/*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 struct SimilarWith { T value; float theta_eps; float rho_eps; SimilarWith(T val, float e, float r_e): value(val), theta_eps(e), rho_eps(r_e) { }; bool operator()(const T& other); }; template<> bool SimilarWith::operator()(const Vec2f& other) { return std::abs(other[0] - value[0]) < rho_eps && std::abs(other[1] - value[1]) < theta_eps; } template<> bool SimilarWith::operator()(const Vec3f& other) { return std::abs(other[0] - value[0]) < rho_eps && std::abs(other[1] - value[1]) < theta_eps; } template<> bool SimilarWith::operator()(const Vec4i& other) { return cv::norm(value, other) < theta_eps; } template int countMatIntersection(const Mat& expect, const Mat& actual, float eps, float rho_eps) { int count = 0; if (!expect.empty() && !actual.empty()) { for (MatConstIterator_ it=expect.begin(); it!=expect.end(); it++) { MatConstIterator_ f = std::find_if(actual.begin(), actual.end(), SimilarWith(*it, eps, rho_eps)); if (f != actual.end()) 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 void run_test(int type, const char* xml_name); string picture_name; double rhoStep; double thetaStep; int threshold; int minLineLength; int maxGap; }; typedef tuple Image_RhoStep_ThetaStep_Threshold_t; class StandartHoughLinesTest : public BaseHoughLineTest, public testing::TestWithParam { 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 Image_RhoStep_ThetaStep_Threshold_MinLine_MaxGap_t; class ProbabilisticHoughLinesTest : public BaseHoughLineTest, public testing::TestWithParam { 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 HoughLinesPointSetInput_t; class HoughLinesPointSetTest : public testing::TestWithParam { 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 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(Mat(exp_lines), Mat(lines), (float) thetaStep + FLT_EPSILON, (float) rhoStep + FLT_EPSILON); else if (type == PROBABILISTIC) count = countMatIntersection(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 pointf; vector pointi; vector 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(STANDART, "HoughLines.xml"); } TEST_P(ProbabilisticHoughLinesTest, regression) { run_test(PROBABILISTIC, "HoughLinesP.xml"); } TEST_P(StandartHoughLinesTest, regression_Vec2f) { run_test, Vec2f>(STANDART, "HoughLines2f.xml"); } TEST_P(StandartHoughLinesTest, regression_Vec3f) { run_test, 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