Argos

"Assorted Recognition General Objects Surveillance System"

A Multiple Camera Multiple Object Tracking System.

Setup

• Requirements

  • Python >= 3.9
  • Pytorch >= 1.13.1

• Installation

  1. Clone this repository.

     git clone https://github.com/Namiki-Laboaratory/Argos.git
     

  2. Setup environment.
     Here using Anaconda as example:

     conda env create -n Argos
     conda activate Argos
     

     Make sure you install Pytorch in your environment

     pip install cython
     pip install -r requirements.txt
     pip install "mmcv>=2.0.0rc1" -f xxxx  # Use mmcv for DCNv2 from https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md
     

Project Structure

Optional content. Highly recommend read this before development.

Structure directory

The whole project is based on a multiprocessing pipeline system, which can be easily configured in *YAML* format. Project structure as following:

src
├─── dataset
├─── trainer
├─── model
│   ├─── cfg
│   └─── networks
├─── multiprocess_pipeline
│   ├─── cfg
│   ├─── process
│   ├─── shared_structure
│   └─── workers
└─── opts

• Dataset

  All dataset related is in dataset directory. Substructure as following:

  dataset
  ├─── cfg
  ├─── data_path
  ├─── convert_labels.py
  ├─── gen_data_path.py
  └─── dataset.py
  

  • cfg: Store dataset config file by JSON.
  • data_path: Store image file paths by specialized file type.
  • convert_labels.py: Contain all utils to convert dataset format to MOT Challenge format.
  • gen_data_path.py: Generate dataset image file container file.
  • dataset.py: All dataset class defined in this file.

• Trainer

  trainer defined basic trainer class for network training. Substructure as following:

  trainer
  ├─── utils
  └─── trainer.py
  

  • utils: util functions, for example: initial check.
  • trainer.py: defined all trainers.

• Model

  model contain all network related content. We use a simple swappable module system to develop tracking networks.

  The whole model can be easily defined by a model configure file in model/cfg. And each part network also can be defined by its own part configure file in model/networks/[part category]/[part name]/cfg.

  Substructure as following:

  model
  ├─── cfg
  ├─── networks
  │    ├─── backbone
  │    │    ├─── DLA
  │    │    ├─── ParNet
  │    │    ├─── ShuffleNetV2
  │    │    ├─── ...
  │    ├─── backbone_with_neck
  │    │    ├─── DLA_DCN
  │    │    ├─── ParNet
  │    │    ├─── ResNet_DCN
  │    │    ├─── ...
  │    ├─── head
  │    │    └─── FairMOT
  │    ├─── loss
  │    ├─── module
  │    ├─── neck
  │    │    ├─── DLA_Fusion
  │    │    ├─── FPN
  │    │    ├─── Ghost_PAN
  │    │    ├─── ...
  │    └─── model_part_config.py
  ├─── model_config.py
  ├─── utils
  └─── base_model.py
  

  • cfg: store overall model config files.
  • network: contain all model parts.
  • model_config.py: defined model configure structure.
  • utils: util functions.
  • base_model.py: define the base class of model.
  • Here is an example model configure file DLA+GhostPAN_mot_8class.yml:

    _description: ''
    backbone:
      _description: ''
      cfg_name: default
      model_name: DLA
    backbone_with_neck:
      _description: ''
      cfg_name: ''
      model_name: ''
    neck:
      _description: ''
      cfg_name: default
      model_name: Ghost_PAN
    head:
      _description: ''
      cfg_name: default
      model_name: FairMOT
    max_classes_num: 8
    max_objects_num: 500

    • _description: human-readable additional information.
    • model_name: model part name.
    • cfg_name: the part configure file name used for this model part.
    • max_classes_num: max tracking object categories in this model.
    • max_objects_num: max tracking object amounts for each category in this model.

  An example part network FairMOT substructure as following:

  ...
  ├─── head
  │    ├─── FairMOT
  │    │    ├─── cfg
  │    │    │    └─── default.yml
  │    │    └─── FairMOT.py
  ...  ... 
  │    ├─── _masterclass.py
  │    └─── __init__.py
  ...   
  

  • cfg: store model configure file. Configure contents are this part model initial structure key arguments. In this example, we have one configure default.yml. In this example, configure content as following:

  head_conv: 256
  reid_dim: 128
  loss_cfg:
    mse_loss: false
    reg_loss: 'l1'
    hm_weight: 1
    off_weight: 1
    wh_weight: 0.1
    id_loss: 'ce'
    id_weight: 1
    norm_wh: false
    dense_wh: false
    cat_spec_wh: false
    reg_offset: true
  
    loss_stats: ['loss', 'hm_loss', 'wh_loss', 'off_loss', 'id_loss']

  • [model name].py: model python file. In this example, we use FairMOT.py model.
  • _masterclass.py: define the base model of this part.
  • __init__.py: all usable networks in this part should be registered in this file to use.

• Multiprocess Pipeline

  multiprocess_pipeline contain all multiprocess pipeline related contents. In this project, we use a multiprocess based configurable pipeline system for tracking implement. You can find example system configure files in multiprocess_pipeline/cfg.

  Example concept as following figure:

  Substructure as following:

  multiprocess_pipeline
  ├─── cfg
  ├─── process
  │   ├─── producer
  │   │   ├─── ...
  │   │   └─── _masterclass.py
  │   ├─── consumer
  │   │   ├─── ...
  │   │   └─── _masterclass.py
  │   ├─── post
  │   │   ├─── ...
  │   │   └─── _masterclass.py
  │   └─── _masterclass.py
  ├─── workers
  │   ├─── image_loader
  │   ├─── tracker
  │   ├─── ...
  │   └─── postprocess
  ├─── shared_structure
  └─── utils
  

  • cfg: store overall implement multiprocess pipeline config files.
  • process: contain all -producer, consumer, post- 3 categories process files. The base class define is in _masterclass.py
  • workers: all function workers files. Some process can have different workers for different methods with the same usage. For example, process ImgaLoader can have worker loader_address load image from an IP address or have worker loader_video load image from a video file.
  • shared_structure: define all shared memory structure like data_hub and output_port
  • utils: util functions.
  • An example structure configure TestFunc.yml as following:

    FuncTest_1:
      producer:
        ImageLoader:
          image_path: "D:\\Output\\OpenShot\\Old\\Test_01.mp4"
          loader: Video
          normalized_image_shape: [ 3, 608, 1088 ]
    
      consumer:
        Track:
          arch: DLA+GhostPAN_mot_8class
          load_model: D:\Project\PythonScripts\Argus\results\train_result\Experiment_02\DLA+GhostPAN_mot_8class\2024-03-08-03-19-33\DLA+GhostPAN_mot_8class.pth
          conf_thres: 0.4
          track_buffer: 30
    
        PathPredict:
          predictor_name: HermiteSpline
    
      post:
        IndiResultsVisual:
          output_format: video
    
      static_shared_value:
        CamIntrinsicPara:
          data_type: SharedArray_Float
          data_shape: [ 3, 4 ]
          data_value: [ [ 11.11, 0., 128.0, 0 ],
                        [ 0, 11.11, 128.0, 0 ],
                        [ 0, 0, 1.0000, 0 ] ]

    • First hierarchy is the pipeline name, here is FuncTest_1.
    • producer contain all producer processes and their own arguments, here we have ImageLoader.
    • consumer contain all consumer processes and their own arguments, here we have Track and PathPredict.
    • post contain all post processes and their own arguments, here we have IndiResultsVisual.
    • All processes are from multiprocess_pipeline/process. Check each category __init__.py file for process name.
    • static_shared_value: for some initial static shared values in this pipeline, here we initialize CamIntrinsicPara as camera intrinsic matrix. Check dict_SharedDataInfoFormat in multiprocess_pipeline/shared_structure/__init__.py for shared value initialize format.

• Parser for command-line options

  opts handles all the command-line input options or arguments for each experiment. Substructure as following:

  opts
  ├─── track.py
  ├─── train.py
  └─── _base_opt.py
  

  • _base_opt.py: master opt class.

  Currently, only 2 optional subclass opt_track and opt_train are used in this project for multi-object tracking and neural network training respectively.

Inference

In this project, we use a multiprocess based configurable pipeline system for any implement. You can find example system configure files in ./src/multiprocess_pipeline/cfg.

• Example:

  An example pipeline system configure TestFunc.yml as following:

  FuncTest_1:
    producer:
      ImageLoader:
        image_path: "D:\\Output\\OpenShot\\Old\\Test_01.mp4"
        loader: Video
        normalized_image_shape: [ 3, 608, 1088 ]
  
    consumer:
      Track:
        arch: DLA+GhostPAN_mot_8class
        load_model: D:\Project\PythonScripts\Argus\results\train_result\Experiment_02\DLA+GhostPAN_mot_8class\2024-03-08-03-19-33\DLA+GhostPAN_mot_8class.pth
        conf_thres: 0.4
        track_buffer: 30
  
      PathPredict:
        predictor_name: HermiteSpline
  
    post:
      IndiResultsVisual:
        output_format: video
  
    static_shared_value:
      CamIntrinsicPara:
        data_type: SharedArray_Float
        data_shape: [ 3, 4 ]
        data_value: [ [ 11.11, 0., 128.0, 0 ],
                      [ 0, 11.11, 128.0, 0 ],
                      [ 0, 0, 1.0000, 0 ] ]

  • First hierarchy is the pipeline name, here is FuncTest_1.
  • producer contain all producer processes and their own arguments, here we have ImageLoader.
  • consumer contain all consumer processes and their own arguments, here we have Track and PathPredict.
  • post contain all post processes and their own arguments, here we have IndiResultsVisual.
  • All processes are from multiprocess_pipeline/process. Check each category __init__.py file for process name.
  • static_shared_value: for some initial static shared values in this pipeline, here we initialize CamIntrinsicPara as camera intrinsic matrix. Check dict_SharedDataInfoFormat in multiprocess_pipeline/shared_structure/__init__.py for shared value initialize format.

• Tracking

  python track.py --exp_id Test --pipeline_cfg ./src/multiprocess_pipeline/cfg/TestFunc.yml
  

  here:

  • --exp_id as experiment name for example here I use Test;
  • --pipeline_cfg as pipeline config file path.

  More arguments details please see in ./src/opts/track.py or run python track.py -h list all arguments.

Dataset

• Format:

  We use the MOT Challenge format and directory structure as training label format, details as following:

  • label format:

    class id x_center/img_width y_center/img_height w/img_width h/img_height
    

  • directory structure example:

    [Dataset name]
    ├─── images
    │    └─── [any sub directories hierarchy]
    │         ├─── [sequence name]
    │         │    ├─── 0000_0001.png
    │         │    ├─── 0000_0002.png
    │         │    └─── ...
    │         └─── ...
    └─── labels_with_ids
         └─── [same sub directories hierarchy as 'images']
              ├─── [sequence name]
              │    ├─── 0000_0001.txt
              │    ├─── 0000_0002.txt
              │    └─── ...
              └─── ...
    

  You can directly use MOT17 or MOT20 dataset for training. We provide a convert tool ./src/dataset/convert_labels.py to convert KITTI Tracking or VisDrone2019 to MOT format.

• Dataset Preparation

  1. Make sure your dataset format and directory structure is in MOT Challenge format.
  2. Generate image paths container file by tool ./src/dataset/gen_data_path.py.
  3. Create a json file for this dataset. You need to specify the "root" and "train" keys in the json file. You can find some examples in ./src/dataset/cfg.

Training

• Model

  We use a simple swappable module system to develop tracking networks. All model configure files are default in ./src/model/cfg.

  Here is an example model configure file DLA+GhostPAN_mot_8class.yml:

  _description: ''
  backbone:
    _description: ''
    cfg_name: default
    model_name: DLA
  backbone_with_neck:
    _description: ''
    cfg_name: ''
    model_name: ''
  neck:
    _description: ''
    cfg_name: default
    model_name: Ghost_PAN
  head:
    _description: ''
    cfg_name: default
    model_name: FairMOT
  max_classes_num: 8
  max_objects_num: 500

  • _description: human-readable additional information.
  • model_name: model part name.
  • cfg_name: the part configure file name used for this model part.
  • max_classes_num: max tracking object categories in this model.
  • max_objects_num: max tracking object amounts for each category in this model.

• Train

  Please make sure you have finished Dataset Preparation and Model Configure before training.

  python train.py --exp_id Train_Test --arch DLA34_mot_8class --data_cfg ./src/dataset/cfg/FunTest.json
  

  here:

  • --exp_id as experiment name for example here I use Train_Test;
  • --arch as model configure file name in model configure directory, which is defined by --arch_cfg_path in _base_opt or manually input with command line;
  • --data_cfg as dataset configure json file;

  More arguments details please see in ./src/opts/train.py or run python train.py -h list all arguments.

Acknowledgement

This research some parts are based on FairMOT, MCMOT and Nanodet. Thanks for their wonderful works.