A few weeks ago Raspbian Jessie was released, bringing in a ton of new, great features.

However, the update to Jessie also broke the previous OpenCV + Python install instructions for Raspbian Wheezy:

Since PyImageSearch has become the online destination for learning computer vision + OpenCV on the Raspberry Pi, I decided to write a new tutorial on installing OpenCV 3 with Python bindings on Raspbian Jessie.

As an additional bonus, I’ve also included a video tutorial that you can use to follow along with me as I install OpenCV 3 on my own Raspberry Pi 2 running Raspbian Jessie.

This video tutorial should help address the most common questions, doubts, and pitfalls that arise when installing OpenCV + Python bindings on the Raspberry Pi for the first time.

Assumptions

For this tutorial I am going to assume that you already own a Raspberry Pi 2 with Raspbian Jessie installed. Other than that, you should either have (1) physical access to your Pi 2 and can open up a terminal or (2) remote access where you can SSH in. I’ll be doing this tutorial via SSH, but as long as you have access to a terminal, it really doesn’t matter.

The quick start video tutorial

Before we get this tutorial underway, let me ask you two quick questions:

Is this your first time installing OpenCV? Are you just getting started learning Linux and how to use the command line?

If you answered yes to either of these questions, I highly suggest that you watch the video below and follow along with me as a guide you step-by-step on how to install OpenCV 3 with Python bindings on your Raspberry Pi 2 running Raspbian Jessie:

Otherwise, if you feel comfortable using the command line or if you have previous experience using the command line, feel free to follow the tutorial below.

Installing OpenCV 3 on Raspbian Jessie

Installing OpenCV 3 is a multi-step (and even time consuming) process requiring you to install many dependencies and pre-requisites. The remainder of this tutorial will guide you step-by-step through the process.

To make the installation process easier, I’ve included timings for each step (when appropriate) so you know when to stick by your terminal, grab a cup of coffee, or go for a nice long walk.

If you’re an experienced Linux user or have already installed OpenCV on a Raspberry Pi (or another other system) before, you can likely just follow the steps outlined below.

However, if this is your first time installing OpenCV (or you don’t have much prior exposure to the Linux operating systems and the command line), I highly recommend that you watch the video above and follow along with me as I show you how to install OpenCV 3 on your Rasberry Pi running Raspbian Jessie.

That said, let’s get started installing OpenCV 3.

Step #1: Install dependencies

The first thing we should do is update and upgrade any existing packages, followed by updating the Raspberry Pi firmware.

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

Timing: 3m 33s

You’ll need to reboot your Raspberry Pi after the firmware update:

$ sudo reboot

Now we need to install a few developer tools:

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

Timing: 51s

Now we can move on to installing image I/O packages which allow us to load image file formats such as JPEG, PNG, TIFF, etc.:

$ sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev

Timing: 42s

Just like we need image I/O packages, we also need video I/O packages. These packages allow us to load various video file formats as well as work with video streams:

$ sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev $ sudo apt-get install libxvidcore-dev libx264-dev

Timing: 58s

We need to install the GTK development library so we can compile the highgui sub-module of OpenCV, which allows us to display images to our screen and build simple GUI interfaces:

$ sudo apt-get install libgtk2.0-dev

Timing: 2m 48s

Various operations inside of OpenCV (such as matrix operations) can be optimized using added dependencies:

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

Timing: 50s

Lastly, we’ll need to install the Python 2.7 and Python 3 header files so we can compile our OpenCV + Python bindings:

$ sudo apt-get install python2.7-dev python3-dev

Step #2: Grab the OpenCV source code

At this point we have all of our prerequisites installed, so let’s grab the 3.0.0 version of OpenCV from the OpenCV repository. (Note: As future versions of OpenCV are released just replace the 3.0.0 with the most recent version number):

$ cd ~ $ wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.0.0.zip $ unzip opencv.zip

Timing: 2m 29s

For the full install of OpenCV 3 (which includes features such as SIFT and SURF), be sure to grab the opencv_contrib repo as well. (Note: Make sure your opencv and opencv_contrib versions match up, otherwise you will run into errors during compilation. For example, if I download v3.0.0 of opencv , then I’ll want to download v3.0.0 of opencv_contrib as well):

$ wget -O opencv_contrib.zip https://github.com/Itseez/opencv_contrib/archive/3.0.0.zip $ unzip opencv_contrib.zip

Timing: 1m 54s

Step #3: Setup Python

The first step in setting up Python for our OpenCV compile is to install pip , a Python package manager:

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

Timing: 26s

I’ve discussed both virtualenv and virtualenvwrapper many times on the PyImageSearch blog before, especially within these installation tutorials. Installing these packages is certainly not a requirement to get OpenCV and Python up and running on your Raspberry Pi, but I highly recommend that you install them!

Using virtualenv and virtualenvwrapper allows you to create isolated Python environments, separate from your system install of Python. This means that you can run multiple versions of Python, with different versions of packages installed into each virtual environment — this solves the “Project A depends on version 1.x, but Project B needs 4.x” problem that often arises in software engineering.

Again, it’s standard practice in the Python community to use virtual environments, so I highly suggest that you start using them if you are not already:

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

Timing: 17s

After virtualenv and virtualenvwrapper have been installed, we need to update our ~/.profile file and insert the following lines at the bottom of the file:

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

You can use your favorite editor to edit this file, such as vim , emacs , nano , or any other graphical editor included in the Raspbian Jessie distribution. Again, all you need to do is open the file located at /home/pi/.profile and insert the lines above at the bottom of the file.

Now that your ~/.profile has been updated, you need to reload it so the changes can take affect. To force a reload of the ~/.profile file you can (1) logout and log back in, (2) close your terminal and open up a new one, or (3) just use the source command:

$ source ~/.profile

Note: You’ll likely need to run the source ~/.profile command each time you open up a new terminal to ensure your environment has been setup correctly.

The next step is to create our Python virtual environment where we’ll be doing our computer vision work:

$ mkvirtualenv cv

The above command will create a virtual environment named cv using Python 2.7.

If you want Python 3, run this command instead:

$ mkvirtualenv cv -p python3

Again, it’s important to note that the cv Python environment is entirely independent from the default version of Python included in the download of Raspbian Jesse.

If you ever reboot your system, logout and log back in, or open up a new terminal, you’ll need to use the workon command to re-access the cv virtual environment, otherwise you’ll be using the system version of Python instead:

$ source ~/.profile $ workon cv

You can ensure you are in the cv virtual environment by examining your command line. If you see the text “(cv)” preceding your prompt, then you are in the cv virtual environment:

Otherwise, you are not in the cv virtual environment:

If this is the case, you need to run the source and workon commands above.

Assuming that you are in the cv virtual environment, we can install NumPy, an important dependency when compiling the Python bindings for OpenCV. You might want to grab a cup of coffee or go for a walk while NumPy downloads and installs:

$ pip install numpy

Timing: 16m 10s

Step #4: Compile and install OpenCV

At this point, we are ready to compile OpenCV.

First, make sure you are in the cv virtual environment:

$ workon cv

Followed by setting up the build:

$ cd ~/opencv-3.0.0/ $ 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-3.0.0/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.

Before you move on to the compilation step, make sure you examine the output of CMake!

Scroll down the section titled Python 2 and Python 3 .

If you’re compiling OpenCV 3 for Python 2.7, then you’ll want to make sure the Python 2 section looks like this (highlighted) in red:

Notice how both the Interpreter and numpy variables point to the cv virtual environment.

Similarly, if you’re compiling OpenCV for Python 3, then make sure the Python 3 section looks like this:

Again, both the Interpreter and numpy variables are pointing to our cv virtual environment.

In either case, if you do not see the cv virtual environment for these variables MAKE SURE YOU ARE IN THE cv VIRTUAL ENVIRONMENT PRIOR TO RUNNING CMAKE!

Now that our build is all setup, we can compile OpenCV:

$ make -j4

Timing: 1h 35m

The -j4 switch stands for the number of cores to use when compiling OpenCV. Since we are using a Raspberry Pi 2, we’ll leverage all four cores of the processor for a faster compilation.

However, if your make command errors out, I would suggest starting the compilation over again and only using one core:

$ make clean $ make

Using only one core will take much longer to compile, but can help reduce any type of strange race dependency condition errors when compiling.

Assuming OpenCV compiled without error, all we need to do is install it on our system:

$ sudo make install $ sudo ldconfig

Step #5: Finishing the install

We’re almost there! Just a few more things and we’ll be 100% done.

For Python 2.7:

Provided you finished Step #4 without error, OpenCV should now be installed in /usr/local/lib/python2.7/site-packages :

$ ls -l /usr/local/lib/python2.7/site-packages/ total 1636 -rw-r--r-- 1 root staff 1675144 Oct 17 15:25 cv2.so

Note: In some instances OpenCV can be installed in /usr/local/lib/python2.7/dist-packages (note the dist-packages rather than site-packages ). If you do not find the cv2.so bindings in site-packages , be sure to check dist-packages as well.

The last step here is to sym-link the OpenCV bindings into 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

For Python 3:

OpenCV should now be installed in /usr/local/lib/python3.4/site-packages :

$ ls /usr/local/lib/python3.4/site-packages/ cv2.cpython-34m.so

For some reason, unbeknownst to me, when compiling the Python 3 bindings the output .so file is named cv2.cpython-34m.so rather than cv2.so .

Luckily, this is an easy fix. All we need to do is rename the file:

$ cd /usr/local/lib/python3.4/site-packages/ $ sudo mv cv2.cpython-34m.so cv2.so

Followed by sym-linking OpenCV into our cv virtual environment:

$ cd ~/.virtualenvs/cv/lib/python3.4/site-packages/ $ ln -s /usr/local/lib/python3.4/site-packages/cv2.so cv2.so

Step #6: Verifying your OpenCV 3 install

At this point, OpenCV 3 should be installed on your Raspberry Pi running Raspbian Jessie!

But before we wrap this tutorial up, let’s verify that your OpenCV installation is working by accessing the cv virtual environment and importing cv2 , the OpenCV + Python bindings:

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

You can see a screenshot of my terminal below, indicating that OpenCV 3 has been successfully installed:

Troubleshooting

Q. When I try to use the mkvirtualenv or workon commands, I get an error saying “command not found”.

A. Go back to Step #3 and ensure your ~/.profile file has been updated properly. Once you have updated it, be sure to run source ~/.profile to reload it.

Q. After I reboot/logout/open up a new terminal, I cannot run the mkvirtualenv or workon commands.

A. Anytime you reboot your system, logout and log back in, or open up a new terminal, you should run source ~/.profile to make sure you have access to your Python virtual environments.

Q. When I open up a Python shell and type import cv2 , I get the dreaded ImportError: No module named cv2 error.

A. The reason for this error is hard to diagnose, mainly because there are multiple issues that could be causing this problem. For starters, make sure you are in the cv virtual environment using workon cv . If the workon command is giving you problems, then see the previous questions in this section. From there, you’ll want to investigate the site-packages directory of your cv virtual environment located in ~/.virtualenvs/cv/lib/python2.7/site-packages/ or ~/.virtualenvs/cv/lib/python3.4/site-packages/ , respectively. Make sure that the sym-link path to the cv2.so file is valid. If you do not know how to do this, please consult the video tutorial at the top of this post.

So, what’s next?

Congrats! You have a brand new, fresh install of OpenCV on your Raspberry Pi — and I’m sure you’re just itching to leverage your Raspberry Pi to build some awesome computer vision apps.

But I’m also willing to bet that you’re just getting started learning computer vision and OpenCV, and you’re probably feeling a bit confused and overwhelmed on where exactly to start.

Personally, I’m a big fan of learning by example, so a good first step would be to read this blog post on accessing your Raspberry Pi Camera with the picamera module. This tutorial details the exact steps you need to take to (1) capture photos from the camera module and (2) access the raw video stream.

And if you’re really interested in leveling-up your computer vision skills, you should definitely check out my book, Practical Python and OpenCV + Case Studies. My book not only covers the basics of computer vision and image processing, but also teaches you how to solve real world computer vision problems including face detection in images and video streams, object tracking in video, and handwriting recognition.

All code examples covered in the book are guaranteed to run on the Raspberry Pi 2 and Pi 3 as well! Most programs will also run on the B+ and Zero models, but might be a bit slow due to the limited computing power of the B+ and Zero.

So let’s put your fresh install of OpenCV on your Raspberry Pi to good use — just click here to learn more about the real-world projects you can solve using your Raspberry Pi + Practical Python and OpenCV .

Summary

In this lesson we learned how to install OpenCV 3 with Python 2.7 and Python 3 bindings on your Raspberry Pi 2 running Raspbian Jessie. I provided timings for each step so ensure you can plan your install accordingly.

It’s also worth mentioning that I provide OpenCV v2.4 and v3 install instructions for Raspbian Wheezy in the following posts:

If you run into any issues during the installation process, please see the Troubleshooting section above. Additionally, I would suggest watching the video tutorial at the top of this post to aid you in the setup process.

Before you go…

I tend to cover a lot of great computer vision projects using OpenCV and the Raspberry Pi, so consider entering your email address in the form below to be notified when these posts go live!