Also, as I’ve mentioned before on this blog, pretty much all of my future blogging is going to happen at blog.danwin.com, which is built in Jekyll. Not coincidentally, the Computational Journalism Lab site is also built in Jekyll — see its Github here.

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On the Stanford FarmShare machines (which run on Ubuntu 13.10), I had trouble getting their installation of OpenCV working, but was able to use the
Anaconda distribution to install both Python 2.7.8 and OpenCV 2.4.9.1 via the Binstar package repo.

Briefly, here are the instructions:

1. Get the Anaconda download link
2. curl (*the-anaconda-URL-script*) -o /tmp/anaconda-install.sh && bash /tmp/anaconda-install.sh
3. conda install binstar
4. conda install -c https://conda.binstar.org/menpo opencv

Note: For Mac users for whom `brew install opencv` isn’t working: Anaconda worked well enough for me, though I had to install from a different pacakge repo:

conda install -c https://conda.binstar.org/jjhelmus opencv

The Anaconda system, which I hadn't used before but find really convenient, automatically upgrades/downgrades the necessary dependencies (such as numpy).

Using Anaconda works fine on fresh Ubuntu installs (I tested on AWS and Digital Ocean), but I wanted to see if I could compile it from source just in case I couldn't use Anaconda. This ended up being a very painful time of wading through blog articles and Github issues. Admittedly, I'm not at all an expert at *nix administration, but it's obvious there's a lot of incomplete and varying answers out there.

The help.ubuntu.docs on OpenCV are the most extensive, but right at the top, they state:

Ubuntu's latest incarnation, Utopic Unicorn, comes with a new version of libav, and opencv sources will fail to build with this new library version. Likewise, some packages required by the script no longer exist (libxine-dev, ffmpeg) in the standard repositories. The procedures and script described below will therefore not work at least since Ubuntu 14.10!

The removal of ffmpeg from the official Ubuntu package repo is, from what I can tell, the main source of errors when trying to compile OpenCV for Ubuntu 14.04/14.10. Many of the instructions deal with getting ffmpeg from a personal-package-archive and then trying to build OpenCV. That approach didn't work for me, but admittedly, I didn't test out all the possible variables (such as version of ffmpeg).

In the end, what worked was to simply just set the flag to build without ffmpeg:

  cmake [etc] -D WITH_FFMPEG=OFF

I've created a gist to build out all the software I want for my class machines, but here are the relevant parts for OpenCV:

sudo apt-get update && sudo apt-get -y upgrade
sudo apt-get -y dist-upgrade && sudo apt-get -y autoremove

# build developer tools. Some of these are probably non-pertinent
sudo apt-get install -y git-core curl zlib1g-dev build-essential \
     libssl-dev libreadline-dev libyaml-dev libsqlite3-dev \
     libxml2-dev libxslt1-dev libcurl4-openssl-dev \
     python-software-properties

# numpy is a dependency for OpenCV, so most of these other
# packages are probably optional
sudo apt-get install -y python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose
## Other scientific libraries (obviously not needed for OpenCV)
pip install -U scikit-learn
pip install -U nltk

### opencv from source
# first, installing some utilities
sudo apt-get install -y qt-sdk unzip
OPENCV_VER=2.4.10
curl "http://fossies.org/linux/misc/opencv-${OPENCV_VER}.zip" -o opencv-${OPENCV_VER}.zip
unzip "opencv-${OPENCV_VER}.zip" && cd "opencv-${OPENCV_VER}"
mkdir build && cd build
# build without ffmpeg
cmake -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON \
      -D WITH_V4L=ON -D INSTALL_C_EXAMPLES=ON \
      -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON \
      -D WITH_QT=ON -D WITH_OPENGL=ON -D WITH_VTK=ON \
      -D WITH_FFMPEG=OFF ..

A recurring issue I had come across – I didn't test it myself, but just saw it in the variety of speculation regarding the difficulty of building OpenCV – is that building with a Python other than the system's Python would cause problems. So, for what it's worth, the above process works with 14.04's Python 2.7.6, and 14.10's 2.7.8. I'm not much of a Python user myself so I don't know much about best practices regarding environment…pyenv works pretty effortlessly (that is, it works just like rbenv), but I didn't try it in relation to building OpenCV.

Also, this isn't the bare minimum…I'm not sure what dev tools or which cmake flags are are absolutely needed, or if qt-sdk is needed if you don't build with Qt support. But it works, so hopefully anyone Googling this issue will be able to make some progress.

Note: Other things I tried that did not work on clean installs of Ubuntu 14.04/14.10:

• Sebastian Montabone's writeup
• sudo apt-get build-dep opencv
• sudo apt-get install libopencv-dev (as per StackOverflow)

The Python code needed to do simple face-detection looks something like this (based off of examples from OpenCV-Python and Practical Python and OpenCV:

(You can find pre=built XML classifiers at the OpenCV repo)

import cv2
face_cascade_path = '/YOUR/PATH/TO/haarcascade_frontalface_default.xml'
face_cascade = cv2.CascadeClassifier(face_cascade_path)

scale_factor = 1.1
min_neighbors = 3
min_size = (30, 30)
flags = cv2.cv.CV_HAAR_SCALE_IMAGE

# load the image
image_path = "YOUR/PATH/TO/image.jpg"
image = cv2.imread(image_path)

# this does the work
rects = face_cascade.detectMultiScale(image, scaleFactor = scale_factor,
  minNeighbors = min_neighbors, minSize = min_size, flags = flags)

for( x, y, w, h ) in rects:
  cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 0), 2)

cv2.imwrite("YOUR/PATH/TO/output.jpg", image)

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