/*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 resizeImpl(const cv::Mat& src, cv::Mat& dst, double fx, double fy) { const int cn = src.channels(); cv::Size dsize(cv::saturate_cast(src.cols * fx), cv::saturate_cast(src.rows * fy)); dst.create(dsize, src.type()); float ifx = static_cast(1.0 / fx); float ify = static_cast(1.0 / fy); 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, y * ify, x * ifx, c, cv::BORDER_REPLICATE); } } } void resizeGold(const cv::Mat& src, cv::Mat& dst, double fx, double fy, int interpolation) { typedef void (*func_t)(const cv::Mat& src, cv::Mat& dst, double fx, double fy); static const func_t nearest_funcs[] = { resizeImpl, resizeImpl, resizeImpl, resizeImpl, resizeImpl, resizeImpl }; static const func_t linear_funcs[] = { resizeImpl, resizeImpl, resizeImpl, resizeImpl, resizeImpl, resizeImpl }; static const func_t cubic_funcs[] = { resizeImpl, resizeImpl, resizeImpl, resizeImpl, resizeImpl, resizeImpl }; static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs}; funcs[interpolation][src.depth()](src, dst, fx, fy); } } /////////////////////////////////////////////////////////////////// // Test PARAM_TEST_CASE(Resize, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi) { cv::gpu::DeviceInfo devInfo; cv::Size size; double coeff; int interpolation; int type; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); type = GET_PARAM(2); coeff = GET_PARAM(3); interpolation = GET_PARAM(4); useRoi = GET_PARAM(5); cv::gpu::setDevice(devInfo.deviceID()); } }; TEST_P(Resize, Accuracy) { cv::Mat src = randomMat(size, type); cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast(src.cols * coeff), cv::saturate_cast(src.rows * coeff)), type, useRoi); cv::gpu::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation); cv::Mat dst_gold; resizeGold(src, dst_gold, coeff, coeff, interpolation); EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0); } INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Resize, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), testing::Values(0.3, 0.5, 1.5, 2.0), testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)), WHOLE_SUBMAT)); ///////////////// PARAM_TEST_CASE(ResizeSameAsHost, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi) { cv::gpu::DeviceInfo devInfo; cv::Size size; double coeff; int interpolation; int type; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); size = GET_PARAM(1); type = GET_PARAM(2); coeff = GET_PARAM(3); interpolation = GET_PARAM(4); useRoi = GET_PARAM(5); cv::gpu::setDevice(devInfo.deviceID()); } }; // downscaling only: used for classifiers TEST_P(ResizeSameAsHost, Accuracy) { cv::Mat src = randomMat(size, type); cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast(src.cols * coeff), cv::saturate_cast(src.rows * coeff)), type, useRoi); cv::gpu::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation); cv::Mat dst_gold; cv::resize(src, dst_gold, cv::Size(), coeff, coeff, interpolation); EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0); } INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeSameAsHost, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), testing::Values(0.3, 0.5), testing::Values(Interpolation(cv::INTER_AREA), Interpolation(cv::INTER_NEAREST)), //, Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC) WHOLE_SUBMAT)); /////////////////////////////////////////////////////////////////// // Test NPP PARAM_TEST_CASE(ResizeNPP, cv::gpu::DeviceInfo, MatType, double, Interpolation) { cv::gpu::DeviceInfo devInfo; double coeff; int interpolation; int type; virtual void SetUp() { devInfo = GET_PARAM(0); type = GET_PARAM(1); coeff = GET_PARAM(2); interpolation = GET_PARAM(3); cv::gpu::setDevice(devInfo.deviceID()); } }; TEST_P(ResizeNPP, Accuracy) { if (type == CV_8UC1 && interpolation == cv::INTER_CUBIC) return; cv::Mat src = readImageType("stereobp/aloe-L.png", type); cv::gpu::GpuMat dst; cv::gpu::resize(loadMat(src), dst, cv::Size(), coeff, coeff, interpolation); cv::Mat dst_gold; resizeGold(src, dst_gold, coeff, coeff, interpolation); EXPECT_MAT_SIMILAR(dst_gold, dst, 1e-1); } INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeNPP, testing::Combine( ALL_DEVICES, testing::Values(MatType(CV_8UC1), MatType(CV_8UC4)), testing::Values(0.3, 0.5, 1.5, 2.0), testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)))); #endif // HAVE_CUDA