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Open Source Computer Vision Library
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275 lines
9.9 KiB
275 lines
9.9 KiB
/*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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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, 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 materials provided with the distribution. |
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// |
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// * The name of Intel Corporation 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 implied warranties, including, but not limited to, the implied |
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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 "precomp.hpp" |
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#ifdef HAVE_CUDA |
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namespace |
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{ |
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cv::Mat createTransfomMatrix(cv::Size srcSize, double angle) |
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{ |
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cv::Mat M(2, 3, CV_64FC1); |
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M.at<double>(0, 0) = std::cos(angle); M.at<double>(0, 1) = -std::sin(angle); M.at<double>(0, 2) = srcSize.width / 2; |
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M.at<double>(1, 0) = std::sin(angle); M.at<double>(1, 1) = std::cos(angle); M.at<double>(1, 2) = 0.0; |
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return M; |
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} |
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} |
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/////////////////////////////////////////////////////////////////// |
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// Test buildWarpAffineMaps |
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PARAM_TEST_CASE(BuildWarpAffineMaps, cv::gpu::DeviceInfo, cv::Size, Inverse) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::Size size; |
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bool inverse; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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size = GET_PARAM(1); |
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inverse = GET_PARAM(2); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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TEST_P(BuildWarpAffineMaps, Accuracy) |
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{ |
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cv::Mat M = createTransfomMatrix(size, CV_PI / 4); |
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cv::gpu::GpuMat xmap, ymap; |
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cv::gpu::buildWarpAffineMaps(M, inverse, size, xmap, ymap); |
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int interpolation = cv::INTER_NEAREST; |
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int borderMode = cv::BORDER_CONSTANT; |
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cv::Mat src = randomMat(randomSize(200, 400), CV_8UC1); |
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cv::Mat dst; |
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cv::remap(src, dst, cv::Mat(xmap), cv::Mat(ymap), interpolation, borderMode); |
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int flags = interpolation; |
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if (inverse) |
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flags |= cv::WARP_INVERSE_MAP; |
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cv::Mat dst_gold; |
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cv::warpAffine(src, dst_gold, M, size, flags, borderMode); |
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EXPECT_MAT_NEAR(dst_gold, dst, 0.0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, BuildWarpAffineMaps, testing::Combine( |
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ALL_DEVICES, |
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DIFFERENT_SIZES, |
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DIRECT_INVERSE)); |
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/////////////////////////////////////////////////////////////////// |
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// Gold implementation |
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namespace |
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{ |
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template <typename T, template <typename> class Interpolator> void warpAffineImpl(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal) |
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{ |
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const int cn = src.channels(); |
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dst.create(dsize, src.type()); |
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for (int y = 0; y < dsize.height; ++y) |
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{ |
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for (int x = 0; x < dsize.width; ++x) |
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{ |
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float xcoo = static_cast<float>(M.at<double>(0, 0) * x + M.at<double>(0, 1) * y + M.at<double>(0, 2)); |
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float ycoo = static_cast<float>(M.at<double>(1, 0) * x + M.at<double>(1, 1) * y + M.at<double>(1, 2)); |
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for (int c = 0; c < cn; ++c) |
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dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, ycoo, xcoo, c, borderType, borderVal); |
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} |
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} |
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} |
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void warpAffineGold(const cv::Mat& src, const cv::Mat& M, bool inverse, cv::Size dsize, cv::Mat& dst, int interpolation, int borderType, cv::Scalar borderVal) |
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{ |
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typedef void (*func_t)(const cv::Mat& src, const cv::Mat& M, cv::Size dsize, cv::Mat& dst, int borderType, cv::Scalar borderVal); |
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static const func_t nearest_funcs[] = |
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{ |
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warpAffineImpl<unsigned char, NearestInterpolator>, |
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warpAffineImpl<signed char, NearestInterpolator>, |
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warpAffineImpl<unsigned short, NearestInterpolator>, |
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warpAffineImpl<short, NearestInterpolator>, |
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warpAffineImpl<int, NearestInterpolator>, |
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warpAffineImpl<float, NearestInterpolator> |
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}; |
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static const func_t linear_funcs[] = |
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{ |
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warpAffineImpl<unsigned char, LinearInterpolator>, |
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warpAffineImpl<signed char, LinearInterpolator>, |
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warpAffineImpl<unsigned short, LinearInterpolator>, |
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warpAffineImpl<short, LinearInterpolator>, |
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warpAffineImpl<int, LinearInterpolator>, |
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warpAffineImpl<float, LinearInterpolator> |
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}; |
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static const func_t cubic_funcs[] = |
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{ |
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warpAffineImpl<unsigned char, CubicInterpolator>, |
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warpAffineImpl<signed char, CubicInterpolator>, |
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warpAffineImpl<unsigned short, CubicInterpolator>, |
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warpAffineImpl<short, CubicInterpolator>, |
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warpAffineImpl<int, CubicInterpolator>, |
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warpAffineImpl<float, CubicInterpolator> |
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}; |
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static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs}; |
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if (inverse) |
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funcs[interpolation][src.depth()](src, M, dsize, dst, borderType, borderVal); |
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else |
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{ |
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cv::Mat iM; |
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cv::invertAffineTransform(M, iM); |
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funcs[interpolation][src.depth()](src, iM, dsize, dst, borderType, borderVal); |
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} |
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} |
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} |
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/////////////////////////////////////////////////////////////////// |
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// Test |
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PARAM_TEST_CASE(WarpAffine, cv::gpu::DeviceInfo, cv::Size, MatType, Inverse, Interpolation, Border, UseRoi) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::Size size; |
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int type; |
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bool inverse; |
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int interpolation; |
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int borderType; |
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bool useRoi; |
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cv::Mat M; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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size = GET_PARAM(1); |
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type = GET_PARAM(2); |
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inverse = GET_PARAM(3); |
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interpolation = GET_PARAM(4); |
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borderType = GET_PARAM(5); |
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useRoi = GET_PARAM(6); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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TEST_P(WarpAffine, Accuracy) |
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{ |
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cv::Mat src = randomMat(size, type); |
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cv::Mat M = createTransfomMatrix(size, CV_PI / 3); |
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int flags = interpolation; |
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if (inverse) |
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flags |= cv::WARP_INVERSE_MAP; |
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cv::Scalar val = randomScalar(0.0, 255.0); |
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cv::gpu::GpuMat dst = createMat(size, type, useRoi); |
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cv::gpu::warpAffine(loadMat(src, useRoi), dst, M, size, flags, borderType, val); |
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cv::Mat dst_gold; |
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warpAffineGold(src, M, inverse, size, dst_gold, interpolation, borderType, val); |
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EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-1 : 1.0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpAffine, testing::Combine( |
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ALL_DEVICES, |
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DIFFERENT_SIZES, |
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testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), |
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DIRECT_INVERSE, |
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testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)), |
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testing::Values(Border(cv::BORDER_REFLECT101), Border(cv::BORDER_REPLICATE), Border(cv::BORDER_REFLECT), Border(cv::BORDER_WRAP)), |
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WHOLE_SUBMAT)); |
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/////////////////////////////////////////////////////////////////// |
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// Test NPP |
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PARAM_TEST_CASE(WarpAffineNPP, cv::gpu::DeviceInfo, MatType, Inverse, Interpolation) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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int type; |
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bool inverse; |
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int interpolation; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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type = GET_PARAM(1); |
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inverse = GET_PARAM(2); |
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interpolation = GET_PARAM(3); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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TEST_P(WarpAffineNPP, Accuracy) |
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{ |
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cv::Mat src = readImageType("stereobp/aloe-L.png", type); |
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cv::Mat M = createTransfomMatrix(src.size(), CV_PI / 4); |
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int flags = interpolation; |
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if (inverse) |
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flags |= cv::WARP_INVERSE_MAP; |
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cv::gpu::GpuMat dst; |
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cv::gpu::warpAffine(loadMat(src), dst, M, src.size(), flags); |
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cv::Mat dst_gold; |
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warpAffineGold(src, M, inverse, src.size(), dst_gold, interpolation, cv::BORDER_CONSTANT, cv::Scalar::all(0)); |
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EXPECT_MAT_SIMILAR(dst_gold, dst, 2e-2); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, WarpAffineNPP, testing::Combine( |
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ALL_DEVICES, |
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testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), |
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DIRECT_INVERSE, |
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testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)))); |
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#endif // HAVE_CUDA
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