point2f rectPoints[4];
track.m_rrect.points(rectPoints);
+ //std::cout << "track: rrect [" << track.m_rrect.size << " from " << track.m_rrect.center << ", " << track.m_rrect.angle << "]" << std::endl;
for (int i = 0; i < 4; ++i)
{
- cv::line(frame, ResizePoint(rectPoints[i]), ResizePoint(rectPoints[(i+1) % 4]), color);
+ cv::line(frame, rectPoints[i], rectPoints[(i+1) % 4], color);
}
+
#if 0
#if 0
track_t minAreaRadiusPix = frame.rows / 20.f;
@@ -471,29 +585,88 @@ void VideoExample::DrawTrack(cv::Mat frame,
#endif
if (drawTrajectory)
{
- cv::Scalar cl = m_colors[track.m_ID % m_colors.size()];
+ cv::Scalar cl = m_colors[track.m_ID.ID2Module(m_colors.size())];
for (size_t j = 0; j < track.m_trace.size() - 1; ++j)
{
const TrajectoryPoint& pt1 = track.m_trace.at(j);
const TrajectoryPoint& pt2 = track.m_trace.at(j + 1);
#if (CV_VERSION_MAJOR >= 4)
- cv::line(frame, ResizePoint(pt1.m_prediction), ResizePoint(pt2.m_prediction), cl, 1, cv::LINE_AA);
+ cv::line(frame, pt1.m_prediction, pt2.m_prediction, cl, 1, cv::LINE_AA);
#else
- cv::line(frame, ResizePoint(pt1.m_prediction), ResizePoint(pt2.m_prediction), cl, 1, CV_AA);
+ cv::line(frame, pt1.m_prediction, pt2.m_prediction, cl, 1, CV_AA);
#endif
if (!pt2.m_hasRaw)
{
#if (CV_VERSION_MAJOR >= 4)
- cv::circle(frame, ResizePoint(pt2.m_prediction), 4, cl, 1, cv::LINE_AA);
+ cv::circle(frame, pt2.m_prediction, 4, cl, 1, cv::LINE_AA);
#else
- cv::circle(frame, ResizePoint(pt2.m_prediction), 4, cl, 1, CV_AA);
+ cv::circle(frame, pt2.m_prediction, 4, cl, 1, CV_AA);
#endif
}
}
}
- cv::Rect brect = track.m_rrect.boundingRect();
+ cv::Rect brect = track.m_rrect.boundingRect();
+ std::stringstream label;
+ label << track.m_ID.ID2Str();
+ if (track.m_type != bad_type)
+ label << ": " << TypeConverter::Type2Str(track.m_type);
+ else if (!userLabel.empty())
+ label << ": " << userLabel;
+ if (track.m_confidence > 0)
+ label << ", " << std::fixed << std::setw(2) << std::setprecision(2) << track.m_confidence;
+#if 0
+ track_t mean = 0;
+ track_t stddev = 0;
+ TrackingObject::LSParams lsParams;
+ if (track.LeastSquares2(10, mean, stddev, lsParams))
+ {
+ std::cout << "LSParams: " << lsParams << std::endl;
+ cv::Scalar cl(255, 0, 255);
+ label += ", [" + std::to_string(cvRound(mean)) + ", " + std::to_string(cvRound(stddev)) + "]";
+ for (size_t j = 0; j < track.m_trace.size() - 1; ++j)
+ {
+ track_t t1 = j;
+ track_t t2 = j + 1;
+ cv::Point pt1(lsParams.m_ax * sqr(t1) + lsParams.m_v0x * t1 + lsParams.m_x0, lsParams.m_ay * sqr(t1) + lsParams.m_v0y * t1 + lsParams.m_y0);
+ cv::Point pt2(lsParams.m_ax * sqr(t2) + lsParams.m_v0x * t2 + lsParams.m_x0, lsParams.m_ay * sqr(t2) + lsParams.m_v0y * t2 + lsParams.m_y0);
+ //std::cout << pt1 << " - " << pt2 << std::endl;
+#if (CV_VERSION_MAJOR >= 4)
+ cv::line(frame, pt1, pt2, cl, 1, cv::LINE_AA);
+#else
+ cv::line(frame, pt1, pt2, cl, 1, CV_AA);
+#endif
+ }
+ }
+ label += ", " + std::to_string(cvRound(sqrt(sqr(track.m_velocity[0]) + sqr(track.m_velocity[1]))));
+#endif
+ int baseLine = 0;
+ double fontScale = (frame.cols < 1920) ? 0.5 : 0.7;
+ cv::Size labelSize = cv::getTextSize(label.str(), cv::FONT_HERSHEY_TRIPLEX, fontScale, 1, &baseLine);
+ if (brect.x < 0)
+ {
+ brect.width = std::min(brect.width, frame.cols - 1);
+ brect.x = 0;
+ }
+ else if (brect.x + brect.width >= frame.cols)
+ {
+ brect.x = std::max(0, frame.cols - brect.width - 1);
+ brect.width = std::min(brect.width, frame.cols - 1);
+ }
+ if (brect.y - labelSize.height < 0)
+ {
+ brect.height = std::min(brect.height, frame.rows - 1);
+ brect.y = labelSize.height;
+ }
+ else if (brect.y + brect.height >= frame.rows)
+ {
+ brect.y = std::max(0, frame.rows - brect.height - 1);
+ brect.height = std::min(brect.height, frame.rows - 1);
+ }
+ DrawFilledRect(frame, cv::Rect(cv::Point(brect.x, brect.y - labelSize.height), cv::Size(labelSize.width, labelSize.height + baseLine)), cv::Scalar(200, 200, 200), 150);
+ cv::putText(frame, label.str(), brect.tl(), cv::FONT_HERSHEY_TRIPLEX, fontScale, cv::Scalar(0, 0, 0));
+
m_resultsLog.AddTrack(framesCounter, track.m_ID, brect, track.m_type, track.m_confidence);
m_resultsLog.AddRobustTrack(track.m_ID);
}
@@ -522,9 +695,13 @@ bool VideoExample::OpenCapture(cv::VideoCapture& capture)
{
capture.set(cv::CAP_PROP_POS_FRAMES, m_startFrame);
- m_fps = std::max(25.f, (float)capture.get(cv::CAP_PROP_FPS));
+ m_fps = std::max(1.f, (float)capture.get(cv::CAP_PROP_FPS));
+
+ m_frameSize.width = cvRound(capture.get(cv::CAP_PROP_FRAME_WIDTH));
+ m_frameSize.height = cvRound(capture.get(cv::CAP_PROP_FRAME_HEIGHT));
+ m_framesCount = cvRound(capture.get(cv::CAP_PROP_FRAME_COUNT));
- std::cout << "Video " << m_inFile << " was started from " << m_startFrame << " frame with " << m_fps << " fps" << std::endl;
+ std::cout << "Video " << m_inFile << " was started from " << m_startFrame << " frame with " << m_fps << " fps, frame size " << m_frameSize << " and length " << m_framesCount << std::endl;
return true;
}
diff --git a/example/VideoExample.h b/example/VideoExample.h
index 2443582a3..b5e0fc693 100644
--- a/example/VideoExample.h
+++ b/example/VideoExample.h
@@ -1,9 +1,7 @@
#pragma once
#include
-#include
#include
-#include
#include
#include
#include
@@ -11,150 +9,213 @@
#include
#include "BaseDetector.h"
-#include "Ctracker.h"
+#include "BaseTracker.h"
+#include "FileLogger.h"
+#include "cvatAnnotationsGenerator.h"
+
+#include "spdlog/spdlog.h"
+#include "spdlog/async.h"
+#include "spdlog/sinks/stdout_color_sinks.h"
+#include "spdlog/sinks/basic_file_sink.h"
+#include "spdlog/sinks/rotating_file_sink.h"
///
-/// \brief The ResultsLog class
+/// \brief The Frame struct
///
-class ResultsLog
+class Frame
{
public:
- ///
- ResultsLog(const std::string& fileName)
- : m_fileName(fileName)
- {
- }
-
- ///
- ~ResultsLog()
- {
- WriteAll(true);
- }
-
- ///
- bool Open()
- {
- m_resCSV.close();
- if (m_fileName.size() > 5)
- {
- m_resCSV.open(m_fileName);
- return m_resCSV.is_open();
- }
- return false;
- }
-
- ///
- bool AddTrack(int framesCounter, size_t trackID, const cv::Rect& brect, objtype_t type, float confidence)
- {
- if (m_resCSV.is_open())
- {
- auto frame = m_frames.find(framesCounter);
- if (frame == std::end(m_frames))
- {
- DetectsOnFrame tmpFrame;
- tmpFrame.m_detects.emplace_back(trackID, brect, type, confidence);
- m_frames.emplace(framesCounter, tmpFrame);
- }
- else
- {
- frame->second.m_detects.emplace_back(trackID, brect, type, confidence);
- }
- return true;
- }
- return false;
- }
-
- ///
- void AddRobustTrack(size_t trackID)
- {
- m_robustIDs.insert(trackID);
- }
+ Frame() = default;
+ Frame(cv::Mat imgBGR, bool useCLAHE)
+ {
+ m_mBGR = imgBGR;
+ if (useCLAHE)
+ {
+ m_clahe = cv::createCLAHE(1.2, cv::Size(4, 4));
+ AdjustMatBGR();
+ }
+ }
+
+ ///
+ void SetUseAdjust(bool useCLAHE)
+ {
+ if (useCLAHE)
+ {
+ m_clahe = cv::createCLAHE(1.2, cv::Size(4, 4));
+ AdjustMatBGR();
+ }
+ else
+ {
+ m_clahe.reset();
+ }
+ }
+
+ ///
+ bool empty() const noexcept
+ {
+ return m_mBGR.empty();
+ }
+
+ ///
+ const cv::Mat& GetMatBGR() const noexcept
+ {
+ return m_mBGR;
+ }
+ ///
+ cv::Mat& GetMatBGRWrite()
+ {
+ m_umBGRGenerated = false;
+ m_mGrayGenerated = false;
+ m_umGrayGenerated = false;
+ return m_mBGR;
+ }
+ ///
+ bool AdjustMatBGR()
+ {
+ if (m_mBGR.empty() || m_clahe.empty())
+ return false;
+
+ cv::cvtColor(m_mBGR, m_mHSV, cv::COLOR_BGR2HSV);
+ cv::split(m_mHSV, m_chansHSV);
+ m_clahe->apply(m_chansHSV[2], m_chansHSV[2]);
+ cv::merge(m_chansHSV, m_mHSV);
+ cv::cvtColor(m_mHSV, m_mBGR, cv::COLOR_HSV2BGR);
+
+ //std::cout << "AdjustMatBGR()" << std::endl;
+
+ return true;
+ }
+ ///
+ const cv::Mat& GetMatGray()
+ {
+ if (m_mGray.empty() || !m_mGrayGenerated)
+ {
+ if (m_umGray.empty() || !m_umGrayGenerated)
+ cv::cvtColor(m_mBGR, m_mGray, cv::COLOR_BGR2GRAY);
+ else
+ m_mGray = m_umGray.getMat(cv::ACCESS_READ);
+ m_mGrayGenerated = true;
+ }
+ return m_mGray;
+ }
+ ///
+ const cv::UMat& GetUMatBGR()
+ {
+ std::thread::id lastThreadID = std::this_thread::get_id();
+
+ if (m_umBGR.empty() || !m_umBGRGenerated || lastThreadID != m_umBGRThreadID)
+ {
+ m_umBGR = m_mBGR.getUMat(cv::ACCESS_READ);
+ m_umBGRGenerated = true;
+ m_umBGRThreadID = lastThreadID;
+ }
+ return m_umBGR;
+ }
+ ///
+ const cv::UMat& GetUMatGray()
+ {
+ std::thread::id lastThreadID = std::this_thread::get_id();
+
+ if (m_umGray.empty() || !m_umGrayGenerated || lastThreadID != m_umGrayThreadID)
+ {
+ if (m_mGray.empty() || !m_mGrayGenerated)
+ {
+ if (m_umBGR.empty() || !m_umBGRGenerated || lastThreadID != m_umGrayThreadID)
+ cv::cvtColor(m_mBGR, m_umGray, cv::COLOR_BGR2GRAY);
+ else
+ cv::cvtColor(m_umBGR, m_umGray, cv::COLOR_BGR2GRAY);
+ }
+ else
+ {
+ m_umGray = m_mGray.getUMat(cv::ACCESS_READ);
+ }
+ m_umGrayGenerated = true;
+ m_umGrayThreadID = lastThreadID;
+ }
+ return m_umGray;
+ }
private:
- std::string m_fileName;
- std::ofstream m_resCSV;
-
- ///
- struct Detection
- {
- cv::Rect m_rect;
- objtype_t m_type;
- float m_conf = 0.f;
- size_t m_trackID = 0;
-
- Detection(size_t trackID, const cv::Rect& brect, objtype_t type, float confidence)
- {
- m_type = type;
- m_rect = brect;
- m_conf = confidence;
- m_trackID = trackID;
- }
- };
-
- ///
- struct DetectsOnFrame
- {
- std::vector m_detects;
- };
- std::map m_frames;
- std::set m_robustIDs;
-
- ///
- void WriteAll(bool byFrames)
- {
- if (byFrames)
- {
-#if 1
- char delim = ',';
- for (const auto& frame : m_frames)
- {
- for (const auto& detect : frame.second.m_detects)
- {
- if (m_robustIDs.find(detect.m_trackID) != std::end(m_robustIDs))
- {
- m_resCSV << frame.first << delim << TypeConverter::Type2Str(detect.m_type) << delim << detect.m_rect.x << delim << detect.m_rect.y << delim <<
- detect.m_rect.width << delim << detect.m_rect.height << delim <<
- detect.m_conf << delim << std::endl;
- }
- }
- }
-#else
- char delim = ' ';
- for (const auto& frame : m_frames)
- {
- for (const auto& detect : frame.second.m_detects)
- {
- if (m_robustIDs.find(detect.m_trackID) != std::end(m_robustIDs))
- {
- m_resCSV << frame.first << delim << TypeConverter::Type2Str(detect.m_type) << delim << detect.m_rect.x << delim << detect.m_rect.y << delim <<
- (detect.m_rect.x + detect.m_rect.width) << delim << (detect.m_rect.y + detect.m_rect.height) << delim <<
- detect.m_conf << delim << detect.m_trackID << std::endl;
- }
- }
- }
-#endif
- }
- else
- {
- char delim = ',';
- for (size_t id : m_robustIDs)
- {
- for (const auto& frame : m_frames)
- {
- for (const auto& detect : frame.second.m_detects)
- {
- if (detect.m_trackID == id)
- {
- m_resCSV << frame.first << delim << id << delim << detect.m_rect.x << delim << detect.m_rect.y << delim <<
- detect.m_rect.width << delim << detect.m_rect.height << delim <<
- detect.m_conf << ",-1,-1,-1," << std::endl;
- break;
- }
- }
- }
- }
- }
- }
+ cv::Mat m_mBGR;
+ cv::Mat m_mGray;
+ cv::UMat m_umBGR;
+ cv::UMat m_umGray;
+ bool m_umBGRGenerated = false;
+ bool m_mGrayGenerated = false;
+ bool m_umGrayGenerated = false;
+ std::thread::id m_umBGRThreadID;
+ std::thread::id m_umGrayThreadID;
+
+ cv::Ptr m_clahe;
+ cv::Mat m_mHSV;
+ std::vector m_chansHSV;
+};
+
+///
+/// \brief The FrameInfo struct
+///
+struct FrameInfo
+{
+ ///
+ FrameInfo()
+ {
+ m_frames.reserve(m_batchSize);
+ m_regions.reserve(m_batchSize);
+ m_frameInds.reserve(m_batchSize);
+ }
+ ///
+ FrameInfo(size_t batchSize)
+ : m_batchSize(batchSize)
+ {
+ m_frames.reserve(m_batchSize);
+ m_regions.reserve(m_batchSize);
+ m_frameInds.reserve(m_batchSize);
+ }
+
+ ///
+ void SetBatchSize(size_t batchSize)
+ {
+ m_batchSize = batchSize;
+ m_frames.reserve(m_batchSize);
+ m_regions.reserve(m_batchSize);
+ m_frameInds.reserve(m_batchSize);
+ }
+
+ ///
+ void CleanRegions()
+ {
+ if (m_regions.size() != m_batchSize)
+ m_regions.resize(m_batchSize);
+ for (auto& regions : m_regions)
+ {
+ regions.clear();
+ }
+ }
+
+ ///
+ void CleanTracks()
+ {
+ if (m_tracks.size() != m_batchSize)
+ m_tracks.resize(m_batchSize);
+ for (auto& tracks : m_tracks)
+ {
+ tracks.clear();
+ }
+ }
+
+ std::vector m_regions;
+ std::vector> m_tracks;
+ std::vector m_frameInds;
+ std::vector m_frameTimeStamps;
+
+ size_t m_batchSize = 1;
+
+ int64 m_dt = 0;
+
+ std::condition_variable m_cond;
+ std::mutex m_mutex;
+ std::atomic m_captured { false };
};
///
@@ -176,56 +237,65 @@ class VideoExample
protected:
std::unique_ptr m_detector;
- std::unique_ptr m_tracker;
+ std::unique_ptr m_tracker;
- std::vector m_tracks;
-
- bool m_showLogs = true;
+ std::string m_showLogsLevel = "debug";
float m_fps = 25;
+ cv::Size m_frameSize;
+ int m_framesCount = 0;
+
+ bool m_useContrastAdjustment = false;
+
+ size_t m_batchSize = 1;
int m_captureTimeOut = 60000;
int m_trackingTimeOut = 60000;
ResultsLog m_resultsLog;
+ CVATAnnotationsGenerator m_cvatAnnotationsGenerator;
static void CaptureAndDetect(VideoExample* thisPtr, std::atomic& stopCapture);
virtual bool InitDetector(cv::UMat frame) = 0;
virtual bool InitTracker(cv::UMat frame) = 0;
- void Detection(cv::Mat frame, regions_t& regions);
- void Tracking(cv::Mat frame, const regions_t& regions);
+ void Detection(FrameInfo& frame);
+ void Tracking(FrameInfo& frame);
+
+ virtual void DrawData(cv::Mat frame, const std::vector& tracks, int framesCounter, int currTime) = 0;
+ virtual void DrawTrack(cv::Mat frame, const TrackingObject& track, bool drawTrajectory, int framesCounter, const std::string& userLabel = "");
- virtual void DrawData(cv::Mat frame, int framesCounter, int currTime) = 0;
+ TrackerSettings m_trackerSettings;
+ bool m_trackerSettingsLoaded = false;
- void DrawTrack(cv::Mat frame, int resizeCoeff, const TrackingObject& track, bool drawTrajectory, int framesCounter);
+ std::vector m_colors;
- TrackerSettings m_trackerSettings;
- bool m_trackerSettingsLoaded = false;
- bool ParseTrackerSettings(const std::string& settingsFile);
+ std::shared_ptr m_consoleSink;
+ std::shared_ptr m_fileSink;
+ std::shared_ptr m_logger;
private:
+ std::vector m_tracks;
+
bool m_isTrackerInitialized = false;
bool m_isDetectorInitialized = false;
std::string m_inFile;
std::string m_outFile;
+#if 0
+ int m_fourcc = cv::VideoWriter::fourcc('h', '2', '6', '4');
+#else
int m_fourcc = cv::VideoWriter::fourcc('M', 'J', 'P', 'G');
+#endif
int m_startFrame = 0;
int m_endFrame = 0;
int m_finishDelay = 0;
- std::vector m_colors;
+
+ time_point_t m_startTimeStamp;
+ bool m_useArchieveTime = true;
- struct FrameInfo
- {
- cv::Mat m_frame;
- regions_t m_regions;
- int64 m_dt = 0;
-
- std::condition_variable m_cond;
- std::mutex m_mutex;
- bool m_captured = false;
- };
FrameInfo m_frameInfo[2];
+
+ time_point_t GetNextTimeStamp(int framesCounter) const;
bool OpenCapture(cv::VideoCapture& capture);
bool WriteFrame(cv::VideoWriter& writer, const cv::Mat& frame);
diff --git a/example/cvatAnnotationsGenerator.h b/example/cvatAnnotationsGenerator.h
new file mode 100644
index 000000000..49f140a62
--- /dev/null
+++ b/example/cvatAnnotationsGenerator.h
@@ -0,0 +1,252 @@
+#pragma once
+
+#include
+#include
+#include "defines.h"
+
+///
+class CVATAnnotationsGenerator
+{
+public:
+ ///
+ CVATAnnotationsGenerator(const std::string& annFileName)
+ : m_annFileName(annFileName)
+ {
+ }
+
+ ///
+ bool NewDetects(int frameInd, const std::vector& tracks, size_t detectorInd)
+ {
+ if (m_annFileName.empty())
+ return false;
+
+ auto it = m_detects.find(frameInd);
+ if (it == m_detects.end())
+ {
+ if (detectorInd == 0)
+ {
+ m_detects.emplace(frameInd, tracks);
+ }
+ else
+ {
+ std::vector tmpTracks = tracks;
+ for (auto& track : tmpTracks)
+ {
+ track.m_ID.m_val += detectorInd * DetectorIDRange;
+ }
+ m_detects.emplace(frameInd, tmpTracks);
+ }
+
+ //it = m_detects.find(frameInd);
+ //std::cout << "New detects 1: Frame " << frameInd << ", detector ind " << detectorInd << std::endl;
+ //for (const auto& track : it->second)
+ //{
+ // std::cout << "track " << track.m_ID.ID2Str() << ", type = " << track.m_type << ", rect = " << track.m_rrect.boundingRect() << std::endl;
+ //}
+ }
+ else
+ {
+ if (detectorInd == 0)
+ {
+ it->second.insert(it->second.end(), tracks.begin(), tracks.end());
+ }
+ else
+ {
+ std::vector tmpTracks = tracks;
+ for (auto& track : tmpTracks)
+ {
+ track.m_ID.m_val += detectorInd * DetectorIDRange;
+ }
+ it->second.insert(it->second.end(), tmpTracks.begin(), tmpTracks.end());
+ }
+
+ //std::cout << "New detects 2: Frame " << frameInd << ", detector ind " << detectorInd << std::endl;
+ //for (const auto& track : it->second)
+ //{
+ // std::cout << "track " << track.m_ID.ID2Str() << ", type = " << track.m_type << ", rect = " << track.m_rrect.boundingRect() << std::endl;
+ //}
+ }
+
+ return true;
+ }
+
+ ///
+ bool Save(const std::string& videoFileName, int framesCount, cv::Size frameSize)
+ {
+ //PrintDetects();
+
+ bool res = !m_annFileName.empty();
+ if (!res)
+ return res;
+
+ std::ofstream annFile(m_annFileName);
+ res = annFile.is_open();
+ if (!res)
+ return res;
+
+ WriteMeta(annFile, videoFileName, framesCount, frameSize);
+
+ auto WritePoly = [&](int frameInd, const cv::RotatedRect& rrect)
+ {
+ cv::Point2f pts[4];
+ rrect.points(pts);
+ annFile << " \n";
+ annFile << " \n";
+ };
+
+ std::unordered_set writedTracks;
+
+ for (auto itStartFrame = std::begin(m_detects); itStartFrame != std::end(m_detects); ++itStartFrame)
+ {
+ for (const auto& track : itStartFrame->second)
+ {
+ if (writedTracks.find(track.m_ID.m_val) != std::end(writedTracks))
+ continue;
+ writedTracks.emplace(track.m_ID.m_val);
+
+ annFile << " \n";
+ WritePoly(itStartFrame->first, track.m_rrect);
+
+ //std::cout << "track " << track.m_ID.ID2Str() << ", type = " << track.m_type << ", rect = " << track.m_rrect.boundingRect() << std::endl;
+
+ auto itNextFrame = itStartFrame;
+ for (++itNextFrame; itNextFrame != std::end(m_detects); ++itNextFrame)
+ {
+ for (const auto& subTrack : itNextFrame->second)
+ {
+ if (track.m_ID.m_val != subTrack.m_ID.m_val)
+ continue;
+
+ WritePoly(itNextFrame->first, subTrack.m_rrect);
+
+ //std::cout << "subTrack " << subTrack.m_ID.ID2Str() << ", type = " << subTrack.m_type << ", rect = " << subTrack.m_rrect.boundingRect() << "\n";
+ break;
+ }
+ }
+
+ annFile << " \n";
+ }
+ }
+
+ FinalMeta(annFile);
+
+ return res;
+ }
+
+private:
+ std::string m_annFileName;
+
+ std::map> m_detects;
+
+ static constexpr track_id_t::value_type DetectorIDRange = 1000000000;
+
+ ///
+ void PrintDetects()
+ {
+ std::cout << "Print detects:\n";
+ for (auto it = m_detects.begin(); it != m_detects.end(); ++it)
+ {
+ std::cout << "Frame " << it->first << ": \n";
+ for (const auto track : it->second)
+ {
+ std::cout << "track " << track.m_ID.ID2Str() << ", type = " << track.m_type << ", rect = " << track.m_rrect.boundingRect() << "\n";
+ }
+ }
+ std::cout.flush();
+ }
+
+ ///
+ template
+ std::string Time2Str(TimePoint now)
+ {
+ // get number of milliseconds for the current second
+ // (remainder after division into seconds)
+ auto ms = std::chrono::duration_cast(now.time_since_epoch()) % 1000;
+
+ // convert to std::time_t in order to convert to std::tm (broken time)
+ auto timer = std::chrono::system_clock::to_time_t(now);
+
+ // convert to broken time
+#ifdef _WIN32
+ std::tm bt;
+ localtime_s(&bt, &timer);
+#else
+ std::tm bt = *std::localtime(&timer);
+#endif
+
+ std::ostringstream oss;
+ oss << std::put_time(&bt, "%Y-%m-%d %H:%M:%S");
+ oss << '.' << std::setfill('0') << std::setw(3) << ms.count() << "+00:00";
+
+ return oss.str();
+ }
+
+ ///
+ void WriteMeta(std::ofstream& annFile, const std::string& videoFileName, int framesCount, cv::Size frameSize)
+ {
+ std::string currTime = Time2Str(std::chrono::system_clock::now());
+
+ annFile << "\n";
+ annFile << "\n";
+ annFile << " 1.1 \n";
+ annFile << " \n";
+ annFile << " 777 \n";
+ annFile << " " << videoFileName << " \n";
+ annFile << " " << framesCount << " \n";
+ annFile << " interpolation \n";
+ annFile << " 5 \n";
+ annFile << " " << currTime << " \n";
+ annFile << " " << currTime << " \n";
+ annFile << " default \n";
+ annFile << " " << 0 << " \n";
+ annFile << " " << (framesCount - 1) << " \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " 777 \n";
+ annFile << " " << 0 << " \n";
+ annFile << " " << (framesCount - 1) << " \n";
+ annFile << " http://127.0.0.1:8080/?id=777 \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " user \n";
+ annFile << " user@de-id.ca \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " face \n";
+ annFile << " #906080 \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " licence_plate \n";
+ annFile << " #d055ce \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " " << frameSize.width << " \n";
+ annFile << " " << frameSize.height << " \n";
+ annFile << " \n";
+ annFile << " \n";
+ annFile << " " << currTime << " \n";
+ annFile << " " << videoFileName << " \n";
+ annFile << " \n";
+ }
+
+ ///
+ void FinalMeta(std::ofstream& annFile)
+ {
+ annFile << " \n";
+ }
+};
diff --git a/example/examples.h b/example/examples.h
index 32e9761ec..bc6db8a1f 100644
--- a/example/examples.h
+++ b/example/examples.h
@@ -6,405 +6,17 @@
#include "VideoExample.h"
-///
-/// \brief DrawFilledRect
-///
-void DrawFilledRect(cv::Mat& frame, const cv::Rect& rect, cv::Scalar cl, int alpha)
-{
- if (alpha)
- {
- const int alpha_1 = 255 - alpha;
- const int nchans = frame.channels();
- int color[3] = { cv::saturate_cast(cl[0]), cv::saturate_cast(cl[1]), cv::saturate_cast(cl[2]) };
- for (int y = rect.y; y < rect.y + rect.height; ++y)
- {
- uchar* ptr = frame.ptr(y) + nchans * rect.x;
- for (int x = rect.x; x < rect.x + rect.width; ++x)
- {
- for (int i = 0; i < nchans; ++i)
- {
- ptr[i] = cv::saturate_cast((alpha_1 * ptr[i] + alpha * color[i]) / 255);
- }
- ptr += nchans;
- }
- }
- }
- else
- {
- cv::rectangle(frame, rect, cl, cv::FILLED);
- }
-}
-
-///
-/// \brief The MotionDetectorExample class
-///
-class MotionDetectorExample : public VideoExample
-{
-public:
- MotionDetectorExample(const cv::CommandLineParser& parser)
- :
- VideoExample(parser),
- m_minObjWidth(10)
- {
- }
-
-protected:
- ///
- /// \brief InitDetector
- /// \param frame
- /// \return
- ///
- bool InitDetector(cv::UMat frame)
- {
- m_minObjWidth = frame.cols / 20;
-
- config_t config;
- config.emplace("useRotatedRect", "0");
-
- tracking::Detectors detectorType = tracking::Detectors::Motion_VIBE;
-
- switch (detectorType)
- {
- case tracking::Detectors::Motion_VIBE:
- config.emplace("samples", "20");
- config.emplace("pixelNeighbor", "1");
- config.emplace("distanceThreshold", "20");
- config.emplace("matchingThreshold", "3");
- config.emplace("updateFactor", "16");
- break;
- case tracking::Detectors::Motion_MOG:
- config.emplace("history", std::to_string(cvRound(50 * m_minStaticTime * m_fps)));
- config.emplace("nmixtures", "3");
- config.emplace("backgroundRatio", "0.7");
- config.emplace("noiseSigma", "0");
- break;
- case tracking::Detectors::Motion_GMG:
- config.emplace("initializationFrames", "50");
- config.emplace("decisionThreshold", "0.7");
- break;
- case tracking::Detectors::Motion_CNT:
- config.emplace("minPixelStability", "15");
- config.emplace("maxPixelStability", std::to_string(cvRound(20 * m_minStaticTime * m_fps)));
- config.emplace("useHistory", "1");
- config.emplace("isParallel", "1");
- break;
- case tracking::Detectors::Motion_SuBSENSE:
- break;
- case tracking::Detectors::Motion_LOBSTER:
- break;
- case tracking::Detectors::Motion_MOG2:
- config.emplace("history", std::to_string(cvRound(20 * m_minStaticTime * m_fps)));
- config.emplace("varThreshold", "10");
- config.emplace("detectShadows", "1");
- break;
- }
- m_detector = std::unique_ptr(CreateDetector(detectorType, config, frame));
-
- if (m_detector.get())
- {
- m_detector->SetMinObjectSize(cv::Size(m_minObjWidth, m_minObjWidth));
- return true;
- }
- return false;
- }
- ///
- /// \brief InitTracker
- /// \param frame
- /// \return
- ///
- bool InitTracker(cv::UMat frame)
- {
- if (!m_trackerSettingsLoaded)
- {
- m_trackerSettings.SetDistance(tracking::DistRects);
- m_trackerSettings.m_kalmanType = tracking::KalmanLinear;
- m_trackerSettings.m_filterGoal = tracking::FilterCenter;
- m_trackerSettings.m_lostTrackType = tracking::TrackCSRT; // Use visual objects tracker for collisions resolving. Used if m_filterGoal == tracking::FilterRect
- m_trackerSettings.m_matchType = tracking::MatchHungrian;
- m_trackerSettings.m_useAcceleration = false; // Use constant acceleration motion model
- m_trackerSettings.m_dt = m_trackerSettings.m_useAcceleration ? 0.05f : 0.2f; // Delta time for Kalman filter
- m_trackerSettings.m_accelNoiseMag = 0.2f; // Accel noise magnitude for Kalman filter
- m_trackerSettings.m_distThres = 0.95f; // Distance threshold between region and object on two frames
-#if 0
- m_trackerSettings.m_minAreaRadiusPix = frame.rows / 20.f;
-#else
- m_trackerSettings.m_minAreaRadiusPix = -1.f;
-#endif
- m_trackerSettings.m_minAreaRadiusK = 0.8f;
-
- m_trackerSettings.m_useAbandonedDetection = true;
- if (m_trackerSettings.m_useAbandonedDetection)
- {
- m_trackerSettings.m_minStaticTime = m_minStaticTime;
- m_trackerSettings.m_maxStaticTime = 10;
- m_trackerSettings.m_maximumAllowedSkippedFrames = cvRound(m_trackerSettings.m_minStaticTime * m_fps); // Maximum allowed skipped frames
- m_trackerSettings.m_maxTraceLength = 2 * m_trackerSettings.m_maximumAllowedSkippedFrames; // Maximum trace length
- }
- else
- {
- m_trackerSettings.m_maximumAllowedSkippedFrames = cvRound(2 * m_fps); // Maximum allowed skipped frames
- m_trackerSettings.m_maxTraceLength = cvRound(4 * m_fps); // Maximum trace length
- }
- }
-
- m_tracker = std::make_unique(m_trackerSettings);
- return true;
- }
-
- ///
- /// \brief DrawData
- /// \param frame
- /// \param framesCounter
- /// \param currTime
- ///
- void DrawData(cv::Mat frame, int framesCounter, int currTime)
- {
- m_tracks = m_tracker->GetTracks();
-
- if (m_showLogs)
- std::cout << "Frame " << framesCounter << ": tracks = " << m_tracks.size() << ", time = " << currTime << std::endl;
-
- for (const auto& track : m_tracks)
- {
- if (track.m_isStatic)
- {
- DrawTrack(frame, 1, track, false, framesCounter);
-
- std::string label = "abandoned " + std::to_string(track.m_ID);
- int baseLine = 0;
- cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
-
- cv::Rect brect = track.m_rrect.boundingRect();
- if (brect.x < 0)
- {
- brect.width = std::min(brect.width, frame.cols - 1);
- brect.x = 0;
- }
- else if (brect.x + brect.width >= frame.cols)
- {
- brect.x = std::max(0, frame.cols - brect.width - 1);
- brect.width = std::min(brect.width, frame.cols - 1);
- }
- if (brect.y - labelSize.height < 0)
- {
- brect.height = std::min(brect.height, frame.rows - 1);
- brect.y = labelSize.height;
- }
- else if (brect.y + brect.height >= frame.rows)
- {
- brect.y = std::max(0, frame.rows - brect.height - 1);
- brect.height = std::min(brect.height, frame.rows - 1);
- }
- DrawFilledRect(frame, cv::Rect(cv::Point(brect.x, brect.y - labelSize.height), cv::Size(labelSize.width, labelSize.height + baseLine)), cv::Scalar(255, 0, 255), 150);
- cv::putText(frame, label, brect.tl(), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
- }
- else
- {
- if (track.IsRobust(cvRound(m_fps / 4), // Minimal trajectory size
- 0.7f, // Minimal ratio raw_trajectory_points / trajectory_lenght
- cv::Size2f(0.1f, 8.0f))) // Min and max ratio: width / height
- DrawTrack(frame, 1, track, true, framesCounter);
- }
- }
- m_detector->CalcMotionMap(frame);
- }
-
-private:
- int m_minObjWidth = 8;
- int m_minStaticTime = 5;
-};
-
-// ----------------------------------------------------------------------
-
-///
-/// \brief The FaceDetectorExample class
-///
-class FaceDetectorExample : public VideoExample
-{
-public:
- FaceDetectorExample(const cv::CommandLineParser& parser)
- :
- VideoExample(parser)
- {
- }
-
-protected:
- ///
- /// \brief InitDetector
- /// \param frame
- /// \return
- ///
- bool InitDetector(cv::UMat frame)
- {
-#ifdef _WIN32
- std::string pathToModel = "../../data/";
-#else
- std::string pathToModel = "../data/";
-#endif
-
- config_t config;
- config.emplace("cascadeFileName", pathToModel + "haarcascade_frontalface_alt2.xml");
- m_detector = std::unique_ptr(CreateDetector(tracking::Detectors::Face_HAAR, config, frame));
- if (m_detector.get())
- {
- m_detector->SetMinObjectSize(cv::Size(frame.cols / 20, frame.rows / 20));
- return true;
- }
- return false;
- }
- ///
- /// \brief InitTracker
- /// \param frame
- /// \return
- ///
- bool InitTracker(cv::UMat frame)
- {
- if (!m_trackerSettingsLoaded)
- {
- m_trackerSettings.SetDistance(tracking::DistJaccard);
- m_trackerSettings.m_kalmanType = tracking::KalmanUnscented;
- m_trackerSettings.m_filterGoal = tracking::FilterRect;
- m_trackerSettings.m_lostTrackType = tracking::TrackCSRT; // Use visual objects tracker for collisions resolving. Used if m_filterGoal == tracking::FilterRect
- m_trackerSettings.m_matchType = tracking::MatchHungrian;
- m_trackerSettings.m_dt = 0.3f; // Delta time for Kalman filter
- m_trackerSettings.m_accelNoiseMag = 0.1f; // Accel noise magnitude for Kalman filter
- m_trackerSettings.m_distThres = 0.8f; // Distance threshold between region and object on two frames
- m_trackerSettings.m_minAreaRadiusPix = frame.rows / 20.f;
- m_trackerSettings.m_maximumAllowedSkippedFrames = cvRound(m_fps / 2); // Maximum allowed skipped frames
- m_trackerSettings.m_maxTraceLength = cvRound(5 * m_fps); // Maximum trace length
- }
- m_tracker = std::make_unique(m_trackerSettings);
-
- return true;
- }
-
- ///
- /// \brief DrawData
- /// \param frame
- /// \param framesCounter
- /// \param currTime
- ///
- void DrawData(cv::Mat frame, int framesCounter, int currTime)
- {
- m_tracks = m_tracker->GetTracks();
-
- if (m_showLogs)
- std::cout << "Frame " << framesCounter << ": tracks = " << m_tracks.size() << ", time = " << currTime << std::endl;
-
- for (const auto& track : m_tracks)
- {
- if (track.IsRobust(8, // Minimal trajectory size
- 0.4f, // Minimal ratio raw_trajectory_points / trajectory_lenght
- cv::Size2f(0.1f, 8.0f))) // Min and max ratio: width / height
- DrawTrack(frame, 1, track, true, framesCounter);
- }
- m_detector->CalcMotionMap(frame);
- }
-};
-
-// ----------------------------------------------------------------------
-
-///
-/// \brief The PedestrianDetectorExample class
-///
-class PedestrianDetectorExample : public VideoExample
-{
-public:
- PedestrianDetectorExample(const cv::CommandLineParser& parser)
- :
- VideoExample(parser)
- {
- }
-
-protected:
- ///
- /// \brief InitDetector
- /// \param frame
- /// \return
- ///
- bool InitDetector(cv::UMat frame)
- {
- tracking::Detectors detectorType = tracking::Detectors::Pedestrian_C4; // tracking::Detectors::Pedestrian_HOG;
-
-#ifdef _WIN32
- std::string pathToModel = "../../data/";
-#else
- std::string pathToModel = "../data/";
-#endif
-
- config_t config;
- config.emplace("detectorType", (detectorType == tracking::Pedestrian_HOG) ? "HOG" : "C4");
- config.emplace("cascadeFileName1", pathToModel + "combined.txt.model");
- config.emplace("cascadeFileName2", pathToModel + "combined.txt.model_");
- m_detector = std::unique_ptr