My script for generate dataset

📖 Table of Contents

Table of Contents

1. ➤ Set up
2. ➤ Dataset Structure
3. ➤ Generate MOT format dataset
4. ➤ Generate reid dataset
5. ➤ Generate bytetrack dataset
6. ➤ References
7. ➤ Contributors

🔨 Set up

git clone https://github.com/LeDuySon/ALL_SCRIPTS.git
conda create -n {name} python=3.8
conda activate {name}
pip install -r requirement.txt

📚 Dataset Structure

• You have to prepare your dataset same as below

• Your dataset structure

  {ROOT}
  ├── video1.mp4
  ├── video1
  │   └── gt
  │       ├── gt.txt
  │       └── labels.txt
  ├── video2.mp4
  ├── video2
  │   └── gt
  │       ├── gt.txt
  │       └── labels.txt
  ├── video3.mp4
  ├── video3
  │   └── gt
  │       ├── gt.txt
  │       └── labels.txt

💾 Generate MOT format dataset

• MOT dataset structure

  {ROOT_MOT}
  ├── images
  │   ├── test
  │   └── train
  └── labels_with_ids

• Generate Steps:

  1. Create a folder that have structure likes MOT dataset

  
        bash create_folder_tree.sh {Name of ROOT_MOT}
        

  2. Go to folder generate_fairmot_dataset/, run to generate frame

  
        python generate_fairmot_dataset.py --video_path {video_path} --save_path {save_path} --frame_interval {frame_interval}
        

  Note:

  • video_path: Path to video file (Only support .mp4)
  • save_path: Save folder path ( eg: {ROOT}/images/train or {ROOT}/images/test)
  • frame_interval: Number of frame between 2 saving frames
  • If you want to run on multiple video, run:

  
            bash gen_frames {folder contain your .mp4 files} {save_path} {frame_interval}
          

  3. After that, run:

  
        python create_dataset.py --root_path {root_path} --gt_path {gt_path}
        

  Note:

  • root_path: Path to your MOT dataset folder train or test(eg: {ROOT_MOT}/images/train or {ROOT_MOT}/images/test
  • gt_path: Path to groundtruth folder ( eg: {ROOT}/train or {ROOT}/test)
  • If you want to run on multiple video, run:
  4. Just wait and have a coffee

💾 Generate reid dataset

• Your dataset(train|test) must be the same as ours datasets

• Generate reid dataset for multiple videos with mode train or test

python generate_reid_dataset_multiple.py --data_path {path to your dataset, eg {ROOT} in our dataset} \
                                          --save_path {name of saving folders} --mode {mode train|test|normal}

• If you want to prepare dataset entirely, run this:

 bash create_data.sh {path to your train dataset} {path to your test dataset} {name of saving folders}

• Notes about {mode} args
• train: only save in train/ folder
• test: save to gallery/ folder and then split to query/ folder
• normal: save to train/ folder and then split to gallery/ and query/ folder

Results after generate:

.          
├── gallery
├── query  
└── train  
    ├── 0  
    ├── 1  
    ├── 2  
    ├── 3  
    ├── 4  
    ├── 5  
    ├── 6  
    ├── 7  
    ├── 8  
    └── 9  

💾 Generate bytetrack dataset

Refer: https://github.com/ifzhang/ByteTrack

BYTETRACK dataset structure

{ROOT_COCO}
├── annotations
├── test
├── train
└── val

Your dataset(train|test) must be the same as ours datasets

1. Create a folder that have structure likes COCO dataset

      bash create_folder_tree.sh {Name of ROOT_COCO}
      

2. Run to generate frame from video

      python generate_frame_from_videos.py --video_path {video_path} --save_path {save_path} --frame_interval {frame_interval}
      

Note:

• video_path: Path to video file (Only support .mp4)
• save_path: Save folder path ( eg: {ROOT_COCO}/train or {ROOT_COCO}/test)
• frame_interval: Number of frame between 2 saving frames
• If you want to run on multiple video, run:

          bash gen_frames {folder contain your .mp4 files} {save_path} {frame_interval}
        

3. After that, run:

      python create_dataset.py --root_path {root_path} --gt_path {gt_path}
      

Note:

• root_path: Path to your MOT dataset folder train or test(eg: {ROOT_COCO}/train or {ROOT_COCO}/test
• gt_path: Path to groundtruth folder ( eg: {ROOT}/train or {ROOT}/test)
4. Finally, run this script to convert your dataset to coco format (get json files in annotation folder):

      python convert_vtx_to_coco.py --data_path {ROOT_COCO}
      

5. Guide for training and testing bytetrack

• Create new Exp class based on MOT examples in ByteTrack/exps/example/mot:
• Change path to your annotations json files in __init__()
• Change data_dir path in get_eval_loader() to your dataset path (default mot/)
• Change name of your dataset type in get_eval_loader() (default "train")