test_templmatchmask.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
CV_ENUM(MatchTemplType, CV_TM_CCORR, CV_TM_CCORR_NORMED,
CV_TM_SQDIFF, CV_TM_SQDIFF_NORMED,
CV_TM_CCOEFF, CV_TM_CCOEFF_NORMED)
class Imgproc_MatchTemplateWithMask : public TestWithParam<std::tuple<MatType,MatType>>
{
protected:
// Member functions inherited from ::testing::Test
void SetUp() override;
// Matrices for test calculations (always CV_32)
Mat img_;
Mat templ_;
Mat mask_;
Mat templ_masked_;
Mat img_roi_masked_;
// Matrices for call to matchTemplate (have test type)
Mat img_testtype_;
Mat templ_testtype_;
Mat mask_testtype_;
Mat result_;
// Constants
static const Size IMG_SIZE;
static const Size TEMPL_SIZE;
static const Point TEST_POINT;
};
// Arbitraryly chosen test constants
const Size Imgproc_MatchTemplateWithMask::IMG_SIZE(160, 100);
const Size Imgproc_MatchTemplateWithMask::TEMPL_SIZE(21, 13);
const Point Imgproc_MatchTemplateWithMask::TEST_POINT(8, 9);
void Imgproc_MatchTemplateWithMask::SetUp()
{
int type = std::get<0>(GetParam());
int type_mask = std::get<1>(GetParam());
// Matrices are created with the depth to test (for the call to matchTemplate()), but are also
// converted to CV_32 for the test calculations, because matchTemplate() also only operates on
// and returns CV_32.
img_testtype_.create(IMG_SIZE, type);
templ_testtype_.create(TEMPL_SIZE, type);
mask_testtype_.create(TEMPL_SIZE, type_mask);
randu(img_testtype_, 0, 10);
randu(templ_testtype_, 0, 10);
randu(mask_testtype_, 0, 5);
img_testtype_.convertTo(img_, CV_32F);
templ_testtype_.convertTo(templ_, CV_32F);
mask_testtype_.convertTo(mask_, CV_32F);
if (CV_MAT_DEPTH(type_mask) == CV_8U)
{
// CV_8U masks are interpreted as binary masks
mask_.setTo(Scalar::all(1), mask_ != 0);
}
if (mask_.channels() != templ_.channels())
{
std::vector<Mat> mask_channels(templ_.channels(), mask_);
merge(mask_channels.data(), templ_.channels(), mask_);
}
Rect roi(TEST_POINT, TEMPL_SIZE);
img_roi_masked_ = img_(roi).mul(mask_);
templ_masked_ = templ_.mul(mask_);
}
TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplSQDIFF)
{
matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_SQDIFF, mask_testtype_);
// Naive implementation for one point
Mat temp = img_roi_masked_ - templ_masked_;
Scalar temp_s = sum(temp.mul(temp));
double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
}
TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplSQDIFF_NORMED)
{
matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_SQDIFF_NORMED, mask_testtype_);
// Naive implementation for one point
Mat temp = img_roi_masked_ - templ_masked_;
Scalar temp_s = sum(temp.mul(temp));
double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
// Normalization
temp_s = sum(templ_masked_.mul(templ_masked_));
double norm = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
temp_s = sum(img_roi_masked_.mul(img_roi_masked_));
norm *= temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
norm = sqrt(norm);
val /= norm;
EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
}
TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplCCORR)
{
matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_CCORR, mask_testtype_);
// Naive implementation for one point
Scalar temp_s = sum(templ_masked_.mul(img_roi_masked_));
double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
}
TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplCCORR_NORMED)
{
matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_CCORR_NORMED, mask_testtype_);
// Naive implementation for one point
Scalar temp_s = sum(templ_masked_.mul(img_roi_masked_));
double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
// Normalization
temp_s = sum(templ_masked_.mul(templ_masked_));
double norm = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
temp_s = sum(img_roi_masked_.mul(img_roi_masked_));
norm *= temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
norm = sqrt(norm);
val /= norm;
EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
}
TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplCCOEFF)
{
matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_CCOEFF, mask_testtype_);
// Naive implementation for one point
Scalar temp_s = sum(mask_);
for (int i = 0; i < 4; i++)
{
if (temp_s[i] != 0.0)
temp_s[i] = 1.0 / temp_s[i];
else
temp_s[i] = 1.0;
}
Mat temp = mask_.clone(); temp = temp_s; // Workaround to multiply Mat by Scalar
Mat temp2 = mask_.clone(); temp2 = sum(templ_masked_); // Workaround to multiply Mat by Scalar
Mat templx = templ_masked_ - mask_.mul(temp).mul(temp2);
temp2 = sum(img_roi_masked_); // Workaround to multiply Mat by Scalar
Mat imgx = img_roi_masked_ - mask_.mul(temp).mul(temp2);
temp_s = sum(templx.mul(imgx));
double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
}
TEST_P(Imgproc_MatchTemplateWithMask, CompareNaiveImplCCOEFF_NORMED)
{
matchTemplate(img_testtype_, templ_testtype_, result_, CV_TM_CCOEFF_NORMED, mask_testtype_);
// Naive implementation for one point
Scalar temp_s = sum(mask_);
for (int i = 0; i < 4; i++)
{
if (temp_s[i] != 0.0)
temp_s[i] = 1.0 / temp_s[i];
else
temp_s[i] = 1.0;
}
Mat temp = mask_.clone(); temp = temp_s; // Workaround to multiply Mat by Scalar
Mat temp2 = mask_.clone(); temp2 = sum(templ_masked_); // Workaround to multiply Mat by Scalar
Mat templx = templ_masked_ - mask_.mul(temp).mul(temp2);
temp2 = sum(img_roi_masked_); // Workaround to multiply Mat by Scalar
Mat imgx = img_roi_masked_ - mask_.mul(temp).mul(temp2);
temp_s = sum(templx.mul(imgx));
double val = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
// Normalization
temp_s = sum(templx.mul(templx));
double norm = temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
temp_s = sum(imgx.mul(imgx));
norm *= temp_s[0] + temp_s[1] + temp_s[2] + temp_s[3];
norm = sqrt(norm);
val /= norm;
EXPECT_NEAR(val, result_.at<float>(TEST_POINT), TEMPL_SIZE.area()*abs(val)*FLT_EPSILON);
}
INSTANTIATE_TEST_CASE_P(SingleChannelMask, Imgproc_MatchTemplateWithMask,
Combine(
Values(CV_32FC1, CV_32FC3, CV_8UC1, CV_8UC3),
Values(CV_32FC1, CV_8UC1)));
INSTANTIATE_TEST_CASE_P(MultiChannelMask, Imgproc_MatchTemplateWithMask,
Combine(
Values(CV_32FC3, CV_8UC3),
Values(CV_32FC3, CV_8UC3)));
class Imgproc_MatchTemplateWithMask2 : public TestWithParam<std::tuple<MatType,MatType,
MatchTemplType>>
{
protected:
// Member functions inherited from ::testing::Test
void SetUp() override;
// Data members
Mat img_;
Mat templ_;
Mat mask_;
Mat result_withoutmask_;
Mat result_withmask_;
// Constants
static const Size IMG_SIZE;
static const Size TEMPL_SIZE;
};
// Arbitraryly chosen test constants
const Size Imgproc_MatchTemplateWithMask2::IMG_SIZE(160, 100);
const Size Imgproc_MatchTemplateWithMask2::TEMPL_SIZE(21, 13);
void Imgproc_MatchTemplateWithMask2::SetUp()
{
int type = std::get<0>(GetParam());
int type_mask = std::get<1>(GetParam());
img_.create(IMG_SIZE, type);
templ_.create(TEMPL_SIZE, type);
mask_.create(TEMPL_SIZE, type_mask);
randu(img_, 0, 100);
randu(templ_, 0, 100);
if (CV_MAT_DEPTH(type_mask) == CV_8U)
{
// CV_8U implies binary mask, so all nonzero values should work
randu(mask_, 1, 255);
}
else
{
mask_ = Scalar(1, 1, 1, 1);
}
}
TEST_P(Imgproc_MatchTemplateWithMask2, CompareWithAndWithoutMask)
{
int method = std::get<2>(GetParam());
matchTemplate(img_, templ_, result_withmask_, method, mask_);
matchTemplate(img_, templ_, result_withoutmask_, method);
// Get maximum result for relative error calculation
double min_val, max_val;
minMaxLoc(abs(result_withmask_), &min_val, &max_val);
// Get maximum of absolute diff for comparison
double mindiff, maxdiff;
minMaxLoc(abs(result_withmask_ - result_withoutmask_), &mindiff, &maxdiff);
EXPECT_LT(maxdiff, max_val*TEMPL_SIZE.area()*FLT_EPSILON);
}
INSTANTIATE_TEST_CASE_P(SingleChannelMask, Imgproc_MatchTemplateWithMask2,
Combine(
Values(CV_32FC1, CV_32FC3, CV_8UC1, CV_8UC3),
Values(CV_32FC1, CV_8UC1),
Values(CV_TM_SQDIFF, CV_TM_SQDIFF_NORMED, CV_TM_CCORR, CV_TM_CCORR_NORMED,
CV_TM_CCOEFF, CV_TM_CCOEFF_NORMED)));
INSTANTIATE_TEST_CASE_P(MultiChannelMask, Imgproc_MatchTemplateWithMask2,
Combine(
Values(CV_32FC3, CV_8UC3),
Values(CV_32FC3, CV_8UC3),
Values(CV_TM_SQDIFF, CV_TM_SQDIFF_NORMED, CV_TM_CCORR, CV_TM_CCORR_NORMED,
CV_TM_CCOEFF, CV_TM_CCOEFF_NORMED)));
}} // namespace