398 lines
13 KiB
C++
398 lines
13 KiB
C++
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/*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) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage 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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// 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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namespace opencv_test { namespace {
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/// phase correlation
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class CV_PhaseCorrelatorTest : public cvtest::ArrayTest
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{
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public:
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CV_PhaseCorrelatorTest();
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protected:
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void run( int );
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};
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CV_PhaseCorrelatorTest::CV_PhaseCorrelatorTest() {}
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void CV_PhaseCorrelatorTest::run( int )
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{
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ts->set_failed_test_info(cvtest::TS::OK);
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Mat r1 = Mat::ones(Size(129, 128), CV_64F);
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Mat r2 = Mat::ones(Size(129, 128), CV_64F);
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double expectedShiftX = -10.0;
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double expectedShiftY = -20.0;
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// draw 10x10 rectangles @ (100, 100) and (90, 80) should see ~(-10, -20) shift here...
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cv::rectangle(r1, Point(100, 100), Point(110, 110), Scalar(0, 0, 0), CV_FILLED);
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cv::rectangle(r2, Point(90, 80), Point(100, 90), Scalar(0, 0, 0), CV_FILLED);
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Mat hann;
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createHanningWindow(hann, r1.size(), CV_64F);
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Point2d phaseShift = phaseCorrelate(r1, r2, hann);
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// test accuracy should be less than 1 pixel...
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if(std::abs(expectedShiftX - phaseShift.x) >= 1 || std::abs(expectedShiftY - phaseShift.y) >= 1)
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{
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
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}
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}
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TEST(Imgproc_PhaseCorrelatorTest, accuracy) { CV_PhaseCorrelatorTest test; test.safe_run(); }
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TEST(Imgproc_PhaseCorrelatorTest, accuracy_real_img)
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{
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Mat img = imread(cvtest::TS::ptr()->get_data_path() + "shared/airplane.png", IMREAD_GRAYSCALE);
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img.convertTo(img, CV_64FC1);
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const int xLen = 129;
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const int yLen = 129;
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const int xShift = 40;
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const int yShift = 14;
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Mat roi1 = img(Rect(xShift, yShift, xLen, yLen));
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Mat roi2 = img(Rect(0, 0, xLen, yLen));
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Mat hann;
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createHanningWindow(hann, roi1.size(), CV_64F);
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Point2d phaseShift = phaseCorrelate(roi1, roi2, hann);
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ASSERT_NEAR(phaseShift.x, (double)xShift, 1.);
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ASSERT_NEAR(phaseShift.y, (double)yShift, 1.);
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}
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TEST(Imgproc_PhaseCorrelatorTest, accuracy_1d_odd_fft) {
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Mat r1 = Mat::ones(Size(129, 1), CV_64F)*255; // 129 will be completed to 135 before FFT
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Mat r2 = Mat::ones(Size(129, 1), CV_64F)*255;
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const int xShift = 10;
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for(int i = 6; i < 20; i++)
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{
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r1.at<double>(i) = 1;
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r2.at<double>(i + xShift) = 1;
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}
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Point2d phaseShift = phaseCorrelate(r1, r2);
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ASSERT_NEAR(phaseShift.x, (double)xShift, 1.);
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}
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////////////////////// DivSpectrums ////////////////////////
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class CV_DivSpectrumsTest : public cvtest::ArrayTest
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{
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public:
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CV_DivSpectrumsTest();
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protected:
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void run_func();
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void get_test_array_types_and_sizes( int, vector<vector<Size> >& sizes, vector<vector<int> >& types );
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void prepare_to_validation( int test_case_idx );
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int flags;
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};
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CV_DivSpectrumsTest::CV_DivSpectrumsTest() : flags(0)
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{
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// Allocate test matrices.
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test_array[INPUT].push_back(NULL); // first input DFT as a CCS-packed array or complex matrix.
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test_array[INPUT].push_back(NULL); // second input DFT as a CCS-packed array or complex matrix.
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test_array[OUTPUT].push_back(NULL); // output DFT as a complex matrix.
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test_array[REF_OUTPUT].push_back(NULL); // reference output DFT as a complex matrix.
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test_array[TEMP].push_back(NULL); // first input DFT converted to a complex matrix.
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test_array[TEMP].push_back(NULL); // second input DFT converted to a complex matrix.
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test_array[TEMP].push_back(NULL); // output DFT as a CCV-packed array.
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}
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void CV_DivSpectrumsTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
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{
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cvtest::ArrayTest::get_test_array_types_and_sizes(test_case_idx, sizes, types);
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RNG& rng = ts->get_rng();
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// Get the flag of the input.
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const int rand_int_flags = cvtest::randInt(rng);
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flags = rand_int_flags & (CV_DXT_MUL_CONJ | CV_DXT_ROWS);
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// Get input type.
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const int rand_int_type = cvtest::randInt(rng);
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int type;
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if (rand_int_type % 4)
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{
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type = CV_32FC1;
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}
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else if (rand_int_type % 4 == 1)
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{
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type = CV_32FC2;
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}
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else if (rand_int_type % 4 == 2)
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{
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type = CV_64FC1;
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}
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else
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{
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type = CV_64FC2;
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}
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for( size_t i = 0; i < types.size(); i++ )
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{
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for( size_t j = 0; j < types[i].size(); j++ )
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{
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types[i][j] = type;
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}
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}
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// Inputs are CCS-packed arrays. Prepare outputs and temporary inputs as complex matrices.
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if( type == CV_32FC1 || type == CV_64FC1 )
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{
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types[OUTPUT][0] += 8;
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types[REF_OUTPUT][0] += 8;
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types[TEMP][0] += 8;
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types[TEMP][1] += 8;
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}
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}
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/// Helper function to convert a ccs array of depth_t into a complex matrix.
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template<typename depth_t>
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static void convert_from_ccs_helper( const Mat& src0, const Mat& src1, Mat& dst )
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{
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const int cn = src0.channels();
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int srcstep = cn;
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int dststep = 1;
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if( !dst.isContinuous() )
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dststep = (int)(dst.step/dst.elemSize());
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if( !src0.isContinuous() )
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srcstep = (int)(src0.step/src0.elemSize1());
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Complex<depth_t> *dst_data = dst.ptr<Complex<depth_t> >();
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const depth_t* src0_data = src0.ptr<depth_t>();
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const depth_t* src1_data = src1.ptr<depth_t>();
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dst_data->re = src0_data[0];
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dst_data->im = 0;
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const int n = dst.cols + dst.rows - 1;
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const int n2 = (n+1) >> 1;
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if( (n & 1) == 0 )
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{
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dst_data[n2*dststep].re = src0_data[(cn == 1 ? n-1 : n2)*srcstep];
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dst_data[n2*dststep].im = 0;
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}
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int delta0 = srcstep;
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int delta1 = delta0 + (cn == 1 ? srcstep : 1);
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if( cn == 1 )
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srcstep *= 2;
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for( int i = 1; i < n2; i++, delta0 += srcstep, delta1 += srcstep )
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{
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depth_t t0 = src0_data[delta0];
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depth_t t1 = src0_data[delta1];
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dst_data[i*dststep].re = t0;
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dst_data[i*dststep].im = t1;
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t0 = src1_data[delta0];
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t1 = -src1_data[delta1];
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dst_data[(n-i)*dststep].re = t0;
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dst_data[(n-i)*dststep].im = t1;
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}
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}
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/// Helper function to convert a ccs array into a complex matrix.
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static void convert_from_ccs( const Mat& src0, const Mat& src1, Mat& dst, const int flags )
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{
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if( dst.rows > 1 && (dst.cols > 1 || (flags & DFT_ROWS)) )
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{
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const int count = dst.rows;
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const int len = dst.cols;
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const bool is2d = (flags & DFT_ROWS) == 0;
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for( int i = 0; i < count; i++ )
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{
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const int j = !is2d || i == 0 ? i : count - i;
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const Mat& src0row = src0.row(i);
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const Mat& src1row = src1.row(j);
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Mat dstrow = dst.row(i);
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convert_from_ccs( src0row, src1row, dstrow, 0 );
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}
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if( is2d )
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{
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const Mat& src0row = src0.col(0);
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Mat dstrow = dst.col(0);
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convert_from_ccs( src0row, src0row, dstrow, 0 );
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if( (len & 1) == 0 )
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{
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const Mat& src0row_even = src0.col(src0.cols - 1);
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Mat dstrow_even = dst.col(len/2);
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convert_from_ccs( src0row_even, src0row_even, dstrow_even, 0 );
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}
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}
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}
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else
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{
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if( dst.depth() == CV_32F )
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{
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convert_from_ccs_helper<float>( src0, src1, dst );
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}
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else
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{
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convert_from_ccs_helper<double>( src0, src1, dst );
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}
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}
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}
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/// Helper function to compute complex number (nu_re + nu_im * i) / (de_re + de_im * i).
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static std::pair<double, double> divide_complex_numbers( const double nu_re, const double nu_im,
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const double de_re, const double de_im,
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const bool conj_de )
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{
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if ( conj_de )
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{
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return divide_complex_numbers( nu_re, nu_im, de_re, -de_im, false /* conj_de */ );
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}
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const double result_de = de_re * de_re + de_im * de_im + DBL_EPSILON;
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const double result_re = nu_re * de_re + nu_im * de_im;
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const double result_im = nu_re * (-de_im) + nu_im * de_re;
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return std::pair<double, double>(result_re / result_de, result_im / result_de);
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};
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/// Helper function to divide a DFT in src1 by a DFT in src2 with depths depth_t. The DFTs are
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/// complex matrices.
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template <typename depth_t>
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static void div_complex_helper( const Mat& src1, const Mat& src2, Mat& dst, int flags )
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{
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CV_Assert( src1.size == src2.size && src1.type() == src2.type() );
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dst.create( src1.rows, src1.cols, src1.type() );
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const int cn = src1.channels();
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int cols = src1.cols * cn;
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for( int i = 0; i < dst.rows; i++ )
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{
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const depth_t *src1_data = src1.ptr<depth_t>(i);
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const depth_t *src2_data = src2.ptr<depth_t>(i);
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depth_t *dst_data = dst.ptr<depth_t>(i);
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for( int j = 0; j < cols; j += 2 )
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{
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std::pair<double, double> result =
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divide_complex_numbers( src1_data[j], src1_data[j + 1],
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src2_data[j], src2_data[j + 1],
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(flags & CV_DXT_MUL_CONJ) != 0 );
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dst_data[j] = (depth_t)result.first;
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dst_data[j + 1] = (depth_t)result.second;
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}
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}
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}
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/// Helper function to divide a DFT in src1 by a DFT in src2. The DFTs are complex matrices.
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static void div_complex( const Mat& src1, const Mat& src2, Mat& dst, const int flags )
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{
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const int type = src1.type();
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CV_Assert( type == CV_32FC2 || type == CV_64FC2 );
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if ( src1.depth() == CV_32F )
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{
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return div_complex_helper<float>( src1, src2, dst, flags );
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}
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else
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{
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return div_complex_helper<double>( src1, src2, dst, flags );
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}
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}
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void CV_DivSpectrumsTest::prepare_to_validation( int /* test_case_idx */ )
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{
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Mat &src1 = test_mat[INPUT][0];
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Mat &src2 = test_mat[INPUT][1];
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Mat &ref_dst = test_mat[REF_OUTPUT][0];
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const int cn = src1.channels();
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// Inputs are CCS-packed arrays. Convert them to complex matrices and get the expected output
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// as a complex matrix.
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if( cn == 1 )
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{
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Mat &converted_src1 = test_mat[TEMP][0];
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Mat &converted_src2 = test_mat[TEMP][1];
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convert_from_ccs( src1, src1, converted_src1, flags );
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convert_from_ccs( src2, src2, converted_src2, flags );
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div_complex( converted_src1, converted_src2, ref_dst, flags );
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}
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// Inputs are complex matrices. Get the expected output as a complex matrix.
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else
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{
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div_complex( src1, src2, ref_dst, flags );
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}
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}
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void CV_DivSpectrumsTest::run_func()
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{
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const Mat &src1 = test_mat[INPUT][0];
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const Mat &src2 = test_mat[INPUT][1];
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const int cn = src1.channels();
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// Inputs are CCS-packed arrays. Get the output as a CCS-packed array and convert it to a
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// complex matrix.
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if ( cn == 1 )
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{
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Mat &dst = test_mat[TEMP][2];
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cv::divSpectrums( src1, src2, dst, flags, (flags & CV_DXT_MUL_CONJ) != 0 );
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Mat &converted_dst = test_mat[OUTPUT][0];
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convert_from_ccs( dst, dst, converted_dst, flags );
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}
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// Inputs are complex matrices. Get the output as a complex matrix.
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else
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{
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Mat &dst = test_mat[OUTPUT][0];
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cv::divSpectrums( src1, src2, dst, flags, (flags & CV_DXT_MUL_CONJ) != 0 );
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}
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}
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TEST(Imgproc_DivSpectrums, accuracy) { CV_DivSpectrumsTest test; test.safe_run(); }
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}} // namespace
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