Compilation

OpenCV with dnn module

Talha Hanif Butt
4 min readFeb 23, 2020
Image taken from https://www.pyimagesearch.com/2017/11/06/deep-learning-opencvs-blobfromimage-works/

I usually work with Tensorflow, Keras, PyTorch or Caffe but recently I had to use OpenCV for detection which requires “dnn” module because of which I compiled it with CUDA and cuDNN. The details are as follows:

NVIDIA driver installation

apt search nvidia-driver

apt-cache search nvidia-driver

sudo apt install nvidia-410

sudo reboot

nvidia-smi

sudo nvidia-settings

Install CUDA and cuDNN

Download cuda-linux.10.0.130–24817639.run

sudo ./cuda-linux.10.0.130–24817639.run

sudo bash -c “echo /usr/local/cuda/lib64/ > /etc/ld.so.conf.d/cuda.conf”

sudo ldconfig

sudo gedit /etc/environment

Append ‘:/usr/local/cuda/bin’ at the end of the PATH

Install cuDNN 7.3 for CUDA 10.0

Download all 3 .deb files: the runtime library, the developer library, and the code samples library for Ubuntu 16.04. Install them in the following order runtime, developer and code samples

sudo dpkg -i libcudnn7_7.3.0.29–1+cuda10.0_amd64.deb

sudo dpkg -i libcudnn7-dev_7.3.0.29–1+cuda10.0_amd64.deb

sudo dpkg -i libcudnn7-doc_7.3.0.29–1+cuda10.0_amd64.deb

export LD_LIBRARY_PATH=”$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64"

OpenCV Compilation

For this part, I have followed this article.

1. Install OpenCV and “dnn” GPU dependencies

sudo apt-get update

sudo apt-get upgrade

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

sudo apt-get install libv4l-dev 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 OpenCV source code

cd ~

wget -O opencv.zip https://github.com/opencv/opencv/archive/4.2.0.zip

wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.2.0.zip

unzip opencv.zip

unzip opencv_contrib.zip

mv opencv-4.2.0 opencv

mv opencv_contrib-4.2.0 opencv_contrib

3. Configure Python virtual environment

wget https://bootstrap.pypa.io/get-pip.py

sudo python3 get-pip.py

sudo pip install virtualenv virtualenvwrapper

sudo rm -rf ~/get-pip.py ~/.cache/pip

nano ~/.bashrc

Once you have the ~/.bashrc file open, scroll to the bottom of the file, and insert the following:

# virtualenv and virtualenvwrapper

export WORKON_HOME=$HOME/.virtualenvs

export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3

source /usr/local/bin/virtualenvwrapper.sh

You can then reload your ~/.bashrc file in your terminal session:

source ~/.bashrc

The final step is to create your Python virtual environment:

mkvirtualenv opencv_cuda -p python3

You should then install NumPy into the opencv_cuda environment:

pip install numpy

If you ever close your terminal or deactivate your Python virtual environment, you can access it again via the workon command:

workon opencv_cuda

4. Determine your CUDA architecture version

nvidia-smi

You can find your NVIDIA GPU architecture version for your particular GPU using this page:

5. Configure OpenCV with NVIDIA GPU support

workon opencv_cuda

cd ~/opencv

mkdir build

cd build

cmake -D CMAKE_BUILD_TYPE=RELEASE \

-D CMAKE_INSTALL_PREFIX=/usr/local \

-D INSTALL_PYTHON_EXAMPLES=ON \

-D INSTALL_C_EXAMPLES=OFF \

-D OPENCV_ENABLE_NONFREE=ON \

-D WITH_CUDA=ON \

-D WITH_CUDNN=ON \

-D OPENCV_DNN_CUDA=ON \

-D ENABLE_FAST_MATH=1 \

-D CUDA_FAST_MATH=1 \

-D CUDA_ARCH_BIN=7.0 \

-D WITH_CUBLAS=1 \

-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \

-D HAVE_opencv_python3=ON \

-D PYTHON_EXECUTABLE=~/.virtualenvs/opencv_cuda/bin/python \

-D BUILD_EXAMPLES=ON ..

The most important, and error-prone, configuration is your CUDA_ARCH_BIN — make sure you set it correctly!

You can verify that your cmake command executed properly by looking at the output.

You can also look at the Python 3 section to verify that both your Interpreter and numpy point to your Python virtual environment.

Make sure you take note of the install path as well!

You’ll be needing that path when we finish the OpenCV install.

6. Compile OpenCV with “dnn” GPU support

make -j8

7. Install OpenCV with “dnn” GPU support

sudo make install

sudo ldconfig

The final step is to sym-link the OpenCV library into your Python virtual environment.

To do so, you need to know the location of where the OpenCV bindings were installed — you can determine that path via the install path configuration in Step #5.

In my case, the install path was

lib/python3.5/site-packages/cv2/python-3.5

That means that my OpenCV bindings should be in

/usr/local/lib/python3.5/site-packages/cv2/python-3.5

I can confirm the location by using the ls command:

ls -l /usr/local/lib/python3.5/site-packages/cv2/python-3.5

Now that I know the location of my OpenCV bindings, I need to sym-link them into my Python virtual environment using the ln command:

cd ~/.virtualenvs/opencv_cuda/lib/python3.5/site-packages/

ln -s /usr/local/lib/python3.5/site-packages/cv2/python-3.5/cv2.cpython 35m-x86_64-linux-gnu.so cv2.so

8. Verify OpenCV installation

workon opencv_cuda

python

import cv2

cv2.__version__

The OpenCV version should be v4.2, which is indeed the OpenCV version we compiled from.

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Talha Hanif Butt

PhD Student -- Signal and Systems Engineering, Halmstad University, Volvo Trucks http://pk.linkedin.com/in/talhahanifbutt