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…vide parallel version of both Wu's and Grana's algorithms (using TBB library)
Extended parallel version to all frameworks supported by OpenCV; Added some documentation notes in modules/imgproc/include/opencv2/imgproc.hpp;
…h getNumberOfCPUs
…into UserColormap
ICV_HLINE is split into several specific cases, according to pixel_size, to optimize memory copies of the same color components along the line.
On MacOS and iOS, the unused opencvBigToHost32 is a warning for buildbot
…g_performance # Conflicts: # modules/imgproc/src/drawing.cpp
added 64b optimization for 3 channels case not added 64b optimization for 4 channels case since timings did not show any improvement split ICV_HLINE cases into inline functions instead of macro for code size reduction, without significand speed drawback at first sight
added ICV_HLINE custom implementations for element sizes up to 32 but timings show that it is not very relevant for sizes >= 12
I took the subScalar.cu code and changed the inner operation
* avoid link error (move the implementation of software version to header) * make getConvertFuncFp16 local (move from precomp.hpp to convert.hpp) * fix error on 32bit x86
C4189: 'clImageUV' : local variable is initialized but not referenced
Fixed snprintf for VS 2013 (#8816) * Fixed snprintf for VS 2013 * snprintf: removed declaration from header, changed implementation * cv_snprintf corrected according to comments * update snprintf patch
There is no cast to wide integer type:
std::numeric_limits<ST>::max() * std::numeric_limits<ST>::max()
ranjiewwen
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Sep 8, 2017
[GSOC] Speeding-up AKAZE, part #2 (opencv#8951) * feature2d: instrument more functions used in AKAZE * rework Compute_Determinant_Hessian_Response * this takes 84% of time of Feature_Detection * run everything in parallel * compute Scharr kernels just once * compute sigma more efficiently * allocate all matrices in evolution without zeroing * features2d: add one bigger image to tests * now test have images: 600x768, 900x600 and 1385x700 to cover different resolutions * explicitly zero Lx and Ly * add Lflow and Lstep to evolution as in original AKAZE code * reworked computing keypoints orientation integrated faster function from https://github.com/h2suzuki/fast_akaze * use standard fastAtan2 instead of getAngle * compute keypoints orientation in parallel * fix visual studio warnings * replace some wrapped functions with direct calls to OpenCV functions * improved readability for people familiar with opencv * do not same image twice in base level * rework diffusity stencil * use one pass stencil for diffusity from https://github.com/h2suzuki/fast_akaze * improve locality in Create_Scale_Space * always compute determinat od hessian and spacial derivatives * this needs to be computed always as we need derivatives while computing descriptors * fixed tests of AKAZE with KAZE descriptors which have been affected by this Currently it computes all first and second order derivatives together and the determiant of the hessian. For descriptors it would be enough to compute just first order derivates, but it is not probably worth it optimize for scenario where descriptors and keypoints are computed separately, since it is already very inefficient. When computing keypoint and descriptors together it is faster to do it the current way (preserves locality). * parallelize non linear diffusion computation * do multiplication right in the nlp diffusity kernel * rework kfactor computation * get rid of sharing buffers when creating scale space pyramid, the performace impact is neglegible * features2d: initialize TBB scheduler in perf tests * ensures more stable output * more reasonable profiles, since the first call of parallel_for_ is not getting big performace hit * compute_kfactor: interleave finding of maximum and computing distance * no need to go twice through the data * start to use UMats in AKAZE to leverage OpenCl in the future * fixed bug that prevented computing determinant for scale pyramid of size 1 (just the base image) * all descriptors now support writing to uninitialized memory * use InputArray and OutputArray for input image and descriptors, allows to make use UMAt that user passes to us * enable use of all existing ocl paths in AKAZE * all parts that uses ocl-enabled functions should use ocl by now * imgproc: fix dispatching of IPP version when OCL is disabled * when OCL is disabled IPP version should be always prefered (even when the dst is UMat) * get rid of copy in DeterminantHessian response * this slows CPU version considerably * do no run in parallel when running with OCL * store derivations as UMat in pyramid * enables OCL path computing of determint hessian * will allow to compute descriptors on GPU in the future * port diffusivity to OCL * diffusivity itself is not a blocker, but this saves us downloading and uploading derivations * implement kernel for nonlinear scalar diffusion step * download the pyramid from GPU just once we don't want to downlaod matrices ad hoc from gpu when the function in AKAZE needs it. There is a HUGE mapping overhead and without shared memory support a LOT of unnecessary transfers. This maps/downloads matrices just once. * fix bug with uninitialized values in non linear diffusion * this was causing spurious segfaults in stitching tests due to propagation of NaNs * added new test, which checks for NaNs (added new debug asserts for NaNs) * valgrind now says everything is ok * add nonlinear diffusion step OCL implementation * Lt in pyramid changed to UMat, it will be downlaoded from GPU along with Lx, Ly * fix bug in pm_g2 kernel. OpenCV mangles dimensions passed to OpenCL, so we need to check for boundaries in each OCL kernel. * port computing of determinant to OCL * computing of determinant is not a blocker, but with this change we don't need to download all spatial derivatives to CPU, we only download determinant * make Ldet in the pyramid UMat, download it from CPU together with the other parts of the pyramid * add profiling macros * fix visual studio warning * instrument non_linear_diffusion * remove changes I have made to TEvolution * TEvolution is used only in KAZE now * Revert "features2d: initialize TBB scheduler in perf tests" This reverts commit ba81e2a.
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