/*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" using namespace cv; using namespace std; 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()(T other); }; template<> bool SimilarWith::operator()(Vec2f other) { return abs(other[0] - value[0]) < rho_eps && abs(other[1] - value[1]) < theta_eps; } template<> bool SimilarWith::operator()(Vec4i other) { return norm(value, other) < theta_eps; } template int countMatIntersection(Mat expect, Mat actual, float eps, float rho_eps) { int count = 0; if (!expect.empty() && !actual.empty()) { for (MatIterator_ it=expect.begin(); it!=expect.end(); it++) { MatIterator_ 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: void run_test(int type); string picture_name; double rhoStep; double thetaStep; int threshold; int minLineLength; int maxGap; }; typedef std::tr1::tuple Image_RhoStep_ThetaStep_Threshold_t; class StandartHoughLinesTest : public BaseHoughLineTest, public testing::TestWithParam { public: StandartHoughLinesTest() { picture_name = std::tr1::get<0>(GetParam()); rhoStep = std::tr1::get<1>(GetParam()); thetaStep = std::tr1::get<2>(GetParam()); threshold = std::tr1::get<3>(GetParam()); minLineLength = 0; maxGap = 0; } }; typedef std::tr1::tuple Image_RhoStep_ThetaStep_Threshold_MinLine_MaxGap_t; class ProbabilisticHoughLinesTest : public BaseHoughLineTest, public testing::TestWithParam { public: ProbabilisticHoughLinesTest() { picture_name = std::tr1::get<0>(GetParam()); rhoStep = std::tr1::get<1>(GetParam()); thetaStep = std::tr1::get<2>(GetParam()); threshold = std::tr1::get<3>(GetParam()); minLineLength = std::tr1::get<4>(GetParam()); maxGap = std::tr1::get<5>(GetParam()); } }; void BaseHoughLineTest::run_test(int type) { string filename = cvtest::TS::ptr()->get_data_path() + picture_name; Mat src = imread(filename, IMREAD_GRAYSCALE); EXPECT_FALSE(src.empty()) << "Invalid test image: " << filename; string xml; if (type == STANDART) xml = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/HoughLines.xml"; else if (type == PROBABILISTIC) xml = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/HoughLinesP.xml"; Mat dst; Canny(src, dst, 100, 150, 3); EXPECT_FALSE(dst.empty()) << "Failed Canny edge detector"; Mat 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); FileStorage fs(xml, FileStorage::READ); FileNode node = fs[test_case_name]; if (node.empty()) { fs.release(); fs.open(xml, FileStorage::APPEND); EXPECT_TRUE(fs.isOpened()) << "Cannot open sanity data file: " << xml; fs << test_case_name << lines; fs.release(); fs.open(xml, FileStorage::READ); EXPECT_TRUE(fs.isOpened()) << "Cannot open sanity data file: " << xml; } Mat exp_lines; read( fs[test_case_name], exp_lines, Mat() ); fs.release(); int count = -1; if (type == STANDART) count = countMatIntersection(exp_lines, lines, (float) thetaStep + FLT_EPSILON, (float) rhoStep + FLT_EPSILON); else if (type == PROBABILISTIC) count = countMatIntersection(exp_lines, lines, 1e-4f, 0.f); #if defined HAVE_IPP && IPP_VERSION_X100 >= 810 && !IPP_DISABLE_HOUGH EXPECT_GE( count, (int) (exp_lines.total() * 0.8) ); #else EXPECT_EQ( count, (int) exp_lines.total()); #endif } TEST_P(StandartHoughLinesTest, regression) { run_test(STANDART); } TEST_P(ProbabilisticHoughLinesTest, regression) { run_test(PROBABILISTIC); } 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 ) ));