Last week we kicked-off the OpenCV 3.0 install fest by detailing how to install OpenCV 3.0 and Python 2.7+ on the OSX platform.
Today we are going to continue the OpenCV 3.0 install instruction series by moving over to the Ubuntu operating system.
In the remainder of the post I will provide instructions on how to configure and install OpenCV 3.0 and Python 2.7+ on Ubuntu. I have personally tested these instructions on Ubuntu 14.04, but they should pretty much work on any Debian-based operating system.
A quick note before we get started: Yes, OpenCV 3.0 is indeed compatible with Python 3+. However, the install instructions are slightly different between Python 2.7+ and Python 3+. In an effort to keep each article self-contained and easy to follow, I am creating separate OpenCV 3.0 install tutorials for Python 2.7 and Python 3+. If you would like to use OpenCV 3.0 and Python 3+ on your Ubuntu system, please keep an eye on this blog — I will be posting OpenCV 3.0 and Python 3+ install instructions later this month. But for the time being, let’s stick with Python 2.7.

How to Install OpenCV 3.0 and Python 2.7+ on Ubuntu

This is the second article in the OpenCV 3.0 install-fest series. Last week we covered how to install OpenCV 3.0 and Python 2.7+ on OSX. Today we are going to perform the same OpenCV 3.0 and Python 2.7 installation, only on the Ubuntu operating system. In general, you should find installing OpenCV 3.0 and Python 2.7+ on Ubuntu much easier than installing on OSX.

Step 1:

Open up a terminal and update the apt-get package manager followed by upgrading any pre-installed packages:

$ sudo apt-get update
$ sudo apt-get upgrade

1 2	$ sudo apt-get update $ sudo apt-get upgrade

Step 2:

Now we need to install our developer tools:

$ sudo apt-get install build-essential cmake git pkg-config

1	$ sudo apt-get install build-essential cmake git pkg-config

The pkg-config is likely already installed, but be sure to include it just in case. We’ll be using git to pull down the OpenCV repositories from GitHub. The cmake package is used to configure our build.

Step 3:

OpenCV needs to be able to load various image file formats from disk, including JPEG, PNG, TIFF, etc. In order to load these image formats from disk, we’ll need our image I/O packages:

$ sudo apt-get install libjpeg8-dev libtiff4-dev libjasper-dev libpng12-dev

1	$ sudo apt-get install libjpeg8-dev libtiff4-dev libjasper-dev libpng12-dev

Step 4:

At this point, we have the ability to load a given image off of disk. But how do we display the actual image to our screen? The answer is the GTK development library, which the highgui module of OpenCV depends on to guild Graphical User Interfaces (GUIs):

$ sudo apt-get install libgtk2.0-dev

1	$ sudo apt-get install libgtk2.0-dev

Step 5:

We can load images using OpenCV, but what about processing video streams and accessing individual frames? We’ve got that covered here:

$ sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev

1	$ sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev

Step 6:

Install libraries that are used to optimize various routines inside of OpenCV:

$ sudo apt-get install libatlas-base-dev gfortran

1	$ sudo apt-get install libatlas-base-dev gfortran

Step 7:

Install pip , a Python package manager:

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

1 2	$ wget https://bootstrap.pypa.io/get-pip.py $ sudo python get-pip.py

Step 8:

Install virtualenv and virtualenvwrapper. These two packages allow us to create separate Python environments for each project we are working on. While installing virtualenv and virtualenvwrapper is not a requirement to get OpenCV 3.0 and Python 2.7+ up and running on your Ubuntu system, I highly recommend it and the rest of this tutorial will assume you have them installed!

$ sudo pip install virtualenv virtualenvwrapper
$ sudo rm -rf ~/.cache/pip

1 2	$ sudo pip install virtualenv virtualenvwrapper $ sudo rm -rf ~/.cache/pip

Now that we have virtualenv and virtualenvwrapper installed, we need to update our ~/.bashrc file:

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

# virtualenv and virtualenvwrapper

export WORKON_HOME=$HOME/.virtualenvs

source /usr/local/bin/virtualenvwrapper.sh

This quick update will ensure that both virtualenv and virtualenvwrapper are loaded each time you login.
To make the changes to our ~/.bashrc file take effect, you can either (1) logout and log back in, (2) close your current terminal window and open a new one, or preferably, (3) reload the contents of your ~/.bashrc file:

$ source ~/.bashrc

1	$ source ~/.bashrc

Lastly, we can create our cv virtual environment where we’ll be doing our computer vision development and OpenCV 3.0 + Python 2.7+ installation:

$ mkvirtualenv cv

1	$ mkvirtualenv cv

Step 9:

As I mentioned above, this tutorial covers how to install OpenCV 3.0 and Python 2.7+ (I’ll have a OpenCV 3.0 + Python 3 tutorial available later this month), so we’ll need to install our Python 2.7 development tools:

$ sudo apt-get install python2.7-dev

1	$ sudo apt-get install python2.7-dev

Since OpenCV represents images as multi-dimensional NumPy arrays, we better install NumPy into our cv virtual environment:

$ pip install numpy

1	$ pip install numpy

Step 10:

Our environment is now all setup — we can proceed to change to our home directory, pull down OpenCV from GitHub, and checkout the 3.0.0 version:

$ cd ~
$ git clone https://github.com/Itseez/opencv.git
$ cd opencv
$ git checkout 3.0.0

$ cd ~

$ git clone https://github.com/Itseez/opencv.git

$ cd opencv

$ git checkout 3.0.0

Update (3 January 2016): You can replace the 3.0.0 version with whatever the current release is (as of right now, it’s 3.1.0 ). Be sure to check OpenCV.org for information on the latest release.
As I mentioned last week, we also need the opencv_contrib repo as well. Without this repository, we won’t have access to standard keypoint detectors and local invariant descriptors (such as SIFT, SURF, etc.) that were available in the OpenCV 2.4.X version. We’ll also be missing out on some of the newer OpenCV 3.0 features like text detection in natural images:

$ cd ~
$ git clone https://github.com/Itseez/opencv_contrib.git
$ cd opencv_contrib
$ git checkout 3.0.0

$ cd ~

$ git clone https://github.com/Itseez/opencv_contrib.git

$ cd opencv_contrib

$ git checkout 3.0.0

Again, make sure that you checkout the same version for opencv_contrib that you did for opencv above, otherwise you could run into compilation errors.
Time to setup the build:

$ cd ~/opencv
$ mkdir build
$ cd build
$ cmake -D CMAKE_BUILD_TYPE=RELEASE \
 -D CMAKE_INSTALL_PREFIX=/usr/local \
 -D INSTALL_C_EXAMPLES=ON \
 -D INSTALL_PYTHON_EXAMPLES=ON \
 -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
 -D BUILD_EXAMPLES=ON ..

$ cd ~/opencv

$ mkdir build

$ cd build

$ cmake -D CMAKE_BUILD_TYPE=RELEASE \

-D CMAKE_INSTALL_PREFIX=/usr/local \

-D INSTALL_C_EXAMPLES=ON \

-D INSTALL_PYTHON_EXAMPLES=ON \

-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \

-D BUILD_EXAMPLES=ON ..

Update (3 January 2016): In order to build OpenCV 3.1.0 , you need to set -D INSTALL_C_EXAMPLES=OFF (rather than ON ) in the cmake command. There is a bug in the OpenCV v3.1.0 CMake build script that can cause errors if you leave this switch on. Once you set this switch to off, CMake should run without a problem.
Notice how compared to last week our CMake command is substantially less verbose and requires less manual tweaking — this is because CMake is able to better automatically tune our install parameters (at least compared to OSX).
Now we can finally compile OpenCV:

$ make -j4

1	$ make -j4

Where you can replace the 4 with the number of available cores on your processor to speedup the compilation.
Here’s an example of OpenCV 3.0 compiling on my system:

Figure 1: OpenCV 3.0 with Python 2.7+ support compiling on my Ubuntu 14.04 system.

Assuming that OpenCV compiled without error, you can now install it on your Ubuntu system:

$ sudo make install
$ sudo ldconfig

1 2	$ sudo make install $ sudo ldconfig

Step 11:

If you’ve reached this step without an error, OpenCV should now be installed in /usr/local/lib/python2.7/site-packages
However, our cv virtual environment is located in our home directory — thus to use OpenCV within our cv environment, we first need to sym-link OpenCV into the site-packages directory of the cv virtual environment:

$ cd ~/.virtualenvs/cv/lib/python2.7/site-packages/
$ ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so

1 2	$ cd ~/.virtualenvs/cv/lib/python2.7/site-packages/ $ ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so

Step 12:

Congratulations! You have successfully installed OpenCV 3.0 with Python 2.7+ bindings on your Ubuntu system!
To confirm your installation, simply ensure that you are in the cv virtual environment, followed by importing cv2 :

$ workon cv
$ python
>>> import cv2
>>> cv2.__version__
'3.0.0'

$ workon cv

$ python

>>> import cv2

>>> cv2.__version__

'3.0.0'

Here’s an example of demonstrating the OpenCV 3.0 and Python 2.7+ install on my own Ubuntu machine:

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