279 lines
10 KiB
C++
279 lines
10 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 "test_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(3, 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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M.at<double>(2, 0) = 0.0 ; M.at<double>(2, 1) = 0.0 ; M.at<double>(2, 2) = 1.0;
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return M;
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}
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}
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///////////////////////////////////////////////////////////////////
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// Test buildWarpPerspectiveMaps
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PARAM_TEST_CASE(BuildWarpPerspectiveMaps, 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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GPU_TEST_P(BuildWarpPerspectiveMaps, 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::buildWarpPerspectiveMaps(M, inverse, size, xmap, ymap);
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cv::Mat src = randomMat(randomSize(200, 400), CV_8UC1);
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int interpolation = cv::INTER_NEAREST;
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int borderMode = cv::BORDER_CONSTANT;
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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;
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cv::remap(src, dst, cv::Mat(xmap), cv::Mat(ymap), interpolation, borderMode);
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cv::Mat dst_gold;
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cv::warpPerspective(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, BuildWarpPerspectiveMaps, 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 warpPerspectiveImpl(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 coeff = static_cast<float>(M.at<double>(2, 0) * x + M.at<double>(2, 1) * y + M.at<double>(2, 2));
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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)) / coeff);
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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)) / coeff);
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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 warpPerspectiveGold(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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warpPerspectiveImpl<unsigned char, NearestInterpolator>,
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warpPerspectiveImpl<signed char, NearestInterpolator>,
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warpPerspectiveImpl<unsigned short, NearestInterpolator>,
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warpPerspectiveImpl<short, NearestInterpolator>,
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warpPerspectiveImpl<int, NearestInterpolator>,
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warpPerspectiveImpl<float, NearestInterpolator>
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};
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static const func_t linear_funcs[] =
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{
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warpPerspectiveImpl<unsigned char, LinearInterpolator>,
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warpPerspectiveImpl<signed char, LinearInterpolator>,
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warpPerspectiveImpl<unsigned short, LinearInterpolator>,
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warpPerspectiveImpl<short, LinearInterpolator>,
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warpPerspectiveImpl<int, LinearInterpolator>,
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warpPerspectiveImpl<float, LinearInterpolator>
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};
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static const func_t cubic_funcs[] =
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{
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warpPerspectiveImpl<unsigned char, CubicInterpolator>,
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warpPerspectiveImpl<signed char, CubicInterpolator>,
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warpPerspectiveImpl<unsigned short, CubicInterpolator>,
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warpPerspectiveImpl<short, CubicInterpolator>,
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warpPerspectiveImpl<int, CubicInterpolator>,
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warpPerspectiveImpl<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::invert(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(WarpPerspective, cv::gpu::DeviceInfo, cv::Size, MatType, Inverse, Interpolation, BorderType, 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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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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GPU_TEST_P(WarpPerspective, 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::warpPerspective(loadMat(src, useRoi), dst, M, size, flags, borderType, val);
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cv::Mat dst_gold;
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warpPerspectiveGold(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, WarpPerspective, 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(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT), BorderType(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(WarpPerspectiveNPP, 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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GPU_TEST_P(WarpPerspectiveNPP, 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::warpPerspective(loadMat(src), dst, M, src.size(), flags);
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cv::Mat dst_gold;
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warpPerspectiveGold(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, WarpPerspectiveNPP, 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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