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430 lines
14 KiB
C++
430 lines
14 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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using namespace cv;
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using namespace std;
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class CV_CannyTest : public cvtest::ArrayTest
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{
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public:
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CV_CannyTest(bool custom_deriv = false);
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protected:
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void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
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double get_success_error_level( int test_case_idx, int i, int j );
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int prepare_test_case( int test_case_idx );
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void run_func();
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void prepare_to_validation( int );
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int validate_test_results( int /*test_case_idx*/ );
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int aperture_size;
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bool use_true_gradient;
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double threshold1, threshold2;
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bool test_cpp;
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bool test_custom_deriv;
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Mat img;
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};
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CV_CannyTest::CV_CannyTest(bool custom_deriv)
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{
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test_array[INPUT].push_back(NULL);
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test_array[OUTPUT].push_back(NULL);
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test_array[REF_OUTPUT].push_back(NULL);
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element_wise_relative_error = true;
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aperture_size = 0;
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use_true_gradient = false;
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threshold1 = threshold2 = 0;
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test_cpp = false;
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test_custom_deriv = custom_deriv;
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const char imgPath[] = "shared/fruits.png";
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img = cv::imread(cvtest::TS::ptr()->get_data_path() + imgPath, IMREAD_GRAYSCALE);
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}
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void CV_CannyTest::get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes,
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vector<vector<int> >& types )
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{
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RNG& rng = ts->get_rng();
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double thresh_range;
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cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
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types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_8U;
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aperture_size = cvtest::randInt(rng) % 2 ? 5 : 3;
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thresh_range = aperture_size == 3 ? 300 : 1000;
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threshold1 = cvtest::randReal(rng)*thresh_range;
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threshold2 = cvtest::randReal(rng)*thresh_range*0.3;
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if( cvtest::randInt(rng) % 2 )
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CV_SWAP( threshold1, threshold2, thresh_range );
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use_true_gradient = cvtest::randInt(rng) % 2 != 0;
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test_cpp = (cvtest::randInt(rng) & 256) == 0;
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ts->printf(cvtest::TS::LOG, "Canny(size = %d x %d, aperture_size = %d, threshold1 = %g, threshold2 = %g, L2 = %s) test_cpp = %s (test case #%d)\n",
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sizes[0][0].width, sizes[0][0].height, aperture_size, threshold1, threshold2, use_true_gradient ? "TRUE" : "FALSE", test_cpp ? "TRUE" : "FALSE", test_case_idx);
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}
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int CV_CannyTest::prepare_test_case( int test_case_idx )
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{
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int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
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if( code > 0 )
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{
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RNG& rng = ts->get_rng();
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Mat& src = test_mat[INPUT][0];
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//GaussianBlur(src, src, Size(11, 11), 5, 5);
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if(src.cols > img.cols || src.rows > img.rows)
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resize(img, src, src.size(), 0, 0, INTER_LINEAR_EXACT);
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else
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img(
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Rect(
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cvtest::randInt(rng) % (img.cols-src.cols),
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cvtest::randInt(rng) % (img.rows-src.rows),
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src.cols,
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src.rows
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)
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).copyTo(src);
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GaussianBlur(src, src, Size(5, 5), 0);
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}
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return code;
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}
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double CV_CannyTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
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{
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return 0;
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}
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void CV_CannyTest::run_func()
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{
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if (test_custom_deriv)
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{
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cv::Mat _out = cv::cvarrToMat(test_array[OUTPUT][0]);
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cv::Mat src = cv::cvarrToMat(test_array[INPUT][0]);
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cv::Mat dx, dy;
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int m = aperture_size;
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Point anchor(m/2, m/2);
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Mat dxkernel = cvtest::calcSobelKernel2D( 1, 0, m, 0 );
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Mat dykernel = cvtest::calcSobelKernel2D( 0, 1, m, 0 );
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cvtest::filter2D(src, dx, CV_16S, dxkernel, anchor, 0, BORDER_REPLICATE);
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cvtest::filter2D(src, dy, CV_16S, dykernel, anchor, 0, BORDER_REPLICATE);
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cv::Canny(dx, dy, _out, threshold1, threshold2, use_true_gradient);
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}
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else if(!test_cpp)
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{
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cvCanny( test_array[INPUT][0], test_array[OUTPUT][0], threshold1, threshold2,
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aperture_size + (use_true_gradient ? CV_CANNY_L2_GRADIENT : 0));
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}
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else
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{
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cv::Mat _out = cv::cvarrToMat(test_array[OUTPUT][0]);
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cv::Canny(cv::cvarrToMat(test_array[INPUT][0]), _out, threshold1, threshold2,
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aperture_size + (use_true_gradient ? CV_CANNY_L2_GRADIENT : 0));
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}
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}
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static void
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cannyFollow( int x, int y, float lowThreshold, const Mat& mag, Mat& dst )
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{
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static const int ofs[][2] = {{1,0},{1,-1},{0,-1},{-1,-1},{-1,0},{-1,1},{0,1},{1,1}};
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int i;
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dst.at<uchar>(y, x) = (uchar)255;
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for( i = 0; i < 8; i++ )
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{
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int x1 = x + ofs[i][0];
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int y1 = y + ofs[i][1];
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if( (unsigned)x1 < (unsigned)mag.cols &&
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(unsigned)y1 < (unsigned)mag.rows &&
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mag.at<float>(y1, x1) > lowThreshold &&
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!dst.at<uchar>(y1, x1) )
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cannyFollow( x1, y1, lowThreshold, mag, dst );
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}
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}
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static void
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test_Canny( const Mat& src, Mat& dst,
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double threshold1, double threshold2,
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int aperture_size, bool use_true_gradient )
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{
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int m = aperture_size;
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Point anchor(m/2, m/2);
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const double tan_pi_8 = tan(CV_PI/8.);
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const double tan_3pi_8 = tan(CV_PI*3/8);
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float lowThreshold = (float)MIN(threshold1, threshold2);
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float highThreshold = (float)MAX(threshold1, threshold2);
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int x, y, width = src.cols, height = src.rows;
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Mat dxkernel = cvtest::calcSobelKernel2D( 1, 0, m, 0 );
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Mat dykernel = cvtest::calcSobelKernel2D( 0, 1, m, 0 );
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Mat dx, dy, mag(height, width, CV_32F);
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cvtest::filter2D(src, dx, CV_16S, dxkernel, anchor, 0, BORDER_REPLICATE);
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cvtest::filter2D(src, dy, CV_16S, dykernel, anchor, 0, BORDER_REPLICATE);
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// calc gradient magnitude
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for( y = 0; y < height; y++ )
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{
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for( x = 0; x < width; x++ )
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{
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int dxval = dx.at<short>(y, x), dyval = dy.at<short>(y, x);
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mag.at<float>(y, x) = use_true_gradient ?
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(float)sqrt((double)(dxval*dxval + dyval*dyval)) :
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(float)(fabs((double)dxval) + fabs((double)dyval));
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}
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}
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// calc gradient direction, do nonmaxima suppression
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for( y = 0; y < height; y++ )
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{
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for( x = 0; x < width; x++ )
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{
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float a = mag.at<float>(y, x), b = 0, c = 0;
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int y1 = 0, y2 = 0, x1 = 0, x2 = 0;
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if( a <= lowThreshold )
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continue;
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int dxval = dx.at<short>(y, x);
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int dyval = dy.at<short>(y, x);
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double tg = dxval ? (double)dyval/dxval : DBL_MAX*CV_SIGN(dyval);
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if( fabs(tg) < tan_pi_8 )
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{
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y1 = y2 = y; x1 = x + 1; x2 = x - 1;
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}
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else if( tan_pi_8 <= tg && tg <= tan_3pi_8 )
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{
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y1 = y + 1; y2 = y - 1; x1 = x + 1; x2 = x - 1;
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}
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else if( -tan_3pi_8 <= tg && tg <= -tan_pi_8 )
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{
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y1 = y - 1; y2 = y + 1; x1 = x + 1; x2 = x - 1;
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}
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else
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{
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assert( fabs(tg) > tan_3pi_8 );
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x1 = x2 = x; y1 = y + 1; y2 = y - 1;
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}
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if( (unsigned)y1 < (unsigned)height && (unsigned)x1 < (unsigned)width )
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b = (float)fabs(mag.at<float>(y1, x1));
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if( (unsigned)y2 < (unsigned)height && (unsigned)x2 < (unsigned)width )
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c = (float)fabs(mag.at<float>(y2, x2));
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if( (a > b || (a == b && ((x1 == x+1 && y1 == y) || (x1 == x && y1 == y+1)))) && a > c )
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;
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else
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mag.at<float>(y, x) = -a;
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}
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}
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dst = Scalar::all(0);
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// hysteresis threshold
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for( y = 0; y < height; y++ )
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{
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for( x = 0; x < width; x++ )
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if( mag.at<float>(y, x) > highThreshold && !dst.at<uchar>(y, x) )
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cannyFollow( x, y, lowThreshold, mag, dst );
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}
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}
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void CV_CannyTest::prepare_to_validation( int )
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{
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Mat src = test_mat[INPUT][0], dst = test_mat[REF_OUTPUT][0];
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test_Canny( src, dst, threshold1, threshold2, aperture_size, use_true_gradient );
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}
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int CV_CannyTest::validate_test_results( int test_case_idx )
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{
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int code = cvtest::TS::OK, nz0;
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prepare_to_validation(test_case_idx);
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double err = cvtest::norm(test_mat[OUTPUT][0], test_mat[REF_OUTPUT][0], CV_L1);
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if( err == 0 )
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return code;
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if( err != cvRound(err) || cvRound(err)%255 != 0 )
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{
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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 );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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return code;
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}
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nz0 = cvRound(cvtest::norm(test_mat[REF_OUTPUT][0], CV_L1)/255);
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err = (err/255/MAX(nz0,100))*100;
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if( err > 1 )
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{
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ts->printf( cvtest::TS::LOG, "Too high percentage of non-matching edge pixels = %g%%\n", err);
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
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}
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return code;
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}
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TEST(Imgproc_Canny, accuracy) { CV_CannyTest test; test.safe_run(); }
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TEST(Imgproc_Canny, accuracy_deriv) { CV_CannyTest test(true); test.safe_run(); }
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/*
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* Comparing OpenVX based implementation with the main one
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*/
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#ifndef IMPLEMENT_PARAM_CLASS
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#define IMPLEMENT_PARAM_CLASS(name, type) \
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class name \
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{ \
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public: \
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name ( type arg = type ()) : val_(arg) {} \
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operator type () const {return val_;} \
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private: \
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type val_; \
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}; \
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inline void PrintTo( name param, std::ostream* os) \
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{ \
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*os << #name << "(" << testing::PrintToString(static_cast< type >(param)) << ")"; \
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}
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#endif // IMPLEMENT_PARAM_CLASS
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IMPLEMENT_PARAM_CLASS(ImagePath, string)
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IMPLEMENT_PARAM_CLASS(ApertureSize, int)
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IMPLEMENT_PARAM_CLASS(L2gradient, bool)
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PARAM_TEST_CASE(CannyVX, ImagePath, ApertureSize, L2gradient)
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{
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string imgPath;
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int kSize;
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bool useL2;
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Mat src, dst;
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virtual void SetUp()
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{
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imgPath = GET_PARAM(0);
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kSize = GET_PARAM(1);
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useL2 = GET_PARAM(2);
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}
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void loadImage()
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{
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src = cv::imread(cvtest::TS::ptr()->get_data_path() + imgPath, IMREAD_GRAYSCALE);
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ASSERT_FALSE(src.empty()) << "cann't load image: " << imgPath;
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}
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};
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TEST_P(CannyVX, Accuracy)
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{
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if(haveOpenVX())
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{
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loadImage();
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setUseOpenVX(false);
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Mat canny;
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cv::Canny(src, canny, 100, 150, 3);
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setUseOpenVX(true);
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Mat cannyVX;
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cv::Canny(src, cannyVX, 100, 150, 3);
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// 'smart' diff check (excluding isolated pixels)
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Mat diff, diff1;
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absdiff(canny, cannyVX, diff);
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boxFilter(diff, diff1, -1, Size(3,3));
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const int minPixelsAroud = 3; // empirical number
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diff1 = diff1 > 255/9 * minPixelsAroud;
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erode(diff1, diff1, Mat());
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double error = cv::norm(diff1, NORM_L1) / 255;
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const int maxError = std::min(10, diff.size().area()/100); // empirical number
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if(error > maxError)
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{
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string outPath =
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string("CannyVX-diff-") +
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imgPath + '-' +
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'k' + char(kSize+'0') + '-' +
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(useL2 ? "l2" : "l1");
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std::replace(outPath.begin(), outPath.end(), '/', '_');
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std::replace(outPath.begin(), outPath.end(), '\\', '_');
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std::replace(outPath.begin(), outPath.end(), '.', '_');
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imwrite(outPath+".png", diff);
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}
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ASSERT_LE(error, maxError);
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}
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}
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INSTANTIATE_TEST_CASE_P(
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ImgProc, CannyVX,
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testing::Combine(
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testing::Values(
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string("shared/baboon.png"),
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string("shared/fruits.png"),
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string("shared/lena.png"),
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string("shared/pic1.png"),
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string("shared/pic3.png"),
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string("shared/pic5.png"),
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string("shared/pic6.png")
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),
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testing::Values(ApertureSize(3), ApertureSize(5)),
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testing::Values(L2gradient(false), L2gradient(true))
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)
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);
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/* End of file. */
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