opencv

Commit Graph

Author	SHA1	Message	Date
Tomoaki Teshima	a61546680b	use only even number for inplace flip	4 years ago
Gil Shotan	1612db5f91	Fix signed integer overflow in connected components	4 years ago
Alexander Alekhin	7dfe68cac6	imgcodecs: lazy on-demand codecs initialization	4 years ago
Shubham Singh	23e71d1aa2	fixes #17187 probably Added Eltwise Layer Support	4 years ago
Liubov Batanina	b542a1804c	Support global reduce ops	4 years ago
Alexander Alekhin	50ff40d684	pre: OpenCV 3.4.12 (version++)	4 years ago
Danny	c31164bf1e	Merge pull request #18126 from danielenricocahall:add-oob-error-sample-weighting Account for sample weights in calculating OOB Error * account for sample weights in oob error calculation * redefine oob error functions * fix ABI compatibility	4 years ago
Danny	20b23da8e2	Merge pull request #18061 from danielenricocahall:fix-kd-tree Fix KD Tree kNN Implementation * Make KDTree mode in kNN functional remove docs and revert change Make KDTree mode in kNN functional spacing Make KDTree mode in kNN functional fix window compilations warnings Make KDTree mode in kNN functional fix window compilations warnings Make KDTree mode in kNN functional casting Make KDTree mode in kNN functional formatting Make KDTree mode in kNN functional * test coding style	4 years ago
Alexander Alekhin	f6795d75a6	videoio: repair build of FFmpeg windows wrapper	4 years ago
Alexander Alekhin	1f2c83845d	backport: checks and fixes from static code analyzers results original commit: `71f665bd8c`	4 years ago
Liubov Batanina	2349a09736	Support Reshape with zero dim	4 years ago
YashasSamaga	1df533c914	fix typo in fusion tests	4 years ago
YashasSamaga	44bf748479	do not allocate UMat in non-OpenCL targets	4 years ago
pemmanuelviel	31dc3e9256	Merge pull request #18211 from pemmanuelviel:pev--handle-dna-vectors * DNA-mode: update miniflann to handle DNA * DNA-mode: update hierarchical kmeans to handle DNA sequences	4 years ago
Yosshi999	698b2bf729	Merge pull request #18167 from Yosshi999:bit-exact-gaussian Bit exact gaussian blur for 16bit unsigned int * bit-exact gaussian kernel for CV_16U * SIMD optimization * template GaussianBlurFixedPoint * remove template specialization * simd support for h3N121 uint16 * test for u16 gaussian blur * remove unnecessary comments * fix return type of raw() * add typedef of native internal type in fixedpoint * update return type of raw()	4 years ago
Alexander Alekhin	efcf307b4c	ocl: cleanup dead code in case of disabled OpenCL	4 years ago
Yosshi999	7495a4722f	Merge pull request #18053 from Yosshi999:bit-exact-resizeNN Bit-exact Nearest Neighbor Resizing * bit exact resizeNN * change the value of method enum * add bitexact-nn to ResizeExactTest * test to compare with non-exact version * add perf for bit-exact resizenn * use cvFloor-equivalent * 1/3 scaling is not stable for floating calculation * stricter test * bugfix: broken data in case of 6 or 12bytes elements * bugfix: broken data in default pix_size * stricter threshold * use raw() for floor * use double instead of int * follow code reviews * fewer cases in perf test * center pixel convention	4 years ago
Pierre-Emmanuel Viel	5376863c0c	bugfix	4 years ago
Sergei Slashchinin	9aa401a7d0	Merge pull request #17978 from sl-sergei:fix_17516_17531 * Fix ONNX loading in issues opencv#17516, opencv#17531 * Add tests for Linear and Matmul layers * Disable tests for IE versions lower than 20.4 * Skip unstable tests with OpenCL FP16 on Intel GPU * Add correct test filtering for OpenCL FP16 tests	4 years ago
catree	379b83e946	Fix cubic root computation to be able to handle negative values. Improve doc. Add regression test.	4 years ago
catree	dda1bf1887	Add broken implementation note for DLS and UPnP. Add CV_LOG_DEBUG.	4 years ago
Vadim Levin	f7e524cbe6	fix: libavcodec version check for AVDISCARD_NONINTRA - AVDISCARD_NONINTRA flag is supported only for FFMPEG libraries pack	4 years ago
Vadim Levin	e503ac508e	fix: libavcodec version check for AV_CODEC_FLAG_GLOBAL_HEADER	4 years ago
Liubov Batanina	d392b11dfb	Supported ONNX Pow op	4 years ago
catree	cd01ee9a54	Use camera intrinsic matrix everywhere. Add cameramatrix, distcoeffs and distcoeffsfisheye macros to avoid copy/paste errors.	4 years ago
Namgoo Lee	a7ffcaab28	Remove compiler warnings	4 years ago
Namgoo Lee	f617f18e46	bit-exact cuda::equalizeHist	4 years ago
Clement Courbet	da555a2c9b	Optimize opencv dft by vectorizing radix2 and radix3. This is useful for non power-of-two sizes when WITH_IPP is not an option. This shows consistent improvement over openCV benchmarks, and we measure even larger improvements on our internal workloads. For example, for 320x480, `32FC`, we can see a ~5% improvement}, as `320=2^65` and `480=2^535`, so the improved radix3 version is used. `64FC*` is flat as expected, as we do not specialize the functors for `double` in this change. ``` dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, 0, false) 1.239 1.153 1.07 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, 0, true) 0.991 0.926 1.07 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, DFT_COMPLEX_OUTPUT, false) 1.367 1.281 1.07 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, DFT_COMPLEX_OUTPUT, true) 1.114 1.049 1.06 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, DFT_INVERSE, false) 1.313 1.254 1.05 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, DFT_INVERSE, true) 1.027 0.977 1.05 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 1.296 1.217 1.06 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 1.039 0.963 1.08 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, DFT_ROWS, false) 0.542 0.524 1.04 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, DFT_ROWS, true) 0.293 0.277 1.06 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, DFT_SCALE, false) 1.265 1.175 1.08 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC1, DFT_SCALE, true) 1.004 0.942 1.07 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, 0, false) 1.292 1.280 1.01 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, 0, true) 1.038 1.030 1.01 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, DFT_COMPLEX_OUTPUT, false) 1.484 1.488 1.00 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, DFT_COMPLEX_OUTPUT, true) 1.222 1.224 1.00 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, DFT_INVERSE, false) 1.380 1.355 1.02 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, DFT_INVERSE, true) 1.117 1.133 0.99 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 1.372 1.383 0.99 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 1.117 1.127 0.99 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, DFT_ROWS, false) 0.546 0.539 1.01 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, DFT_ROWS, true) 0.293 0.299 0.98 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, DFT_SCALE, false) 1.351 1.339 1.01 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 64FC1, DFT_SCALE, true) 1.099 1.092 1.01 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, 0, false) 2.235 2.123 1.05 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, 0, true) 1.843 1.727 1.07 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, DFT_COMPLEX_OUTPUT, false) 2.189 2.109 1.04 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, DFT_COMPLEX_OUTPUT, true) 1.827 1.754 1.04 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, DFT_INVERSE, false) 2.392 2.309 1.04 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, DFT_INVERSE, true) 1.951 1.865 1.05 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 2.391 2.293 1.04 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 1.954 1.882 1.04 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, DFT_ROWS, false) 0.811 0.815 0.99 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, DFT_ROWS, true) 0.426 0.437 0.98 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, DFT_SCALE, false) 2.268 2.152 1.05 dft::Size_MatType_FlagsType_NzeroRows::(320x480, 32FC2, DFT_SCALE, true) 1.893 1.788 1.06 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, 0, false) 4.546 4.395 1.03 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, 0, true) 3.616 3.426 1.06 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, DFT_COMPLEX_OUTPUT, false) 4.843 4.668 1.04 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, DFT_COMPLEX_OUTPUT, true) 3.825 3.748 1.02 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, DFT_INVERSE, false) 4.720 4.525 1.04 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, DFT_INVERSE, true) 3.743 3.601 1.04 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 4.755 4.527 1.05 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 3.744 3.586 1.04 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, DFT_ROWS, false) 1.992 2.012 0.99 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, DFT_ROWS, true) 1.048 1.048 1.00 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, DFT_SCALE, false) 4.625 4.451 1.04 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC1, DFT_SCALE, true) 3.643 3.491 1.04 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, 0, false) 4.499 4.488 1.00 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, 0, true) 3.559 3.555 1.00 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, DFT_COMPLEX_OUTPUT, false) 5.155 5.165 1.00 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, DFT_COMPLEX_OUTPUT, true) 4.103 4.101 1.00 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, DFT_INVERSE, false) 5.484 5.474 1.00 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, DFT_INVERSE, true) 4.617 4.518 1.02 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 5.547 5.509 1.01 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 4.553 4.554 1.00 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, DFT_ROWS, false) 2.067 2.018 1.02 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, DFT_ROWS, true) 1.104 1.079 1.02 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, DFT_SCALE, false) 4.665 4.619 1.01 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 64FC1, DFT_SCALE, true) 3.698 3.681 1.00 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, 0, false) 8.774 8.275 1.06 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, 0, true) 6.975 6.527 1.07 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, DFT_COMPLEX_OUTPUT, false) 8.720 8.270 1.05 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, DFT_COMPLEX_OUTPUT, true) 6.928 6.532 1.06 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, DFT_INVERSE, false) 9.272 8.862 1.05 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, DFT_INVERSE, true) 7.323 6.946 1.05 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 9.262 8.768 1.06 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 7.298 6.871 1.06 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, DFT_ROWS, false) 3.766 3.639 1.03 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, DFT_ROWS, true) 1.932 1.889 1.02 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, DFT_SCALE, false) 8.865 8.417 1.05 dft::Size_MatType_FlagsType_NzeroRows::(800x600, 32FC2, DFT_SCALE, true) 7.067 6.643 1.06 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, 0, false) 10.014 10.141 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, 0, true) 7.600 7.632 1.00 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, DFT_COMPLEX_OUTPUT, false) 11.059 11.283 0.98 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, DFT_COMPLEX_OUTPUT, true) 8.475 8.552 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, DFT_INVERSE, false) 12.678 12.789 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, DFT_INVERSE, true) 10.445 10.359 1.01 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 12.626 12.925 0.98 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 10.538 10.553 1.00 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, DFT_ROWS, false) 5.041 5.084 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, DFT_ROWS, true) 2.595 2.607 1.00 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, DFT_SCALE, false) 10.231 10.330 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC1, DFT_SCALE, true) 7.786 7.815 1.00 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, 0, false) 13.597 13.302 1.02 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, 0, true) 10.377 10.207 1.02 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, DFT_COMPLEX_OUTPUT, false) 15.940 15.545 1.03 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, DFT_COMPLEX_OUTPUT, true) 12.299 12.230 1.01 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, DFT_INVERSE, false) 15.270 15.181 1.01 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, DFT_INVERSE, true) 12.757 12.339 1.03 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 15.512 15.157 1.02 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 12.505 12.635 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, DFT_ROWS, false) 6.359 6.255 1.02 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, DFT_ROWS, true) 3.314 3.248 1.02 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, DFT_SCALE, false) 13.937 13.733 1.01 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 64FC1, DFT_SCALE, true) 10.782 10.495 1.03 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, 0, false) 18.985 18.926 1.00 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, 0, true) 14.256 14.509 0.98 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, DFT_COMPLEX_OUTPUT, false) 18.696 19.021 0.98 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, DFT_COMPLEX_OUTPUT, true) 14.290 14.429 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, DFT_INVERSE, false) 20.135 20.296 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, DFT_INVERSE, true) 15.390 15.512 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 20.121 20.354 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 15.341 15.605 0.98 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, DFT_ROWS, false) 8.932 9.084 0.98 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, DFT_ROWS, true) 4.539 4.649 0.98 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, DFT_SCALE, false) 19.137 19.303 0.99 dft::Size_MatType_FlagsType_NzeroRows::(1280x1024, 32FC2, DFT_SCALE, true) 14.565 14.808 0.98 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, 0, false) 22.553 21.171 1.07 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, 0, true) 17.850 16.390 1.09 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, DFT_COMPLEX_OUTPUT, false) 24.062 22.634 1.06 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, DFT_COMPLEX_OUTPUT, true) 19.342 17.932 1.08 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, DFT_INVERSE, false) 28.609 27.326 1.05 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, DFT_INVERSE, true) 24.591 23.289 1.06 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 28.667 27.467 1.04 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 24.671 23.309 1.06 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, DFT_ROWS, false) 9.458 9.077 1.04 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, DFT_ROWS, true) 4.709 4.566 1.03 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, DFT_SCALE, false) 22.791 21.583 1.06 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC1, DFT_SCALE, true) 18.029 16.691 1.08 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, 0, false) 25.238 24.427 1.03 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, 0, true) 19.636 19.270 1.02 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, DFT_COMPLEX_OUTPUT, false) 28.342 27.957 1.01 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, DFT_COMPLEX_OUTPUT, true) 22.413 22.477 1.00 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, DFT_INVERSE, false) 26.465 26.085 1.01 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, DFT_INVERSE, true) 21.972 21.704 1.01 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 26.497 26.127 1.01 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 22.010 21.523 1.02 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, DFT_ROWS, false) 11.188 10.774 1.04 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, DFT_ROWS, true) 6.094 5.916 1.03 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, DFT_SCALE, false) 25.728 24.934 1.03 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 64FC1, DFT_SCALE, true) 20.077 19.653 1.02 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, 0, false) 43.834 40.726 1.08 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, 0, true) 35.198 32.218 1.09 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, DFT_COMPLEX_OUTPUT, false) 43.743 40.897 1.07 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, DFT_COMPLEX_OUTPUT, true) 35.240 32.226 1.09 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, DFT_INVERSE, false) 46.022 42.612 1.08 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, DFT_INVERSE, true) 36.779 33.961 1.08 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 46.396 42.723 1.09 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 37.025 33.874 1.09 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, DFT_ROWS, false) 17.334 16.832 1.03 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, DFT_ROWS, true) 9.212 8.970 1.03 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, DFT_SCALE, false) 44.190 41.211 1.07 dft::Size_MatType_FlagsType_NzeroRows::(1920x1080, 32FC2, DFT_SCALE, true) 35.900 32.888 1.09 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, 0, false) 40.948 38.256 1.07 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, 0, true) 33.825 30.759 1.10 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, DFT_COMPLEX_OUTPUT, false) 53.210 53.584 0.99 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, DFT_COMPLEX_OUTPUT, true) 46.356 46.712 0.99 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, DFT_INVERSE, false) 47.471 47.213 1.01 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, DFT_INVERSE, true) 40.491 41.363 0.98 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 46.724 47.049 0.99 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 40.834 41.381 0.99 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, DFT_ROWS, false) 14.508 14.490 1.00 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, DFT_ROWS, true) 7.832 7.828 1.00 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, DFT_SCALE, false) 41.491 38.341 1.08 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC1, DFT_SCALE, true) 34.587 31.208 1.11 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, 0, false) 65.155 63.173 1.03 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, 0, true) 56.091 54.752 1.02 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, DFT_COMPLEX_OUTPUT, false) 71.549 70.626 1.01 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, DFT_COMPLEX_OUTPUT, true) 62.319 61.437 1.01 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, DFT_INVERSE, false) 61.480 59.540 1.03 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, DFT_INVERSE, true) 54.047 52.650 1.03 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 61.752 61.366 1.01 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 54.400 53.665 1.01 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, DFT_ROWS, false) 20.219 19.704 1.03 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, DFT_ROWS, true) 11.145 10.868 1.03 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, DFT_SCALE, false) 66.220 64.525 1.03 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 64FC1, DFT_SCALE, true) 57.389 56.114 1.02 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, 0, false) 86.761 88.128 0.98 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, 0, true) 75.528 76.725 0.98 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, DFT_COMPLEX_OUTPUT, false) 86.750 88.223 0.98 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, DFT_COMPLEX_OUTPUT, true) 75.830 76.809 0.99 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, DFT_INVERSE, false) 91.728 92.161 1.00 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, DFT_INVERSE, true) 78.797 79.876 0.99 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, false) 92.163 92.177 1.00 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, DFT_INVERSE\|DFT_COMPLEX_OUTPUT, true) 78.957 79.863 0.99 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, DFT_ROWS, false) 24.781 25.576 0.97 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, DFT_ROWS, true) 13.226 13.695 0.97 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, DFT_SCALE, false) 87.990 89.324 0.99 dft::Size_MatType_FlagsType_NzeroRows::(2048x2048, 32FC2, DFT_SCALE, true) 76.732 77.869 0.99 ```	4 years ago
Alexander Alekhin	cd00d8f3f0	core(trace): lazy quering for OPENCV_TRACE_LOCATION - fixes proper initialization of non-trivial variable	4 years ago
Alexander Alekhin	b3755e617c	ocl: silence warning in case of async cleanup - OpenCL kernel cleanup processing is asynchronous and can be called even after forced clFinish() - buffers are released later in asynchronous mode - silence these false positive cases for asynchronous cleanup	4 years ago
danielenricocahall	ac177b849c	Improve initialization performance of Brisk reformatting Improve initialization performance of Brisk fix formatting Improve initialization performance of Brisk formatting Improve initialization performance of Brisk make a lookup table for ring use cosine/sine lookup table for theta in brisk and utilize trig identity fix ring lookup table use cosine/sine lookup table for theta in brisk and utilize trig identity formatting use cosine/sine lookup table for theta in brisk and utilize trig identity move scale radius product to ring loop to ensure it's not recomputed for each rot revert change move scale radius product to ring loop to ensure it's not recomputed for each rot remove rings lookup table move scale radius product to ring loop to ensure it's not recomputed for each rot fix formatting of for loop move scale radius product to ring loop to ensure it's not recomputed for each rot use sine/cosine approximations for brisk lookup table. add documentation for sine/cosine lookup tables Improve initialization performance of BRISK	4 years ago
Tomoaki Teshima	cc769ff19d	fix build error on odroid-n2-plus	4 years ago
Yosshi999	1834eed809	Merge pull request #18001 from Yosshi999:sift-8bit-descr * 8-bit SIFT descriptors * use clearer parameter * update docs * propagate type info * overload function for avoiding ABI-break * bugfix: some values are undefined when CV_SIMD is absent	4 years ago
Alexander Alekhin	7ec9f52509	highgui: don't terminate if we can't initialize GTK backend - allow Users to handle such case - exception will be thrown instead	4 years ago
nhlsm	68f527267b	Merge pull request #18080 from nhlsm:improve-mat-operator-assign-scalar * improve Mat::operator=(Scalar) * touch * remove trailing whitespace * TEST: check if old code pass test or not * remove CV_Error * remove warning * fix: is -> Scalar * 1) Mat mat -> Mat &mat 2) return bool, add output param add comment	4 years ago
Liubov Batanina	ad63d24dba	Merge pull request #18096 from l-bat:update_onnx_importer * Added ReduceSum to ONNX importer * Fix comments * Fix Mul	4 years ago
Liubov Batanina	339b963e6b	Fix MatMul and Add axes	4 years ago
Alexander Alekhin	00890aecdf	core(ocl): fix ocl::Image2d::isFormatSupported() in case of OPENCV_OPENCL_DEVICE=disabled	4 years ago
Yashas Samaga B L	2171cae8ff	Merge pull request #17976 from YashasSamaga:dnn-fusion-tests-fix-ocl dnn: add exhaustive fusion tests, enable more eltwise fusions * add eltwise fusion tests, enable more eltwise fusions * merge weighted eltwise tests with eltwise tests	4 years ago
Pierre-Emmanuel Viel	3f55152ca0	Add debug assert to check in FLANN the vectors size is multiple of the architecture word size	4 years ago
Liubov Batanina	f3cebb3e1b	Merge pull request #18077 from l-bat:reduce_sum * Supported ReduceSum op * Skip test	4 years ago
Pierre-Emmanuel Viel	98de57c6c4	Refactoring to prepare for other vector types while mutualizing some methods	4 years ago
zhaoyue-zephyrus	e231be86b7	support flownet2 with arbitary input size revise default proto to match the filename in documentations fix a bug beautify python codes fix bug beautify codes add test samples with larger/smaller size remove unless code using bytearray without creating tmp file remove useless codes	4 years ago
Liubov Batanina	6226ea0085	Fix bug in ONNX Gather op	4 years ago
Vadim Pisarevsky	1537ecd931	* added depth-wise convolution; gives ~20-30% performance improvement in MobileSSD networks * hopefully, eliminated compile warnings, errors, as well as failure in one test * * fixed a few typos * decreased buffer size in some cases * added more optimal im2row branch in the case of 1x1 convolutions * tuned fastConv to reduce the number of passes over arrays backport of commit `77b01deb80`	4 years ago
Gabriel	96ce65f021	Document PatchNANs input type	4 years ago
danielenricocahall	8457e471fd	add relu as activation option in darknet add relu option add relu as activation option in darknet simplify the setParams if-else ladder add relu as activation option in darknet correct activation_param type format format add relu as activation option in darknet spacing spacing add relu as activation option in darknet	4 years ago
Ilya Churaev	246de2b7f5	Replaced copy_with_new_args to clone_with_new_inputs	4 years ago
Ilya Churaev	e8c26963e9	Fixed removing is_parameter, is_constant, is_output	4 years ago
pemmanuelviel	793e7c0d9f	Merge pull request #18019 from pemmanuelviel:pev--multiple-kmeans-trees * Possibility to set more than one tree for the hierarchical KMeans (default is still 1 tree). This particularly improves NN retrieval results with binary vectors, allowing better quality compared to LSH for similar processing time when speed is the criterium. * Add explanations on the FLANN's hierarchical KMeans for binary data.	4 years ago

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19929 Commits (0be18f5cb0aa6e45febde51ea7c8af253d03f144)