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opencv/modules/imgproc/test/test_thresh.cpp
Pierre Chatelier 0db6a496ba
Merge pull request #26842 from chacha21:threshold_with_mask 
Added optional mask to cv::threshold #26842
 
Proposal for #26777

To avoid code duplication, and keep performance when no mask is used, inner implementation always propagate the const cv::Mat& mask, but they use a template<bool useMask> parameter that let the compiler optimize out unnecessary tests when the mask is not to be used.

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [X] There is a reference to the original bug report and related work
- [X] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2025-03-12 17:55:07 +03:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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#include "test_precomp.hpp"
namespace opencv_test { namespace {
class CV_ThreshTest : public cvtest::ArrayTest
{
public:
CV_ThreshTest(int test_type = 0);
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void prepare_to_validation( int );
int thresh_type;
double thresh_val;
double max_val;
int extra_type;
};
CV_ThreshTest::CV_ThreshTest(int test_type)
{
CV_Assert( (test_type & cv::THRESH_MASK) == 0 );
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
optional_mask = false;
element_wise_relative_error = true;
extra_type = test_type;
// Reduce number of test with automated thresholding
if (extra_type != 0)
test_case_count = 250;
}
void CV_ThreshTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int depth = cvtest::randInt(rng) % 5, cn = cvtest::randInt(rng) % 4 + 1;
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
depth = depth == 0 ? CV_8U : depth == 1 ? CV_16S : depth == 2 ? CV_16U : depth == 3 ? CV_32F : CV_64F;
if ( extra_type == cv::THRESH_OTSU )
{
depth = cvtest::randInt(rng) % 2 == 0 ? CV_8U : CV_16U;
cn = 1;
}
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth,cn);
thresh_type = cvtest::randInt(rng) % 5;
if( depth == CV_8U )
{
thresh_val = (cvtest::randReal(rng)*350. - 50.);
max_val = (cvtest::randReal(rng)*350. - 50.);
if( cvtest::randInt(rng)%4 == 0 )
max_val = 255.f;
}
else if( depth == CV_16S )
{
double min_val = SHRT_MIN-100.f;
max_val = SHRT_MAX+100.f;
thresh_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val);
max_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val);
if( cvtest::randInt(rng)%4 == 0 )
max_val = (double)SHRT_MAX;
}
else if( depth == CV_16U )
{
double min_val = -100.f;
max_val = USHRT_MAX+100.f;
thresh_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val);
max_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val);
if( cvtest::randInt(rng)%4 == 0 )
max_val = (double)USHRT_MAX;
}
else
{
thresh_val = (cvtest::randReal(rng)*1000. - 500.);
max_val = (cvtest::randReal(rng)*1000. - 500.);
}
}
double CV_ThreshTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return FLT_EPSILON*10;
}
void CV_ThreshTest::run_func()
{
cvThreshold( test_array[INPUT][0], test_array[OUTPUT][0],
thresh_val, max_val, thresh_type | extra_type);
}
static double compute_otsu_thresh(const Mat& _src)
{
int depth = _src.depth();
int width = _src.cols, height = _src.rows;
const int N = 65536;
std::vector<int> h(N, 0);
int i, j;
double mu = 0, scale = 1./(width*height);
for(i = 0; i < height; ++i)
{
for(j = 0; j < width; ++j)
{
const int val = depth == CV_16UC1 ? (int)_src.at<ushort>(i, j) : (int)_src.at<uchar>(i,j);
h[val]++;
}
}
for( i = 0; i < N; i++ )
{
mu += i*(double)h[i];
}
mu *= scale;
double mu1 = 0, q1 = 0;
double max_sigma = 0, max_val = 0;
for( i = 0; i < N; i++ )
{
double p_i, q2, mu2, sigma;
p_i = h[i]*scale;
mu1 *= q1;
q1 += p_i;
q2 = 1. - q1;
if( std::min(q1,q2) < FLT_EPSILON || std::max(q1,q2) > 1. - FLT_EPSILON )
continue;
mu1 = (mu1 + i*p_i)/q1;
mu2 = (mu - q1*mu1)/q2;
sigma = q1*q2*(mu1 - mu2)*(mu1 - mu2);
if( sigma > max_sigma )
{
max_sigma = sigma;
max_val = i;
}
}
return max_val;
}
static void test_threshold( const Mat& _src, Mat& _dst,
double thresh, double maxval, int thresh_type, int extra_type )
{
int i, j;
int depth = _src.depth(), cn = _src.channels();
int width_n = _src.cols*cn, height = _src.rows;
int ithresh = cvFloor(thresh);
int imaxval, ithresh2;
if (extra_type == cv::THRESH_OTSU)
{
thresh = compute_otsu_thresh(_src);
ithresh = cvFloor(thresh);
}
if( depth == CV_8U )
{
ithresh2 = saturate_cast<uchar>(ithresh);
imaxval = saturate_cast<uchar>(maxval);
}
else if( depth == CV_16S )
{
ithresh2 = saturate_cast<short>(ithresh);
imaxval = saturate_cast<short>(maxval);
}
else if( depth == CV_16U )
{
ithresh2 = saturate_cast<ushort>(ithresh);
imaxval = saturate_cast<ushort>(maxval);
}
else
{
ithresh2 = cvRound(ithresh);
imaxval = cvRound(maxval);
}
CV_Assert( depth == CV_8U || depth == CV_16S || depth == CV_16U || depth == CV_32F || depth == CV_64F );
switch( thresh_type )
{
case cv::THRESH_BINARY:
for( i = 0; i < height; i++ )
{
if( depth == CV_8U )
{
const uchar* src = _src.ptr<uchar>(i);
uchar* dst = _dst.ptr<uchar>(i);
for( j = 0; j < width_n; j++ )
dst[j] = (uchar)(src[j] > ithresh ? imaxval : 0);
}
else if( depth == CV_16S )
{
const short* src = _src.ptr<short>(i);
short* dst = _dst.ptr<short>(i);
for( j = 0; j < width_n; j++ )
dst[j] = (short)(src[j] > ithresh ? imaxval : 0);
}
else if( depth == CV_16U )
{
const ushort* src = _src.ptr<ushort>(i);
ushort* dst = _dst.ptr<ushort>(i);
for( j = 0; j < width_n; j++ )
dst[j] = (ushort)(src[j] > ithresh ? imaxval : 0);
}
else if( depth == CV_32F )
{
const float* src = _src.ptr<float>(i);
float* dst = _dst.ptr<float>(i);
for( j = 0; j < width_n; j++ )
dst[j] = (float)(src[j] > thresh ? maxval : 0.f);
}
else
{
const double* src = _src.ptr<double>(i);
double* dst = _dst.ptr<double>(i);
for( j = 0; j < width_n; j++ )
dst[j] = src[j] > thresh ? maxval : 0.0;
}
}
break;
case cv::THRESH_BINARY_INV:
for( i = 0; i < height; i++ )
{
if( depth == CV_8U )
{
const uchar* src = _src.ptr<uchar>(i);
uchar* dst = _dst.ptr<uchar>(i);
for( j = 0; j < width_n; j++ )
dst[j] = (uchar)(src[j] > ithresh ? 0 : imaxval);
}
else if( depth == CV_16S )
{
const short* src = _src.ptr<short>(i);
short* dst = _dst.ptr<short>(i);
for( j = 0; j < width_n; j++ )
dst[j] = (short)(src[j] > ithresh ? 0 : imaxval);
}
else if( depth == CV_16U )
{
const ushort* src = _src.ptr<ushort>(i);
ushort* dst = _dst.ptr<ushort>(i);
for( j = 0; j < width_n; j++ )
dst[j] = (ushort)(src[j] > ithresh ? 0 : imaxval);
}
else if( depth == CV_32F )
{
const float* src = _src.ptr<float>(i);
float* dst = _dst.ptr<float>(i);
for( j = 0; j < width_n; j++ )
dst[j] = (float)(src[j] > thresh ? 0.f : maxval);
}
else
{
const double* src = _src.ptr<double>(i);
double* dst = _dst.ptr<double>(i);
for( j = 0; j < width_n; j++ )
dst[j] = src[j] > thresh ? 0.0 : maxval;
}
}
break;
case cv::THRESH_TRUNC:
for( i = 0; i < height; i++ )
{
if( depth == CV_8U )
{
const uchar* src = _src.ptr<uchar>(i);
uchar* dst = _dst.ptr<uchar>(i);
for( j = 0; j < width_n; j++ )
{
int s = src[j];
dst[j] = (uchar)(s > ithresh ? ithresh2 : s);
}
}
else if( depth == CV_16S )
{
const short* src = _src.ptr<short>(i);
short* dst = _dst.ptr<short>(i);
for( j = 0; j < width_n; j++ )
{
int s = src[j];
dst[j] = (short)(s > ithresh ? ithresh2 : s);
}
}
else if( depth == CV_16U )
{
const ushort* src = _src.ptr<ushort>(i);
ushort* dst = _dst.ptr<ushort>(i);
for( j = 0; j < width_n; j++ )
{
int s = src[j];
dst[j] = (ushort)(s > ithresh ? ithresh2 : s);
}
}
else if( depth == CV_32F )
{
const float* src = _src.ptr<float>(i);
float* dst = _dst.ptr<float>(i);
for( j = 0; j < width_n; j++ )
{
float s = src[j];
dst[j] = (float)(s > thresh ? thresh : s);
}
}
else
{
const double* src = _src.ptr<double>(i);
double* dst = _dst.ptr<double>(i);
for( j = 0; j < width_n; j++ )
{
double s = src[j];
dst[j] = s > thresh ? thresh : s;
}
}
}
break;
case cv::THRESH_TOZERO:
for( i = 0; i < height; i++ )
{
if( depth == CV_8U )
{
const uchar* src = _src.ptr<uchar>(i);
uchar* dst = _dst.ptr<uchar>(i);
for( j = 0; j < width_n; j++ )
{
int s = src[j];
dst[j] = (uchar)(s > ithresh ? s : 0);
}
}
else if( depth == CV_16S )
{
const short* src = _src.ptr<short>(i);
short* dst = _dst.ptr<short>(i);
for( j = 0; j < width_n; j++ )
{
int s = src[j];
dst[j] = (short)(s > ithresh ? s : 0);
}
}
else if( depth == CV_16U )
{
const ushort* src = _src.ptr<ushort>(i);
ushort* dst = _dst.ptr<ushort>(i);
for( j = 0; j < width_n; j++ )
{
int s = src[j];
dst[j] = (ushort)(s > ithresh ? s : 0);
}
}
else if( depth == CV_32F )
{
const float* src = _src.ptr<float>(i);
float* dst = _dst.ptr<float>(i);
for( j = 0; j < width_n; j++ )
{
float s = src[j];
dst[j] = s > thresh ? s : 0.f;
}
}
else
{
const double* src = _src.ptr<double>(i);
double* dst = _dst.ptr<double>(i);
for( j = 0; j < width_n; j++ )
{
double s = src[j];
dst[j] = s > thresh ? s : 0.0;
}
}
}
break;
case cv::THRESH_TOZERO_INV:
for( i = 0; i < height; i++ )
{
if( depth == CV_8U )
{
const uchar* src = _src.ptr<uchar>(i);
uchar* dst = _dst.ptr<uchar>(i);
for( j = 0; j < width_n; j++ )
{
int s = src[j];
dst[j] = (uchar)(s > ithresh ? 0 : s);
}
}
else if( depth == CV_16S )
{
const short* src = _src.ptr<short>(i);
short* dst = _dst.ptr<short>(i);
for( j = 0; j < width_n; j++ )
{
int s = src[j];
dst[j] = (short)(s > ithresh ? 0 : s);
}
}
else if( depth == CV_16U )
{
const ushort* src = _src.ptr<ushort>(i);
ushort* dst = _dst.ptr<ushort>(i);
for( j = 0; j < width_n; j++ )
{
int s = src[j];
dst[j] = (ushort)(s > ithresh ? 0 : s);
}
}
else if (depth == CV_32F)
{
const float* src = _src.ptr<float>(i);
float* dst = _dst.ptr<float>(i);
for( j = 0; j < width_n; j++ )
{
float s = src[j];
dst[j] = s > thresh ? 0.f : s;
}
}
else
{
const double* src = _src.ptr<double>(i);
double* dst = _dst.ptr<double>(i);
for( j = 0; j < width_n; j++ )
{
double s = src[j];
dst[j] = s > thresh ? 0.0 : s;
}
}
}
break;
default:
CV_Assert(0);
}
}
void CV_ThreshTest::prepare_to_validation( int /*test_case_idx*/ )
{
test_threshold( test_mat[INPUT][0], test_mat[REF_OUTPUT][0],
thresh_val, max_val, thresh_type, extra_type );
}
TEST(Imgproc_Threshold, accuracy) { CV_ThreshTest test; test.safe_run(); }
TEST(Imgproc_Threshold, accuracyOtsu) { CV_ThreshTest test(cv::THRESH_OTSU); test.safe_run(); }
BIGDATA_TEST(Imgproc_Threshold, huge)
{
Mat m(65000, 40000, CV_8U);
ASSERT_FALSE(m.isContinuous());
uint64 i, n = (uint64)m.rows*m.cols;
for( i = 0; i < n; i++ )
m.data[i] = (uchar)(i & 255);
cv::threshold(m, m, 127, 255, cv::THRESH_BINARY);
int nz = cv::countNonZero(m); // FIXIT 'int' is not enough here (overflow is possible with other inputs)
ASSERT_EQ((uint64)nz, n / 2);
}
TEST(Imgproc_Threshold, threshold_dryrun)
{
Size sz(16, 16);
Mat input_original(sz, CV_8U, Scalar::all(2));
Mat input = input_original.clone();
std::vector<int> threshTypes = {THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV};
std::vector<int> threshFlags = {0, THRESH_OTSU, THRESH_TRIANGLE};
for(int threshType : threshTypes)
{
for(int threshFlag : threshFlags)
{
const int _threshType = threshType | threshFlag | THRESH_DRYRUN;
cv::threshold(input, input, 2.0, 0.0, _threshType);
EXPECT_MAT_NEAR(input, input_original, 0);
}
}
}
typedef tuple < bool, int, int, int, int > Imgproc_Threshold_Masked_Params_t;
typedef testing::TestWithParam< Imgproc_Threshold_Masked_Params_t > Imgproc_Threshold_Masked_Fixed;
TEST_P(Imgproc_Threshold_Masked_Fixed, threshold_mask_fixed)
{
bool useROI = get<0>(GetParam());
int depth = get<1>(GetParam());
int cn = get<2>(GetParam());
int threshType = get<3>(GetParam());
int threshFlag = get<4>(GetParam());
const int _threshType = threshType | threshFlag;
Size sz(127, 127);
Size wrapperSize = useROI ? Size(sz.width+4, sz.height+4) : sz;
Mat wrapper(wrapperSize, CV_MAKETYPE(depth, cn));
Mat input = useROI ? Mat(wrapper, Rect(Point(), sz)) : wrapper;
cv::randu(input, cv::Scalar::all(0), cv::Scalar::all(255));
Mat mask = cv::Mat::zeros(sz, CV_8UC1);
cv::RotatedRect ellipseRect((cv::Point2f)cv::Point(sz.width/2, sz.height/2), (cv::Size2f)sz, 0);
cv::ellipse(mask, ellipseRect, cv::Scalar::all(255), cv::FILLED);//for very different mask alignments
Mat output_with_mask = cv::Mat::zeros(sz, input.type());
cv::thresholdWithMask(input, output_with_mask, mask, 127, 255, _threshType);
cv::bitwise_not(mask, mask);
input.copyTo(output_with_mask, mask);
Mat output_without_mask;
cv::threshold(input, output_without_mask, 127, 255, _threshType);
input.copyTo(output_without_mask, mask);
EXPECT_MAT_NEAR(output_with_mask, output_without_mask, 0);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Imgproc_Threshold_Masked_Fixed,
testing::Combine(
testing::Values(false, true),//use roi
testing::Values(CV_8U, CV_16U, CV_16S, CV_32F, CV_64F),//depth
testing::Values(1, 3),//channels
testing::Values(THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV),// threshTypes
testing::Values(0)
)
);
typedef testing::TestWithParam< Imgproc_Threshold_Masked_Params_t > Imgproc_Threshold_Masked_Auto;
TEST_P(Imgproc_Threshold_Masked_Auto, threshold_mask_auto)
{
bool useROI = get<0>(GetParam());
int depth = get<1>(GetParam());
int cn = get<2>(GetParam());
int threshType = get<3>(GetParam());
int threshFlag = get<4>(GetParam());
if (threshFlag == THRESH_TRIANGLE && depth != CV_8U)
throw SkipTestException("THRESH_TRIANGLE option supports CV_8UC1 input only");
const int _threshType = threshType | threshFlag;
Size sz(127, 127);
Size wrapperSize = useROI ? Size(sz.width+4, sz.height+4) : sz;
Mat wrapper(wrapperSize, CV_MAKETYPE(depth, cn));
Mat input = useROI ? Mat(wrapper, Rect(Point(), sz)) : wrapper;
cv::randu(input, cv::Scalar::all(0), cv::Scalar::all(255));
//for OTSU and TRIANGLE, we use a rectangular mask that can be just cropped
//in order to compute the threshold of the non-masked version
Mat mask = cv::Mat::zeros(sz, CV_8UC1);
cv::Rect roiRect(sz.width/4, sz.height/4, sz.width/2, sz.height/2);
cv::rectangle(mask, roiRect, cv::Scalar::all(255), cv::FILLED);
Mat output_with_mask = cv::Mat::zeros(sz, input.type());
const double autoThreshWithMask = cv::thresholdWithMask(input, output_with_mask, mask, 127, 255, _threshType);
output_with_mask = Mat(output_with_mask, roiRect);
Mat output_without_mask;
const double autoThresholdWithoutMask = cv::threshold(Mat(input, roiRect), output_without_mask, 127, 255, _threshType);
ASSERT_EQ(autoThreshWithMask, autoThresholdWithoutMask);
EXPECT_MAT_NEAR(output_with_mask, output_without_mask, 0);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Imgproc_Threshold_Masked_Auto,
testing::Combine(
testing::Values(false, true),//use roi
testing::Values(CV_8U, CV_16U),//depth
testing::Values(1),//channels
testing::Values(THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV),// threshTypes
testing::Values(THRESH_OTSU, THRESH_TRIANGLE)
)
);
TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_16085)
{
Size sz(16, 16);
Mat input(sz, CV_32F, Scalar::all(2));
Mat result;
cv::threshold(input, result, 2.0, 0.0, THRESH_TOZERO);
EXPECT_EQ(0, cv::norm(result, NORM_INF));
}
TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258)
{
Size sz(16, 16);
float val = nextafterf(16.0f, 0.0f); // 0x417fffff, all bits in mantissa are 1
Mat input(sz, CV_32F, Scalar::all(val));
Mat result;
cv::threshold(input, result, val, 0.0, THRESH_TOZERO);
EXPECT_EQ(0, cv::norm(result, NORM_INF));
}
TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258_Min)
{
Size sz(16, 16);
float min_val = -std::numeric_limits<float>::max();
Mat input(sz, CV_32F, Scalar::all(min_val));
Mat result;
cv::threshold(input, result, min_val, 0.0, THRESH_TOZERO);
EXPECT_EQ(0, cv::norm(result, NORM_INF));
}
TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258_Max)
{
Size sz(16, 16);
float max_val = std::numeric_limits<float>::max();
Mat input(sz, CV_32F, Scalar::all(max_val));
Mat result;
cv::threshold(input, result, max_val, 0.0, THRESH_TOZERO);
EXPECT_EQ(0, cv::norm(result, NORM_INF));
}
TEST(Imgproc_AdaptiveThreshold, mean)
{
const string input_path = cvtest::findDataFile("../cv/shared/baboon.png");
Mat input = imread(input_path, IMREAD_GRAYSCALE);
Mat result;
cv::adaptiveThreshold(input, result, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, 15, 8);
const string gt_path = cvtest::findDataFile("../cv/imgproc/adaptive_threshold1.png");
Mat gt = imread(gt_path, IMREAD_GRAYSCALE);
EXPECT_EQ(0, cv::norm(result, gt, NORM_INF));
}
TEST(Imgproc_AdaptiveThreshold, mean_inv)
{
const string input_path = cvtest::findDataFile("../cv/shared/baboon.png");
Mat input = imread(input_path, IMREAD_GRAYSCALE);
Mat result;
cv::adaptiveThreshold(input, result, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY_INV, 15, 8);
const string gt_path = cvtest::findDataFile("../cv/imgproc/adaptive_threshold1.png");
Mat gt = imread(gt_path, IMREAD_GRAYSCALE);
gt = Mat(gt.rows, gt.cols, CV_8UC1, cv::Scalar(255)) - gt;
EXPECT_EQ(0, cv::norm(result, gt, NORM_INF));
}
TEST(Imgproc_AdaptiveThreshold, gauss)
{
const string input_path = cvtest::findDataFile("../cv/shared/baboon.png");
Mat input = imread(input_path, IMREAD_GRAYSCALE);
Mat result;
cv::adaptiveThreshold(input, result, 200, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 21, -5);
const string gt_path = cvtest::findDataFile("../cv/imgproc/adaptive_threshold2.png");
Mat gt = imread(gt_path, IMREAD_GRAYSCALE);
EXPECT_EQ(0, cv::norm(result, gt, NORM_INF));
}
TEST(Imgproc_AdaptiveThreshold, gauss_inv)
{
const string input_path = cvtest::findDataFile("../cv/shared/baboon.png");
Mat input = imread(input_path, IMREAD_GRAYSCALE);
Mat result;
cv::adaptiveThreshold(input, result, 200, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY_INV, 21, -5);
const string gt_path = cvtest::findDataFile("../cv/imgproc/adaptive_threshold2.png");
Mat gt = imread(gt_path, IMREAD_GRAYSCALE);
gt = Mat(gt.rows, gt.cols, CV_8UC1, cv::Scalar(200)) - gt;
EXPECT_EQ(0, cv::norm(result, gt, NORM_INF));
}
}} // namespace
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