mirror of
https://github.com/opencv/opencv.git
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4c94804bb0
New computeECC function, and updated findTransformECC function to make gaussian filtering optional (#13837) * fix for https://github.com/opencv/opencv/issues/12432 with doc and tests * Added doc string for new parameter. * Fixes suggested by Alalek for getting around ABI incompatibility. * Update to docstring, to remove parameter that isn't relevant. * More updates based on Alalek's usggestions.
511 lines
15 KiB
C++
511 lines
15 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) 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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class CV_ECC_BaseTest : public cvtest::BaseTest
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{
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public:
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CV_ECC_BaseTest();
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protected:
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double computeRMS(const Mat& mat1, const Mat& mat2);
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bool isMapCorrect(const Mat& mat);
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double MAX_RMS_ECC;//upper bound for RMS error
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int ntests;//number of tests per motion type
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int ECC_iterations;//number of iterations for ECC
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double ECC_epsilon; //we choose a negative value, so that
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// ECC_iterations are always executed
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};
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CV_ECC_BaseTest::CV_ECC_BaseTest()
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{
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MAX_RMS_ECC=0.1;
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ntests = 3;
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ECC_iterations = 50;
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ECC_epsilon = -1; //-> negative value means that ECC_Iterations will be executed
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}
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bool CV_ECC_BaseTest::isMapCorrect(const Mat& map)
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{
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bool tr = true;
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float mapVal;
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for(int i =0; i<map.rows; i++)
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for(int j=0; j<map.cols; j++){
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mapVal = map.at<float>(i, j);
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tr = tr & (!cvIsNaN(mapVal) && (fabs(mapVal) < 1e9));
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}
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return tr;
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}
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double CV_ECC_BaseTest::computeRMS(const Mat& mat1, const Mat& mat2){
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CV_Assert(mat1.rows == mat2.rows);
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CV_Assert(mat1.cols == mat2.cols);
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Mat errorMat;
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subtract(mat1, mat2, errorMat);
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return sqrt(errorMat.dot(errorMat)/(mat1.rows*mat1.cols));
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}
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class CV_ECC_Test_Translation : public CV_ECC_BaseTest
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{
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public:
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CV_ECC_Test_Translation();
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protected:
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void run(int);
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bool testTranslation(int);
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};
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CV_ECC_Test_Translation::CV_ECC_Test_Translation(){}
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bool CV_ECC_Test_Translation::testTranslation(int from)
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{
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Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0);
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if (img.empty())
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{
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ts->printf( ts->LOG, "test image can not be read");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return false;
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}
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Mat testImg;
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resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT);
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cv::RNG rng = ts->get_rng();
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int progress=0;
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for (int k=from; k<ntests; k++){
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ts->update_context( this, k, true );
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progress = update_progress(progress, k, ntests, 0);
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Mat translationGround = (Mat_<float>(2,3) << 1, 0, (rng.uniform(10.f, 20.f)),
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0, 1, (rng.uniform(10.f, 20.f)));
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Mat warpedImage;
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warpAffine(testImg, warpedImage, translationGround,
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Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP);
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Mat mapTranslation = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0);
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findTransformECC(warpedImage, testImg, mapTranslation, 0,
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon));
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if (!isMapCorrect(mapTranslation)){
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
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return false;
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}
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if (computeRMS(mapTranslation, translationGround)>MAX_RMS_ECC){
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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ts->printf( ts->LOG, "RMS = %f",
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computeRMS(mapTranslation, translationGround));
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return false;
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}
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}
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return true;
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}
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void CV_ECC_Test_Translation::run(int from)
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{
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if (!testTranslation(from))
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return;
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ts->set_failed_test_info(cvtest::TS::OK);
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}
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class CV_ECC_Test_Euclidean : public CV_ECC_BaseTest
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{
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public:
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CV_ECC_Test_Euclidean();
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protected:
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void run(int);
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bool testEuclidean(int);
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};
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CV_ECC_Test_Euclidean::CV_ECC_Test_Euclidean() { }
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bool CV_ECC_Test_Euclidean::testEuclidean(int from)
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{
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Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0);
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if (img.empty())
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{
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ts->printf( ts->LOG, "test image can not be read");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return false;
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}
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Mat testImg;
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resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT);
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cv::RNG rng = ts->get_rng();
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int progress = 0;
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for (int k=from; k<ntests; k++){
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ts->update_context( this, k, true );
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progress = update_progress(progress, k, ntests, 0);
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double angle = CV_PI/30 + CV_PI*rng.uniform((double)-2.f, (double)2.f)/180;
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Mat euclideanGround = (Mat_<float>(2,3) << cos(angle), -sin(angle), (rng.uniform(10.f, 20.f)),
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sin(angle), cos(angle), (rng.uniform(10.f, 20.f)));
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Mat warpedImage;
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warpAffine(testImg, warpedImage, euclideanGround,
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Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP);
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Mat mapEuclidean = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0);
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findTransformECC(warpedImage, testImg, mapEuclidean, 1,
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon));
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if (!isMapCorrect(mapEuclidean)){
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
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return false;
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}
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if (computeRMS(mapEuclidean, euclideanGround)>MAX_RMS_ECC){
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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ts->printf( ts->LOG, "RMS = %f",
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computeRMS(mapEuclidean, euclideanGround));
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return false;
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}
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}
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return true;
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}
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void CV_ECC_Test_Euclidean::run(int from)
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{
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if (!testEuclidean(from))
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return;
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ts->set_failed_test_info(cvtest::TS::OK);
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}
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class CV_ECC_Test_Affine : public CV_ECC_BaseTest
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{
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public:
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CV_ECC_Test_Affine();
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protected:
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void run(int);
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bool testAffine(int);
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};
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CV_ECC_Test_Affine::CV_ECC_Test_Affine(){}
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bool CV_ECC_Test_Affine::testAffine(int from)
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{
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Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0);
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if (img.empty())
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{
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ts->printf( ts->LOG, "test image can not be read");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return false;
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}
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Mat testImg;
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resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT);
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cv::RNG rng = ts->get_rng();
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int progress = 0;
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for (int k=from; k<ntests; k++){
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ts->update_context( this, k, true );
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progress = update_progress(progress, k, ntests, 0);
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Mat affineGround = (Mat_<float>(2,3) << (1-rng.uniform(-0.05f, 0.05f)),
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(rng.uniform(-0.03f, 0.03f)), (rng.uniform(10.f, 20.f)),
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(rng.uniform(-0.03f, 0.03f)), (1-rng.uniform(-0.05f, 0.05f)),
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(rng.uniform(10.f, 20.f)));
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Mat warpedImage;
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warpAffine(testImg, warpedImage, affineGround,
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Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP);
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Mat mapAffine = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0);
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findTransformECC(warpedImage, testImg, mapAffine, 2,
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon));
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if (!isMapCorrect(mapAffine)){
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
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return false;
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}
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if (computeRMS(mapAffine, affineGround)>MAX_RMS_ECC){
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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ts->printf( ts->LOG, "RMS = %f",
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computeRMS(mapAffine, affineGround));
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return false;
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}
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}
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return true;
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}
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void CV_ECC_Test_Affine::run(int from)
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{
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if (!testAffine(from))
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return;
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ts->set_failed_test_info(cvtest::TS::OK);
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}
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class CV_ECC_Test_Homography : public CV_ECC_BaseTest
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{
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public:
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CV_ECC_Test_Homography();
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protected:
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void run(int);
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bool testHomography(int);
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};
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CV_ECC_Test_Homography::CV_ECC_Test_Homography(){}
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bool CV_ECC_Test_Homography::testHomography(int from)
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{
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Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0);
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if (img.empty())
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{
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ts->printf( ts->LOG, "test image can not be read");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return false;
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}
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Mat testImg;
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resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT);
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cv::RNG rng = ts->get_rng();
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int progress = 0;
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for (int k=from; k<ntests; k++){
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ts->update_context( this, k, true );
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progress = update_progress(progress, k, ntests, 0);
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Mat homoGround = (Mat_<float>(3,3) << (1-rng.uniform(-0.05f, 0.05f)),
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(rng.uniform(-0.03f, 0.03f)), (rng.uniform(10.f, 20.f)),
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(rng.uniform(-0.03f, 0.03f)), (1-rng.uniform(-0.05f, 0.05f)),(rng.uniform(10.f, 20.f)),
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(rng.uniform(0.0001f, 0.0003f)), (rng.uniform(0.0001f, 0.0003f)), 1.f);
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Mat warpedImage;
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warpPerspective(testImg, warpedImage, homoGround,
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Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP);
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Mat mapHomography = Mat::eye(3, 3, CV_32F);
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findTransformECC(warpedImage, testImg, mapHomography, 3,
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon));
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if (!isMapCorrect(mapHomography)){
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
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return false;
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}
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if (computeRMS(mapHomography, homoGround)>MAX_RMS_ECC){
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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ts->printf( ts->LOG, "RMS = %f",
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computeRMS(mapHomography, homoGround));
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return false;
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}
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}
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return true;
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}
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void CV_ECC_Test_Homography::run(int from)
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{
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if (!testHomography(from))
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return;
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ts->set_failed_test_info(cvtest::TS::OK);
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}
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class CV_ECC_Test_Mask : public CV_ECC_BaseTest
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{
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public:
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CV_ECC_Test_Mask();
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protected:
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void run(int);
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bool testMask(int);
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};
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CV_ECC_Test_Mask::CV_ECC_Test_Mask(){}
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bool CV_ECC_Test_Mask::testMask(int from)
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{
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Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0);
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if (img.empty())
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{
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ts->printf( ts->LOG, "test image can not be read");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return false;
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}
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Mat scaledImage;
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resize(img, scaledImage, Size(216, 216), 0, 0, INTER_LINEAR_EXACT );
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Mat_<float> testImg;
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scaledImage.convertTo(testImg, testImg.type());
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cv::RNG rng = ts->get_rng();
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int progress=0;
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for (int k=from; k<ntests; k++){
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ts->update_context( this, k, true );
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progress = update_progress(progress, k, ntests, 0);
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Mat translationGround = (Mat_<float>(2,3) << 1, 0, (rng.uniform(10.f, 20.f)),
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0, 1, (rng.uniform(10.f, 20.f)));
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Mat warpedImage;
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warpAffine(testImg, warpedImage, translationGround,
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Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP);
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Mat mapTranslation = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0);
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Mat_<unsigned char> mask = Mat_<unsigned char>::ones(testImg.rows, testImg.cols);
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for (int i=testImg.rows*2/3; i<testImg.rows; i++) {
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for (int j=testImg.cols*2/3; j<testImg.cols; j++) {
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testImg(i, j) = 0;
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mask(i, j) = 0;
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}
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}
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findTransformECC(warpedImage, testImg, mapTranslation, 0,
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon), mask);
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if (!isMapCorrect(mapTranslation)){
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
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return false;
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}
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if (computeRMS(mapTranslation, translationGround)>MAX_RMS_ECC){
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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ts->printf( ts->LOG, "RMS = %f",
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computeRMS(mapTranslation, translationGround));
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return false;
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}
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// Test with non-default gaussian blur.
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findTransformECC(warpedImage, testImg, mapTranslation, 0,
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon), mask, 1);
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if (!isMapCorrect(mapTranslation)){
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
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return false;
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}
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if (computeRMS(mapTranslation, translationGround)>MAX_RMS_ECC){
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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ts->printf( ts->LOG, "RMS = %f",
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computeRMS(mapTranslation, translationGround));
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return false;
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}
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}
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return true;
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}
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void CV_ECC_Test_Mask::run(int from)
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{
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if (!testMask(from))
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return;
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ts->set_failed_test_info(cvtest::TS::OK);
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}
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TEST(Video_ECC_Test_Compute, accuracy)
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{
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Mat testImg = (Mat_<float>(3, 3) << 1, 0, 0, 1, 0, 0, 1, 0, 0);
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Mat warpedImage = (Mat_<float>(3, 3) << 0, 1, 0, 0, 1, 0, 0, 1, 0);
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Mat_<unsigned char> mask = Mat_<unsigned char>::ones(testImg.rows, testImg.cols);
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double ecc = computeECC(warpedImage, testImg, mask);
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EXPECT_NEAR(ecc, -0.5f, 1e-5f);
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}
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TEST(Video_ECC_Translation, accuracy) { CV_ECC_Test_Translation test; test.safe_run();}
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TEST(Video_ECC_Euclidean, accuracy) { CV_ECC_Test_Euclidean test; test.safe_run(); }
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TEST(Video_ECC_Affine, accuracy) { CV_ECC_Test_Affine test; test.safe_run(); }
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TEST(Video_ECC_Homography, accuracy) { CV_ECC_Test_Homography test; test.safe_run(); }
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TEST(Video_ECC_Mask, accuracy) { CV_ECC_Test_Mask test; test.safe_run(); }
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}} // namespace
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