A Python wrapper on Darknet. Compatible with YOLO V3.
Image source: http://absfreepic.com/free-photos/download/crowded-cars-on-street-4032x2272_48736.html
Refer the following link to preview YOLO3-4-Py in Google Colab: [Google Colab].
Copy the notebook to your drive and run all cells. Ensure that you are in a GPU runtime. You can change the runtime by accessing the menu Runtime/Change runtime type.
1) Python 3.5+ 2) Python3-Dev (For Ubuntu,
sudo apt-get install python3-dev) 3) Numpy
pip3 install numpy4) Cython
pip3 install cython5) Optionally, OpenCV 3.x with Python bindings. (Tested on OpenCV 3.4.0) - You can use this script to automate Open CV 3.4 installation (Tested on Ubuntu 16.04). - Performance of this approach is better than not using OpenCV. - Installations from PyPI distributions does not use OpenCV.
NOTE: OpenCV 3.4.1 has a bug which causes Darknet to fail. Therefore this wrapper would not work with OpenCV 3.4.1. More details are available at https://github.com/pjreddie/darknet/issues/502
Installation from PyPI distribution (as described below) is the most convenient approach if you intend to use yolo34py for your projects.
python3 -m pip install yolo34py
python3 -m pip install yolo34py-gpu
NOTE: PyPI Deployments does not use OpenCV due to complexity involved in installation. To get best performance, it is recommended to install from source with OpenCV enabled.
NOTE: Make sure CUDA_HOME environment variable is set.
1) If you have not installed already, run
python3 setup.py build_ext --inplaceto install library locally. 2) Download "yolov3" model file and config files using
sh download_models.sh. 3) Run
1) Navigate to docker directory. 2) Copy sample images into the
inputdirectory. Or else run input/downloadsampleimages.sh 3) Run
sh run-gpu.sh4) Observe the outputs generated in
GPU Version requires nvidia-docker
1) Set environment variables - To enable GPU acceleration,
export GPU=1. - To enable OpenCV,
2) Navigate to
pip3 install .to install library.
1) Set environment variable DARKNETHOME to download location of darknet. 2) Add DARKNETHOME to LDLIBRARYPATH.
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$DARKNET_HOME3) Continue instructions for installation from source.
Kindly raise your issues in the issues section of GitHub repository.
Feel free to send PRs or discuss on possible future improvements in issues section. Your contributions are most welcome!