/*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. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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" using namespace cv; using namespace std; class CV_CannyTest : public cvtest::ArrayTest { public: CV_CannyTest(); protected: void get_test_array_types_and_sizes( int test_case_idx, vector >& sizes, vector >& types ); double get_success_error_level( int test_case_idx, int i, int j ); int prepare_test_case( int test_case_idx ); void run_func(); void prepare_to_validation( int ); int validate_test_results( int /*test_case_idx*/ ); int aperture_size; bool use_true_gradient; double threshold1, threshold2; bool test_cpp; }; CV_CannyTest::CV_CannyTest() { test_array[INPUT].push_back(NULL); test_array[OUTPUT].push_back(NULL); test_array[REF_OUTPUT].push_back(NULL); element_wise_relative_error = true; aperture_size = 0; use_true_gradient = false; threshold1 = threshold2 = 0; test_cpp = false; } void CV_CannyTest::get_test_array_types_and_sizes( int test_case_idx, vector >& sizes, vector >& types ) { RNG& rng = ts->get_rng(); double thresh_range; cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types ); types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_8U; aperture_size = cvtest::randInt(rng) % 2 ? 5 : 3; thresh_range = aperture_size == 3 ? 300 : 1000; threshold1 = cvtest::randReal(rng)*thresh_range; threshold2 = cvtest::randReal(rng)*thresh_range*0.3; if( cvtest::randInt(rng) % 2 ) CV_SWAP( threshold1, threshold2, thresh_range ); use_true_gradient = cvtest::randInt(rng) % 2 != 0; test_cpp = (cvtest::randInt(rng) & 256) == 0; } int CV_CannyTest::prepare_test_case( int test_case_idx ) { int code = cvtest::ArrayTest::prepare_test_case( test_case_idx ); if( code > 0 ) { Mat& src = test_mat[INPUT][0]; GaussianBlur(src, src, Size(11, 11), 5, 5); } return code; } double CV_CannyTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ ) { return 0; } void CV_CannyTest::run_func() { if(!test_cpp) cvCanny( test_array[INPUT][0], test_array[OUTPUT][0], threshold1, threshold2, aperture_size + (use_true_gradient ? CV_CANNY_L2_GRADIENT : 0)); else { cv::Mat _out = cv::cvarrToMat(test_array[OUTPUT][0]); cv::Canny(cv::cvarrToMat(test_array[INPUT][0]), _out, threshold1, threshold2, aperture_size + (use_true_gradient ? CV_CANNY_L2_GRADIENT : 0)); } } static void cannyFollow( int x, int y, float lowThreshold, const Mat& mag, Mat& dst ) { static const int ofs[][2] = {{1,0},{1,-1},{0,-1},{-1,-1},{-1,0},{-1,1},{0,1},{1,1}}; int i; dst.at(y, x) = (uchar)255; for( i = 0; i < 8; i++ ) { int x1 = x + ofs[i][0]; int y1 = y + ofs[i][1]; if( (unsigned)x1 < (unsigned)mag.cols && (unsigned)y1 < (unsigned)mag.rows && mag.at(y1, x1) > lowThreshold && !dst.at(y1, x1) ) cannyFollow( x1, y1, lowThreshold, mag, dst ); } } static void test_Canny( const Mat& src, Mat& dst, double threshold1, double threshold2, int aperture_size, bool use_true_gradient ) { int m = aperture_size; Point anchor(m/2, m/2); const double tan_pi_8 = tan(CV_PI/8.); const double tan_3pi_8 = tan(CV_PI*3/8); float lowThreshold = (float)MIN(threshold1, threshold2); float highThreshold = (float)MAX(threshold1, threshold2); int x, y, width = src.cols, height = src.rows; Mat dxkernel = cvtest::calcSobelKernel2D( 1, 0, m, 0 ); Mat dykernel = cvtest::calcSobelKernel2D( 0, 1, m, 0 ); Mat dx, dy, mag(height, width, CV_32F); cvtest::filter2D(src, dx, CV_16S, dxkernel, anchor, 0, BORDER_REPLICATE); cvtest::filter2D(src, dy, CV_16S, dykernel, anchor, 0, BORDER_REPLICATE); // calc gradient magnitude for( y = 0; y < height; y++ ) { for( x = 0; x < width; x++ ) { int dxval = dx.at(y, x), dyval = dy.at(y, x); mag.at(y, x) = use_true_gradient ? (float)sqrt((double)(dxval*dxval + dyval*dyval)) : (float)(fabs((double)dxval) + fabs((double)dyval)); } } // calc gradient direction, do nonmaxima suppression for( y = 0; y < height; y++ ) { for( x = 0; x < width; x++ ) { float a = mag.at(y, x), b = 0, c = 0; int y1 = 0, y2 = 0, x1 = 0, x2 = 0; if( a <= lowThreshold ) continue; int dxval = dx.at(y, x); int dyval = dy.at(y, x); double tg = dxval ? (double)dyval/dxval : DBL_MAX*CV_SIGN(dyval); if( fabs(tg) < tan_pi_8 ) { y1 = y2 = y; x1 = x + 1; x2 = x - 1; } else if( tan_pi_8 <= tg && tg <= tan_3pi_8 ) { y1 = y + 1; y2 = y - 1; x1 = x + 1; x2 = x - 1; } else if( -tan_3pi_8 <= tg && tg <= -tan_pi_8 ) { y1 = y - 1; y2 = y + 1; x1 = x + 1; x2 = x - 1; } else { assert( fabs(tg) > tan_3pi_8 ); x1 = x2 = x; y1 = y + 1; y2 = y - 1; } if( (unsigned)y1 < (unsigned)height && (unsigned)x1 < (unsigned)width ) b = (float)fabs(mag.at(y1, x1)); if( (unsigned)y2 < (unsigned)height && (unsigned)x2 < (unsigned)width ) c = (float)fabs(mag.at(y2, x2)); if( (a > b || (a == b && ((x1 == x+1 && y1 == y) || (x1 == x && y1 == y+1)))) && a > c ) ; else mag.at(y, x) = -a; } } dst = Scalar::all(0); // hysteresis threshold for( y = 0; y < height; y++ ) { for( x = 0; x < width; x++ ) if( mag.at(y, x) > highThreshold && !dst.at(y, x) ) cannyFollow( x, y, lowThreshold, mag, dst ); } } void CV_CannyTest::prepare_to_validation( int ) { Mat src = test_mat[INPUT][0], dst = test_mat[REF_OUTPUT][0]; test_Canny( src, dst, threshold1, threshold2, aperture_size, use_true_gradient ); } int CV_CannyTest::validate_test_results( int test_case_idx ) { int code = cvtest::TS::OK, nz0; prepare_to_validation(test_case_idx); double err = cvtest::norm(test_mat[OUTPUT][0], test_mat[REF_OUTPUT][0], CV_L1); if( err == 0 ) return code; if( err != cvRound(err) || cvRound(err)%255 != 0 ) { ts->printf( cvtest::TS::LOG, "Some of the pixels, produced by Canny, are not 0's or 255's; the difference is %g\n", err ); ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); return code; } nz0 = cvRound(cvtest::norm(test_mat[REF_OUTPUT][0], CV_L1)/255); err = (err/255/MAX(nz0,100))*100; if( err > 1 ) { ts->printf( cvtest::TS::LOG, "Too high percentage of non-matching edge pixels = %g%%\n", err); ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); } return code; } TEST(Imgproc_Canny, accuracy) { CV_CannyTest test; test.safe_run(); } /* End of file. */