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Build OpenCV DNN Module with Nvidia GPU Support on Ubuntu 18.04

A thorough guide to teach you building OpenCV CUDA-enabled DNN module with example code.

Finally we get OpenCV DNN module work with Nvidia GPU for waiting for years.

Use this friend link to read the rest of the story if you are blocked behind the paywall.

In 2017, OpenCV 3.3 brought a revolutionary DNN module. As time passes, it currently supports plenty of deep learning framework such as TensorFlow, Caffe, and Darknet, etc. With the help of this module, we can use OpenCV to:

  1. Load a pre-trained model from disk.
  2. Making a preprocessing to an input image.
  3. Pass the image through the network and obtain the output results.
  4. Least dependency (only OpenCV !).

Although we cannot train a deep learning models with OpenCV (this does not make sense, though), we can take the models trained by other deep learning frameworks and perform inference using OpenCV with incredible speed on CPU.

However, the DNN module does not support Nvidia GPU until now. In Google Summer of Code (GSoC) 2019, OpenCV DNN module finally supports Nvidia GPU for inference with the work of Davis King — the creator of dlib — and Yashas Samaga, and this work was made in public in version 4.2.0. As a programmer expertised with OpenCV, I think I should share this exciting news in the first moment. Besides, I will teach you how to compile and install OpenCV with the utility of Nvidia GPU for deep neural network inference, and I will provide the minimal workable example code in both C++ and Python.

Menu with Compiling OpenCV with CUDA and cuDNN-enabled DNN Module


I assume you are using the following settings:

  1. A Nvidia GPU (of course!).
  2. Ubuntu 18.04 (or other equivalent Debian-based distro).
  3. CUDA 10.0 and corresponding version of cuDNN (in here I use v7.6.5).
  4. Your system account has sudo privilege.
  5. OpenCV version 4.2.0.

Step #1: Install Nvidia CUDA driver, CUDA toolkit, and cuDNN

  1. Backup your system, period. To my experience, you may accidentally break the system when installing any kind of driver. Although you can recover the system doing the installation backwards, sometimes you just can’t recover successfully because you forget the steps or even the driver does some changes and you don’t notice at all. As a person who broke the system twice (both with install CUDA drivers), I strongly suggest you backup your system in any condition.
  2. Pre-installation Actions I make some outlines here. For detailed information you can visit here.
  • lspci | grep -i nvidia: verify you have a CUDA-capable GPU.
  • uname -m && cat /etc/*release: verify you have a supported version of Linux.
  • gcc --version: verify the system has GCC installed.
  • uname -r: check the system has the correct kernel headers and development packages installed.
  • sudo apt-get install linux-headers-$(uname -r): install the kernel headers and development packages of current running kernel.

3. Install CUDA driver Install CUDA driver by typing the following commands:

$ sudo add-apt-repository ppa:graphics-drivers/ppa
$ sudo apt-get update
$ sudo apt-get install nvidia-driver-418

4. After the installation, reboot the system:

Once your system brings up, type nvidia-smi and it should give a similar output:

A sample output of nvidia-smi.

5. Install CUDA toolkit I will choose runfile packages to install CUDA drivers because you may upgrade the packages if you install with distribution-specific packages methods, which will cause huge problem afterwards.

First, download the runfile suitable of your system from here.

Then type the following command to run the runfile: sudo sh cuda_<version> --override

After finishing the installation, update your bash profile (~/.bashrc) by insert the following lines at the bottom of the profile:

export PATH=/usr/local/cuda-10.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH

Then source the profile:

$ source ~/.bashrc

Type nvcc -V. It you get the similar output, you're done!

$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130

6. Install cuDNN Installing cuDNN is much less tedious than CUDA driver and CUDA toolkit.

In the beginning, download cuDNN v7.6.5 for CUDA 10.0 from here (you may need to login or signup to download cuDNN).

Next, extract the zip file and put the headers and shared libraries:

$ tar -zvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
$ cd cuda
$ sudo cp -P lib64/* /usr/local/cuda/lib64/
$ sudo cp -P include/* /usr/local/cuda/include/
$ cd ~

If you are here and do not encounter any problem, Congrats! You have finish the hardest part!

Step #2 Install OpenCV Dependencies

OpenCV has a huge of dependencies, and I write in here so that you just copy and past the following command:

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

Step #3 Download OpenCV Source Code

Currently there is no python wheels or dpkg packages that built with Nvidia GPU support. So we have to compile OpenCV from source.

In this tutorial, I put both opencv and opencv_contrib repository in ~/opencv directory. I will use git to download the source code so that I can change the version I like:

$ cd ~
$ mkdir opencv
$ cd opencv
$ git clone
$ git clone
$ cd opencv
$ git checkout 4.2.0
$ cd ..
$ cd opencv_contrib
$ git checkout 4.2.0
$ cd ../opencv

Step #4 Configure Python Virtual Environment

For developping with Python, it is a good practice to use virtual environment due to using different versions of Python libraries in isolation and causing less problem in production environment.

In this part, I will use virtualenv and virtualenvwrapper as the virtual environment.

  1. Install virtualenv and virtualenvwrapper using pip.
$ sudo pip install virtualenv virtualenvwrapper

After installing these two packages, you need to add these lines in ~/.bashrc in order to let bash load virtualenv and virtualenvwrapper each time when terminal is up:

# virtualenv and virtualenvwrapper
export WORKON_HOME=$HOME/.virtualenvs
export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
source /usr/local/bin/

Then reload you ~/.bashrc to let the settings activate immediately:

source ~/.bashrc

2. Create a virtual environment. The first step is to create the virtual environment:

$ mkvirtualenv opencv_cuda -p python3

This command will create a virtual environment called opencv_cuda with Python 3. After the creation, your current working virtual environment should be opencv_cuda.

NOTE: If you ever close your terminal or deactivate the virtual environment, you can re-activate it by typing:

$ workon opencv_cuda

Because OpenCV Python will use numpy, we then install numpy:

$ pip install numpy

Step #5 Determine Your CUDA Architecture Version

As a experienced CUDA programmer, determining the CUDA architecture version is a required practice because it lets the compiler generate more efficient code on your GPU. Furthermore, setting architecture params which does not include the architecture of your Nvidia GPU will let your program not working while executing.

We can use nvidia-smi to figure out what model of your Nvidia GPU is:

You can see that I am using an Nvidia Geforce GTX 1080 GPU written in the Name section. Please make sure you have verfied your GPU model by running nvidia-smi before you continue the next part.

After you get the model of Nvidia GPU, you can find your CUDA Architecture using this page:

Scroll down to the Paragraph of “Your GPU Compute Capability”. As I’m using GTX 1080, I will click on the “CUDA-Enabled GeForce and TITAN Products” sections.

After examining it, I realize my Nvidia GPU architecture version is 6.1. As a reminder, your GPU architecture version may vary.

Once you got the GPU architecture version, leave a note of it because we will use it on the next step.

Step #6 Configure OpenCV with Nvidia GPU Support

OpenCV uses CMake to configure and generate the build. First of all activate the opencv_cuda virtual environment:

$ workon opencv_cuda

Then, change directory to the location you cloned the OpenCV source code (e.g., ~/opencv, and then create a build directory (we use out-of-source building):

$ cd ~/opencv
$ cd opencv
$ mkdir build && cd build

Next, run the following cmake command, and change the CUDA_ARCH_BIN variable you wrote down in step #5:

-D OPENCV_EXTRA_MODULES_PATH=~/opencv/opencv_contrib-4.2.0/modules/ \
-D HAVE_opencv_python3=ON \
-D PYTHON_EXECUTABLE=~/.virtualenvs/opencv_cuda/bin/python \

For one more point, check the install path in Python 3 section of CMake output. We will use install path in step #8. So please leave a note of install path.

--   Python 3:
-- Interpreter: /home/cudachen/.virtualenvs/opencv_cuda/bin/python3 (ver 3.6.9)
-- Libraries: /usr/lib/x86_64-linux-gnu/ (ver 3.6.9)
-- numpy: /home/cudachen/.virtualenvs/opencv_cuda/lib/python3.6/site-packages/numpy/core/include (ver 1.18.1)
-- install path: lib/python3.6/site-packages/cv2/python-3.6

Step #7 Compile OpenCV

If cmake exited with no errors, you then compile OpenCV with the following command:

$ make -j$(nproc)

Step #8 Install OpenCV with CUDA DNN Module

If make completed in success, you the type the following commands to install OpenCV:

$ sudo make install
$ sudo ldconfig

Then, we are going to create a sym-link the OpenCV Python bindings into your Python virtual environment. Mentioned in Step #6, we know that the install path is /usr/local/lib/python3.6/site-packages/cv2/python-3.6.

To confirm, you can use the ls command:

$ ls -l /usr/local/lib/python3.6/site-packages/cv2/python-3.6

You can see the name of my OpenCV Python bindings is (you may have the similar name of your own built bindings).

Next, create a sym-link to your virtual environment:

$ ln -s /usr/local/lib/python3.6/site-packages/cv2/python-3.6 ~/.virtualenvs/opencv_cuda/lib/python3.6/site-packages/

Remember to take a second to check your file paths because ln will slient fail if the path of OpenCV bindings are not correct.

Verify the Installation


We can verify the installation is successful with two means:

  1. The program compiles with OpenCV in no problem.
  2. The program executes with no errors.

Steps to Verify, C++ Part

  1. Download the repo and the weights mentioned in README.
  2. Go to cpp_code directory and type this command:g++ -o opencv_dnn_cuda_test_cpp main.cpp -I/usr/local/include/opencv4 -lopencv_core -lopencv_dnn -lopencv_highgui -lopencv_imgcodecs
  3. Run the executable by using this command: opencv_dnn_cuda_test_cpp
  4. If terminal outputs similar message, you’re done!
$ ./opencv_dnn_cuda_test_cpp 
Time per inference: 14 ms
FPS: 71.4286


We can verify the installation is successful with two means:

  1. We can import OpenCV in Python script.
  2. We are able to use Nvidia GPU via the DNN module.

Steps to Verify, Python Part

  1. Download the repo and the weights mentioned in README.
  2. Activate the virtual environment (that is, opencv_cuda).
  3. Go to python_code directory and type the command: python
  4. If terminal outputs similar message, you’re done!
$ python 
Time per inference: 14.480803 ms
FPS: 69.05694381124881


In this post, I teach you how to install OpenCV with CUDA-enabled DNN modules from scratch on Ubuntu 18.04. Also, I provided a minimal sample code both in C++ and Python so that you can adapt them in the later projects for your convenience.

To use CUDA as the backend of OpenCV DNN module, you can simply add these two lines after you load the pre-trained model:

// C++


# Python

Special Thanks

I would like to give a big gratitude to this post on Without this post, I would not complete this post in simplicity.

I would also like to give specia thanks to YashasSamaga, the main contributor of OpenCV DNN module with CUDA. He also teaches a lot in the issues on OpenCV GitHub repo which helps plenty of people to solve the problems of compilation with OpenCV CUDA-enabled DNN modules.

Originally published at on February 22, 2020.

If you have any thoughts and questions to share, please contact me at clh960524[at] Also, you can check my GitHub repositories for other works. If you are, like me passionate about machine learning, image processing and parallel computing, feel free to add me on LinkedIn.



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Lu-Hsuan Chen

Lu-Hsuan Chen

Enthusiastic of image processing, machine learning, and parallel computing. Current status: beggar on the street.