What

Dead simple python wrapper for Yolo V3 using AlexyAB’s darknet fork. Works with CUDA 10.1 and OpenCV 4.1 or later (I use OpenCV master as of Jun 23, 2019)

Why

  • OpenCV’s DNN module, as of today, does not support NVIDIA GPUs. There is a GSOC WIP that will change this. Till then, this library is what I needed.

  • I used Alexy’s fork because he keeps it more updated with required changes (like using std++-11 etc.).
    W

  • Other excellent libraries such as pyyolo, Yolo34Py did not work for me with CUDA 10.1 and OpenCV 4.1. They all had compiler issues

How to use this library

By dead simple, I mean dead simple.

  • This module doesn’t bother cloning/building darknet. Build it whichever way you want, and simply make libdarknet.so accessible to this module.

  • Modify cfg/coco.data names= to point to where you have the labels (typically coco.names)

  • See example.py

Sample:

import simpleyolo.simpleYolo as yolo

configPath='./cfg/yolov3.cfg'
weightPath='./yolov3.weights'
metaPath='./cfg/coco.data'
imagePath='data/dog.jpg'

# initialize
m = yolo.SimpleYolo(configPath=configPath, 
                    weightPath=weightPath, 
                    metaPath=metaPath, 
                    darknetLib='./libdarknet_gpu.so', 
                    useGPU=True)
print ('detecting...')
detections = m.detect(imagePath)
print (detections)

When to use/not to use

  • Use this library if you want GPU support for YoloV3.
  • DON’T USE THIS LIBRARY if you want CPU support. It will work, but OpenCV’s DNN module for YoloV3 is around 10x faster than using darknet directly. Really.
  • On CPU, Intel Xeon 32GB RAM, 4 core, 3.1GHz, OpenCV DNN YoloV3 with blas/atlas takes ~2-4s
  • On CPU, Intel Xeon 32GB RAM, 4 core, 3.1GHz, darkneti YoloV3 takes ~45s (gaah!)
  • BUT, on GPU, NVIDIA GeForce 1050 Ti, 4GB, same CPU, darknet YoloV3 takes 91ms (woot!)

If you really want to know how to get darknet working with OpenCV 4.1

Assuming you have built/installed CUDA/cuDNN and optionally OpenCV 4.1:

git clone https://github.com/AlexeyAB/darknet
cd darknet

Edit the Makefile, set:
GPU=1
CUDNN=1
LIBSO=1

If you want darknet to use OPENCV (not necessary), also set

OPENCV=1

Notes:

  • You will make to change the Makefile to change pkg-config --libs opencv to pkg-config --libs opencv4 (2 instances). This will not be needed after Alexy fixes this issue

  • The above will only work if you previously compiled OpenCV 4+ with OPENCV_GENERATE_PKGCONFIG=ON and then copied the generated pc file like so: sudo cp unix-install/opencv4.pc /usr/lib/pkgconfig/

Pretty, please, how do we build OpenCV 4.1 with CUDA 10.1?

Assuming you have built/installed CUDA/cuDNN:

git clone https://github.com/opencv/opencv
git clone https://github.com/opencv/opencv_contrib
cd opencv
mkdir build

cmake -D CMAKE_BUILD_TYPE=RELEASE \
        -D CMAKE_INSTALL_PREFIX=/usr/local \
        -D PYTHON_DEFAULT_EXECUTABLE=$(which python3) \
        -D INSTALL_PYTHON_EXAMPLES=OFF \
        -D INSTALL_C_EXAMPLES=OFF \
        -D OPENCV_ENABLE_NONFREE=ON \
        -D OPENCV_EXTRA_MODULES_PATH=/home/pp/opencv_contrib/modules \
        -D BUILD_EXAMPLES=OFF \
        -D WITH_CUDA=ON \
        -D ENABLE_FAST_MATH=ON \
        -D CUDA_FAST_MATH=ON \
        -D WITH_CUBLAS=ON \
        -D WITH_OPENCL=ON \
        -D BUILD_opencv_cudacodec=OFF \
        -D BUILD_opencv_world=OFF \
        -D WITH_NVCUVID=OFF \
        -D WITH_OPENGL=ON \
        -D BUILD_opencv_python3=ON \
        -D OPENCV_GENERATE_PKGCONFIG=ON \
        ..
make -j$(nproc)
sudo make install

# don't forget this, for darknet and other libs to find opencv4 later
sudo cp unix-install/opencv4.pc /usr/lib/pkgconfig/

Pretty pretty please, how do I build CUDA 10.1 and nvidia drivers?

Maybe later.

GitHub

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