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#include "test_precomp.hpp"
#include "test_chessboardgenerator.hpp"

#include <functional>

namespace opencv_test { namespace {

#define _L2_ERR

//#define DEBUG_CHESSBOARD

#ifdef DEBUG_CHESSBOARD
void show_points( const Mat& gray, const Mat& expected, const vector<Point2f>& actual, bool was_found )
{
    Mat rgb( gray.size(), CV_8U);
    merge(vector<Mat>(3, gray), rgb);

    for(size_t i = 0; i < actual.size(); i++ )
        circle( rgb, actual[i], 5, Scalar(0, 0, 200), 1, LINE_AA);

    if( !expected.empty() )
    {
        const Point2f* u_data = expected.ptr<Point2f>();
        size_t count = expected.cols * expected.rows;
        for(size_t i = 0; i < count; i++ )
            circle(rgb, u_data[i], 4, Scalar(0, 240, 0), 1, LINE_AA);
    }
    putText(rgb, was_found ? "FOUND !!!" : "NOT FOUND", Point(5, 20), FONT_HERSHEY_PLAIN, 1, Scalar(0, 240, 0));
    imshow( "test", rgb ); while ((uchar)waitKey(0) != 'q') {};
}
#else
#define show_points(...)
#endif

enum Pattern { CHESSBOARD, CIRCLES_GRID, ASYMMETRIC_CIRCLES_GRID };

class CV_ChessboardDetectorTest : public cvtest::BaseTest
{
public:
    CV_ChessboardDetectorTest( Pattern pattern, int algorithmFlags = 0 );
protected:
    void run(int);
    void run_batch(const string& filename);
    bool checkByGenerator();

    Pattern pattern;
    int algorithmFlags;
};

CV_ChessboardDetectorTest::CV_ChessboardDetectorTest( Pattern _pattern, int _algorithmFlags )
{
    pattern = _pattern;
    algorithmFlags = _algorithmFlags;
}

double calcError(const vector<Point2f>& v, const Mat& u)
{
    int count_exp = u.cols * u.rows;
    const Point2f* u_data = u.ptr<Point2f>();

    double err = std::numeric_limits<double>::max();
    for( int k = 0; k < 2; ++k )
    {
        double err1 = 0;
        for( int j = 0; j < count_exp; ++j )
        {
            int j1 = k == 0 ? j : count_exp - j - 1;
            double dx = fabs( v[j].x - u_data[j1].x );
            double dy = fabs( v[j].y - u_data[j1].y );

#if defined(_L2_ERR)
            err1 += dx*dx + dy*dy;
#else
            dx = MAX( dx, dy );
            if( dx > err1 )
                err1 = dx;
#endif //_L2_ERR
            //printf("dx = %f\n", dx);
        }
        //printf("\n");
        err = min(err, err1);
    }

#if defined(_L2_ERR)
    err = sqrt(err/count_exp);
#endif //_L2_ERR

    return err;
}

const double rough_success_error_level = 2.5;
const double precise_success_error_level = 2;


/* ///////////////////// chess_corner_test ///////////////////////// */
void CV_ChessboardDetectorTest::run( int /*start_from */)
{
    ts->set_failed_test_info( cvtest::TS::OK );

    /*if (!checkByGenerator())
        return;*/
    switch( pattern )
    {
        case CHESSBOARD:
            checkByGenerator();
            if (ts->get_err_code() != cvtest::TS::OK)
            {
                break;
            }

            run_batch("negative_list.dat");
            if (ts->get_err_code() != cvtest::TS::OK)
            {
                break;
            }

            run_batch("chessboard_list.dat");
            if (ts->get_err_code() != cvtest::TS::OK)
            {
                break;
            }

            run_batch("chessboard_list_subpixel.dat");
            break;
        case CIRCLES_GRID:
            run_batch("circles_list.dat");
            break;
        case ASYMMETRIC_CIRCLES_GRID:
            run_batch("acircles_list.dat");
            break;
    }
}

void CV_ChessboardDetectorTest::run_batch( const string& filename )
{
    ts->printf(cvtest::TS::LOG, "\nRunning batch %s\n", filename.c_str());
//#define WRITE_POINTS 1
#ifndef WRITE_POINTS
    double max_rough_error = 0, max_precise_error = 0;
#endif
    string folder;
    switch( pattern )
    {
        case CHESSBOARD:
            folder = string(ts->get_data_path()) + "cv/cameracalibration/";
            break;
        case CIRCLES_GRID:
            folder = string(ts->get_data_path()) + "cv/cameracalibration/circles/";
            break;
        case ASYMMETRIC_CIRCLES_GRID:
            folder = string(ts->get_data_path()) + "cv/cameracalibration/asymmetric_circles/";
            break;
    }

    FileStorage fs( folder + filename, FileStorage::READ );
    FileNode board_list = fs["boards"];

    if( !fs.isOpened() || board_list.empty() || !board_list.isSeq() || board_list.size() % 2 != 0 )
    {
        ts->printf( cvtest::TS::LOG, "%s can not be read or is not valid\n", (folder + filename).c_str() );
        ts->printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n",
            fs.isOpened(), (int)board_list.empty(), board_list.isSeq(), board_list.size()%2);
        ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
        return;
    }

    int progress = 0;
    int max_idx = (int)board_list.size()/2;
    double sum_error = 0.0;
    int count = 0;

    for(int idx = 0; idx < max_idx; ++idx )
    {
        ts->update_context( this, idx, true );

        /* read the image */
        String img_file = board_list[idx * 2];
        Mat gray = imread( folder + img_file, IMREAD_GRAYSCALE);

        if( gray.empty() )
        {
            ts->printf( cvtest::TS::LOG, "one of chessboard images can't be read: %s\n", img_file.c_str() );
            ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
            return;
        }

        String _filename = folder + (String)board_list[idx * 2 + 1];
        bool doesContatinChessboard;
        Mat expected;
        {
            FileStorage fs1(_filename, FileStorage::READ);
            fs1["corners"] >> expected;
            fs1["isFound"] >> doesContatinChessboard;
            fs1.release();
        }
        size_t count_exp = static_cast<size_t>(expected.cols * expected.rows);
        Size pattern_size = expected.size();

        vector<Point2f> v;
        bool result = false;
        switch( pattern )
        {
            case CHESSBOARD:
                result = findChessboardCorners(gray, pattern_size, v, CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE);
                break;
            case CIRCLES_GRID:
                result = findCirclesGrid(gray, pattern_size, v);
                break;
            case ASYMMETRIC_CIRCLES_GRID:
                result = findCirclesGrid(gray, pattern_size, v, CALIB_CB_ASYMMETRIC_GRID | algorithmFlags);
                break;
        }

        if( result ^ doesContatinChessboard || v.size() != count_exp )
        {
            ts->printf( cvtest::TS::LOG, "chessboard is detected incorrectly in %s\n", img_file.c_str() );
            ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
            return;
        }

        if( result )
        {

#ifndef WRITE_POINTS
            double err = calcError(v, expected);
            max_rough_error = MAX( max_rough_error, err );
#endif
            if( pattern == CHESSBOARD )
                cornerSubPix( gray, v, Size(5, 5), Size(-1,-1), TermCriteria(TermCriteria::EPS|TermCriteria::MAX_ITER, 30, 0.1));
            //find4QuadCornerSubpix(gray, v, Size(5, 5));
            show_points( gray, expected, v, result  );
#ifndef WRITE_POINTS
    //        printf("called find4QuadCornerSubpix\n");
            err = calcError(v, expected);
            sum_error += err;
            count++;
            if( err > precise_success_error_level )
            {
                ts->printf( cvtest::TS::LOG, "Image %s: bad accuracy of adjusted corners %f\n", img_file.c_str(), err );
                ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
                return;
            }
            ts->printf(cvtest::TS::LOG, "Error on %s is %f\n", img_file.c_str(), err);
            max_precise_error = MAX( max_precise_error, err );
#endif
        }
        else
        {
            show_points( gray, Mat(), v, result );
        }

#ifdef WRITE_POINTS
        Mat mat_v(pattern_size, CV_32FC2, (void*)&v[0]);
        FileStorage fs(_filename, FileStorage::WRITE);
        fs << "isFound" << result;
        fs << "corners" << mat_v;
        fs.release();
#endif
        progress = update_progress( progress, idx, max_idx, 0 );
    }

    if (count != 0)
        sum_error /= count;
    ts->printf(cvtest::TS::LOG, "Average error is %f (%d patterns have been found)\n", sum_error, count);
}

double calcErrorMinError(const Size& cornSz, const vector<Point2f>& corners_found, const vector<Point2f>& corners_generated)
{
    Mat m1(cornSz, CV_32FC2, (Point2f*)&corners_generated[0]);
    Mat m2; flip(m1, m2, 0);

    Mat m3; flip(m1, m3, 1); m3 = m3.t(); flip(m3, m3, 1);

    Mat m4 = m1.t(); flip(m4, m4, 1);

    double min1 =  min(calcError(corners_found, m1), calcError(corners_found, m2));
    double min2 =  min(calcError(corners_found, m3), calcError(corners_found, m4));
    return min(min1, min2);
}

bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz,
                  const vector<Point2f>& corners_generated)
{
    Size cornersSize = cbg.cornersSize();
    Mat_<Point2f> mat(cornersSize.height, cornersSize.width, (Point2f*)&corners_generated[0]);

    double minNeibDist = std::numeric_limits<double>::max();
    double tmp = 0;
    for(int i = 1; i < mat.rows - 2; ++i)
        for(int j = 1; j < mat.cols - 2; ++j)
        {
            const Point2f& cur = mat(i, j);

            tmp = cv::norm(cur - mat(i + 1, j + 1)); // TODO cvtest
            if (tmp < minNeibDist)
                minNeibDist = tmp;

            tmp = cv::norm(cur - mat(i - 1, j + 1)); // TODO cvtest
            if (tmp < minNeibDist)
                minNeibDist = tmp;

            tmp = cv::norm(cur - mat(i + 1, j - 1)); // TODO cvtest
            if (tmp < minNeibDist)
                minNeibDist = tmp;

            tmp = cv::norm(cur - mat(i - 1, j - 1)); // TODO cvtest
            if (tmp < minNeibDist)
                minNeibDist = tmp;
        }

    const double threshold = 0.25;
    double cbsize = (max(cornersSize.width, cornersSize.height) + 1) * minNeibDist;
    int imgsize =  min(imgSz.height, imgSz.width);
    return imgsize * threshold < cbsize;
}

bool CV_ChessboardDetectorTest::checkByGenerator()
{
    bool res = true;

    //theRNG() = 0x58e6e895b9913160;
    //cv::DefaultRngAuto dra;
    //theRNG() = *ts->get_rng();

    Mat bg(Size(800, 600), CV_8UC3, Scalar::all(255));
    randu(bg, Scalar::all(0), Scalar::all(255));
    GaussianBlur(bg, bg, Size(7,7), 3.0);

    Mat_<float> camMat(3, 3);
    camMat << 300.f, 0.f, bg.cols/2.f, 0, 300.f, bg.rows/2.f, 0.f, 0.f, 1.f;

    Mat_<float> distCoeffs(1, 5);
    distCoeffs << 1.2f, 0.2f, 0.f, 0.f, 0.f;

    const Size sizes[] = { Size(6, 6), Size(8, 6), Size(11, 12),  Size(5, 4) };
    const size_t sizes_num = sizeof(sizes)/sizeof(sizes[0]);
    const int test_num = 16;
    int progress = 0;
    for(int i = 0; i < test_num; ++i)
    {
        progress = update_progress( progress, i, test_num, 0 );
        ChessBoardGenerator cbg(sizes[i % sizes_num]);

        vector<Point2f> corners_generated;

        Mat cb = cbg(bg, camMat, distCoeffs, corners_generated);

        if(!validateData(cbg, cb.size(), corners_generated))
        {
            ts->printf( cvtest::TS::LOG, "Chess board skipped - too small" );
            continue;
        }

        /*cb = cb * 0.8 + Scalar::all(30);
        GaussianBlur(cb, cb, Size(3, 3), 0.8);     */
        //cv::addWeighted(cb, 0.8, bg, 0.2, 20, cb);
        //cv::namedWindow("CB"); cv::imshow("CB", cb); cv::waitKey();

        vector<Point2f> corners_found;
        int flags = i % 8; // need to check branches for all flags
        bool found = findChessboardCorners(cb, cbg.cornersSize(), corners_found, flags);
        if (!found)
        {
            ts->printf( cvtest::TS::LOG, "Chess board corners not found\n" );
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            res = false;
            return res;
        }

        double err = calcErrorMinError(cbg.cornersSize(), corners_found, corners_generated);
        if( err > rough_success_error_level )
        {
            ts->printf( cvtest::TS::LOG, "bad accuracy of corner guesses" );
            ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
            res = false;
            return res;
        }
    }

    /* ***** negative ***** */
    {
        vector<Point2f> corners_found;
        bool found = findChessboardCorners(bg, Size(8, 7), corners_found);
        if (found)
            res = false;

        ChessBoardGenerator cbg(Size(8, 7));

        vector<Point2f> cg;
        Mat cb = cbg(bg, camMat, distCoeffs, cg);

        found = findChessboardCorners(cb, Size(3, 4), corners_found);
        if (found)
            res = false;

        Point2f c = std::accumulate(cg.begin(), cg.end(), Point2f(), std::plus<Point2f>()) * (1.f/cg.size());

        Mat_<double> aff(2, 3);
        aff << 1.0, 0.0, -(double)c.x, 0.0, 1.0, 0.0;
        Mat sh;
        warpAffine(cb, sh, aff, cb.size());

        found = findChessboardCorners(sh, cbg.cornersSize(), corners_found);
        if (found)
            res = false;

        vector< vector<Point> > cnts(1);
        vector<Point>& cnt = cnts[0];
        cnt.push_back(cg[  0]); cnt.push_back(cg[0+2]);
        cnt.push_back(cg[7+0]); cnt.push_back(cg[7+2]);
        cv::drawContours(cb, cnts, -1, Scalar::all(128), FILLED);

        found = findChessboardCorners(cb, cbg.cornersSize(), corners_found);
        if (found)
            res = false;

        cv::drawChessboardCorners(cb, cbg.cornersSize(), Mat(corners_found), found);
    }

    return res;
}

TEST(Calib3d_ChessboardDetector, accuracy) {  CV_ChessboardDetectorTest test( CHESSBOARD ); test.safe_run(); }
TEST(Calib3d_CirclesPatternDetector, accuracy) { CV_ChessboardDetectorTest test( CIRCLES_GRID ); test.safe_run(); }
TEST(Calib3d_AsymmetricCirclesPatternDetector, accuracy) { CV_ChessboardDetectorTest test( ASYMMETRIC_CIRCLES_GRID ); test.safe_run(); }
#ifdef HAVE_OPENCV_FLANN
TEST(Calib3d_AsymmetricCirclesPatternDetectorWithClustering, accuracy) { CV_ChessboardDetectorTest test( ASYMMETRIC_CIRCLES_GRID, CALIB_CB_CLUSTERING ); test.safe_run(); }
#endif

TEST(Calib3d_CirclesPatternDetectorWithClustering, accuracy)
{
    cv::String dataDir = string(TS::ptr()->get_data_path()) + "cv/cameracalibration/circles/";

    cv::Mat expected;
    FileStorage fs(dataDir + "circles_corners15.dat", FileStorage::READ);
    fs["corners"] >> expected;
    fs.release();

    cv::Mat image = cv::imread(dataDir + "circles15.png");

    std::vector<Point2f> centers;
    cv::findCirclesGrid(image, Size(10, 8), centers, CALIB_CB_SYMMETRIC_GRID | CALIB_CB_CLUSTERING);
    ASSERT_EQ(expected.total(), centers.size());

    double error = calcError(centers, expected);
    ASSERT_LE(error, precise_success_error_level);
}

TEST(Calib3d_AsymmetricCirclesPatternDetector, regression_18713)
{
    float pts_[][2] = {
        { 166.5, 107 }, { 146, 236 }, { 147, 92 }, { 184, 162 }, { 150, 185.5 },
        { 215, 105 }, { 270.5, 186 }, { 159, 142 }, { 6, 205.5 }, { 32, 148.5 },
        { 126, 163.5 }, { 181, 208.5 }, { 240.5, 62 }, { 84.5, 76.5 }, { 190, 120.5 },
        { 10, 189 }, { 266, 104 }, { 307.5, 207.5 }, { 97, 184 }, { 116.5, 210 },
        { 114, 139 }, { 84.5, 233 }, { 269.5, 139 }, { 136, 126.5 }, { 120, 107.5 },
        { 129.5, 65.5 }, { 212.5, 140.5 }, { 204.5, 60.5 }, { 207.5, 241 }, { 61.5, 94.5 },
        { 186.5, 61.5 }, { 220, 63 }, { 239, 120.5 }, { 212, 186 }, { 284, 87.5 },
        { 62, 114.5 }, { 283, 61.5 }, { 238.5, 88.5 }, { 243, 159 }, { 245, 208 },
        { 298.5, 158.5 }, { 57, 129 }, { 156.5, 63.5 }, { 192, 90.5 }, { 281, 235.5 },
        { 172, 62.5 }, { 291.5, 119.5 }, { 90, 127 }, { 68.5, 166.5 }, { 108.5, 83.5 },
        { 22, 176 }
    };
    Mat candidates(51, 1, CV_32FC2, (void*)pts_);
    Size patternSize(4, 9);

    std::vector< Point2f > result;
    bool res = false;

    // issue reports about hangs
    EXPECT_NO_THROW(res = findCirclesGrid(candidates, patternSize, result, CALIB_CB_ASYMMETRIC_GRID, Ptr<FeatureDetector>()/*blobDetector=NULL*/));
    EXPECT_FALSE(res);

    if (cvtest::debugLevel > 0)
    {
        std::cout << Mat(candidates) << std::endl;
        std::cout << Mat(result) << std::endl;
        Mat img(Size(400, 300), CV_8UC3, Scalar::all(0));

        std::vector< Point2f > centers;
        candidates.copyTo(centers);

        for (size_t i = 0; i < centers.size(); i++)
        {
            const Point2f& pt = centers[i];
            //printf("{ %g, %g }, \n", pt.x, pt.y);
            circle(img, pt, 5, Scalar(0, 255, 0));
        }
        for (size_t i = 0; i < result.size(); i++)
        {
            const Point2f& pt = result[i];
            circle(img, pt, 10, Scalar(0, 0, 255));
        }
        imwrite("test_18713.png", img);
        if (cvtest::debugLevel >= 10)
        {
            imshow("result", img);
            waitKey();
        }
    }
}

TEST(Calib3d_AsymmetricCirclesPatternDetector, regression_19498)
{
    float pts_[121][2] = {
        { 84.7462f, 404.504f }, { 49.1586f, 404.092f }, { 12.3362f, 403.434f }, { 102.542f, 386.214f }, { 67.6042f, 385.475f },
        { 31.4982f, 384.569f }, { 141.231f, 377.856f }, { 332.834f, 370.745f }, { 85.7663f, 367.261f }, { 50.346f, 366.051f },
        { 13.7726f, 364.663f }, { 371.746f, 362.011f }, { 68.8543f, 347.883f }, { 32.9334f, 346.263f }, { 331.926f, 343.291f },
        { 351.535f, 338.112f }, { 51.7951f, 328.247f }, { 15.4613f, 326.095f }, { 311.719f, 319.578f }, { 330.947f, 313.708f },
        { 256.706f, 307.584f }, { 34.6834f, 308.167f }, { 291.085f, 295.429f }, { 17.4316f, 287.824f }, { 252.928f, 277.92f },
        { 270.19f, 270.93f }, { 288.473f, 263.484f }, { 216.401f, 260.94f }, { 232.195f, 253.656f }, { 266.757f, 237.708f },
        { 211.323f, 229.005f }, { 227.592f, 220.498f }, { 154.749f, 188.52f }, { 222.52f, 184.906f }, { 133.85f, 163.968f },
        { 200.024f, 158.05f }, { 147.485f, 153.643f }, { 161.967f, 142.633f }, { 177.396f, 131.059f }, { 125.909f, 128.116f },
        { 139.817f, 116.333f }, { 91.8639f, 114.454f }, { 104.343f, 102.542f }, { 117.635f, 89.9116f }, { 70.9465f, 89.4619f },
        { 82.8524f, 76.7862f }, { 131.738f, 76.4741f }, { 95.5012f, 63.3351f }, { 109.034f, 49.0424f }, { 314.886f, 374.711f },
        { 351.735f, 366.489f }, { 279.113f, 357.05f }, { 313.371f, 348.131f }, { 260.123f, 335.271f }, { 276.346f, 330.325f },
        { 293.588f, 325.133f }, { 240.86f, 313.143f }, { 273.436f, 301.667f }, { 206.762f, 296.574f }, { 309.877f, 288.796f },
        { 187.46f, 274.319f }, { 201.521f, 267.804f }, { 248.973f, 245.918f }, { 181.644f, 244.655f }, { 196.025f, 237.045f },
        { 148.41f, 229.131f }, { 161.604f, 221.215f }, { 175.455f, 212.873f }, { 244.748f, 211.459f }, { 128.661f, 206.109f },
        { 190.217f, 204.108f }, { 141.346f, 197.568f }, { 205.876f, 194.781f }, { 168.937f, 178.948f }, { 121.006f, 173.714f },
        { 183.998f, 168.806f }, { 88.9095f, 159.731f }, { 100.559f, 149.867f }, { 58.553f, 146.47f }, { 112.849f, 139.302f },
        { 80.0968f, 125.74f }, { 39.24f, 123.671f }, { 154.582f, 103.85f }, { 59.7699f, 101.49f }, { 266.334f, 385.387f },
        { 234.053f, 368.718f }, { 263.347f, 361.184f }, { 244.763f, 339.958f }, { 198.16f, 328.214f }, { 211.675f, 323.407f },
        { 225.905f, 318.426f }, { 192.98f, 302.119f }, { 221.267f, 290.693f }, { 161.437f, 286.46f }, { 236.656f, 284.476f },
        { 168.023f, 251.799f }, { 105.385f, 221.988f }, { 116.724f, 214.25f }, { 97.2959f, 191.81f }, { 108.89f, 183.05f },
        { 77.9896f, 169.242f }, { 48.6763f, 156.088f }, { 68.9635f, 136.415f }, { 29.8484f, 133.886f }, { 49.1966f, 112.826f },
        { 113.059f, 29.003f }, { 251.698f, 388.562f }, { 281.689f, 381.929f }, { 297.875f, 378.518f }, { 248.376f, 365.025f },
        { 295.791f, 352.763f }, { 216.176f, 348.586f }, { 230.143f, 344.443f }, { 179.89f, 307.457f }, { 174.083f, 280.51f },
        { 142.867f, 265.085f }, { 155.127f, 258.692f }, { 124.187f, 243.661f }, { 136.01f, 236.553f }, { 86.4651f, 200.13f },
        { 67.5711f, 178.221f }
    };

    Mat candidates(121, 1, CV_32FC2, (void*)pts_);
    Size patternSize(13, 8);

    std::vector< Point2f > result;
    bool res = false;

    EXPECT_NO_THROW(res = findCirclesGrid(candidates, patternSize, result, CALIB_CB_SYMMETRIC_GRID, Ptr<FeatureDetector>()/*blobDetector=NULL*/));
    EXPECT_FALSE(res);
}

}} // namespace
/* End of file. */