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