From cd7c3449075ee8ec9ab5e70b918483b1d5c276e1 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Mon, 6 Mar 2023 22:40:55 +0800
Subject: [PATCH 01/17] remove pycache

---
 models/__pycache__/__init__.cpython-37.pyc      | Bin 142 -> 0 bytes
 models/__pycache__/common.cpython-37.pyc        | Bin 76867 -> 0 bytes
 models/__pycache__/experimental.cpython-37.pyc  | Bin 4533 -> 0 bytes
 models/__pycache__/yolo.cpython-37.pyc          | Bin 20665 -> 0 bytes
 tracker/basetrack.py                            |   2 +-
 .../__pycache__/OSNet.cpython-37.pyc            | Bin 13273 -> 0 bytes
 .../__pycache__/__init__.cpython-37.pyc         | Bin 196 -> 0 bytes
 .../__pycache__/deepsort_reid.cpython-37.pyc    | Bin 4783 -> 0 bytes
 .../__pycache__/load_model_tools.cpython-37.pyc | Bin 7366 -> 0 bytes
 .../__pycache__/__init__.cpython-37.pyc         | Bin 176 -> 0 bytes
 .../__pycache__/burst_base.cpython-37.pyc       | Bin 26263 -> 0 bytes
 .../__pycache__/burst_ow_base.cpython-37.pyc    | Bin 27791 -> 0 bytes
 .../__pycache__/format_converter.cpython-37.pyc | Bin 9401 -> 0 bytes
 .../metrics/__pycache__/__init__.cpython-36.pyc | Bin 432 -> 0 bytes
 .../metrics/__pycache__/__init__.cpython-37.pyc | Bin 454 -> 0 bytes
 .../metrics/__pycache__/__init__.cpython-38.pyc | Bin 450 -> 0 bytes
 .../__pycache__/_base_metric.cpython-36.pyc     | Bin 5559 -> 0 bytes
 .../__pycache__/_base_metric.cpython-37.pyc     | Bin 5581 -> 0 bytes
 .../__pycache__/_base_metric.cpython-38.pyc     | Bin 5606 -> 0 bytes
 .../metrics/__pycache__/clear.cpython-36.pyc    | Bin 6358 -> 0 bytes
 .../metrics/__pycache__/clear.cpython-37.pyc    | Bin 6380 -> 0 bytes
 .../metrics/__pycache__/clear.cpython-38.pyc    | Bin 6351 -> 0 bytes
 .../metrics/__pycache__/count.cpython-36.pyc    | Bin 1931 -> 0 bytes
 .../metrics/__pycache__/count.cpython-37.pyc    | Bin 1953 -> 0 bytes
 .../metrics/__pycache__/count.cpython-38.pyc    | Bin 1932 -> 0 bytes
 .../metrics/__pycache__/hota.cpython-36.pyc     | Bin 7878 -> 0 bytes
 .../metrics/__pycache__/hota.cpython-37.pyc     | Bin 7869 -> 0 bytes
 .../metrics/__pycache__/hota.cpython-38.pyc     | Bin 7699 -> 0 bytes
 .../metrics/__pycache__/identity.cpython-36.pyc | Bin 5038 -> 0 bytes
 .../metrics/__pycache__/identity.cpython-37.pyc | Bin 5060 -> 0 bytes
 .../metrics/__pycache__/identity.cpython-38.pyc | Bin 5004 -> 0 bytes
 .../metrics/__pycache__/ideucl.cpython-36.pyc   | Bin 5802 -> 0 bytes
 .../metrics/__pycache__/ideucl.cpython-37.pyc   | Bin 5824 -> 0 bytes
 .../metrics/__pycache__/ideucl.cpython-38.pyc   | Bin 5781 -> 0 bytes
 .../metrics/__pycache__/j_and_f.cpython-36.pyc  | Bin 10691 -> 0 bytes
 .../metrics/__pycache__/j_and_f.cpython-37.pyc  | Bin 10660 -> 0 bytes
 .../metrics/__pycache__/j_and_f.cpython-38.pyc  | Bin 10535 -> 0 bytes
 .../__pycache__/track_map.cpython-36.pyc        | Bin 15284 -> 0 bytes
 .../__pycache__/track_map.cpython-37.pyc        | Bin 15170 -> 0 bytes
 .../__pycache__/track_map.cpython-38.pyc        | Bin 15129 -> 0 bytes
 .../metrics/__pycache__/vace.cpython-36.pyc     | Bin 4576 -> 0 bytes
 .../metrics/__pycache__/vace.cpython-37.pyc     | Bin 4598 -> 0 bytes
 .../metrics/__pycache__/vace.cpython-38.pyc     | Bin 4611 -> 0 bytes
 utils/__pycache__/__init__.cpython-37.pyc       | Bin 141 -> 0 bytes
 utils/__pycache__/autoanchor.cpython-37.pyc     | Bin 6229 -> 0 bytes
 utils/__pycache__/datasets.cpython-37.pyc       | Bin 45143 -> 0 bytes
 utils/__pycache__/general.cpython-37.pyc        | Bin 25911 -> 0 bytes
 utils/__pycache__/google_utils.cpython-37.pyc   | Bin 3151 -> 0 bytes
 utils/__pycache__/loss.cpython-37.pyc           | Bin 41699 -> 0 bytes
 utils/__pycache__/metrics.cpython-37.pyc        | Bin 7665 -> 0 bytes
 utils/__pycache__/plots.cpython-37.pyc          | Bin 16100 -> 0 bytes
 utils/__pycache__/torch_utils.cpython-37.pyc    | Bin 13244 -> 0 bytes
 52 files changed, 1 insertion(+), 1 deletion(-)
 delete mode 100644 models/__pycache__/__init__.cpython-37.pyc
 delete mode 100644 models/__pycache__/common.cpython-37.pyc
 delete mode 100644 models/__pycache__/experimental.cpython-37.pyc
 delete mode 100644 models/__pycache__/yolo.cpython-37.pyc
 delete mode 100644 tracker/reid_models/__pycache__/OSNet.cpython-37.pyc
 delete mode 100644 tracker/reid_models/__pycache__/__init__.cpython-37.pyc
 delete mode 100644 tracker/reid_models/__pycache__/deepsort_reid.cpython-37.pyc
 delete mode 100644 tracker/reid_models/__pycache__/load_model_tools.cpython-37.pyc
 delete mode 100644 tracker/trackeval/datasets/burst_helpers/__pycache__/__init__.cpython-37.pyc
 delete mode 100644 tracker/trackeval/datasets/burst_helpers/__pycache__/burst_base.cpython-37.pyc
 delete mode 100644 tracker/trackeval/datasets/burst_helpers/__pycache__/burst_ow_base.cpython-37.pyc
 delete mode 100644 tracker/trackeval/datasets/burst_helpers/__pycache__/format_converter.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/__init__.cpython-36.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/__init__.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/__init__.cpython-38.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/_base_metric.cpython-36.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/_base_metric.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/_base_metric.cpython-38.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/clear.cpython-36.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/clear.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/clear.cpython-38.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/count.cpython-36.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/count.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/count.cpython-38.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/hota.cpython-36.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/hota.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/hota.cpython-38.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/identity.cpython-36.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/identity.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/identity.cpython-38.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/ideucl.cpython-36.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/ideucl.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/ideucl.cpython-38.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/j_and_f.cpython-36.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/j_and_f.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/j_and_f.cpython-38.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/track_map.cpython-36.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/track_map.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/track_map.cpython-38.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/vace.cpython-36.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/vace.cpython-37.pyc
 delete mode 100644 tracker/trackeval/metrics/__pycache__/vace.cpython-38.pyc
 delete mode 100644 utils/__pycache__/__init__.cpython-37.pyc
 delete mode 100644 utils/__pycache__/autoanchor.cpython-37.pyc
 delete mode 100644 utils/__pycache__/datasets.cpython-37.pyc
 delete mode 100644 utils/__pycache__/general.cpython-37.pyc
 delete mode 100644 utils/__pycache__/google_utils.cpython-37.pyc
 delete mode 100644 utils/__pycache__/loss.cpython-37.pyc
 delete mode 100644 utils/__pycache__/metrics.cpython-37.pyc
 delete mode 100644 utils/__pycache__/plots.cpython-37.pyc
 delete mode 100644 utils/__pycache__/torch_utils.cpython-37.pyc

diff --git a/models/__pycache__/__init__.cpython-37.pyc b/models/__pycache__/__init__.cpython-37.pyc
deleted file mode 100644
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diff --git a/tracker/basetrack.py b/tracker/basetrack.py
index a0090a3..6bcae71 100644
--- a/tracker/basetrack.py
+++ b/tracker/basetrack.py
@@ -1,7 +1,7 @@
 """
 Copyed from ByteTrack
 """
-from numba import jit
+# from numba import jit
 
 import numpy as np
 from collections import OrderedDict
diff --git a/tracker/reid_models/__pycache__/OSNet.cpython-37.pyc b/tracker/reid_models/__pycache__/OSNet.cpython-37.pyc
deleted file mode 100644
index 3c6db89de3f7a61841510d43542ea308eb79d86b..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/utils/__pycache__/metrics.cpython-37.pyc b/utils/__pycache__/metrics.cpython-37.pyc
deleted file mode 100644
index 0dc87ba940608bfd5fefae8250010dbbc03924e2..0000000000000000000000000000000000000000
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From 7def40361b9e2b55b08ff7c0b71d9d2ac83fa4e9 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Tue, 14 Mar 2023 11:36:01 +0800
Subject: [PATCH 02/17] fix bug of deepsort and c_biou

---
 tracker/c_biou_tracker.py | 6 ++++++
 tracker/deepsort.py       | 4 ++--
 tracker/track.py          | 4 ++--
 3 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/tracker/c_biou_tracker.py b/tracker/c_biou_tracker.py
index bb2ce66..6db4869 100644
--- a/tracker/c_biou_tracker.py
+++ b/tracker/c_biou_tracker.py
@@ -146,6 +146,12 @@ def update(self, new_track, frame_id):
             self.motion_state1 = self.get_buffer_bbox(level=1, bbox=new_tlwh)
             self.motion_state2 = self.get_buffer_bbox(level=2, bbox=new_tlwh)
 
+        # update status
+        self.state = TrackState.Tracked
+        self.is_activated = True
+
+        self.time_since_update = 0
+
     @staticmethod
     def xywh2tlbr(xywh):
         """
diff --git a/tracker/deepsort.py b/tracker/deepsort.py
index 44770ca..a112d78 100644
--- a/tracker/deepsort.py
+++ b/tracker/deepsort.py
@@ -190,8 +190,8 @@ def update(self, det_results, ori_img):
             lost_stracks.append(track)
 
         # deal with unconfirmed tracks, match new track of last frame and new high conf det
-        matched_pair2, u_tracks2_idx, u_det2_idx = matching.matching_cascade(self.gated_metric, 0.7, self.max_time_lost, 
-                                                                unconfirmed, u_det1)
+        IoU_dist = matching.iou_distance(atracks=unconfirmed, btracks=u_det1)
+        matched_pair2, u_tracks2_idx, u_det2_idx = matching.linear_assignment(IoU_dist, thresh=0.9)
 
         for itrack_match, idet_match in matched_pair2:
             track = unconfirmed[itrack_match]
diff --git a/tracker/track.py b/tracker/track.py
index 83b9a7a..61b37f8 100644
--- a/tracker/track.py
+++ b/tracker/track.py
@@ -184,7 +184,7 @@ def main(opts, cfgs):
             timer.toc()  # end timing this image
             
             if opts.save_images:
-                plot_img(img0, frame_id, [cur_tlwh, cur_id, cur_cls], save_dir=os.path.join(DATASET_ROOT, 'reuslt_images', seq))
+                plot_img(img0, frame_id, [cur_tlwh, cur_id, cur_cls], save_dir=os.path.join(DATASET_ROOT, 'result_images', seq))
         
             frame_id += 1
 
@@ -306,7 +306,7 @@ def save_videos(seq_names):
         seq_names = [seq_names]
 
     for seq in seq_names:
-        images_path = os.path.join(DATASET_ROOT, 'reuslt_images', seq)
+        images_path = os.path.join(DATASET_ROOT, 'result_images', seq)
         images_name = sorted(os.listdir(images_path))
 
         to_video_path = os.path.join(images_path, '../', seq + '.mp4')

From 4082cbfd7375b586b53f98952b9b86899787e606 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Tue, 14 Mar 2023 11:39:58 +0800
Subject: [PATCH 03/17] update readme

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index c818be1..b8d31f8 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,8 @@
 
 ## 0. 更新记录
 
+**2023.3.14**解决了`DeepSORT`和`C_BIoUTracker`后面出现的目标不跟踪的bug.  
+
 **2023.2.28**优化了`track_demo.py`, 减少了内存占用.  
 
 **2023.2.24**加入了**推理单个视频或图片文件夹**以及**YOLO v8**的推理功能, 对应的代码为`tracker/track_demo.py`与`tracker/track_yolov8.py`.  推理单个视频或图片文件夹不需要指定数据集与真值, 也没有评测指标的功能, 只需要在命令行中指定`obj`即可, 例如:

From 3b5adf6f10785327f7d01a1a8869251ab38f73e2 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Wed, 15 Mar 2023 17:19:50 +0800
Subject: [PATCH 04/17] fix txt bug of track_demo.py

---
 tracker/track_demo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tracker/track_demo.py b/tracker/track_demo.py
index 3d34a7e..371770c 100644
--- a/tracker/track_demo.py
+++ b/tracker/track_demo.py
@@ -159,7 +159,7 @@ def main(opts):
     timer.clear()  # clear for next seq
     # thirdly, save results
     # every time assign a different name
-    if opts.save_txt: save_results(obj_name, '', results)
+    if opts.save_txt: save_results(obj_name, results)
 
     ## finally, save videos
     save_videos(obj_name)

From fc70fc4e2848e855df34678c254c6ee0c663f9ee Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Tue, 11 Apr 2023 19:51:59 +0800
Subject: [PATCH 05/17] update gitignore

---
 .gitgnore => .gitignore | 49 ++++++++++++++++++++++++++++++++---------
 1 file changed, 38 insertions(+), 11 deletions(-)
 rename .gitgnore => .gitignore (54%)

diff --git a/.gitgnore b/.gitignore
similarity index 54%
rename from .gitgnore
rename to .gitignore
index ae99c53..1054e35 100644
--- a/.gitgnore
+++ b/.gitignore
@@ -1,3 +1,9 @@
+# Customized
+weights/*
+!weights/ckpt.t7
+!weights/DHN.pth 
+!weights/osnet_x0_25.pth
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -20,7 +26,6 @@ parts/
 sdist/
 var/
 wheels/
-pip-wheel-metadata/
 share/python-wheels/
 *.egg-info/
 .installed.cfg
@@ -50,6 +55,7 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
+cover/
 
 # Translations
 *.mo
@@ -72,6 +78,7 @@ instance/
 docs/_build/
 
 # PyBuilder
+.pybuilder/
 target/
 
 # Jupyter Notebook
@@ -81,11 +88,10 @@ target/
 profile_default/
 ipython_config.py
 
-# Profiling
-*.pclprof
-
 # pyenv
-.python-version
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
 
 # pipenv
 #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
@@ -94,7 +100,22 @@ ipython_config.py
 #   install all needed dependencies.
 #Pipfile.lock
 
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
 __pypackages__/
 
 # Celery stuff
@@ -131,9 +152,15 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
-# datasets and projects
-datasets/
-runs/
-wandb/
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
 
-.DS_Store
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
\ No newline at end of file

From 3b803f1012664242919d6f6a60f46f25b57c2954 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Thu, 13 Apr 2023 22:12:48 +0800
Subject: [PATCH 06/17] fix bug of track_demo.py

---
 tracker/basetrack.py  | 2 +-
 tracker/track_demo.py | 9 ++++++++-
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/tracker/basetrack.py b/tracker/basetrack.py
index 6bcae71..1719a2b 100644
--- a/tracker/basetrack.py
+++ b/tracker/basetrack.py
@@ -362,7 +362,7 @@ def update(self, det_results, ori_img):
         ori_img: original image, np.ndarray, shape(H, W, C)
         """
         if isinstance(det_results, torch.Tensor):
-            det_results = det_results.cpu().numpy()
+            det_results = det_results.detach().cpu().numpy()
         if isinstance(ori_img, torch.Tensor):
             ori_img = ori_img.numpy()
 
diff --git a/tracker/track_demo.py b/tracker/track_demo.py
index 371770c..ca9b9fa 100644
--- a/tracker/track_demo.py
+++ b/tracker/track_demo.py
@@ -72,6 +72,7 @@ def main(opts):
         model = TracedModel(model, device, opts.img_size)
     else:
         model.to(device)
+    model.eval()
 
     """
     2. load videos or images
@@ -85,14 +86,19 @@ def main(opts):
     # check path
     assert os.path.exists(obj_name), 'the path does not exist! '
     obj, get_next_frame = None, None  # init obj
-    if 'mp4' in opts.obj:  # if it is a video
+    if 'mp4' in opts.obj or 'MP4' in opts.obj:  # if it is a video
         obj = cv2.VideoCapture(obj_name) 
         get_next_frame = lambda _ : obj.read()
+
+        if os.path.isabs(obj_name): obj_name = obj_name.split('/')[-1][:-4]
+        else: obj_name = obj_name[:-4]
     
     else:  
         obj = my_queue(os.listdir(obj_name))
         get_next_frame = lambda _ : obj.pop_front()
 
+        if os.path.isabs(obj_name): obj_name = obj_name.split('/')[-1]
+
 
     """
     3. start tracking
@@ -262,6 +268,7 @@ def save_videos(obj_name):
 
     seq_names: List[str] or str, seqs that will be generated
     """
+
     if not isinstance(obj_name, list):
         obj_name = [obj_name]
 

From 356410cd3196c79b975cea844e017b257da26db2 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Sat, 6 May 2023 10:52:24 +0800
Subject: [PATCH 07/17] change preprocess and postprocess

---
 .gitignore                     |  7 ++++
 README.md                      |  6 ++-
 data/uavdt.yaml                |  7 ++++
 run_yolov7.txt                 |  4 +-
 tools/convert_UAVDT_to_yolo.py |  4 +-
 tracker/basetrack.py           | 29 ++++++--------
 tracker/botsort.py             | 29 ++++----------
 tracker/bytetrack.py           | 28 ++++---------
 tracker/c_biou_tracker.py      | 31 +++++++--------
 tracker/deepmot.py             | 23 +++--------
 tracker/deepsort.py            | 23 +++--------
 tracker/strongsort.py          | 24 +++--------
 tracker/track.py               | 56 ++++++++++++++------------
 tracker/track_yolov5.py        | 41 +++++++++++--------
 tracker/track_yolov8.py        | 15 +++++--
 tracker/tracker_dataloader.py  | 73 ++++++++++++++++++++++++++++------
 tracker/uavmot.py              | 29 ++++----------
 utils/datasets.py              | 20 ++--------
 18 files changed, 217 insertions(+), 232 deletions(-)
 create mode 100644 data/uavdt.yaml

diff --git a/.gitignore b/.gitignore
index 1054e35..d4ab509 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,13 @@ weights/*
 !weights/ckpt.t7
 !weights/DHN.pth 
 !weights/osnet_x0_25.pth
+visdrone/*
+uavdt/*
+mot17/*
+runs/*
+wandb/*
+track_result.txt
+.idea/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/README.md b/README.md
index b8d31f8..80d8466 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,8 @@
 
 ## 0. 更新记录
 
+**2023.5.6[大更新]**: 对于v5, v7, 改变前处理和后处理方式(采用原有方式), ***解决了部分边界框近大远小的bug, 边界框更加精确***. 此外, 对于v8, 弃用了resize步骤, 直接推理.
+
 **2023.3.14**解决了`DeepSORT`和`C_BIoUTracker`后面出现的目标不跟踪的bug.  
 
 **2023.2.28**优化了`track_demo.py`, 减少了内存占用.  
@@ -34,7 +36,7 @@ YOLO v8 代码的参数与之前完全相同. 安装YOLO v8以及训练步骤请
 
 **2022.10.15**增加了对yolo v5的支持, 只需替换track.py, 将tracker文件夹放到v5的根目录(我测试的是官方的[repo](https://github.com/ultralytics/yolov5))下即可. 代码在[yolo v5](https://github.com/JackWoo0831/Yolov7-tracker/blob/master/tracker/track_yolov5.py). 
 
-**2022.09.27**修复了STrack类中update不更新外观的问题, 代码有较大更改, **您可能需要重新下载```./tracker```文件夹**. 
+**2022.09.27[大更新]**修复了STrack类中update不更新外观的问题, 代码有较大更改, **您可能需要重新下载```./tracker```文件夹**. 
 尝试加入StrongSORT, 但是目前还不work:(, 尽力调一调
 
 ## 1. 亮点  
@@ -144,7 +146,7 @@ origin意味着您直接使用数据集原本的路径, **而不是通过yolo要
 DATA_ROOT的值应为/data/datasets/VisDrone2019/VisDrone2019-MOT-test-dev/sequences, 即DATA_ROOT目录下应该为各个视频序列的文件夹.
 ```
 
-***2. yolo***
+***2. yolo[推荐]***
 
 yolo格式意味着您通过yolo训练时所要求的txt文件读取序列. 我们知道yolo要求txt文件记录图片的路径, 例如:
 
diff --git a/data/uavdt.yaml b/data/uavdt.yaml
new file mode 100644
index 0000000..3f0e02e
--- /dev/null
+++ b/data/uavdt.yaml
@@ -0,0 +1,7 @@
+train: ./uavdt/train.txt
+val: ./uavdt/test.txt
+test: ./uavdt/test.txt
+
+nc: 1
+
+names: ['car']
\ No newline at end of file
diff --git a/run_yolov7.txt b/run_yolov7.txt
index 1e9f1b9..f808d42 100644
--- a/run_yolov7.txt
+++ b/run_yolov7.txt
@@ -17,6 +17,8 @@ python train_aux.py --dataset visdrone --workers 8 --device 4 --batch-size 16 --
 ## all category
 python train_aux.py --dataset visdrone --workers 8 --device 0 --batch-size 16 --data data/visdrone_all.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights './weights/yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
 
+uavdt:
+python train_aux.py --dataset uavdt --workers 8 --device 5 --batch-size 16 --data data/uavdt.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights './weights/yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
 
 ## all category, no mosaic, rect training
 NOTE: edited augment = False in line 246, train_aux.py
@@ -61,4 +63,4 @@ CUDA_VISIBLE_DEVICES=0 python train_aux.py --dataset mot17 --workers 8 --device
 track:
 python tracker/track.py --dataset mot17 --data_format yolo --tracker sort --model_path runs/train/yolov7-w6-custom3/weights/best.pt --save_images
 python tracker/track.py --dataset mot17 --data_format yolo --tracker bytetrack --model_path weights/best.pt --save_images
-python tracker/track.py --dataset mot17 --data_format yolo --tracker deepsort --model_path weights/best.pt --save_images
\ No newline at end of file
+python tracker/track.py --dataset mot17 --data_format yolo --tracker deepsort --model_path weights/best.pt --save_images
diff --git a/tools/convert_UAVDT_to_yolo.py b/tools/convert_UAVDT_to_yolo.py
index 217b9a0..7793c4c 100644
--- a/tools/convert_UAVDT_to_yolo.py
+++ b/tools/convert_UAVDT_to_yolo.py
@@ -47,7 +47,7 @@ def generate_imgs_and_labels(opts):
 
     # 分别处理train与test
     process_train_test(train_seq_list, frame_range, CATEGOTY_ID, 'train')
-    process_train_test(test_seq_list, frame_range, CATEGOTY_ID, 'test')
+    process_train_test(test_seq_list, {'start': 0.0, 'end': 1.0}, CATEGOTY_ID, 'test')
     print('All Done!!')
                 
 
@@ -156,4 +156,4 @@ def process_train_test(seqs: list, frame_range: dict, cat_id: int = 0, split: st
     opts = parser.parse_args()
 
     generate_imgs_and_labels(opts)
-    # python uav_tools/convert_UAVDT_to_yolo.py --generate_imgs --half --random
\ No newline at end of file
+    # python tools/convert_UAVDT_to_yolo.py --generate_imgs --half --random
\ No newline at end of file
diff --git a/tracker/basetrack.py b/tracker/basetrack.py
index 1719a2b..7744aab 100644
--- a/tracker/basetrack.py
+++ b/tracker/basetrack.py
@@ -107,6 +107,17 @@ def __init__(self, cls, tlwh, score, kalman_format='default',
         self.mean, self.cov = None, None  # for kalman predict
 
     # some tool funcs 
+    @staticmethod
+    def tlbr2tlwh(tlbr):
+        """
+        convert tlbr to tlwh
+        """
+        result = np.asarray(tlbr).copy()
+        result[2] -= result[0]
+        result[3] -= result[1]
+
+        return result
+
     @staticmethod
     def tlwh2xyah(tlwh):
         """
@@ -375,26 +386,12 @@ def update(self, det_results, ori_img):
         """step 1. filter results and init tracks"""
         det_results = det_results[det_results[:, 4] > self.det_thresh]
 
-        # convert the scale to origin size
-        # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-        # TODO: check here, if nesscessary use two ratio
-        img_h, img_w = ori_img.shape[0], ori_img.shape[1]
-        ratio = [img_h / self.model_img_size[0], img_w / self.model_img_size[1]]  # usually > 1
-        det_results[:, 0], det_results[:, 2] =  det_results[:, 0]*ratio[1], det_results[:, 2]*ratio[1]
-        det_results[:, 1], det_results[:, 3] =  det_results[:, 1]*ratio[0], det_results[:, 3]*ratio[0]
-
 
         if det_results.shape[0] > 0:
-            if self.NMS:
-                # TODO: Note nms need tlbr format
-                bbox_temp = STrack.xywh2tlbr(det_results[:, :4])
-                nms_indices = nms(torch.from_numpy(bbox_temp), torch.from_numpy(det_results[:, 4]), 
-                                self.opts.nms_thresh)
-                det_results = det_results[nms_indices.numpy()]
 
             # detections: List[Strack]
-            detections = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format)
-                            for (cls, xywh, score) in zip(det_results[:, -1], det_results[:, :4], det_results[:, 4])]
+            detections = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
+                            for (cls, tlbr, score) in zip(det_results[:, -1], det_results[:, :4], det_results[:, 4])]
 
         else:
             detections = []
diff --git a/tracker/botsort.py b/tracker/botsort.py
index f1f8489..66799e3 100644
--- a/tracker/botsort.py
+++ b/tracker/botsort.py
@@ -328,14 +328,6 @@ def update(self, det_results, ori_img):
         lost_stracks = []           # The tracks which are not obtained in the current frame but are not removed.(Lost for some time lesser than the threshold for removing)
         removed_stracks = []
 
-        # convert the scale to origin size
-        # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-        # TODO: check here, if nesscessary use two ratio
-        img_h, img_w = ori_img.shape[0], ori_img.shape[1]
-        ratio = [img_h / self.model_img_size[0], img_w / self.model_img_size[1]]  # usually > 1
-        det_results[:, 0], det_results[:, 2] =  det_results[:, 0]*ratio[1], det_results[:, 2]*ratio[1]
-        det_results[:, 1], det_results[:, 3] =  det_results[:, 1]*ratio[0], det_results[:, 3]*ratio[0]
-
         """step 1. filter results and init tracks"""
                
         # filter small area bboxs
@@ -343,13 +335,6 @@ def update(self, det_results, ori_img):
             small_indicies = det_results[:, 2]*det_results[:, 3] > 50
             det_results = det_results[small_indicies]
 
-        # run NMS
-        if self.NMS:
-            # NOTE: Note nms need tlbr format
-            nms_indices = nms(torch.from_numpy(STrack.xywh2tlbr(det_results[:, :4])), torch.from_numpy(det_results[:, 4]), 
-                            self.opts.nms_thresh)
-            det_results = det_results[nms_indices.numpy()]
-
         # cal high and low indicies
         det_high_indicies = det_results[:, 4] >= self.det_thresh
         det_low_indicies = np.logical_and(np.logical_not(det_high_indicies), det_results[:, 4] > self.low_conf_thresh)
@@ -358,19 +343,19 @@ def update(self, det_results, ori_img):
         det_high, det_low = det_results[det_high_indicies], det_results[det_low_indicies]
         if det_high.shape[0] > 0:
             if self.use_apperance_model:
-                features = self.get_feature(STrack.xywh2tlbr(det_high[:, :4]), ori_img)
+                features = self.get_feature(det_high[:, :4], ori_img)
                 # detections: List[Strack]
-                D_high = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format, feature=feature)
-                                for (cls, xywh, score, feature) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4], features)]
+                D_high = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format, feature=feature)
+                                for (cls, tlbr, score, feature) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4], features)]
             else:
-                D_high = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format)
-                            for (cls, xywh, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
+                D_high = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
+                            for (cls, tlbr, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
         else:
             D_high = []
 
         if det_low.shape[0] > 0:
-            D_low = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format)
-                            for (cls, xywh, score) in zip(det_low[:, -1], det_low[:, :4], det_low[:, 4])]
+            D_low = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
+                            for (cls, tlbr, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
         else:
             D_low = []
 
diff --git a/tracker/bytetrack.py b/tracker/bytetrack.py
index 3b987a0..61eeee9 100644
--- a/tracker/bytetrack.py
+++ b/tracker/bytetrack.py
@@ -57,14 +57,6 @@ def update(self, det_results, ori_img):
         lost_stracks = []           # The tracks which are not obtained in the current frame but are not removed.(Lost for some time lesser than the threshold for removing)
         removed_stracks = []
 
-        # convert the scale to origin size
-        # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-        # TODO: check here, if nesscessary use two ratio
-        img_h, img_w = ori_img.shape[0], ori_img.shape[1]
-        ratio = [img_h / self.model_img_size[0], img_w / self.model_img_size[1]]  # usually > 1
-        det_results[:, 0], det_results[:, 2] =  det_results[:, 0]*ratio[1], det_results[:, 2]*ratio[1]
-        det_results[:, 1], det_results[:, 3] =  det_results[:, 1]*ratio[0], det_results[:, 3]*ratio[0]
-
         """step 1. filter results and init tracks"""
                
         # filter small area bboxs
@@ -72,12 +64,6 @@ def update(self, det_results, ori_img):
             small_indicies = det_results[:, 2]*det_results[:, 3] > 50
             det_results = det_results[small_indicies]
 
-        # run NMS
-        if self.NMS:
-            # NOTE: Note nms need tlbr format
-            nms_indices = nms(torch.from_numpy(STrack.xywh2tlbr(det_results[:, :4])), torch.from_numpy(det_results[:, 4]), 
-                            self.opts.nms_thresh)
-            det_results = det_results[nms_indices.numpy()]
 
         # cal high and low indicies
         det_high_indicies = det_results[:, 4] >= self.det_thresh
@@ -88,19 +74,19 @@ def update(self, det_results, ori_img):
         det_high, det_low = det_results[det_high_indicies], det_results[det_low_indicies]
         if det_high.shape[0] > 0:
             if self.use_apperance_model:
-                features = self.get_feature(STrack.xywh2tlbr(det_high[:, :4]), ori_img)
+                features = self.get_feature(det_high[:, :4], ori_img)
                 # detections: List[Strack]
-                D_high = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format, feature=feature)
-                                for (cls, xywh, score, feature) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4], features)]
+                D_high = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format, feature=feature)
+                                for (cls, tlbr, score, feature) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4], features)]
             else:
-                D_high = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format)
-                            for (cls, xywh, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
+                D_high = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
+                            for (cls, tlbr, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
         else:
             D_high = []
 
         if det_low.shape[0] > 0:
-            D_low = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format)
-                            for (cls, xywh, score) in zip(det_low[:, -1], det_low[:, :4], det_low[:, 4])]
+            D_low = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
+                            for (cls, tlbr, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
         else:
             D_low = []
 
diff --git a/tracker/c_biou_tracker.py b/tracker/c_biou_tracker.py
index 6db4869..7381532 100644
--- a/tracker/c_biou_tracker.py
+++ b/tracker/c_biou_tracker.py
@@ -61,7 +61,6 @@ def get_buffer_bbox(self, level=1, bbox=None):
             buffer_bbox = bbox + np.array([-b*bbox[2], -b*bbox[3], 2*b*bbox[2], 2*b*bbox[3]])
         return np.maximum(0.0, buffer_bbox)
 
-
     @property
     def tlbr(self):
         ret = self.origin_bbox_buffer[-1].copy()
@@ -152,6 +151,18 @@ def update(self, new_track, frame_id):
 
         self.time_since_update = 0
 
+
+    @staticmethod
+    def tlbr2tlwh(tlbr):
+        """
+        convert tlbr to tlwh
+        """
+        result = np.asarray(tlbr).copy()
+        result[2] -= result[0]
+        result[3] -= result[1]
+
+        return result
+
     @staticmethod
     def xywh2tlbr(xywh):
         """
@@ -226,25 +237,11 @@ def update(self, det_results, ori_img):
         """step 1. filter results and init tracks"""
         det_results = det_results[det_results[:, 4] > self.det_thresh]
 
-        # convert the scale to origin size
-        # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-        # TODO: check here, if nesscessary use two ratio
-        img_h, img_w = ori_img.shape[0], ori_img.shape[1]
-        ratio = [img_h / self.model_img_size[0], img_w / self.model_img_size[1]]  # usually > 1
-        det_results[:, 0], det_results[:, 2] =  det_results[:, 0]*ratio[1], det_results[:, 2]*ratio[1]
-        det_results[:, 1], det_results[:, 3] =  det_results[:, 1]*ratio[0], det_results[:, 3]*ratio[0]
-
         if det_results.shape[0] > 0:
-            if self.NMS:
-                # TODO: Note nms need tlbr format
-                bbox_temp = C_BIoUSTrack.xywh2tlbr(det_results[:, :4])
-                nms_indices = nms(torch.from_numpy(bbox_temp), torch.from_numpy(det_results[:, 4]), 
-                                self.opts.nms_thresh)
-                det_results = det_results[nms_indices.numpy()]
 
             # detections: List[Strack]
-            detections = [C_BIoUSTrack(cls, C_BIoUSTrack.xywh2tlwh(xywh), score)
-                            for (cls, xywh, score) in zip(det_results[:, -1], det_results[:, :4], det_results[:, 4])]
+            detections = [C_BIoUSTrack(cls, C_BIoUSTrack.tlbr2tlwh(tlbr), score)
+                            for (cls, tlbr, score) in zip(det_results[:, -1], det_results[:, :4], det_results[:, 4])]
 
         else:
             detections = []
diff --git a/tracker/deepmot.py b/tracker/deepmot.py
index 0448f53..d32e32b 100644
--- a/tracker/deepmot.py
+++ b/tracker/deepmot.py
@@ -176,14 +176,6 @@ def update(self, det_results, ori_img):
         lost_stracks = []           # The tracks which are not obtained in the current frame but are not removed.(Lost for some time lesser than the threshold for removing)
         removed_stracks = []
 
-        # convert the scale to origin size
-        # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-        # TODO: check here, if nesscessary use two ratio
-        img_h, img_w = ori_img.shape[0], ori_img.shape[1]
-        ratio = [img_h / self.model_img_size[0], img_w / self.model_img_size[1]]  # usually > 1
-        det_results[:, 0], det_results[:, 2] =  det_results[:, 0]*ratio[1], det_results[:, 2]*ratio[1]
-        det_results[:, 1], det_results[:, 3] =  det_results[:, 1]*ratio[0], det_results[:, 3]*ratio[0]
-
         """step 1. filter results and init tracks"""
                
         # filter small area bboxs
@@ -191,27 +183,22 @@ def update(self, det_results, ori_img):
             small_indicies = det_results[:, 2]*det_results[:, 3] > 50
             det_results = det_results[small_indicies]
 
-        # run NMS
-        if self.NMS:
-            # NOTE: Note nms need tlbr format
-            nms_indices = nms(torch.from_numpy(STrack.xywh2tlbr(det_results[:, :4])), torch.from_numpy(det_results[:, 4]), 
-                            self.opts.nms_thresh)
-            det_results = det_results[nms_indices.numpy()]
 
         # cal high and low indicies
         det_high_indicies = det_results[:, 4] >= self.det_thresh
         det_low_indicies = np.logical_and(np.logical_not(det_high_indicies), det_results[:, 4] > self.low_conf_thresh)
 
         det_high, det_low = det_results[det_high_indicies], det_results[det_low_indicies]
+
         if det_high.shape[0] > 0:
-            D_high = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format)
-                        for (cls, xywh, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
+            D_high = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
+                        for (cls, tlbr, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
         else:
             D_high = []
 
         if det_low.shape[0] > 0:
-            D_low = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format)
-                            for (cls, xywh, score) in zip(det_low[:, -1], det_low[:, :4], det_low[:, 4])]
+            D_low = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
+                            for (cls, tlbr, score) in zip(det_low[:, -1], det_low[:, :4], det_low[:, 4])]
         else:
             D_low = []
 
diff --git a/tracker/deepsort.py b/tracker/deepsort.py
index a112d78..c4858ae 100644
--- a/tracker/deepsort.py
+++ b/tracker/deepsort.py
@@ -96,34 +96,21 @@ def update(self, det_results, ori_img):
 
         """step 1. filter results and init tracks"""
         det_results = det_results[det_results[:, 4] > self.det_thresh]
-        # convert the scale to origin size
-        # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-        # TODO: check here, if nesscessary use two ratio
-        img_h, img_w = ori_img.shape[0], ori_img.shape[1]
-        ratio = [img_h / self.model_img_size[0], img_w / self.model_img_size[1]]  # usually > 1
-        det_results[:, 0], det_results[:, 2] =  det_results[:, 0]*ratio[1], det_results[:, 2]*ratio[1]
-        det_results[:, 1], det_results[:, 3] =  det_results[:, 1]*ratio[0], det_results[:, 3]*ratio[0]
+
 
         if det_results.shape[0] > 0:
 
-            bbox_temp = STrack.xywh2tlbr(det_results[:, :4])
+            bbox_temp = det_results[:, :4]
             if self.filter_small_area:  # filter small area bboxs
                 small_indicies = det_results[:, 2]*det_results[:, 3] > 50
                 det_results = det_results[small_indicies]
                 bbox_temp = bbox_temp[small_indicies]
             
-            if self.NMS:
-                # NOTE: Note nms need tlbr format
-                nms_indices = nms(torch.from_numpy(bbox_temp), torch.from_numpy(det_results[:, 4]), 
-                                self.opts.nms_thresh)
-                det_results = det_results[nms_indices.numpy()]
-                features = self.get_feature(bbox_temp[nms_indices.numpy()], ori_img)
-            else:
-                features = self.get_feature(bbox_temp, ori_img)
+            features = self.get_feature(bbox_temp, ori_img)
 
             # detections: List[Strack]
-            detections = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format, feature=feature, use_avg_of_feature=False)
-                            for (cls, xywh, score, feature) in zip(det_results[:, -1], det_results[:, :4], det_results[:, 4], features)]
+            detections = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format, feature=feature, use_avg_of_feature=False)
+                            for (cls, tlbr, score, feature) in zip(det_results[:, -1], det_results[:, :4], det_results[:, 4], features)]
 
         else:
             detections = []
diff --git a/tracker/strongsort.py b/tracker/strongsort.py
index b56d19a..65109ef 100644
--- a/tracker/strongsort.py
+++ b/tracker/strongsort.py
@@ -108,29 +108,17 @@ def update(self, det_results, ori_img):
 
         """step 1. filter results and init tracks"""
         det_results = det_results[det_results[:, 4] > self.det_thresh]
-        # convert the scale to origin size
-        # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-        # TODO: check here, if nesscessary use two ratio
-        img_h, img_w = ori_img.shape[0], ori_img.shape[1]
-        ratio = [img_h / self.model_img_size[0], img_w / self.model_img_size[1]]  # usually > 1
-        det_results[:, 0], det_results[:, 2] =  det_results[:, 0]*ratio[1], det_results[:, 2]*ratio[1]
-        det_results[:, 1], det_results[:, 3] =  det_results[:, 1]*ratio[0], det_results[:, 3]*ratio[0]
+
 
         if det_results.shape[0] > 0:
 
-            bbox_temp = STrack.xywh2tlbr(det_results[:, :4])            
-            if self.NMS:
-                # NOTE: Note nms need tlbr format
-                nms_indices = nms(torch.from_numpy(bbox_temp), torch.from_numpy(det_results[:, 4]), 
-                                self.opts.nms_thresh)
-                det_results = det_results[nms_indices.numpy()]
-                features = self.get_feature(bbox_temp[nms_indices.numpy()], ori_img)
-            else:
-                features = self.get_feature(bbox_temp, ori_img)
+            bbox_temp = det_results[:, :4]        
+
+            features = self.get_feature(bbox_temp, ori_img)
 
             # detections: List[Strack]
-            detections = [STrack(cls, STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format, feature=feature)
-                            for (cls, xywh, score, feature) in zip(det_results[:, -1], det_results[:, :4], det_results[:, 4], features)]
+            detections = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format, feature=feature)
+                            for (cls, tlbr, score, feature) in zip(det_results[:, -1], det_results[:, :4], det_results[:, 4], features)]
 
         else:
             detections = []
diff --git a/tracker/track.py b/tracker/track.py
index 61b37f8..88b19fc 100644
--- a/tracker/track.py
+++ b/tracker/track.py
@@ -29,6 +29,8 @@
     from models.experimental import attempt_load
     from evaluate import evaluate
     from utils.torch_utils import select_device, time_synchronized, TracedModel
+    from utils.general import non_max_suppression, scale_coords, check_img_size
+
     print('Note: running yolo v7 detector')
 
 except:
@@ -73,17 +75,16 @@ def main(opts, cfgs):
         opts.save_images = True
 
     """
-    1. load model
+    1. load model for yolo v7
     """
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    ckpt = torch.load(opts.model_path, map_location=device)
-    model = ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()  # for yolo v7
+
+    model = attempt_load(opts.model_path, map_location=device)  # for yolo v7
+    stride = int(model.stride.max())  # model stride
+    opts.img_size = check_img_size(opts.img_size, s=stride)  # check img_size
 
     if opts.trace:
-        print(opts.img_size)
         model = TracedModel(model, device, opts.img_size)
-    else:
-        model.to(device)
 
     """
     2. load dataset and track
@@ -124,7 +125,7 @@ def main(opts, cfgs):
 
         path = os.path.join(DATA_ROOT, seq) if opts.data_format == 'origin' else os.path.join('./', f'{opts.dataset}', 'test.txt')
 
-        loader = tracker_dataloader.TrackerLoader(path, opts.img_size, opts.data_format, seq)
+        loader = tracker_dataloader.TrackerLoader(path, opts.img_size, opts.data_format, seq, pre_process_method='v7', model_stride=stride)
 
         data_loader = torch.utils.data.DataLoader(loader, batch_size=1)
 
@@ -139,27 +140,16 @@ def main(opts, cfgs):
             timer.tic()  # start timing this img
 
             if not i % opts.detect_per_frame:  # if it's time to detect
-
+                
                 out = model(img.to(device))  # model forward             
                 out = out[0]  # NOTE: for yolo v7
-            
-                if len(out.shape) == 3:  # case (bs, num_obj, ...)
-                    # out = out.squeeze()
-                    # NOTE: assert batch size == 1
-                    out = out.squeeze(0)
-                    img0 = img0.squeeze(0)
-                # remove some low conf detections
-                out = out[out[:, 4] > 0.001]
-                
-            
-                # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-                if opts.det_output_format == 'yolo':
-                    cls_conf, cls_idx = torch.max(out[:, 5:], dim=1)
-                    # out[:, 4] *= cls_conf  # fuse object and cls conf
-                    out[:, 5] = cls_idx
-                    out = out[:, :6]
+                img0 = img0.squeeze(0)
+
+                # post process
+                out = post_process_v7(out, img_size=img.shape[2:], ori_img_size=img0.shape)
             
                 current_tracks = tracker.update(out, img0)  # List[class(STracks)]
+                
             else:  # otherwize
                 # make the img shape (bs, C, H, W) as (C, H, W)
                 if len(img0.shape) == 4:
@@ -239,6 +229,21 @@ def main(opts, cfgs):
         evaluate(sorted(os.listdir(f'./tracker/results/{folder_name}')), 
                     sorted([seq + '.txt' for seq in seqs]), data_type='visdrone', result_folder=folder_name)  
 
+
+
+def post_process_v7(out, img_size, ori_img_size):
+    """ post process for v5 and v7
+    
+    """
+
+    out = non_max_suppression(out, conf_thres=0.01, )[0]
+    out[:, :4] = scale_coords(img_size, out[:, :4], ori_img_size, ratio_pad=None).round()
+
+    # out: tlbr, conf, cls
+
+    return out
+
+
 def save_results(folder_name, seq_name, results, data_type='default'):
     """
     write results to txt file
@@ -346,7 +351,7 @@ def get_color(idx):
     parser.add_argument('--model_path', type=str, default='./weights/best.pt', help='model path')
     parser.add_argument('--trace', type=bool, default=False, help='traced model of YOLO v7')
 
-    parser.add_argument('--img_size', nargs='+', type=int, default=[1280, 1280], help='[train, test] image sizes')
+    parser.add_argument('--img_size', nargs='+', type=int, default=1280, help='[train, test] image sizes')
 
     """For tracker"""
     # model path
@@ -377,4 +382,5 @@ def get_color(idx):
     # NOTE: read path of datasets, sequences and TrackEval configs
     with open(f'./tracker/config_files/{opts.dataset}.yaml', 'r') as f:
         cfgs = yaml.load(f, Loader=yaml.FullLoader)
+    
     main(opts, cfgs)
diff --git a/tracker/track_yolov5.py b/tracker/track_yolov5.py
index 329b6bb..c283750 100644
--- a/tracker/track_yolov5.py
+++ b/tracker/track_yolov5.py
@@ -27,6 +27,7 @@
     
     from models.common import DetectMultiBackend
     from evaluate import evaluate
+    from utils.general import check_img_size, non_max_suppression, scale_boxes
     print('Note: running yolo v5 detector')
 
 except:
@@ -75,6 +76,9 @@ def main(opts, cfgs):
     """
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     model = DetectMultiBackend(opts.model_path, device=device, dnn=False, data=YAML_DICT, fp16=False)
+    stride = model.stride
+    opts.img_size = check_img_size(opts.img_size, s=stride)  # check img_size
+
     model.eval()
     # warm up
     model.warmup(imgsz=(1, 3, 640, 640))
@@ -115,7 +119,7 @@ def main(opts, cfgs):
 
         path = os.path.join(DATA_ROOT, seq) if opts.data_format == 'origin' else os.path.join('./', f'{opts.dataset}', 'test.txt')
 
-        loader = tracker_dataloader.TrackerLoader(path, opts.img_size, opts.data_format, seq)
+        loader = tracker_dataloader.TrackerLoader(path, opts.img_size, opts.data_format, seq, pre_process_method='v5', model_stride=stride)
 
         data_loader = torch.utils.data.DataLoader(loader, batch_size=1)
 
@@ -132,23 +136,11 @@ def main(opts, cfgs):
             if not i % opts.detect_per_frame:  # if it's time to detect
 
                 out = model(img.to(device))  # model forward             
-                out = out[0]  # NOTE: for yolo v7
-            
-                if len(out.shape) == 3:  # case (bs, num_obj, ...)
-                    # out = out.squeeze()
-                    # NOTE: assert batch size == 1
-                    out = out.squeeze(0)
-                    img0 = img0.squeeze(0)
-                # remove some low conf detections
-                out = out[out[:, 4] > 0.001]
-                
+                out = out[0]  
             
-                # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-                if opts.det_output_format == 'yolo':
-                    cls_conf, cls_idx = torch.max(out[:, 5:], dim=1)
-                    # out[:, 4] *= cls_conf  # fuse object and cls conf
-                    out[:, 5] = cls_idx
-                    out = out[:, :6]
+                img0 = img0.squeeze(0)                
+
+                out = post_process_v5(out, img_size=img.shape[2:], ori_img_size=img0.shape)
             
                 current_tracks = tracker.update(out, img0)  # List[class(STracks)]
             else:  # otherwize
@@ -223,6 +215,21 @@ def main(opts, cfgs):
                     sorted([seq + '.txt' for seq in seqs]), data_type='visdrone', result_folder=folder_name)  
 
 
+
+def post_process_v5(out, img_size, ori_img_size):
+    """ post process for v5 and v7
+    
+    """
+
+    out = non_max_suppression(out, conf_thres=0.01, )[0]
+    out[:, :4] = scale_boxes(img_size, out[:, :4], ori_img_size, ratio_pad=None).round()
+
+    # out: tlbr, conf, cls
+
+    return out
+
+
+
 def save_results(folder_name, seq_name, results, data_type='default'):
     """
     write results to txt file
diff --git a/tracker/track_yolov8.py b/tracker/track_yolov8.py
index e3a8728..d786f03 100644
--- a/tracker/track_yolov8.py
+++ b/tracker/track_yolov8.py
@@ -115,7 +115,7 @@ def main(opts, cfgs):
 
         # NOTE: yolo v8 requires img to be original shape that openCV reads, that is
         # (H, W, C) inseatd of (C, H, W)
-        loader = tracker_dataloader.TrackerLoader(path, opts.img_size, opts.data_format, seq, convert_and_norm=False)
+        loader = tracker_dataloader.TrackerLoader(path, opts.img_size, opts.data_format, seq, pre_process_method='v8')
 
         data_loader = torch.utils.data.DataLoader(loader, batch_size=1)
 
@@ -141,13 +141,15 @@ def main(opts, cfgs):
                 out_ = model.predict(img)  # out: list['ultralytics.yolo.engine.results.Results']
                 out_ = out_[0].boxes  # out: ultralytics.yolo.engine.results.Results
 
+                out_ = postprocess_v8(out_)
+
                 # NOTE: convert out to (N, 6) which means [xc, yc, w, h, conf, cls]
-                out = torch.cat([out_.xywh, out_.conf[:, None], out_.cls[:, None]], dim=1)
+                out = torch.cat([out_.xyxy, out_.conf[:, None], out_.cls[:, None]], dim=1)
             
                 if len(img0.shape) == 4:
                     img0 = img0.squeeze(0)
                 # remove some low conf detections
-                out = out[out[:, 4] > 0.001]
+                out = out[out[:, 4] > 0.01]
                            
                 current_tracks = tracker.update(out, img0)  # List[class(STracks)]
             else:  # otherwize
@@ -229,6 +231,13 @@ def main(opts, cfgs):
         evaluate(sorted(os.listdir(f'./tracker/results/{folder_name}')), 
                     sorted([seq + '.txt' for seq in seqs]), data_type='visdrone', result_folder=folder_name)  
 
+
+
+def postprocess_v8(out):
+
+    return out
+
+
 def save_results(folder_name, seq_name, results, data_type='default'):
     """
     write results to txt file
diff --git a/tracker/tracker_dataloader.py b/tracker/tracker_dataloader.py
index 86af736..62dc0fd 100644
--- a/tracker/tracker_dataloader.py
+++ b/tracker/tracker_dataloader.py
@@ -18,19 +18,24 @@ def letterbox(img, height=608, width=1088, color=(127.5, 127.5, 127.5)):  # resi
 
 
 class TrackerLoader(torch.utils.data.Dataset):
-    def __init__(self, path, img_size=1280, format='origin', seq=None, convert_and_norm=True) -> None:
+    def __init__(self, path, img_size=1280, format='origin', seq=None, pre_process_method='v5',
+                 model_stride=32) -> None:
         """
         Load images for EACH SEQUENCE
+
         path: file for img paths(format == 'yolo') or dataset path(format == 'origin')
         img_size: image size for model, tuple or int
         format: 'origin' or 'yolo'. origin for direct read imgs under seqs, yolo for read imgs by train.txt
-        convert_and_norm: whether convert origin image from (H, W, C) to (C, H, W) and normed between(0, 1)
+        pre_process_method: how to resize origin image
+        model_stride: stride of the model, only valid for v5 or v7
         """
         super().__init__()
         self.DATA_ROOT = '/data/wujiapeng/datasets/' if format == 'yolo' else path  # to get image
         self.img_files = []
         self.format = format
-        self.convert_and_norm = convert_and_norm
+        self.pre_process_method = pre_process_method
+        self.model_stride = model_stride
+
         if format == 'origin':
             assert os.path.isdir(path), f'your path is {path}, path must be your dataset path'
            
@@ -65,22 +70,64 @@ def __getitem__(self, index):
         if self.format == 'origin':
             current_img_path = os.path.join(self.DATA_ROOT, current_img_path)
               
-        img = cv2.imread(current_img_path)  # (H, W, C)
+        ori_img = cv2.imread(current_img_path)  # (H, W, C)
 
-        assert img is not None, f'Fail to load image{current_img_path}'
+        assert ori_img is not None, f'Fail to load image{current_img_path}'
         
-        # img_resized, *_ = letterbox(img, self.height, self.width)
-        img_resized = cv2.resize(img, (self.width, self.height))
+        if self.pre_process_method in ['v5', 'v7']:
 
-        if self.convert_and_norm:
-            # convert BGR to RGB and to (C, H, W)
-            img_resized = img_resized[:, :, ::-1].transpose(2, 0, 1)
+            img_resized = self._letterbox(ori_img, new_shape=(self.height, self.width), stride=self.model_stride)[0]
 
-            img_resized = np.ascontiguousarray(img_resized, dtype=np.float32)
-            img_resized /= 255.0
+            img_resized = img_resized[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB
+            img_resized = np.ascontiguousarray(img_resized)
 
-        return torch.from_numpy(img_resized), torch.from_numpy(img)
+            img_resized = torch.from_numpy(img_resized).float()
+            img_resized /= 255.0
 
+        elif self.pre_process_method in ['v8']:
+            # NOTE: abort resize step
+            # img_resized = cv2.resize(ori_img, (self.height, self.width))
+            img_resized = torch.from_numpy(ori_img)
+
+        else:
+            raise NotImplementedError
+
+
+        return img_resized, torch.from_numpy(ori_img)
+    
+
+    
+    def _letterbox(self, img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
+        # Resize and pad image while meeting stride-multiple constraints
+        shape = img.shape[:2]  # current shape [height, width]
+        if isinstance(new_shape, int):
+            new_shape = (new_shape, new_shape)
+
+        # Scale ratio (new / old)
+        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+        if not scaleup:  # only scale down, do not scale up (for better test mAP)
+            r = min(r, 1.0)
+
+        # Compute padding
+        ratio = r, r  # width, height ratios
+        new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+        dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+        if auto:  # minimum rectangle
+            dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
+        elif scaleFill:  # stretch
+            dw, dh = 0.0, 0.0
+            new_unpad = (new_shape[1], new_shape[0])
+            ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
+
+        dw /= 2  # divide padding into 2 sides
+        dh /= 2
+
+        if shape[::-1] != new_unpad:  # resize
+            img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+        return img, ratio, (dw, dh)
 
     def __len__(self):
         return len(self.img_files)
diff --git a/tracker/uavmot.py b/tracker/uavmot.py
index ac08b88..0dccd17 100644
--- a/tracker/uavmot.py
+++ b/tracker/uavmot.py
@@ -122,14 +122,6 @@ def update(self, det_results, ori_img):
         lost_stracks = []           # The tracks which are not obtained in the current frame but are not removed.(Lost for some time lesser than the threshold for removing)
         removed_stracks = []
 
-        # convert the scale to origin size
-        # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-        # TODO: check here, if nesscessary use two ratio
-        img_h, img_w = ori_img.shape[0], ori_img.shape[1]
-        ratio = [img_h / self.model_img_size[0], img_w / self.model_img_size[1]]  # usually > 1
-        det_results[:, 0], det_results[:, 2] =  det_results[:, 0]*ratio[1], det_results[:, 2]*ratio[1]
-        det_results[:, 1], det_results[:, 3] =  det_results[:, 1]*ratio[0], det_results[:, 3]*ratio[0]
-
         """step 1. filter results and init tracks"""
                
         # filter small area bboxs
@@ -137,35 +129,28 @@ def update(self, det_results, ori_img):
             small_indicies = det_results[:, 2]*det_results[:, 3] > 50
             det_results = det_results[small_indicies]
 
-        # run NMS
-        if self.NMS:
-            # NOTE: Note nms need tlbr format
-            nms_indices = nms(torch.from_numpy(AMF_STrack.xywh2tlbr(det_results[:, :4])), torch.from_numpy(det_results[:, 4]), 
-                            self.opts.nms_thresh)
-            det_results = det_results[nms_indices.numpy()]
 
         # cal high and low indicies
         det_high_indicies = det_results[:, 4] >= self.det_thresh
         det_low_indicies = np.logical_and(np.logical_not(det_high_indicies), det_results[:, 4] > self.low_conf_thresh)
 
-        
         # init saperatly
         det_high, det_low = det_results[det_high_indicies], det_results[det_low_indicies]
         if det_high.shape[0] > 0:
             if self.use_apperance_model:
-                features = self.get_feature(AMF_STrack.xywh2tlbr(det_high[:, :4]), ori_img)
+                features = self.get_feature(det_high[:, :4], ori_img)
                 # detections: List[Strack]
-                D_high = [AMF_STrack(cls, AMF_STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format, feature=feature)
-                                for (cls, xywh, score, feature) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4], features)]
+                D_high = [AMF_STrack(cls, AMF_STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format, feature=feature)
+                                for (cls, tlbr, score, feature) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4], features)]
             else:
-                D_high = [AMF_STrack(cls, AMF_STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format)
-                            for (cls, xywh, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
+                D_high = [AMF_STrack(cls, AMF_STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
+                            for (cls, tlbr, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
         else:
             D_high = []
 
         if det_low.shape[0] > 0:
-            D_low = [AMF_STrack(cls, AMF_STrack.xywh2tlwh(xywh), score, kalman_format=self.opts.kalman_format)
-                            for (cls, xywh, score) in zip(det_low[:, -1], det_low[:, :4], det_low[:, 4])]
+            D_low = [AMF_STrack(cls, AMF_STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
+                            for (cls, tlbr, score) in zip(det_low[:, -1], det_low[:, :4], det_low[:, 4])]
         else:
             D_low = []
 
diff --git a/utils/datasets.py b/utils/datasets.py
index 1f491aa..98886ff 100644
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -77,20 +77,8 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
                                         pad=pad,
                                         image_weights=image_weights,
                                         prefix=prefix)
-        elif opt.dataset == 'visdrone':
-            dataset = LoadImagesAndLabelsVisDrone(path, imgsz, batch_size,
-                                        augment=augment,  # augment images
-                                        hyp=hyp,  # augmentation hyperparameters
-                                        rect=rect,  # rectangular training
-                                        cache_images=cache,
-                                        single_cls=opt.single_cls,
-                                        stride=int(stride),
-                                        pad=pad,
-                                        image_weights=image_weights,
-                                        prefix=prefix)
-
-        elif opt.dataset == 'mot17':
-            dataset = LoadImagesAndLabelsVisDrone(path, imgsz, batch_size,
+        else:
+            dataset = LoadImagesAndLabelsCustom(path, imgsz, batch_size,
                                         augment=augment,  # augment images
                                         hyp=hyp,  # augmentation hyperparameters
                                         rect=rect,  # rectangular training
@@ -100,8 +88,6 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
                                         pad=pad,
                                         image_weights=image_weights,
                                         prefix=prefix)
-        else:
-            raise NotImplementedError
 
     batch_size = min(batch_size, len(dataset))
     nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers])  # number of workers
@@ -699,7 +685,7 @@ def img2label_paths_VisDrone(img_paths):
 
 
 
-class LoadImagesAndLabelsVisDrone(LoadImagesAndLabels):
+class LoadImagesAndLabelsCustom(LoadImagesAndLabels):
     def __init__(self, path, img_size=640, batch_size=16, augment=False, 
             hyp=None, rect=False, image_weights=False, cache_images=False, single_cls=False, stride=32, pad=0, prefix=''):
         # super().__init__(path, img_size, batch_size, augment, hyp, rect, image_weights, cache_images, single_cls, stride, pad, prefix)

From 38ba3c54ffecd07f694ef4851df14c7e0da2b46d Mon Sep 17 00:00:00 2001
From: Jiapeng Wu <49953745+JackWoo0831@users.noreply.github.com>
Date: Sat, 6 May 2023 10:54:28 +0800
Subject: [PATCH 08/17] Delete mot17 directory

---
 mot17/test.txt  | 17757 ------------------
 mot17/train.txt | 44628 ----------------------------------------------
 mot17/val.txt   | 18648 -------------------
 3 files changed, 81033 deletions(-)
 delete mode 100644 mot17/test.txt
 delete mode 100644 mot17/train.txt
 delete mode 100644 mot17/val.txt

diff --git a/mot17/test.txt b/mot17/test.txt
deleted file mode 100644
index fa1d903..0000000
--- a/mot17/test.txt
+++ /dev/null
@@ -1,17757 +0,0 @@
-MOT17/images/test/MOT17-07-SDP/000001.jpg
-MOT17/images/test/MOT17-07-SDP/000002.jpg
-MOT17/images/test/MOT17-07-SDP/000003.jpg
-MOT17/images/test/MOT17-07-SDP/000004.jpg
-MOT17/images/test/MOT17-07-SDP/000005.jpg
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-MOT17/images/test/MOT17-07-SDP/000007.jpg
-MOT17/images/test/MOT17-07-SDP/000008.jpg
-MOT17/images/test/MOT17-07-SDP/000009.jpg
-MOT17/images/test/MOT17-07-SDP/000010.jpg
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-MOT17/images/test/MOT17-07-SDP/000012.jpg
-MOT17/images/test/MOT17-07-SDP/000013.jpg
-MOT17/images/test/MOT17-07-SDP/000014.jpg
-MOT17/images/test/MOT17-07-SDP/000015.jpg
-MOT17/images/test/MOT17-07-SDP/000016.jpg
-MOT17/images/test/MOT17-07-SDP/000017.jpg
-MOT17/images/test/MOT17-07-SDP/000018.jpg
-MOT17/images/test/MOT17-07-SDP/000019.jpg
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-MOT17/images/test/MOT17-07-SDP/000021.jpg
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-MOT17/images/test/MOT17-07-SDP/000023.jpg
-MOT17/images/test/MOT17-07-SDP/000024.jpg
-MOT17/images/test/MOT17-07-SDP/000025.jpg
-MOT17/images/test/MOT17-07-SDP/000026.jpg
-MOT17/images/test/MOT17-07-SDP/000027.jpg
-MOT17/images/test/MOT17-07-SDP/000028.jpg
-MOT17/images/test/MOT17-07-SDP/000029.jpg
-MOT17/images/test/MOT17-07-SDP/000030.jpg
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diff --git a/mot17/train.txt b/mot17/train.txt
deleted file mode 100644
index 3296661..0000000
--- a/mot17/train.txt
+++ /dev/null
@@ -1,44628 +0,0 @@
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diff --git a/mot17/val.txt b/mot17/val.txt
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From 40ce97ae77421b4e2ecf5c29f60ba515e5bcaa30 Mon Sep 17 00:00:00 2001
From: Jiapeng Wu <49953745+JackWoo0831@users.noreply.github.com>
Date: Sat, 6 May 2023 11:00:20 +0800
Subject: [PATCH 09/17] Delete visdrone directory

---
 visdrone/test.txt    |  6635 -----------
 visdrone/train.cache |   Bin 34046767 -> 0 bytes
 visdrone/train.txt   | 24198 -----------------------------------------
 visdrone/val.cache   |   Bin 3610799 -> 0 bytes
 visdrone/val.txt     |  2846 -----
 5 files changed, 33679 deletions(-)
 delete mode 100644 visdrone/test.txt
 delete mode 100644 visdrone/train.cache
 delete mode 100644 visdrone/train.txt
 delete mode 100644 visdrone/val.cache
 delete mode 100644 visdrone/val.txt

diff --git a/visdrone/test.txt b/visdrone/test.txt
deleted file mode 100644
index 168ee08..0000000
--- a/visdrone/test.txt
+++ /dev/null
@@ -1,6635 +0,0 @@
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diff --git a/visdrone/train.txt b/visdrone/train.txt
deleted file mode 100644
index 2aaf73d..0000000
--- a/visdrone/train.txt
+++ /dev/null
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diff --git a/visdrone/val.txt b/visdrone/val.txt
deleted file mode 100644
index b6ee3aa..0000000
--- a/visdrone/val.txt
+++ /dev/null
@@ -1,2846 +0,0 @@
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-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000226.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000227.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000228.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000229.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000230.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000231.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000232.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000233.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000234.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000235.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000236.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000237.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000238.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000239.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000240.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000241.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000242.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000243.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000244.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000245.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000246.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000247.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000248.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000249.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000250.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000251.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000252.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000253.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000254.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000255.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000256.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000257.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000258.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000259.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000260.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000261.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000262.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000263.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000264.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000265.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000266.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000267.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000268.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000269.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000270.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000271.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000272.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000273.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000274.jpg
-VisDrone2019/VisDrone2019/images/VisDrone2019-MOT-val/uav0000339_00001_v/0000275.jpg

From 76bb885909a13d35f985189907591cd75a6eb1be Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Sat, 6 May 2023 22:16:12 +0800
Subject: [PATCH 10/17] update save_results

---
 .gitignore              |  1 +
 tracker/track.py        | 16 ++++++++--------
 tracker/track_yolov5.py | 14 +++++++-------
 tracker/track_yolov8.py | 14 +++++++-------
 4 files changed, 23 insertions(+), 22 deletions(-)

diff --git a/.gitignore b/.gitignore
index d4ab509..dd84e0d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,6 +10,7 @@ runs/*
 wandb/*
 track_result.txt
 .idea/
+tracker/results/*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/tracker/track.py b/tracker/track.py
index 88b19fc..a7224ba 100644
--- a/tracker/track.py
+++ b/tracker/track.py
@@ -244,17 +244,15 @@ def post_process_v7(out, img_size, ori_img_size):
     return out
 
 
-def save_results(folder_name, seq_name, results, data_type='default'):
+def save_results(folder_name, seq_name, results, data_type='mot17'):
     """
     write results to txt file
 
     results: list  row format: frame id, target id, box coordinate, class(optional)
     to_file: file path(optional)
-    data_type: write data format
+    data_type: write data format, default or mot17 format.
     """
     assert len(results)
-    if not data_type == 'default':
-        raise NotImplementedError  # TODO
 
     if not os.path.exists(f'./tracker/results/{folder_name}'):
 
@@ -263,11 +261,13 @@ def save_results(folder_name, seq_name, results, data_type='default'):
     with open(os.path.join('./tracker/results', folder_name, seq_name + '.txt'), 'w') as f:
         for frame_id, target_ids, tlwhs, clses in results:
             if data_type == 'default':
-
-                # f.write(f'{frame_id},{target_id},{tlwh[0]},{tlwh[1]},\
-                #             {tlwh[2]},{tlwh[3]},{cls}\n')
+                
                 for id, tlwh, cls in zip(target_ids, tlwhs, clses):
                     f.write(f'{frame_id},{id},{tlwh[0]:.2f},{tlwh[1]:.2f},{tlwh[2]:.2f},{tlwh[3]:.2f},{int(cls)}\n')
+            
+            elif data_type == 'mot17':
+                for id, tlwh, cls in zip(target_ids, tlwhs, clses):
+                    f.write(f'{frame_id},{id},{tlwh[0]:.2f},{tlwh[1]:.2f},{tlwh[2]:.2f},{tlwh[3]:.2f},1.0,-1,-1,-1\n')
     f.close()
 
     return folder_name
@@ -359,7 +359,7 @@ def get_color(idx):
     parser.add_argument('--dhn_path', type=str, default='./weights/DHN.pth', help='path of DHN path for DeepMOT')
 
     # threshs
-    parser.add_argument('--conf_thresh', type=float, default=0.5, help='filter tracks')
+    parser.add_argument('--conf_thresh', type=float, default=0.2, help='filter tracks')
     parser.add_argument('--nms_thresh', type=float, default=0.7, help='thresh for NMS')
     parser.add_argument('--iou_thresh', type=float, default=0.5, help='IOU thresh to filter tracks')
 
diff --git a/tracker/track_yolov5.py b/tracker/track_yolov5.py
index c283750..0e5c627 100644
--- a/tracker/track_yolov5.py
+++ b/tracker/track_yolov5.py
@@ -230,17 +230,15 @@ def post_process_v5(out, img_size, ori_img_size):
 
 
 
-def save_results(folder_name, seq_name, results, data_type='default'):
+def save_results(folder_name, seq_name, results, data_type='mot17'):
     """
     write results to txt file
 
     results: list  row format: frame id, target id, box coordinate, class(optional)
     to_file: file path(optional)
-    data_type: write data format
+    data_type: write data format, default or mot17 format.
     """
     assert len(results)
-    if not data_type == 'default':
-        raise NotImplementedError  # TODO
 
     if not os.path.exists(f'./tracker/results/{folder_name}'):
 
@@ -249,11 +247,13 @@ def save_results(folder_name, seq_name, results, data_type='default'):
     with open(os.path.join('./tracker/results', folder_name, seq_name + '.txt'), 'w') as f:
         for frame_id, target_ids, tlwhs, clses in results:
             if data_type == 'default':
-
-                # f.write(f'{frame_id},{target_id},{tlwh[0]},{tlwh[1]},\
-                #             {tlwh[2]},{tlwh[3]},{cls}\n')
+                
                 for id, tlwh, cls in zip(target_ids, tlwhs, clses):
                     f.write(f'{frame_id},{id},{tlwh[0]:.2f},{tlwh[1]:.2f},{tlwh[2]:.2f},{tlwh[3]:.2f},{int(cls)}\n')
+            
+            elif data_type == 'mot17':
+                for id, tlwh, cls in zip(target_ids, tlwhs, clses):
+                    f.write(f'{frame_id},{id},{tlwh[0]:.2f},{tlwh[1]:.2f},{tlwh[2]:.2f},{tlwh[3]:.2f},1.0,-1,-1,-1\n')
     f.close()
 
     return folder_name
diff --git a/tracker/track_yolov8.py b/tracker/track_yolov8.py
index d786f03..f3aaa75 100644
--- a/tracker/track_yolov8.py
+++ b/tracker/track_yolov8.py
@@ -238,17 +238,15 @@ def postprocess_v8(out):
     return out
 
 
-def save_results(folder_name, seq_name, results, data_type='default'):
+def save_results(folder_name, seq_name, results, data_type='mot17'):
     """
     write results to txt file
 
     results: list  row format: frame id, target id, box coordinate, class(optional)
     to_file: file path(optional)
-    data_type: write data format
+    data_type: write data format, default or mot17 format.
     """
     assert len(results)
-    if not data_type == 'default':
-        raise NotImplementedError  # TODO
 
     if not os.path.exists(f'./tracker/results/{folder_name}'):
 
@@ -257,11 +255,13 @@ def save_results(folder_name, seq_name, results, data_type='default'):
     with open(os.path.join('./tracker/results', folder_name, seq_name + '.txt'), 'w') as f:
         for frame_id, target_ids, tlwhs, clses in results:
             if data_type == 'default':
-
-                # f.write(f'{frame_id},{target_id},{tlwh[0]},{tlwh[1]},\
-                #             {tlwh[2]},{tlwh[3]},{cls}\n')
+                
                 for id, tlwh, cls in zip(target_ids, tlwhs, clses):
                     f.write(f'{frame_id},{id},{tlwh[0]:.2f},{tlwh[1]:.2f},{tlwh[2]:.2f},{tlwh[3]:.2f},{int(cls)}\n')
+            
+            elif data_type == 'mot17':
+                for id, tlwh, cls in zip(target_ids, tlwhs, clses):
+                    f.write(f'{frame_id},{id},{tlwh[0]:.2f},{tlwh[1]:.2f},{tlwh[2]:.2f},{tlwh[3]:.2f},1.0,-1,-1,-1\n')
     f.close()
 
     return folder_name

From 75d99ca92974e8c04509872783f1c4bc9812ba8f Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Sun, 7 May 2023 15:53:32 +0800
Subject: [PATCH 11/17] just change a default parse

---
 tracker/track_yolov5.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tracker/track_yolov5.py b/tracker/track_yolov5.py
index 0e5c627..90c33dc 100644
--- a/tracker/track_yolov5.py
+++ b/tracker/track_yolov5.py
@@ -336,7 +336,7 @@ def get_color(idx):
 
     parser.add_argument('--model_path', type=str, default=None, help='model path')
 
-    parser.add_argument('--img_size', nargs='+', type=int, default=[1280, 1280], help='[train, test] image sizes')
+    parser.add_argument('--img_size', nargs='+', type=int, default=1280, help='[train, test] image sizes')
 
     """For tracker"""
     # model path

From dfdcf9445fa06ddbacc48bd71da8b0b3975e4be0 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Mon, 3 Jul 2023 23:04:23 +0800
Subject: [PATCH 12/17] update track_demo

---
 tracker/botsort.py              |  2 +-
 tracker/config_files/uavdt.yaml |  2 +-
 tracker/track_demo.py           | 87 +++++++++++++++++++++++++--------
 tracker/track_yolov5.py         |  4 +-
 4 files changed, 70 insertions(+), 25 deletions(-)

diff --git a/tracker/botsort.py b/tracker/botsort.py
index 66799e3..bc41812 100644
--- a/tracker/botsort.py
+++ b/tracker/botsort.py
@@ -270,7 +270,7 @@ def multi_gmc(stracks, H=np.eye(2, 3)):
 
 
 class BoTSORT(BaseTracker):
-    def __init__(self, opts, frame_rate=30, gamma=0.02, use_GMC=False, *args, **kwargs) -> None:
+    def __init__(self, opts, frame_rate=30, gamma=0.02, use_GMC=True, *args, **kwargs) -> None:
         super().__init__(opts, frame_rate, *args, **kwargs)
 
         self.use_apperance_model = False
diff --git a/tracker/config_files/uavdt.yaml b/tracker/config_files/uavdt.yaml
index d17248c..af4906b 100644
--- a/tracker/config_files/uavdt.yaml
+++ b/tracker/config_files/uavdt.yaml
@@ -8,7 +8,7 @@ CATEGORY_DICT:
   0: 'car'
 
 CERTAIN_SEQS:
-  - 'M0101'
+  - 
 IGNORE_SEQS:  # Seqs you want to ignore
   - 
 
diff --git a/tracker/track_demo.py b/tracker/track_demo.py
index ca9b9fa..5ffc9dd 100644
--- a/tracker/track_demo.py
+++ b/tracker/track_demo.py
@@ -30,6 +30,7 @@
     from models.experimental import attempt_load
     from evaluate import evaluate
     from utils.torch_utils import select_device, time_synchronized, TracedModel
+    from utils.general import non_max_suppression, scale_coords, check_img_size
     print('Note: running yolo v7 detector')
 
 except:
@@ -64,8 +65,11 @@ def main(opts):
     1. load model
     """
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
     ckpt = torch.load(opts.model_path, map_location=device)
     model = ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()  # for yolo v7
+    stride = int(model.stride.max())  # model stride
+    opts.img_size = check_img_size(opts.img_size, s=stride)  # check img_size
 
     if opts.trace:
         print(opts.img_size)
@@ -116,13 +120,15 @@ def main(opts):
         if not is_valid: 
             break  # end of reading 
 
-        img = resize_a_frame(img0, [opts.img_size, opts.img_size])
+        img, img0 = preprocess_v7(ori_img=img0, model_size=(opts.img_size, opts.img_size), model_stride=stride)
 
         timer.tic()  # start timing this img
         img = img.unsqueeze(0)  # （C, H, W) -> (bs == 1, C, H, W)
         out = model(img.to(device))  # model forward             
         out = out[0]  # NOTE: for yolo v7
-    
+
+        out = post_process_v7(out, img_size=img.shape[2:], ori_img_size=img0.shape)
+
         if len(out.shape) == 3:  # case (bs, num_obj, ...)
             # out = out.squeeze()
             # NOTE: assert batch size == 1
@@ -157,7 +163,7 @@ def main(opts):
         results.append((frame_id + 1, cur_id, cur_tlwh, cur_cls))
         timer.toc()  # end timing this image
         
-        plot_img(img0, frame_id, [cur_tlwh, cur_id, cur_cls], save_dir=os.path.join(SAVE_FOLDER, 'reuslt_images', obj_name))
+        plot_img(img0, frame_id, [cur_tlwh, cur_id, cur_cls], save_dir=os.path.join(SAVE_FOLDER, 'result_images', obj_name))
     
         frame_id += 1
 
@@ -191,25 +197,63 @@ def is_empty(self):
         return self.start_idx == len(self.arr)
 
 
-def resize_a_frame(frame, target_size):
-    """
-    resize a frame to target size
-
-    frame: np.ndarray, shape (H, W, C)
-    target_size: List[int, int] | Tuple[int, int]
+def post_process_v7(out, img_size, ori_img_size):
+    """ post process for v5 and v7
+    
     """
-    # resize to input to the YOLO net
-    frame_resized = cv2.resize(frame, (target_size[0], target_size[1]))  # (H', W', C)
-    # convert BGR to RGB and to (C, H, W)
-    frame_resized = frame_resized[:, :, ::-1].transpose(2, 0, 1)
 
-    frame_resized = np.ascontiguousarray(frame_resized, dtype=np.float32)
-    frame_resized /= 255.0
+    out = non_max_suppression(out, conf_thres=0.01, )[0]
+    out[:, :4] = scale_coords(img_size, out[:, :4], ori_img_size, ratio_pad=None).round()
 
-    frame_resized = torch.from_numpy(frame_resized)
+    # out: tlbr, conf, cls
 
-    return frame_resized
+    return out
 
+def preprocess_v7(ori_img, model_size, model_stride):
+    """ simple preprocess for a single image
+    
+    """
+    img_resized = _letterbox(ori_img, new_shape=model_size, stride=model_stride)[0]
+
+    img_resized = img_resized[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB
+    img_resized = np.ascontiguousarray(img_resized)
+
+    img_resized = torch.from_numpy(img_resized).float()
+    img_resized /= 255.0
+
+    return img_resized, ori_img
+
+def _letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
+    # Resize and pad image while meeting stride-multiple constraints
+    shape = img.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+    if not scaleup:  # only scale down, do not scale up (for better test mAP)
+        r = min(r, 1.0)
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+    if auto:  # minimum rectangle
+        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
+    elif scaleFill:  # stretch
+        dw, dh = 0.0, 0.0
+        new_unpad = (new_shape[1], new_shape[0])
+        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return img, ratio, (dw, dh)
 
 def save_results(obj_name, results, data_type='default'):
     """
@@ -273,7 +317,8 @@ def save_videos(obj_name):
         obj_name = [obj_name]
 
     for seq in obj_name:
-        images_path = os.path.join(SAVE_FOLDER, 'reuslt_images', seq)
+        if 'mp4' in seq: seq = seq[:-4]
+        images_path = os.path.join(SAVE_FOLDER, 'result_images', seq)
         images_name = sorted(os.listdir(images_path))
 
         to_video_path = os.path.join(images_path, '../', seq + '.mp4')
@@ -303,12 +348,12 @@ def get_color(idx):
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
 
-    parser.add_argument('--obj', type=str, default='M1305.mp4', help='video NAME or images FOLDER NAME')
+    parser.add_argument('--obj', type=str, default='demo.mp4', help='video NAME or images FOLDER NAME')
 
     parser.add_argument('--save_txt', type=bool, default=False, help='whether save txt')
 
     parser.add_argument('--tracker', type=str, default='sort', help='sort, deepsort, etc')
-    parser.add_argument('--model_path', type=str, default='./weights/best.pt', help='model path')
+    parser.add_argument('--model_path', type=str, default='./weights/yolov7_UAVDT_35epochs_20230507.pt', help='model path')
     parser.add_argument('--trace', type=bool, default=False, help='traced model of YOLO v7')
 
     parser.add_argument('--img_size', type=int, default=1280, help='[train, test] image sizes')
@@ -319,7 +364,7 @@ def get_color(idx):
     parser.add_argument('--dhn_path', type=str, default='./weights/DHN.pth', help='path of DHN path for DeepMOT')
 
     # threshs
-    parser.add_argument('--conf_thresh', type=float, default=0.5, help='filter tracks')
+    parser.add_argument('--conf_thresh', type=float, default=0.1, help='filter tracks')
     parser.add_argument('--nms_thresh', type=float, default=0.7, help='thresh for NMS')
     parser.add_argument('--iou_thresh', type=float, default=0.5, help='IOU thresh to filter tracks')
 
diff --git a/tracker/track_yolov5.py b/tracker/track_yolov5.py
index 90c33dc..3798324 100644
--- a/tracker/track_yolov5.py
+++ b/tracker/track_yolov5.py
@@ -167,7 +167,7 @@ def main(opts, cfgs):
             timer.toc()  # end timing this image
             
             if opts.save_images:
-                plot_img(img0, frame_id, [cur_tlwh, cur_id, cur_cls], save_dir=os.path.join(DATASET_ROOT, 'reuslt_images', seq))
+                plot_img(img0, frame_id, [cur_tlwh, cur_id, cur_cls], save_dir=os.path.join(DATASET_ROOT, 'result_images', seq))
         
             frame_id += 1
 
@@ -297,7 +297,7 @@ def save_videos(seq_names):
         seq_names = [seq_names]
 
     for seq in seq_names:
-        images_path = os.path.join(DATASET_ROOT, 'reuslt_images', seq)
+        images_path = os.path.join(DATASET_ROOT, 'result_images', seq)
         images_name = sorted(os.listdir(images_path))
 
         to_video_path = os.path.join(images_path, '../', seq + '.mp4')

From 98f03661f94b3d43db0d3e264191d2e5a2be9628 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Fri, 15 Sep 2023 12:55:01 +0800
Subject: [PATCH 13/17] fix some bugs of track_demo

---
 .gitignore            |  4 ++++
 tracker/botsort.py    |  2 +-
 tracker/track_demo.py | 18 ++++++++++--------
 3 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/.gitignore b/.gitignore
index dd84e0d..2f57332 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,6 +11,10 @@ wandb/*
 track_result.txt
 .idea/
 tracker/results/*
+*.mp4
+*.mkv
+temp.py
+demo_result/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/tracker/botsort.py b/tracker/botsort.py
index bc41812..b8108a6 100644
--- a/tracker/botsort.py
+++ b/tracker/botsort.py
@@ -318,7 +318,7 @@ def update(self, det_results, ori_img):
         ori_img: original image, np.ndarray, shape(H, W, C)
         """
         if isinstance(det_results, torch.Tensor):
-            det_results = det_results.cpu().numpy()
+            det_results = det_results.detach().cpu().numpy()
         if isinstance(ori_img, torch.Tensor):
             ori_img = ori_img.numpy()
 
diff --git a/tracker/track_demo.py b/tracker/track_demo.py
index 5ffc9dd..d1fd02b 100644
--- a/tracker/track_demo.py
+++ b/tracker/track_demo.py
@@ -90,7 +90,7 @@ def main(opts):
     # check path
     assert os.path.exists(obj_name), 'the path does not exist! '
     obj, get_next_frame = None, None  # init obj
-    if 'mp4' in opts.obj or 'MP4' in opts.obj:  # if it is a video
+    if 'mp4' in opts.obj or 'MP4' in opts.obj or 'mkv' in opts.obj:  # if it is a video
         obj = cv2.VideoCapture(obj_name) 
         get_next_frame = lambda _ : obj.read()
 
@@ -98,7 +98,7 @@ def main(opts):
         else: obj_name = obj_name[:-4]
     
     else:  
-        obj = my_queue(os.listdir(obj_name))
+        obj = my_queue(os.listdir(obj_name), obj_name)
         get_next_frame = lambda _ : obj.pop_front()
 
         if os.path.isabs(obj_name): obj_name = obj_name.split('/')[-1]
@@ -124,8 +124,9 @@ def main(opts):
 
         timer.tic()  # start timing this img
         img = img.unsqueeze(0)  # （C, H, W) -> (bs == 1, C, H, W)
-        out = model(img.to(device))  # model forward             
-        out = out[0]  # NOTE: for yolo v7
+        with torch.no_grad():
+            out = model(img.to(device))  # model forward             
+            out = out[0]  # NOTE: for yolo v7
 
         out = post_process_v7(out, img_size=img.shape[2:], ori_img_size=img0.shape)
 
@@ -181,15 +182,16 @@ class my_queue:
     """
     implement a queue for image seq reading
     """
-    def __init__(self, arr: list) -> None:
+    def __init__(self, arr: list, root_path: str) -> None:
         self.arr = arr 
         self.start_idx = 0
+        self.root_path = root_path
 
     def push_back(self, item):
         self.arr.append(item)
     
     def pop_front(self):
-        ret = cv2.imread(self.arr[self.start_idx])
+        ret = cv2.imread(os.path.join(self.root_path, self.arr[self.start_idx]))
         self.start_idx += 1
         return not self.is_empty(), ret
     
@@ -299,7 +301,7 @@ def plot_img(img, frame_id, results, save_dir):
         # draw a rect
         cv2.rectangle(img_, tlbr[:2], tlbr[2:], get_color(id), thickness=3, )
         # note the id and cls
-        text = f'{CATEGORY_DICT[cls]}-{id}'
+        text = f'id: {id}'
         cv2.putText(img_, text, (tlbr[0], tlbr[1]), fontFace=cv2.FONT_HERSHEY_PLAIN, fontScale=1, 
                         color=(255, 164, 0), thickness=2)
 
@@ -364,7 +366,7 @@ def get_color(idx):
     parser.add_argument('--dhn_path', type=str, default='./weights/DHN.pth', help='path of DHN path for DeepMOT')
 
     # threshs
-    parser.add_argument('--conf_thresh', type=float, default=0.1, help='filter tracks')
+    parser.add_argument('--conf_thresh', type=float, default=0.05, help='filter tracks')
     parser.add_argument('--nms_thresh', type=float, default=0.7, help='thresh for NMS')
     parser.add_argument('--iou_thresh', type=float, default=0.5, help='IOU thresh to filter tracks')
 

From e31226b4bace01cd3ceb0cd7f6ed6add9a64cb73 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Sat, 16 Sep 2023 13:11:02 +0800
Subject: [PATCH 14/17] fix some bugs

---
 README.md             | 76 ++++++++++++++++++++++++-------------------
 tracker/track_demo.py | 10 +++---
 2 files changed, 47 insertions(+), 39 deletions(-)

diff --git a/README.md b/README.md
index 80d8466..d55a8bc 100644
--- a/README.md
+++ b/README.md
@@ -1,43 +1,12 @@
 # YOLO v7 + 各种tracker实现多目标跟踪
 
-## 0. 更新记录
+## 0. 近期更新
 
-**2023.5.6[大更新]**: 对于v5, v7, 改变前处理和后处理方式(采用原有方式), ***解决了部分边界框近大远小的bug, 边界框更加精确***. 此外, 对于v8, 弃用了resize步骤, 直接推理.
-
-**2023.3.14**解决了`DeepSORT`和`C_BIoUTracker`后面出现的目标不跟踪的bug.  
-
-**2023.2.28**优化了`track_demo.py`, 减少了内存占用.  
-
-**2023.2.24**加入了**推理单个视频或图片文件夹**以及**YOLO v8**的推理功能, 对应的代码为`tracker/track_demo.py`与`tracker/track_yolov8.py`.  推理单个视频或图片文件夹不需要指定数据集与真值, 也没有评测指标的功能, 只需要在命令行中指定`obj`即可, 例如:
-
-```shell
-python tracker/track_demo.py --obj demo.mp4 
-```
-YOLO v8 代码的参数与之前完全相同. 安装YOLO v8以及训练步骤请参照[YOLO v8](https://github.com/ultralytics/ultralytics)  
+**2023.9.16**: 对于`track_demo.py`, 由于`yolov7`的`non_maximum_supress`函数中已经对类别做了筛选, 因此删去了主程序中类别计算的部分, 并修复了一些小bug  
 
+**2023.5.6[大更新]**: 对于v5, v7, 改变前处理和后处理方式(采用原有方式), ***解决了部分边界框近大远小的bug, 边界框更加精确***. 此外, 对于v8, 弃用了resize步骤, 直接推理.
 
-**2023.2.11**修复了TrackEval路径报错的问题, 详见[issue35](https://github.com/JackWoo0831/Yolov7-tracker/issues/35)  
-
-**2023.2.10**修改了[DeepSORT](https://github.com/JackWoo0831/Yolov7-tracker/blob/master/tracker/deepsort.py)的代码与相关部分代码, 遵循了DeepSORT原论文**级联匹配和余弦距离计算**的原则, 并且解决了原有DeepSORT代码出现莫名漂移跟踪框的问题.  
-
-**2023.1.15**加入了**MOT17数据集**的训练与测试功能, 增加了MOT17转yolo格式的代码(`./tools/convert_MOT17_to_yolo.py`), 转换的过程中强制使得坐标合法, 且忽略了遮挡率>=0.75的目标. 您可以采用此代码转换并训练, 具体请见后面的说明. 在使用tracker的时候, 注意将`data_format`设置为yolo, 这样可以直接根据txt文件的路径读取图片.  
-
-**2023.1.14**加入了当前DanceTrack的SOTA[C_BIoUTracker](https://arxiv.org/pdf/2211.14317v2.pdf), 该论文提出了一种增广的IoU来避免目标的瞬间大范围移动, 且弃用了Kalman滤波. 该代码没有开源, 我是按照自己的理解进行了复现. **有错误非常欢迎指出**.  
-
-**2022.11.26**加入了[TrackEval](https://github.com/JonathonLuiten/TrackEval)评测的方式, 支持MOT, VisDrone和UAVDT三种数据集. 此外将一些路径变量选择了按照`yaml`的方式读取, 尽量让代码可读性高一些. 如果您不想用TrackEval进行评测, 则可以将`track.py`或`track_yolov5.py`的命令配置代码`parser.add_argument('--track_eval', type=bool, default=True, help='Use TrackEval to evaluate')`改为`False`.
-
-**2022.11.10**更新了如何设置数据集路径的说明, 请参见README的`track.py路径读取说明`部分.
-
-**2022.11.09**修复了BoT-SORT中的一处错误[issue 16](https://github.com/JackWoo0831/Yolov7-tracker/issues/16), 加粗了边界框与字体.  
-
-**2022.11.08**更新了track.py, track_yolov5.py, basetrack.py和tracker_dataloader.py, 修复了yolo格式读取数据以及保存视频功能的一些bug, 并增加了隔帧检测的功能(大多数时候用不到). 
-
-**2022.10.22**本代码的匹配代码比较简单, 不一定会达到最好的效果(每次匹配只用一次linear assignment, 没有和历史帧的特征相匹配), 您可以使用cascade matching的方式(参见[StrongSORT](https://github.com/dyhBUPT/StrongSORT/blob/master/deep_sort/tracker.py)的line94-134)  
-
-**2022.10.15**增加了对yolo v5的支持, 只需替换track.py, 将tracker文件夹放到v5的根目录(我测试的是官方的[repo](https://github.com/ultralytics/yolov5))下即可. 代码在[yolo v5](https://github.com/JackWoo0831/Yolov7-tracker/blob/master/tracker/track_yolov5.py). 
 
-**2022.09.27[大更新]**修复了STrack类中update不更新外观的问题, 代码有较大更改, **您可能需要重新下载```./tracker```文件夹**. 
-尝试加入StrongSORT, 但是目前还不work:(, 尽力调一调
 
 ## 1. 亮点  
 1. 统一代码风格, 对多种tracker重新整理, 详细注释, 方便阅读, 适合初学者 
@@ -249,3 +218,42 @@ python tracker/track_demo.py --obj demo.mp4
 > 注意: 推理的时候batch_size要求为1. 
 
 ## 更多运行命令参考 run_yolov7.txt文件
+
+
+## 10. 更新记录
+
+
+**2023.3.14**解决了`DeepSORT`和`C_BIoUTracker`后面出现的目标不跟踪的bug.  
+
+**2023.2.28**优化了`track_demo.py`, 减少了内存占用.  
+
+**2023.2.24**加入了**推理单个视频或图片文件夹**以及**YOLO v8**的推理功能, 对应的代码为`tracker/track_demo.py`与`tracker/track_yolov8.py`.  推理单个视频或图片文件夹不需要指定数据集与真值, 也没有评测指标的功能, 只需要在命令行中指定`obj`即可, 例如:
+
+```shell
+python tracker/track_demo.py --obj demo.mp4 
+```
+YOLO v8 代码的参数与之前完全相同. 安装YOLO v8以及训练步骤请参照[YOLO v8](https://github.com/ultralytics/ultralytics)  
+
+
+**2023.2.11**修复了TrackEval路径报错的问题, 详见[issue35](https://github.com/JackWoo0831/Yolov7-tracker/issues/35)  
+
+**2023.2.10**修改了[DeepSORT](https://github.com/JackWoo0831/Yolov7-tracker/blob/master/tracker/deepsort.py)的代码与相关部分代码, 遵循了DeepSORT原论文**级联匹配和余弦距离计算**的原则, 并且解决了原有DeepSORT代码出现莫名漂移跟踪框的问题.  
+
+**2023.1.15**加入了**MOT17数据集**的训练与测试功能, 增加了MOT17转yolo格式的代码(`./tools/convert_MOT17_to_yolo.py`), 转换的过程中强制使得坐标合法, 且忽略了遮挡率>=0.75的目标. 您可以采用此代码转换并训练, 具体请见后面的说明. 在使用tracker的时候, 注意将`data_format`设置为yolo, 这样可以直接根据txt文件的路径读取图片.  
+
+**2023.1.14**加入了当前DanceTrack的SOTA[C_BIoUTracker](https://arxiv.org/pdf/2211.14317v2.pdf), 该论文提出了一种增广的IoU来避免目标的瞬间大范围移动, 且弃用了Kalman滤波. 该代码没有开源, 我是按照自己的理解进行了复现. **有错误非常欢迎指出**.  
+
+**2022.11.26**加入了[TrackEval](https://github.com/JonathonLuiten/TrackEval)评测的方式, 支持MOT, VisDrone和UAVDT三种数据集. 此外将一些路径变量选择了按照`yaml`的方式读取, 尽量让代码可读性高一些. 如果您不想用TrackEval进行评测, 则可以将`track.py`或`track_yolov5.py`的命令配置代码`parser.add_argument('--track_eval', type=bool, default=True, help='Use TrackEval to evaluate')`改为`False`.
+
+**2022.11.10**更新了如何设置数据集路径的说明, 请参见README的`track.py路径读取说明`部分.
+
+**2022.11.09**修复了BoT-SORT中的一处错误[issue 16](https://github.com/JackWoo0831/Yolov7-tracker/issues/16), 加粗了边界框与字体.  
+
+**2022.11.08**更新了track.py, track_yolov5.py, basetrack.py和tracker_dataloader.py, 修复了yolo格式读取数据以及保存视频功能的一些bug, 并增加了隔帧检测的功能(大多数时候用不到). 
+
+**2022.10.22**本代码的匹配代码比较简单, 不一定会达到最好的效果(每次匹配只用一次linear assignment, 没有和历史帧的特征相匹配), 您可以使用cascade matching的方式(参见[StrongSORT](https://github.com/dyhBUPT/StrongSORT/blob/master/deep_sort/tracker.py)的line94-134)  
+
+**2022.10.15**增加了对yolo v5的支持, 只需替换track.py, 将tracker文件夹放到v5的根目录(我测试的是官方的[repo](https://github.com/ultralytics/yolov5))下即可. 代码在[yolo v5](https://github.com/JackWoo0831/Yolov7-tracker/blob/master/tracker/track_yolov5.py). 
+
+**2022.09.27[大更新]**修复了STrack类中update不更新外观的问题, 代码有较大更改, **您可能需要重新下载```./tracker```文件夹**. 
+尝试加入StrongSORT, 但是目前还不work:(, 尽力调一调
\ No newline at end of file
diff --git a/tracker/track_demo.py b/tracker/track_demo.py
index d1fd02b..0a564d6 100644
--- a/tracker/track_demo.py
+++ b/tracker/track_demo.py
@@ -139,11 +139,11 @@ def main(opts):
         
     
         # NOTE: yolo v7 origin out format: [xc, yc, w, h, conf, cls0_conf, cls1_conf, ..., clsn_conf]
-        cls_conf, cls_idx = torch.max(out[:, 5:], dim=1)
+        # cls_conf, cls_idx = torch.max(out[:, 5:], dim=1)
         # out[:, 4] *= cls_conf  # fuse object and cls conf
-        out[:, 5] = cls_idx
-        out = out[:, :6]
-    
+        # out[:, 5] = cls_idx
+        # out = out[:, :6]
+
         current_tracks = tracker.update(out, img0)  # List[class(STracks)]      
 
 
@@ -327,7 +327,7 @@ def save_videos(obj_name):
         fourcc = cv2.VideoWriter_fourcc(*"mp4v")
 
         img0 = Image.open(os.path.join(images_path, images_name[0]))
-        vw = cv2.VideoWriter(to_video_path, fourcc, 15, img0.size)
+        vw = cv2.VideoWriter(to_video_path, fourcc, 30, img0.size)
 
         for img in images_name:
             if img.endswith('.jpg'):

From 23dbf40045141f9200c2de70d27d17331c7788f0 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Thu, 12 Oct 2023 16:01:04 +0800
Subject: [PATCH 15/17] update README

---
 README.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/README.md b/README.md
index d55a8bc..eb796d8 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,17 @@
 # YOLO v7 + 各种tracker实现多目标跟踪
 
+****
+
+本人在写这个代码的时候, 没想到会有这么多人看到. 然而, 必须承认我这份代码是以尽量整合为目的, 加了我自己的理解, 所以有的部分也许和原论文有出入, 导致效果不一定是最好的.
+
+为此, 给大家推荐一个成熟的repo: (https://github.com/mikel-brostrom/yolo_tracking)[https://github.com/mikel-brostrom/yolo_tracking]
+
+我这个代码大家可以作为学习之用, 也就是熟悉MOT的流程. 如果追求更好的效果, 我建议采纳更成熟的那些.
+
+我会不断听取大家的问题和建议, 希望和大家一起学习!
+
+****
+
 ## 0. 近期更新
 
 **2023.9.16**: 对于`track_demo.py`, 由于`yolov7`的`non_maximum_supress`函数中已经对类别做了筛选, 因此删去了主程序中类别计算的部分, 并修复了一些小bug  

From c292bde8e2647cd551796aa9aa7727cde74167a6 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Tue, 24 Oct 2023 17:10:11 +0800
Subject: [PATCH 16/17] fix bugs of bot and byte

---
 README.md            | 2 ++
 tracker/botsort.py   | 2 +-
 tracker/bytetrack.py | 2 +-
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index eb796d8..dc1f791 100644
--- a/README.md
+++ b/README.md
@@ -14,6 +14,8 @@
 
 ## 0. 近期更新
 
+**2023.10.24**: 对于botsort(`tracker/botsort.py line 358`)和bytetrack(`tracker/bytetrack.py line 89`), 在低置信度检测列表初始化, 改正原有错误 (原本写错了, 写的用`det_high`初始化). 
+
 **2023.9.16**: 对于`track_demo.py`, 由于`yolov7`的`non_maximum_supress`函数中已经对类别做了筛选, 因此删去了主程序中类别计算的部分, 并修复了一些小bug  
 
 **2023.5.6[大更新]**: 对于v5, v7, 改变前处理和后处理方式(采用原有方式), ***解决了部分边界框近大远小的bug, 边界框更加精确***. 此外, 对于v8, 弃用了resize步骤, 直接推理.
diff --git a/tracker/botsort.py b/tracker/botsort.py
index b8108a6..c3cc65c 100644
--- a/tracker/botsort.py
+++ b/tracker/botsort.py
@@ -355,7 +355,7 @@ def update(self, det_results, ori_img):
 
         if det_low.shape[0] > 0:
             D_low = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
-                            for (cls, tlbr, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
+                            for (cls, tlbr, score) in zip(det_low[:, -1], det_low[:, :4], det_low[:, 4])]
         else:
             D_low = []
 
diff --git a/tracker/bytetrack.py b/tracker/bytetrack.py
index 61eeee9..19cf74b 100644
--- a/tracker/bytetrack.py
+++ b/tracker/bytetrack.py
@@ -86,7 +86,7 @@ def update(self, det_results, ori_img):
 
         if det_low.shape[0] > 0:
             D_low = [STrack(cls, STrack.tlbr2tlwh(tlbr), score, kalman_format=self.opts.kalman_format)
-                            for (cls, tlbr, score) in zip(det_high[:, -1], det_high[:, :4], det_high[:, 4])]
+                            for (cls, tlbr, score) in zip(det_low[:, -1], det_low[:, :4], det_low[:, 4])]
         else:
             D_low = []
 

From 261e62afb9af324910ee95df8b20b69c3441b3b0 Mon Sep 17 00:00:00 2001
From: JackWoo0831 <wujiapeng0831@qq.com>
Date: Tue, 31 Oct 2023 22:40:43 +0800
Subject: [PATCH 17/17] fix spelling problem

---
 tracker/track_yolov8.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tracker/track_yolov8.py b/tracker/track_yolov8.py
index f3aaa75..5765f51 100644
--- a/tracker/track_yolov8.py
+++ b/tracker/track_yolov8.py
@@ -176,7 +176,7 @@ def main(opts, cfgs):
             timer.toc()  # end timing this image
             
             if opts.save_images:
-                plot_img(img0, frame_id, [cur_tlwh, cur_id, cur_cls], save_dir=os.path.join(DATASET_ROOT, 'reuslt_images', seq))
+                plot_img(img0, frame_id, [cur_tlwh, cur_id, cur_cls], save_dir=os.path.join(DATASET_ROOT, 'result_images', seq))
         
             frame_id += 1
 
@@ -305,7 +305,7 @@ def save_videos(seq_names):
         seq_names = [seq_names]
 
     for seq in seq_names:
-        images_path = os.path.join(DATASET_ROOT, 'reuslt_images', seq)
+        images_path = os.path.join(DATASET_ROOT, 'result_images', seq)
         images_name = sorted(os.listdir(images_path))
 
         to_video_path = os.path.join(images_path, '../', seq + '.mp4')