Hu Chunming / vpt_ascend

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#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