268 lines
9.0 KiB
C++
268 lines
9.0 KiB
C++
/*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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#include <iostream>
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#ifdef HAVE_CUDA
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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>
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void resizeImpl(const cv::Mat& src, cv::Mat& dst, double fx, double fy)
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{
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const int cn = src.channels();
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cv::Size dsize(cv::saturate_cast<int>(src.cols * fx), cv::saturate_cast<int>(src.rows * fy));
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dst.create(dsize, src.type());
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float ifx = static_cast<float>(1.0 / fx);
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float ify = static_cast<float>(1.0 / fy);
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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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for (int c = 0; c < cn; ++c)
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dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, y * ify, x * ifx, c, cv::BORDER_REPLICATE);
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}
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}
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}
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void resizeGold(const cv::Mat& src, cv::Mat& dst, double fx, double fy, int interpolation)
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{
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typedef void (*func_t)(const cv::Mat& src, cv::Mat& dst, double fx, double fy);
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static const func_t nearest_funcs[] =
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{
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resizeImpl<unsigned char, NearestInterpolator>,
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resizeImpl<signed char, NearestInterpolator>,
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resizeImpl<unsigned short, NearestInterpolator>,
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resizeImpl<short, NearestInterpolator>,
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resizeImpl<int, NearestInterpolator>,
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resizeImpl<float, NearestInterpolator>
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};
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static const func_t linear_funcs[] =
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{
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resizeImpl<unsigned char, LinearInterpolator>,
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resizeImpl<signed char, LinearInterpolator>,
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resizeImpl<unsigned short, LinearInterpolator>,
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resizeImpl<short, LinearInterpolator>,
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resizeImpl<int, LinearInterpolator>,
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resizeImpl<float, LinearInterpolator>
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};
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static const func_t cubic_funcs[] =
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{
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resizeImpl<unsigned char, CubicInterpolator>,
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resizeImpl<signed char, CubicInterpolator>,
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resizeImpl<unsigned short, CubicInterpolator>,
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resizeImpl<short, CubicInterpolator>,
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resizeImpl<int, CubicInterpolator>,
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resizeImpl<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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funcs[interpolation][src.depth()](src, dst, fx, fy);
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}
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}
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///////////////////////////////////////////////////////////////////
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// Test
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PARAM_TEST_CASE(Resize, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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double coeff;
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int interpolation;
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int type;
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bool useRoi;
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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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coeff = GET_PARAM(3);
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interpolation = GET_PARAM(4);
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useRoi = GET_PARAM(5);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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TEST_P(Resize, Accuracy)
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{
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cv::Mat src = randomMat(size, type);
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cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
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cv::gpu::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation);
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cv::Mat dst_gold;
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resizeGold(src, dst_gold, coeff, coeff, interpolation);
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EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Resize, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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testing::Values(MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
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testing::Values(0.3, 0.5, 1.5, 2.0),
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testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
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WHOLE_SUBMAT));
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/////////////////
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PARAM_TEST_CASE(ResizeArea, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Size size;
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double coeff;
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int interpolation;
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int type;
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bool useRoi;
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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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coeff = GET_PARAM(3);
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interpolation = GET_PARAM(4);
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useRoi = GET_PARAM(5);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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TEST_P(ResizeArea, Accuracy)
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{
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cv::Mat src = randomMat(size, type);
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cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
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cv::gpu::GpuMat buffer = createMat(cv::Size(dst.cols, src.rows), CV_32FC1);
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cv::gpu::resize(loadMat(src, useRoi), dst, buffer, cv::Size(), coeff, coeff, interpolation);
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cv::Mat dst_cpu;
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cv::resize(src, dst_cpu, cv::Size(), coeff, coeff, interpolation);
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cv::Mat gpu_buff;
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buffer.download(gpu_buff);
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cv::Mat gpu;
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dst.download(gpu);
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// std::cout << src
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// << std::endl << std::endl
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// << gpu_buff
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// << std::endl << std::endl
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// << gpu
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// << std::endl << std::endl
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// << dst_cpu<< std::endl;
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EXPECT_MAT_NEAR(dst_cpu, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeArea, testing::Combine(
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ALL_DEVICES,
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testing::Values(cv::Size(512, 256)),//DIFFERENT_SIZES,
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testing::Values(MatType(CV_8UC1)/*MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)*/),
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testing::Values(0.5),
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testing::Values(Interpolation(cv::INTER_AREA)),
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WHOLE_SUBMAT));
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///////////////////////////////////////////////////////////////////
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// Test NPP
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PARAM_TEST_CASE(ResizeNPP, cv::gpu::DeviceInfo, MatType, double, Interpolation)
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{
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cv::gpu::DeviceInfo devInfo;
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double coeff;
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int interpolation;
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int type;
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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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coeff = 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(ResizeNPP, Accuracy)
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{
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if (type == CV_8UC1 && interpolation == cv::INTER_CUBIC)
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return;
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cv::Mat src = readImageType("stereobp/aloe-L.png", type);
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cv::gpu::GpuMat dst;
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cv::gpu::resize(loadMat(src), dst, cv::Size(), coeff, coeff, interpolation);
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cv::Mat dst_gold;
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resizeGold(src, dst_gold, coeff, coeff, interpolation);
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EXPECT_MAT_SIMILAR(dst_gold, dst, 1e-1);
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeNPP, testing::Combine(
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ALL_DEVICES,
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testing::Values(MatType(CV_8UC1), MatType(CV_8UC4)),
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testing::Values(0.3, 0.5, 1.5, 2.0),
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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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