test_houghlines.cpp
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/*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();
}
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