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
UPDATE: The tutorial you are reading now covers how to install OpenCV 3.0 with Python 2.7+ bindings on Ubuntu 14.04. This tutorial still works perfectly, but if you want to install OpenCV on the newer Ubuntu 16.04 with OpenCV 3.1 and Python 2.7 (or Python 3.5) bindings, please use this freshly updated tutorial:
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.
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
Now we need to install our developer tools:
$ sudo apt-get install build-essential cmake git pkg-config
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.
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
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
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
Install libraries that are used to optimize various routines inside of OpenCV:
$ sudo apt-get install libatlas-base-dev gfortran
pip , a Python package manager:
$ wget https://bootstrap.pypa.io/get-pip.py $ sudo python get-pip.py
Install virtualenv and virtualenvwrapper. These two packages allow us to create separate Python environments for each project we are working on. While installing
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
Now that we have
virtualenvwrapper installed, we need to update our
# virtualenv and virtualenvwrapper export WORKON_HOME=$HOME/.virtualenvs source /usr/local/bin/virtualenvwrapper.sh
This quick update will ensure that both
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
$ 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
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
$ pip install numpy $ pip install imutils
Our environment is now all setup — we can proceed to change to our home directory, pull down OpenCV from GitHub, and checkout the
$ 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
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 ..
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
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:
Assuming that OpenCV compiled without error, you can now install it on your Ubuntu system:
$ sudo make install $ sudo ldconfig
If you’ve reached this step without an error, OpenCV should now be installed in
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
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
$ 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:
Now that OpenCV has been configured and installed, let’s build a quick Python script to detect the red game cartridge in the image named
Open up your favorite editor, create a new file, name it
find_game.py , and insert the following code:
# import the necessary packages import numpy as np import imutils import cv2 # load the games image image = cv2.imread("games.jpg") # find the red color game in the image upper = np.array([65, 65, 255]) lower = np.array([0, 0, 200]) mask = cv2.inRange(image, lower, upper) # find contours in the masked image and keep the largest one cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = imutils.grab_contours(cnts) c = max(cnts, key=cv2.contourArea) # approximate the contour peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.05 * peri, True) # draw a green bounding box surrounding the red game cv2.drawContours(image, [approx], -1, (0, 255, 0), 4) cv2.imshow("Image", image) cv2.waitKey(0)
You’ll also need to download the games.jpg image and place it in the same directory as your
find_game.py file. Once the
games.jpg file has been downloaded, you can execute the script via:
$ python find_game.py
Assuming that you have downloaded the
games.jpg image and placed it in the same directory as our
find_game.py script, you should see the following output:
Notice how our script was able to successfully detect the red game cartridge in the right portion of the image, followed by drawing a green bounding box surrounding it.
Obviously this isn’t the most exciting example in the world — but it has demonstrated that we have OpenCV 3.0 with Python 2.7+ bindings up and running on our Ubuntu system!
So, what’s next?
Congrats! You have a brand new, fresh install of OpenCV on your Ubuntu system — and I’m sure you’re just itching to leverage your new install 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 webcam with OpenCV. This tutorial details the exact steps you need to take to (1) capture photos from your webcam 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.
So let’s put your fresh install of OpenCV on your Ubuntu system to good use — just click here to learn more about the real-world projects you can solve using your new OpenCV install + Practical Python and OpenCV .
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To celebrate the OpenCV 3.0 release, I am working my way through OpenCV 3.0 and Python 2.7/Python 3.4 installation instructions on OSX, Ubuntu, and the Raspberry Pi.
Last week I covered how to install OpenCV 3.0 and Python 2.7+ on OSX.
And today we covered how to install OpenCV 3.0 with Python 2.7 bindings on Ubuntu. I have personally tested these instructions on my own Ubuntu 14.04 machine, but they should work on any Debian-based system.
Next week we’ll continue the install-fest and hop back to OSX — this time installing OpenCV 3.0 and Python 3!
This will be the first time we’ve used Python 3 on the PyImageSearch blog, so you won’t want to miss it!
And please consider subscribing to the PyImageSearch Newsletter by entering your email address in the form below. As we work through the OpenCV install-fest, I’ll be sending out updates as each new OpenCV 3.0 + Python install tutorial is released!
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