mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
321 lines
12 KiB
321 lines
12 KiB
/*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" |
|
|
|
namespace opencv_test { namespace { |
|
|
|
#ifndef DEBUG_IMAGES |
|
#define DEBUG_IMAGES 0 |
|
#endif |
|
|
|
//#define GENERATE_DATA // generate data in debug mode via CPU code path (without IPP / OpenCL and other accelerators) |
|
|
|
using namespace cv; |
|
using namespace std; |
|
|
|
static string getTestCaseName(const string& picture_name, double minDist, double edgeThreshold, double accumThreshold, int minRadius, int maxRadius) |
|
{ |
|
string results_name = cv::format("circles_%s_%.0f_%.0f_%.0f_%d_%d", |
|
picture_name.c_str(), minDist, edgeThreshold, accumThreshold, minRadius, maxRadius); |
|
string temp(results_name); |
|
size_t pos = temp.find_first_of("\\/."); |
|
while (pos != string::npos) { |
|
temp.replace(pos, 1, "_"); |
|
pos = temp.find_first_of("\\/."); |
|
} |
|
return temp; |
|
} |
|
|
|
#if DEBUG_IMAGES |
|
static void highlightCircles(const string& imagePath, const vector<Vec3f>& circles, const string& outputImagePath) |
|
{ |
|
Mat imgDebug = imread(imagePath, IMREAD_COLOR); |
|
const Scalar yellow(0, 255, 255); |
|
|
|
for (vector<Vec3f>::const_iterator iter = circles.begin(); iter != circles.end(); ++iter) |
|
{ |
|
const Vec3f& circle = *iter; |
|
float x = circle[0]; |
|
float y = circle[1]; |
|
float r = max(circle[2], 2.0f); |
|
cv::circle(imgDebug, Point(int(x), int(y)), int(r), yellow); |
|
} |
|
imwrite(outputImagePath, imgDebug); |
|
} |
|
#endif |
|
|
|
typedef tuple<string, double, double, double, int, int> Image_MinDist_EdgeThreshold_AccumThreshold_MinRadius_MaxRadius_t; |
|
class HoughCirclesTestFixture : public testing::TestWithParam<Image_MinDist_EdgeThreshold_AccumThreshold_MinRadius_MaxRadius_t> |
|
{ |
|
string picture_name; |
|
double minDist; |
|
double edgeThreshold; |
|
double accumThreshold; |
|
int minRadius; |
|
int maxRadius; |
|
|
|
public: |
|
HoughCirclesTestFixture() |
|
{ |
|
picture_name = get<0>(GetParam()); |
|
minDist = get<1>(GetParam()); |
|
edgeThreshold = get<2>(GetParam()); |
|
accumThreshold = get<3>(GetParam()); |
|
minRadius = get<4>(GetParam()); |
|
maxRadius = get<5>(GetParam()); |
|
} |
|
|
|
HoughCirclesTestFixture(const string& picture, double minD, double edge, double accum, int minR, int maxR) : |
|
picture_name(picture), minDist(minD), edgeThreshold(edge), accumThreshold(accum), minRadius(minR), maxRadius(maxR) |
|
{ |
|
} |
|
|
|
template <typename CircleType> |
|
void run_test(const char* xml_name) |
|
{ |
|
string test_case_name = getTestCaseName(picture_name, minDist, edgeThreshold, accumThreshold, minRadius, maxRadius); |
|
string filename = cvtest::TS::ptr()->get_data_path() + picture_name; |
|
Mat src = imread(filename, IMREAD_GRAYSCALE); |
|
EXPECT_FALSE(src.empty()) << "Invalid test image: " << filename; |
|
|
|
GaussianBlur(src, src, Size(9, 9), 2, 2); |
|
|
|
vector<CircleType> circles; |
|
const double dp = 1.0; |
|
HoughCircles(src, circles, CV_HOUGH_GRADIENT, dp, minDist, edgeThreshold, accumThreshold, minRadius, maxRadius); |
|
|
|
string imgProc = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/"; |
|
#if DEBUG_IMAGES |
|
highlightCircles(filename, circles, imgProc + test_case_name + ".png"); |
|
#endif |
|
|
|
string xml = imgProc + xml_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 << circles; |
|
} |
|
#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; |
|
vector<CircleType> exp_circles; |
|
read(fs[test_case_name], exp_circles, vector<CircleType>()); |
|
fs.release(); |
|
EXPECT_EQ(exp_circles.size(), circles.size()); |
|
#endif |
|
} |
|
}; |
|
|
|
TEST_P(HoughCirclesTestFixture, regression) |
|
{ |
|
run_test<Vec3f>("HoughCircles.xml"); |
|
} |
|
|
|
TEST_P(HoughCirclesTestFixture, regression4f) |
|
{ |
|
run_test<Vec4f>("HoughCircles4f.xml"); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(ImgProc, HoughCirclesTestFixture, testing::Combine( |
|
// picture_name: |
|
testing::Values("imgproc/stuff.jpg"), |
|
// minDist: |
|
testing::Values(20), |
|
// edgeThreshold: |
|
testing::Values(20), |
|
// accumThreshold: |
|
testing::Values(30), |
|
// minRadius: |
|
testing::Values(20), |
|
// maxRadius: |
|
testing::Values(200) |
|
)); |
|
|
|
|
|
class HoughCirclesTest : public testing::TestWithParam<HoughModes> |
|
{ |
|
protected: |
|
HoughModes method; |
|
public: |
|
HoughCirclesTest() { method = GetParam(); } |
|
}; |
|
|
|
TEST_P(HoughCirclesTest, DefaultMaxRadius) |
|
{ |
|
string picture_name = "imgproc/stuff.jpg"; |
|
string filename = cvtest::TS::ptr()->get_data_path() + picture_name; |
|
Mat src = imread(filename, IMREAD_GRAYSCALE); |
|
EXPECT_FALSE(src.empty()) << "Invalid test image: " << filename; |
|
GaussianBlur(src, src, Size(9, 9), 2, 2); |
|
|
|
double dp = 1.0; |
|
double minDist = 20.0; |
|
double edgeThreshold = 20.0; |
|
double param2 = method == HOUGH_GRADIENT_ALT ? 0.9 : 30.; |
|
int minRadius = method == HOUGH_GRADIENT_ALT ? 10 : 20; |
|
int maxRadius = 0; |
|
|
|
vector<Vec3f> circles; |
|
vector<Vec4f> circles4f; |
|
HoughCircles(src, circles, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius); |
|
HoughCircles(src, circles4f, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius); |
|
|
|
#if DEBUG_IMAGES |
|
string imgProc = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/"; |
|
highlightCircles(filename, circles, imgProc + "HoughCirclesTest_DefaultMaxRadius.png"); |
|
#endif |
|
|
|
int maxDimension = std::max(src.rows, src.cols); |
|
|
|
if(method == HOUGH_GRADIENT_ALT) |
|
{ |
|
EXPECT_EQ(circles.size(), size_t(3)) << "Should find 3 circles"; |
|
} |
|
else |
|
{ |
|
EXPECT_GT(circles.size(), size_t(0)) << "Should find at least some circles"; |
|
} |
|
|
|
for (size_t i = 0; i < circles.size(); ++i) |
|
{ |
|
EXPECT_GE(circles[i][2], minRadius) << "Radius should be >= minRadius"; |
|
EXPECT_LE(circles[i][2], maxDimension) << "Radius should be <= max image dimension"; |
|
} |
|
} |
|
|
|
TEST_P(HoughCirclesTest, CentersOnly) |
|
{ |
|
string picture_name = "imgproc/stuff.jpg"; |
|
string filename = cvtest::TS::ptr()->get_data_path() + picture_name; |
|
Mat src = imread(filename, IMREAD_GRAYSCALE); |
|
EXPECT_FALSE(src.empty()) << "Invalid test image: " << filename; |
|
|
|
GaussianBlur(src, src, Size(9, 9), 2, 2); |
|
double dp = 1.0; |
|
double minDist = 20.0; |
|
double edgeThreshold = 20.0; |
|
double param2 = method == HOUGH_GRADIENT_ALT ? 0.9 : 30.; |
|
int minRadius = method == HOUGH_GRADIENT_ALT ? 10 : 20; |
|
int maxRadius = -1; |
|
|
|
vector<Vec3f> circles; |
|
vector<Vec4f> circles4f; |
|
|
|
HoughCircles(src, circles, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius); |
|
HoughCircles(src, circles4f, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius); |
|
|
|
#if DEBUG_IMAGES |
|
string imgProc = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/"; |
|
highlightCircles(filename, circles, imgProc + "HoughCirclesTest_DefaultMaxRadius.png"); |
|
#endif |
|
|
|
if(method == HOUGH_GRADIENT_ALT) |
|
{ |
|
EXPECT_EQ(circles.size(), size_t(3)) << "Should find 3 circles"; |
|
} |
|
else |
|
{ |
|
EXPECT_GT(circles.size(), size_t(0)) << "Should find at least some circles"; |
|
} |
|
|
|
for (size_t i = 0; i < circles.size(); ++i) |
|
{ |
|
if( method == HOUGH_GRADIENT ) |
|
{ |
|
EXPECT_EQ(circles[i][2], 0.0f) << "Did not ask for radius"; |
|
} |
|
EXPECT_EQ(circles[i][0], circles4f[i][0]); |
|
EXPECT_EQ(circles[i][1], circles4f[i][1]); |
|
EXPECT_EQ(circles[i][2], circles4f[i][2]); |
|
} |
|
} |
|
|
|
TEST_P(HoughCirclesTest, ManySmallCircles) |
|
{ |
|
string picture_name = "imgproc/beads.jpg"; |
|
|
|
string filename = cvtest::TS::ptr()->get_data_path() + picture_name; |
|
Mat src = imread(filename, IMREAD_GRAYSCALE); |
|
EXPECT_FALSE(src.empty()) << "Invalid test image: " << filename; |
|
|
|
const double dp = method == HOUGH_GRADIENT_ALT ? 1.5 : 1.0; |
|
double minDist = 10; |
|
double edgeThreshold = 90; |
|
double accumThreshold = 11; |
|
double minCos2 = 0.85; |
|
double param2 = method == HOUGH_GRADIENT_ALT ? minCos2 : accumThreshold; |
|
int minRadius = 7; |
|
int maxRadius = 18; |
|
int ncircles_min = method == HOUGH_GRADIENT_ALT ? 2000 : 3000; |
|
|
|
Mat src_smooth; |
|
if( method == HOUGH_GRADIENT_ALT ) |
|
GaussianBlur(src, src_smooth, Size(7, 7), 1.5, 1.5); |
|
else |
|
src.copyTo(src_smooth); |
|
vector<Vec3f> circles; |
|
vector<Vec4f> circles4f; |
|
HoughCircles(src_smooth, circles, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius); |
|
HoughCircles(src_smooth, circles4f, method, dp, minDist, edgeThreshold, param2, minRadius, maxRadius); |
|
|
|
#if DEBUG_IMAGES |
|
string imgProc = string(cvtest::TS::ptr()->get_data_path()) + "imgproc/"; |
|
string test_case_name = getTestCaseName(picture_name, minDist, edgeThreshold, accumThreshold, minRadius, maxRadius); |
|
highlightCircles(filename, circles, imgProc + test_case_name + ".png"); |
|
#endif |
|
|
|
EXPECT_GT(circles.size(), size_t(ncircles_min)) << "Should find a lot of circles"; |
|
EXPECT_EQ(circles.size(), circles4f.size()); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(HoughGradient, HoughCirclesTest, testing::Values(HOUGH_GRADIENT)); |
|
INSTANTIATE_TEST_CASE_P(HoughGradientAlt, HoughCirclesTest, testing::Values(HOUGH_GRADIENT_ALT)); |
|
|
|
}} // namespace
|
|
|