1 /*M/////////////////////////////////////////////////////////////////////////////////////// 2 // 3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 4 // 5 // By downloading, copying, installing or using the software you agree to this license. 6 // If you do not agree to this license, do not download, install, 7 // copy or use the software. 8 // 9 // 10 // Intel License Agreement 11 // For Open Source Computer Vision Library 12 // 13 // Copyright (C) 2000, Intel Corporation, all rights reserved. 14 // Third party copyrights are property of their respective owners. 15 // 16 // Redistribution and use in source and binary forms, with or without modification, 17 // are permitted provided that the following conditions are met: 18 // 19 // * Redistribution's of source code must retain the above copyright notice, 20 // this list of conditions and the following disclaimer. 21 // 22 // * Redistribution's in binary form must reproduce the above copyright notice, 23 // this list of conditions and the following disclaimer in the documentation 24 // and/or other materials provided with the distribution. 25 // 26 // * The name of Intel Corporation may not be used to endorse or promote products 27 // derived from this software without specific prior written permission. 28 // 29 // This software is provided by the copyright holders and contributors "as is" and 30 // any express or implied warranties, including, but not limited to, the implied 31 // warranties of merchantability and fitness for a particular purpose are disclaimed. 32 // In no event shall the Intel Corporation or contributors be liable for any direct, 33 // indirect, incidental, special, exemplary, or consequential damages 34 // (including, but not limited to, procurement of substitute goods or services; 35 // loss of use, data, or profits; or business interruption) however caused 36 // and on any theory of liability, whether in contract, strict liability, 37 // or tort (including negligence or otherwise) arising in any way out of 38 // the use of this software, even if advised of the possibility of such damage. 39 // 40 //M*/ 41 42 #include "test_precomp.hpp" 43 44 using namespace cv; 45 using namespace std; 46 47 class CV_TemplMatchTest : public cvtest::ArrayTest 48 { 49 public: 50 CV_TemplMatchTest(); 51 52 protected: 53 int read_params( CvFileStorage* fs ); 54 void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types ); 55 void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high ); 56 double get_success_error_level( int test_case_idx, int i, int j ); 57 void run_func(); 58 void prepare_to_validation( int ); 59 60 int max_template_size; 61 int method; 62 bool test_cpp; 63 }; 64 65 66 CV_TemplMatchTest::CV_TemplMatchTest() 67 { 68 test_array[INPUT].push_back(NULL); 69 test_array[INPUT].push_back(NULL); 70 test_array[OUTPUT].push_back(NULL); 71 test_array[REF_OUTPUT].push_back(NULL); 72 element_wise_relative_error = false; 73 max_template_size = 100; 74 method = 0; 75 test_cpp = false; 76 } 77 78 79 int CV_TemplMatchTest::read_params( CvFileStorage* fs ) 80 { 81 int code = cvtest::ArrayTest::read_params( fs ); 82 if( code < 0 ) 83 return code; 84 85 max_template_size = cvReadInt( find_param( fs, "max_template_size" ), max_template_size ); 86 max_template_size = cvtest::clipInt( max_template_size, 1, 100 ); 87 88 return code; 89 } 90 91 92 void CV_TemplMatchTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high ) 93 { 94 cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high ); 95 int depth = CV_MAT_DEPTH(type); 96 if( depth == CV_32F ) 97 { 98 low = Scalar::all(-10.); 99 high = Scalar::all(10.); 100 } 101 } 102 103 104 void CV_TemplMatchTest::get_test_array_types_and_sizes( int test_case_idx, 105 vector<vector<Size> >& sizes, vector<vector<int> >& types ) 106 { 107 RNG& rng = ts->get_rng(); 108 int depth = cvtest::randInt(rng) % 2, cn = cvtest::randInt(rng) & 1 ? 3 : 1; 109 cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types ); 110 depth = depth == 0 ? CV_8U : CV_32F; 111 112 types[INPUT][0] = types[INPUT][1] = CV_MAKETYPE(depth,cn); 113 types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_32FC1; 114 115 sizes[INPUT][1].width = cvtest::randInt(rng)%MIN(sizes[INPUT][1].width,max_template_size) + 1; 116 sizes[INPUT][1].height = cvtest::randInt(rng)%MIN(sizes[INPUT][1].height,max_template_size) + 1; 117 sizes[OUTPUT][0].width = sizes[INPUT][0].width - sizes[INPUT][1].width + 1; 118 sizes[OUTPUT][0].height = sizes[INPUT][0].height - sizes[INPUT][1].height + 1; 119 sizes[REF_OUTPUT][0] = sizes[OUTPUT][0]; 120 121 method = cvtest::randInt(rng)%6; 122 test_cpp = (cvtest::randInt(rng) & 256) == 0; 123 } 124 125 126 double CV_TemplMatchTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ ) 127 { 128 if( test_mat[INPUT][1].depth() == CV_8U || 129 (method >= CV_TM_CCOEFF && test_mat[INPUT][1].cols*test_mat[INPUT][1].rows <= 2) ) 130 return 1e-2; 131 else 132 return 1e-3; 133 } 134 135 136 void CV_TemplMatchTest::run_func() 137 { 138 if(!test_cpp) 139 cvMatchTemplate( test_array[INPUT][0], test_array[INPUT][1], test_array[OUTPUT][0], method ); 140 else 141 { 142 cv::Mat _out = cv::cvarrToMat(test_array[OUTPUT][0]); 143 cv::matchTemplate(cv::cvarrToMat(test_array[INPUT][0]), cv::cvarrToMat(test_array[INPUT][1]), _out, method); 144 } 145 } 146 147 148 static void cvTsMatchTemplate( const CvMat* img, const CvMat* templ, CvMat* result, int method ) 149 { 150 int i, j, k, l; 151 int depth = CV_MAT_DEPTH(img->type), cn = CV_MAT_CN(img->type); 152 int width_n = templ->cols*cn, height = templ->rows; 153 int a_step = img->step / CV_ELEM_SIZE(img->type & CV_MAT_DEPTH_MASK); 154 int b_step = templ->step / CV_ELEM_SIZE(templ->type & CV_MAT_DEPTH_MASK); 155 CvScalar b_mean, b_sdv; 156 double b_denom = 1., b_sum2 = 0; 157 int area = templ->rows*templ->cols; 158 159 cvAvgSdv(templ, &b_mean, &b_sdv); 160 161 for( i = 0; i < cn; i++ ) 162 b_sum2 += (b_sdv.val[i]*b_sdv.val[i] + b_mean.val[i]*b_mean.val[i])*area; 163 164 if( b_sdv.val[0]*b_sdv.val[0] + b_sdv.val[1]*b_sdv.val[1] + 165 b_sdv.val[2]*b_sdv.val[2] + b_sdv.val[3]*b_sdv.val[3] < DBL_EPSILON && 166 method == CV_TM_CCOEFF_NORMED ) 167 { 168 cvSet( result, cvScalarAll(1.) ); 169 return; 170 } 171 172 if( method & 1 ) 173 { 174 b_denom = 0; 175 if( method != CV_TM_CCOEFF_NORMED ) 176 { 177 b_denom = b_sum2; 178 } 179 else 180 { 181 for( i = 0; i < cn; i++ ) 182 b_denom += b_sdv.val[i]*b_sdv.val[i]*area; 183 } 184 b_denom = sqrt(b_denom); 185 if( b_denom == 0 ) 186 b_denom = 1.; 187 } 188 189 assert( CV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMED ); 190 191 for( i = 0; i < result->rows; i++ ) 192 { 193 for( j = 0; j < result->cols; j++ ) 194 { 195 CvScalar a_sum(0), a_sum2(0); 196 CvScalar ccorr(0); 197 double value = 0.; 198 199 if( depth == CV_8U ) 200 { 201 const uchar* a = img->data.ptr + i*img->step + j*cn; 202 const uchar* b = templ->data.ptr; 203 204 if( cn == 1 || method < CV_TM_CCOEFF ) 205 { 206 for( k = 0; k < height; k++, a += a_step, b += b_step ) 207 for( l = 0; l < width_n; l++ ) 208 { 209 ccorr.val[0] += a[l]*b[l]; 210 a_sum.val[0] += a[l]; 211 a_sum2.val[0] += a[l]*a[l]; 212 } 213 } 214 else 215 { 216 for( k = 0; k < height; k++, a += a_step, b += b_step ) 217 for( l = 0; l < width_n; l += 3 ) 218 { 219 ccorr.val[0] += a[l]*b[l]; 220 ccorr.val[1] += a[l+1]*b[l+1]; 221 ccorr.val[2] += a[l+2]*b[l+2]; 222 a_sum.val[0] += a[l]; 223 a_sum.val[1] += a[l+1]; 224 a_sum.val[2] += a[l+2]; 225 a_sum2.val[0] += a[l]*a[l]; 226 a_sum2.val[1] += a[l+1]*a[l+1]; 227 a_sum2.val[2] += a[l+2]*a[l+2]; 228 } 229 } 230 } 231 else 232 { 233 const float* a = (const float*)(img->data.ptr + i*img->step) + j*cn; 234 const float* b = (const float*)templ->data.ptr; 235 236 if( cn == 1 || method < CV_TM_CCOEFF ) 237 { 238 for( k = 0; k < height; k++, a += a_step, b += b_step ) 239 for( l = 0; l < width_n; l++ ) 240 { 241 ccorr.val[0] += a[l]*b[l]; 242 a_sum.val[0] += a[l]; 243 a_sum2.val[0] += a[l]*a[l]; 244 } 245 } 246 else 247 { 248 for( k = 0; k < height; k++, a += a_step, b += b_step ) 249 for( l = 0; l < width_n; l += 3 ) 250 { 251 ccorr.val[0] += a[l]*b[l]; 252 ccorr.val[1] += a[l+1]*b[l+1]; 253 ccorr.val[2] += a[l+2]*b[l+2]; 254 a_sum.val[0] += a[l]; 255 a_sum.val[1] += a[l+1]; 256 a_sum.val[2] += a[l+2]; 257 a_sum2.val[0] += a[l]*a[l]; 258 a_sum2.val[1] += a[l+1]*a[l+1]; 259 a_sum2.val[2] += a[l+2]*a[l+2]; 260 } 261 } 262 } 263 264 switch( method ) 265 { 266 case CV_TM_CCORR: 267 case CV_TM_CCORR_NORMED: 268 value = ccorr.val[0]; 269 break; 270 case CV_TM_SQDIFF: 271 case CV_TM_SQDIFF_NORMED: 272 value = (a_sum2.val[0] + b_sum2 - 2*ccorr.val[0]); 273 break; 274 default: 275 value = (ccorr.val[0] - a_sum.val[0]*b_mean.val[0]+ 276 ccorr.val[1] - a_sum.val[1]*b_mean.val[1]+ 277 ccorr.val[2] - a_sum.val[2]*b_mean.val[2]); 278 } 279 280 if( method & 1 ) 281 { 282 double denom; 283 284 // calc denominator 285 if( method != CV_TM_CCOEFF_NORMED ) 286 { 287 denom = a_sum2.val[0] + a_sum2.val[1] + a_sum2.val[2]; 288 } 289 else 290 { 291 denom = a_sum2.val[0] - (a_sum.val[0]*a_sum.val[0])/area; 292 denom += a_sum2.val[1] - (a_sum.val[1]*a_sum.val[1])/area; 293 denom += a_sum2.val[2] - (a_sum.val[2]*a_sum.val[2])/area; 294 } 295 denom = sqrt(MAX(denom,0))*b_denom; 296 if( fabs(value) < denom ) 297 value /= denom; 298 else if( fabs(value) < denom*1.125 ) 299 value = value > 0 ? 1 : -1; 300 else 301 value = method != CV_TM_SQDIFF_NORMED ? 0 : 1; 302 } 303 304 ((float*)(result->data.ptr + result->step*i))[j] = (float)value; 305 } 306 } 307 } 308 309 310 void CV_TemplMatchTest::prepare_to_validation( int /*test_case_idx*/ ) 311 { 312 CvMat _input = test_mat[INPUT][0], _templ = test_mat[INPUT][1]; 313 CvMat _output = test_mat[REF_OUTPUT][0]; 314 cvTsMatchTemplate( &_input, &_templ, &_output, method ); 315 316 //if( ts->get_current_test_info()->test_case_idx == 0 ) 317 /*{ 318 CvFileStorage* fs = cvOpenFileStorage( "_match_template.yml", 0, CV_STORAGE_WRITE ); 319 cvWrite( fs, "image", &test_mat[INPUT][0] ); 320 cvWrite( fs, "template", &test_mat[INPUT][1] ); 321 cvWrite( fs, "ref", &test_mat[REF_OUTPUT][0] ); 322 cvWrite( fs, "opencv", &test_mat[OUTPUT][0] ); 323 cvWriteInt( fs, "method", method ); 324 cvReleaseFileStorage( &fs ); 325 }*/ 326 327 if( method >= CV_TM_CCOEFF ) 328 { 329 // avoid numerical stability problems in singular cases (when the results are near to 0) 330 const double delta = 10.; 331 test_mat[REF_OUTPUT][0] += Scalar::all(delta); 332 test_mat[OUTPUT][0] += Scalar::all(delta); 333 } 334 } 335 336 TEST(Imgproc_MatchTemplate, accuracy) { CV_TemplMatchTest test; test.safe_run(); } 337
