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Open Source Computer Vision Library
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337 lines
12 KiB
337 lines
12 KiB
/*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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namespace opencv_test { namespace { |
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class CV_TemplMatchTest : public cvtest::ArrayTest |
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{ |
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public: |
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CV_TemplMatchTest(); |
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protected: |
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int read_params( const cv::FileStorage& fs ); |
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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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void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high ); |
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double get_success_error_level( int test_case_idx, int i, int j ); |
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void run_func(); |
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void prepare_to_validation( int ); |
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int max_template_size; |
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int method; |
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bool test_cpp; |
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}; |
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CV_TemplMatchTest::CV_TemplMatchTest() |
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{ |
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test_array[INPUT].push_back(NULL); |
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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 = false; |
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max_template_size = 100; |
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method = 0; |
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test_cpp = false; |
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} |
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int CV_TemplMatchTest::read_params( const cv::FileStorage& fs ) |
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{ |
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int code = cvtest::ArrayTest::read_params( fs ); |
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if( code < 0 ) |
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return code; |
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read( find_param( fs, "max_template_size" ), max_template_size, max_template_size ); |
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max_template_size = cvtest::clipInt( max_template_size, 1, 100 ); |
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return code; |
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} |
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void CV_TemplMatchTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high ) |
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{ |
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cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high ); |
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int depth = CV_MAT_DEPTH(type); |
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if( depth == CV_32F ) |
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{ |
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low = Scalar::all(-10.); |
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high = Scalar::all(10.); |
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} |
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} |
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void CV_TemplMatchTest::get_test_array_types_and_sizes( int test_case_idx, |
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vector<vector<Size> >& sizes, vector<vector<int> >& types ) |
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{ |
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RNG& rng = ts->get_rng(); |
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int depth = cvtest::randInt(rng) % 2, cn = cvtest::randInt(rng) & 1 ? 3 : 1; |
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cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types ); |
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depth = depth == 0 ? CV_8U : CV_32F; |
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types[INPUT][0] = types[INPUT][1] = CV_MAKETYPE(depth,cn); |
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types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_32FC1; |
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sizes[INPUT][1].width = cvtest::randInt(rng)%MIN(sizes[INPUT][1].width,max_template_size) + 1; |
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sizes[INPUT][1].height = cvtest::randInt(rng)%MIN(sizes[INPUT][1].height,max_template_size) + 1; |
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sizes[OUTPUT][0].width = sizes[INPUT][0].width - sizes[INPUT][1].width + 1; |
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sizes[OUTPUT][0].height = sizes[INPUT][0].height - sizes[INPUT][1].height + 1; |
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sizes[REF_OUTPUT][0] = sizes[OUTPUT][0]; |
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method = cvtest::randInt(rng)%6; |
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test_cpp = (cvtest::randInt(rng) & 256) == 0; |
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} |
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double CV_TemplMatchTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ ) |
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{ |
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if( test_mat[INPUT][1].depth() == CV_8U || |
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(method >= CV_TM_CCOEFF && test_mat[INPUT][1].cols*test_mat[INPUT][1].rows <= 2) ) |
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return 1e-2; |
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else |
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return 1e-3; |
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} |
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void CV_TemplMatchTest::run_func() |
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{ |
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if(!test_cpp) |
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cvMatchTemplate( test_array[INPUT][0], test_array[INPUT][1], test_array[OUTPUT][0], method ); |
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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::matchTemplate(cv::cvarrToMat(test_array[INPUT][0]), cv::cvarrToMat(test_array[INPUT][1]), _out, method); |
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} |
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} |
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static void cvTsMatchTemplate( const CvMat* img, const CvMat* templ, CvMat* result, int method ) |
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{ |
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int i, j, k, l; |
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int depth = CV_MAT_DEPTH(img->type), cn = CV_MAT_CN(img->type); |
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int width_n = templ->cols*cn, height = templ->rows; |
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int a_step = img->step / CV_ELEM_SIZE(img->type & CV_MAT_DEPTH_MASK); |
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int b_step = templ->step / CV_ELEM_SIZE(templ->type & CV_MAT_DEPTH_MASK); |
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CvScalar b_mean = CV_STRUCT_INITIALIZER, b_sdv = CV_STRUCT_INITIALIZER; |
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double b_denom = 1., b_sum2 = 0; |
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int area = templ->rows*templ->cols; |
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cvAvgSdv(templ, &b_mean, &b_sdv); |
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for( i = 0; i < cn; i++ ) |
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b_sum2 += (b_sdv.val[i]*b_sdv.val[i] + b_mean.val[i]*b_mean.val[i])*area; |
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if( b_sdv.val[0]*b_sdv.val[0] + b_sdv.val[1]*b_sdv.val[1] + |
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b_sdv.val[2]*b_sdv.val[2] + b_sdv.val[3]*b_sdv.val[3] < DBL_EPSILON && |
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method == CV_TM_CCOEFF_NORMED ) |
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{ |
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cvSet( result, cvScalarAll(1.) ); |
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return; |
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} |
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if( method & 1 ) |
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{ |
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b_denom = 0; |
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if( method != CV_TM_CCOEFF_NORMED ) |
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{ |
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b_denom = b_sum2; |
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} |
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else |
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{ |
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for( i = 0; i < cn; i++ ) |
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b_denom += b_sdv.val[i]*b_sdv.val[i]*area; |
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} |
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b_denom = sqrt(b_denom); |
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if( b_denom == 0 ) |
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b_denom = 1.; |
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} |
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assert( CV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMED ); |
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for( i = 0; i < result->rows; i++ ) |
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{ |
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for( j = 0; j < result->cols; j++ ) |
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{ |
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Scalar a_sum(0), a_sum2(0); |
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Scalar ccorr(0); |
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double value = 0.; |
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if( depth == CV_8U ) |
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{ |
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const uchar* a = img->data.ptr + i*img->step + j*cn; |
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const uchar* b = templ->data.ptr; |
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if( cn == 1 || method < CV_TM_CCOEFF ) |
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{ |
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for( k = 0; k < height; k++, a += a_step, b += b_step ) |
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for( l = 0; l < width_n; l++ ) |
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{ |
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ccorr.val[0] += a[l]*b[l]; |
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a_sum.val[0] += a[l]; |
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a_sum2.val[0] += a[l]*a[l]; |
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} |
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} |
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else |
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{ |
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for( k = 0; k < height; k++, a += a_step, b += b_step ) |
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for( l = 0; l < width_n; l += 3 ) |
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{ |
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ccorr.val[0] += a[l]*b[l]; |
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ccorr.val[1] += a[l+1]*b[l+1]; |
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ccorr.val[2] += a[l+2]*b[l+2]; |
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a_sum.val[0] += a[l]; |
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a_sum.val[1] += a[l+1]; |
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a_sum.val[2] += a[l+2]; |
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a_sum2.val[0] += a[l]*a[l]; |
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a_sum2.val[1] += a[l+1]*a[l+1]; |
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a_sum2.val[2] += a[l+2]*a[l+2]; |
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} |
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} |
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} |
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else |
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{ |
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const float* a = (const float*)(img->data.ptr + i*img->step) + j*cn; |
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const float* b = (const float*)templ->data.ptr; |
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if( cn == 1 || method < CV_TM_CCOEFF ) |
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{ |
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for( k = 0; k < height; k++, a += a_step, b += b_step ) |
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for( l = 0; l < width_n; l++ ) |
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{ |
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ccorr.val[0] += a[l]*b[l]; |
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a_sum.val[0] += a[l]; |
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a_sum2.val[0] += a[l]*a[l]; |
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} |
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} |
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else |
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{ |
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for( k = 0; k < height; k++, a += a_step, b += b_step ) |
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for( l = 0; l < width_n; l += 3 ) |
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{ |
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ccorr.val[0] += a[l]*b[l]; |
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ccorr.val[1] += a[l+1]*b[l+1]; |
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ccorr.val[2] += a[l+2]*b[l+2]; |
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a_sum.val[0] += a[l]; |
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a_sum.val[1] += a[l+1]; |
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a_sum.val[2] += a[l+2]; |
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a_sum2.val[0] += a[l]*a[l]; |
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a_sum2.val[1] += a[l+1]*a[l+1]; |
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a_sum2.val[2] += a[l+2]*a[l+2]; |
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} |
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} |
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} |
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switch( method ) |
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{ |
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case CV_TM_CCORR: |
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case CV_TM_CCORR_NORMED: |
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value = ccorr.val[0]; |
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break; |
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case CV_TM_SQDIFF: |
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case CV_TM_SQDIFF_NORMED: |
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value = (a_sum2.val[0] + b_sum2 - 2*ccorr.val[0]); |
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break; |
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default: |
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value = (ccorr.val[0] - a_sum.val[0]*b_mean.val[0]+ |
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ccorr.val[1] - a_sum.val[1]*b_mean.val[1]+ |
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ccorr.val[2] - a_sum.val[2]*b_mean.val[2]); |
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} |
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if( method & 1 ) |
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{ |
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double denom; |
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// calc denominator |
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if( method != CV_TM_CCOEFF_NORMED ) |
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{ |
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denom = a_sum2.val[0] + a_sum2.val[1] + a_sum2.val[2]; |
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} |
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else |
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{ |
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denom = a_sum2.val[0] - (a_sum.val[0]*a_sum.val[0])/area; |
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denom += a_sum2.val[1] - (a_sum.val[1]*a_sum.val[1])/area; |
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denom += a_sum2.val[2] - (a_sum.val[2]*a_sum.val[2])/area; |
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} |
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denom = sqrt(MAX(denom,0))*b_denom; |
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if( fabs(value) < denom ) |
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value /= denom; |
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else if( fabs(value) < denom*1.125 ) |
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value = value > 0 ? 1 : -1; |
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else |
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value = method != CV_TM_SQDIFF_NORMED ? 0 : 1; |
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} |
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((float*)(result->data.ptr + result->step*i))[j] = (float)value; |
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} |
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} |
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} |
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void CV_TemplMatchTest::prepare_to_validation( int /*test_case_idx*/ ) |
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{ |
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CvMat _input = cvMat(test_mat[INPUT][0]), _templ = cvMat(test_mat[INPUT][1]); |
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CvMat _output = cvMat(test_mat[REF_OUTPUT][0]); |
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cvTsMatchTemplate( &_input, &_templ, &_output, method ); |
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//if( ts->get_current_test_info()->test_case_idx == 0 ) |
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/*{ |
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CvFileStorage* fs = cvOpenFileStorage( "_match_template.yml", 0, CV_STORAGE_WRITE ); |
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cvWrite( fs, "image", &test_mat[INPUT][0] ); |
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cvWrite( fs, "template", &test_mat[INPUT][1] ); |
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cvWrite( fs, "ref", &test_mat[REF_OUTPUT][0] ); |
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cvWrite( fs, "opencv", &test_mat[OUTPUT][0] ); |
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cvWriteInt( fs, "method", method ); |
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cvReleaseFileStorage( &fs ); |
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}*/ |
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if( method >= CV_TM_CCOEFF ) |
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{ |
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// avoid numerical stability problems in singular cases (when the results are near to 0) |
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const double delta = 10.; |
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test_mat[REF_OUTPUT][0] += Scalar::all(delta); |
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test_mat[OUTPUT][0] += Scalar::all(delta); |
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} |
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} |
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TEST(Imgproc_MatchTemplate, accuracy) { CV_TemplMatchTest test; test.safe_run(); } |
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}} // namespace
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