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Building OpenCV with Conda on Linux

December 29, 2017  [opencv]  [cmake]  [conda]  [python]  [linux] 

When it comes to building and installing OpenCV with Python support on *nix platforms, the collection of tutorials by Adrian Rosebrock is the best. He provides detailed description of the required steps, as well as motivation for better development practices. In particular, Adrian creates a dedicated virtualenv environment, installs NumPy in it, and builds the whole thing having this environment activated.

Such a solution worked pretty well for me, both on Linux and macOS. However, in my work I mostly use conda for managing my Python environments. It especially shines on Raspberry Pies, where conda’s pre-built binaries get installed way faster than the pip-installed ones.

It is worth to mention that there exist pre-built OpenCV 3 binaries in the menpo channel on Anaconda Cloud. Their availability vary by operating systems and Python versions, but they are in general a pretty good reproducible solution when OpenCV is only used in Python. My aim, however, is to build OpenCV so that it could be used in both C++ and Python.

Further in this blog post I am going to summarize my notes on building OpenCV 3 on Ubuntu using conda’s Python 3.6, with further symlinking of the Python extension to other conda environments. My Ubuntu version as of time of this writing is 16.04.3 (Xenial Xerus).

The list of prerequisites (to install via apt), according to Adrian, is the following:

sudo apt install build-essential cmake pkg-config \
libjpeg8-dev libtiff5-dev libjasper-dev libpng12-dev \
libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
libxvidcore-dev libx264-dev \
libgtk-3-dev libatlas-base-dev gfortran python2.7-dev python3.5-dev

After the required Ubuntu packages are installed, create a dedicated conda environment (to be used specifically for the build process) with only NumPy inside:

conda create -n opencv_build python=3.6 numpy

Before proceeding to the build process the new invironment has to be activated:

source activate opencv_build

Download the source code of OpenCV and opencv_contrib and checkout the newest stable version (in my case, 3.4.1)

mkdir opencv_install
cd opencv_install
git clone https://github.com/opencv/opencv.git
git clone https://github.com/opencv/opencv_contrib.git
cd opencv; git checkout 3.4.1
cd ../opencv_contrib; git checkout 3.4.1
cd .. # in opencv_install

Create an xcmake.sh script for more precise control of the build generation:




Create the build directory and generate makefiles.

mkdir build # in opencv_install
cd build

If everything went correctly, the summary section on Python 3 should look similar to mine:

--   Python 3:
--     Interpreter:                 /home/alex/.conda/envs/opencv_build/bin/python (ver 3.6.3)
--     Libraries:                   /home/alex/.conda/envs/opencv_build/lib/libpython3.6m.so (ver 3.6.3)
--     numpy:                       /home/alex/.conda/envs/opencv_build/lib/python3.6/site-packages/numpy/core/include (ver 1.13.3)
--     packages path:               lib/python3.6/site-packages

You are now ready to build and install the whole thing:

# in opencv_install
make -j4
sudo make install
sudo ldconfig

As a result, you get OpenCV installed throughout /usr/local (the whole list of installed files can be found in the install_manifest.txt file, generated in build directory) To use OpenCV with a conda environment of your choice (say, myenv), just create a symlink in the environment’s packages directory (this may not the perfect solution from the point of environment reproducibility, but, as I mentioned earlier, if you intend to use OpenCV in both Python and C++, the approach is pretty good):

ln -s \
    /usr/local/lib/python3.6/site-packages/cv2.cpython-36m-x86_64-linux-gnu.so \

Then go ahead, activate myenv, and be happy with the fresh and workable OpenCV installation.

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