mirror of
https://github.com/opencv/opencv.git
synced 2024-12-05 09:49:12 +08:00
1f0bfc8d83
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.
364 lines
13 KiB
C++
364 lines
13 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
// Copyright (C) 2014, Itseez, Inc, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#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
|