fixed test ocl_MatchTemplate for sparse matrix
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@ -454,14 +454,15 @@ static bool matchTemplate_CCOEFF(InputArray _image, InputArray _templ, OutputArr
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if (cn==1)
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if (cn==1)
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{
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{
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float templ_sum = static_cast<float>(sum(_templ)[0]) / tsize.area();
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Scalar templMean = mean(templ);
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float templ_sum = (float)templMean[0];
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k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, templ_sum);
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k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, templ_sum);
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}
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}
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else
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else
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{
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{
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Vec4f templ_sum = Vec4f::all(0);
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Vec4f templ_sum = Vec4f::all(0);
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templ_sum = sum(templ) / tsize.area();
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templ_sum = (Vec4f)mean(templ);
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k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, templ_sum); }
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k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, templ_sum); }
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@ -97,9 +97,17 @@ PARAM_TEST_CASE(MatchTemplate, MatDepth, Channels, MatchTemplType, bool)
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UMAT_UPLOAD_OUTPUT_PARAMETER(result);
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UMAT_UPLOAD_OUTPUT_PARAMETER(result);
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}
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}
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void Near(double threshold = 0.0)
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void Near()
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{
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{
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OCL_EXPECT_MATS_NEAR(result, threshold);
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bool isNormed =
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method == TM_CCORR_NORMED ||
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method == TM_SQDIFF_NORMED ||
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method == TM_CCOEFF_NORMED;
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if (isNormed)
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OCL_EXPECT_MATS_NEAR(result, 3e-2);
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else
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OCL_EXPECT_MATS_NEAR_RELATIVE_SPARSE(result, 1.5e-2);
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}
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}
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};
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};
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@ -112,14 +120,7 @@ OCL_TEST_P(MatchTemplate, Mat)
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OCL_OFF(cv::matchTemplate(image_roi, templ_roi, result_roi, method));
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OCL_OFF(cv::matchTemplate(image_roi, templ_roi, result_roi, method));
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OCL_ON(cv::matchTemplate(uimage_roi, utempl_roi, uresult_roi, method));
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OCL_ON(cv::matchTemplate(uimage_roi, utempl_roi, uresult_roi, method));
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bool isNormed =
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Near();
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method == TM_CCORR_NORMED ||
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method == TM_SQDIFF_NORMED ||
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method == TM_CCOEFF_NORMED;
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double eps = isNormed ? 3e-2 : 255.0 * 255.0 * templ.total() * 2e-5;
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Near(eps);
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}
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}
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}
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}
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@ -159,6 +159,25 @@ do \
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<< "Size: " << name ## _roi.size() << std::endl; \
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<< "Size: " << name ## _roi.size() << std::endl; \
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} while ((void)0, 0)
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} while ((void)0, 0)
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//for sparse matrix
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#define OCL_EXPECT_MATS_NEAR_RELATIVE_SPARSE(name, eps) \
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do \
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{ \
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ASSERT_EQ(name ## _roi.type(), u ## name ## _roi.type()); \
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ASSERT_EQ(name ## _roi.size(), u ## name ## _roi.size()); \
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EXPECT_LE(TestUtils::checkNormRelativeSparse(name ## _roi, u ## name ## _roi), eps) \
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<< "Size: " << name ## _roi.size() << std::endl; \
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Point _offset; \
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Size _wholeSize; \
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name ## _roi.locateROI(_wholeSize, _offset); \
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Mat _mask(name.size(), CV_8UC1, Scalar::all(255)); \
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_mask(Rect(_offset, name ## _roi.size())).setTo(Scalar::all(0)); \
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ASSERT_EQ(name.type(), u ## name.type()); \
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ASSERT_EQ(name.size(), u ## name.size()); \
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EXPECT_LE(TestUtils::checkNormRelativeSparse(name, u ## name, _mask), eps) \
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<< "Size: " << name ## _roi.size() << std::endl; \
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} while ((void)0, 0)
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#define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \
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#define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \
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do \
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do \
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{ \
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{ \
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@ -274,6 +293,16 @@ struct CV_EXPORTS TestUtils
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std::max((double)std::numeric_limits<float>::epsilon(),
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std::max((double)std::numeric_limits<float>::epsilon(),
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(double)std::max(cvtest::norm(m1.getMat(), cv::NORM_INF), cvtest::norm(m2.getMat(), cv::NORM_INF)));
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(double)std::max(cvtest::norm(m1.getMat(), cv::NORM_INF), cvtest::norm(m2.getMat(), cv::NORM_INF)));
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}
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}
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static inline double checkNormRelativeSparse(InputArray m1, InputArray m2, InputArray mask = noArray())
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{
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double norm_inf = cvtest::norm(m1.getMat(), m2.getMat(), cv::NORM_INF, mask);
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double norm_rel = norm_inf /
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std::max((double)std::numeric_limits<float>::epsilon(),
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(double)std::max(cvtest::norm(m1.getMat(), cv::NORM_INF), cvtest::norm(m2.getMat(), cv::NORM_INF)));
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return std::min(norm_inf, norm_rel);
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}
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};
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};
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#define TEST_DECLARE_INPUT_PARAMETER(name) Mat name, name ## _roi; UMat u ## name, u ## name ## _roi
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#define TEST_DECLARE_INPUT_PARAMETER(name) Mat name, name ## _roi; UMat u ## name, u ## name ## _roi
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