Simon, Straight from the OpenCV 3.0 documentation site:
While in the future we hope to validate (and enable) the OCL module in all > OpenCL capable devices, we currently develop and test on GPU devices only. > This includes both discrete GPUs (NVidia, AMD), as well as integrated chips > (AMD APU and Intel HD devices). Performance of any particular algorithm > will depend on the particular platform characteristics and capabilities. > However, currently, accuracy and mathematical correctness has been verified > to be identical to that of the pure CPU implementation on all tested GPU > devices and platforms (both Windows and Linux). Does this mean that they have not tested on OSX or is this more likely due to hardware support? Someone has encountered problems running OpenCL on OSX, but perhaps this is an isolated case (OpenCL example failed on OSX <http://answers.opencv.org/question/12158/opencl-example-failed-on-mac-os-x-1083/> ). According to the OpenCV 3.0 documentation Many kernels use 256 as its workgroup size if possible, so the max work > group size of the device must larger than 256. All GPU devices we are aware > of indeed support 256 workitems in a workgroup, however non GPU devices may > not. This will be improved in the future. However, there is a nice and useful link here for how to support OpenCV-OpenCL on OSX so it maybe worth trying: https://fogofcode.wordpress.com/2014/02/23/running-opencv-on-osx-with-opencl-support/ Apparently, building OpenCV with OpenCL requires AMDBLAS and AMDFFT. I guess you are using Linux or Windows, right? clAmdFft and clAmdBlas are only built for Windows and Linux so I will have to build them first from source in OSX. Max On Saturday, December 6, 2014 1:28:08 PM UTC+1, Simon Danisch wrote: > > Oh this is odd, why do they name the NVIDIA restricted > version GPU-accelerated Computer Vision, and the hardware vendor > independent version OpenCL-accelerated Computer Vision? > I personally would first go for the OpenCL module, as its supported by > more GPU's (I don't even have an NVIDIA card)... > > 2014-12-06 13:19 GMT+01:00 Tim Holy <[email protected] <javascript:>>: > >> This looks really cool. Nice work! >> >> As you know well, it looks like most or all of what's in your README is >> covered with native Julia solutions (Images, VideoIO, and ImageView). But >> clearly the fun is going to come via the "advanced applications," for >> which >> there is very little publicly available in julia. >> >> I'm really looking forward to playing with this! >> >> Best, >> --Tim >> >> On Saturday, December 06, 2014 02:44:45 AM Max Suster wrote: >> > Hi all, >> > >> > A few months ago I set out to learn Julia in an attempt to find an >> > alternative to MATLAB for developing computer vision applications. >> > Given the interest (1 >> > < >> https://groups.google.com/forum/#!searchin/julia-users/OpenCV/julia-users/P >> > jyfzxPt8Gk/SuwKtjTd9j4J> ,2 >> > < >> https://groups.google.com/forum/#!searchin/julia-users/OpenCV/julia-users/8 >> > 1V5zSNJY3Q/DRUT0dR2qhQJ> ,3 >> > < >> https://groups.google.com/forum/%23!searchin/julia-users/OpenCV/julia-users >> > /iUPqo8drYek/pUeHECk91AQJ> ,4 >> > < >> https://groups.google.com/forum/%23!searchin/julia-users/OpenCV/julia-users >> > /6QunG66MfNs/C63pDfI-EMAJ> ) and wide application of OpenCV for fast >> > real-time computer vision applications, I set myself to put together a >> > simple interface for OpenCV in Julia. Coding in Julia and developing >> the >> > interface between C++ and Julia has been a lot of fun! >> > >> > OpenCV.jl aims to provide an interface for OpenCV <http://opencv.org/> >> > computer vision applications (C++) directly in Julia >> > <http://julia.readthedocs.org/en/latest/manual/>. It relies primarily >> on >> > Keno´s amazing Cxx.jl <https://github.com/Keno/Cxx.jl>, the Julia C++ >> > foreign function interface (FFI). You can find all the information on >> my >> > package at https://github.com/maxruby/OpenCV.jl. >> > >> > You can download and run the package as follows: >> > >> > Pkg.clone("git://github.com/maxruby/OpenCV.jl.git")using OpenCV >> > >> > >> > For MacOSX, OpenCV.jl comes with pre-compiled shared libraries, so it is >> > extremely easy to run. For Windows and Linux, you will need to first >> > compile the OpenCV libraries, but this is well documented and links to >> the >> > instructions for doing so are included in the README.md file. >> > >> > The package currently supports most of OpenCV´s C++ API; however, at >> this >> > point I have created custom wrappings for core, imgproc, videoio and >> > highgui modules so that these are easy to use for anyone. >> > >> > The package also demonstrates/contains >> > >> > - preliminary interface with the Qt GUI framework (see imread() and >> > imwrite() functions) >> > - thin-wrappers for C++ objects such as std::vectors, std::strings >> > - conversion from Julia arrays to C++ std::vector >> > - conversion of Julia images (Images.jl) to Mat (OpenCV) - though >> this >> > has much room for improvement (i.e., color handling) >> > >> > Please let me know if there are any features you would like to see added >> > and I will try my best to integrate them. In the meantime, I will >> continue >> > to integrate more advanced algorithms for computer vision and eventually >> > extend the documentation as needed. >> > >> > Cheers, >> > Max >> >> >
