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opencv/modules/imgproc/test/test_thresh.cpp
Alexander Lyulkov 30889f4a90 Added tests for adaptiveThreshold and sepFilter2D
2024-04-08 13:04:50 +03:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// are permitted provided that the following conditions are met:
//
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// this list of conditions and the following disclaimer.
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// and on any theory of liability, whether in contract, strict liability,
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//M*/
#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, 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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