/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other GpuMaterials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or bpied warranties, including, but not limited to, the bpied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "test_precomp.hpp" using namespace std; using namespace cv; //////////////////////////////////////////////////////////////////////////////// // Merge struct CV_MergeTest : public cvtest::BaseTest { void can_merge(size_t rows, size_t cols); void can_merge_submatrixes(size_t rows, size_t cols); void run(int); }; void CV_MergeTest::can_merge(size_t rows, size_t cols) { bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) && gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE); size_t depth_end = double_ok ? CV_64F : CV_32F; for (size_t num_channels = 1; num_channels <= 4; ++num_channels) for (size_t depth = CV_8U; depth <= depth_end; ++depth) { vector src; for (size_t i = 0; i < num_channels; ++i) src.push_back(Mat(rows, cols, depth, Scalar::all(static_cast(i)))); Mat dst(rows, cols, CV_MAKETYPE(depth, num_channels)); cv::merge(src, dst); vector dev_src; for (size_t i = 0; i < num_channels; ++i) dev_src.push_back(gpu::GpuMat(src[i])); gpu::GpuMat dev_dst(rows, cols, CV_MAKETYPE(depth, num_channels)); cv::gpu::merge(dev_src, dev_dst); Mat host_dst = dev_dst; double err = norm(dst, host_dst, NORM_INF); if (err > 1e-3) { //ts->printf(cvtest::TS::CONSOLE, "\nNorm: %f\n", err); //ts->printf(cvtest::TS::CONSOLE, "Depth: %d\n", depth); //ts->printf(cvtest::TS::CONSOLE, "Rows: %d\n", rows); //ts->printf(cvtest::TS::CONSOLE, "Cols: %d\n", cols); //ts->printf(cvtest::TS::CONSOLE, "NumChannels: %d\n", num_channels); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); return; } } } void CV_MergeTest::can_merge_submatrixes(size_t rows, size_t cols) { bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) && gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE); size_t depth_end = double_ok ? CV_64F : CV_32F; for (size_t num_channels = 1; num_channels <= 4; ++num_channels) for (size_t depth = CV_8U; depth <= depth_end; ++depth) { vector src; for (size_t i = 0; i < num_channels; ++i) { Mat m(rows * 2, cols * 2, depth, Scalar::all(static_cast(i))); src.push_back(m(Range(rows / 2, rows / 2 + rows), Range(cols / 2, cols / 2 + cols))); } Mat dst(rows, cols, CV_MAKETYPE(depth, num_channels)); cv::merge(src, dst); vector dev_src; for (size_t i = 0; i < num_channels; ++i) dev_src.push_back(gpu::GpuMat(src[i])); gpu::GpuMat dev_dst(rows, cols, CV_MAKETYPE(depth, num_channels)); cv::gpu::merge(dev_src, dev_dst); Mat host_dst = dev_dst; double err = norm(dst, host_dst, NORM_INF); if (err > 1e-3) { //ts->printf(cvtest::TS::CONSOLE, "\nNorm: %f\n", err); //ts->printf(cvtest::TS::CONSOLE, "Depth: %d\n", depth); //ts->printf(cvtest::TS::CONSOLE, "Rows: %d\n", rows); //ts->printf(cvtest::TS::CONSOLE, "Cols: %d\n", cols); //ts->printf(cvtest::TS::CONSOLE, "NumChannels: %d\n", num_channels); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); return; } } } void CV_MergeTest::run(int) { can_merge(1, 1); can_merge(1, 7); can_merge(53, 7); can_merge_submatrixes(1, 1); can_merge_submatrixes(1, 7); can_merge_submatrixes(53, 7); } //////////////////////////////////////////////////////////////////////////////// // Split struct CV_SplitTest : public cvtest::BaseTest { void can_split(size_t rows, size_t cols); void can_split_submatrix(size_t rows, size_t cols); void run(int); }; void CV_SplitTest::can_split(size_t rows, size_t cols) { bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) && gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE); size_t depth_end = double_ok ? CV_64F : CV_32F; for (size_t num_channels = 1; num_channels <= 4; ++num_channels) for (size_t depth = CV_8U; depth <= depth_end; ++depth) { Mat src(rows, cols, CV_MAKETYPE(depth, num_channels), Scalar(1.0, 2.0, 3.0, 4.0)); vector dst; cv::split(src, dst); gpu::GpuMat dev_src(src); vector dev_dst; cv::gpu::split(dev_src, dev_dst); if (dev_dst.size() != dst.size()) { ts->printf(cvtest::TS::CONSOLE, "Bad output sizes"); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); } for (size_t i = 0; i < num_channels; ++i) { Mat host_dst = dev_dst[i]; double err = norm(dst[i], host_dst, NORM_INF); if (err > 1e-3) { //ts->printf(cvtest::TS::CONSOLE, "\nNorm: %f\n", err); //ts->printf(cvtest::TS::CONSOLE, "Depth: %d\n", depth); //ts->printf(cvtest::TS::CONSOLE, "Rows: %d\n", rows); //ts->printf(cvtest::TS::CONSOLE, "Cols: %d\n", cols); //ts->printf(cvtest::TS::CONSOLE, "NumChannels: %d\n", num_channels); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); return; } } } } void CV_SplitTest::can_split_submatrix(size_t rows, size_t cols) { bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) && gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE); size_t depth_end = double_ok ? CV_64F : CV_32F; for (size_t num_channels = 1; num_channels <= 4; ++num_channels) for (size_t depth = CV_8U; depth <= depth_end; ++depth) { Mat src_data(rows * 2, cols * 2, CV_MAKETYPE(depth, num_channels), Scalar(1.0, 2.0, 3.0, 4.0)); Mat src(src_data(Range(rows / 2, rows / 2 + rows), Range(cols / 2, cols / 2 + cols))); vector dst; cv::split(src, dst); gpu::GpuMat dev_src(src); vector dev_dst; cv::gpu::split(dev_src, dev_dst); if (dev_dst.size() != dst.size()) { ts->printf(cvtest::TS::CONSOLE, "Bad output sizes"); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); } for (size_t i = 0; i < num_channels; ++i) { Mat host_dst = dev_dst[i]; double err = norm(dst[i], host_dst, NORM_INF); if (err > 1e-3) { //ts->printf(cvtest::TS::CONSOLE, "\nNorm: %f\n", err); //ts->printf(cvtest::TS::CONSOLE, "Depth: %d\n", depth); //ts->printf(cvtest::TS::CONSOLE, "Rows: %d\n", rows); //ts->printf(cvtest::TS::CONSOLE, "Cols: %d\n", cols); //ts->printf(cvtest::TS::CONSOLE, "NumChannels: %d\n", num_channels); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); return; } } } } void CV_SplitTest::run(int) { can_split(1, 1); can_split(1, 7); can_split(7, 53); can_split_submatrix(1, 1); can_split_submatrix(1, 7); can_split_submatrix(7, 53); } //////////////////////////////////////////////////////////////////////////////// // Split and merge struct CV_SplitMergeTest : public cvtest::BaseTest { void can_split_merge(size_t rows, size_t cols); void run(int); }; void CV_SplitMergeTest::can_split_merge(size_t rows, size_t cols) { bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) && gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE); size_t depth_end = double_ok ? CV_64F : CV_32F; for (size_t num_channels = 1; num_channels <= 4; ++num_channels) for (size_t depth = CV_8U; depth <= depth_end; ++depth) { Mat orig(rows, cols, CV_MAKETYPE(depth, num_channels), Scalar(1.0, 2.0, 3.0, 4.0)); gpu::GpuMat dev_orig(orig); vector dev_vec; cv::gpu::split(dev_orig, dev_vec); gpu::GpuMat dev_final(rows, cols, CV_MAKETYPE(depth, num_channels)); cv::gpu::merge(dev_vec, dev_final); double err = cv::norm((Mat)dev_orig, (Mat)dev_final, NORM_INF); if (err > 1e-3) { //ts->printf(cvtest::TS::CONSOLE, "\nNorm: %f\n", err); //ts->printf(cvtest::TS::CONSOLE, "Depth: %d\n", depth); //ts->printf(cvtest::TS::CONSOLE, "Rows: %d\n", rows); //ts->printf(cvtest::TS::CONSOLE, "Cols: %d\n", cols); //ts->printf(cvtest::TS::CONSOLE, "NumChannels: %d\n", num_channels); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); return; } } } void CV_SplitMergeTest::run(int) { can_split_merge(1, 1); can_split_merge(1, 7); can_split_merge(7, 53); } TEST(merge, accuracy) { CV_MergeTest test; test.safe_run(); } TEST(split, accuracy) { CV_SplitTest test; test.safe_run(); } TEST(split, merge_consistency) { CV_SplitMergeTest test; test.safe_run(); }