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chore: import upstream snapshot with attribution

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2026-07-13 12:06:04 +08:00
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modules/features2d/test/ocl/test_akaze.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#include "../test_precomp.hpp"
#include "cvconfig.h"
#include "opencv2/ts/ocl_test.hpp"
#ifdef HAVE_OPENCL
namespace opencv_test {
namespace ocl {
TEST(Features2d_AKAZE, ocl_accuracy)
{
Mat testImg(640, 480, CV_8U);
theRNG().fill(testImg, RNG::UNIFORM, Scalar(0), Scalar(255), true);
// CPU version - use MLDB_UPRIGHT to match GPU implementation
Ptr<Feature2D> akaze_cpu = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 0, 3, 0.001f, 1, 1, KAZE::DIFF_PM_G2);
vector<KeyPoint> kp_cpu;
Mat desc_cpu;
akaze_cpu->detectAndCompute(testImg, noArray(), kp_cpu, desc_cpu);
// OpenCL version - use MLDB_UPRIGHT to match GPU implementation
UMat testImg_umat;
testImg.copyTo(testImg_umat);
Ptr<Feature2D> akaze_ocl = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 0, 3, 0.001f, 1, 1, KAZE::DIFF_PM_G2);
vector<KeyPoint> kp_ocl;
UMat desc_ocl_umat;
akaze_ocl->detectAndCompute(testImg_umat, noArray(), kp_ocl, desc_ocl_umat);
Mat desc_ocl = desc_ocl_umat.getMat(ACCESS_READ);
// Check that both detected keypoints
ASSERT_FALSE(kp_cpu.empty()) << "CPU should detect keypoints";
ASSERT_FALSE(kp_ocl.empty()) << "OpenCL should detect keypoints";
// Allow small keypoint count difference due to border handling
float kp_ratio = (float)kp_ocl.size() / kp_cpu.size();
EXPECT_GT(kp_ratio, 0.95f) << "OpenCL keypoint count should be within 5% of CPU, got " << kp_ratio;
EXPECT_LT(kp_ratio, 1.05f) << "OpenCL keypoint count should be within 5% of CPU, got " << kp_ratio;
// Check descriptor dimensions match
ASSERT_EQ(desc_cpu.cols, desc_ocl.cols) << "Descriptor size should match";
ASSERT_EQ(desc_cpu.type(), desc_ocl.type()) << "Descriptor type should match";
// Match keypoints by position (within 1 pixel tolerance)
int matched_kpts = 0;
int total_compared = 0;
int matching_bytes = 0;
for (size_t i = 0; i < kp_cpu.size(); i++) {
// Find matching keypoint in OpenCL results
for (size_t j = 0; j < kp_ocl.size(); j++) {
float dx = fabs(kp_cpu[i].pt.x - kp_ocl[j].pt.x);
float dy = fabs(kp_cpu[i].pt.y - kp_ocl[j].pt.y);
if (dx < 1.0f && dy < 1.0f) {
// Found matching keypoint, compare descriptors
matched_kpts++;
for (int k = 0; k < desc_cpu.cols; k++) {
if (desc_cpu.at<uchar>((int)i, k) == desc_ocl.at<uchar>((int)j, k)) {
matching_bytes++;
}
}
total_compared += desc_cpu.cols;
break;
}
}
}
float match_rate = total_compared > 0 ? (float)matching_bytes / total_compared : 0.0f;
EXPECT_GT(matched_kpts, (int)(kp_cpu.size() * 0.9f)) << "Should match at least 90% of keypoints by position";
EXPECT_GT(match_rate, 0.95f) << "Descriptor match rate should be > 95%, got " << match_rate;
}
TEST(Features2d_KAZE, ocl_accuracy)
{
Mat testImg(640, 480, CV_8U);
theRNG().fill(testImg, RNG::UNIFORM, Scalar(0), Scalar(255), true);
// CPU version - use upright=true to match GPU implementation
Ptr<KAZE> kaze_cpu = KAZE::create(false, true, 0.001f, 4, 4, KAZE::DIFF_PM_G2);
vector<KeyPoint> kp_cpu;
Mat desc_cpu;
kaze_cpu->detectAndCompute(testImg, noArray(), kp_cpu, desc_cpu);
// OpenCL version - use upright=true to match GPU implementation
UMat testImg_umat;
testImg.copyTo(testImg_umat);
Ptr<KAZE> kaze_ocl = KAZE::create(false, true, 0.001f, 4, 4, KAZE::DIFF_PM_G2);
vector<KeyPoint> kp_ocl;
UMat desc_ocl_umat;
kaze_ocl->detectAndCompute(testImg_umat, noArray(), kp_ocl, desc_ocl_umat);
Mat desc_ocl = desc_ocl_umat.getMat(ACCESS_READ);
// Check that both detected keypoints
ASSERT_FALSE(kp_cpu.empty()) << "CPU should detect keypoints";
ASSERT_FALSE(kp_ocl.empty()) << "OpenCL should detect keypoints";
// Allow small keypoint count difference due to border handling
float kp_ratio = (float)kp_ocl.size() / kp_cpu.size();
EXPECT_GT(kp_ratio, 0.95f) << "OpenCL keypoint count should be within 5% of CPU, got " << kp_ratio;
EXPECT_LT(kp_ratio, 1.05f) << "OpenCL keypoint count should be within 5% of CPU, got " << kp_ratio;
// Check descriptor dimensions match
ASSERT_EQ(desc_cpu.cols, desc_ocl.cols) << "Descriptor size should match";
ASSERT_EQ(desc_cpu.type(), desc_ocl.type()) << "Descriptor type should match";
// Match keypoints by position (within 1 pixel tolerance)
int matched_kpts = 0;
int total_compared = 0;
double total_diff = 0;
for (size_t i = 0; i < kp_cpu.size(); i++) {
// Find matching keypoint in OpenCL results
for (size_t j = 0; j < kp_ocl.size(); j++) {
float dx = fabs(kp_cpu[i].pt.x - kp_ocl[j].pt.x);
float dy = fabs(kp_cpu[i].pt.y - kp_ocl[j].pt.y);
if (dx < 1.0f && dy < 1.0f) {
// Found matching keypoint, compare descriptors
matched_kpts++;
for (int k = 0; k < desc_cpu.cols; k++) {
double diff = fabs(desc_cpu.at<float>((int)i, k) - desc_ocl.at<float>((int)j, k));
total_diff += diff;
total_compared++;
}
break;
}
}
}
float avg_diff = total_compared > 0 ? (float)(total_diff / total_compared) : 0.0f;
EXPECT_GT(matched_kpts, (int)(kp_cpu.size() * 0.9f)) << "Should match at least 90% of keypoints by position";
EXPECT_LT(avg_diff, 0.01f) << "Average descriptor difference should be < 0.01, got " << avg_diff;
}
}} // namespace
#endif