1. 环境配置及安装：

Prerequisites

• Linux | macOS | Windows
• Python 3.6+
• PyTorch 1.3+
• CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
• GCC 5+
• MMCV
• MMDetection

The compatible MMTracking, MMCV, and MMDetection versions are as below. Please install the correct version to avoid installation issues.

| MMTracking version | MMCV version | MMDetection version | | :----------------: | :------------------------: | :-----------------: | | master | mmcv-full>=1.3.17, <1.6.0 | MMDetection>=2.19.1 | | 0.13.0 | mmcv-full>=1.3.17, <1.6.0 | MMDetection>=2.19.1 | | 0.12.0 | mmcv-full>=1.3.17, <1.5.0 | MMDetection>=2.19.1 | | 0.11.0 | mmcv-full>=1.3.17, <1.5.0 | MMDetection>=2.19.1 | | 0.10.0 | mmcv-full>=1.3.17, <1.5.0 | MMDetection>=2.19.1 | | 0.9.0 | mmcv-full>=1.3.17, <1.5.0 | MMDetection>=2.19.1 | | 0.8.0 | mmcv-full>=1.3.8, <1.4.0 | MMDetection>=2.14.0 | | 0.7.0 | mmcv-full>=1.3.8, <1.4.0 | MMDetection>=2.14.0 | | 0.6.0 | mmcv-full>=1.3.8, <1.4.0 | MMDetection>=2.14.0 |

Installation

Detailed Instructions

1. Create a conda virtual environment and activate it.

shell conda create -n open-mmlab python=3.7 -y conda activate open-mmlab

1. Install PyTorch and torchvision following the official instructions, e.g.,

shell conda install pytorch torchvision -c pytorch

Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.

E.g.1 If you have CUDA 10.1 installed under /usr/local/cuda and would like to install PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.

shell conda install pytorch==1.5 cudatoolkit=10.1 torchvision -c pytorch

E.g. 2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.

shell conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch

If you build PyTorch from source instead of installing the prebuilt package, you can use more CUDA versions such as 9.0.

1. Install extra dependencies for VOT evaluation (optional)

If you need to evaluate on VOT Challenge, please install the vot-toolkit before the installation of mmcv and mmdetection to avoid possible numpy version requirement conflict among some dependencies.

```shell pip install git+https://github.com/votchallenge/toolkit.git

```

1. Install mmcv-full, we recommend you to install the pre-build package as below.

shell # pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.10.0/index.html

mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well.

shell # We can ignore the micro version of PyTorch pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.10/index.html

See here for different versions of MMCV compatible to different PyTorch and CUDA versions. Optionally you can choose to compile mmcv from source by the following command

shell git clone https://github.com/open-mmlab/mmcv.git cd mmcv MMCV_WITH_OPS=1 pip install -e . # package mmcv-full will be installed after this step cd ..

1. Install MMDetection

shell pip install mmdet

Optionally, you can also build MMDetection from source in case you want to modify the code:

shell git clone https://github.com/open-mmlab/mmdetection.git cd mmdetection pip install -r requirements/build.txt pip install -v -e . # or "python setup.py develop"

1. Clone the MMTracking repository.

shell git clone https://github.com/open-mmlab/mmtracking.git cd mmtracking

1. Install build requirements and then install MMTracking.

shell pip install -r requirements/build.txt pip install -v -e . # or "python setup.py develop"

1. Install extra dependencies

• For MOTChallenge evaluation:

```shell

pip install git+https://github.com/JonathonLuiten/TrackEval.git ```

• For LVIS evaluation:

shell pip install git+https://github.com/lvis-dataset/lvis-api.git

• For TAO evaluation:

shell pip install git+https://github.com/TAO-Dataset/tao.git

Note:

a. Following the above instructions, MMTracking is installed on dev mode , any local modifications made to the code will take effect without the need to reinstall it.

b. If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.

A from-scratch setup script

Assuming that you already have CUDA 10.1 installed, here is a full script for setting up MMTracking with conda.

```shell conda create -n open-mmlab python=3.7 -y conda activate open-mmlab

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch -y

pip install git+https://github.com/votchallenge/toolkit.git (optional)

install the latest mmcv

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html

install mmdetection

pip install mmdet

install mmtracking

git clone https://github.com/open-mmlab/mmtracking.git cd mmtracking pip install -r requirements/build.txt pip install -v -e . pip install git+https://github.com/JonathonLuiten/TrackEval.git pip install git+https://github.com/lvis-dataset/lvis-api.git pip install git+https://github.com/TAO-Dataset/tao.git ```

Developing with multiple MMTracking versions

The train and test scripts already modify the PYTHONPATH to ensure the script use the MMTracking in the current directory.

To use the default MMTracking installed in the environment rather than that you are working with, you can remove the following line in those scripts

shell PYTHONPATH="$(dirname $0)/..":$PYTHONPATH

Verification

To verify whether MMTracking and the required environment are installed correctly, we can run MOT, VID, SOT demo script.

For example, run MOT demo and you will see a output video named mot.mp4:

shell python demo/demo_mot_vis.py configs/mot/deepsort/sort_faster-rcnn_fpn_4e_mot17-private.py --input demo/demo.mp4 --output mot.mp4

2. 主要文件结构介绍

1. configs文件夹包括所有的运行配置文件，自定义的配置文件在configs/mot/myconfig路径下，包括ByteTrack和Deepsort两种方法的配置文件
2. mmtrack/datasets路径放置自定义的数据集注册文件
3. mmtrack/models/trackers路径放置跟踪方法具体实现文件
4. mmtracktest路径下放置跟踪方法的推理代码文件testbytetrack.py和test_deepsort.py

3. 运行步骤说明（主要运行testbytetrack.py或testdeepsort.py）

1. 下载模型参数文件，并放置到一定的位置（可以在配置文件中修改路径）

百度网盘链接：

链接：https://pan.baidu.com/s/1f1xHOIa9hrlCreCLbgEpaA?pwd=4321 提取码：4321

1. 准备待推理视频作为输入，如果是图片序列需要使用ffmpeg等工具合成为MP4格式，输入视频路径可以在推理代码文件中修改

2. 修改推理代码文件中配置文件的路径，修改输出视频的路径

3. 运行mmtracktest/testbytetrack.py或mmtracktest/testdeepsort.py即可输出跟踪结果视频，同时会生成跟踪结果txt文件，其中txt文件的路径以及名称在mmtrack/core/track/transforms.py中的results2outs函数中修改

4. 准备gt文件和跟踪结果文件，运行evaluate.py可以得到模型MOT评估指标