That's a good point! Are you using CV_8UC3? Video cards like everything that has 32 bits, so if you use RGB (3*8=24) depending on how OpenCV optimizes things, it could mean quite a slow down for the UMat case.
2014-12-08 19:29 GMT+01:00 Max Suster <mxsst...@gmail.com>: > Thanks! That is very useful. > > Why is cvColor so slow in the Intel tests? > I notice they used a 4000x4000, CV_8UC4 (Uint8, 4 channels) for the > GaussianBlur test! > > > On Monday, December 8, 2014 7:16:42 PM UTC+1, Simon Danisch wrote: >> >> To give it some more underpinning, I searched for a benchmark. >> I only found one for cuda so far from 2012: http://www.timzaman.com/ >> ?p=2256 >> Results in a table: >> https://docs.google.com/spreadsheet/pub?key= >> 0AkfBSyx6TpqMdDFnclY5UjlyNmVsSnhGV0hscnJQcVE&output=html >> Resulting graph: >> >> >> I think the scale is speedup from using the gpu, so the cpu is always one. >> >> 2014-12-08 19:07 GMT+01:00 Simon Danisch <sdan...@gmail.com>: >> >>> Strictly faster is probably a little bit exaggerated >>> <http://www.dict.cc/englisch-deutsch/exaggerated.html>, but simple >>> image manipulations fit the GPU very well. This should be valid for all >>> algorithms which can be massively parallelized >>> <http://www.dict.cc/englisch-deutsch/parallelized.html> and don't use >>> scatter writes or reads. >>> So if you have decent video card, the cpu should have a hard time to >>> match the performance. >>> I'm not sure about the transfers, as OpenCV might actually do transfers >>> even in the UMat case... It's not that obvious how they manage their memory. >>> >>> 2014-12-08 18:58 GMT+01:00 Tim Holy <tim....@gmail.com>: >>> >>> Now that you posted the code (along with timing markers), it's much >>>> clearer. >>>> Your timings are just for the computation, and don't include transfer >>>> time. >>>> >>>> My "misleading" statement was not intended as an accusation :-), merely >>>> trying >>>> to help explain the gaussian blur result: if you included transfer time >>>> in one >>>> but not the other, that might explain it. But from your posted code, >>>> it's >>>> clear that wasn't the problem. >>>> >>>> --Tim >>>> >>>> On Monday, December 08, 2014 09:54:03 AM Max Suster wrote: >>>> > Its an exact comparison side-by-side of the same code, and actually >>>> already >>>> > tested by others in the OpenCV forum. >>>> > The Mat/UMat image is available for display with imshow -- the step >>>> "imshow( >>>> > "edges", gray);" in both cases -- which is how the test was set up. >>>> The >>>> > main point is the time it takes to complete the entire process and >>>> the fact >>>> > that cvtColor with OpenCL can generate an image for viewing much more >>>> > quickly. >>>> > >>>> > I never intended to be misleading, and I am sorry that you interpret >>>> it >>>> > this way. >>>> > >>>> > Max >>>> > >>>> > On Monday, December 8, 2014 6:22:39 PM UTC+1, Tim Holy wrote: >>>> > > I wonder if the bigger problem might be that your numbers for the >>>> > > grayscale >>>> > > conversion (which were very promising) might be misleading. Are you >>>> sure >>>> > > the >>>> > > calculation is "done" (and the results are available to the CPU) by >>>> the >>>> > > time >>>> > > it finishes? If we assume a best-case scenario of 6GB/s of data >>>> transfer >>>> > > to the >>>> > > GPU, then transferring 3MB to the GPU and 1MB back takes about >>>> 0.7ms. >>>> > > That's >>>> > > many times longer than what you reported for that calculation. Or >>>> did you >>>> > > not >>>> > > include transfer time in your results? >>>> > > >>>> > > --Tim >>>> > > >>>> > > On Monday, December 08, 2014 05:50:32 PM Simon Danisch wrote: >>>> > > > That's interesting, gaussian blur should definitely be faster on >>>> the >>>> > > >>>> > > gpu! >>>> > > >>>> > > > Maybe this thread helps? >>>> > > > http://answers.opencv.org/question/34127/opencl-in-opencv-300/ >>>> > > > It seems like things are a little complicated, as it isn't really >>>> clear >>>> > > >>>> > > if >>>> > > >>>> > > > the data is currently in VRAM or RAM... >>>> > > > >>>> > > > 2014-12-08 17:39 GMT+01:00 Max Suster <mxss...@gmail.com >>>> <javascript:>>: >>>> > > > > Thanks for the feedback. I realize that the copying needs to be >>>> > > >>>> > > skipped >>>> > > >>>> > > > > if possible . . . >>>> > > > > I have been playing a bit with the OpenCL UMat and it will need >>>> indeed >>>> > > > > some tweeking because UMat is not always advantageous. >>>> > > > > While there 10x gain with cvtColor and other functions such as >>>> > > > > GasussianBlur are actually a little slower. >>>> > > > > >>>> > > > > I will have closer look at this tonight. >>>> > > > > >>>> > > > > Max >>>> > > > > >>>> > > > > On Monday, December 8, 2014 4:15:28 PM UTC+1, Simon Danisch >>>> wrote: >>>> > > > >> If you're interested here are some more links: >>>> > > > >> https://software.intel.com/en-us/articles/opencl-and-opengl-> >>>> >> >>>> > > >>>> > > interoperability-tutorial >>>> > > >>>> > > > >> Valentine's and mine prototype for OpenGL OpenCL >>>> interoperability in >>>> > > > >> Julia: >>>> > > > >> https://github.com/vchuravy/qjulia_gpu >>>> > > > >> >>>> > > > >> Am Samstag, 6. Dezember 2014 11:44:45 UTC+1 schrieb Max Suster: >>>> > > > >>> 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-use >>>> > > >>>> > > > >>> rs/PjyfzxPt8Gk/SuwKtjTd9j4J> ,2 >>>> > > > >>> < >>>> > > >>>> > > https://groups.google.com/forum/#!searchin/julia-users/ >>>> OpenCV/julia-use >>>> > > >>>> > > > >>> rs/81V5zSNJY3Q/DRUT0dR2qhQJ> ,3 >>>> > > > >>> < >>>> > > >>>> > > https://groups.google.com/forum/%23!searchin/julia- >>>> users/OpenCV/julia-u >>>> > > >>>> > > > >>> sers/iUPqo8drYek/pUeHECk91AQJ> ,4 >>>> > > > >>> < >>>> > > >>>> > > https://groups.google.com/forum/%23!searchin/julia- >>>> users/OpenCV/julia-u >>>> > > >>>> > > > >>> sers/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 >>>> >>>> >>> >>
