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 /* Haar features calculation */ 43 44 #include "_cv.h" 45 #include <stdio.h> 46 47 /* these settings affect the quality of detection: change with care */ 48 #define CV_ADJUST_FEATURES 1 49 #define CV_ADJUST_WEIGHTS 1 50 51 typedef int sumtype; 52 typedef double sqsumtype; 53 54 typedef struct MyCvHidHaarFeature 55 { 56 struct 57 { 58 sumtype *p0, *p1, *p2, *p3; 59 int weight; 60 } 61 rect[CV_HAAR_FEATURE_MAX]; 62 } 63 MyCvHidHaarFeature; 64 65 66 typedef struct MyCvHidHaarTreeNode 67 { 68 MyCvHidHaarFeature feature; 69 int threshold; 70 int left; 71 int right; 72 } 73 MyCvHidHaarTreeNode; 74 75 76 typedef struct MyCvHidHaarClassifier 77 { 78 int count; 79 //CvHaarFeature* orig_feature; 80 MyCvHidHaarTreeNode* node; 81 float* alpha; 82 } 83 MyCvHidHaarClassifier; 84 85 86 typedef struct MyCvHidHaarStageClassifier 87 { 88 int count; 89 float threshold; 90 MyCvHidHaarClassifier* classifier; 91 int two_rects; 92 93 struct MyCvHidHaarStageClassifier* next; 94 struct MyCvHidHaarStageClassifier* child; 95 struct MyCvHidHaarStageClassifier* parent; 96 } 97 MyCvHidHaarStageClassifier; 98 99 100 struct MyCvHidHaarClassifierCascade 101 { 102 int count; 103 int is_stump_based; 104 int has_tilted_features; 105 int is_tree; 106 double inv_window_area; 107 CvMat sum, sqsum, tilted; 108 MyCvHidHaarStageClassifier* stage_classifier; 109 sqsumtype *pq0, *pq1, *pq2, *pq3; 110 sumtype *p0, *p1, *p2, *p3; 111 112 void** ipp_stages; 113 }; 114 115 116 const int icv_object_win_border = 1; 117 const float icv_stage_threshold_bias = 0.0001f; 118 119 static int myis_equal( const void* _r1, const void* _r2, void* ) 120 { 121 const CvRect* r1 = (const CvRect*)_r1; 122 const CvRect* r2 = (const CvRect*)_r2; 123 int distance = cvRound(r1->width*0.2); 124 125 return r2->x <= r1->x + distance && 126 r2->x >= r1->x - distance && 127 r2->y <= r1->y + distance && 128 r2->y >= r1->y - distance && 129 r2->width <= cvRound( r1->width * 1.2 ) && 130 cvRound( r2->width * 1.2 ) >= r1->width; 131 } 132 133 static void 134 myicvReleaseHidHaarClassifierCascade( MyCvHidHaarClassifierCascade** _cascade ) 135 { 136 if( _cascade && *_cascade ) 137 { 138 /*CvHidHaarClassifierCascade* cascade = *_cascade; 139 if( cascade->ipp_stages && icvHaarClassifierFree_32f_p ) 140 { 141 int i; 142 for( i = 0; i < cascade->count; i++ ) 143 { 144 if( cascade->ipp_stages[i] ) 145 icvHaarClassifierFree_32f_p( cascade->ipp_stages[i] ); 146 } 147 } 148 cvFree( &cascade->ipp_stages );*/ 149 cvFree( _cascade ); 150 } 151 } 152 153 /* create more efficient internal representation of haar classifier cascade */ 154 static MyCvHidHaarClassifierCascade* 155 myicvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade ) 156 { 157 CvRect* ipp_features = 0; 158 float *ipp_weights = 0, *ipp_thresholds = 0, *ipp_val1 = 0, *ipp_val2 = 0; 159 int* ipp_counts = 0; 160 161 MyCvHidHaarClassifierCascade* out = 0; 162 163 CV_FUNCNAME( "icvCreateHidHaarClassifierCascade" ); 164 165 __BEGIN__; 166 167 int i, j, k, l; 168 int datasize; 169 int total_classifiers = 0; 170 int total_nodes = 0; 171 char errorstr[100]; 172 MyCvHidHaarClassifier* haar_classifier_ptr; 173 MyCvHidHaarTreeNode* haar_node_ptr; 174 CvSize orig_window_size; 175 int has_tilted_features = 0; 176 int max_count = 0; 177 178 if( !CV_IS_HAAR_CLASSIFIER(cascade) ) 179 CV_ERROR( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" ); 180 181 if( cascade->hid_cascade ) 182 CV_ERROR( CV_StsError, "hid_cascade has been already created" ); 183 184 if( !cascade->stage_classifier ) 185 CV_ERROR( CV_StsNullPtr, "" ); 186 187 if( cascade->count <= 0 ) 188 CV_ERROR( CV_StsOutOfRange, "Negative number of cascade stages" ); 189 190 orig_window_size = cascade->orig_window_size; 191 192 /* check input structure correctness and calculate total memory size needed for 193 internal representation of the classifier cascade */ 194 for( i = 0; i < cascade->count; i++ ) 195 { 196 CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i; 197 198 if( !stage_classifier->classifier || 199 stage_classifier->count <= 0 ) 200 { 201 sprintf( errorstr, "header of the stage classifier #%d is invalid " 202 "(has null pointers or non-positive classfier count)", i ); 203 CV_ERROR( CV_StsError, errorstr ); 204 } 205 206 max_count = MAX( max_count, stage_classifier->count ); 207 total_classifiers += stage_classifier->count; 208 209 for( j = 0; j < stage_classifier->count; j++ ) 210 { 211 CvHaarClassifier* classifier = stage_classifier->classifier + j; 212 213 total_nodes += classifier->count; 214 for( l = 0; l < classifier->count; l++ ) 215 { 216 for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ ) 217 { 218 if( classifier->haar_feature[l].rect[k].r.width ) 219 { 220 CvRect r = classifier->haar_feature[l].rect[k].r; 221 int tilted = classifier->haar_feature[l].tilted; 222 has_tilted_features |= tilted != 0; 223 if( r.width < 0 || r.height < 0 || r.y < 0 || 224 r.x + r.width > orig_window_size.width 225 || 226 (!tilted && 227 (r.x < 0 || r.y + r.height > orig_window_size.height)) 228 || 229 (tilted && (r.x - r.height < 0 || 230 r.y + r.width + r.height > orig_window_size.height))) 231 { 232 sprintf( errorstr, "rectangle #%d of the classifier #%d of " 233 "the stage classifier #%d is not inside " 234 "the reference (original) cascade window", k, j, i ); 235 CV_ERROR( CV_StsNullPtr, errorstr ); 236 } 237 } 238 } 239 } 240 } 241 } 242 243 // this is an upper boundary for the whole hidden cascade size 244 datasize = sizeof(MyCvHidHaarClassifierCascade) + 245 sizeof(MyCvHidHaarStageClassifier)*cascade->count + 246 sizeof(MyCvHidHaarClassifier) * total_classifiers + 247 sizeof(MyCvHidHaarTreeNode) * total_nodes + 248 sizeof(void*)*(total_nodes + total_classifiers); 249 250 CV_CALL( out = (MyCvHidHaarClassifierCascade*)cvAlloc( datasize )); 251 memset( out, 0, sizeof(*out) ); 252 253 /* init header */ 254 out->count = cascade->count; 255 out->stage_classifier = (MyCvHidHaarStageClassifier*)(out + 1); 256 haar_classifier_ptr = (MyCvHidHaarClassifier*)(out->stage_classifier + cascade->count); 257 haar_node_ptr = (MyCvHidHaarTreeNode*)(haar_classifier_ptr + total_classifiers); 258 259 out->is_stump_based = 1; 260 out->has_tilted_features = has_tilted_features; 261 out->is_tree = 0; 262 263 /* initialize internal representation */ 264 for( i = 0; i < cascade->count; i++ ) 265 { 266 CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i; 267 MyCvHidHaarStageClassifier* hid_stage_classifier = out->stage_classifier + i; 268 269 hid_stage_classifier->count = stage_classifier->count; 270 hid_stage_classifier->threshold = stage_classifier->threshold - icv_stage_threshold_bias; 271 hid_stage_classifier->classifier = haar_classifier_ptr; 272 hid_stage_classifier->two_rects = 1; 273 haar_classifier_ptr += stage_classifier->count; 274 275 hid_stage_classifier->parent = (stage_classifier->parent == -1) 276 ? NULL : out->stage_classifier + stage_classifier->parent; 277 hid_stage_classifier->next = (stage_classifier->next == -1) 278 ? NULL : out->stage_classifier + stage_classifier->next; 279 hid_stage_classifier->child = (stage_classifier->child == -1) 280 ? NULL : out->stage_classifier + stage_classifier->child; 281 282 out->is_tree |= hid_stage_classifier->next != NULL; 283 284 for( j = 0; j < stage_classifier->count; j++ ) 285 { 286 CvHaarClassifier* classifier = stage_classifier->classifier + j; 287 MyCvHidHaarClassifier* hid_classifier = hid_stage_classifier->classifier + j; 288 int node_count = classifier->count; 289 float* alpha_ptr = (float*)(haar_node_ptr + node_count); 290 291 hid_classifier->count = node_count; 292 hid_classifier->node = haar_node_ptr; 293 hid_classifier->alpha = alpha_ptr; 294 295 for( l = 0; l < node_count; l++ ) 296 { 297 MyCvHidHaarTreeNode* node = hid_classifier->node + l; 298 CvHaarFeature* feature = classifier->haar_feature + l; 299 memset( node, -1, sizeof(*node) ); 300 node->threshold = (int)((classifier->threshold[l]) * 65536.0); 301 node->left = classifier->left[l]; 302 node->right = classifier->right[l]; 303 304 if( fabs(feature->rect[2].weight) < DBL_EPSILON || 305 feature->rect[2].r.width == 0 || 306 feature->rect[2].r.height == 0 ) 307 memset( &(node->feature.rect[2]), 0, sizeof(node->feature.rect[2]) ); 308 else 309 hid_stage_classifier->two_rects = 0; 310 } 311 312 memcpy( alpha_ptr, classifier->alpha, (node_count+1)*sizeof(alpha_ptr[0])); 313 haar_node_ptr = 314 (MyCvHidHaarTreeNode*)cvAlignPtr(alpha_ptr+node_count+1, sizeof(void*)); 315 316 out->is_stump_based &= node_count == 1; 317 } 318 } 319 320 /*{ 321 int can_use_ipp = icvHaarClassifierInitAlloc_32f_p != 0 && 322 icvHaarClassifierFree_32f_p != 0 && 323 icvApplyHaarClassifier_32f_C1R_p != 0 && 324 icvRectStdDev_32f_C1R_p != 0 && 325 !out->has_tilted_features && !out->is_tree && out->is_stump_based; 326 327 if( can_use_ipp ) 328 { 329 int ipp_datasize = cascade->count*sizeof(out->ipp_stages[0]); 330 float ipp_weight_scale=(float)(1./((orig_window_size.width-icv_object_win_border*2)* 331 (orig_window_size.height-icv_object_win_border*2))); 332 333 CV_CALL( out->ipp_stages = (void**)cvAlloc( ipp_datasize )); 334 memset( out->ipp_stages, 0, ipp_datasize ); 335 336 CV_CALL( ipp_features = (CvRect*)cvAlloc( max_count*3*sizeof(ipp_features[0]) )); 337 CV_CALL( ipp_weights = (float*)cvAlloc( max_count*3*sizeof(ipp_weights[0]) )); 338 CV_CALL( ipp_thresholds = (float*)cvAlloc( max_count*sizeof(ipp_thresholds[0]) )); 339 CV_CALL( ipp_val1 = (float*)cvAlloc( max_count*sizeof(ipp_val1[0]) )); 340 CV_CALL( ipp_val2 = (float*)cvAlloc( max_count*sizeof(ipp_val2[0]) )); 341 CV_CALL( ipp_counts = (int*)cvAlloc( max_count*sizeof(ipp_counts[0]) )); 342 343 for( i = 0; i < cascade->count; i++ ) 344 { 345 CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i; 346 for( j = 0, k = 0; j < stage_classifier->count; j++ ) 347 { 348 CvHaarClassifier* classifier = stage_classifier->classifier + j; 349 int rect_count = 2 + (classifier->haar_feature->rect[2].r.width != 0); 350 351 ipp_thresholds[j] = classifier->threshold[0]; 352 ipp_val1[j] = classifier->alpha[0]; 353 ipp_val2[j] = classifier->alpha[1]; 354 ipp_counts[j] = rect_count; 355 356 for( l = 0; l < rect_count; l++, k++ ) 357 { 358 ipp_features[k] = classifier->haar_feature->rect[l].r; 359 //ipp_features[k].y = orig_window_size.height - ipp_features[k].y - ipp_features[k].height; 360 ipp_weights[k] = classifier->haar_feature->rect[l].weight*ipp_weight_scale; 361 } 362 } 363 364 if( icvHaarClassifierInitAlloc_32f_p( &out->ipp_stages[i], 365 ipp_features, ipp_weights, ipp_thresholds, 366 ipp_val1, ipp_val2, ipp_counts, stage_classifier->count ) < 0 ) 367 break; 368 } 369 370 if( i < cascade->count ) 371 { 372 for( j = 0; j < i; j++ ) 373 if( icvHaarClassifierFree_32f_p && out->ipp_stages[i] ) 374 icvHaarClassifierFree_32f_p( out->ipp_stages[i] ); 375 cvFree( &out->ipp_stages ); 376 } 377 } 378 }*/ 379 380 cascade->hid_cascade = (CvHidHaarClassifierCascade*)out; 381 assert( (char*)haar_node_ptr - (char*)out <= datasize ); 382 383 __END__; 384 385 if( cvGetErrStatus() < 0 ) 386 myicvReleaseHidHaarClassifierCascade( &out ); 387 388 cvFree( &ipp_features ); 389 cvFree( &ipp_weights ); 390 cvFree( &ipp_thresholds ); 391 cvFree( &ipp_val1 ); 392 cvFree( &ipp_val2 ); 393 cvFree( &ipp_counts ); 394 395 return out; 396 } 397 398 #define calc_sum(rect,offset) \ 399 ((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset]) 400 401 402 CV_INLINE 403 double myicvEvalHidHaarClassifier( MyCvHidHaarClassifier* classifier, 404 double variance_norm_factor, 405 size_t p_offset ) 406 { 407 int idx = 0; 408 do 409 { 410 MyCvHidHaarTreeNode* node = classifier->node + idx; 411 double t = node->threshold * variance_norm_factor; 412 413 double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight; 414 sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight; 415 416 if( node->feature.rect[2].p0 ) 417 sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight; 418 419 idx = sum < t ? node->left : node->right; 420 } 421 while( idx > 0 ); 422 return classifier->alpha[-idx]; 423 } 424 425 /*********************** Special integer sqrt **************************/ 426 427 int 428 isqrt(int x) 429 { 430 /* 431 * Logically, these are unsigned. We need the sign bit to test 432 * whether (op - res - one) underflowed. 433 */ 434 435 register int op, res, one; 436 437 op = x; 438 res = 0; 439 440 /* "one" starts at the highest power of four <= than the argument. */ 441 442 one = 1 << 30; /* second-to-top bit set */ 443 while (one > op) one >>= 2; 444 445 while (one != 0) { 446 if (op >= res + one) { 447 op = op - (res + one); 448 res = res + 2 * one; 449 } 450 res /= 2; 451 one /= 4; 452 } 453 return(res); 454 } 455 456 #define NEXT(n, i) (((n) + (i)/(n)) >> 1) 457 458 unsigned int isqrt1(int number) { 459 unsigned int n = 1; 460 unsigned int n1 = NEXT(n, number); 461 462 while(abs(n1 - n) > 1) { 463 n = n1; 464 n1 = NEXT(n, number); 465 } 466 while((n1*n1) > number) { 467 n1 -= 1; 468 } 469 return n1; 470 } 471 /***********************************************************************/ 472 473 CV_IMPL int 474 mycvRunHaarClassifierCascade( CvHaarClassifierCascade* _cascade, 475 CvPoint pt, int start_stage ) 476 { 477 int result = -1; 478 CV_FUNCNAME("mycvRunHaarClassifierCascade"); 479 480 __BEGIN__; 481 482 int p_offset, pq_offset; 483 int pq0, pq1, pq2, pq3; 484 int i, j; 485 double mean; 486 int variance_norm_factor; 487 MyCvHidHaarClassifierCascade* cascade; 488 489 if( !CV_IS_HAAR_CLASSIFIER(_cascade) ) 490 CV_ERROR( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid cascade pointer" ); 491 492 cascade = (MyCvHidHaarClassifierCascade*)_cascade->hid_cascade; 493 if( !cascade ) 494 CV_ERROR( CV_StsNullPtr, "Hidden cascade has not been created.\n" 495 "Use cvSetImagesForHaarClassifierCascade" ); 496 497 if( pt.x < 0 || pt.y < 0 || 498 pt.x + _cascade->real_window_size.width >= cascade->sum.width-2 || 499 pt.y + _cascade->real_window_size.height >= cascade->sum.height-2 ) 500 EXIT; 501 502 p_offset = pt.y * (cascade->sum.step/sizeof(sumtype)) + pt.x; 503 pq_offset = pt.y * (cascade->sqsum.step/sizeof(sqsumtype)) + pt.x; 504 mean = calc_sum(*cascade,p_offset) * cascade->inv_window_area; 505 pq0 = cascade->pq0[pq_offset]; 506 pq1 = cascade->pq1[pq_offset]; 507 pq2 = cascade->pq2[pq_offset]; 508 pq3 = cascade->pq3[pq_offset]; 509 variance_norm_factor = pq0 - pq1 - pq2 + pq3; 510 variance_norm_factor = variance_norm_factor * cascade->inv_window_area - mean * mean; 511 if( variance_norm_factor >= 0. ) 512 variance_norm_factor = sqrt(variance_norm_factor); 513 else 514 variance_norm_factor = 1.; 515 516 // if( cascade->is_tree ) 517 // { 518 // MyCvHidHaarStageClassifier* ptr; 519 // assert( start_stage == 0 ); 520 // 521 // result = 1; 522 // ptr = cascade->stage_classifier; 523 // 524 // while( ptr ) 525 // { 526 // double stage_sum = 0; 527 // 528 // for( j = 0; j < ptr->count; j++ ) 529 // { 530 // stage_sum += myicvEvalHidHaarClassifier( ptr->classifier + j, 531 // variance_norm_factor, p_offset ); 532 // } 533 // 534 // if( stage_sum >= ptr->threshold ) 535 // { 536 // ptr = ptr->child; 537 // } 538 // else 539 // { 540 // while( ptr && ptr->next == NULL ) ptr = ptr->parent; 541 // if( ptr == NULL ) 542 // { 543 // result = 0; 544 // EXIT; 545 // } 546 // ptr = ptr->next; 547 // } 548 // } 549 // } 550 // else if( cascade->is_stump_based ) 551 { 552 for( i = start_stage; i < cascade->count; i++ ) 553 { 554 double stage_sum = 0; 555 556 if( cascade->stage_classifier[i].two_rects ) 557 { 558 for( j = 0; j < cascade->stage_classifier[i].count; j++ ) 559 { 560 MyCvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j; 561 MyCvHidHaarTreeNode* node = classifier->node; 562 int t = node->threshold * variance_norm_factor; 563 int sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight; 564 sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight; 565 stage_sum += classifier->alpha[sum >= t]; 566 } 567 } 568 else 569 { 570 for( j = 0; j < cascade->stage_classifier[i].count; j++ ) 571 { 572 MyCvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j; 573 MyCvHidHaarTreeNode* node = classifier->node; 574 int t = node->threshold * variance_norm_factor; 575 int sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight; 576 sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight; 577 if( node->feature.rect[2].p0 ) 578 sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight; 579 580 stage_sum += classifier->alpha[sum >= t]; 581 } 582 } 583 584 if( stage_sum < cascade->stage_classifier[i].threshold ) 585 { 586 result = -i; 587 EXIT; 588 } 589 } 590 } 591 // else 592 // { 593 // for( i = start_stage; i < cascade->count; i++ ) 594 // { 595 // double stage_sum = 0; 596 // 597 // for( j = 0; j < cascade->stage_classifier[i].count; j++ ) 598 // { 599 // stage_sum += myicvEvalHidHaarClassifier( 600 // cascade->stage_classifier[i].classifier + j, 601 // variance_norm_factor, p_offset ); 602 // } 603 // 604 // if( stage_sum < cascade->stage_classifier[i].threshold ) 605 // { 606 // result = -i; 607 // EXIT; 608 // } 609 // } 610 // } 611 612 result = 1; 613 614 __END__; 615 616 return result; 617 } 618 619 #define sum_elem_ptr(sum,row,col) \ 620 ((sumtype*)CV_MAT_ELEM_PTR_FAST((sum),(row),(col),sizeof(sumtype))) 621 622 #define sqsum_elem_ptr(sqsum,row,col) \ 623 ((sqsumtype*)CV_MAT_ELEM_PTR_FAST((sqsum),(row),(col),sizeof(sqsumtype))) 624 625 626 CV_IMPL void 627 mycvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade, 628 const CvArr* _sum, 629 const CvArr* _sqsum, 630 const CvArr* _tilted_sum, 631 double scale ) 632 { 633 CV_FUNCNAME("cvSetImagesForHaarClassifierCascade"); 634 635 __BEGIN__; 636 637 CvMat sum_stub, *sum = (CvMat*)_sum; 638 CvMat sqsum_stub, *sqsum = (CvMat*)_sqsum; 639 CvMat tilted_stub, *tilted = (CvMat*)_tilted_sum; 640 MyCvHidHaarClassifierCascade* cascade; 641 int coi0 = 0, coi1 = 0; 642 int i; 643 CvRect equ_rect; 644 double weight_scale; 645 646 if( !CV_IS_HAAR_CLASSIFIER(_cascade) ) 647 CV_ERROR( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" ); 648 649 if( scale <= 0 ) 650 CV_ERROR( CV_StsOutOfRange, "Scale must be positive" ); 651 652 CV_CALL( sum = cvGetMat( sum, &sum_stub, &coi0 )); 653 CV_CALL( sqsum = cvGetMat( sqsum, &sqsum_stub, &coi1 )); 654 655 if( coi0 || coi1 ) 656 CV_ERROR( CV_BadCOI, "COI is not supported" ); 657 658 if( !CV_ARE_SIZES_EQ( sum, sqsum )) 659 CV_ERROR( CV_StsUnmatchedSizes, "All integral images must have the same size" ); 660 661 if( CV_MAT_TYPE(sqsum->type) != CV_64FC1 || 662 CV_MAT_TYPE(sum->type) != CV_32SC1 ) 663 CV_ERROR( CV_StsUnsupportedFormat, 664 "Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" ); 665 666 if( !_cascade->hid_cascade ) 667 CV_CALL( myicvCreateHidHaarClassifierCascade(_cascade) ); 668 669 cascade = (MyCvHidHaarClassifierCascade*)_cascade->hid_cascade; 670 671 if( cascade->has_tilted_features ) 672 { 673 CV_CALL( tilted = cvGetMat( tilted, &tilted_stub, &coi1 )); 674 675 if( CV_MAT_TYPE(tilted->type) != CV_32SC1 ) 676 CV_ERROR( CV_StsUnsupportedFormat, 677 "Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" ); 678 679 if( sum->step != tilted->step ) 680 CV_ERROR( CV_StsUnmatchedSizes, 681 "Sum and tilted_sum must have the same stride (step, widthStep)" ); 682 683 if( !CV_ARE_SIZES_EQ( sum, tilted )) 684 CV_ERROR( CV_StsUnmatchedSizes, "All integral images must have the same size" ); 685 cascade->tilted = *tilted; 686 } 687 688 _cascade->scale = scale; 689 _cascade->real_window_size.width = cvRound( _cascade->orig_window_size.width * scale ); 690 _cascade->real_window_size.height = cvRound( _cascade->orig_window_size.height * scale ); 691 692 cascade->sum = *sum; 693 cascade->sqsum = *sqsum; 694 695 equ_rect.x = equ_rect.y = cvRound(scale); 696 equ_rect.width = cvRound((_cascade->orig_window_size.width-2)*scale); 697 equ_rect.height = cvRound((_cascade->orig_window_size.height-2)*scale); 698 weight_scale = 1./(equ_rect.width*equ_rect.height); 699 cascade->inv_window_area = weight_scale; 700 701 cascade->p0 = sum_elem_ptr(*sum, equ_rect.y, equ_rect.x); 702 cascade->p1 = sum_elem_ptr(*sum, equ_rect.y, equ_rect.x + equ_rect.width ); 703 cascade->p2 = sum_elem_ptr(*sum, equ_rect.y + equ_rect.height, equ_rect.x ); 704 cascade->p3 = sum_elem_ptr(*sum, equ_rect.y + equ_rect.height, 705 equ_rect.x + equ_rect.width ); 706 707 cascade->pq0 = sqsum_elem_ptr(*sqsum, equ_rect.y, equ_rect.x); 708 cascade->pq1 = sqsum_elem_ptr(*sqsum, equ_rect.y, equ_rect.x + equ_rect.width ); 709 cascade->pq2 = sqsum_elem_ptr(*sqsum, equ_rect.y + equ_rect.height, equ_rect.x ); 710 cascade->pq3 = sqsum_elem_ptr(*sqsum, equ_rect.y + equ_rect.height, 711 equ_rect.x + equ_rect.width ); 712 713 /* init pointers in haar features according to real window size and 714 given image pointers */ 715 { 716 #ifdef _OPENMP 717 int max_threads = cvGetNumThreads(); 718 #pragma omp parallel for num_threads(max_threads) schedule(dynamic) 719 #endif // _OPENMP 720 for( i = 0; i < _cascade->count; i++ ) 721 { 722 int j, k, l; 723 for( j = 0; j < cascade->stage_classifier[i].count; j++ ) 724 { 725 for( l = 0; l < cascade->stage_classifier[i].classifier[j].count; l++ ) 726 { 727 CvHaarFeature* feature = 728 &_cascade->stage_classifier[i].classifier[j].haar_feature[l]; 729 /* CvHidHaarClassifier* classifier = 730 cascade->stage_classifier[i].classifier + j; */ 731 MyCvHidHaarFeature* hidfeature = 732 &cascade->stage_classifier[i].classifier[j].node[l].feature; 733 double sum0 = 0, area0 = 0; 734 CvRect r[3]; 735 #if CV_ADJUST_FEATURES 736 int base_w = -1, base_h = -1; 737 int new_base_w = 0, new_base_h = 0; 738 int kx, ky; 739 int flagx = 0, flagy = 0; 740 int x0 = 0, y0 = 0; 741 #endif 742 int nr; 743 744 /* align blocks */ 745 for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ ) 746 { 747 if( !hidfeature->rect[k].p0 ) 748 break; 749 #if CV_ADJUST_FEATURES 750 r[k] = feature->rect[k].r; 751 base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].width-1) ); 752 base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].x - r[0].x-1) ); 753 base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].height-1) ); 754 base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].y - r[0].y-1) ); 755 #endif 756 } 757 758 nr = k; 759 760 #if CV_ADJUST_FEATURES 761 base_w += 1; 762 base_h += 1; 763 kx = r[0].width / base_w; 764 ky = r[0].height / base_h; 765 766 if( kx <= 0 ) 767 { 768 flagx = 1; 769 new_base_w = cvRound( r[0].width * scale ) / kx; 770 x0 = cvRound( r[0].x * scale ); 771 } 772 773 if( ky <= 0 ) 774 { 775 flagy = 1; 776 new_base_h = cvRound( r[0].height * scale ) / ky; 777 y0 = cvRound( r[0].y * scale ); 778 } 779 #endif 780 781 float tmpweight[3] = {0}; 782 783 for( k = 0; k < nr; k++ ) 784 { 785 CvRect tr; 786 double correction_ratio; 787 788 #if CV_ADJUST_FEATURES 789 if( flagx ) 790 { 791 tr.x = (r[k].x - r[0].x) * new_base_w / base_w + x0; 792 tr.width = r[k].width * new_base_w / base_w; 793 } 794 else 795 #endif 796 { 797 tr.x = cvRound( r[k].x * scale ); 798 tr.width = cvRound( r[k].width * scale ); 799 } 800 801 #if CV_ADJUST_FEATURES 802 if( flagy ) 803 { 804 tr.y = (r[k].y - r[0].y) * new_base_h / base_h + y0; 805 tr.height = r[k].height * new_base_h / base_h; 806 } 807 else 808 #endif 809 { 810 tr.y = cvRound( r[k].y * scale ); 811 tr.height = cvRound( r[k].height * scale ); 812 } 813 814 #if CV_ADJUST_WEIGHTS 815 { 816 // RAINER START 817 const float orig_feature_size = (float)(feature->rect[k].r.width)*feature->rect[k].r.height; 818 const float orig_norm_size = (float)(_cascade->orig_window_size.width)*(_cascade->orig_window_size.height); 819 const float feature_size = float(tr.width*tr.height); 820 //const float normSize = float(equ_rect.width*equ_rect.height); 821 float target_ratio = orig_feature_size / orig_norm_size; 822 //float isRatio = featureSize / normSize; 823 //correctionRatio = targetRatio / isRatio / normSize; 824 correction_ratio = target_ratio / feature_size; 825 // RAINER END 826 } 827 #else 828 correction_ratio = weight_scale * (!feature->tilted ? 1 : 0.5); 829 #endif 830 831 if( !feature->tilted ) 832 { 833 hidfeature->rect[k].p0 = sum_elem_ptr(*sum, tr.y, tr.x); 834 hidfeature->rect[k].p1 = sum_elem_ptr(*sum, tr.y, tr.x + tr.width); 835 hidfeature->rect[k].p2 = sum_elem_ptr(*sum, tr.y + tr.height, tr.x); 836 hidfeature->rect[k].p3 = sum_elem_ptr(*sum, tr.y + tr.height, tr.x + tr.width); 837 } 838 else 839 { 840 hidfeature->rect[k].p2 = sum_elem_ptr(*tilted, tr.y + tr.width, tr.x + tr.width); 841 hidfeature->rect[k].p3 = sum_elem_ptr(*tilted, tr.y + tr.width + tr.height, 842 tr.x + tr.width - tr.height); 843 hidfeature->rect[k].p0 = sum_elem_ptr(*tilted, tr.y, tr.x); 844 hidfeature->rect[k].p1 = sum_elem_ptr(*tilted, tr.y + tr.height, tr.x - tr.height); 845 } 846 847 // hidfeature->rect[k].weight = (float)(feature->rect[k].weight * correction_ratio); 848 tmpweight[k] = (float)(feature->rect[k].weight * correction_ratio); 849 850 if( k == 0 ) 851 area0 = tr.width * tr.height; 852 else 853 // sum0 += hidfeature->rect[k].weight * tr.width * tr.height; 854 sum0 += tmpweight[k] * tr.width * tr.height; 855 } 856 857 tmpweight[0] = (float)(-sum0/area0); 858 859 for(int ii = 0; ii < nr; hidfeature->rect[ii].weight = (int)(tmpweight[ii] * 65536.0), ii++); 860 } /* l */ 861 } /* j */ 862 } 863 } 864 865 __END__; 866 } 867 868 CvMat *temp = 0, *sum = 0, *sqsum = 0; 869 double tickFreqTimes1000 = ((double)cvGetTickFrequency()*1000.); 870 871 CV_IMPL CvSeq* 872 mycvHaarDetectObjects( const CvArr* _img, 873 CvHaarClassifierCascade* cascade, 874 CvMemStorage* storage, double scale_factor, 875 int min_neighbors, int flags, CvSize min_size ) 876 { 877 int split_stage = 2; 878 879 CvMat stub, *img = (CvMat*)_img; 880 CvMat *tilted = 0, *norm_img = 0, *sumcanny = 0, *img_small = 0; 881 CvSeq* result_seq = 0; 882 CvMemStorage* temp_storage = 0; 883 CvAvgComp* comps = 0; 884 CvSeq* seq_thread[CV_MAX_THREADS] = {0}; 885 int i, max_threads = 0; 886 double t1; 887 888 CV_FUNCNAME( "cvHaarDetectObjects" ); 889 890 __BEGIN__; 891 892 double t = (double)cvGetTickCount(); 893 894 CvSeq *seq = 0, *seq2 = 0, *idx_seq = 0, *big_seq = 0; 895 CvAvgComp result_comp = {{0,0,0,0},0}; 896 double factor; 897 int npass = 2, coi; 898 bool do_canny_pruning = (flags & CV_HAAR_DO_CANNY_PRUNING) != 0; 899 bool find_biggest_object = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0; 900 bool rough_search = (flags & CV_HAAR_DO_ROUGH_SEARCH) != 0; 901 902 if( !CV_IS_HAAR_CLASSIFIER(cascade) ) 903 CV_ERROR( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" ); 904 905 if( !storage ) 906 CV_ERROR( CV_StsNullPtr, "Null storage pointer" ); 907 908 CV_CALL( img = cvGetMat( img, &stub, &coi )); 909 if( coi ) 910 CV_ERROR( CV_BadCOI, "COI is not supported" ); 911 912 if( CV_MAT_DEPTH(img->type) != CV_8U ) 913 CV_ERROR( CV_StsUnsupportedFormat, "Only 8-bit images are supported" ); 914 915 if( scale_factor <= 1 ) 916 CV_ERROR( CV_StsOutOfRange, "scale factor must be > 1" ); 917 918 if( find_biggest_object ) 919 flags &= ~CV_HAAR_SCALE_IMAGE; 920 921 if(!temp) { 922 CV_CALL( temp = cvCreateMat( img->rows, img->cols, CV_8UC1 )); 923 } 924 if(!sum) { 925 CV_CALL( sum = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 )); 926 } 927 if(!sqsum) { 928 CV_CALL( sqsum = cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 )); 929 } 930 CV_CALL( temp_storage = cvCreateChildMemStorage( storage )); 931 932 if( !cascade->hid_cascade ) 933 CV_CALL( myicvCreateHidHaarClassifierCascade(cascade) ); 934 935 if( ((MyCvHidHaarClassifierCascade*)cascade->hid_cascade)->has_tilted_features ) 936 tilted = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ); 937 938 seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvRect), temp_storage ); 939 seq2 = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), temp_storage ); 940 result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage ); 941 942 max_threads = cvGetNumThreads(); 943 if( max_threads > 1 ) 944 for( i = 0; i < max_threads; i++ ) 945 { 946 CvMemStorage* temp_storage_thread; 947 CV_CALL( temp_storage_thread = cvCreateMemStorage(0)); 948 CV_CALL( seq_thread[i] = cvCreateSeq( 0, sizeof(CvSeq), 949 sizeof(CvRect), temp_storage_thread )); 950 } 951 else 952 seq_thread[0] = seq; 953 954 if( CV_MAT_CN(img->type) > 1 ) 955 { 956 cvCvtColor( img, temp, CV_BGR2GRAY ); 957 img = temp; 958 } 959 960 if( flags & CV_HAAR_FIND_BIGGEST_OBJECT ) 961 flags &= ~(CV_HAAR_SCALE_IMAGE|CV_HAAR_DO_CANNY_PRUNING); 962 963 // if( flags & CV_HAAR_SCALE_IMAGE ) 964 // { 965 // CvSize win_size0 = cascade->orig_window_size; 966 // /*int use_ipp = cascade->hid_cascade->ipp_stages != 0 && 967 // icvApplyHaarClassifier_32f_C1R_p != 0; 968 // 969 // if( use_ipp ) 970 // CV_CALL( norm_img = cvCreateMat( img->rows, img->cols, CV_32FC1 ));*/ 971 // CV_CALL( img_small = cvCreateMat( img->rows + 1, img->cols + 1, CV_8UC1 )); 972 // 973 // for( factor = 1; ; factor *= scale_factor ) 974 // { 975 // int strip_count, strip_size; 976 // int ystep = factor > 2. ? 1 : 2; 977 // CvSize win_size = { cvRound(win_size0.width*factor), 978 // cvRound(win_size0.height*factor) }; 979 // CvSize sz = { cvRound( img->cols/factor ), cvRound( img->rows/factor ) }; 980 // CvSize sz1 = { sz.width - win_size0.width, sz.height - win_size0.height }; 981 // /*CvRect equ_rect = { icv_object_win_border, icv_object_win_border, 982 // win_size0.width - icv_object_win_border*2, 983 // win_size0.height - icv_object_win_border*2 };*/ 984 // CvMat img1, sum1, sqsum1, norm1, tilted1, mask1; 985 // CvMat* _tilted = 0; 986 // 987 // if( sz1.width <= 0 || sz1.height <= 0 ) 988 // break; 989 // if( win_size.width < min_size.width || win_size.height < min_size.height ) 990 // continue; 991 // 992 // img1 = cvMat( sz.height, sz.width, CV_8UC1, img_small->data.ptr ); 993 // sum1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, sum->data.ptr ); 994 // sqsum1 = cvMat( sz.height+1, sz.width+1, CV_64FC1, sqsum->data.ptr ); 995 // if( tilted ) 996 // { 997 // tilted1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, tilted->data.ptr ); 998 // _tilted = &tilted1; 999 // } 1000 // norm1 = cvMat( sz1.height, sz1.width, CV_32FC1, norm_img ? norm_img->data.ptr : 0 ); 1001 // mask1 = cvMat( sz1.height, sz1.width, CV_8UC1, temp->data.ptr ); 1002 // 1003 // cvResize( img, &img1, CV_INTER_LINEAR ); 1004 // cvIntegral( &img1, &sum1, &sqsum1, _tilted ); 1005 // 1006 // if( max_threads > 1 ) 1007 // { 1008 // strip_count = MAX(MIN(sz1.height/ystep, max_threads*3), 1); 1009 // strip_size = (sz1.height + strip_count - 1)/strip_count; 1010 // strip_size = (strip_size / ystep)*ystep; 1011 // } 1012 // else 1013 // { 1014 // strip_count = 1; 1015 // strip_size = sz1.height; 1016 // } 1017 // 1018 // //if( !use_ipp ) 1019 // cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, 0, 1. ); 1020 // /*else 1021 // { 1022 // for( i = 0; i <= sz.height; i++ ) 1023 // { 1024 // const int* isum = (int*)(sum1.data.ptr + sum1.step*i); 1025 // float* fsum = (float*)isum; 1026 // const int FLT_DELTA = -(1 << 24); 1027 // int j; 1028 // for( j = 0; j <= sz.width; j++ ) 1029 // fsum[j] = (float)(isum[j] + FLT_DELTA); 1030 // } 1031 // }*/ 1032 // 1033 //#ifdef _OPENMP 1034 //#pragma omp parallel for num_threads(max_threads) schedule(dynamic) 1035 //#endif 1036 // for( i = 0; i < strip_count; i++ ) 1037 // { 1038 // int thread_id = cvGetThreadNum(); 1039 // int positive = 0; 1040 // int y1 = i*strip_size, y2 = (i+1)*strip_size/* - ystep + 1*/; 1041 // CvSize ssz; 1042 // int x, y; 1043 // if( i == strip_count - 1 || y2 > sz1.height ) 1044 // y2 = sz1.height; 1045 // ssz = cvSize(sz1.width, y2 - y1); 1046 // 1047 // /*if( use_ipp ) 1048 // { 1049 // icvRectStdDev_32f_C1R_p( 1050 // (float*)(sum1.data.ptr + y1*sum1.step), sum1.step, 1051 // (double*)(sqsum1.data.ptr + y1*sqsum1.step), sqsum1.step, 1052 // (float*)(norm1.data.ptr + y1*norm1.step), norm1.step, ssz, equ_rect ); 1053 // 1054 // positive = (ssz.width/ystep)*((ssz.height + ystep-1)/ystep); 1055 // memset( mask1.data.ptr + y1*mask1.step, ystep == 1, mask1.height*mask1.step); 1056 // 1057 // if( ystep > 1 ) 1058 // { 1059 // for( y = y1, positive = 0; y < y2; y += ystep ) 1060 // for( x = 0; x < ssz.width; x += ystep ) 1061 // mask1.data.ptr[mask1.step*y + x] = (uchar)1; 1062 // } 1063 // 1064 // for( int j = 0; j < cascade->count; j++ ) 1065 // { 1066 // if( icvApplyHaarClassifier_32f_C1R_p( 1067 // (float*)(sum1.data.ptr + y1*sum1.step), sum1.step, 1068 // (float*)(norm1.data.ptr + y1*norm1.step), norm1.step, 1069 // mask1.data.ptr + y1*mask1.step, mask1.step, ssz, &positive, 1070 // cascade->hid_cascade->stage_classifier[j].threshold, 1071 // cascade->hid_cascade->ipp_stages[j]) < 0 ) 1072 // { 1073 // positive = 0; 1074 // break; 1075 // } 1076 // if( positive <= 0 ) 1077 // break; 1078 // } 1079 // } 1080 // else*/ 1081 // { 1082 // for( y = y1, positive = 0; y < y2; y += ystep ) 1083 // for( x = 0; x < ssz.width; x += ystep ) 1084 // { 1085 // mask1.data.ptr[mask1.step*y + x] = 1086 // mycvRunHaarClassifierCascade( cascade, cvPoint(x,y), 0 ) > 0; 1087 // positive += mask1.data.ptr[mask1.step*y + x]; 1088 // } 1089 // } 1090 // 1091 // if( positive > 0 ) 1092 // { 1093 // for( y = y1; y < y2; y += ystep ) 1094 // for( x = 0; x < ssz.width; x += ystep ) 1095 // if( mask1.data.ptr[mask1.step*y + x] != 0 ) 1096 // { 1097 // CvRect obj_rect = { cvRound(x*factor), cvRound(y*factor), 1098 // win_size.width, win_size.height }; 1099 // cvSeqPush( seq_thread[thread_id], &obj_rect ); 1100 // } 1101 // } 1102 // } 1103 // 1104 // // gather the results 1105 // if( max_threads > 1 ) 1106 // for( i = 0; i < max_threads; i++ ) 1107 // { 1108 // CvSeq* s = seq_thread[i]; 1109 // int j, total = s->total; 1110 // CvSeqBlock* b = s->first; 1111 // for( j = 0; j < total; j += b->count, b = b->next ) 1112 // cvSeqPushMulti( seq, b->data, b->count ); 1113 // } 1114 // } 1115 // } 1116 // else 1117 t1 = (double)cvGetTickCount(); 1118 // printf( "init time = %gms\n", (t1 - t)/tickFreqTimes1000); 1119 t = t1; 1120 1121 { 1122 int n_factors = 0; 1123 CvRect scan_roi_rect = {0,0,0,0}; 1124 bool is_found = false, scan_roi = false; 1125 1126 cvIntegral( img, sum, sqsum, tilted ); 1127 1128 // if( do_canny_pruning ) 1129 // { 1130 // sumcanny = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ); 1131 // cvCanny( img, temp, 0, 50, 3 ); 1132 // cvIntegral( temp, sumcanny ); 1133 // } 1134 1135 if( (unsigned)split_stage >= (unsigned)cascade->count || 1136 ((MyCvHidHaarClassifierCascade*)cascade->hid_cascade)->is_tree ) 1137 { 1138 split_stage = cascade->count; 1139 npass = 1; 1140 } 1141 1142 for( n_factors = 0, factor = 1; 1143 factor*cascade->orig_window_size.width < img->cols - 10 && 1144 factor*cascade->orig_window_size.height < img->rows - 10; 1145 n_factors++, factor *= scale_factor ) 1146 ; 1147 1148 if( find_biggest_object ) 1149 { 1150 scale_factor = 1./scale_factor; 1151 factor *= scale_factor; 1152 big_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvRect), temp_storage ); 1153 } 1154 else 1155 factor = 1; 1156 1157 for( ; n_factors-- > 0 && !is_found; factor *= scale_factor ) 1158 { 1159 const double ystep = MAX( 2, factor ); 1160 CvSize win_size = { cvRound( cascade->orig_window_size.width * factor ), 1161 cvRound( cascade->orig_window_size.height * factor )}; 1162 CvRect equ_rect = { 0, 0, 0, 0 }; 1163 int *p0 = 0, *p1 = 0, *p2 = 0, *p3 = 0; 1164 int *pq0 = 0, *pq1 = 0, *pq2 = 0, *pq3 = 0; 1165 int pass, stage_offset = 0; 1166 int start_x = 0, start_y = 0; 1167 int end_x = cvRound((img->cols - win_size.width) / ystep); 1168 int end_y = cvRound((img->rows - win_size.height) / ystep); 1169 1170 if( win_size.width < min_size.width || win_size.height < min_size.height ) 1171 { 1172 if( find_biggest_object ) 1173 break; 1174 continue; 1175 } 1176 1177 mycvSetImagesForHaarClassifierCascade( cascade, sum, sqsum, tilted, factor ); 1178 cvZero( temp ); 1179 1180 // if( do_canny_pruning ) 1181 // { 1182 // equ_rect.x = cvRound(win_size.width*0.15); 1183 // equ_rect.y = cvRound(win_size.height*0.15); 1184 // equ_rect.width = cvRound(win_size.width*0.7); 1185 // equ_rect.height = cvRound(win_size.height*0.7); 1186 // 1187 // p0 = (int*)(sumcanny->data.ptr + equ_rect.y*sumcanny->step) + equ_rect.x; 1188 // p1 = (int*)(sumcanny->data.ptr + equ_rect.y*sumcanny->step) 1189 // + equ_rect.x + equ_rect.width; 1190 // p2 = (int*)(sumcanny->data.ptr + (equ_rect.y + equ_rect.height)*sumcanny->step) + equ_rect.x; 1191 // p3 = (int*)(sumcanny->data.ptr + (equ_rect.y + equ_rect.height)*sumcanny->step) 1192 // + equ_rect.x + equ_rect.width; 1193 // 1194 // pq0 = (int*)(sum->data.ptr + equ_rect.y*sum->step) + equ_rect.x; 1195 // pq1 = (int*)(sum->data.ptr + equ_rect.y*sum->step) 1196 // + equ_rect.x + equ_rect.width; 1197 // pq2 = (int*)(sum->data.ptr + (equ_rect.y + equ_rect.height)*sum->step) + equ_rect.x; 1198 // pq3 = (int*)(sum->data.ptr + (equ_rect.y + equ_rect.height)*sum->step) 1199 // + equ_rect.x + equ_rect.width; 1200 // } 1201 1202 if( scan_roi ) 1203 { 1204 //adjust start_height and stop_height 1205 start_y = cvRound(scan_roi_rect.y / ystep); 1206 end_y = cvRound((scan_roi_rect.y + scan_roi_rect.height - win_size.height) / ystep); 1207 1208 start_x = cvRound(scan_roi_rect.x / ystep); 1209 end_x = cvRound((scan_roi_rect.x + scan_roi_rect.width - win_size.width) / ystep); 1210 } 1211 1212 ((MyCvHidHaarClassifierCascade*)cascade->hid_cascade)->count = split_stage; 1213 1214 for( pass = 0; pass < npass; pass++ ) 1215 { 1216 #ifdef _OPENMP 1217 #pragma omp parallel for num_threads(max_threads) schedule(dynamic) 1218 #endif 1219 for( int _iy = start_y; _iy < end_y; _iy++ ) 1220 { 1221 int thread_id = cvGetThreadNum(); 1222 int iy = cvRound(_iy*ystep); 1223 int _ix, _xstep = 1; 1224 uchar* mask_row = temp->data.ptr + temp->step * iy; 1225 1226 for( _ix = start_x; _ix < end_x; _ix += _xstep ) 1227 { 1228 int ix = cvRound(_ix*ystep); // it really should be ystep 1229 1230 if( pass == 0 ) 1231 { 1232 int result; 1233 _xstep = 2; 1234 1235 // if( do_canny_pruning ) 1236 // { 1237 // int offset; 1238 // int s, sq; 1239 // 1240 // offset = iy*(sum->step/sizeof(p0[0])) + ix; 1241 // s = p0[offset] - p1[offset] - p2[offset] + p3[offset]; 1242 // sq = pq0[offset] - pq1[offset] - pq2[offset] + pq3[offset]; 1243 // if( s < 100 || sq < 20 ) 1244 // continue; 1245 // } 1246 1247 result = mycvRunHaarClassifierCascade( cascade, cvPoint(ix,iy), 0 ); 1248 if( result > 0 ) 1249 { 1250 if( pass < npass - 1 ) 1251 mask_row[ix] = 1; 1252 else 1253 { 1254 CvRect rect = cvRect(ix,iy,win_size.width,win_size.height); 1255 cvSeqPush( seq_thread[thread_id], &rect ); 1256 } 1257 } 1258 if( result < 0 ) 1259 _xstep = 1; 1260 } 1261 else if( mask_row[ix] ) 1262 { 1263 int result = mycvRunHaarClassifierCascade( cascade, cvPoint(ix,iy), 1264 stage_offset ); 1265 if( result > 0 ) 1266 { 1267 if( pass == npass - 1 ) 1268 { 1269 CvRect rect = cvRect(ix,iy,win_size.width,win_size.height); 1270 cvSeqPush( seq_thread[thread_id], &rect ); 1271 } 1272 } 1273 else 1274 mask_row[ix] = 0; 1275 } 1276 } 1277 } 1278 stage_offset = ((MyCvHidHaarClassifierCascade*)cascade->hid_cascade)->count; 1279 ((MyCvHidHaarClassifierCascade*)cascade->hid_cascade)->count = cascade->count; 1280 } 1281 1282 // gather the results 1283 if( max_threads > 1 ) 1284 for( i = 0; i < max_threads; i++ ) 1285 { 1286 CvSeq* s = seq_thread[i]; 1287 int j, total = s->total; 1288 CvSeqBlock* b = s->first; 1289 for( j = 0; j < total; j += b->count, b = b->next ) 1290 cvSeqPushMulti( seq, b->data, b->count ); 1291 } 1292 1293 if( find_biggest_object ) 1294 { 1295 CvSeq* bseq = min_neighbors > 0 ? big_seq : seq; 1296 1297 if( min_neighbors > 0 && !scan_roi ) 1298 { 1299 // group retrieved rectangles in order to filter out noise 1300 int ncomp = cvSeqPartition( seq, 0, &idx_seq, myis_equal, 0 ); 1301 CV_CALL( comps = (CvAvgComp*)cvAlloc( (ncomp+1)*sizeof(comps[0]))); 1302 memset( comps, 0, (ncomp+1)*sizeof(comps[0])); 1303 1304 #if VERY_ROUGH_SEARCH 1305 if( rough_search ) 1306 { 1307 for( i = 0; i < seq->total; i++ ) 1308 { 1309 CvRect r1 = *(CvRect*)cvGetSeqElem( seq, i ); 1310 int idx = *(int*)cvGetSeqElem( idx_seq, i ); 1311 assert( (unsigned)idx < (unsigned)ncomp ); 1312 1313 comps[idx].neighbors++; 1314 comps[idx].rect.x += r1.x; 1315 comps[idx].rect.y += r1.y; 1316 comps[idx].rect.width += r1.width; 1317 comps[idx].rect.height += r1.height; 1318 } 1319 1320 // calculate average bounding box 1321 for( i = 0; i < ncomp; i++ ) 1322 { 1323 int n = comps[i].neighbors; 1324 if( n >= min_neighbors ) 1325 { 1326 CvAvgComp comp; 1327 comp.rect.x = (comps[i].rect.x*2 + n)/(2*n); 1328 comp.rect.y = (comps[i].rect.y*2 + n)/(2*n); 1329 comp.rect.width = (comps[i].rect.width*2 + n)/(2*n); 1330 comp.rect.height = (comps[i].rect.height*2 + n)/(2*n); 1331 comp.neighbors = n; 1332 cvSeqPush( bseq, &comp ); 1333 } 1334 } 1335 } 1336 else 1337 #endif 1338 { 1339 for( i = 0 ; i <= ncomp; i++ ) 1340 comps[i].rect.x = comps[i].rect.y = INT_MAX; 1341 1342 // count number of neighbors 1343 for( i = 0; i < seq->total; i++ ) 1344 { 1345 CvRect r1 = *(CvRect*)cvGetSeqElem( seq, i ); 1346 int idx = *(int*)cvGetSeqElem( idx_seq, i ); 1347 assert( (unsigned)idx < (unsigned)ncomp ); 1348 1349 comps[idx].neighbors++; 1350 1351 // rect.width and rect.height will store coordinate of right-bottom corner 1352 comps[idx].rect.x = MIN(comps[idx].rect.x, r1.x); 1353 comps[idx].rect.y = MIN(comps[idx].rect.y, r1.y); 1354 comps[idx].rect.width = MAX(comps[idx].rect.width, r1.x+r1.width-1); 1355 comps[idx].rect.height = MAX(comps[idx].rect.height, r1.y+r1.height-1); 1356 } 1357 1358 // calculate enclosing box 1359 for( i = 0; i < ncomp; i++ ) 1360 { 1361 int n = comps[i].neighbors; 1362 if( n >= min_neighbors ) 1363 { 1364 CvAvgComp comp; 1365 int t; 1366 double min_scale = rough_search ? 0.6 : 0.4; 1367 comp.rect.x = comps[i].rect.x; 1368 comp.rect.y = comps[i].rect.y; 1369 comp.rect.width = comps[i].rect.width - comps[i].rect.x + 1; 1370 comp.rect.height = comps[i].rect.height - comps[i].rect.y + 1; 1371 1372 // update min_size 1373 t = cvRound( comp.rect.width*min_scale ); 1374 min_size.width = MAX( min_size.width, t ); 1375 1376 t = cvRound( comp.rect.height*min_scale ); 1377 min_size.height = MAX( min_size.height, t ); 1378 1379 //expand the box by 20% because we could miss some neighbours 1380 //see 'is_equal' function 1381 #if 1 1382 int offset = cvRound(comp.rect.width * 0.2); 1383 int right = MIN( img->cols-1, comp.rect.x+comp.rect.width-1 + offset ); 1384 int bottom = MIN( img->rows-1, comp.rect.y+comp.rect.height-1 + offset); 1385 comp.rect.x = MAX( comp.rect.x - offset, 0 ); 1386 comp.rect.y = MAX( comp.rect.y - offset, 0 ); 1387 comp.rect.width = right - comp.rect.x + 1; 1388 comp.rect.height = bottom - comp.rect.y + 1; 1389 #endif 1390 1391 comp.neighbors = n; 1392 cvSeqPush( bseq, &comp ); 1393 } 1394 } 1395 } 1396 1397 cvFree( &comps ); 1398 } 1399 1400 // extract the biggest rect 1401 if( bseq->total > 0 ) 1402 { 1403 int max_area = 0; 1404 for( i = 0; i < bseq->total; i++ ) 1405 { 1406 CvAvgComp* comp = (CvAvgComp*)cvGetSeqElem( bseq, i ); 1407 int area = comp->rect.width * comp->rect.height; 1408 if( max_area < area ) 1409 { 1410 max_area = area; 1411 result_comp.rect = comp->rect; 1412 result_comp.neighbors = bseq == seq ? 1 : comp->neighbors; 1413 } 1414 } 1415 1416 //Prepare information for further scanning inside the biggest rectangle 1417 1418 #if VERY_ROUGH_SEARCH 1419 // change scan ranges to roi in case of required 1420 if( !rough_search && !scan_roi ) 1421 { 1422 scan_roi = true; 1423 scan_roi_rect = result_comp.rect; 1424 cvClearSeq(bseq); 1425 } 1426 else if( rough_search ) 1427 is_found = true; 1428 #else 1429 if( !scan_roi ) 1430 { 1431 scan_roi = true; 1432 scan_roi_rect = result_comp.rect; 1433 cvClearSeq(bseq); 1434 } 1435 #endif 1436 } 1437 } 1438 } 1439 } 1440 1441 // t1 = (double)cvGetTickCount(); 1442 // printf( "factors time = %gms\n", (t1 - t)/tickFreqTimes1000); 1443 // t = t1; 1444 1445 if( min_neighbors == 0 && !find_biggest_object ) 1446 { 1447 for( i = 0; i < seq->total; i++ ) 1448 { 1449 CvRect* rect = (CvRect*)cvGetSeqElem( seq, i ); 1450 CvAvgComp comp; 1451 comp.rect = *rect; 1452 comp.neighbors = 1; 1453 cvSeqPush( result_seq, &comp ); 1454 } 1455 } 1456 1457 if( min_neighbors != 0 1458 #if VERY_ROUGH_SEARCH 1459 && (!find_biggest_object || !rough_search) 1460 #endif 1461 ) 1462 { 1463 // group retrieved rectangles in order to filter out noise 1464 int ncomp = cvSeqPartition( seq, 0, &idx_seq, myis_equal, 0 ); 1465 CV_CALL( comps = (CvAvgComp*)cvAlloc( (ncomp+1)*sizeof(comps[0]))); 1466 memset( comps, 0, (ncomp+1)*sizeof(comps[0])); 1467 1468 // count number of neighbors 1469 for( i = 0; i < seq->total; i++ ) 1470 { 1471 CvRect r1 = *(CvRect*)cvGetSeqElem( seq, i ); 1472 int idx = *(int*)cvGetSeqElem( idx_seq, i ); 1473 assert( (unsigned)idx < (unsigned)ncomp ); 1474 1475 comps[idx].neighbors++; 1476 1477 comps[idx].rect.x += r1.x; 1478 comps[idx].rect.y += r1.y; 1479 comps[idx].rect.width += r1.width; 1480 comps[idx].rect.height += r1.height; 1481 } 1482 1483 // calculate average bounding box 1484 for( i = 0; i < ncomp; i++ ) 1485 { 1486 int n = comps[i].neighbors; 1487 if( n >= min_neighbors ) 1488 { 1489 CvAvgComp comp; 1490 comp.rect.x = (comps[i].rect.x*2 + n)/(2*n); 1491 comp.rect.y = (comps[i].rect.y*2 + n)/(2*n); 1492 comp.rect.width = (comps[i].rect.width*2 + n)/(2*n); 1493 comp.rect.height = (comps[i].rect.height*2 + n)/(2*n); 1494 comp.neighbors = comps[i].neighbors; 1495 1496 cvSeqPush( seq2, &comp ); 1497 } 1498 } 1499 1500 if( !find_biggest_object ) 1501 { 1502 // filter out small face rectangles inside large face rectangles 1503 for( i = 0; i < seq2->total; i++ ) 1504 { 1505 CvAvgComp r1 = *(CvAvgComp*)cvGetSeqElem( seq2, i ); 1506 int j, flag = 1; 1507 1508 for( j = 0; j < seq2->total; j++ ) 1509 { 1510 CvAvgComp r2 = *(CvAvgComp*)cvGetSeqElem( seq2, j ); 1511 int distance = cvRound( r2.rect.width * 0.2 ); 1512 1513 if( i != j && 1514 r1.rect.x >= r2.rect.x - distance && 1515 r1.rect.y >= r2.rect.y - distance && 1516 r1.rect.x + r1.rect.width <= r2.rect.x + r2.rect.width + distance && 1517 r1.rect.y + r1.rect.height <= r2.rect.y + r2.rect.height + distance && 1518 (r2.neighbors > MAX( 3, r1.neighbors ) || r1.neighbors < 3) ) 1519 { 1520 flag = 0; 1521 break; 1522 } 1523 } 1524 1525 if( flag ) 1526 cvSeqPush( result_seq, &r1 ); 1527 } 1528 } 1529 else 1530 { 1531 int max_area = 0; 1532 for( i = 0; i < seq2->total; i++ ) 1533 { 1534 CvAvgComp* comp = (CvAvgComp*)cvGetSeqElem( seq2, i ); 1535 int area = comp->rect.width * comp->rect.height; 1536 if( max_area < area ) 1537 { 1538 max_area = area; 1539 result_comp = *comp; 1540 } 1541 } 1542 } 1543 } 1544 1545 t1 = (double)cvGetTickCount(); 1546 // printf( "results eval time = %gms\n", (t1 - t)/tickFreqTimes1000); 1547 t = t1; 1548 1549 if( find_biggest_object && result_comp.rect.width > 0 ) 1550 cvSeqPush( result_seq, &result_comp ); 1551 1552 __END__; 1553 1554 if( max_threads > 1 ) 1555 for( i = 0; i < max_threads; i++ ) 1556 { 1557 if( seq_thread[i] ) 1558 cvReleaseMemStorage( &seq_thread[i]->storage ); 1559 } 1560 1561 cvReleaseMemStorage( &temp_storage ); 1562 cvReleaseMat( &sum ); 1563 cvReleaseMat( &sqsum ); 1564 cvReleaseMat( &tilted ); 1565 cvReleaseMat( &temp ); 1566 cvReleaseMat( &sumcanny ); 1567 cvReleaseMat( &norm_img ); 1568 cvReleaseMat( &img_small ); 1569 cvFree( &comps ); 1570 1571 return result_seq; 1572 } 1573
