python设计源码之yolov7目标跟踪项目

# YOLO detector and SOTA Multi-object tracker Toolbox ## ❗❗Important Notes Compared to the previous version, this is an ***entirely new version (branch v2)***!!! **Please use this version directly, as I have almost rewritten all the code to ensure better readability and improved results, as well as to correct some errors in the past code.** ```bash git clone https://github.com/JackWoo0831/Yolov7-tracker.git git checkout v2 # change to v2 branch !! ``` 🙌 ***If you have any suggestions for adding trackers***, please leave a comment in the Issues section with the paper title or link! Everyone is welcome to contribute to making this repo better. <div align="center"> **Language**: English | [简体中文](README_CN.md) </div> ## 🗺️ Latest News - ***2024.10.24*** Add Hybrid SORT and fix some errors and bugs of OC-SORT. ## ❤️ Introduction This repo is a toolbox that implements the **tracking-by-detection paradigm multi-object tracker**. The detector supports: - YOLOX - YOLO v7 - YOLO v8, and the tracker supports: - SORT - DeepSORT - ByteTrack ([ECCV2022](https://arxiv.org/pdf/2110.06864)) - Bot-SORT ([arxiv2206](https://arxiv.org/pdf/2206.14651.pdf)) - OCSORT ([CVPR2023](https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Observation-Centric_SORT_Rethinking_SORT_for_Robust_Multi-Object_Tracking_CVPR_2023_paper.pdf)) - C_BIoU Track ([arxiv2211](https://arxiv.org/pdf/2211.14317v2.pdf)) - Strong SORT ([IEEE TMM 2023](https://arxiv.org/pdf/2202.13514)) - Sparse Track ([arxiv 2306](https://arxiv.org/pdf/2306.05238)) - UCMC Track ([AAAI 2024](http://arxiv.org/abs/2312.08952)) - Hybrid SORT([AAAI 2024](https://ojs.aaai.org/index.php/AAAI/article/view/28471)) and the reid model supports: - OSNet - Extractor from DeepSort The highlights are: - Supporting more trackers than MMTracking - Rewrite multiple trackers with a ***unified code style***, without the need to configure multiple environments for each tracker - Modular design, which ***decouples*** the detector, tracker, reid model and Kalman filter for easy conducting experiments  ## 🔨 Installation The basic env is: - Ubuntu 18.04 - Python：3.9, Pytorch: 1.12 Run following commond to install other packages: ```bash pip3 install -r requirements.txt ``` ### 🔍 Detector installation 1. YOLOX: The version of YOLOX is **0.1.0 (same as ByteTrack)**. To install it, you can clone the ByteTrack repo somewhere, and run: ``` bash https://github.com/ifzhang/ByteTrack.git python3 setup.py develop ``` 2. YOLO v7: There is no need to execute addtional steps as the repo itself is based on YOLOv7. 3. YOLO v8: Please run: ```bash pip3 install ultralytics==8.0.94 ``` ### 📑 Data preparation ***If you do not want to test on the specific dataset, instead, you only want to run demos, please skip this section.*** ***No matter what dataset you want to test, please organize it in the following way (YOLO style):*** ``` dataset_name |---images |---train |---sequence_name1 |---000001.jpg |---000002.jpg ... |---val ... |---test ... | ``` You can refer to the codes in `./tools` to see how to organize the datasets. ***Then, you need to prepare a `yaml` file to indicate the path so that the code can find the images.*** Some examples are in `tracker/config_files`. The important keys are: ``` DATASET_ROOT: '/data/xxxx/datasets/MOT17' # your dataset root SPLIT: test # train, test or val CATEGORY_NAMES: # same in YOLO training - 'pedestrian' CATEGORY_DICT: 0: 'pedestrian' ``` ## 🚗 Practice ### 🏃 Training Trackers generally do not require parameters to be trained. Please refer to the training methods of different detectors to train YOLOs. Some references may help you: - YOLOX: `tracker/yolox_utils/train_yolox.py` - YOLO v7: ```shell python train_aux.py --dataset visdrone --workers 8 --device <$GPU_id$> --batch-size 16 --data data/visdrone_all.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights <$YOLO v7 pretrained model path$> --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml ``` - YOLO v8: `tracker/yolov8_utils/train_yolov8.py` ### 😊 Tracking ! If you only want to run a demo: ```bash python tracker/track_demo.py --obj ${video path or images folder path} --detector ${yolox, yolov8 or yolov7} --tracker ${tracker name} --kalman_format ${kalman format, sort, byte, ...} --detector_model_path ${detector weight path} --save_images ``` For example: ```bash python tracker/track_demo.py --obj M0203.mp4 --detector yolov8 --tracker deepsort --kalman_format byte --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt --save_images ``` If you want to run trackers on dataset: ```bash python tracker/track.py --dataset ${dataset name, related with the yaml file} --detector ${yolox, yolov8 or yolov7} --tracker ${tracker name} --kalman_format ${kalman format, sort, byte, ...} --detector_model_path ${detector weight path} ``` For example: - SORT: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker sort --kalman_format sort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt ` - DeepSORT: `python tracker/track.py --dataset uavdt --detector yolov7 --tracker deepsort --kalman_format byte --detector_model_path weights/yolov7_UAVDT_35epochs_20230507.pt` - ByteTrack: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker bytetrack --kalman_format byte --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt` - OCSort: `python tracker/track.py --dataset mot17 --detector yolox --tracker ocsort --kalman_format ocsort --detector_model_path weights/bytetrack_m_mot17.pth.tar` - C-BIoU Track: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker c_bioutrack --kalman_format bot --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt` - BoT-SORT: `python tracker/track.py --dataset uavdt --detector yolox --tracker botsort --kalman_format bot --detector_model_path weights/yolox_m_uavdt_50epochs.pth.tar` - Strong SORT: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker strongsort --kalman_format strongsort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt` - Sparse Track: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker sparsetrack --kalman_format bot --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt` - UCMC Track: `python tracker/track.py --dataset mot17 --detector yolox --tracker ucmctrack --kalman_format ucmc --detector_model_path weights/bytetrack_m_mot17.pth.tar --camera_parameter_folder ./tracker/cam_param_files` - Hybrid SORT: `python tracker/track.py --dataset mot17 --detector yolox --tracker hybridsort --kalman_format hybridsort --detector_model_path weights/bytetrack_m_mot17.pth.tar --save_images` > **Important notes for UCMC Track:** > > 1. Camera parameters. The UCMC Track need the intrinsic and extrinsic parameter of camera. Please organize like the format of `tracker/cam_param_files/uavdt/M0101.txt`. One video sequence corresponds to one txt file. If you do not have the labelled parameters, you can refer to the estimating toolbox in original repo ([https://github.com/corfyi/UCMCTrack](https://github.com/corfyi/UCMCTrack)). > > 2. The code does not contain the camera motion compensation part between every two frame, please refer to [https://github.com/corfyi/UCMCTrack/issues/12](https://github.com/corfyi/UCMCTrack/issues/12). From my perspective, since the algorithm name is 'uniform', the update of compensation between every two frames is not necessary. ### ✅ Evaluation Coming Soon. As an alternative, after obtaining the result txt file, you can use the [Easier to use TrackEval repo](https://github.com/JackWoo0831/Easier_To_Use_TrackEval).