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opencv/modules/imgproc/test/test_houghlines.cpp
lamm45 1f0bfc8d83 Fix angle discretization in Hough transforms 
In some situations the last value was missing from the discrete theta
values. Now, the last value is chosen such that it is close to the
user-provided maximum theta, while the distance to pi remains always
at least theta_step/2. This should avoid duplicate detections.

A better way would probably be to use max_theta as is and adjust the
resolution (theta_step) instead, such that the discretization would
always be uniform (in a circular sense) when full angle range is used.
2022-08-30 18:46:16 -04:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "test_precomp.hpp"
//#define GENERATE_DATA // generate data in debug mode via CPU code path (without IPP / OpenCL and other accelerators)
namespace opencv_test { namespace {
template<typename T>
struct SimilarWith
{
T value;
float theta_eps;
float rho_eps;
SimilarWith<T>(T val, float e, float r_e): value(val), theta_eps(e), rho_eps(r_e) { };
bool operator()(const T& other);
};
template<>
bool SimilarWith<Vec2f>::operator()(const Vec2f& other)
{
return std::abs(other[0] - value[0]) < rho_eps && std::abs(other[1] - value[1]) < theta_eps;
}
template<>
bool SimilarWith<Vec3f>::operator()(const Vec3f& other)
{
return std::abs(other[0] - value[0]) < rho_eps && std::abs(other[1] - value[1]) < theta_eps;
}
template<>
bool SimilarWith<Vec4i>::operator()(const Vec4i& other)
{
return cv::norm(value, other) < theta_eps;
}
template <typename T>
int countMatIntersection(const Mat& expect, const Mat& actual, float eps, float rho_eps)
{
int count = 0;
if (!expect.empty() && !actual.empty())
{
for (MatConstIterator_<T> it=expect.begin<T>(); it!=expect.end<T>(); it++)
{
MatConstIterator_<T> f = std::find_if(actual.begin<T>(), actual.end<T>(), SimilarWith<T>(*it, eps, rho_eps));
if (f != actual.end<T>())
count++;
}
}
return count;
}
String getTestCaseName(String filename)
{
string temp(filename);
size_t pos = temp.find_first_of("\\/.");
while ( pos != string::npos ) {
temp.replace( pos, 1, "_" );
pos = temp.find_first_of("\\/.");
}
return String(temp);
}
class BaseHoughLineTest
{
public:
enum {STANDART = 0, PROBABILISTIC};
protected:
template<typename LinesType, typename LineType>
void run_test(int type, const char* xml_name);
string picture_name;
double rhoStep;
double thetaStep;
int threshold;
int minLineLength;
int maxGap;
};
typedef tuple<string, double, double, int> Image_RhoStep_ThetaStep_Threshold_t;
class StandartHoughLinesTest : public BaseHoughLineTest, public testing::TestWithParam<Image_RhoStep_ThetaStep_Threshold_t>
{
public:
StandartHoughLinesTest()
{
picture_name = get<0>(GetParam());
rhoStep = get<1>(GetParam());
thetaStep = get<2>(GetParam());
threshold = get<3>(GetParam());
minLineLength = 0;
maxGap = 0;
}
};
typedef tuple<string, double, double, int, int, int> Image_RhoStep_ThetaStep_Threshold_MinLine_MaxGap_t;
class ProbabilisticHoughLinesTest : public BaseHoughLineTest, public testing::TestWithParam<Image_RhoStep_ThetaStep_Threshold_MinLine_MaxGap_t>
{
public:
ProbabilisticHoughLinesTest()
{
picture_name = get<0>(GetParam());
rhoStep = get<1>(GetParam());
thetaStep = get<2>(GetParam());
threshold = get<3>(GetParam());
minLineLength = get<4>(GetParam());
maxGap = get<5>(GetParam());
}
};
typedef tuple<double, double, double, double> HoughLinesPointSetInput_t;
class HoughLinesPointSetTest : public testing::TestWithParam<HoughLinesPointSetInput_t>
{
protected:
void run_test();
double Rho;
double Theta;
double rhoMin, rhoMax, rhoStep;
double thetaMin, thetaMax, thetaStep;
public:
HoughLinesPointSetTest()
{
rhoMin = get<0>(GetParam());
rhoMax = get<1>(GetParam());
rhoStep = (rhoMax - rhoMin) / 360.0f;
thetaMin = get<2>(GetParam());
thetaMax = get<3>(GetParam());
thetaStep = CV_PI / 180.0f;
Rho = 320.00000;
Theta = 1.04719;
}
};
template<typename LinesType, typename LineType>
void BaseHoughLineTest::run_test(int type, const char* xml_name)
{
string filename = cvtest::TS::ptr()->get_data_path() + picture_name;
Mat src = imread(filename, IMREAD_GRAYSCALE);
ASSERT_FALSE(src.empty()) << "Invalid test image: " << filename;
string xml = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/" + xml_name;
Mat dst;
Canny(src, dst, 100, 150, 3);
ASSERT_FALSE(dst.empty()) << "Failed Canny edge detector";
LinesType lines;
if (type == STANDART)
HoughLines(dst, lines, rhoStep, thetaStep, threshold, 0, 0);
else if (type == PROBABILISTIC)
HoughLinesP(dst, lines, rhoStep, thetaStep, threshold, minLineLength, maxGap);
String test_case_name = format("lines_%s_%.0f_%.2f_%d_%d_%d", picture_name.c_str(), rhoStep, thetaStep,
threshold, minLineLength, maxGap);
test_case_name = getTestCaseName(test_case_name);
#ifdef GENERATE_DATA
{
FileStorage fs(xml, FileStorage::READ);
ASSERT_TRUE(!fs.isOpened() || fs[test_case_name].empty());
}
{
FileStorage fs(xml, FileStorage::APPEND);
EXPECT_TRUE(fs.isOpened()) << "Cannot open sanity data file: " << xml;
fs << test_case_name << Mat(lines);
}
#else
FileStorage fs(xml, FileStorage::READ);
FileNode node = fs[test_case_name];
ASSERT_FALSE(node.empty()) << "Missing test data: " << test_case_name << std::endl << "XML: " << xml;
Mat exp_lines_;
read(fs[test_case_name], exp_lines_, Mat());
fs.release();
LinesType exp_lines;
exp_lines_.copyTo(exp_lines);
int count = -1;
if (type == STANDART)
count = countMatIntersection<LineType>(Mat(exp_lines), Mat(lines), (float) thetaStep + FLT_EPSILON, (float) rhoStep + FLT_EPSILON);
else if (type == PROBABILISTIC)
count = countMatIntersection<LineType>(Mat(exp_lines), Mat(lines), 1e-4f, 0.f);
#if defined HAVE_IPP && IPP_VERSION_X100 >= 810 && !IPP_DISABLE_HOUGH
EXPECT_LE(std::abs((double)count - Mat(exp_lines).total()), Mat(exp_lines).total() * 0.25)
<< "count=" << count << " expected=" << Mat(exp_lines).total();
#else
EXPECT_EQ(count, (int)Mat(exp_lines).total());
#endif
#endif // GENERATE_DATA
}
void HoughLinesPointSetTest::run_test(void)
{
Mat lines_f, lines_i;
vector<Point2f> pointf;
vector<Point2i> pointi;
vector<Vec3d> line_polar_f, line_polar_i;
const float Points[20][2] = {
{ 0.0f, 369.0f }, { 10.0f, 364.0f }, { 20.0f, 358.0f }, { 30.0f, 352.0f },
{ 40.0f, 346.0f }, { 50.0f, 341.0f }, { 60.0f, 335.0f }, { 70.0f, 329.0f },
{ 80.0f, 323.0f }, { 90.0f, 318.0f }, { 100.0f, 312.0f }, { 110.0f, 306.0f },
{ 120.0f, 300.0f }, { 130.0f, 295.0f }, { 140.0f, 289.0f }, { 150.0f, 284.0f },
{ 160.0f, 277.0f }, { 170.0f, 271.0f }, { 180.0f, 266.0f }, { 190.0f, 260.0f }
};
// Float
for (int i = 0; i < 20; i++)
{
pointf.push_back(Point2f(Points[i][0],Points[i][1]));
}
HoughLinesPointSet(pointf, lines_f, 20, 1,
rhoMin, rhoMax, rhoStep,
thetaMin, thetaMax, thetaStep);
lines_f.copyTo( line_polar_f );
// Integer
for( int i = 0; i < 20; i++ )
{
pointi.push_back( Point2i( (int)Points[i][0], (int)Points[i][1] ) );
}
HoughLinesPointSet( pointi, lines_i, 20, 1,
rhoMin, rhoMax, rhoStep,
thetaMin, thetaMax, thetaStep );
lines_i.copyTo( line_polar_i );
EXPECT_EQ((int)(line_polar_f.at(0).val[1] * 100000.0f), (int)(Rho * 100000.0f));
EXPECT_EQ((int)(line_polar_f.at(0).val[2] * 100000.0f), (int)(Theta * 100000.0f));
EXPECT_EQ((int)(line_polar_i.at(0).val[1] * 100000.0f), (int)(Rho * 100000.0f));
EXPECT_EQ((int)(line_polar_i.at(0).val[2] * 100000.0f), (int)(Theta * 100000.0f));
}
TEST_P(StandartHoughLinesTest, regression)
{
run_test<Mat, Vec2f>(STANDART, "HoughLines.xml");
}
TEST_P(ProbabilisticHoughLinesTest, regression)
{
run_test<Mat, Vec4i>(PROBABILISTIC, "HoughLinesP.xml");
}
TEST_P(StandartHoughLinesTest, regression_Vec2f)
{
run_test<std::vector<Vec2f>, Vec2f>(STANDART, "HoughLines2f.xml");
}
TEST_P(StandartHoughLinesTest, regression_Vec3f)
{
run_test<std::vector<Vec3f>, Vec3f>(STANDART, "HoughLines3f.xml");
}
TEST_P(HoughLinesPointSetTest, regression)
{
run_test();
}
TEST(HoughLinesPointSet, regression_21029)
{
std::vector<Point2f> points;
points.push_back(Point2f(100, 100));
points.push_back(Point2f(1000, 1000));
points.push_back(Point2f(10000, 10000));
points.push_back(Point2f(100000, 100000));
double rhoMin = 0;
double rhoMax = 10;
double rhoStep = 0.1;
double thetaMin = 85 * CV_PI / 180.0;
double thetaMax = 95 * CV_PI / 180.0;
double thetaStep = 1 * CV_PI / 180.0;
int lines_max = 5;
int threshold = 100;
Mat lines;
HoughLinesPointSet(points, lines,
lines_max, threshold,
rhoMin, rhoMax, rhoStep,
thetaMin, thetaMax, thetaStep
);
EXPECT_TRUE(lines.empty());
}
TEST(HoughLines, regression_21983)
{
Mat img(200, 200, CV_8UC1, Scalar(0));
line(img, Point(0, 100), Point(100, 100), Scalar(255));
std::vector<Vec2f> lines;
HoughLines(img, lines, 1, CV_PI/180, 90, 0, 0, 0.001, 1.58);
ASSERT_EQ(lines.size(), 1U);
EXPECT_EQ(lines[0][0], 100);
EXPECT_NEAR(lines[0][1], 1.57179642, 1e-4);
}
INSTANTIATE_TEST_CASE_P( ImgProc, StandartHoughLinesTest, testing::Combine(testing::Values( "shared/pic5.png", "../stitching/a1.png" ),
testing::Values( 1, 10 ),
testing::Values( 0.05, 0.1 ),
testing::Values( 80, 150 )
));
INSTANTIATE_TEST_CASE_P( ImgProc, ProbabilisticHoughLinesTest, testing::Combine(testing::Values( "shared/pic5.png", "shared/pic1.png" ),
testing::Values( 5, 10 ),
testing::Values( 0.05, 0.1 ),
testing::Values( 75, 150 ),
testing::Values( 0, 10 ),
testing::Values( 0, 4 )
));
INSTANTIATE_TEST_CASE_P( Imgproc, HoughLinesPointSetTest, testing::Combine(testing::Values( 0.0f, 120.0f ),
testing::Values( 360.0f, 480.0f ),
testing::Values( 0.0f, (CV_PI / 18.0f) ),
testing::Values( (CV_PI / 2.0f), (CV_PI * 5.0f / 12.0f) )
));
}} // namespace
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