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https://github.com/opencv/opencv.git
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ed207d79e7
* Added accumulator value to the output of HoughLines and HoughCircles * imgproc: refactor Hough patch - eliminate code duplication - fix type handling, fix OpenCL code - fix test data generation - re-generated test data in debug mode via plain CPU code path
323 lines
12 KiB
C++
323 lines
12 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2014, Itseez, Inc, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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//#define GENERATE_DATA // generate data in debug mode via CPU code path (without IPP / OpenCL and other accelerators)
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namespace opencv_test { namespace {
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template<typename T>
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struct SimilarWith
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{
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T value;
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float theta_eps;
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float rho_eps;
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SimilarWith<T>(T val, float e, float r_e): value(val), theta_eps(e), rho_eps(r_e) { };
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bool operator()(const T& other);
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};
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template<>
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bool SimilarWith<Vec2f>::operator()(const Vec2f& other)
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{
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return std::abs(other[0] - value[0]) < rho_eps && std::abs(other[1] - value[1]) < theta_eps;
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}
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template<>
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bool SimilarWith<Vec3f>::operator()(const Vec3f& other)
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{
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return std::abs(other[0] - value[0]) < rho_eps && std::abs(other[1] - value[1]) < theta_eps;
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}
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template<>
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bool SimilarWith<Vec4i>::operator()(const Vec4i& other)
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{
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return cv::norm(value, other) < theta_eps;
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}
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template <typename T>
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int countMatIntersection(const Mat& expect, const Mat& actual, float eps, float rho_eps)
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{
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int count = 0;
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if (!expect.empty() && !actual.empty())
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{
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for (MatConstIterator_<T> it=expect.begin<T>(); it!=expect.end<T>(); it++)
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{
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MatConstIterator_<T> f = std::find_if(actual.begin<T>(), actual.end<T>(), SimilarWith<T>(*it, eps, rho_eps));
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if (f != actual.end<T>())
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count++;
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}
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}
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return count;
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}
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String getTestCaseName(String filename)
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{
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string temp(filename);
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size_t pos = temp.find_first_of("\\/.");
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while ( pos != string::npos ) {
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temp.replace( pos, 1, "_" );
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pos = temp.find_first_of("\\/.");
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}
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return String(temp);
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}
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class BaseHoughLineTest
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{
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public:
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enum {STANDART = 0, PROBABILISTIC};
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protected:
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template<typename LinesType, typename LineType>
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void run_test(int type, const char* xml_name);
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string picture_name;
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double rhoStep;
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double thetaStep;
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int threshold;
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int minLineLength;
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int maxGap;
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};
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typedef tuple<string, double, double, int> Image_RhoStep_ThetaStep_Threshold_t;
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class StandartHoughLinesTest : public BaseHoughLineTest, public testing::TestWithParam<Image_RhoStep_ThetaStep_Threshold_t>
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{
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public:
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StandartHoughLinesTest()
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{
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picture_name = get<0>(GetParam());
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rhoStep = get<1>(GetParam());
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thetaStep = get<2>(GetParam());
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threshold = get<3>(GetParam());
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minLineLength = 0;
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maxGap = 0;
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}
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};
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typedef tuple<string, double, double, int, int, int> Image_RhoStep_ThetaStep_Threshold_MinLine_MaxGap_t;
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class ProbabilisticHoughLinesTest : public BaseHoughLineTest, public testing::TestWithParam<Image_RhoStep_ThetaStep_Threshold_MinLine_MaxGap_t>
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{
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public:
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ProbabilisticHoughLinesTest()
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{
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picture_name = get<0>(GetParam());
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rhoStep = get<1>(GetParam());
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thetaStep = get<2>(GetParam());
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threshold = get<3>(GetParam());
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minLineLength = get<4>(GetParam());
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maxGap = get<5>(GetParam());
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}
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};
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typedef tuple<double, double, double, double> HoughLinesPointSetInput_t;
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class HoughLinesPointSetTest : public testing::TestWithParam<HoughLinesPointSetInput_t>
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{
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protected:
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void run_test();
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double Rho;
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double Theta;
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double rhoMin, rhoMax, rhoStep;
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double thetaMin, thetaMax, thetaStep;
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public:
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HoughLinesPointSetTest()
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{
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rhoMin = get<0>(GetParam());
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rhoMax = get<1>(GetParam());
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rhoStep = (rhoMax - rhoMin) / 360.0f;
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thetaMin = get<2>(GetParam());
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thetaMax = get<3>(GetParam());
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thetaStep = CV_PI / 180.0f;
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Rho = 320.00000;
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Theta = 1.04719;
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}
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};
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template<typename LinesType, typename LineType>
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void BaseHoughLineTest::run_test(int type, const char* xml_name)
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{
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string filename = cvtest::TS::ptr()->get_data_path() + picture_name;
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Mat src = imread(filename, IMREAD_GRAYSCALE);
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ASSERT_FALSE(src.empty()) << "Invalid test image: " << filename;
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string xml = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/" + xml_name;
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Mat dst;
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Canny(src, dst, 100, 150, 3);
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ASSERT_FALSE(dst.empty()) << "Failed Canny edge detector";
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LinesType lines;
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if (type == STANDART)
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HoughLines(dst, lines, rhoStep, thetaStep, threshold, 0, 0);
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else if (type == PROBABILISTIC)
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HoughLinesP(dst, lines, rhoStep, thetaStep, threshold, minLineLength, maxGap);
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String test_case_name = format("lines_%s_%.0f_%.2f_%d_%d_%d", picture_name.c_str(), rhoStep, thetaStep,
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threshold, minLineLength, maxGap);
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test_case_name = getTestCaseName(test_case_name);
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#ifdef GENERATE_DATA
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{
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FileStorage fs(xml, FileStorage::READ);
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ASSERT_TRUE(!fs.isOpened() || fs[test_case_name].empty());
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}
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{
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FileStorage fs(xml, FileStorage::APPEND);
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EXPECT_TRUE(fs.isOpened()) << "Cannot open sanity data file: " << xml;
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fs << test_case_name << Mat(lines);
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}
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#else
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FileStorage fs(xml, FileStorage::READ);
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FileNode node = fs[test_case_name];
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ASSERT_FALSE(node.empty()) << "Missing test data: " << test_case_name << std::endl << "XML: " << xml;
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Mat exp_lines_;
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read(fs[test_case_name], exp_lines_, Mat());
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fs.release();
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LinesType exp_lines;
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exp_lines_.copyTo(exp_lines);
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int count = -1;
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if (type == STANDART)
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count = countMatIntersection<LineType>(Mat(exp_lines), Mat(lines), (float) thetaStep + FLT_EPSILON, (float) rhoStep + FLT_EPSILON);
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else if (type == PROBABILISTIC)
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count = countMatIntersection<LineType>(Mat(exp_lines), Mat(lines), 1e-4f, 0.f);
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#if defined HAVE_IPP && IPP_VERSION_X100 >= 810 && !IPP_DISABLE_HOUGH
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EXPECT_LE(std::abs((double)count - Mat(exp_lines).total()), Mat(exp_lines).total() * 0.25)
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<< "count=" << count << " expected=" << Mat(exp_lines).total();
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#else
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EXPECT_EQ(count, (int)Mat(exp_lines).total());
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#endif
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#endif // GENERATE_DATA
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}
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void HoughLinesPointSetTest::run_test(void)
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{
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Mat lines_f, lines_i;
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vector<Point2f> pointf;
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vector<Point2i> pointi;
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vector<Vec3d> line_polar_f, line_polar_i;
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const float Points[20][2] = {
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{ 0.0f, 369.0f }, { 10.0f, 364.0f }, { 20.0f, 358.0f }, { 30.0f, 352.0f },
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{ 40.0f, 346.0f }, { 50.0f, 341.0f }, { 60.0f, 335.0f }, { 70.0f, 329.0f },
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{ 80.0f, 323.0f }, { 90.0f, 318.0f }, { 100.0f, 312.0f }, { 110.0f, 306.0f },
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{ 120.0f, 300.0f }, { 130.0f, 295.0f }, { 140.0f, 289.0f }, { 150.0f, 284.0f },
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{ 160.0f, 277.0f }, { 170.0f, 271.0f }, { 180.0f, 266.0f }, { 190.0f, 260.0f }
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};
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// Float
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for (int i = 0; i < 20; i++)
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{
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pointf.push_back(Point2f(Points[i][0],Points[i][1]));
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}
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HoughLinesPointSet(pointf, lines_f, 20, 1,
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rhoMin, rhoMax, rhoStep,
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thetaMin, thetaMax, thetaStep);
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lines_f.copyTo( line_polar_f );
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// Integer
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for( int i = 0; i < 20; i++ )
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{
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pointi.push_back( Point2i( (int)Points[i][0], (int)Points[i][1] ) );
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}
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HoughLinesPointSet( pointi, lines_i, 20, 1,
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rhoMin, rhoMax, rhoStep,
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thetaMin, thetaMax, thetaStep );
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lines_i.copyTo( line_polar_i );
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EXPECT_EQ((int)(line_polar_f.at(0).val[1] * 100000.0f), (int)(Rho * 100000.0f));
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EXPECT_EQ((int)(line_polar_f.at(0).val[2] * 100000.0f), (int)(Theta * 100000.0f));
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EXPECT_EQ((int)(line_polar_i.at(0).val[1] * 100000.0f), (int)(Rho * 100000.0f));
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EXPECT_EQ((int)(line_polar_i.at(0).val[2] * 100000.0f), (int)(Theta * 100000.0f));
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}
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TEST_P(StandartHoughLinesTest, regression)
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{
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run_test<Mat, Vec2f>(STANDART, "HoughLines.xml");
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}
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TEST_P(ProbabilisticHoughLinesTest, regression)
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{
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run_test<Mat, Vec4i>(PROBABILISTIC, "HoughLinesP.xml");
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}
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TEST_P(StandartHoughLinesTest, regression_Vec2f)
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{
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run_test<std::vector<Vec2f>, Vec2f>(STANDART, "HoughLines2f.xml");
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}
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TEST_P(StandartHoughLinesTest, regression_Vec3f)
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{
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run_test<std::vector<Vec3f>, Vec3f>(STANDART, "HoughLines3f.xml");
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}
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TEST_P(HoughLinesPointSetTest, regression)
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{
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run_test();
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}
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INSTANTIATE_TEST_CASE_P( ImgProc, StandartHoughLinesTest, testing::Combine(testing::Values( "shared/pic5.png", "../stitching/a1.png" ),
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testing::Values( 1, 10 ),
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testing::Values( 0.05, 0.1 ),
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testing::Values( 80, 150 )
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));
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INSTANTIATE_TEST_CASE_P( ImgProc, ProbabilisticHoughLinesTest, testing::Combine(testing::Values( "shared/pic5.png", "shared/pic1.png" ),
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testing::Values( 5, 10 ),
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testing::Values( 0.05, 0.1 ),
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testing::Values( 75, 150 ),
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testing::Values( 0, 10 ),
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testing::Values( 0, 4 )
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));
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INSTANTIATE_TEST_CASE_P( Imgproc, HoughLinesPointSetTest, testing::Combine(testing::Values( 0.0f, 120.0f ),
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testing::Values( 360.0f, 480.0f ),
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testing::Values( 0.0f, (CV_PI / 18.0f) ),
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testing::Values( (CV_PI / 2.0f), (CV_PI * 5.0f / 12.0f) )
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));
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}} // namespace
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