322 lines
12 KiB
C++
322 lines
12 KiB
C++
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/*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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namespace opencv_test { namespace {
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#ifndef DEBUG_IMAGES
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#define DEBUG_IMAGES 0
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#endif
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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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using namespace cv;
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using namespace std;
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static string getTestCaseName(const string& picture_name, double minDist, double edgeThreshold, double accumThreshold, int minRadius, int maxRadius)
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{
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string results_name = cv::format("circles_%s_%.0f_%.0f_%.0f_%d_%d",
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picture_name.c_str(), minDist, edgeThreshold, accumThreshold, minRadius, maxRadius);
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string temp(results_name);
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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 temp;
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}
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#if DEBUG_IMAGES
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static void highlightCircles(const string& imagePath, const vector<Vec3f>& circles, const string& outputImagePath)
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{
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Mat imgDebug = imread(imagePath, IMREAD_COLOR);
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const Scalar yellow(0, 255, 255);
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for (vector<Vec3f>::const_iterator iter = circles.begin(); iter != circles.end(); ++iter)
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{
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const Vec3f& circle = *iter;
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float x = circle[0];
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float y = circle[1];
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float r = max(circle[2], 2.0f);
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cv::circle(imgDebug, Point(int(x), int(y)), int(r), yellow);
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}
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imwrite(outputImagePath, imgDebug);
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}
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#endif
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typedef tuple<string, double, double, double, int, int> Image_MinDist_EdgeThreshold_AccumThreshold_MinRadius_MaxRadius_t;
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class HoughCirclesTestFixture : public testing::TestWithParam<Image_MinDist_EdgeThreshold_AccumThreshold_MinRadius_MaxRadius_t>
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{
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string picture_name;
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double minDist;
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double edgeThreshold;
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double accumThreshold;
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int minRadius;
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int maxRadius;
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public:
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HoughCirclesTestFixture()
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{
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picture_name = get<0>(GetParam());
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minDist = get<1>(GetParam());
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edgeThreshold = get<2>(GetParam());
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accumThreshold = get<3>(GetParam());
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minRadius = get<4>(GetParam());
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maxRadius = get<5>(GetParam());
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}
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HoughCirclesTestFixture(const string& picture, double minD, double edge, double accum, int minR, int maxR) :
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picture_name(picture), minDist(minD), edgeThreshold(edge), accumThreshold(accum), minRadius(minR), maxRadius(maxR)
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{
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}
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template <typename CircleType>
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void run_test(const char* xml_name)
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{
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string test_case_name = getTestCaseName(picture_name, minDist, edgeThreshold, accumThreshold, minRadius, maxRadius);
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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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EXPECT_FALSE(src.empty()) << "Invalid test image: " << filename;
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GaussianBlur(src, src, Size(9, 9), 2, 2);
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vector<CircleType> circles;
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const double dp = 1.0;
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HoughCircles(src, circles, CV_HOUGH_GRADIENT, dp, minDist, edgeThreshold, accumThreshold, minRadius, maxRadius);
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string imgProc = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/";
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#if DEBUG_IMAGES
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highlightCircles(filename, circles, imgProc + test_case_name + ".png");
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#endif
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string xml = imgProc + xml_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 << circles;
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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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vector<CircleType> exp_circles;
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read(fs[test_case_name], exp_circles, vector<CircleType>());
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fs.release();
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EXPECT_EQ(exp_circles.size(), circles.size());
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#endif
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}
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};
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TEST_P(HoughCirclesTestFixture, regression)
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{
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run_test<Vec3f>("HoughCircles.xml");
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}
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TEST_P(HoughCirclesTestFixture, regression4f)
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{
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run_test<Vec4f>("HoughCircles4f.xml");
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}
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INSTANTIATE_TEST_CASE_P(ImgProc, HoughCirclesTestFixture, testing::Combine(
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// picture_name:
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testing::Values("imgproc/stuff.jpg"),
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// minDist:
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testing::Values(20),
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// edgeThreshold:
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testing::Values(20),
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// accumThreshold:
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testing::Values(30),
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// minRadius:
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testing::Values(20),
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// maxRadius:
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testing::Values(200)
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));
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class HoughCirclesTest : public testing::TestWithParam<HoughModes>
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{
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protected:
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HoughModes method;
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public:
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HoughCirclesTest() { method = GetParam(); }
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};
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TEST_P(HoughCirclesTest, DefaultMaxRadius)
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{
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string picture_name = "imgproc/stuff.jpg";
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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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EXPECT_FALSE(src.empty()) << "Invalid test image: " << filename;
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GaussianBlur(src, src, Size(9, 9), 2, 2);
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double dp = 1.0;
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double minDist = 20.0;
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double edgeThreshold = 20.0;
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double param2 = method == HOUGH_GRADIENT_ALT ? 0.9 : 30.;
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int minRadius = method == HOUGH_GRADIENT_ALT ? 10 : 20;
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int maxRadius = 0;
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vector<Vec3f> circles;
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vector<Vec4f> circles4f;
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HoughCircles(src, circles, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius);
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HoughCircles(src, circles4f, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius);
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#if DEBUG_IMAGES
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string imgProc = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/";
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highlightCircles(filename, circles, imgProc + "HoughCirclesTest_DefaultMaxRadius.png");
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#endif
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int maxDimension = std::max(src.rows, src.cols);
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if(method == HOUGH_GRADIENT_ALT)
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{
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EXPECT_EQ(circles.size(), size_t(3)) << "Should find 3 circles";
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}
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else
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{
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EXPECT_GT(circles.size(), size_t(0)) << "Should find at least some circles";
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}
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for (size_t i = 0; i < circles.size(); ++i)
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{
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EXPECT_GE(circles[i][2], minRadius) << "Radius should be >= minRadius";
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EXPECT_LE(circles[i][2], maxDimension) << "Radius should be <= max image dimension";
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}
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}
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TEST_P(HoughCirclesTest, CentersOnly)
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{
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string picture_name = "imgproc/stuff.jpg";
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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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EXPECT_FALSE(src.empty()) << "Invalid test image: " << filename;
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GaussianBlur(src, src, Size(9, 9), 2, 2);
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double dp = 1.0;
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double minDist = 20.0;
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double edgeThreshold = 20.0;
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double param2 = method == HOUGH_GRADIENT_ALT ? 0.9 : 30.;
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int minRadius = method == HOUGH_GRADIENT_ALT ? 10 : 20;
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int maxRadius = -1;
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vector<Vec3f> circles;
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vector<Vec4f> circles4f;
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HoughCircles(src, circles, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius);
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HoughCircles(src, circles4f, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius);
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#if DEBUG_IMAGES
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string imgProc = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/";
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highlightCircles(filename, circles, imgProc + "HoughCirclesTest_DefaultMaxRadius.png");
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#endif
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if(method == HOUGH_GRADIENT_ALT)
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{
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EXPECT_EQ(circles.size(), size_t(3)) << "Should find 3 circles";
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}
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else
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{
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EXPECT_GT(circles.size(), size_t(0)) << "Should find at least some circles";
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}
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for (size_t i = 0; i < circles.size(); ++i)
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{
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if( method == HOUGH_GRADIENT )
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{
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EXPECT_EQ(circles[i][2], 0.0f) << "Did not ask for radius";
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}
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EXPECT_EQ(circles[i][0], circles4f[i][0]);
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EXPECT_EQ(circles[i][1], circles4f[i][1]);
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EXPECT_EQ(circles[i][2], circles4f[i][2]);
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}
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}
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TEST_P(HoughCirclesTest, ManySmallCircles)
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{
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string picture_name = "imgproc/beads.jpg";
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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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EXPECT_FALSE(src.empty()) << "Invalid test image: " << filename;
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const double dp = method == HOUGH_GRADIENT_ALT ? 1.5 : 1.0;
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double minDist = 10;
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double edgeThreshold = 90;
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double accumThreshold = 11;
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double minCos2 = 0.85;
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double param2 = method == HOUGH_GRADIENT_ALT ? minCos2 : accumThreshold;
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int minRadius = 7;
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int maxRadius = 18;
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int ncircles_min = method == HOUGH_GRADIENT_ALT ? 2000 : 3000;
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Mat src_smooth;
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if( method == HOUGH_GRADIENT_ALT )
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GaussianBlur(src, src_smooth, Size(7, 7), 1.5, 1.5);
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else
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src.copyTo(src_smooth);
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vector<Vec3f> circles;
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vector<Vec4f> circles4f;
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HoughCircles(src_smooth, circles, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius);
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HoughCircles(src_smooth, circles4f, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius);
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#if DEBUG_IMAGES
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string imgProc = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/";
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string test_case_name = getTestCaseName(picture_name, minDist, edgeThreshold, accumThreshold, minRadius, maxRadius);
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highlightCircles(filename, circles, imgProc + test_case_name + ".png");
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#endif
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EXPECT_GT(circles.size(), size_t(ncircles_min)) << "Should find a lot of circles";
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EXPECT_EQ(circles.size(), circles4f.size());
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}
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INSTANTIATE_TEST_CASE_P(HoughGradient, HoughCirclesTest, testing::Values(HOUGH_GRADIENT));
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INSTANTIATE_TEST_CASE_P(HoughGradientAlt, HoughCirclesTest, testing::Values(HOUGH_GRADIENT_ALT));
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}} // namespace
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