/*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. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, 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 materials provided with the distribution. // // * The name of Intel Corporation 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 implied warranties, including, but not limited to, the implied // 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 "precomp.hpp" #ifdef HAVE_CUDA /////////////////////////////////////////////////////////////////// // Gold implementation namespace { template class Interpolator> void remapImpl(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int borderType, cv::Scalar borderVal) { const int cn = src.channels(); cv::Size dsize = xmap.size(); dst.create(dsize, src.type()); for (int y = 0; y < dsize.height; ++y) { for (int x = 0; x < dsize.width; ++x) { for (int c = 0; c < cn; ++c) dst.at(y, x * cn + c) = Interpolator::getValue(src, ymap.at(y, x), xmap.at(y, x), c, borderType, borderVal); } } } void remapGold(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int interpolation, int borderType, cv::Scalar borderVal) { typedef void (*func_t)(const cv::Mat& src, const cv::Mat& xmap, const cv::Mat& ymap, cv::Mat& dst, int borderType, cv::Scalar borderVal); static const func_t nearest_funcs[] = { remapImpl, remapImpl, remapImpl, remapImpl, remapImpl, remapImpl }; static const func_t linear_funcs[] = { remapImpl, remapImpl, remapImpl, remapImpl, remapImpl, remapImpl }; static const func_t cubic_funcs[] = { remapImpl, remapImpl, remapImpl, remapImpl, remapImpl, remapImpl }; static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs}; funcs[interpolation][src.depth()](src, xmap, ymap, dst, borderType, borderVal); } } /////////////////////////////////////////////////////////////////// // Test PARAM_TEST_CASE(Remap, cv::gpu::DeviceInfo, cv::Size, MatType, Interpolation, Border, UseRoi) { cv::gpu::DeviceInfo devInfo; cv::Size size; int type; int interpolation; int borderType; bool useRoi; cv::Mat xmap; cv::Mat ymap; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); type = GET_PARAM(2); interpolation = GET_PARAM(3); borderType = GET_PARAM(4); useRoi = GET_PARAM(5); cv::gpu::setDevice(devInfo.deviceID()); // rotation matrix const double aplha = CV_PI / 4; static double M[2][3] = { {std::cos(aplha), -std::sin(aplha), size.width / 2.0}, {std::sin(aplha), std::cos(aplha), 0.0}}; xmap.create(size, CV_32FC1); ymap.create(size, CV_32FC1); for (int y = 0; y < size.height; ++y) { for (int x = 0; x < size.width; ++x) { xmap.at(y, x) = static_cast(M[0][0] * x + M[0][1] * y + M[0][2]); ymap.at(y, x) = static_cast(M[1][0] * x + M[1][1] * y + M[1][2]); } } } }; TEST_P(Remap, Accuracy) { cv::Mat src = randomMat(size, type); cv::Scalar val = randomScalar(0.0, 255.0); cv::gpu::GpuMat dst = createMat(xmap.size(), type, useRoi); cv::gpu::remap(loadMat(src, useRoi), dst, loadMat(xmap, useRoi), loadMat(ymap, useRoi), interpolation, borderType, val); cv::Mat dst_gold; remapGold(src, xmap, ymap, dst_gold, interpolation, borderType, val); EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-4 : 1.0); } INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Remap, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)), testing::Values(Border(cv::BORDER_REFLECT101), Border(cv::BORDER_REPLICATE), Border(cv::BORDER_CONSTANT), Border(cv::BORDER_REFLECT), Border(cv::BORDER_WRAP)), WHOLE_SUBMAT)); #endif // HAVE_CUDA