mirror of
https://github.com/opencv/opencv.git
synced 2026-07-29 15:23:05 +04:00
4.x
170 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Alexander Smorkalov
|
24030ff3a2 |
Merge pull request #29390 from amd:fast_countNonZero
core: Optimized countNonZero with AVX-512 signmask path |
||
|
Alexander Smorkalov
|
9460e30793 |
Merge pull request #29400 from amd:fast_sum
core: enable AVX-512 dispatch for sum |
||
 Madan mohan Manokar
|
579a505734 |
Merge pull request #29335 from amd:fast_norm_simd
core: SIMD optimizations for norm, distance and Hamming APIs. (Improves ORB & BRISK) #29335 Generic universal-intrinsic kernels (benefit all SIMD backends: NEON, AVX2, AVX-512, etc.), plus enabling wider dispatch for the norm module. - hal::normHamming: cached-pointer dispatch (resolve once, no per-call dispatch chain or trace region) + vector popcount path. cv::norm(NORM_HAMMING) and binary-descriptor matching (BFMatcher ORB/BRISK/FREAK via cv::batchDistance). - hal::normL2Sqr_ / normL1_: direct inlinable kernels with single-vector tail (cv::batchDistance / BFMatcher float L2/L1, cv::kmeans). - cv::norm masked NORM_INF: deinterleave SIMD for multichannel + back-step tail. - cv::norm(src1, src2, type, mask): SIMD masked norm-diff; INF is one templated kernel for all element types, plus uchar L1/L2 and int L1 kernels. - Enable AVX512_SKX/AVX512_ICL dispatch for the norm module. - features2d: add BFMatcher knnMatch perf tests (float L2/L1, binary Hamming). - ORB and BRISK performance improved ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake |
||
|
Madan mohan Manokar
|
d4005e3cb1 |
core: enable AVX-512 dispatch for sum
- Add AVX512_SKX/AVX512_ICL to sum dispatch |
||
|
Madan mohan Manokar
|
d6a187480f |
core: speed up countNonZero with AVX-512 signmask path
Use signmask+popcount only in AVX-512 dispatch units; keep the legacy batched SIMD kernels for AVX2, NEON, and LASX to avoid regressions on narrow SIMD widths. |
||
|
Alexander Smorkalov
|
639fd5d07a |
Merge pull request #29340 from amd:fast_minmax_simd
core: Optimize minMaxLoc |
||
|
Madan mohan Manokar
|
4eb3f8ecb6 |
Merge pull request #29339 from amd:fast_mean_simd
core: Vectorize meanStdDev #29339 - Add SIMD SumSqr_SIMD reductions (previously scalar) plus AVX-512 dispatch for the mean TU. - Uses universal intrinsics, so all SIMD backends benefit. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake |
||
|
Madan mohan Manokar
|
550251b3c2 |
core: Optimize minMaxIdx/minMaxLoc
- Replace the fixed-128-bit per-depth minMaxIdx kernels with a single templated universal-intrinsic core - added simd dispatch AVX-512 dispatch - A dual-accumulator inner loop added Uses universal intrinsics, so all SIMD backends benefit. |
||
|
Madan mohan Manokar
|
70a2c50b43 |
Merge pull request #29250 from amd:fast_lut8u_simd
imgproc: optimized LUT and equalizeHist with SIMD #29250 - optimized lut with SIMD - support equalizeHist with v_lut ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake |
||
 kjg0724
|
5d88781f74 |
Merge pull request #28782 from kjg0724:reduce-simd-optimization
core: add platform-specific SIMD for cv::reduce REDUCE_SUM ### Pull Request Rewrite `cv::reduce` SIMD optimization using platform-specific instructions as discussed in #28763. #### Changes **Col reduce (dim=1) — horizontal sum per row:** - ARM DOTPROD: `vdotq_u32()` — single-instruction byte sum, no intermediate flush needed - ARM AArch64 fallback: `vpaddlq` chain (u8→u16→u32) - Intel AVX2: `_mm256_sad_epu8()` — 32 bytes → 4×u64 partial sums per cycle - Intel SSSE3: `_mm_shuffle_epi8` + `_mm_sad_epu8` for cn=4 channel separation - Intel SSE2: `_mm_sad_epu8` for cn=1 - cn=4: hardware deinterleave (`vld4q_u8` on ARM, shuffle+SAD on Intel) **Row reduce (dim=0) — vertical accumulation across rows:** - u16 intermediate accumulator with 256-row flush (halves memory bandwidth vs direct u32) - ARM AArch64: `vaddw_u8` widening add (single instruction vs expand+add) - Intel: unpack + add with u16 buffer **Coverage:** All REDUCE_SUM type combinations — 8U→32S, 8U→32F, 16U→32F, 16S→32F, 32F→32F, 32F→64F, 64F→64F. Non-8U types use universal intrinsics where platform-specific gain is minimal (widening is single-stage, FP has limited alternatives). `REDUCE_AVG` benefits automatically (uses SUM internally). **Dispatch:** `CV_CPU_DISPATCH` with SSE2/AVX2/NEON_DOTPROD/LASX. Fallback hierarchy: DOTPROD → AArch64 NEON → Universal Intrinsics → scalar. #### Benchmark (Apple M3 Pro, MacBook Pro 16-inch 2023) | Path | Type | Speedup vs scalar | |------|------|-------------------| | Col reduce (dim=1) | 8UC1 | **3.0–4.5x** | | Col reduce (dim=1) | 8UC4 | **2.7–5.0x** | | Col reduce (dim=1) | 32FC1 | 1.7–2.3x | | Row reduce (dim=0) | 8UC1 | 1.1–1.5x | Row reduce gains are modest due to memory-bandwidth bound (as expected for vertical accumulation). No regressions on non-target paths (MAX, MIN, SUM2). #### Testing - `opencv_test_core --gtest_filter="*Reduce*:*reduce*"` — 483 tests PASSED - Edge cases: non-aligned dimensions (127×61), cn=2/3 scalar fallback, REDUCE_SUM2 unmodified #### Files changed - `modules/core/src/reduce.simd.hpp` — new, platform-dispatched SIMD implementation - `modules/core/src/reduce.dispatch.cpp` — new, CV_CPU_DISPATCH wrapper - `modules/core/CMakeLists.txt` — add `NEON_DOTPROD` to dispatch list - `modules/core/src/matrix_operations.cpp` — wire dispatch functions into ReduceC/R_Invoker |
||
 cudawarped
|
6adb985679 | [cuda] Add cudart to all modules which use it | ||
 Yuantao Feng
|
e2803bee5c |
Merge pull request #26885 from fengyuentau:4x/core/normalize_simd
core: vectorize cv::normalize / cv::norm #26885 Checklist: | | normInf | normL1 | normL2 | | ---- | ------- | ------ | ------ | | bool | - | - | - | | 8u | √ | √ | √ | | 8s | √ | √ | √ | | 16u | √ | √ | √ | | 16s | √ | √ | √ | | 16f | - | - | - | | 16bf | - | - | - | | 32u | - | - | - | | 32s | √ | √ | √ | | 32f | √ | √ | √ | | 64u | - | - | - | | 64s | - | - | - | | 64f | √ | √ | √ | *: Vectorization of data type bool, 16f, 16bf, 32u, 64u and 64s needs to be done on 5.x. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake |
||
 Brad Smith
|
918196ec1b |
Add CMake checks for getauxval and elf_aux_info for POWER
- Change __unix__ check for feature detection. NetBSD does not have either API. - Adds support for OpenBSD/powerpc64. |
||
 Maksim Shabunin
|
04818d6dd5 | build: made environment access a separate feature | ||
|
Alexander Smorkalov
|
15783d6598 |
Merge pull request #25792 from asmorkalov:as/HAL_fast_GaussianBlur
Added flag to GaussianBlur for faster but not bit-exact implementation #25792 Rationale: Current implementation of GaussianBlur is almost always bit-exact. It helps to get predictable results according platforms, but prohibits most of approximations and optimization tricks. The patch converts `borderType` parameter to more generic `flags` and introduces `GAUSS_ALLOW_APPROXIMATIONS` flag to allow not bit-exact implementation. With the flag IPP and generic HAL implementation are called first. The flag naming and location is a subject for discussion. Replaces https://github.com/opencv/opencv/pull/22073 Possibly related issue: https://github.com/opencv/opencv/issues/24135 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake |
||
|
cudawarped
|
7d681cf80d | build: first class cuda support | ||
 Giles Payne
|
3d9cb5329c |
Merge pull request #24136 from komakai:visionos_support
Add experimental support for Apple VisionOS platform #24136 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch This is dependent on cmake support for VisionOs which is currently in progress. Creating PR now to test that there are no regressions in iOS and macOS builds |
||
 Hao Chen
|
c19adb4953 |
Change the lsx to baseline features.
This patch change lsx to baseline feature, and lasx to dispatch feature. Additionally, the runtime detection methods for lasx and lsx have been modified. |
||
 Kumataro
|
81cc89a3ce |
Merge pull request #24179 from Kumataro:fix24145
* core:add OPENCV_IPP_MEAN/MINMAX/SUM option to enable IPP optimizations * fix: to use guard HAVE_IPP and ocv_append_source_file_compile_definitions() macro. * support OPENCV_IPP_ENABLE_ALL * add document for OPENCV_IPP_ENABLE_ALL * fix OPENCV_IPP_ENABLE_ALL comment |
||
 Pierre Chatelier
|
60b806f9b8 |
Merge pull request #22947 from chacha21:hasNonZero
Added cv::hasNonZero() #22947 `cv::hasNonZero()` is semantically equivalent to (`cv::countNonZero()>0`) but stops parsing the image when a non-zero value is found, for a performance gain - [X] I agree to contribute to the project under Apache 2 License. - [X] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [X] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake This pull request might be refused, but I submit it to know if further work is needed or if I just stop working on it. The idea is only a performance gain vs `countNonZero()>0` at the cost of more code. Reasons why it might be refused : - this is just more code - the execution time is "unfair"/"unpredictable" since it depends on the position of the first non-zero value - the user must be aware that default search is from first row/col to last row/col and has no way to customize that, even if his use case lets him know where a non zero could be found - the PR in its current state is using, for the ocl implementation, a mere `countNonZero()>0` ; there is not much sense in trying to break early the ocl kernel call when non-zero is encountered. So the ocl implementation does not bring any improvement. - there is no IPP function that can help (`countNonZero()` is based in `ippCountInRange`) - the PR in its current state might be slower than a call to `countNonZero()>0` in some cases (see "challenges" below) Reasons why it might be accepted : - the performance gain is huge on average, if we consider that "on average" means "non zero in the middle of the image" - the "missing" IPP implementation is replaced by an "Open-CV universal intrinsics" implementation - the PR in its current state is almost always faster than a call to `countNonZero()>0`, is only slightly slower in the worst cases, and not even for all matrices **Challenges** The worst case is either an all-zero matrix, or a non-zero at the very last position. In such a case, the `hasNonZero()` implementation will parse the whole matrix like `countNonZero()` would do. But we expect the performance to be the same in this case. And `ippCountInRange` is hard to beat ! There is also the case of very small matrices (<=32x32...) in 8b, where the SIMD can be hard to feed. For all cases but the worse, my custom `hasNonZero()` performs better than `ippCountInRange()` For the worst case, my custom `hasNonZero()` performs better than `ippCountInRange()` *except for large matrices of type CV_32S or CV_64F* (but surprisingly, not CV_32F). The difference is small, but it exists (and I don't understand why). For very small CV_8U matrices `ippCountInRange()` seems unbeatable. Here is the code that I use to check timings ``` //test cv::hasNonZero() vs (cv::countNonZero()>0) for different matrices sizes, types, strides... { cv::setRNGSeed(1234); const std::vector<cv::Size> sizes = {{32, 32}, {64, 64}, {128, 128}, {320, 240}, {512, 512}, {640, 480}, {1024, 768}, {2048, 2048}, {1031, 1000}}; const std::vector<int> types = {CV_8U, CV_16U, CV_32S, CV_32F, CV_64F}; const size_t iterations = 1000; for(const cv::Size& size : sizes) { for(const int type : types) { for(int c = 0 ; c<2 ; ++c) { const bool continuous = !c; for(int i = 0 ; i<4 ; ++i) { cv::Mat m = continuous ? cv::Mat::zeros(size, type) : cv::Mat(cv::Mat::zeros(cv::Size(2*size.width, size.height), type), cv::Rect(cv::Point(0, 0), size)); const bool nz = (i <= 2); const unsigned int nzOffsetRange = 10; const unsigned int nzOffset = cv::randu<unsigned int>()%nzOffsetRange; const cv::Point pos = (i == 0) ? cv::Point(nzOffset, 0) : (i == 1) ? cv::Point(size.width/2-nzOffsetRange/2+nzOffset, size.height/2) : (i == 2) ? cv::Point(size.width-1-nzOffset, size.height-1) : cv::Point(0, 0); std::cout << "============================================================" << std::endl; std::cout << "size:" << size << " type:" << type << " continuous = " << (continuous ? "true" : "false") << " iterations:" << iterations << " nz=" << (nz ? "true" : "false"); std::cout << " pos=" << ((i == 0) ? "begin" : (i == 1) ? "middle" : (i == 2) ? "end" : "none"); std::cout << std::endl; cv::Mat mask = cv::Mat::zeros(size, CV_8UC1); mask.at<unsigned char>(pos) = 0xFF; m.setTo(cv::Scalar::all(0)); m.setTo(cv::Scalar::all(nz ? 1 : 0), mask); std::vector<bool> results; std::vector<double> timings; { bool res = false; auto ref = cv::getTickCount(); for(size_t k = 0 ; k<iterations ; ++k) res = cv::hasNonZero(m); auto now = cv::getTickCount(); const bool error = (res != nz); if (error) printf("!!ERROR!!\r\n"); results.push_back(res); timings.push_back(1000.*(now-ref)/cv::getTickFrequency()); } { bool res = false; auto ref = cv::getTickCount(); for(size_t k = 0 ; k<iterations ; ++k) res = (cv::countNonZero(m)>0); auto now = cv::getTickCount(); const bool error = (res != nz); if (error) printf("!!ERROR!!\r\n"); results.push_back(res); timings.push_back(1000.*(now-ref)/cv::getTickFrequency()); } const size_t bestTimingIndex = (std::min_element(timings.begin(), timings.end())-timings.begin()); if ((bestTimingIndex != 0) || (std::find_if_not(results.begin(), results.end(), [&](bool r) {return (r == nz);}) != results.end())) { std::cout << "cv::hasNonZero\t\t=>" << results[0] << ((results[0] != nz) ? " ERROR" : "") << " perf:" << timings[0] << "ms => " << (iterations/timings[0]*1000) << " im/s" << ((bestTimingIndex == 0) ? " * " : "") << std::endl; std::cout << "cv::countNonZero\t=>" << results[1] << ((results[1] != nz) ? " ERROR" : "") << " perf:" << timings[1] << "ms => " << (iterations/timings[1]*1000) << " im/s" << ((bestTimingIndex == 1) ? " * " : "") << std::endl; } } } } } } ``` Here is a report of this benchmark (it only reports timings when `cv::countNonZero()` is faster) My CPU is an Intel Core I7 4790 @ 3.60Ghz ``` ============================================================ size:[32 x 32] type:0 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[32 x 32] type:0 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[32 x 32] type:0 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[32 x 32] type:0 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[32 x 32] type:0 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[32 x 32] type:0 continuous = false iterations:1000 nz=true pos=middle cv::hasNonZero =>1 perf:0.353764ms => 2.82674e+06 im/s cv::countNonZero =>1 perf:0.282044ms => 3.54555e+06 im/s * ============================================================ size:[32 x 32] type:0 continuous = false iterations:1000 nz=true pos=end cv::hasNonZero =>1 perf:0.610478ms => 1.63806e+06 im/s cv::countNonZero =>1 perf:0.283182ms => 3.5313e+06 im/s * ============================================================ size:[32 x 32] type:0 continuous = false iterations:1000 nz=false pos=none cv::hasNonZero =>0 perf:0.630115ms => 1.58701e+06 im/s cv::countNonZero =>0 perf:0.282044ms => 3.54555e+06 im/s * ============================================================ size:[32 x 32] type:2 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[32 x 32] type:2 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[32 x 32] type:2 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[32 x 32] type:2 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[32 x 32] type:2 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[32 x 32] type:2 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[32 x 32] type:2 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[32 x 32] type:2 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[32 x 32] type:4 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[32 x 32] type:4 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[32 x 32] type:4 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[32 x 32] type:4 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[32 x 32] type:4 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[32 x 32] type:4 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[32 x 32] type:4 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[32 x 32] type:4 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[32 x 32] type:5 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[32 x 32] type:5 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[32 x 32] type:5 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[32 x 32] type:5 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[32 x 32] type:5 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[32 x 32] type:5 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[32 x 32] type:5 continuous = false iterations:1000 nz=true pos=end cv::hasNonZero =>1 perf:0.607347ms => 1.64651e+06 im/s cv::countNonZero =>1 perf:0.467037ms => 2.14116e+06 im/s * ============================================================ size:[32 x 32] type:5 continuous = false iterations:1000 nz=false pos=none cv::hasNonZero =>0 perf:0.618162ms => 1.6177e+06 im/s cv::countNonZero =>0 perf:0.468175ms => 2.13595e+06 im/s * ============================================================ size:[32 x 32] type:6 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[32 x 32] type:6 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[32 x 32] type:6 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[32 x 32] type:6 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[32 x 32] type:6 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[32 x 32] type:6 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[32 x 32] type:6 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[32 x 32] type:6 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[64 x 64] type:0 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[64 x 64] type:0 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[64 x 64] type:0 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[64 x 64] type:0 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[64 x 64] type:0 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[64 x 64] type:0 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[64 x 64] type:0 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[64 x 64] type:0 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[64 x 64] type:2 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[64 x 64] type:2 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[64 x 64] type:2 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[64 x 64] type:2 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[64 x 64] type:2 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[64 x 64] type:2 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[64 x 64] type:2 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[64 x 64] type:2 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[64 x 64] type:4 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[64 x 64] type:4 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[64 x 64] type:4 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[64 x 64] type:4 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[64 x 64] type:4 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[64 x 64] type:4 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[64 x 64] type:4 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[64 x 64] type:4 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[64 x 64] type:5 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[64 x 64] type:5 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[64 x 64] type:5 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[64 x 64] type:5 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[64 x 64] type:5 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[64 x 64] type:5 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[64 x 64] type:5 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[64 x 64] type:5 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[64 x 64] type:6 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[64 x 64] type:6 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[64 x 64] type:6 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[64 x 64] type:6 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[64 x 64] type:6 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[64 x 64] type:6 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[64 x 64] type:6 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[64 x 64] type:6 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[128 x 128] type:0 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[128 x 128] type:0 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[128 x 128] type:0 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[128 x 128] type:0 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[128 x 128] type:0 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[128 x 128] type:0 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[128 x 128] type:0 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[128 x 128] type:0 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[128 x 128] type:2 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[128 x 128] type:2 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[128 x 128] type:2 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[128 x 128] type:2 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[128 x 128] type:2 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[128 x 128] type:2 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[128 x 128] type:2 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[128 x 128] type:2 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[128 x 128] type:4 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[128 x 128] type:4 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[128 x 128] type:4 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[128 x 128] type:4 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[128 x 128] type:4 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[128 x 128] type:4 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[128 x 128] type:4 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[128 x 128] type:4 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[128 x 128] type:5 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[128 x 128] type:5 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[128 x 128] type:5 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[128 x 128] type:5 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[128 x 128] type:5 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[128 x 128] type:5 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[128 x 128] type:5 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[128 x 128] type:5 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[128 x 128] type:6 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[128 x 128] type:6 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[128 x 128] type:6 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[128 x 128] type:6 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[128 x 128] type:6 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[128 x 128] type:6 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[128 x 128] type:6 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[128 x 128] type:6 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[320 x 240] type:0 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[320 x 240] type:0 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[320 x 240] type:0 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[320 x 240] type:0 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[320 x 240] type:0 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[320 x 240] type:0 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[320 x 240] type:0 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[320 x 240] type:0 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[320 x 240] type:2 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[320 x 240] type:2 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[320 x 240] type:2 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[320 x 240] type:2 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[320 x 240] type:2 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[320 x 240] type:2 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[320 x 240] type:2 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[320 x 240] type:2 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[320 x 240] type:4 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[320 x 240] type:4 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[320 x 240] type:4 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[320 x 240] type:4 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[320 x 240] type:4 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[320 x 240] type:4 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[320 x 240] type:4 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[320 x 240] type:4 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[320 x 240] type:5 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[320 x 240] type:5 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[320 x 240] type:5 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[320 x 240] type:5 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[320 x 240] type:5 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[320 x 240] type:5 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[320 x 240] type:5 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[320 x 240] type:5 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[320 x 240] type:6 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[320 x 240] type:6 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[320 x 240] type:6 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[320 x 240] type:6 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[320 x 240] type:6 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[320 x 240] type:6 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[320 x 240] type:6 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[320 x 240] type:6 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[512 x 512] type:0 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[512 x 512] type:0 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[512 x 512] type:0 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[512 x 512] type:0 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[512 x 512] type:0 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[512 x 512] type:0 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[512 x 512] type:0 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[512 x 512] type:0 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[512 x 512] type:2 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[512 x 512] type:2 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[512 x 512] type:2 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[512 x 512] type:2 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[512 x 512] type:2 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[512 x 512] type:2 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[512 x 512] type:2 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[512 x 512] type:2 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[512 x 512] type:4 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[512 x 512] type:4 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[512 x 512] type:4 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[512 x 512] type:4 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[512 x 512] type:4 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[512 x 512] type:4 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[512 x 512] type:4 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[512 x 512] type:4 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[512 x 512] type:5 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[512 x 512] type:5 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[512 x 512] type:5 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[512 x 512] type:5 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[512 x 512] type:5 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[512 x 512] type:5 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[512 x 512] type:5 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[512 x 512] type:5 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[512 x 512] type:6 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[512 x 512] type:6 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[512 x 512] type:6 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[512 x 512] type:6 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[512 x 512] type:6 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[512 x 512] type:6 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[512 x 512] type:6 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[512 x 512] type:6 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[640 x 480] type:0 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[640 x 480] type:0 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[640 x 480] type:0 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[640 x 480] type:0 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[640 x 480] type:0 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[640 x 480] type:0 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[640 x 480] type:0 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[640 x 480] type:0 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[640 x 480] type:2 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[640 x 480] type:2 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[640 x 480] type:2 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[640 x 480] type:2 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[640 x 480] type:2 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[640 x 480] type:2 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[640 x 480] type:2 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[640 x 480] type:2 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[640 x 480] type:4 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[640 x 480] type:4 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[640 x 480] type:4 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[640 x 480] type:4 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[640 x 480] type:4 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[640 x 480] type:4 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[640 x 480] type:4 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[640 x 480] type:4 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[640 x 480] type:5 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[640 x 480] type:5 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[640 x 480] type:5 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[640 x 480] type:5 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[640 x 480] type:5 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[640 x 480] type:5 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[640 x 480] type:5 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[640 x 480] type:5 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[640 x 480] type:6 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[640 x 480] type:6 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[640 x 480] type:6 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[640 x 480] type:6 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[640 x 480] type:6 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[640 x 480] type:6 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[640 x 480] type:6 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[640 x 480] type:6 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[1024 x 768] type:0 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[1024 x 768] type:0 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[1024 x 768] type:0 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[1024 x 768] type:0 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[1024 x 768] type:0 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[1024 x 768] type:0 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[1024 x 768] type:0 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[1024 x 768] type:0 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[1024 x 768] type:2 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[1024 x 768] type:2 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[1024 x 768] type:2 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[1024 x 768] type:2 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[1024 x 768] type:2 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[1024 x 768] type:2 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[1024 x 768] type:2 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[1024 x 768] type:2 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[1024 x 768] type:4 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[1024 x 768] type:4 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[1024 x 768] type:4 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[1024 x 768] type:4 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[1024 x 768] type:4 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[1024 x 768] type:4 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[1024 x 768] type:4 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[1024 x 768] type:4 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[1024 x 768] type:5 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[1024 x 768] type:5 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[1024 x 768] type:5 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[1024 x 768] type:5 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[1024 x 768] type:5 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[1024 x 768] type:5 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[1024 x 768] type:5 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[1024 x 768] type:5 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[1024 x 768] type:6 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[1024 x 768] type:6 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[1024 x 768] type:6 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[1024 x 768] type:6 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[1024 x 768] type:6 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[1024 x 768] type:6 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[1024 x 768] type:6 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[1024 x 768] type:6 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[2048 x 2048] type:0 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[2048 x 2048] type:0 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[2048 x 2048] type:0 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[2048 x 2048] type:0 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[2048 x 2048] type:0 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[2048 x 2048] type:0 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[2048 x 2048] type:0 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[2048 x 2048] type:0 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[2048 x 2048] type:2 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[2048 x 2048] type:2 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[2048 x 2048] type:2 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[2048 x 2048] type:2 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[2048 x 2048] type:2 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[2048 x 2048] type:2 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[2048 x 2048] type:2 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[2048 x 2048] type:2 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[2048 x 2048] type:4 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[2048 x 2048] type:4 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[2048 x 2048] type:4 continuous = true iterations:1000 nz=true pos=end cv::hasNonZero =>1 perf:895.381ms => 1116.84 im/s cv::countNonZero =>1 perf:882.569ms => 1133.06 im/s * ============================================================ size:[2048 x 2048] type:4 continuous = true iterations:1000 nz=false pos=none cv::hasNonZero =>0 perf:899.53ms => 1111.69 im/s cv::countNonZero =>0 perf:870.894ms => 1148.24 im/s * ============================================================ size:[2048 x 2048] type:4 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[2048 x 2048] type:4 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[2048 x 2048] type:4 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[2048 x 2048] type:4 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[2048 x 2048] type:5 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[2048 x 2048] type:5 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[2048 x 2048] type:5 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[2048 x 2048] type:5 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[2048 x 2048] type:5 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[2048 x 2048] type:5 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[2048 x 2048] type:5 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[2048 x 2048] type:5 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[2048 x 2048] type:6 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[2048 x 2048] type:6 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[2048 x 2048] type:6 continuous = true iterations:1000 nz=true pos=end cv::hasNonZero =>1 perf:2018.92ms => 495.313 im/s cv::countNonZero =>1 perf:1966.37ms => 508.552 im/s * ============================================================ size:[2048 x 2048] type:6 continuous = true iterations:1000 nz=false pos=none cv::hasNonZero =>0 perf:2005.87ms => 498.537 im/s cv::countNonZero =>0 perf:1992.78ms => 501.812 im/s * ============================================================ size:[2048 x 2048] type:6 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[2048 x 2048] type:6 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[2048 x 2048] type:6 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[2048 x 2048] type:6 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[1031 x 1000] type:0 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[1031 x 1000] type:0 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[1031 x 1000] type:0 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[1031 x 1000] type:0 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[1031 x 1000] type:0 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[1031 x 1000] type:0 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[1031 x 1000] type:0 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[1031 x 1000] type:0 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[1031 x 1000] type:2 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[1031 x 1000] type:2 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[1031 x 1000] type:2 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[1031 x 1000] type:2 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[1031 x 1000] type:2 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[1031 x 1000] type:2 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[1031 x 1000] type:2 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[1031 x 1000] type:2 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[1031 x 1000] type:4 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[1031 x 1000] type:4 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[1031 x 1000] type:4 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[1031 x 1000] type:4 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[1031 x 1000] type:4 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[1031 x 1000] type:4 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[1031 x 1000] type:4 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[1031 x 1000] type:4 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[1031 x 1000] type:5 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[1031 x 1000] type:5 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[1031 x 1000] type:5 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[1031 x 1000] type:5 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[1031 x 1000] type:5 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[1031 x 1000] type:5 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[1031 x 1000] type:5 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[1031 x 1000] type:5 continuous = false iterations:1000 nz=false pos=none ============================================================ size:[1031 x 1000] type:6 continuous = true iterations:1000 nz=true pos=begin ============================================================ size:[1031 x 1000] type:6 continuous = true iterations:1000 nz=true pos=middle ============================================================ size:[1031 x 1000] type:6 continuous = true iterations:1000 nz=true pos=end ============================================================ size:[1031 x 1000] type:6 continuous = true iterations:1000 nz=false pos=none ============================================================ size:[1031 x 1000] type:6 continuous = false iterations:1000 nz=true pos=begin ============================================================ size:[1031 x 1000] type:6 continuous = false iterations:1000 nz=true pos=middle ============================================================ size:[1031 x 1000] type:6 continuous = false iterations:1000 nz=true pos=end ============================================================ size:[1031 x 1000] type:6 continuous = false iterations:1000 nz=false pos=none done ``` |
||
 Tinson Lai
|
f8f425e34c | Change custom_hal.hpp output location | ||
 Yuantao Feng
|
c63d79c5b1 |
Merge pull request #23095 from fengyuentau:fix_omp_macos
* fix openmp include and link issue on macos * turn off have_openmp if OpenMP_CXX_INCLUDE_DIRS is empty * test commit * use condition HAVE_OPENMP and OpenMP_CXX_LIBRARIES for linking * remove trailing whitespace * remove notes * update conditions * use OpenMP_CXX_LIBRARIES for linking |
||
 Alexander Alekhin
|
2ebdc04787 | Merge remote-tracking branch 'upstream/3.4' into merge-3.4 | ||
|
Alexander Alekhin
|
d0d115321d | Merge pull request #22350 from alalek:rework_psabi_warning | ||
|
Alexander Alekhin
|
44b2f9637a |
Revert "suppress warning on GCC 7 and later"
This reverts commit
|
||
 Tomoaki Teshima
|
b3269b08a1 |
neon: add dotprod dispatch implementation
* read vector at runtime
* add enum
|
||
|
Tomoaki Teshima
|
a630ad73cb | suppress warning on GCC 7 and later | ||
 Joel Winarske
|
0769bf416f |
highgui Wayland xdg_shell
-enable using -DWITH_WAYLAND=ON -adapted from https://github.com/pfpacket/opencv-wayland -using xdg_shell stable protocol -overrides HAVE_QT if HAVE_WAYLAND and WITH_WAYLAND are set Signed-off-by: Joel Winarske <joel.winarske@gmail.com> Co-authored-by: Ryo Munakata <afpacket@gmail.com> |
||
|
Alexander Alekhin
|
c80b270678 | cmake: force lowercase plugins internal names | ||
|
Alexander Alekhin
|
d24befa0bc | Merge remote-tracking branch 'upstream/3.4' into merge-3.4 | ||
|
Alexander Alekhin
|
65392d5e6b | cmake: fix OPENGL_LIBRARIES handling | ||
 Francesco Petrogalli
|
d29c7e7871 |
Merge pull request #20392 from fpetrogalli:aarch64-semihosting
AArch64 semihosting
* [ts] Disable filesystem support in the TS module.
Because of this change, all the tests loading data will file, but tat
least the core module can be tested with the following line:
opencv_test_core --gtest_filter=-"*Core_InputOutput*:*Core_globbing.accuracy*"
* [aarch64] Build OpenCV for AArch64 semihosting.
This patch provide a toolchain file that allows to build the library
for semihosting applications [1]. Minimal changes have been applied to
the code to be able to compile with a baremetal toolchain.
[1] https://developer.arm.com/documentation/100863/latest
The option `CV_SEMIHOSTING` is used to guard the bits in the code that
are specific to the target.
To build the code:
cmake ../opencv/ \
-DCMAKE_TOOLCHAIN_FILE=../opencv/platforms/semihosting/aarch64-semihosting.toolchain.cmake \
-DSEMIHOSTING_TOOLCHAIN_PATH=/path/to/baremetal-toolchain/bin/ \
-DBUILD_EXAMPLES=ON -GNinja
A barematel toolchain for targeting aarch64 semihosting can be found
at [2], under `aarch64-none-elf`.
[2] https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-a/downloads
The folder `samples/semihosting` provides two example semihosting
applications.
The two binaries can be executed on the host platform with:
qemu-aarch64 ./bin/example_semihosting_histogram
qemu-aarch64 ./bin/example_semihosting_norm
Similarly, the test and perf executables of the modules can be run
with:
qemu-aarch64 ./bin/opecv_[test|perf]_<module>
Notice that filesystem support is disabled by the toolchain file,
hence some of the test that depend on filesystem support will fail.
* [semihosting] Remove blank like at the end of file. [NFC]
The spurious blankline was reported by
https://pullrequest.opencv.org/buildbot/builders/precommit_docs/builds/31158.
* [semihosting] Make the raw pixel file generation OS independent.
Use the facilities provided by Cmake to generate the header file
instead of a shell script, so that the build doesn't fail on systems
that do not have a unix shell.
* [semihosting] Rename variable for semihosting compilation.
* [semihosting] Move the cmake configuration to a variable file.
* [semihosting] Make the guard macro private for the core module.
* [semihosting] Remove space. [NFC]
* [semihosting] Improve comment with information about semihosting. [NFC]
* [semihosting] Update license statement on top of sourvce file. [NFC]
* [semihosting] Replace BM_SUFFIX with SEMIHOSTING_SUFFIX. [NFC]
* [semihosting] Remove double space. [NFC]
* [semihosting] Add some text output to the sample applications.
* [semihosting] Remove duplicate entry in cmake configuration. [NFCI]
* [semihosting] Replace `long` with `int` in sample apps. [NFCI]
* [semihosting] Use `configure_file` to create the random pixels. [NFCI]
* [semihosting][bugfix] Fix name of cmakedefine variable.
* [semihosting][samples] Use CV_8UC1 for grayscale images. [NFCI]
* [semihosting] Add readme file.
* [semihosting] Remove blank like at the end of README. [NFC]
This fixes the failure at
https://pullrequest.opencv.org/buildbot/builders/precommit_docs/builds/31272.
|
||
|
Francesco Petrogalli
|
b928ebdd53 |
Merge pull request #19985 from fpetrogalli:disable_threads
* [build][option] Introduce `OPENCV_DISABLE_THREAD_SUPPORT` option. The option forces the library to build without thread support. * update handling of OPENCV_DISABLE_THREAD_SUPPORT - reduce amount of #if conditions * [to squash] cmake: apply mode vars in toolchains too Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com> |
||
|
Alexander Alekhin
|
a41394c885 | core(parallel): fix plugins handling if no filesystem available | ||
|
Alexander Alekhin
|
cbfd38bd41 |
core: rework code locality
- to reduce binaries size of FFmpeg Windows wrapper
- MinGW linker doesn't support -ffunction-sections (used for FFmpeg Windows wrapper)
- move code to improve locality with its used dependencies
- move UMat::dot() to matmul.dispatch.cpp (Mat::dot() is already there)
- move UMat::inv() to lapack.cpp
- move UMat::mul() to arithm.cpp
- move UMat:eye() to matrix_operations.cpp (near setIdentity() implementation)
- move normalize(): convert_scale.cpp => norm.cpp
- move convertAndUnrollScalar(): arithm.cpp => copy.cpp
- move scalarToRawData(): array.cpp => copy.cpp
- move transpose(): matrix_operations.cpp => matrix_transform.cpp
- move flip(), rotate(): copy.cpp => matrix_transform.cpp (rotate90 uses flip and transpose)
- add 'OPENCV_CORE_EXCLUDE_C_API' CMake variable to exclude compilation of C-API functions from the core module
- matrix_wrap.cpp: add compile-time checks for CUDA/OpenGL calls
- the steps above allow to reduce FFmpeg wrapper size for ~1.5Mb (initial size of OpenCV part is about 3Mb)
backport is done to improve merge experience (less conflicts)
backport of commit:
|
||
|
Alexander Alekhin
|
65eb946756 |
core: rework code locality
- to reduce binaries size of FFmpeg Windows wrapper - MinGW linker doesn't support -ffunction-sections (used for FFmpeg Windows wrapper) - move code to improve locality with its used dependencies - move UMat::dot() to matmul.dispatch.cpp (Mat::dot() is already there) - move UMat::inv() to lapack.cpp - move UMat::mul() to arithm.cpp - move UMat:eye() to matrix_operations.cpp (near setIdentity() implementation) - move normalize(): convert_scale.cpp => norm.cpp - move convertAndUnrollScalar(): arithm.cpp => copy.cpp - move scalarToRawData(): array.cpp => copy.cpp - move transpose(): matrix_operations.cpp => matrix_transform.cpp - move flip(), rotate(): copy.cpp => matrix_transform.cpp (rotate90 uses flip and transpose) - add 'OPENCV_CORE_EXCLUDE_C_API' CMake variable to exclude compilation of C-API functions from the core module - matrix_wrap.cpp: add compile-time checks for CUDA/OpenGL calls - the steps above allow to reduce FFmpeg wrapper size for ~1.5Mb (initial size of OpenCV part is about 3Mb) |
||
|
Alexander Alekhin
|
3dd55d284d | core(libva): use dynamic loader | ||
|
Alexander Alekhin
|
cc73c36e32 | core(parallel): plugins support | ||
|
Alexander Alekhin
|
37c12db366 | Merge pull request #19365 from alalek:parallel_api | ||
|
Alexander Alekhin
|
b73bf03bfc |
core: parallel backends API
- allow to replace parallel_for() backend |
||
|
Alexander Alekhin
|
cd68cc1f46 | Merge pull request #19195 from diablodale:win32AlignAlloc | ||
 Dale Phurrough
|
109255a730 |
add windows native aligned malloc + unit test case
* implements https://github.com/opencv/opencv/issues/19147 * CAUTION: this PR will only functions safely in the 4+ branches that already include PR 19029 * CAUTION: this PR requires thread-safe startup of the alloc.cpp translation unit as implemented in PR 19029 |
||
|
Alexander Alekhin
|
de385009ae | Merge remote-tracking branch 'upstream/3.4' into merge-3.4 | ||
|
Alexander Alekhin
|
26e8048a0a |
core: update handling of allocator stats type
- don't use OPENCV_ALLOCATOR_STATS_COUNTER_TYPE definition in non C++11 builds - don't use with MinGW |
||
 Odianosen Ejale
|
862fc06b6f | Fixed and updated OpenCL-VA interoperability | ||
|
Giles Payne
|
02385472b6 |
Merge pull request #17165 from komakai:objc-binding
Objc binding * Initial work on Objective-C wrapper * Objective-C generator script; update manually generated wrappers * Add Mat tests * Core Tests * Imgproc wrapper generation and tests * Fixes for Imgcodecs wrapper * Miscellaneous fixes. Swift build support * Objective-C wrapper build/install * Add Swift wrappers for videoio/objdetect/feature2d * Framework build;iOS support * Fix toArray functions;Use enum types whenever possible * Use enum types where possible;prepare test build * Update test * Add test runner scripts for iOS and macOS * Add test scripts and samples * Build fixes * Fix build (cmake 3.17.x compatibility) * Fix warnings * Fix enum name conflicting handling * Add support for document generation with Jazzy * Swift/Native fast accessor functions * Add Objective-C wrapper for calib3d, dnn, ml, photo and video modules * Remove IntOut/FloatOut/DoubleOut classes * Fix iOS default test platform value * Fix samples * Revert default framework name to opencv2 * Add converter util functions * Fix failing test * Fix whitespace * Add handling for deprecated methods;fix warnings;define __OPENCV_BUILD * Suppress cmake warnings * Reduce severity of "jazzy not found" log message * Fix incorrect #include of compatibility header in ios.h * Use explicit returns in subscript/get implementation * Reduce minimum required cmake version to 3.15 for Objective-C/Swift binding |
||
 Alexander Alekhin
|
ca23c0e630 | Merge remote-tracking branch 'upstream/3.4' into merge-3.4 | ||
|
Alexander Alekhin
|
4e56c1326f | core: adjust type of allocator_stats counter, allow to disable | ||
|
Alexander Alekhin
|
ba7b0f4c54 | Merge remote-tracking branch 'upstream/3.4' into merge-3.4 | ||
|
Alexander Alekhin
|
a45928045a |
Merge pull request #16150 from alalek:cmake_avoid_deprecated_link_private
* cmake: avoid deprecated LINK_PRIVATE/LINK_PUBLIC see CMP0023 (CMake 2.8.12+) * cmake: fix 3rdparty list - don't include OpenCV modules |