This repository contains the code for Getting Started with OpenCV CUDA Module

Our code is tested using Python 3.7.5, but it should also work with any other python3.x. If you'd like to check your version run:

python3 -V

Let's create a new virtual environment. You'll need to install virtualenv package if you don't have it:

pip install virtualenv

Now we can create a new virtualenv variable and call it env:

python3 -m venv env

The last thing we have to do is to activate it:

source env/bin/activate

Install numpy package by running:

pip install numpy

The code was tested using CUDA Toolkit 10.2. Please follow the official instruction to download CUDA Toolkit 10.2 or higher.

In this blog post, we're using OpenCV with CUDA support to accelerate OpenCV algorithms. That is why we will need to customize the OpenCV library build and make it from scratch. To do so:

1. Install dependencies:

sudo apt-get update sudo apt-get install build-essential cmake unzip pkg-config sudo apt-get install libjpeg-dev libpng-dev libtiff-dev sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev sudo apt-get install libxvidcore-dev libx264-dev sudo apt-get install libgtk-3-dev sudo apt-get install libatlas-base-dev gfortran sudo apt-get install python3-dev 

2. Download the latest OpenCV version from the official repository:

wget -O opencv.zip https://github.com/opencv/opencv/archive/4.4.0.zip wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.4.0.zip

3. Unzip the downloaded archives:

unzip opencv.zip unzip opencv_contrib.zip

4. Rename the directories to match CMake paths:

mv opencv-4.4.0 opencv mv opencv_contrib-4.4.0 opencv_contrib

5. Compile OpenCV:

Create and enter a build directory:

cd opencv mkdir build cd build

Run CMake to configure the OpenCV build. Don't forget to set the right pass to the PYTHON_EXECUTABLE. If you are using the CUDA version different from 10.2, please change the last 3 arguments accordingly.

cmake -D CMAKE_BUILD_TYPE=RELEASE \ -D CMAKE_INSTALL_PREFIX=/usr/local \ -D INSTALL_PYTHON_EXAMPLES=OFF \ -D INSTALL_C_EXAMPLES=OFF \ -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \ -D PYTHON_EXECUTABLE=env/bin/python3 \ -D BUILD_EXAMPLES=ON \ -D WITH_CUDA=ON \ -D CUDA_FAST_MATH=ON \ -D WITH_CUBLAS=ON \ -D CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.2 \ -D OpenCL_LIBRARY=/usr/local/cuda-10.2/lib64/libOpenCL.so \ -DOpenCL_INCLUDE_DIR=/usr/local/cuda-10.2/include/ \ ..

Check the output and make sure that everything is set correctly. After that we're ready to build it with:

make -j4

Make sure, you didn't get any errors. Then run the following command:

sudo ldconfig

which creates the necessary links and cache to our freshly built shared library.

Rename the created Python3 bindings for OpenCV to cv2.so:

mv lib/python3/cv2.cpython-37m-x86_64-linux-gnu.so cv2.so

The last step is to create a symlink of our OpenCV cv2.so into the virtual environment installed packages:

cd env/lib/python3.7/site-packages/ ln -s ~/opencv/build/cv2.so cv2.so

C++

You first need to compile .cpp file with the following command:

g++ `pkg-config --cflags --libs opencv4` demo.cpp -o demo.out -lopencv_core -lopencv_highgui -lopencv_imgcodecs -lopencv_imgproc -lopencv_videoio -lopencv_video -lopencv_cudaarithm -lopencv_cudaoptflow -lopencv_cudaimgproc -lopencv_cudawarping -std=c++11 

After that to run the demo, you will need to pass:

• path to the video file,
• device, to choose between CPU and GPU inference. By default, the device is set to "cpu".

For example:

./demo.out video/boat.mp4 gpu

Python

To run the demo, you will need to pass:

• --video argument to set the path to the video file,
• --device to choose between CPU and GPU inference. By default, the device is set to "cpu".

For example:

python3 demo.py --video video/boat.mp4 --device "cpu"

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