Open Source Computer Vision Library https://opencv.org/
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#include <iostream>
#include <string>
#include <iosfwd>
#include "test_precomp.hpp"
using namespace cv;
using namespace cv::gpu;
using namespace std;
struct CV_GpuMeanShiftSegmentationTest : public cvtest::BaseTest {
CV_GpuMeanShiftSegmentationTest() {}
void run(int)
{
bool cc12_ok = TargetArchs::builtWith(FEATURE_SET_COMPUTE_12) && DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
if (!cc12_ok)
{
ts->printf(cvtest::TS::CONSOLE, "\nCompute capability 1.2 is required");
ts->set_failed_test_info(cvtest::TS::FAIL_GENERIC);
return;
}
Mat img_rgb = imread(string(ts->get_data_path()) + "meanshift/cones.png");
if (img_rgb.empty())
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
Mat img;
cvtColor(img_rgb, img, CV_BGR2BGRA);
for (int minsize = 0; minsize < 2000; minsize = (minsize + 1) * 4)
{
stringstream path;
path << ts->get_data_path() << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize;
if (TargetArchs::builtWith(FEATURE_SET_COMPUTE_20) && DeviceInfo().supports(FEATURE_SET_COMPUTE_20))
path << ".png";
else
path << "_CC1X.png";
Mat dst;
meanShiftSegmentation((GpuMat)img, dst, 10, 10, minsize);
Mat dst_rgb;
cvtColor(dst, dst_rgb, CV_BGRA2BGR);
//imwrite(path.str(), dst_rgb);
Mat dst_ref = imread(path.str());
if (dst_ref.empty())
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
if (CheckSimilarity(dst_rgb, dst_ref, 1e-3f) != cvtest::TS::OK)
{
ts->printf(cvtest::TS::LOG, "\ndiffers from image *minsize%d.png\n", minsize);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
}
ts->set_failed_test_info(cvtest::TS::OK);
}
int CheckSimilarity(const Mat& m1, const Mat& m2, float max_err)
{
Mat diff;
cv::matchTemplate(m1, m2, diff, CV_TM_CCORR_NORMED);
float err = abs(diff.at<float>(0, 0) - 1.f);
if (err > max_err)
return cvtest::TS::FAIL_INVALID_OUTPUT;
return cvtest::TS::OK;
}
};
TEST(meanShiftSegmentation, regression) { CV_GpuMeanShiftSegmentationTest test; test.safe_run(); }