update

Author: vvEverett

README.md

@@ -1,16 +1,12 @@
-[cars-yolo-output]: examples/assets/cars.gif "Sample Output with YOLO"
-[cows-tf-ssd-output]: examples/assets/cows.gif "Sample Output with SSD"
+# 应用领域
 
-# Multi-object trackers in Python
-Easy to use implementation of various multi-object tracking algorithms.
+本文立足于**将超声悬浮技术应用于超疏水表面上的液滴操控系统**，并在此基础上搭建**以机器视觉**为辅助的三轴式液滴操控系统。本文目的是利用神经网络实现液滴的目标检测与目标跟踪问题，实现液滴的自动化操控，并且提高液滴的操控精度。通过一个轻量化网络，使得在边缘计算设备上也能运行精准的液滴目标检测与跟踪算法。
 
-[![DOI](https://zenodo.org/badge/148338463.svg)](https://zenodo.org/badge/latestdoi/148338463)
+## Available Object Detector
 
-
-`YOLOv3 + CentroidTracker` |  `TF-MobileNetSSD + CentroidTracker`
-:-------------------------:|:-------------------------:
-![Cars with YOLO][cars-yolo-output]  |  ![Cows with tf-SSD][cows-tf-ssd-output]
-Video source: [link](https://flic.kr/p/L6qyxj) | Video source: [link](https://flic.kr/p/26WeEWy)
+```
+NanoDet-Plus
+```
 
 ## Available Multi Object Trackers
 
@@ -21,18 +17,8 @@ CentroidKF_Tracker
 SORT
 ```
 
-## Available OpenCV-based object detectors:
-
-```
-detector.TF_SSDMobileNetV2
-detector.Caffe_SSDMobileNet
-detector.YOLOv3
-```
-
 ## Installation
 
-Pip install for OpenCV (version 3.4.3 or later) is available [here](https://pypi.org/project/opencv-python/) and can be done with the following command:
-
 ```
 git clone https://github.com/vvEverett/multi-object-tracker.git
 cd multi-object-tracker
@@ -42,65 +28,7 @@ python setup.py develop
 python setup_nanodet.py develop
 ```
 
-**Note - for using neural network models with GPU**  
-For using the opencv `dnn`-based object detection modules provided in this repository with GPU, you may have to compile a CUDA enabled version of OpenCV from source.  
-* To build opencv from source, refer the following links:
-[[link-1](https://docs.opencv.org/master/df/d65/tutorial_table_of_content_introduction.html)],
-[[link-2](https://www.pyimagesearch.com/2020/02/03/how-to-use-opencvs-dnn-module-with-nvidia-gpus-cuda-and-cudnn/)]
-
-## How to use?: Examples
+## How to use?
 
-The interface for each tracker is simple and similar. Please refer the example template below.
-
-```
-from motrackers import CentroidTracker # or IOUTracker, CentroidKF_Tracker, SORT
-input_data = ...
-detector = ...
-tracker = CentroidTracker(...) # or IOUTracker(...), CentroidKF_Tracker(...), SORT(...)
-while True:
-    done, image = <read(input_data)>
-    if done:
-        break
-    detection_bboxes, detection_confidences, detection_class_ids = detector.detect(image)
-    # NOTE: 
-    # * `detection_bboxes` are numpy.ndarray of shape (n, 4) with each row containing (bb_left, bb_top, bb_width, bb_height)
-    # * `detection_confidences` are numpy.ndarray of shape (n,);
-    # * `detection_class_ids` are numpy.ndarray of shape (n,).
-    output_tracks = tracker.update(detection_bboxes, detection_confidences, detection_class_ids)
-    # `output_tracks` is a list with each element containing tuple of
-    # (<frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>)
-    for track in output_tracks:
-        frame, id, bb_left, bb_top, bb_width, bb_height, confidence, x, y, z = track
-        assert len(track) == 10
-        print(track)
-```
-
-Please refer [examples](https://github.com/adipandas/multi-object-tracker/tree/master/examples) folder of this repository for more details. You can clone and run the examples.
-
-## Pretrained object detection models
-
-You will have to download the pretrained weights for the neural-network models. 
-The shell scripts for downloading these are provided [here](https://github.com/adipandas/multi-object-tracker/tree/master/examples/pretrained_models) below respective folders.
-Please refer [DOWNLOAD_WEIGHTS.md](https://github.com/adipandas/multi-object-tracker/blob/master/DOWNLOAD_WEIGHTS.md) for more details.
-
-### Notes
-* There are some variations in implementations as compared to what appeared in papers of `SORT` and `IoU Tracker`.
-* In case you find any bugs in the algorithm, I will be happy to accept your pull request or you can create an issue to point it out.
-
-## References, Credits and Contributions
-Please see [REFERENCES.md](https://github.com/adipandas/multi-object-tracker/blob/master/docs/readme/REFERENCES.md) and [CONTRIBUTING.md](https://github.com/adipandas/multi-object-tracker/blob/master/docs/readme/CONTRIBUTING.md).
-
-## Citation
-
-If you use this repository in your work, please consider citing it with:
-```
-@misc{multiobjtracker_amd2018,
-  author = {Deshpande, Aditya M.},
-  title = {Multi-object trackers in Python},
-  year = {2020},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/adipandas/multi-object-tracker}},
-}
-```
+运行main.py即可开启对test.avi的液滴目标检测与跟踪。
 

main.py

@@ -5,8 +5,8 @@
 from motrackers.utils import draw_tracks
 from nanodet.util import Logger, cfg, load_config, load_model_weight
 
-VIDEO_FILE = r"D:\shijue\LiquidDrop\22.avi"
-WEIGHTS_PATH = 'weight/LiquidV4.pth'
+VIDEO_FILE = "test.avi"
+WEIGHTS_PATH = 'weight/LiquidV5.pth'
 CONFIG_FILE_PATH = 'config/LiquidDetect416.yml'
 CHOSEN_TRACKER = 'SORT'
 CONFIDENCE_THRESHOLD = 0.4 # 目标检测的置信度筛选