sudo apt-get install libopencv-devIf you do not care about having the latest version you could skip the rest of the post.
sudo apt-get install build-essential checkinstall cmake pkg-config yasm
sudo apt-get install libtiff4-dev libjpeg-dev libjasper-dev
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev
sudo apt-get install python-dev python-numpy
sudo apt-get install libtbb-dev
sudo apt-get install libqt4-dev libgtk2.0-dev
tar -xvf OpenCV-2.4.0.tar.bz2 cd OpenCV-2.4.0/ mkdir build cd buildConfigure using CMake. You have a lot of options in this step. This is what I use:
cmake -D WITH_QT=ON -D WITH_XINE=ON -D WITH_OPENGL=ON -D WITH_TBB=ON -D BUILD_EXAMPLES=ON ..Notice, that by adding the -D WITH_QT=ON, the highgui module will use QT instead of GTK. If you want to go with GTK just remove this. For more information on the options, look at the CMakeLists.txt file. When you are happy with the configuration you have, you can start compiling:
makeIf compilation finishes without errors, you can install by saying:
sudo make installFinally, make sure that your programs can link to the OpenCV library in run-time by adding the following line at the end of your /etc/ld.so.conf:
/usr/local/libAnd then configure dynamic linker run-time bindings:
sudo ldconfig
./opencv_test_core
makeThis should compile and run with a test image, so you should see something like this:
T1=$(date +%s)
# Do something here
T2=$(date +%s)
diffsec="$(expr $T2 - $T1)"
echo | awk -v D=$diffsec '{printf "Elapsed time: %02d:%02d:%02d\n",D/(60*60),D%(60*60)/60,D%60}'
This will tell you the elapsed time between setting T1 and setting T2. The output is formatted as HH:MM:SS and printed to the console. You can easily redirect to a file for logging.
sudo apt-get install ffmpeg
ffmpeg -i myvideo.avi
ffmpeg -i myvideo.avi -vcodec mpg4 video_out.avi
ffmpeg -i myvideo.avi -vcodec mpg4 -b 1M -r 12 video_out.aviOther values for the -vcodec option include:
copy:
|
Copy raw codec data as is (do not reencode) |
rawvideo:
|
Raw video (uncompressed) |
xvid:
|
XviD (MPEG-4 Part2) |
wmv2:
|
Windows Video Media |
ffmpeg -i myvideo.avi -f image2 frame_%03d.png
ffmpeg -i myvideo.avi -ss 00:01:45 -t 00:00:45 myscene.avi
sudo apt-get install mjpegtoolsFor this example the original video has a frame rate of 30fps and we want to make it play at 1/3 of the speed. So we have to trick ffmpeg to believe that the original video had a 10fps frame rate. The yuvfps tool does exactly that. Make sure that the input frame rate (-s option) and the output frame rate (-r option) are the same.
ffmpeg -i myVideo.avi -f yuv4mpegpipe - | yuvfps -s 10:1 -r 10:1 | ffmpeg -f yuv4mpegpipe -i - -vcodec mpeg4 -b 12M mySlowVideo.aviNote that this will not work unless your video has one of the yuv pixel formats. If your pixel format is not compatible (rgba for example) you would have to convert it like this:
ffmpeg -i myVideo.avi -pix_fmt yuv420p myVideo2.avi
sudo apt-get install build-essential checkinstall git cmake ffmpeg libfaac-dev libjack-jackd2-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libsdl1.2-dev libtheora-dev libva-dev libvdpau-dev libvorbis-dev libx11-dev libxfixes-dev libxvidcore-dev texi2html yasm zlib1g-dev libtbb-dev libv4l-dev libopenexr-dev libunicap2-dev libavformat-dev libswscale-dev libdc1394-22-dev libgstreamer0.10-0 libgstreamer0.10-dev gstreamer0.10-tools gstreamer0.10-plugins-base libgstreamer-plugins-base0.10-dev gstreamer0.10-plugins-good gstreamer0.10-plugins-ugly gstreamer0.10-plugins-bad gstreamer0.10-ffmpeg python-numpyThis is not a minimal set of libraries, but it's a collection that will give you most of the functionality. For example, if you are sure that you will never need to use a firewire camera, you can ignore libdc1394-22-dev. But if you actually need to use this feature you will have to recompile OpenCV. Also, if you need CUDA support, follow the instructions in this post before continuing here.
wget downloads.sourceforge.net/project/opencvlibrary/opencv-unix/2.3.1/OpenCV-2.3.1a.tar.bz2Extract and create a build directory:
tar -xvf OpenCV-2.3.1a.tar.bz2 cd OpenCV-2.3.1/ mkdir build cd buildNow run cmake:
cmake -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=OFF -D INSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON ..At this point check the output of cmake to make sure that all the features you are going to need are supported. If not you will have to install the corresponding libraries and run cmake again. Compile:
makeIf everything went well you can go ahead and install:
sudo make installFinally, add the following lines in your .bashrc:
# OpenCV stuff PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfigTo test that everything works, let's run the face detection demo. Go to the bin directory:
cd binAnd run:
./facedetect --cascade="../../data/haarcascades/haarcascade_frontalface_alt.xml" --nested-cascade="../../data/haarcascades/haarcascade_eye.xml" --scale=1.3 ../../samples/c/lena.jpgYou should see something like this:
sudo telinit 3Login again using your username, then cd to the directory where you downloaded the drivers (e.g. ~/Downloads):
cd ~/Downloads chmod u+x devdriver_4.0_linux_64_270.41.19.run sudo ./devdriver_4.0_linux_64_270.41.19.runFollow the instructions on the screen. Make sure you write to your xorg.conf file. When done reboot:
sudo rebootDownload the CUDA Toolkit from this page (Linux -> CUDA Toolkit for Ubuntu Linux 10.10).
cd ~/Downloads chmod u+x cudatoolkit_4.0.17_linux_64_ubuntu10.10.run sudo ./cudatoolkit_4.0.17_linux_64_ubuntu10.10.runFinally, download the GPU Computing SDK from the above website.
cd ~/Downloads chmod u+x gpucomputingsdk_4.0.17_linux ./gpucomputingsdk_4.0.17_linuxAdd the these lines at the end of your ~/.profile (if your OS is 32-bit, remove the lib64 entry):
#CUDA stuff export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATHNow you just need to compile the samples. Assuming you installed the SDK in your home directory:
cd ~/NVIDIA_GPU_Computing_SDK makeYou can now run one of the demos, e.g. the particle demo
~/NVIDIA_GPU_Computing_SDK/C/bin/linux/release/particlesand you should see something like this:
