[GSOC] Speeding-up AKAZE, part #3 (#9249)
* use finding of scale extremas from fast_akaze
* incorporade finding of extremas and subpixel refinement from Hideaki Suzuki's fast_akaze (https://github.com/h2suzuki/fast_akaze)
* use opencv parallel framework
* do not search for keypoints near the border, where we can't compute sensible descriptors (bugs fixed in ffd9ad99f4, 2c5389594b), but the descriptors were not 100% correct. this is a better solution
this version produces less keypoints with the same treshold. It is more effective in pruning similar keypoints (which do not bring any new information), so we have less keypoints, but with high quality. Accuracy is about the same.
* incorporate bugfix from upstream
* fix bug in subpixel refinement
* see commit db3dc22981e856ca8111f2f7fe57d9c2e0286efc in Pablo's repo
* rework finding of scale space extremas
* store just keypoints positions
* store positions in uchar mask for effective spatial search for neighbours
* construct keypoints structs at the very end
* lower inlier threshold in test
* win32 has lower accuracy
[GSOC] Speeding-up AKAZE, part #2 (#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 ba81e2a711.
Remove unnecessary Non-ASCII characters from source code (#9075)
* Remove unnecessary Non-ASCII characters from source code
Remove unnecessary Non-ASCII characters and replace them with ASCII
characters
* Remove dashes in the @param statement
Remove dashes and place single space in the @param statement to keep
coding style
* misc: more fixes for non-ASCII symbols
* misc: fix non-ASCII symbol in CMake file
[GSOC] Speeding-up AKAZE, part #1 (#8869)
* ts: expand arguments before stringifications in CV_ENUM and CV_FLAGS
added protective macros to always force macro expansion of arguments. This allows using CV_ENUM and CV_FLAGS with macro arguments.
* feature2d: unify perf test
use the same test for all detectors/descriptors we have.
* added AKAZE tests
* features2d: extend perf tests
* add BRISK, KAZE, MSER
* run all extract tests on AKAZE keypoints, so that the test si more comparable for the speed of extraction
* feature2d: rework opencl perf tests
use the same configuration as cpu tests
* feature2d: fix descriptors allocation for AKAZE and KAZE
fix crash when descriptors are UMat
* feature2d: name enum to fix build with older gcc
* Revert "ts: expand arguments before stringifications in CV_ENUM and CV_FLAGS"
This reverts commit 19538cac1e.
This wasn't a great idea after all. There is a lot of flags implemented as #define, that we don't want to expand.
* feature2d: fix expansion problems with CV_ENUM in perf
* expand arguments before passing them to CV_ENUM. This does not need modifications of CV_ENUM.
* added include guards to `perf_feature2d.hpp`
* feature2d: fix crash in AKAZE when using KAZE descriptors
* out-of-bound access in Get_MSURF_Descriptor_64
* this happened reliably when running on provided keypoints (not computed by the same instance)
* feature2d: added regression tests for AKAZE
* test with both MLDB and KAZE keypoints
* feature2d: do not compute keypoints orientation twice
* always compute keypoints orientation, when computing keypoints
* do not recompute keypoint orientation when computing descriptors
this allows to test detection and extraction separately
* features2d: fix crash in AKAZE
* out-of-bound reads near the image edge
* same as the bug in KAZE descriptors
* feature2d: refactor invariance testing
* split detectors and descriptors tests
* rewrite to google test to simplify debugging
* add tests for AKAZE and one test for ORB
* stitching: add tests with AKAZE feature finder
* added basic stitching cpu and ocl tests
* fix bug in AKAZE wrapper for stitching pipeline causing lots of
! OPENCV warning: getUMat()/getMat() call chain possible problem.
! Base object is dead, while nested/derived object is still alive or processed.
! Please check lifetime of UMat/Mat objects!
Added OpenVX based processing to FAST (#7720)
* added wrapper for OVX FAST & fixes to IVX wrappers
* fixed type checks in wrappers, array downloading code simplified
* rewritten for new macro use
1. if a component's variation is a global minimum than it should be a local minimum
2. for the small image with invert and blur, the MSERs number should be 20
the merge() calls growHistory() too many times such that:
1. some CompHistory nodes not used have been created
2. some CompHistory node's val equal their parents
This is found in the original akaze repo. Previous sub pixel localization method assumes the coordinate (0, 0) is the up-left corner of the up-left pixel. But as far as I know, opencv uses the center of the up-left corner, in this case it should be done in this way.
35aeb83a71db3dc22981
All of these: (performance) Prefer prefix ++/-- operators for non-primitive types.
[modules/calib3d/src/fundam.cpp:1049] -> [modules/calib3d/src/fundam.cpp:1049]: (style) Same expression on both sides of '&&'.
* Adds the main features2d header to the parse list for the generator.
* Removes the manual definition of drawKeypoints and drawMatches since these are now included in the main header.
* Updates the generator to ignore SimpleBlobDetector, FlannBasedMatcher and DescriptorMatcher as these cause conflicts with the generator. This is okay since these were not previously included in the distribution anyway, so no harm is done.
FlannBasedMatcher::add is overloaded, but the style of parsing the
InputArrayOfArrays does not match the style from
DescriptorMatcher::add. The issue is that InputArrayOfArrays
must be properly marshalled so that the data can be read
correctly. In this case, the method expects the training
descriptors to be either a vector of matrices or a single matrix
(as is shown in DescriptorMatcher::add). These code
replicates that for the case of the FlannBasedMatcher::add.
In fact, a similar commit to this was added by 26d9a7c but was
ultimately not accepted in #4111. This is likely due to the
fact that the input arrays were not parsed properly and the
case of a single matrix was being improperly handled. I believe
this commit to be correct given the logic from
DescriptorMatcher::add.
PR #2968: cce2d998578f9c
Fixed bug which caused crash of GPU version of feature matcher in stitcher
The bug caused crash of GPU version of feature matcher in stitcher when
we use ORB features.
PR #3236: 5947519
Check sure that we're not already below required leaf false alarm rate before continuing to get negative samples.
PR #3190
fix blobdetector
PR #3562 (part): 82bd82e
TBB updated to 4.3u2. Fix for aarch64 support.
PR #3604 (part): 091c7a3
OpenGL interop sample reworked not ot use cvconfig.h
PR #3792: afdf319
Add -L for CUDA libs path to pkg-config
Add all dirs from CUDA_LIBS_PATH as -L linker options to
OPENCV_LINKER_LIBS. These will end up in opencv.pc.
PR #3893: 122b9f8
Turn ocv_convert_to_lib_name into a function
PR #5490: ec5244a
fixed memory leak in findHomography tests
PR #5491: 0d5b739
delete video readers
PR #5574
PR #5202
- IPP is disabled by default when compiler is mingw (couldn't make it
work)
- fixed some warnings
- fixed some `__GNUC__` version checks (for correctness and convenience)
- removed UTF-8 BOM from hough.cpp (fixes#5253)
Current implementation of miniflann is releasing the trained index, and
rebuilding the index from the beginning.
But, some indexing algorithms like the LSH are able to add the indexing
data after that.
This branch is implementation of that optimization for LshIndex
FlannBasedMatcher in the feature2d.
