mirror of
https://github.com/opencv/opencv.git
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Anand Mahesh
ad50964f78
Feat: Add OpenCL support for AKAZE features #28879 Implement OpenCL-accelerated AKAZE feature detection and descriptor extraction Benchmarked on RTX 5060 Ti: 1.2x to 3.31x faster for image sizes from 640x480 to 3840x2160 - Added 8 OpenCL kernels for feature detection and descriptor extraction - Refactored compute_kcontrast to use OpenCV magnitude() for consistency - Added comprehensive tests with >95% accuracy vs CPU baseline ### 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
88 lines
3.1 KiB
C++
88 lines
3.1 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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TEST(Features2d_AKAZE, detect_and_compute_split)
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{
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Mat testImg(100, 100, CV_8U);
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theRNG().fill(testImg, RNG::UNIFORM, Scalar(0), Scalar(255), true);
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Ptr<Feature2D> ext = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.001f, 1, 1, KAZE::DIFF_PM_G2);
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vector<KeyPoint> detAndCompKps;
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Mat desc;
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ext->detectAndCompute(testImg, noArray(), detAndCompKps, desc);
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vector<KeyPoint> detKps;
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ext->detect(testImg, detKps);
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ASSERT_EQ(detKps.size(), detAndCompKps.size());
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for(size_t i = 0; i < detKps.size(); i++)
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ASSERT_EQ(detKps[i].hash(), detAndCompKps[i].hash());
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}
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/**
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* This test is here to guard propagation of NaNs that happens on this image. NaNs are guarded
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* by debug asserts in AKAZE, which should fire for you if you are lucky.
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*
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* This test also reveals problems with uninitialized memory that happens only on this image.
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* This is very hard to hit and depends a lot on particular allocator. Run this test in valgrind and check
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* for uninitialized values if you think you are hitting this problem again.
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*/
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TEST(Features2d_AKAZE, uninitialized_and_nans)
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{
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Mat b1 = imread(cvtest::TS::ptr()->get_data_path() + "../stitching/b1.png");
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ASSERT_FALSE(b1.empty());
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vector<KeyPoint> keypoints;
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Mat desc;
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Ptr<Feature2D> akaze = AKAZE::create();
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akaze->detectAndCompute(b1, noArray(), keypoints, desc);
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}
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// Test for https://github.com/opencv/opencv/issues/27134
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TEST(Features2d_KAZE, diffusivity_charbonnier)
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{
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Mat testImg(200, 200, CV_8U);
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theRNG().fill(testImg, RNG::UNIFORM, Scalar(0), Scalar(255), true);
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// KAZE with DIFF_CHARBONNIER
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Ptr<KAZE> kaze_charbonnier = KAZE::create(false, false, 0.001f, 4, 4, KAZE::DIFF_CHARBONNIER);
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vector<KeyPoint> kps_charbonnier;
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Mat desc_charbonnier;
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kaze_charbonnier->detectAndCompute(testImg, noArray(), kps_charbonnier, desc_charbonnier);
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// KAZE with DIFF_PM_G2 (default)
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Ptr<KAZE> kaze_pm_g2 = KAZE::create(false, false, 0.001f, 4, 4, KAZE::DIFF_PM_G2);
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vector<KeyPoint> kps_pm_g2;
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Mat desc_pm_g2;
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kaze_pm_g2->detectAndCompute(testImg, noArray(), kps_pm_g2, desc_pm_g2);
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// Both should detect keypoints
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ASSERT_FALSE(kps_charbonnier.empty());
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ASSERT_FALSE(kps_pm_g2.empty());
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// Check subpixel accuracy for DIFF_CHARBONNIER (issue #27134)
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bool hasSubpixel = false;
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for (size_t i = 0; i < kps_charbonnier.size(); i++)
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{
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float fx = kps_charbonnier[i].pt.x - std::floor(kps_charbonnier[i].pt.x);
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float fy = kps_charbonnier[i].pt.y - std::floor(kps_charbonnier[i].pt.y);
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if (fx > 1e-5f || fy > 1e-5f)
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{
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hasSubpixel = true;
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break;
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}
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}
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EXPECT_TRUE(hasSubpixel) << "KAZE with DIFF_CHARBONNIER should have subpixel keypoint coordinates";
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// Descriptor dimensions should match
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ASSERT_EQ(desc_charbonnier.cols, desc_pm_g2.cols);
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}
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}} // namespace
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