Installation using Anaconda

1. Install anaconda python distribution. You can download the free Individual Edition here: https://www.anaconda.com/products/individual#Downloads.

2. Create an environment with required packages (enter this commands in Anaconda prompt):

   conda create -n track -c conda-forge python cython scipy numpy scikit-learn pyqt pyyaml matplotlib pandas pytables ffmpeg sortedcontainers
   

   This will create a virtual Python environment called track.

3. Activate the environment (enter this commands in Anaconda prompt):

   conda activate track
   

4. Install OpenCV with contributed modules (required for some recent tracking algorithms, but not part of the main OpenCV distribution available in conda):

   pip install opencv-contrib-python
   

5. Install PyTorch.

   If you have a CUDA capable GPU, see pytorch website to select the right command. But note that you will need to install the appropriate NVIDIA driver for it to work.

   In case you do not have a CUDA capable GPU, you have to use CPU-only version (which can be ~10 times slower), in the Anaconda prompt:

   conda install pytorch torchvision cpuonly -c pytorch
   

6. Install pycocotools.

   On Windows:

   1. Install MS Visual Studio Build Tools. The installer for Visual Studio 2019 Build Tools is available here: https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019

      You can skip this if you have a functioning Visual C++ installation with the build tools >= 14.0.

   2. Install git from here: https://git-scm.com/downloads or enter in the Anaconda command prompt:

      conda install git
      

   3. In the Anaconda command prompt run (after conda activate track):

      "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvars64.bat"
      

      or the appropriate .bat file for your installation (see https://docs.microsoft.com/en-us/cpp/build/building-on-the-command-line?view=msvc-160#use-the-developer-tools-in-an-existing-command-window). The exact location of this file can vary between installations. You can run it using the following steps (for 64 bit systems):

      1. Go to Start menu-> Visual Studio 2019

      2. Right-click x64 Native Tools Command Prompt and in the popup menu select More->Open File Location.

      3. In the folder that opens, right click on the x64 Native … shortcut and select Properties.

      4. Copy the Target field and paste it in the Anaconda command prompt and press Enter.

   4. In the same prompt run:

      pip install "git+https://github.com/philferriere/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI"
      

      If this throws this error:

      fatal: unable to access 'https://github.com/philferriere/cocoapi.git/': SSL certificate problem: unable to get local issuer certificate
      

      then you may be able to resolve this by entering the following in the Anaconda prompt:

      git config --global http.sslbackend schannel
      

      and try:

      pip install "git+https://github.com/philferriere/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI"
      

      again.

   On Linux/Unix/Mac you need to have make and g++ installed, and then in the Anaconda command prompt enter:

   pip install pycocotools
   

See also

• https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/62381

• https://docs.microsoft.com/en-us/answers/questions/136595/error-microsoft-visual-c-140-or-greater-is-require.html

• https://stackoverflow.com/questions/23885449/unable-to-resolve-unable-to-get-local-issuer-certificate-using-git-on-windows

7. Finally, install the argos toolkit and the tracker with these commands:

   pip install argos-toolkit
   pip install argos-tracker
   

8. Download pretrained models for testing and for training.

   To try Argos tracking on objects in COCO dataset, download the pretrained model released with YOLACT here or go to YOLACT repository to find a mirror. The corresponding configuration file is already installed in {your_python_environment}/lib/site-packages/argos/config/yolact_base/yolact_base_config.yml. If you used Anaconda as described here, {your_python_environment} should be C:\Users\{username}\Anaconda3\env\track\ for Anaconda3 on Windows, ~/.conda/envs/track on Linux.

   To train on your own images, use this backbone distributed with YOLACT: resnet101_reducedfc.pth. Argos Annotation tool will generate the corresponding configuration for you.

Installation using venv (useful on Mac)

On Mac: you can use venv module to create virtual environment like conda (this does not require admin access):

python3 -m venv track

source track/bin/activate

pip install torch torchvision torchaudio opencv-contrib-python Cython

Followed by:

pip install pycocotools argos-toolkit argos-tracker

If you have Mac with Intel CPU, and encounter an error after the command above, (like #error: architecture not supported, error: command ‘clang’ failed with exit status 1) try the following:

export ARCHFLAGS="-arch x86_64"

CC=clang CXX=clang++ python -m pip install pycocotools argos-toolkit argos-tracker

After this, try running the review tool:

python -m argos.review

Installing DCNv2 for YOLACT++

YOLACT++ is an improved version of YOLACT that uses DCNv2 library for Deformable Convolution Network. This library comes with YOLACT source code (yoact/external/DCNv2). You can install it with pip:

pip install DCNv2.

To build this library on your own you need CUDA toolkit from NVidia installed. Also, on MS Windows you need the Visual Studio Build Tools described above. After that

• First activate your conda environment where YOLACT and Argos are installed.

• Change directory to yoact/external/DCNv2.

• Run python setup.py build develop

• Run pip install .

You can find some details in the YOLACT README file.