Currently I am experimenting with stereo vision usb webcams, where an essential step is good calibration. It felt a bit strange to see the image feature detection working on live data:
I’ll try to form the gained experiences into a small step by step guide and probably a github repository soon. Currently I am aiming towards depth data gained by webcams only, especially to compare the results to previous setups, and to see if visual odometry can be an option in low cost environments.
OpenCV is a powerful tool for building computer vision based applications. For one of my projects, I needed it to be compiled on my Raspberry.
Installing it from repositories was not an option because of its too old version.
If this wouldn’t bother you, consider using:
sudo apt-get install libopencv-dev
Here is how I installed it on my Rasbian Wheezy from source:
sudo apt-get -y install build-essential cmake cmake-qt-gui pkg-config libpng12-0 libpng12-dev libpng++-dev libpng3 libpnglite-dev zlib1g-dbg zlib1g zlib1g-dev pngtools libtiff4-dev libtiff4 libtiffxx0c2 libtiff-tools libjpeg8 libjpeg8-dev libjpeg8-dbg libjpeg-progs ffmpeg libavcodec-dev libavcodec53 libavformat53 libavformat-dev libgstreamer0.10-0-dbg libgstreamer0.10-0 libgstreamer0.10-dev libxine1-ffmpeg libxine-dev libxine1-bin libunicap2 libunicap2-dev libdc1394-22-dev libdc1394-22 libdc1394-utils swig libv4l-0 libv4l-dev python-numpy libpython2.6 python-dev python2.6-dev libgtk2.0-dev pkg-config
downloading the zipfile into a folder located in $HOME
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D BUILD_PYTHON_SUPPORT=ON -D BUILD_EXAMPLES=ON
sudo make install
Make took about 6-7 hours on my Raspberry Pi, so be sure you have a stable connection or consider using a tool like ‘screen’.
Please also see:
For openni2_camera it’s not necessary to go further like other posts suggested.