Added gradiantSize param into goodFeaturesToTrack API (#9618)
* Added gradiantSize param into goodFeaturesToTrack API
Removed hardcode value 3 in goodFeaturesToTrack API, and
added new param 'gradinatSize' in this API so that user can
pass any gradiant size as 3, 5 or 7.
Signed-off-by: Vipin Anand <anand.vipin@gmail.com>
Signed-off-by: Nilaykumar Patel<nilay.nilpat@gmail.com>
Signed-off-by: Prashanth Voora <prashanthx85@gmail.com>
* fixed compilation error for java test
Signed-off-by: Vipin Anand <anand.vipin@gmail.com>
* Modifying code for previous binary compatibility and fixing other warnings
fixed ABI break issue
resolved merged conflict
compilation error fix
Signed-off-by: Vipin Anand <anand.vipin@gmail.com>
Signed-off-by: Patel, Nilaykumar K <nilay.nilpat@gmail.com>
* lab_tetra squashed
* initial version is almost written
* unfinished work
* compilation fixed, to be debugged
* Lab test removed
* more fixes
* Luv2RGBinteger: channels order fixed
* Lab structs removed
* good trilinear interpolation added
* several fixes
* removed Luv2RGB interpolations, XYZ tables; 8-cell LUT added
* no_interpolate made 8-cell
* interpolations rewritten to 8-cell, minor fixes
* packed interpolation added for RGB2Luv
* tetra implemented
* removing unnecessary code
* LUT building merged
* changes ported to color.cpp
* minor fixes; try to suppress warnings
* fixed v range of Luv
* fixed incorrect src channel number
* minor fixes
* preliminary version of Luv2RGBinteger is done
* Luv2RGB_b is in progress
* XYZ color constants converted to softfloat
* Luv test: precision fixed
* Luv bit-exactness test added
* warnings fixed
* compilation fixed, error message fixed
* Luv check is limited to [0-2,0-2,0-2] by XYZ
* L->Y generation moved to LUT
* LUTs added for up and vp of Luv2RGB_b
* still works
* fixed-point is done, works at maxerr 2
* vectorized code is done, 2x slower than original
* perf improved by 10%
* extra comments removed
* code moved to color.cpp
* test_lab.cpp updated
* minor refactoring
* test added for Luv2RGB
* OCL Luv2RGB_b: XYZ are limited to [0, 2]; docs updated
* Luv2RGB_b rewritten to universal intrinsics
* test_lab.cpp moved to luv_tetra branch
* Imgproc_ColorLab_Full.accuracy test fixed
* Lab and Luv tests: rewritten, constants explained
* CV_ColorCvtBaseTest: added methods for 8u implementations
* Lab2RGB_b: bit-exactness enabled for all modes; non-vectorized code fixed to comply with vectorized
* srgb support added
* XYZ constants made softdouble
* bit-exact tests written for Lab
* ColorLab_full test fixed
* reverted: no 8u convertors for CV_ColorCvtBaseTest
* added checksum-based test for Lab bit-exactness
* extra declarations removed
* Lab test fix: stop at first mismatch
* test info output improved
* error message fixed
* lab_tetra squashed
* initial version is almost written
* unfinished work
* compilation fixed, to be debugged
* Lab test removed
* more fixes
* Luv2RGBinteger: channels order fixed
* Lab structs removed
* good trilinear interpolation added
* several fixes
* removed Luv2RGB interpolations, XYZ tables; 8-cell LUT added
* no_interpolate made 8-cell
* interpolations rewritten to 8-cell, minor fixes
* packed interpolation added for RGB2Luv
* tetra implemented
* removing unnecessary code
* LUT building merged
* changes ported to color.cpp
* minor fixes; try to suppress warnings
* fixed v range of Luv
* fixed incorrect src channel number
* minor fixes
* preliminary version of Luv2RGBinteger is done
* Luv2RGB_b is in progress
* XYZ color constants converted to softfloat
* Luv test: precision fixed
* Luv bit-exactness test added
* warnings fixed
* compilation fixed, error message fixed
* test_lab.cpp removed
- Optimizations set change. Now IPP integrations will provide code for SSE42, AVX2 and AVX512 (SKX) CPUs only. For HW below SSE42 IPP code is disabled.
- Performance regressions fixes for IPP code paths;
- cv::boxFilter integration improvement;
- cv::filter2D integration improvement;
[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.
RGB2Lab_f added, bugs fixed, moved to float
several bugs fixed
LUT fixed, no switch in tetraInterpolate()
temporary code; to be removed and rewritten
before refactoring
extra interpolations removed, some things to do left
added Lab2RGB_b +XYZ version, etc.
basic version is done, to be sped up
tetra refactored
interpolations: LUT for weights, refactor., etc.
address arithm optimized
initial version of vectorized code added (not compiling now)
compilation fixed, now segfaults
a lot of fixes, vectorization temp. disabled
fixed trilinear shift size, max error dropped from 19 to 10
fixed several bugs (255 vs 256, signed vs unsigned, bIdx)
minor changes
packed: address arithmetics fixed
shorter code
experiments with pure integer calculations
Lab2RGB max error decreased to 2; need to clean the code
ready for vectorization; need cleaning
vectorized, to be debugged
precision fixed, max error is 2
Lab->XYZ shortened
minor fixes
Lab2RGB_f version fixed, to be completely rewritten using _b code
RGB2Lab_f vectorized
minors
moved to separate file
refactored Lab2RGB to float and int versions
minor fix
Lab2RGB_f vectorized
minor refactoring
Lab2RGBint refactored: process methods, vectorize by 4 pix
Lab2RGB_f int version is done
cleanup extra code
code copied to color.cpp
fixed blue idx bug
optimizations enabled when testing; mulFracConst introduced
divConst -> mulFracConst
calc min time in perf instead of avg
minors
process() slightly sped up
Lab2RGB_f: disabled int version
reinterpret added, minor fixes in names
some warnings fixed
changes transferred to color.cpp
RGB2Lab_f code (and trilinear interpolation code) moved to rgb2lab_faster
whitespace
shift negative fixed
more warnings fixed
"constant condition" warnings fixed, little speed up
minor changes
test_photo decolor fixed
changes copied to test_lab.cpp
idx bounds checking in LUT init
several fixes
WIP: softfloat almost integrated
test_lab partially rewritten to SoftFloat
color.cpp rewritten to SoftFloat
test_lab.cpp: accuracy code added
several fixes
RGB2Lab_b testing fixed
splineBuild() rewritten to SoftFloat
accuracy control improved
rounding fixed
Luv <=> RGB: rewritten to SoftFloat
OCL cvtColor Lab and Lut rewritten to SoftFloat
minor fixes
refactored to new SoftFloat interface
round() -> cvRound, etc.
fixed OCL tests
softfloat.cpp: internal functions made static, unused ones removed
meaningful constants
extra lines removed
unused function removed
unfinished work
it works, need to fix TODOs
refactoring; more calls rewritten
mulFracConst removed
constants made bit exact; minors
changes moved to color.cpp
fixed 1 bug and 4 warnings
OCL: fixed constants
pow(x, _1_3f) replaced by cubeRoot(x)
fixed compilation on MSVC32
magic constants explained
file with internal accuracy&speed tests moved to lab_tetra branch
merge_histogram kernel only need "BINS" theads to accumulate the
histgrams, it is not efficient to directly use maxGroupSize as
local size if maxGroupSize is far greater then BINS.
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
* another round of dnn optimization:
* increased malloc alignment across OpenCV from 16 to 64 bytes to make it AVX2 and even AVX-512 friendly
* improved SIMD optimization of pooling layer, optimized average pooling
* cleaned up convolution layer implementation
* made activation layer "attacheable" to all other layers, including fully connected and addition layer.
* fixed bug in the fusion algorithm: "LayerData::consumers" should not be cleared, because it desctibes the topology.
* greatly optimized permutation layer, which improved SSD performance
* parallelized element-wise binary/ternary/... ops (sum, prod, max)
* also, added missing copyrights to many of the layer implementation files
* temporarily disabled (again) the check for intermediate blobs consistency; fixed warnings from various builders
vipinanand4
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Dmitry Kurtaev
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Vladislav Sovrasov
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Maksim Shabunin
Alexander Alekhin
Vitaly Tuzov
Pavel Vlasov
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KUANG, Fangjun
Jiri Horner
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