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#include "test_precomp.hpp"

using namespace cv;
using namespace std;

class CV_FindContourTest : public cvtest::BaseTest
{
public:
    enum { NUM_IMG = 4 };

    CV_FindContourTest();
    ~CV_FindContourTest();
    void clear();

protected:
    int read_params( CvFileStorage* fs );
    int prepare_test_case( int test_case_idx );
    int validate_test_results( int test_case_idx );
    void run_func();

    int min_blob_size, max_blob_size;
    int blob_count, max_log_blob_count;
    int retr_mode, approx_method;

    int min_log_img_size, max_log_img_size;
    CvSize img_size;
    int count, count2;

    IplImage* img[NUM_IMG];
    CvMemStorage* storage;
    CvSeq *contours, *contours2, *chain;
};


CV_FindContourTest::CV_FindContourTest()
{
    int i;

    test_case_count    = 300;
    min_blob_size      = 1;
    max_blob_size      = 50;
    max_log_blob_count = 10;

    min_log_img_size   = 3;
    max_log_img_size   = 10;

    for( i = 0; i < NUM_IMG; i++ )
        img[i] = 0;

    storage = 0;
}


CV_FindContourTest::~CV_FindContourTest()
{
    clear();
}


void CV_FindContourTest::clear()
{
    int i;

    cvtest::BaseTest::clear();

    for( i = 0; i < NUM_IMG; i++ )
        cvReleaseImage( &img[i] );

    cvReleaseMemStorage( &storage );
}


int CV_FindContourTest::read_params( CvFileStorage* fs )
{
    int t;
    int code = cvtest::BaseTest::read_params( fs );

    if( code < 0 )
        return code;

    min_blob_size      = cvReadInt( find_param( fs, "min_blob_size" ), min_blob_size );
    max_blob_size      = cvReadInt( find_param( fs, "max_blob_size" ), max_blob_size );
    max_log_blob_count = cvReadInt( find_param( fs, "max_log_blob_count" ), max_log_blob_count );
    min_log_img_size   = cvReadInt( find_param( fs, "min_log_img_size" ), min_log_img_size );
    max_log_img_size   = cvReadInt( find_param( fs, "max_log_img_size" ), max_log_img_size );

    min_blob_size = cvtest::clipInt( min_blob_size, 1, 100 );
    max_blob_size = cvtest::clipInt( max_blob_size, 1, 100 );

    if( min_blob_size > max_blob_size )
        CV_SWAP( min_blob_size, max_blob_size, t );

    max_log_blob_count = cvtest::clipInt( max_log_blob_count, 1, 10 );

    min_log_img_size = cvtest::clipInt( min_log_img_size, 1, 10 );
    max_log_img_size = cvtest::clipInt( max_log_img_size, 1, 10 );

    if( min_log_img_size > max_log_img_size )
        CV_SWAP( min_log_img_size, max_log_img_size, t );

    return 0;
}


static void
cvTsGenerateBlobImage( IplImage* img, int min_blob_size, int max_blob_size,
                       int blob_count, int min_brightness, int max_brightness,
                       RNG& rng )
{
    int i;
    CvSize size;

    assert( img->depth == IPL_DEPTH_8U && img->nChannels == 1 );

    cvZero( img );

    // keep the border clear
    cvSetImageROI( img, cvRect(1,1,img->width-2,img->height-2) );
    size = cvGetSize( img );

    for( i = 0; i < blob_count; i++ )
    {
        CvPoint center;
        CvSize  axes;
        int angle = cvtest::randInt(rng) % 180;
        int brightness = cvtest::randInt(rng) %
                         (max_brightness - min_brightness) + min_brightness;
        center.x = cvtest::randInt(rng) % size.width;
        center.y = cvtest::randInt(rng) % size.height;

        axes.width = (cvtest::randInt(rng) %
                     (max_blob_size - min_blob_size) + min_blob_size + 1)/2;
        axes.height = (cvtest::randInt(rng) %
                      (max_blob_size - min_blob_size) + min_blob_size + 1)/2;

        cvEllipse( img, center, axes, angle, 0, 360, cvScalar(brightness), CV_FILLED );
    }

    cvResetImageROI( img );
}


static void
cvTsMarkContours( IplImage* img, int val )
{
    int i, j;
    int step = img->widthStep;

    assert( img->depth == IPL_DEPTH_8U && img->nChannels == 1 && (val&1) != 0);

    for( i = 1; i < img->height - 1; i++ )
        for( j = 1; j < img->width - 1; j++ )
        {
            uchar* t = (uchar*)(img->imageData + img->widthStep*i + j);
            if( *t == 1 && (t[-step] == 0 || t[-1] == 0 || t[1] == 0 || t[step] == 0))
                *t = (uchar)val;
        }

    cvThreshold( img, img, val - 2, val, CV_THRESH_BINARY );
}


int CV_FindContourTest::prepare_test_case( int test_case_idx )
{
    RNG& rng = ts->get_rng();
    const int  min_brightness = 0, max_brightness = 2;
    int i, code = cvtest::BaseTest::prepare_test_case( test_case_idx );

    if( code < 0 )
        return code;

    clear();

    blob_count = cvRound(exp(cvtest::randReal(rng)*max_log_blob_count*CV_LOG2));

    img_size.width = cvRound(exp((cvtest::randReal(rng)*
        (max_log_img_size - min_log_img_size) + min_log_img_size)*CV_LOG2));
    img_size.height = cvRound(exp((cvtest::randReal(rng)*
        (max_log_img_size - min_log_img_size) + min_log_img_size)*CV_LOG2));

    approx_method = cvtest::randInt( rng ) % 4 + 1;
    retr_mode = cvtest::randInt( rng ) % 4;

    storage = cvCreateMemStorage( 1 << 10 );

    for( i = 0; i < NUM_IMG; i++ )
        img[i] = cvCreateImage( img_size, 8, 1 );

    cvTsGenerateBlobImage( img[0], min_blob_size, max_blob_size,
        blob_count, min_brightness, max_brightness, rng );

    cvCopy( img[0], img[1] );
    cvCopy( img[0], img[2] );

    cvTsMarkContours( img[1], 255 );

    return 1;
}


void CV_FindContourTest::run_func()
{
    contours = contours2 = chain = 0;
    count = cvFindContours( img[2], storage, &contours, sizeof(CvContour), retr_mode, approx_method );

    cvZero( img[3] );

    if( contours && retr_mode != CV_RETR_EXTERNAL && approx_method < CV_CHAIN_APPROX_TC89_L1 )
        cvDrawContours( img[3], contours, cvScalar(255), cvScalar(255), INT_MAX, -1 );

    cvCopy( img[0], img[2] );

    count2 = cvFindContours( img[2], storage, &chain, sizeof(CvChain), retr_mode, CV_CHAIN_CODE );

    if( chain )
        contours2 = cvApproxChains( chain, storage, approx_method, 0, 0, 1 );

    cvZero( img[2] );

    if( contours && retr_mode != CV_RETR_EXTERNAL && approx_method < CV_CHAIN_APPROX_TC89_L1 )
        cvDrawContours( img[2], contours2, cvScalar(255), cvScalar(255), INT_MAX );
}


// the whole testing is done here, run_func() is not utilized in this test
int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ )
{
    int code = cvtest::TS::OK;

    cvCmpS( img[0], 0, img[0], CV_CMP_GT );

    if( count != count2 )
    {
        ts->printf( cvtest::TS::LOG, "The number of contours retrieved with different "
            "approximation methods is not the same\n"
            "(%d contour(s) for method %d vs %d contour(s) for method %d)\n",
            count, approx_method, count2, CV_CHAIN_CODE );
        code = cvtest::TS::FAIL_INVALID_OUTPUT;
    }

    if( retr_mode != CV_RETR_EXTERNAL && approx_method < CV_CHAIN_APPROX_TC89_L1 )
    {
        Mat _img[4];
        for( int i = 0; i < 4; i++ )
            _img[i] = cvarrToMat(img[i]);

        code = cvtest::cmpEps2(ts, _img[0], _img[3], 0, true, "Comparing original image with the map of filled contours" );

        if( code < 0 )
            goto _exit_;

        code = cvtest::cmpEps2( ts, _img[1], _img[2], 0, true,
            "Comparing contour outline vs manually produced edge map" );

        if( code < 0 )
            goto _exit_;
    }

    if( contours )
    {
        CvTreeNodeIterator iterator1;
        CvTreeNodeIterator iterator2;
        int count3;

        for(int i = 0; i < 2; i++ )
        {
            CvTreeNodeIterator iterator;
            cvInitTreeNodeIterator( &iterator, i == 0 ? contours : contours2, INT_MAX );

            for( count3 = 0; cvNextTreeNode( &iterator ) != 0; count3++ )
                ;

            if( count3 != count )
            {
                ts->printf( cvtest::TS::LOG,
                    "The returned number of retrieved contours (using the approx_method = %d) does not match\n"
                    "to the actual number of contours in the tree/list (returned %d, actual %d)\n",
                    i == 0 ? approx_method : 0, count, count3 );
                code = cvtest::TS::FAIL_INVALID_OUTPUT;
                goto _exit_;
            }
        }

        cvInitTreeNodeIterator( &iterator1, contours, INT_MAX );
        cvInitTreeNodeIterator( &iterator2, contours2, INT_MAX );

        for( count3 = 0; count3 < count; count3++ )
        {
            CvSeq* seq1 = (CvSeq*)cvNextTreeNode( &iterator1 );
            CvSeq* seq2 = (CvSeq*)cvNextTreeNode( &iterator2 );
            CvSeqReader reader1;
            CvSeqReader reader2;

            if( !seq1 || !seq2 )
            {
                ts->printf( cvtest::TS::LOG,
                    "There are NULL pointers in the original contour tree or the "
                    "tree produced by cvApproxChains\n" );
                code = cvtest::TS::FAIL_INVALID_OUTPUT;
                goto _exit_;
            }

            cvStartReadSeq( seq1, &reader1 );
            cvStartReadSeq( seq2, &reader2 );

            if( seq1->total != seq2->total )
            {
                ts->printf( cvtest::TS::LOG,
                    "The original contour #%d has %d points, while the corresponding contour has %d point\n",
                    count3, seq1->total, seq2->total );
                code = cvtest::TS::FAIL_INVALID_OUTPUT;
                goto _exit_;
            }

            for(int i = 0; i < seq1->total; i++ )
            {
                CvPoint pt1;
                CvPoint pt2;

                CV_READ_SEQ_ELEM( pt1, reader1 );
                CV_READ_SEQ_ELEM( pt2, reader2 );

                if( pt1.x != pt2.x || pt1.y != pt2.y )
                {
                    ts->printf( cvtest::TS::LOG,
                    "The point #%d in the contour #%d is different from the corresponding point "
                    "in the approximated chain ((%d,%d) vs (%d,%d)", count3, i, pt1.x, pt1.y, pt2.x, pt2.y );
                    code = cvtest::TS::FAIL_INVALID_OUTPUT;
                    goto _exit_;
                }
            }
        }
    }

_exit_:
    if( code < 0 )
    {
#if 0
        cvNamedWindow( "test", 0 );
        cvShowImage( "test", img[0] );
        cvWaitKey();
#endif
        ts->set_failed_test_info( code );
    }

    return code;
}

TEST(Imgproc_FindContours, accuracy) { CV_FindContourTest test; test.safe_run(); }

TEST(Core_Drawing, _914)
{
    const int rows = 256;
    const int cols = 256;

    Mat img(rows, cols, CV_8UC1, Scalar(255));

    line(img, Point(0, 10), Point(255, 10), Scalar(0), 2, 4);
    line(img, Point(-5, 20), Point(260, 20), Scalar(0), 2, 4);
    line(img, Point(10, 0), Point(10, 255), Scalar(0), 2, 4);

    double x0 = 0.0/pow(2.0, -2.0);
    double x1 = 255.0/pow(2.0, -2.0);
    double y = 30.5/pow(2.0, -2.0);

    line(img, Point(int(x0), int(y)), Point(int(x1), int(y)), Scalar(0), 2, 4, 2);

    int pixelsDrawn = rows*cols - countNonZero(img);
    ASSERT_EQ( (3*rows + cols)*3 - 3*9, pixelsDrawn);
}

TEST(Core_Drawing, polylines_empty)
{
    Mat img(100, 100, CV_8UC1, Scalar(0));
    vector<Point> pts; // empty
    polylines(img, pts, false, Scalar(255));
    int cnt = countNonZero(img);
    ASSERT_EQ(cnt, 0);
}

TEST(Core_Drawing, polylines)
{
    Mat img(100, 100, CV_8UC1, Scalar(0));
    vector<Point> pts;
    pts.push_back(Point(0, 0));
    pts.push_back(Point(20, 0));
    polylines(img, pts, false, Scalar(255));
    int cnt = countNonZero(img);
    ASSERT_EQ(cnt, 21);
}
/* End of file. */