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245 lines
8.0 KiB
C++
245 lines
8.0 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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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// @Authors
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// Peng Xiao, pengxiao@multicorewareinc.com
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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 the copyright holders 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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using namespace std;
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////////////////////////////////////////////////////////////////////////////
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// Dft
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PARAM_TEST_CASE(Dft, cv::Size, int, bool)
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{
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cv::Size dft_size;
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int dft_flags;
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bool doubleFP;
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virtual void SetUp()
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{
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dft_size = GET_PARAM(0);
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dft_flags = GET_PARAM(1);
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doubleFP = GET_PARAM(2);
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}
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};
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OCL_TEST_P(Dft, C2C)
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{
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cv::Mat a = randomMat(dft_size, doubleFP ? CV_64FC2 : CV_32FC2, 0.0, 100.0);
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cv::Mat b_gold;
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cv::ocl::oclMat d_b;
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cv::dft(a, b_gold, dft_flags);
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cv::ocl::dft(cv::ocl::oclMat(a), d_b, a.size(), dft_flags);
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EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), a.size().area() * 1e-4);
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}
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OCL_TEST_P(Dft, R2C)
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{
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cv::Mat a = randomMat(dft_size, doubleFP ? CV_64FC1 : CV_32FC1, 0.0, 100.0);
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cv::Mat b_gold, b_gold_roi;
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cv::ocl::oclMat d_b, d_c;
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cv::ocl::dft(cv::ocl::oclMat(a), d_b, a.size(), dft_flags);
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cv::dft(a, b_gold, cv::DFT_COMPLEX_OUTPUT | dft_flags);
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b_gold_roi = b_gold(cv::Rect(0, 0, d_b.cols, d_b.rows));
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EXPECT_MAT_NEAR(b_gold_roi, cv::Mat(d_b), a.size().area() * 1e-4);
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cv::Mat c_gold;
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cv::dft(b_gold, c_gold, cv::DFT_INVERSE | cv::DFT_REAL_OUTPUT | cv::DFT_SCALE);
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EXPECT_MAT_NEAR(b_gold_roi, cv::Mat(d_b), a.size().area() * 1e-4);
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}
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OCL_TEST_P(Dft, R2CthenC2R)
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{
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cv::Mat a = randomMat(dft_size, doubleFP ? CV_64FC1 : CV_32FC1, 0.0, 10.0);
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cv::ocl::oclMat d_b, d_c;
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cv::ocl::dft(cv::ocl::oclMat(a), d_b, a.size(), 0);
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cv::ocl::dft(d_b, d_c, a.size(), cv::DFT_SCALE | cv::DFT_INVERSE | cv::DFT_REAL_OUTPUT);
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EXPECT_MAT_NEAR(a, d_c, a.size().area() * 1e-4);
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}
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INSTANTIATE_TEST_CASE_P(OCL_ImgProc, Dft, testing::Combine(
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testing::Values(cv::Size(2, 3), cv::Size(5, 4), cv::Size(25, 20), cv::Size(512, 1), cv::Size(1024, 768)),
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testing::Values(0, (int)cv::DFT_ROWS, (int)cv::DFT_SCALE), testing::Bool()));
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////////////////////////////////////////////////////////////////////////////
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// MulSpectrums
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PARAM_TEST_CASE(MulSpectrums, cv::Size, DftFlags, bool)
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{
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cv::Size size;
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int flag;
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bool ccorr;
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cv::Mat a, b;
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virtual void SetUp()
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{
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size = GET_PARAM(0);
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flag = GET_PARAM(1);
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ccorr = GET_PARAM(2);
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a = randomMat(size, CV_32FC2, -100, 100, false);
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b = randomMat(size, CV_32FC2, -100, 100, false);
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}
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};
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OCL_TEST_P(MulSpectrums, Simple)
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{
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cv::ocl::oclMat c;
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cv::ocl::mulSpectrums(cv::ocl::oclMat(a), cv::ocl::oclMat(b), c, flag, 1.0, ccorr);
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cv::Mat c_gold;
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cv::mulSpectrums(a, b, c_gold, flag, ccorr);
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EXPECT_MAT_NEAR(c_gold, c, 1e-2);
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}
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OCL_TEST_P(MulSpectrums, Scaled)
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{
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float scale = 1.f / size.area();
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cv::ocl::oclMat c;
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cv::ocl::mulSpectrums(cv::ocl::oclMat(a), cv::ocl::oclMat(b), c, flag, scale, ccorr);
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cv::Mat c_gold;
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cv::mulSpectrums(a, b, c_gold, flag, ccorr);
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c_gold.convertTo(c_gold, c_gold.type(), scale);
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EXPECT_MAT_NEAR(c_gold, c, 1e-2);
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}
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INSTANTIATE_TEST_CASE_P(OCL_ImgProc, MulSpectrums, testing::Combine(
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DIFFERENT_SIZES,
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testing::Values(DftFlags(0)),
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testing::Values(false, true)));
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////////////////////////////////////////////////////////
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// Convolve
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void static convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false)
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{
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// reallocate the output array if needed
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C.create(std::abs(A.rows - B.rows) + 1, std::abs(A.cols - B.cols) + 1, A.type());
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cv::Size dftSize;
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// compute the size of DFT transform
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dftSize.width = cv::getOptimalDFTSize(A.cols + B.cols - 1);
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dftSize.height = cv::getOptimalDFTSize(A.rows + B.rows - 1);
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// allocate temporary buffers and initialize them with 0s
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cv::Mat tempA(dftSize, A.type(), cv::Scalar::all(0));
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cv::Mat tempB(dftSize, B.type(), cv::Scalar::all(0));
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// copy A and B to the top-left corners of tempA and tempB, respectively
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cv::Mat roiA(tempA, cv::Rect(0, 0, A.cols, A.rows));
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A.copyTo(roiA);
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cv::Mat roiB(tempB, cv::Rect(0, 0, B.cols, B.rows));
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B.copyTo(roiB);
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// now transform the padded A & B in-place;
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// use "nonzeroRows" hint for faster processing
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cv::dft(tempA, tempA, 0, A.rows);
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cv::dft(tempB, tempB, 0, B.rows);
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// multiply the spectrums;
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// the function handles packed spectrum representations well
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cv::mulSpectrums(tempA, tempB, tempA, 0, ccorr);
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// transform the product back from the frequency domain.
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// Even though all the result rows will be non-zero,
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// you need only the first C.rows of them, and thus you
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// pass nonzeroRows == C.rows
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cv::dft(tempA, tempA, cv::DFT_INVERSE + cv::DFT_SCALE, C.rows);
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// now copy the result back to C.
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tempA(cv::Rect(0, 0, C.cols, C.rows)).copyTo(C);
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}
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IMPLEMENT_PARAM_CLASS(KSize, int);
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IMPLEMENT_PARAM_CLASS(Ccorr, bool);
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PARAM_TEST_CASE(Convolve_DFT, cv::Size, KSize, Ccorr)
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{
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cv::Size size;
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int ksize;
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bool ccorr;
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cv::Mat src;
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cv::Mat kernel;
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cv::Mat dst_gold;
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virtual void SetUp()
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{
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size = GET_PARAM(0);
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ksize = GET_PARAM(1);
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ccorr = GET_PARAM(2);
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}
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};
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OCL_TEST_P(Convolve_DFT, Accuracy)
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{
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cv::Mat src = randomMat(size, CV_32FC1, 0.0, 100.0);
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cv::Mat kernel = randomMat(cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0);
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cv::ocl::oclMat dst;
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cv::ocl::convolve(cv::ocl::oclMat(src), cv::ocl::oclMat(kernel), dst, ccorr);
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cv::Mat dst_gold;
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convolveDFT(src, kernel, dst_gold, ccorr);
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EXPECT_MAT_NEAR(dst, dst_gold, 1e-1);
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}
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#define DIFFERENT_CONVOLVE_SIZES testing::Values(cv::Size(251, 257), cv::Size(113, 113), cv::Size(200, 480), cv::Size(1300, 1300))
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INSTANTIATE_TEST_CASE_P(OCL_ImgProc, Convolve_DFT, testing::Combine(
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DIFFERENT_CONVOLVE_SIZES,
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testing::Values(KSize(19), KSize(23), KSize(45)),
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testing::Values(Ccorr(true)/*, Ccorr(false)*/))); // TODO false ccorr cannot pass for some instances
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