mirror of
https://github.com/opencv/opencv.git
synced 2024-12-21 05:28:01 +08:00
402 lines
12 KiB
C++
402 lines
12 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "test_precomp.hpp"
|
|
|
|
using namespace cv;
|
|
using namespace std;
|
|
|
|
class CV_ECC_BaseTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_ECC_BaseTest();
|
|
|
|
protected:
|
|
|
|
double computeRMS(const Mat& mat1, const Mat& mat2);
|
|
bool isMapCorrect(const Mat& mat);
|
|
|
|
|
|
double MAX_RMS_ECC;//upper bound for RMS error
|
|
int ntests;//number of tests per motion type
|
|
int ECC_iterations;//number of iterations for ECC
|
|
double ECC_epsilon; //we choose a negative value, so that
|
|
// ECC_iterations are always executed
|
|
};
|
|
|
|
CV_ECC_BaseTest::CV_ECC_BaseTest()
|
|
{
|
|
MAX_RMS_ECC=0.1;
|
|
ntests = 3;
|
|
ECC_iterations = 50;
|
|
ECC_epsilon = -1; //-> negative value means that ECC_Iterations will be executed
|
|
}
|
|
|
|
|
|
bool CV_ECC_BaseTest::isMapCorrect(const Mat& map)
|
|
{
|
|
bool tr = true;
|
|
float mapVal;
|
|
for(int i =0; i<map.rows; i++)
|
|
for(int j=0; j<map.cols; j++){
|
|
mapVal = map.at<float>(i, j);
|
|
tr = tr & (!cvIsNaN(mapVal) && (fabs(mapVal) < 1e9));
|
|
}
|
|
|
|
return tr;
|
|
}
|
|
|
|
double CV_ECC_BaseTest::computeRMS(const Mat& mat1, const Mat& mat2){
|
|
|
|
CV_Assert(mat1.rows == mat2.rows);
|
|
CV_Assert(mat1.cols == mat2.cols);
|
|
|
|
Mat errorMat;
|
|
subtract(mat1, mat2, errorMat);
|
|
|
|
return sqrt(errorMat.dot(errorMat)/(mat1.rows*mat1.cols));
|
|
}
|
|
|
|
|
|
class CV_ECC_Test_Translation : public CV_ECC_BaseTest
|
|
{
|
|
public:
|
|
CV_ECC_Test_Translation();
|
|
protected:
|
|
void run(int);
|
|
|
|
bool testTranslation(int);
|
|
};
|
|
|
|
CV_ECC_Test_Translation::CV_ECC_Test_Translation(){};
|
|
|
|
bool CV_ECC_Test_Translation::testTranslation(int from)
|
|
{
|
|
Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0);
|
|
|
|
|
|
if (img.empty())
|
|
{
|
|
ts->printf( ts->LOG, "test image can not be read");
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
|
return false;
|
|
}
|
|
Mat testImg;
|
|
resize(img, testImg, Size(216, 216));
|
|
|
|
cv::RNG rng = ts->get_rng();
|
|
|
|
int progress=0;
|
|
|
|
for (int k=from; k<ntests; k++){
|
|
|
|
ts->update_context( this, k, true );
|
|
progress = update_progress(progress, k, ntests, 0);
|
|
|
|
Mat translationGround = (Mat_<float>(2,3) << 1, 0, (rng.uniform(10.f, 20.f)),
|
|
0, 1, (rng.uniform(10.f, 20.f)));
|
|
|
|
Mat warpedImage;
|
|
|
|
warpAffine(testImg, warpedImage, translationGround,
|
|
Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP);
|
|
|
|
Mat mapTranslation = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0);
|
|
|
|
findTransformECC(warpedImage, testImg, mapTranslation, 0,
|
|
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon));
|
|
|
|
if (!isMapCorrect(mapTranslation)){
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return false;
|
|
}
|
|
|
|
if (computeRMS(mapTranslation, translationGround)>MAX_RMS_ECC){
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
ts->printf( ts->LOG, "RMS = %f",
|
|
computeRMS(mapTranslation, translationGround));
|
|
return false;
|
|
}
|
|
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void CV_ECC_Test_Translation::run(int from)
|
|
{
|
|
|
|
if (!testTranslation(from))
|
|
return;
|
|
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
}
|
|
|
|
|
|
|
|
class CV_ECC_Test_Euclidean : public CV_ECC_BaseTest
|
|
{
|
|
public:
|
|
CV_ECC_Test_Euclidean();
|
|
protected:
|
|
void run(int);
|
|
|
|
bool testEuclidean(int);
|
|
};
|
|
|
|
CV_ECC_Test_Euclidean::CV_ECC_Test_Euclidean() { }
|
|
|
|
bool CV_ECC_Test_Euclidean::testEuclidean(int from)
|
|
{
|
|
Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0);
|
|
|
|
|
|
if (img.empty())
|
|
{
|
|
ts->printf( ts->LOG, "test image can not be read");
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
|
return false;
|
|
}
|
|
Mat testImg;
|
|
resize(img, testImg, Size(216, 216));
|
|
|
|
cv::RNG rng = ts->get_rng();
|
|
|
|
int progress = 0;
|
|
for (int k=from; k<ntests; k++){
|
|
ts->update_context( this, k, true );
|
|
progress = update_progress(progress, k, ntests, 0);
|
|
|
|
double angle = CV_PI/30 + CV_PI*rng.uniform((double)-2.f, (double)2.f)/180;
|
|
|
|
Mat euclideanGround = (Mat_<float>(2,3) << cos(angle), -sin(angle), (rng.uniform(10.f, 20.f)),
|
|
sin(angle), cos(angle), (rng.uniform(10.f, 20.f)));
|
|
|
|
Mat warpedImage;
|
|
|
|
warpAffine(testImg, warpedImage, euclideanGround,
|
|
Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP);
|
|
|
|
Mat mapEuclidean = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0);
|
|
|
|
findTransformECC(warpedImage, testImg, mapEuclidean, 1,
|
|
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon));
|
|
|
|
if (!isMapCorrect(mapEuclidean)){
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return false;
|
|
}
|
|
|
|
if (computeRMS(mapEuclidean, euclideanGround)>MAX_RMS_ECC){
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
ts->printf( ts->LOG, "RMS = %f",
|
|
computeRMS(mapEuclidean, euclideanGround));
|
|
return false;
|
|
}
|
|
|
|
}
|
|
return true;
|
|
}
|
|
|
|
|
|
void CV_ECC_Test_Euclidean::run(int from)
|
|
{
|
|
|
|
if (!testEuclidean(from))
|
|
return;
|
|
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
}
|
|
|
|
class CV_ECC_Test_Affine : public CV_ECC_BaseTest
|
|
{
|
|
public:
|
|
CV_ECC_Test_Affine();
|
|
protected:
|
|
void run(int);
|
|
|
|
bool testAffine(int);
|
|
};
|
|
|
|
CV_ECC_Test_Affine::CV_ECC_Test_Affine(){};
|
|
|
|
|
|
bool CV_ECC_Test_Affine::testAffine(int from)
|
|
{
|
|
Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0);
|
|
|
|
if (img.empty())
|
|
{
|
|
ts->printf( ts->LOG, "test image can not be read");
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
|
return false;
|
|
}
|
|
Mat testImg;
|
|
resize(img, testImg, Size(216, 216));
|
|
|
|
cv::RNG rng = ts->get_rng();
|
|
|
|
int progress = 0;
|
|
for (int k=from; k<ntests; k++){
|
|
ts->update_context( this, k, true );
|
|
progress = update_progress(progress, k, ntests, 0);
|
|
|
|
|
|
Mat affineGround = (Mat_<float>(2,3) << (1-rng.uniform(-0.05f, 0.05f)),
|
|
(rng.uniform(-0.03f, 0.03f)), (rng.uniform(10.f, 20.f)),
|
|
(rng.uniform(-0.03f, 0.03f)), (1-rng.uniform(-0.05f, 0.05f)),
|
|
(rng.uniform(10.f, 20.f)));
|
|
|
|
Mat warpedImage;
|
|
|
|
warpAffine(testImg, warpedImage, affineGround,
|
|
Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP);
|
|
|
|
Mat mapAffine = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0);
|
|
|
|
findTransformECC(warpedImage, testImg, mapAffine, 2,
|
|
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon));
|
|
|
|
if (!isMapCorrect(mapAffine)){
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return false;
|
|
}
|
|
|
|
if (computeRMS(mapAffine, affineGround)>MAX_RMS_ECC){
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
ts->printf( ts->LOG, "RMS = %f",
|
|
computeRMS(mapAffine, affineGround));
|
|
return false;
|
|
}
|
|
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
|
|
void CV_ECC_Test_Affine::run(int from)
|
|
{
|
|
|
|
if (!testAffine(from))
|
|
return;
|
|
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
}
|
|
|
|
class CV_ECC_Test_Homography : public CV_ECC_BaseTest
|
|
{
|
|
public:
|
|
CV_ECC_Test_Homography();
|
|
protected:
|
|
void run(int);
|
|
|
|
bool testHomography(int);
|
|
};
|
|
|
|
CV_ECC_Test_Homography::CV_ECC_Test_Homography(){};
|
|
|
|
bool CV_ECC_Test_Homography::testHomography(int from)
|
|
{
|
|
Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0);
|
|
|
|
|
|
if (img.empty())
|
|
{
|
|
ts->printf( ts->LOG, "test image can not be read");
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
|
return false;
|
|
}
|
|
Mat testImg;
|
|
resize(img, testImg, Size(216, 216));
|
|
|
|
cv::RNG rng = ts->get_rng();
|
|
|
|
int progress = 0;
|
|
for (int k=from; k<ntests; k++){
|
|
ts->update_context( this, k, true );
|
|
progress = update_progress(progress, k, ntests, 0);
|
|
|
|
Mat homoGround = (Mat_<float>(3,3) << (1-rng.uniform(-0.05f, 0.05f)),
|
|
(rng.uniform(-0.03f, 0.03f)), (rng.uniform(10.f, 20.f)),
|
|
(rng.uniform(-0.03f, 0.03f)), (1-rng.uniform(-0.05f, 0.05f)),(rng.uniform(10.f, 20.f)),
|
|
(rng.uniform(0.0001f, 0.0003f)), (rng.uniform(0.0001f, 0.0003f)), 1.f);
|
|
|
|
Mat warpedImage;
|
|
|
|
warpPerspective(testImg, warpedImage, homoGround,
|
|
Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP);
|
|
|
|
Mat mapHomography = Mat::eye(3, 3, CV_32F);
|
|
|
|
findTransformECC(warpedImage, testImg, mapHomography, 3,
|
|
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon));
|
|
|
|
if (!isMapCorrect(mapHomography)){
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return false;
|
|
}
|
|
|
|
if (computeRMS(mapHomography, homoGround)>MAX_RMS_ECC){
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
ts->printf( ts->LOG, "RMS = %f",
|
|
computeRMS(mapHomography, homoGround));
|
|
return false;
|
|
}
|
|
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void CV_ECC_Test_Homography::run(int from)
|
|
{
|
|
if (!testHomography(from))
|
|
return;
|
|
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
}
|
|
|
|
|
|
TEST(Video_ECC_Translation, accuracy) { CV_ECC_Test_Translation test; test.safe_run();}
|
|
TEST(Video_ECC_Euclidean, accuracy) { CV_ECC_Test_Euclidean test; test.safe_run(); }
|
|
TEST(Video_ECC_Affine, accuracy) { CV_ECC_Test_Affine test; test.safe_run(); }
|
|
TEST(Video_ECC_Homography, accuracy) { CV_ECC_Test_Homography test; test.safe_run(); }
|