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Merge branch 4.x

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This commit is contained in:
Alexander Smorkalov
2026-04-23 11:00:34 +03:00
parent 323ea1782d 273e52ce8d
commit 7bde3f9ae9
38 changed files with 3437 additions and 293 deletions
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modules/video/test/test_ecc.cpp
+184 -184
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@@ -45,16 +45,30 @@
namespace opencv_test {
namespace {
class CV_ECC_BaseTest : public cvtest::BaseTest {
PARAM_TEST_CASE(Video_ECC, int, bool)
{
int motionType;
bool usePyramids;
virtual void SetUp()
{
motionType = GET_PARAM(0);
usePyramids = GET_PARAM(1);
}
};
class CV_ECC_Test : public cvtest::BaseTest {
public:
CV_ECC_BaseTest();
virtual ~CV_ECC_BaseTest();
CV_ECC_Test(int motionType, bool usePyramids);
virtual ~CV_ECC_Test();
protected:
int motionType;
double MAX_RMS; // upper bound for RMS error
double computeRMS(const Mat& mat1, const Mat& mat2);
bool isMapCorrect(const Mat& mat);
virtual bool test(const Mat) { return true; }; // single test
virtual bool test(const Mat img);
bool testAllTypes(const Mat img); // run test for all supported data types (U8, U16, F32, F64)
bool testAllChNum(const Mat img); // run test for all supported channels count (gray, RGB)
@@ -62,25 +76,28 @@ class CV_ECC_BaseTest : public cvtest::BaseTest {
bool checkMap(const Mat& map, const Mat& ground);
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
TermCriteria criteria;
bool usePyramids; // use version of findTransformECC with pyramids
};
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
criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, ECC_iterations, ECC_epsilon);
}
CV_ECC_BaseTest::~CV_ECC_BaseTest() {}
CV_ECC_Test::CV_ECC_Test(int a_motionType, bool a_usePyramids) : motionType(a_motionType)
, MAX_RMS(0.1)
, ntests(3)
, ECC_iterations(50)
, ECC_epsilon(-1)
, criteria(TermCriteria::COUNT + TermCriteria::EPS, ECC_iterations, ECC_epsilon)
, usePyramids(a_usePyramids)
{}
bool CV_ECC_BaseTest::isMapCorrect(const Mat& map) {
CV_ECC_Test::~CV_ECC_Test() {}
bool CV_ECC_Test::isMapCorrect(const Mat& map) {
bool tr = true;
float mapVal;
for (int i = 0; i < map.rows; i++)
@@ -92,7 +109,7 @@ bool CV_ECC_BaseTest::isMapCorrect(const Mat& map) {
return tr;
}
double CV_ECC_BaseTest::computeRMS(const Mat& mat1, const Mat& mat2) {
double CV_ECC_Test::computeRMS(const Mat& mat1, const Mat& mat2) {
CV_Assert(mat1.rows == mat2.rows);
CV_Assert(mat1.cols == mat2.cols);
@@ -102,13 +119,13 @@ double CV_ECC_BaseTest::computeRMS(const Mat& mat1, const Mat& mat2) {
return sqrt(errorMat.dot(errorMat) / (mat1.rows * mat1.cols * mat1.channels()));
}
bool CV_ECC_BaseTest::checkMap(const Mat& map, const Mat& ground) {
bool CV_ECC_Test::checkMap(const Mat& map, const Mat& ground) {
if (!isMapCorrect(map)) {
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return false;
}
if (computeRMS(map, ground) > MAX_RMS_ECC) {
if (computeRMS(map, ground) > MAX_RMS) {
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
ts->printf(ts->LOG, "RMS = %f", computeRMS(map, ground));
return false;
@@ -116,7 +133,77 @@ bool CV_ECC_BaseTest::checkMap(const Mat& map, const Mat& ground) {
return true;
}
bool CV_ECC_BaseTest::testAllTypes(const Mat img) {
bool CV_ECC_Test::test(const Mat img)
{
cv::RNG rng = ts->get_rng();
int progress = 0;
for (int k = 0; k < ntests; k++) {
ts->update_context(this, k, true);
progress = update_progress(progress, k, ntests, 0);
Mat groundMap;
switch(motionType)
{
case MOTION_TRANSLATION:
groundMap = (Mat_<float>(2, 3) << 1, 0, (rng.uniform(10.f, 20.f)), 0, 1, (rng.uniform(10.f, 20.f)));
break;
case MOTION_EUCLIDEAN:
{
double angle = CV_PI / 30 + CV_PI * rng.uniform((double)-2.f, (double)2.f) / 180;
groundMap = (Mat_<float>(2, 3) << cos(angle), -sin(angle), (rng.uniform(10.f, 20.f)), sin(angle),
cos(angle), (rng.uniform(10.f, 20.f)));
break;
}
case MOTION_AFFINE:
groundMap = (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)));
break;
case MOTION_HOMOGRAPHY:
groundMap =
(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);
break;
default:
CV_Error(Error::StsBadArg, "Incorrect motion type");
break;
}
Mat warpedImage;
Mat foundMap;
if(motionType == MOTION_HOMOGRAPHY)
{
warpPerspective(img, warpedImage, groundMap, Size(200, 200), INTER_LINEAR + WARP_INVERSE_MAP);
foundMap = Mat::eye(3, 3, CV_32F);
}
else
{
warpAffine(img, warpedImage, groundMap, Size(200, 200), INTER_LINEAR + WARP_INVERSE_MAP);
foundMap = Mat((Mat_<float>(2, 3) << 1, 0, 0, 0, 1, 0));
}
if(usePyramids)
{
ECCParameters params;
params.criteria = criteria;
params.motionType = motionType;
findTransformECCMultiScale(warpedImage, img, foundMap, params);
}
else
findTransformECC(warpedImage, img, foundMap, motionType, criteria);
if (!checkMap(foundMap, groundMap))
return false;
}
return true;
}
bool CV_ECC_Test::testAllTypes(const Mat img) {
auto types = {CV_8U, CV_16U, CV_32F, CV_64F};
for (auto type : types) {
Mat timg;
@@ -127,9 +214,10 @@ bool CV_ECC_BaseTest::testAllTypes(const Mat img) {
return true;
}
bool CV_ECC_BaseTest::testAllChNum(const Mat img) {
if (!testAllTypes(img))
return false;
bool CV_ECC_Test::testAllChNum(const Mat img) {
if(!usePyramids)
if (!testAllTypes(img))
return false;
Mat gray;
cvtColor(img, gray, COLOR_RGB2GRAY);
@@ -139,7 +227,7 @@ bool CV_ECC_BaseTest::testAllChNum(const Mat img) {
return true;
}
void CV_ECC_BaseTest::run(int) {
void CV_ECC_Test::run(int) {
Mat img = imread(string(ts->get_data_path()) + "shared/fruits.png");
if (img.empty()) {
ts->printf(ts->LOG, "test image can not be read");
@@ -155,153 +243,22 @@ void CV_ECC_BaseTest::run(int) {
ts->set_failed_test_info(cvtest::TS::OK);
}
class CV_ECC_Test_Translation : public CV_ECC_BaseTest {
public:
CV_ECC_Test_Translation();
protected:
bool test(const Mat);
};
CV_ECC_Test_Translation::CV_ECC_Test_Translation() {}
bool CV_ECC_Test_Translation::test(const Mat testImg) {
cv::RNG rng = ts->get_rng();
int progress = 0;
for (int k = 0; 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, criteria);
if (!checkMap(mapTranslation, translationGround))
return false;
}
return true;
TEST_P(Video_ECC, accuracy) {
CV_ECC_Test test(motionType, usePyramids);
test.safe_run();
}
class CV_ECC_Test_Euclidean : public CV_ECC_BaseTest {
public:
CV_ECC_Test_Euclidean();
INSTANTIATE_TEST_CASE_P(ECCfixtures, Video_ECC,
testing::Values(testing::make_tuple(MOTION_TRANSLATION, false),
testing::make_tuple(MOTION_TRANSLATION, true),
testing::make_tuple(MOTION_EUCLIDEAN, false),
testing::make_tuple(MOTION_EUCLIDEAN, true),
testing::make_tuple(MOTION_AFFINE, false),
testing::make_tuple(MOTION_AFFINE, true),
testing::make_tuple(MOTION_HOMOGRAPHY, false),
testing::make_tuple(MOTION_HOMOGRAPHY, true)));
protected:
bool test(const Mat);
};
CV_ECC_Test_Euclidean::CV_ECC_Test_Euclidean() {}
bool CV_ECC_Test_Euclidean::test(const Mat testImg) {
cv::RNG rng = ts->get_rng();
int progress = 0;
for (int k = 0; 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, criteria);
if (!checkMap(mapEuclidean, euclideanGround))
return false;
}
return true;
}
class CV_ECC_Test_Affine : public CV_ECC_BaseTest {
public:
CV_ECC_Test_Affine();
protected:
bool test(const Mat img);
};
CV_ECC_Test_Affine::CV_ECC_Test_Affine() {}
bool CV_ECC_Test_Affine::test(const Mat testImg) {
cv::RNG rng = ts->get_rng();
int progress = 0;
for (int k = 0; 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, criteria);
if (!checkMap(mapAffine, affineGround))
return false;
}
return true;
}
class CV_ECC_Test_Homography : public CV_ECC_BaseTest {
public:
CV_ECC_Test_Homography();
protected:
bool test(const Mat testImg);
};
CV_ECC_Test_Homography::CV_ECC_Test_Homography() {}
bool CV_ECC_Test_Homography::test(const Mat testImg) {
cv::RNG rng = ts->get_rng();
int progress = 0;
for (int k = 0; 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, criteria);
if (!checkMap(mapHomography, homoGround))
return false;
}
return true;
}
class CV_ECC_Test_Mask : public CV_ECC_BaseTest {
class CV_ECC_Test_Mask : public CV_ECC_Test {
public:
CV_ECC_Test_Mask();
@@ -309,7 +266,7 @@ class CV_ECC_Test_Mask : public CV_ECC_BaseTest {
bool test(const Mat);
};
CV_ECC_Test_Mask::CV_ECC_Test_Mask() {}
CV_ECC_Test_Mask::CV_ECC_Test_Mask():CV_ECC_Test(MOTION_TRANSLATION, false) {}
bool CV_ECC_Test_Mask::test(const Mat testImg) {
cv::RNG rng = ts->get_rng();
@@ -368,6 +325,58 @@ bool CV_ECC_Test_Mask::test(const Mat testImg) {
return true;
}
class CV_ECC_BigPictureTest : public CV_ECC_Test {
public:
CV_ECC_BigPictureTest(bool a_maskedVersion) : CV_ECC_Test(MOTION_HOMOGRAPHY, true), maskedVersion(a_maskedVersion) {}
virtual ~CV_ECC_BigPictureTest() {}
protected:
void run(int);
bool maskedVersion;
};
void CV_ECC_BigPictureTest::run(int)
{
Mat largeGray0 = imread(string(ts->get_data_path()) + "shared/halmosh0.jpg", IMREAD_GRAYSCALE);
Mat largeGray1;
Mat roiMask0;
Mat roiMask1;
Mat expectedRes;
bool readError = false;
if(maskedVersion)
{
largeGray1 = imread(string(ts->get_data_path()) + "shared/halmosh2.jpg", IMREAD_GRAYSCALE);
roiMask0 = imread(string(ts->get_data_path()) + "shared/halmosh0mask.png", IMREAD_GRAYSCALE);
roiMask1 = imread(string(ts->get_data_path()) + "shared/halmosh2mask.png", IMREAD_GRAYSCALE);
readError = largeGray0.empty() || largeGray1.empty() || roiMask0.empty() || roiMask1.empty();
expectedRes = (Mat_<float>(3, 3) << 1.0225, 0.0606, -28.6452, -0.0475, 1.0314, 11.819, 8.21e-06, -3.65e-07, 1);
}
else
{
largeGray1 = imread(string(ts->get_data_path()) + "shared/halmosh1.jpg", IMREAD_GRAYSCALE);
readError = largeGray0.empty() || largeGray1.empty();
expectedRes = (Mat_<float>(3, 3) << 0.9756, -0.0319, 24.685, 0.013, 0.9808, 7.7453, -2.35e-05, -9.12e-06, 1);
}
if(readError)
{
ts->printf(ts->LOG, "test image can not be read");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
cv::Mat found = cv::Mat::eye(3, 3, CV_32F);
constexpr int N_ITERS = 20;
constexpr double TERMINATION_EPS = 1e-6;
ECCParameters params;
params.criteria = cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, N_ITERS, TERMINATION_EPS);
params.motionType = MOTION_HOMOGRAPHY;
params.nlevels = 5;
params.itersPerLevel = {5, 10, 300, 300, 1000};
findTransformECCMultiScale(largeGray0, largeGray1, found, params, roiMask0, roiMask1);
ASSERT_EQ(checkMap(found, expectedRes), true);
ts->set_failed_test_info(cvtest::TS::OK);
}
void testECCProperties(Mat x, float eps) {
// The channels are independent
Mat y = x.t();
@@ -450,26 +459,17 @@ TEST(Video_ECC_Test_Compute, bug_14657) {
EXPECT_NEAR(computeECC(img, img), 1.0f, 1e-5f);
}
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();
}
TEST(Video_ECC_Mask, accuracy) {
CV_ECC_Test_Mask test;
test.safe_run();
}
TEST(Video_ECC_BigMS, accuracy) {
CV_ECC_BigPictureTest test(false);
test.safe_run();
}
TEST(Video_ECC_BigMS_Mask, accuracy) {
CV_ECC_BigPictureTest test(true);
test.safe_run();
}
} // namespace
} // namespace opencv_test