1 /*M///////////////////////////////////////////////////////////////////////////////////////
3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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11 // For Open Source Computer Vision Library
13 // Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
14 // Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
15 // Third party copyrights are property of their respective owners.
18 // Niko Li, newlife20080214@gmail.com
19 // Wang Weiyan, wangweiyanster@gmail.com
20 // Jia Haipeng, jiahaipeng95@gmail.com
21 // Wu Xinglong, wxl370@126.com
22 // Wang Yao, bitwangyaoyao@gmail.com
23 // Sen Liu, swjtuls1987@126.com
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26 // are permitted provided that the following conditions are met:
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51 #include "precomp.hpp"
52 #include "opencl_kernels.hpp"
55 using namespace cv::ocl;
57 /* these settings affect the quality of detection: change with care */
58 #define CV_ADJUST_FEATURES 1
59 #define CV_ADJUST_WEIGHTS 0
62 typedef double sqsumtype;
64 typedef struct CvHidHaarFeature
68 sumtype *p0, *p1, *p2, *p3;
71 rect[CV_HAAR_FEATURE_MAX];
76 typedef struct CvHidHaarTreeNode
78 CvHidHaarFeature feature;
86 typedef struct CvHidHaarClassifier
89 //CvHaarFeature* orig_feature;
90 CvHidHaarTreeNode *node;
96 typedef struct CvHidHaarStageClassifier
100 CvHidHaarClassifier *classifier;
103 struct CvHidHaarStageClassifier *next;
104 struct CvHidHaarStageClassifier *child;
105 struct CvHidHaarStageClassifier *parent;
107 CvHidHaarStageClassifier;
110 struct CvHidHaarClassifierCascade
114 int has_tilted_features;
116 double inv_window_area;
117 CvMat sum, sqsum, tilted;
118 CvHidHaarStageClassifier *stage_classifier;
119 sqsumtype *pq0, *pq1, *pq2, *pq3;
120 sumtype *p0, *p1, *p2, *p3;
127 int grpnumperline_totalgrp;
130 } detect_piramid_info;
132 #define _ALIGNED_ON(_ALIGNMENT) __declspec(align(_ALIGNMENT))
134 typedef _ALIGNED_ON(128) struct GpuHidHaarTreeNode
136 _ALIGNED_ON(64) int p[CV_HAAR_FEATURE_MAX][4];
137 float weight[CV_HAAR_FEATURE_MAX] ;
139 _ALIGNED_ON(16) float alpha[3] ;
140 _ALIGNED_ON(4) int left ;
141 _ALIGNED_ON(4) int right ;
146 typedef _ALIGNED_ON(32) struct GpuHidHaarClassifier
148 _ALIGNED_ON(4) int count;
149 _ALIGNED_ON(8) GpuHidHaarTreeNode *node ;
150 _ALIGNED_ON(8) float *alpha ;
152 GpuHidHaarClassifier;
155 typedef _ALIGNED_ON(64) struct GpuHidHaarStageClassifier
157 _ALIGNED_ON(4) int count ;
158 _ALIGNED_ON(4) float threshold ;
159 _ALIGNED_ON(4) int two_rects ;
160 _ALIGNED_ON(8) GpuHidHaarClassifier *classifier ;
161 _ALIGNED_ON(8) struct GpuHidHaarStageClassifier *next;
162 _ALIGNED_ON(8) struct GpuHidHaarStageClassifier *child ;
163 _ALIGNED_ON(8) struct GpuHidHaarStageClassifier *parent ;
165 GpuHidHaarStageClassifier;
168 typedef _ALIGNED_ON(64) struct GpuHidHaarClassifierCascade
170 _ALIGNED_ON(4) int count ;
171 _ALIGNED_ON(4) int is_stump_based ;
172 _ALIGNED_ON(4) int has_tilted_features ;
173 _ALIGNED_ON(4) int is_tree ;
174 _ALIGNED_ON(4) int pq0 ;
175 _ALIGNED_ON(4) int pq1 ;
176 _ALIGNED_ON(4) int pq2 ;
177 _ALIGNED_ON(4) int pq3 ;
178 _ALIGNED_ON(4) int p0 ;
179 _ALIGNED_ON(4) int p1 ;
180 _ALIGNED_ON(4) int p2 ;
181 _ALIGNED_ON(4) int p3 ;
182 _ALIGNED_ON(4) float inv_window_area ;
183 } GpuHidHaarClassifierCascade;
185 #define _ALIGNED_ON(_ALIGNMENT) __attribute__((aligned(_ALIGNMENT) ))
187 typedef struct _ALIGNED_ON(128) GpuHidHaarTreeNode
189 int p[CV_HAAR_FEATURE_MAX][4] _ALIGNED_ON(64);
190 float weight[CV_HAAR_FEATURE_MAX];// _ALIGNED_ON(16);
191 float threshold;// _ALIGNED_ON(4);
192 float alpha[3] _ALIGNED_ON(16);
193 int left _ALIGNED_ON(4);
194 int right _ALIGNED_ON(4);
198 typedef struct _ALIGNED_ON(32) GpuHidHaarClassifier
200 int count _ALIGNED_ON(4);
201 GpuHidHaarTreeNode *node _ALIGNED_ON(8);
202 float *alpha _ALIGNED_ON(8);
204 GpuHidHaarClassifier;
207 typedef struct _ALIGNED_ON(64) GpuHidHaarStageClassifier
209 int count _ALIGNED_ON(4);
210 float threshold _ALIGNED_ON(4);
211 int two_rects _ALIGNED_ON(4);
212 GpuHidHaarClassifier *classifier _ALIGNED_ON(8);
213 struct GpuHidHaarStageClassifier *next _ALIGNED_ON(8);
214 struct GpuHidHaarStageClassifier *child _ALIGNED_ON(8);
215 struct GpuHidHaarStageClassifier *parent _ALIGNED_ON(8);
217 GpuHidHaarStageClassifier;
220 typedef struct _ALIGNED_ON(64) GpuHidHaarClassifierCascade
222 int count _ALIGNED_ON(4);
223 int is_stump_based _ALIGNED_ON(4);
224 int has_tilted_features _ALIGNED_ON(4);
225 int is_tree _ALIGNED_ON(4);
226 int pq0 _ALIGNED_ON(4);
227 int pq1 _ALIGNED_ON(4);
228 int pq2 _ALIGNED_ON(4);
229 int pq3 _ALIGNED_ON(4);
230 int p0 _ALIGNED_ON(4);
231 int p1 _ALIGNED_ON(4);
232 int p2 _ALIGNED_ON(4);
233 int p3 _ALIGNED_ON(4);
234 float inv_window_area _ALIGNED_ON(4);
235 } GpuHidHaarClassifierCascade;
238 const int icv_object_win_border = 1;
239 const float icv_stage_threshold_bias = 0.0001f;
240 double globaltime = 0;
242 /* create more efficient internal representation of haar classifier cascade */
243 static GpuHidHaarClassifierCascade * gpuCreateHidHaarClassifierCascade( CvHaarClassifierCascade *cascade, int *size, int *totalclassifier)
245 GpuHidHaarClassifierCascade *out = 0;
249 int total_classifiers = 0;
253 GpuHidHaarStageClassifier *stage_classifier_ptr;
254 GpuHidHaarClassifier *haar_classifier_ptr;
255 GpuHidHaarTreeNode *haar_node_ptr;
257 CvSize orig_window_size;
258 int has_tilted_features = 0;
260 if( !CV_IS_HAAR_CLASSIFIER(cascade) )
261 CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );
263 if( cascade->hid_cascade )
264 CV_Error( CV_StsError, "hid_cascade has been already created" );
266 if( !cascade->stage_classifier )
267 CV_Error( CV_StsNullPtr, "" );
269 if( cascade->count <= 0 )
270 CV_Error( CV_StsOutOfRange, "Negative number of cascade stages" );
272 orig_window_size = cascade->orig_window_size;
274 /* check input structure correctness and calculate total memory size needed for
275 internal representation of the classifier cascade */
276 for( i = 0; i < cascade->count; i++ )
278 CvHaarStageClassifier *stage_classifier = cascade->stage_classifier + i;
280 if( !stage_classifier->classifier ||
281 stage_classifier->count <= 0 )
283 sprintf( errorstr, "header of the stage classifier #%d is invalid "
284 "(has null pointers or non-positive classfier count)", i );
285 CV_Error( CV_StsError, errorstr );
288 total_classifiers += stage_classifier->count;
290 for( j = 0; j < stage_classifier->count; j++ )
292 CvHaarClassifier *classifier = stage_classifier->classifier + j;
294 total_nodes += classifier->count;
295 for( l = 0; l < classifier->count; l++ )
297 for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
299 if( classifier->haar_feature[l].rect[k].r.width )
301 CvRect r = classifier->haar_feature[l].rect[k].r;
302 int tilted = classifier->haar_feature[l].tilted;
303 has_tilted_features |= tilted != 0;
304 if( r.width < 0 || r.height < 0 || r.y < 0 ||
305 r.x + r.width > orig_window_size.width
308 (r.x < 0 || r.y + r.height > orig_window_size.height))
310 (tilted && (r.x - r.height < 0 ||
311 r.y + r.width + r.height > orig_window_size.height)))
313 sprintf( errorstr, "rectangle #%d of the classifier #%d of "
314 "the stage classifier #%d is not inside "
315 "the reference (original) cascade window", k, j, i );
316 CV_Error( CV_StsNullPtr, errorstr );
324 // this is an upper boundary for the whole hidden cascade size
325 datasize = sizeof(GpuHidHaarClassifierCascade) +
326 sizeof(GpuHidHaarStageClassifier) * cascade->count +
327 sizeof(GpuHidHaarClassifier) * total_classifiers +
328 sizeof(GpuHidHaarTreeNode) * total_nodes;
330 *totalclassifier = total_classifiers;
332 out = (GpuHidHaarClassifierCascade *)cvAlloc( datasize );
333 memset( out, 0, sizeof(*out) );
336 out->count = cascade->count;
337 stage_classifier_ptr = (GpuHidHaarStageClassifier *)(out + 1);
338 haar_classifier_ptr = (GpuHidHaarClassifier *)(stage_classifier_ptr + cascade->count);
339 haar_node_ptr = (GpuHidHaarTreeNode *)(haar_classifier_ptr + total_classifiers);
341 out->is_stump_based = 1;
342 out->has_tilted_features = has_tilted_features;
345 /* initialize internal representation */
346 for( i = 0; i < cascade->count; i++ )
348 CvHaarStageClassifier *stage_classifier = cascade->stage_classifier + i;
349 GpuHidHaarStageClassifier *hid_stage_classifier = stage_classifier_ptr + i;
351 hid_stage_classifier->count = stage_classifier->count;
352 hid_stage_classifier->threshold = stage_classifier->threshold - icv_stage_threshold_bias;
353 hid_stage_classifier->classifier = haar_classifier_ptr;
354 hid_stage_classifier->two_rects = 1;
355 haar_classifier_ptr += stage_classifier->count;
357 for( j = 0; j < stage_classifier->count; j++ )
359 CvHaarClassifier *classifier = stage_classifier->classifier + j;
360 GpuHidHaarClassifier *hid_classifier = hid_stage_classifier->classifier + j;
361 int node_count = classifier->count;
363 float *alpha_ptr = &haar_node_ptr->alpha[0];
365 hid_classifier->count = node_count;
366 hid_classifier->node = haar_node_ptr;
367 hid_classifier->alpha = alpha_ptr;
369 for( l = 0; l < node_count; l++ )
371 GpuHidHaarTreeNode *node = hid_classifier->node + l;
372 CvHaarFeature *feature = classifier->haar_feature + l;
374 memset( node, -1, sizeof(*node) );
375 node->threshold = classifier->threshold[l];
376 node->left = classifier->left[l];
377 node->right = classifier->right[l];
379 if( fabs(feature->rect[2].weight) < DBL_EPSILON ||
380 feature->rect[2].r.width == 0 ||
381 feature->rect[2].r.height == 0 )
390 hid_stage_classifier->two_rects = 0;
392 memcpy( node->alpha, classifier->alpha, (node_count + 1)*sizeof(alpha_ptr[0]));
393 haar_node_ptr = haar_node_ptr + 1;
395 out->is_stump_based &= node_count == 1;
399 cascade->hid_cascade = (CvHidHaarClassifierCascade *)out;
400 assert( (char *)haar_node_ptr - (char *)out <= datasize );
406 #define sum_elem_ptr(sum,row,col) \
407 ((sumtype*)CV_MAT_ELEM_PTR_FAST((sum),(row),(col),sizeof(sumtype)))
409 #define sqsum_elem_ptr(sqsum,row,col) \
410 ((sqsumtype*)CV_MAT_ELEM_PTR_FAST((sqsum),(row),(col),sizeof(sqsumtype)))
412 #define calc_sum(rect,offset) \
413 ((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])
416 static void gpuSetImagesForHaarClassifierCascade( CvHaarClassifierCascade *_cascade,
420 GpuHidHaarClassifierCascade *cascade;
421 int coi0 = 0, coi1 = 0;
427 GpuHidHaarStageClassifier *stage_classifier;
429 if( !CV_IS_HAAR_CLASSIFIER(_cascade) )
430 CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );
433 CV_Error( CV_StsOutOfRange, "Scale must be positive" );
436 CV_Error( CV_BadCOI, "COI is not supported" );
438 if( !_cascade->hid_cascade )
439 gpuCreateHidHaarClassifierCascade(_cascade, &datasize, &total);
441 cascade = (GpuHidHaarClassifierCascade *) _cascade->hid_cascade;
442 stage_classifier = (GpuHidHaarStageClassifier *) (cascade + 1);
444 _cascade->scale = scale;
445 _cascade->real_window_size.width = cvRound( _cascade->orig_window_size.width * scale );
446 _cascade->real_window_size.height = cvRound( _cascade->orig_window_size.height * scale );
448 equRect.x = equRect.y = cvRound(scale);
449 equRect.width = cvRound((_cascade->orig_window_size.width - 2) * scale);
450 equRect.height = cvRound((_cascade->orig_window_size.height - 2) * scale);
451 weight_scale = 1. / (equRect.width * equRect.height);
452 cascade->inv_window_area = weight_scale;
454 cascade->pq0 = equRect.x;
455 cascade->pq1 = equRect.y;
456 cascade->pq2 = equRect.x + equRect.width;
457 cascade->pq3 = equRect.y + equRect.height;
459 cascade->p0 = equRect.x;
460 cascade->p1 = equRect.y;
461 cascade->p2 = equRect.x + equRect.width;
462 cascade->p3 = equRect.y + equRect.height;
465 /* init pointers in haar features according to real window size and
466 given image pointers */
467 for( i = 0; i < _cascade->count; i++ )
470 for( j = 0; j < stage_classifier[i].count; j++ )
472 for( l = 0; l < stage_classifier[i].classifier[j].count; l++ )
474 CvHaarFeature *feature =
475 &_cascade->stage_classifier[i].classifier[j].haar_feature[l];
476 GpuHidHaarTreeNode *hidnode = &stage_classifier[i].classifier[j].node[l];
477 double sum0 = 0, area0 = 0;
480 int base_w = -1, base_h = -1;
481 int new_base_w = 0, new_base_h = 0;
483 int flagx = 0, flagy = 0;
488 for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
490 if(!hidnode->p[k][0])
492 r[k] = feature->rect[k].r;
493 base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].width - 1) );
494 base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].x - r[0].x - 1) );
495 base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].height - 1) );
496 base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].y - r[0].y - 1) );
504 kx = r[0].width / base_w;
507 ky = r[0].height / base_h;
512 new_base_w = cvRound( r[0].width * scale ) / kx;
513 x0 = cvRound( r[0].x * scale );
519 new_base_h = cvRound( r[0].height * scale ) / ky;
520 y0 = cvRound( r[0].y * scale );
523 for( k = 0; k < nr; k++ )
526 double correction_ratio;
530 tr.x = (r[k].x - r[0].x) * new_base_w / base_w + x0;
531 tr.width = r[k].width * new_base_w / base_w;
535 tr.x = cvRound( r[k].x * scale );
536 tr.width = cvRound( r[k].width * scale );
541 tr.y = (r[k].y - r[0].y) * new_base_h / base_h + y0;
542 tr.height = r[k].height * new_base_h / base_h;
546 tr.y = cvRound( r[k].y * scale );
547 tr.height = cvRound( r[k].height * scale );
550 #if CV_ADJUST_WEIGHTS
553 const float orig_feature_size = (float)(feature->rect[k].r.width) * feature->rect[k].r.height;
554 const float orig_norm_size = (float)(_cascade->orig_window_size.width) * (_cascade->orig_window_size.height);
555 const float feature_size = float(tr.width * tr.height);
556 //const float normSize = float(equRect.width*equRect.height);
557 float target_ratio = orig_feature_size / orig_norm_size;
558 //float isRatio = featureSize / normSize;
559 //correctionRatio = targetRatio / isRatio / normSize;
560 correction_ratio = target_ratio / feature_size;
564 correction_ratio = weight_scale * (!feature->tilted ? 1 : 0.5);
567 if( !feature->tilted )
569 hidnode->p[k][0] = tr.x;
570 hidnode->p[k][1] = tr.y;
571 hidnode->p[k][2] = tr.x + tr.width;
572 hidnode->p[k][3] = tr.y + tr.height;
576 hidnode->p[k][2] = (tr.y + tr.width) * step + tr.x + tr.width;
577 hidnode->p[k][3] = (tr.y + tr.width + tr.height) * step + tr.x + tr.width - tr.height;
578 hidnode->p[k][0] = tr.y * step + tr.x;
579 hidnode->p[k][1] = (tr.y + tr.height) * step + tr.x - tr.height;
581 hidnode->weight[k] = (float)(feature->rect[k].weight * correction_ratio);
583 area0 = tr.width * tr.height;
585 sum0 += hidnode->weight[k] * tr.width * tr.height;
587 hidnode->weight[0] = (float)(-sum0 / area0);
593 static void gpuSetHaarClassifierCascade( CvHaarClassifierCascade *_cascade)
595 GpuHidHaarClassifierCascade *cascade;
601 GpuHidHaarStageClassifier *stage_classifier;
603 if( !CV_IS_HAAR_CLASSIFIER(_cascade) )
604 CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );
606 if( !_cascade->hid_cascade )
607 gpuCreateHidHaarClassifierCascade(_cascade, &datasize, &total);
609 cascade = (GpuHidHaarClassifierCascade *) _cascade->hid_cascade;
610 stage_classifier = (GpuHidHaarStageClassifier *) cascade + 1;
612 _cascade->scale = 1.0;
613 _cascade->real_window_size.width = _cascade->orig_window_size.width ;
614 _cascade->real_window_size.height = _cascade->orig_window_size.height;
616 equRect.x = equRect.y = 1;
617 equRect.width = _cascade->orig_window_size.width - 2;
618 equRect.height = _cascade->orig_window_size.height - 2;
620 cascade->inv_window_area = weight_scale;
622 cascade->p0 = equRect.x;
623 cascade->p1 = equRect.y;
624 cascade->p2 = equRect.height;
625 cascade->p3 = equRect.width ;
626 for( i = 0; i < _cascade->count; i++ )
629 for( j = 0; j < stage_classifier[i].count; j++ )
631 for( l = 0; l < stage_classifier[i].classifier[j].count; l++ )
633 const CvHaarFeature *feature =
634 &_cascade->stage_classifier[i].classifier[j].haar_feature[l];
635 GpuHidHaarTreeNode *hidnode = &stage_classifier[i].classifier[j].node[l];
637 for( int k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
639 const CvRect tr = feature->rect[k].r;
642 double correction_ratio = weight_scale * (!feature->tilted ? 1 : 0.5);
643 hidnode->p[k][0] = tr.x;
644 hidnode->p[k][1] = tr.y;
645 hidnode->p[k][2] = tr.width;
646 hidnode->p[k][3] = tr.height;
647 hidnode->weight[k] = (float)(feature->rect[k].weight * correction_ratio);
654 CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemStorage *storage, double scaleFactor,
655 int minNeighbors, int flags, CvSize minSize, CvSize maxSize)
657 CvHaarClassifierCascade *cascade = oldCascade;
659 const double GROUP_EPS = 0.2;
660 CvSeq *result_seq = 0;
662 cv::ConcurrentRectVector allCandidates;
663 std::vector<cv::Rect> rectList;
664 std::vector<int> rweights;
667 int totalclassifier=0;
669 GpuHidHaarClassifierCascade *gcascade;
670 GpuHidHaarStageClassifier *stage;
671 GpuHidHaarClassifier *classifier;
672 GpuHidHaarTreeNode *node;
677 bool findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0;
679 if( maxSize.height == 0 || maxSize.width == 0 )
681 maxSize.height = gimg.rows;
682 maxSize.width = gimg.cols;
685 if( !CV_IS_HAAR_CLASSIFIER(cascade) )
686 CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" );
689 CV_Error( CV_StsNullPtr, "Null storage pointer" );
691 if( CV_MAT_DEPTH(gimg.type()) != CV_8U )
692 CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" );
694 if( scaleFactor <= 1 )
695 CV_Error( CV_StsOutOfRange, "scale factor must be > 1" );
697 if( findBiggestObject )
698 flags &= ~CV_HAAR_SCALE_IMAGE;
700 if( !cascade->hid_cascade )
701 gpuCreateHidHaarClassifierCascade(cascade, &datasize, &totalclassifier);
703 result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage );
705 if( CV_MAT_CN(gimg.type()) > 1 )
708 cvtColor( gimg, gtemp, CV_BGR2GRAY );
712 if( findBiggestObject )
713 flags &= ~(CV_HAAR_SCALE_IMAGE | CV_HAAR_DO_CANNY_PRUNING);
715 if( gimg.cols < minSize.width || gimg.rows < minSize.height )
716 CV_Error(CV_StsError, "Image too small");
718 cl_command_queue qu = getClCommandQueue(Context::getContext());
719 if( (flags & CV_HAAR_SCALE_IMAGE) )
721 CvSize winSize0 = cascade->orig_window_size;
725 vector<CvSize> sizev;
726 vector<float> scalev;
727 for(factor = 1.f;; factor *= scaleFactor)
729 CvSize winSize = { cvRound(winSize0.width * factor), cvRound(winSize0.height * factor) };
730 sz.width = cvRound( gimg.cols / factor ) + 1;
731 sz.height = cvRound( gimg.rows / factor ) + 1;
732 CvSize sz1 = { sz.width - winSize0.width - 1, sz.height - winSize0.height - 1 };
734 if( sz1.width <= 0 || sz1.height <= 0 )
736 if( winSize.width > maxSize.width || winSize.height > maxSize.height )
738 if( winSize.width < minSize.width || winSize.height < minSize.height )
741 totalheight += sz.height;
743 scalev.push_back(factor);
746 oclMat gimg1(gimg.rows, gimg.cols, CV_8UC1);
747 oclMat gsum(totalheight + 4, gimg.cols + 1, CV_32SC1);
748 oclMat gsqsum(totalheight + 4, gimg.cols + 1, CV_32FC1);
751 if(Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE))
755 sdepth = CV_MAT_DEPTH(sdepth);
756 int type = CV_MAKE_TYPE(sdepth, 1);
757 oclMat gsqsum_t(totalheight + 4, gimg.cols + 1, type);
761 cl_mem candidatebuffer;
762 cl_mem scaleinfobuffer;
764 cv::Mat imgroi, imgroisq;
765 cv::ocl::oclMat resizeroi, gimgroi, gimgroisq;
769 size_t blocksize = 8;
770 size_t localThreads[3] = { blocksize, blocksize , 1 };
771 size_t globalThreads[3] = { grp_per_CU *(gsum.clCxt->getDeviceInfo().maxComputeUnits) *localThreads[0],
774 int outputsz = 256 * globalThreads[0] / localThreads[0];
775 int loopcount = sizev.size();
776 detect_piramid_info *scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
778 for( int i = 0; i < loopcount; i++ )
782 roi = Rect(0, indexy, sz.width, sz.height);
783 roi2 = Rect(0, 0, sz.width - 1, sz.height - 1);
784 resizeroi = gimg1(roi2);
786 gimgroisq = gsqsum_t(roi);
787 int width = gimgroi.cols - 1 - cascade->orig_window_size.width;
788 int height = gimgroi.rows - 1 - cascade->orig_window_size.height;
789 scaleinfo[i].width_height = (width << 16) | height;
792 int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
793 int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
795 scaleinfo[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
796 scaleinfo[i].imgoff = gimgroi.offset >> 2;
797 scaleinfo[i].factor = factor;
798 cv::ocl::resize(gimg, resizeroi, Size(sz.width - 1, sz.height - 1), 0, 0, INTER_LINEAR);
799 cv::ocl::integral(resizeroi, gimgroi, gimgroisq);
803 if(gsqsum_t.depth() == CV_64F)
804 gsqsum_t.convertTo(gsqsum, CV_32FC1);
808 gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
809 stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
810 classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
811 node = (GpuHidHaarTreeNode *)(classifier->node);
813 int nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) -
814 sizeof(GpuHidHaarStageClassifier) * gcascade->count - sizeof(GpuHidHaarClassifier) * totalclassifier) / sizeof(GpuHidHaarTreeNode);
816 candidate = (int *)malloc(4 * sizeof(int) * outputsz);
818 gpuSetImagesForHaarClassifierCascade( cascade, 1., gsum.step / 4 );
820 stagebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(GpuHidHaarStageClassifier) * gcascade->count);
821 openCLSafeCall(clEnqueueWriteBuffer(qu, stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
823 nodebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, nodenum * sizeof(GpuHidHaarTreeNode));
825 openCLSafeCall(clEnqueueWriteBuffer(qu, nodebuffer, 1, 0, nodenum * sizeof(GpuHidHaarTreeNode),
826 node, 0, NULL, NULL));
827 candidatebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_WRITE_ONLY, 4 * sizeof(int) * outputsz);
829 scaleinfobuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
830 openCLSafeCall(clEnqueueWriteBuffer(qu, scaleinfobuffer, 1, 0, sizeof(detect_piramid_info)*loopcount, scaleinfo, 0, NULL, NULL));
833 int endstage = gcascade->count;
835 int pixelstep = gsum.step / 4;
837 int splitnode = stage[0].count + stage[1].count + stage[2].count;
839 p.s[0] = gcascade->p0;
840 p.s[1] = gcascade->p1;
841 p.s[2] = gcascade->p2;
842 p.s[3] = gcascade->p3;
843 pq.s[0] = gcascade->pq0;
844 pq.s[1] = gcascade->pq1;
845 pq.s[2] = gcascade->pq2;
846 pq.s[3] = gcascade->pq3;
847 float correction = gcascade->inv_window_area;
849 vector<pair<size_t, const void *> > args;
850 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&stagebuffer ));
851 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&scaleinfobuffer ));
852 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&nodebuffer ));
853 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
854 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
855 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&candidatebuffer ));
856 args.push_back ( make_pair(sizeof(cl_int) , (void *)&pixelstep ));
857 args.push_back ( make_pair(sizeof(cl_int) , (void *)&loopcount ));
858 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
859 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
860 args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
861 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
862 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitnode ));
863 args.push_back ( make_pair(sizeof(cl_int4) , (void *)&p ));
864 args.push_back ( make_pair(sizeof(cl_int4) , (void *)&pq ));
865 args.push_back ( make_pair(sizeof(cl_float) , (void *)&correction ));
867 if(gcascade->is_stump_based && gsum.clCxt->supportsFeature(FEATURE_CL_INTEL_DEVICE))
869 //setup local group size
871 localThreads[1] = 16;
874 //init maximal number of workgroups
875 int WGNumX = 1+(sizev[0].width /(localThreads[0]));
876 int WGNumY = 1+(sizev[0].height/(localThreads[1]));
877 int WGNumZ = loopcount;
878 int WGNum = 0; //accurate number of non -empty workgroups
879 oclMat oclWGInfo(1,sizeof(cl_int4) * WGNumX*WGNumY*WGNumZ,CV_8U);
881 cl_int4* pWGInfo = (cl_int4*)clEnqueueMapBuffer(getClCommandQueue(oclWGInfo.clCxt),(cl_mem)oclWGInfo.datastart,true,CL_MAP_WRITE, 0, oclWGInfo.step, 0,0,0,&status);
882 openCLVerifyCall(status);
883 for(int z=0;z<WGNumZ;++z)
885 int Width = (scaleinfo[z].width_height >> 16)&0xFFFF;
886 int Height = (scaleinfo[z].width_height >> 0 )& 0xFFFF;
887 for(int y=0;y<WGNumY;++y)
889 int gy = y*localThreads[1];
890 if(gy>=(Height-cascade->orig_window_size.height))
891 continue; // no data to process
892 for(int x=0;x<WGNumX;++x)
894 int gx = x*localThreads[0];
895 if(gx>=(Width-cascade->orig_window_size.width))
896 continue; // no data to process
898 // save no-empty workgroup info into array
899 pWGInfo[WGNum].s[0] = scaleinfo[z].width_height;
900 pWGInfo[WGNum].s[1] = (gx << 16) | gy;
901 pWGInfo[WGNum].s[2] = scaleinfo[z].imgoff;
902 memcpy(&(pWGInfo[WGNum].s[3]),&(scaleinfo[z].factor),sizeof(float));
907 openCLSafeCall(clEnqueueUnmapMemObject(getClCommandQueue(oclWGInfo.clCxt),(cl_mem)oclWGInfo.datastart,pWGInfo,0,0,0));
911 // setup global sizes to have linear array of workgroups with WGNum size
912 globalThreads[0] = localThreads[0]*WGNum;
913 globalThreads[1] = localThreads[1];
914 globalThreads[2] = 1;
917 // pack node info to have less memory loads
918 oclMat oclNodesPK(1,sizeof(cl_int) * NODE_SIZE * nodenum,CV_8U);
921 cl_int* pNodesPK = (cl_int*)clEnqueueMapBuffer(getClCommandQueue(oclNodesPK.clCxt),(cl_mem)oclNodesPK.datastart,true,CL_MAP_WRITE, 0, oclNodesPK.step, 0,0,0,&status);
922 openCLVerifyCall(status);
923 //use known local data stride to precalulate indexes
924 int DATA_SIZE_X = (localThreads[0]+cascade->orig_window_size.width);
925 // check that maximal value is less than maximal unsigned short
926 assert(DATA_SIZE_X*cascade->orig_window_size.height+cascade->orig_window_size.width < USHRT_MAX);
927 for(int i = 0;i<nodenum;++i)
928 {//process each node from classifier
931 unsigned short slm_index[3][4];
936 struct NodePK * pOut = (struct NodePK *)(pNodesPK + NODE_SIZE*i);
938 {// calc 4 short indexes in shared local mem for each rectangle instead of 2 (x,y) pair.
939 int* p = &(node[i].p[k][0]);
940 pOut->slm_index[k][0] = (unsigned short)(p[1]*DATA_SIZE_X+p[0]);
941 pOut->slm_index[k][1] = (unsigned short)(p[1]*DATA_SIZE_X+p[2]);
942 pOut->slm_index[k][2] = (unsigned short)(p[3]*DATA_SIZE_X+p[0]);
943 pOut->slm_index[k][3] = (unsigned short)(p[3]*DATA_SIZE_X+p[2]);
945 //store used float point values for each node
946 pOut->weight[0] = node[i].weight[0];
947 pOut->weight[1] = node[i].weight[1];
948 pOut->weight[2] = node[i].weight[2];
949 pOut->threshold = node[i].threshold;
950 pOut->alpha[0] = node[i].alpha[0];
951 pOut->alpha[1] = node[i].alpha[1];
953 openCLSafeCall(clEnqueueUnmapMemObject(getClCommandQueue(oclNodesPK.clCxt),(cl_mem)oclNodesPK.datastart,pNodesPK,0,0,0));
956 // add 2 additional buffers (WGinfo and packed nodes) as 2 last args
957 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&oclNodesPK.datastart ));
958 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&oclWGInfo.datastart ));
960 //form build options for kernel
961 string options = "-D PACKED_CLASSIFIER";
962 options += format(" -D NODE_SIZE=%d",NODE_SIZE);
963 options += format(" -D WND_SIZE_X=%d",cascade->orig_window_size.width);
964 options += format(" -D WND_SIZE_Y=%d",cascade->orig_window_size.height);
965 options += format(" -D STUMP_BASED=%d",gcascade->is_stump_based);
966 options += format(" -D LSx=%d",localThreads[0]);
967 options += format(" -D LSy=%d",localThreads[1]);
968 options += format(" -D SPLITNODE=%d",splitnode);
969 options += format(" -D SPLITSTAGE=%d",splitstage);
970 options += format(" -D OUTPUTSZ=%d",outputsz);
972 // init candiate global count by 0
974 openCLSafeCall(clEnqueueWriteBuffer(qu, candidatebuffer, 1, 0, 1 * sizeof(pattern),&pattern, 0, NULL, NULL));
975 // execute face detector
976 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascadePacked", globalThreads, localThreads, args, -1, -1, options.c_str());
977 //read candidate buffer back and put it into host list
978 openCLReadBuffer( gsum.clCxt, candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
979 assert(candidate[0]<outputsz);
980 //printf("candidate[0]=%d\n",candidate[0]);
981 for(int i = 1; i <= candidate[0]; i++)
983 allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],candidate[4 * i + 2], candidate[4 * i + 3]));
988 const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
990 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascade", globalThreads, localThreads, args, -1, -1, build_options);
992 openCLReadBuffer( gsum.clCxt, candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
994 for(int i = 0; i < outputsz; i++)
995 if(candidate[4 * i + 2] != 0)
996 allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
997 candidate[4 * i + 2], candidate[4 * i + 3]));
1002 openCLSafeCall(clReleaseMemObject(stagebuffer));
1003 openCLSafeCall(clReleaseMemObject(scaleinfobuffer));
1004 openCLSafeCall(clReleaseMemObject(nodebuffer));
1005 openCLSafeCall(clReleaseMemObject(candidatebuffer));
1010 CvSize winsize0 = cascade->orig_window_size;
1015 cv::ocl::integral(gimg, gsum, gsqsum_t);
1016 if(gsqsum_t.depth() == CV_64F)
1017 gsqsum_t.convertTo(gsqsum, CV_32FC1);
1021 vector<CvSize> sizev;
1022 vector<float> scalev;
1023 gpuSetHaarClassifierCascade(cascade);
1024 gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
1025 stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
1026 classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
1027 node = (GpuHidHaarTreeNode *)(classifier->node);
1030 cl_mem candidatebuffer;
1031 cl_mem scaleinfobuffer;
1033 cl_mem correctionbuffer;
1034 for( n_factors = 0, factor = 1;
1035 cvRound(factor * winsize0.width) < gimg.cols - 10 &&
1036 cvRound(factor * winsize0.height) < gimg.rows - 10;
1037 n_factors++, factor *= scaleFactor )
1039 CvSize winSize = { cvRound( winsize0.width * factor ),
1040 cvRound( winsize0.height * factor )
1042 if( winSize.width < minSize.width || winSize.height < minSize.height )
1046 sizev.push_back(winSize);
1047 scalev.push_back(factor);
1049 int loopcount = scalev.size();
1054 sizev.push_back(minSize);
1055 scalev.push_back( std::min(cvRound(minSize.width / winsize0.width), cvRound(minSize.height / winsize0.height)) );
1058 detect_piramid_info *scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
1059 cl_int4 *p = (cl_int4 *)malloc(sizeof(cl_int4) * loopcount);
1060 float *correction = (float *)malloc(sizeof(float) * loopcount);
1061 int grp_per_CU = 12;
1062 size_t blocksize = 8;
1063 size_t localThreads[3] = { blocksize, blocksize , 1 };
1064 size_t globalThreads[3] = { grp_per_CU *gsum.clCxt->getDeviceInfo().maxComputeUnits *localThreads[0],
1065 localThreads[1], 1 };
1066 int outputsz = 256 * globalThreads[0] / localThreads[0];
1067 int nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) -
1068 sizeof(GpuHidHaarStageClassifier) * gcascade->count - sizeof(GpuHidHaarClassifier) * totalclassifier) / sizeof(GpuHidHaarTreeNode);
1069 nodebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY,
1070 nodenum * sizeof(GpuHidHaarTreeNode));
1071 openCLSafeCall(clEnqueueWriteBuffer(qu, nodebuffer, 1, 0,
1072 nodenum * sizeof(GpuHidHaarTreeNode),
1073 node, 0, NULL, NULL));
1074 cl_mem newnodebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_WRITE,
1075 loopcount * nodenum * sizeof(GpuHidHaarTreeNode));
1077 int endstage = gcascade->count;
1078 for(int i = 0; i < loopcount; i++)
1082 double ystep = std::max(2., factor);
1083 int equRect_x = cvRound(factor * gcascade->p0);
1084 int equRect_y = cvRound(factor * gcascade->p1);
1085 int equRect_w = cvRound(factor * gcascade->p3);
1086 int equRect_h = cvRound(factor * gcascade->p2);
1087 p[i].s[0] = equRect_x;
1088 p[i].s[1] = equRect_y;
1089 p[i].s[2] = equRect_x + equRect_w;
1090 p[i].s[3] = equRect_y + equRect_h;
1091 correction[i] = 1. / (equRect_w * equRect_h);
1092 int width = (gsum.cols - 1 - sz.width + ystep - 1) / ystep;
1093 int height = (gsum.rows - 1 - sz.height + ystep - 1) / ystep;
1094 int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
1095 int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
1097 scaleinfo[i].width_height = (width << 16) | height;
1098 scaleinfo[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
1099 scaleinfo[i].imgoff = 0;
1100 scaleinfo[i].factor = factor;
1101 int startnodenum = nodenum * i;
1102 float factor2 = (float)factor;
1104 vector<pair<size_t, const void *> > args1;
1105 args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&nodebuffer ));
1106 args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&newnodebuffer ));
1107 args1.push_back ( make_pair(sizeof(cl_float) , (void *)&factor2 ));
1108 args1.push_back ( make_pair(sizeof(cl_float) , (void *)&correction[i] ));
1109 args1.push_back ( make_pair(sizeof(cl_int) , (void *)&startnodenum ));
1111 size_t globalThreads2[3] = {nodenum, 1, 1};
1112 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuscaleclassifier", globalThreads2, NULL/*localThreads2*/, args1, -1, -1);
1115 int step = gsum.step / 4;
1118 stagebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(GpuHidHaarStageClassifier) * gcascade->count);
1119 openCLSafeCall(clEnqueueWriteBuffer(qu, stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
1120 candidatebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_WRITE_ONLY | CL_MEM_ALLOC_HOST_PTR, 4 * sizeof(int) * outputsz);
1121 scaleinfobuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
1122 openCLSafeCall(clEnqueueWriteBuffer(qu, scaleinfobuffer, 1, 0, sizeof(detect_piramid_info)*loopcount, scaleinfo, 0, NULL, NULL));
1123 pbuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(cl_int4) * loopcount);
1124 openCLSafeCall(clEnqueueWriteBuffer(qu, pbuffer, 1, 0, sizeof(cl_int4)*loopcount, p, 0, NULL, NULL));
1125 correctionbuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(cl_float) * loopcount);
1126 openCLSafeCall(clEnqueueWriteBuffer(qu, correctionbuffer, 1, 0, sizeof(cl_float)*loopcount, correction, 0, NULL, NULL));
1128 vector<pair<size_t, const void *> > args;
1129 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&stagebuffer ));
1130 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&scaleinfobuffer ));
1131 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&newnodebuffer ));
1132 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
1133 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
1134 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&candidatebuffer ));
1135 args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.rows ));
1136 args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.cols ));
1137 args.push_back ( make_pair(sizeof(cl_int) , (void *)&step ));
1138 args.push_back ( make_pair(sizeof(cl_int) , (void *)&loopcount ));
1139 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
1140 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
1141 args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
1142 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
1143 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&pbuffer ));
1144 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&correctionbuffer ));
1145 args.push_back ( make_pair(sizeof(cl_int) , (void *)&nodenum ));
1146 const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
1147 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuRunHaarClassifierCascade_scaled2", globalThreads, localThreads, args, -1, -1, build_options);
1149 candidate = (int *)clEnqueueMapBuffer(qu, candidatebuffer, 1, CL_MAP_READ, 0, 4 * sizeof(int) * outputsz, 0, 0, 0, &status);
1151 for(int i = 0; i < outputsz; i++)
1153 if(candidate[4 * i + 2] != 0)
1154 allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1], candidate[4 * i + 2], candidate[4 * i + 3]));
1160 clEnqueueUnmapMemObject(qu, candidatebuffer, candidate, 0, 0, 0);
1161 openCLSafeCall(clReleaseMemObject(stagebuffer));
1162 openCLSafeCall(clReleaseMemObject(scaleinfobuffer));
1163 openCLSafeCall(clReleaseMemObject(nodebuffer));
1164 openCLSafeCall(clReleaseMemObject(newnodebuffer));
1165 openCLSafeCall(clReleaseMemObject(candidatebuffer));
1166 openCLSafeCall(clReleaseMemObject(pbuffer));
1167 openCLSafeCall(clReleaseMemObject(correctionbuffer));
1170 cvFree(&cascade->hid_cascade);
1171 rectList.resize(allCandidates.size());
1172 if(!allCandidates.empty())
1173 std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
1175 if( minNeighbors != 0 || findBiggestObject )
1176 groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
1178 rweights.resize(rectList.size(), 0);
1180 if( findBiggestObject && rectList.size() )
1182 CvAvgComp result_comp = {{0, 0, 0, 0}, 0};
1184 for( size_t i = 0; i < rectList.size(); i++ )
1186 cv::Rect r = rectList[i];
1187 if( r.area() > cv::Rect(result_comp.rect).area() )
1189 result_comp.rect = r;
1190 result_comp.neighbors = rweights[i];
1193 cvSeqPush( result_seq, &result_comp );
1197 for( size_t i = 0; i < rectList.size(); i++ )
1200 c.rect = rectList[i];
1201 c.neighbors = rweights[i];
1202 cvSeqPush( result_seq, &c );
1212 Rect operator()(const CvAvgComp &e) const
1218 void cv::ocl::OclCascadeClassifier::detectMultiScale(oclMat &gimg, CV_OUT std::vector<cv::Rect>& faces,
1219 double scaleFactor, int minNeighbors, int flags,
1220 Size minSize, Size maxSize)
1223 MemStorage storage(cvCreateMemStorage(0));
1224 _objects = oclHaarDetectObjects(gimg, storage, scaleFactor, minNeighbors, flags, minSize, maxSize);
1225 vector<CvAvgComp> vecAvgComp;
1226 Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
1227 faces.resize(vecAvgComp.size());
1228 std::transform(vecAvgComp.begin(), vecAvgComp.end(), faces.begin(), getRect());
1235 cl_mem candidatebuffer;
1236 cl_mem scaleinfobuffer;
1238 cl_mem correctionbuffer;
1239 cl_mem newnodebuffer;
1243 void cv::ocl::OclCascadeClassifierBuf::detectMultiScale(oclMat &gimg, CV_OUT std::vector<cv::Rect>& faces,
1244 double scaleFactor, int minNeighbors, int flags,
1245 Size minSize, Size maxSize)
1248 int grp_per_CU = 12;
1249 size_t localThreads[3] = { blocksize, blocksize, 1 };
1250 size_t globalThreads[3] = { grp_per_CU * cv::ocl::Context::getContext()->getDeviceInfo().maxComputeUnits *localThreads[0],
1253 int outputsz = 256 * globalThreads[0] / localThreads[0];
1255 Init(gimg.rows, gimg.cols, scaleFactor, flags, outputsz, localThreads, minSize, maxSize);
1257 const double GROUP_EPS = 0.2;
1259 cv::ConcurrentRectVector allCandidates;
1260 std::vector<cv::Rect> rectList;
1261 std::vector<int> rweights;
1263 CvHaarClassifierCascade *cascade = oldCascade;
1264 GpuHidHaarClassifierCascade *gcascade;
1265 GpuHidHaarStageClassifier *stage;
1267 if( CV_MAT_DEPTH(gimg.type()) != CV_8U )
1268 CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" );
1270 if( CV_MAT_CN(gimg.type()) > 1 )
1273 cvtColor( gimg, gtemp, CV_BGR2GRAY );
1278 cl_command_queue qu = getClCommandQueue(Context::getContext());
1279 if( (flags & CV_HAAR_SCALE_IMAGE) )
1285 cv::ocl::oclMat resizeroi, gimgroi, gimgroisq;
1287 for( int i = 0; i < m_loopcount; i++ )
1290 roi = Rect(0, indexy, sz.width, sz.height);
1291 roi2 = Rect(0, 0, sz.width - 1, sz.height - 1);
1292 resizeroi = gimg1(roi2);
1293 gimgroi = gsum(roi);
1294 gimgroisq = gsqsum_t(roi);
1296 cv::ocl::resize(gimg, resizeroi, Size(sz.width - 1, sz.height - 1), 0, 0, INTER_LINEAR);
1297 cv::ocl::integral(resizeroi, gimgroi, gimgroisq);
1298 indexy += sz.height;
1300 if(gsqsum_t.depth() == CV_64F)
1301 gsqsum_t.convertTo(gsqsum, CV_32FC1);
1305 gcascade = (GpuHidHaarClassifierCascade *)(cascade->hid_cascade);
1306 stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
1309 int endstage = gcascade->count;
1311 int pixelstep = gsum.step / 4;
1313 int splitnode = stage[0].count + stage[1].count + stage[2].count;
1315 p.s[0] = gcascade->p0;
1316 p.s[1] = gcascade->p1;
1317 p.s[2] = gcascade->p2;
1318 p.s[3] = gcascade->p3;
1319 pq.s[0] = gcascade->pq0;
1320 pq.s[1] = gcascade->pq1;
1321 pq.s[2] = gcascade->pq2;
1322 pq.s[3] = gcascade->pq3;
1323 float correction = gcascade->inv_window_area;
1325 vector<pair<size_t, const void *> > args;
1326 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->stagebuffer ));
1327 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->scaleinfobuffer ));
1328 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->nodebuffer ));
1329 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
1330 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
1331 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->candidatebuffer ));
1332 args.push_back ( make_pair(sizeof(cl_int) , (void *)&pixelstep ));
1333 args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_loopcount ));
1334 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
1335 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
1336 args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
1337 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
1338 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitnode ));
1339 args.push_back ( make_pair(sizeof(cl_int4) , (void *)&p ));
1340 args.push_back ( make_pair(sizeof(cl_int4) , (void *)&pq ));
1341 args.push_back ( make_pair(sizeof(cl_float) , (void *)&correction ));
1343 const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
1345 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascade", globalThreads, localThreads, args, -1, -1, build_options);
1347 candidate = (int *)malloc(4 * sizeof(int) * outputsz);
1348 memset(candidate, 0, 4 * sizeof(int) * outputsz);
1350 openCLReadBuffer( gsum.clCxt, ((OclBuffers *)buffers)->candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
1352 for(int i = 0; i < outputsz; i++)
1354 if(candidate[4 * i + 2] != 0)
1356 allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
1357 candidate[4 * i + 2], candidate[4 * i + 3]));
1360 free((void *)candidate);
1365 cv::ocl::integral(gimg, gsum, gsqsum_t);
1366 if(gsqsum_t.depth() == CV_64F)
1367 gsqsum_t.convertTo(gsqsum, CV_32FC1);
1371 gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
1373 int step = gsum.step / 4;
1378 int endstage = gcascade->count;
1380 vector<pair<size_t, const void *> > args;
1381 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->stagebuffer ));
1382 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->scaleinfobuffer ));
1383 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->newnodebuffer ));
1384 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
1385 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
1386 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->candidatebuffer ));
1387 args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.rows ));
1388 args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.cols ));
1389 args.push_back ( make_pair(sizeof(cl_int) , (void *)&step ));
1390 args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_loopcount ));
1391 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
1392 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
1393 args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
1394 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
1395 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->pbuffer ));
1396 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->correctionbuffer ));
1397 args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_nodenum ));
1399 const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
1400 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuRunHaarClassifierCascade_scaled2", globalThreads, localThreads, args, -1, -1, build_options);
1402 candidate = (int *)clEnqueueMapBuffer(qu, ((OclBuffers *)buffers)->candidatebuffer, 1, CL_MAP_READ, 0, 4 * sizeof(int) * outputsz, 0, 0, 0, NULL);
1404 for(int i = 0; i < outputsz; i++)
1406 if(candidate[4 * i + 2] != 0)
1407 allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
1408 candidate[4 * i + 2], candidate[4 * i + 3]));
1410 clEnqueueUnmapMemObject(qu, ((OclBuffers *)buffers)->candidatebuffer, candidate, 0, 0, 0);
1412 rectList.resize(allCandidates.size());
1413 if(!allCandidates.empty())
1414 std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
1416 if( minNeighbors != 0 || findBiggestObject )
1417 groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
1419 rweights.resize(rectList.size(), 0);
1421 GenResult(faces, rectList, rweights);
1424 void cv::ocl::OclCascadeClassifierBuf::Init(const int rows, const int cols,
1425 double scaleFactor, int flags,
1426 const int outputsz, const size_t localThreads[],
1427 CvSize minSize, CvSize maxSize)
1431 return; // we only allow one time initialization
1433 CvHaarClassifierCascade *cascade = oldCascade;
1435 if( !CV_IS_HAAR_CLASSIFIER(cascade) )
1436 CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" );
1438 if( scaleFactor <= 1 )
1439 CV_Error( CV_StsOutOfRange, "scale factor must be > 1" );
1441 if( cols < minSize.width || rows < minSize.height )
1442 CV_Error(CV_StsError, "Image too small");
1445 int totalclassifier=0;
1447 if( !cascade->hid_cascade )
1449 gpuCreateHidHaarClassifierCascade(cascade, &datasize, &totalclassifier);
1452 if( maxSize.height == 0 || maxSize.width == 0 )
1454 maxSize.height = rows;
1455 maxSize.width = cols;
1458 findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0;
1459 if( findBiggestObject )
1460 flags &= ~(CV_HAAR_SCALE_IMAGE | CV_HAAR_DO_CANNY_PRUNING);
1462 CreateBaseBufs(datasize, totalclassifier, flags, outputsz);
1463 CreateFactorRelatedBufs(rows, cols, flags, scaleFactor, localThreads, minSize, maxSize);
1465 m_scaleFactor = scaleFactor;
1469 m_minSize = minSize;
1470 m_maxSize = maxSize;
1473 GpuHidHaarClassifierCascade *gcascade;
1474 GpuHidHaarStageClassifier *stage;
1475 GpuHidHaarClassifier *classifier;
1476 GpuHidHaarTreeNode *node;
1477 cl_command_queue qu = getClCommandQueue(Context::getContext());
1478 if( (flags & CV_HAAR_SCALE_IMAGE) )
1480 gcascade = (GpuHidHaarClassifierCascade *)(cascade->hid_cascade);
1481 stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
1482 classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
1483 node = (GpuHidHaarTreeNode *)(classifier->node);
1485 gpuSetImagesForHaarClassifierCascade( cascade, 1., gsum.step / 4 );
1487 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->stagebuffer, 1, 0,
1488 sizeof(GpuHidHaarStageClassifier) * gcascade->count,
1489 stage, 0, NULL, NULL));
1491 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->nodebuffer, 1, 0,
1492 m_nodenum * sizeof(GpuHidHaarTreeNode),
1493 node, 0, NULL, NULL));
1497 gpuSetHaarClassifierCascade(cascade);
1499 gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
1500 stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
1501 classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
1502 node = (GpuHidHaarTreeNode *)(classifier->node);
1504 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->nodebuffer, 1, 0,
1505 m_nodenum * sizeof(GpuHidHaarTreeNode),
1506 node, 0, NULL, NULL));
1508 cl_int4 *p = (cl_int4 *)malloc(sizeof(cl_int4) * m_loopcount);
1509 float *correction = (float *)malloc(sizeof(float) * m_loopcount);
1511 for(int i = 0; i < m_loopcount; i++)
1514 int equRect_x = (int)(factor * gcascade->p0 + 0.5);
1515 int equRect_y = (int)(factor * gcascade->p1 + 0.5);
1516 int equRect_w = (int)(factor * gcascade->p3 + 0.5);
1517 int equRect_h = (int)(factor * gcascade->p2 + 0.5);
1518 p[i].s[0] = equRect_x;
1519 p[i].s[1] = equRect_y;
1520 p[i].s[2] = equRect_x + equRect_w;
1521 p[i].s[3] = equRect_y + equRect_h;
1522 correction[i] = 1. / (equRect_w * equRect_h);
1523 int startnodenum = m_nodenum * i;
1524 float factor2 = (float)factor;
1526 vector<pair<size_t, const void *> > args1;
1527 args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->nodebuffer ));
1528 args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->newnodebuffer ));
1529 args1.push_back ( make_pair(sizeof(cl_float) , (void *)&factor2 ));
1530 args1.push_back ( make_pair(sizeof(cl_float) , (void *)&correction[i] ));
1531 args1.push_back ( make_pair(sizeof(cl_int) , (void *)&startnodenum ));
1533 size_t globalThreads2[3] = {m_nodenum, 1, 1};
1535 openCLExecuteKernel(Context::getContext(), &haarobjectdetect_scaled2, "gpuscaleclassifier", globalThreads2, NULL/*localThreads2*/, args1, -1, -1);
1537 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
1538 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->pbuffer, 1, 0, sizeof(cl_int4)*m_loopcount, p, 0, NULL, NULL));
1539 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->correctionbuffer, 1, 0, sizeof(cl_float)*m_loopcount, correction, 0, NULL, NULL));
1547 void cv::ocl::OclCascadeClassifierBuf::CreateBaseBufs(const int datasize, const int totalclassifier,
1548 const int flags, const int outputsz)
1552 buffers = malloc(sizeof(OclBuffers));
1555 sizeof(GpuHidHaarStageClassifier) * ((GpuHidHaarClassifierCascade *)oldCascade->hid_cascade)->count;
1556 m_nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) - tempSize - sizeof(GpuHidHaarClassifier) * totalclassifier)
1557 / sizeof(GpuHidHaarTreeNode);
1559 ((OclBuffers *)buffers)->stagebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, tempSize);
1560 ((OclBuffers *)buffers)->nodebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, m_nodenum * sizeof(GpuHidHaarTreeNode));
1564 && ((m_flags & CV_HAAR_SCALE_IMAGE) ^ (flags & CV_HAAR_SCALE_IMAGE)))
1566 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->candidatebuffer));
1569 if (flags & CV_HAAR_SCALE_IMAGE)
1571 ((OclBuffers *)buffers)->candidatebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(),
1573 4 * sizeof(int) * outputsz);
1577 ((OclBuffers *)buffers)->candidatebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(),
1578 CL_MEM_WRITE_ONLY | CL_MEM_ALLOC_HOST_PTR,
1579 4 * sizeof(int) * outputsz);
1583 void cv::ocl::OclCascadeClassifierBuf::CreateFactorRelatedBufs(
1584 const int rows, const int cols, const int flags,
1585 const double scaleFactor, const size_t localThreads[],
1586 CvSize minSize, CvSize maxSize)
1590 if ((m_flags & CV_HAAR_SCALE_IMAGE) && !(flags & CV_HAAR_SCALE_IMAGE))
1597 else if (!(m_flags & CV_HAAR_SCALE_IMAGE) && (flags & CV_HAAR_SCALE_IMAGE))
1599 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
1600 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
1601 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
1603 else if ((m_flags & CV_HAAR_SCALE_IMAGE) && (flags & CV_HAAR_SCALE_IMAGE))
1605 if (fabs(m_scaleFactor - scaleFactor) < 1e-6
1606 && (rows == m_rows && cols == m_cols)
1607 && (minSize.width == m_minSize.width)
1608 && (minSize.height == m_minSize.height)
1609 && (maxSize.width == m_maxSize.width)
1610 && (maxSize.height == m_maxSize.height))
1617 if (fabs(m_scaleFactor - scaleFactor) < 1e-6
1618 && (rows == m_rows && cols == m_cols)
1619 && (minSize.width == m_minSize.width)
1620 && (minSize.height == m_minSize.height)
1621 && (maxSize.width == m_maxSize.width)
1622 && (maxSize.height == m_maxSize.height))
1628 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
1629 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
1630 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
1637 int totalheight = 0;
1641 CvSize winSize0 = oldCascade->orig_window_size;
1642 detect_piramid_info *scaleinfo;
1643 cl_command_queue qu = getClCommandQueue(Context::getContext());
1644 if (flags & CV_HAAR_SCALE_IMAGE)
1646 for(factor = 1.f;; factor *= scaleFactor)
1648 CvSize winSize = { cvRound(winSize0.width * factor), cvRound(winSize0.height * factor) };
1649 sz.width = cvRound( cols / factor ) + 1;
1650 sz.height = cvRound( rows / factor ) + 1;
1651 CvSize sz1 = { sz.width - winSize0.width - 1, sz.height - winSize0.height - 1 };
1653 if( sz1.width <= 0 || sz1.height <= 0 )
1655 if( winSize.width > maxSize.width || winSize.height > maxSize.height )
1657 if( winSize.width < minSize.width || winSize.height < minSize.height )
1660 totalheight += sz.height;
1661 sizev.push_back(sz);
1662 scalev.push_back(static_cast<float>(factor));
1665 loopcount = sizev.size();
1666 gimg1.create(rows, cols, CV_8UC1);
1667 gsum.create(totalheight + 4, cols + 1, CV_32SC1);
1668 gsqsum.create(totalheight + 4, cols + 1, CV_32FC1);
1671 if(Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE))
1675 sdepth = CV_MAT_DEPTH(sdepth);
1676 int type = CV_MAKE_TYPE(sdepth, 1);
1678 gsqsum_t.create(totalheight + 4, cols + 1, type);
1680 scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
1681 for( int i = 0; i < loopcount; i++ )
1684 roi = Rect(0, indexy, sz.width, sz.height);
1685 int width = sz.width - 1 - oldCascade->orig_window_size.width;
1686 int height = sz.height - 1 - oldCascade->orig_window_size.height;
1687 int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
1688 int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
1690 ((detect_piramid_info *)scaleinfo)[i].width_height = (width << 16) | height;
1691 ((detect_piramid_info *)scaleinfo)[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
1692 ((detect_piramid_info *)scaleinfo)[i].imgoff = gsum(roi).offset >> 2;
1693 ((detect_piramid_info *)scaleinfo)[i].factor = scalev[i];
1695 indexy += sz.height;
1701 cvRound(factor * winSize0.width) < cols - 10 && cvRound(factor * winSize0.height) < rows - 10;
1702 factor *= scaleFactor)
1704 CvSize winSize = { cvRound( winSize0.width * factor ), cvRound( winSize0.height * factor ) };
1705 if( winSize.width < minSize.width || winSize.height < minSize.height )
1709 sizev.push_back(winSize);
1710 scalev.push_back(factor);
1713 loopcount = scalev.size();
1717 sizev.push_back(minSize);
1718 scalev.push_back( std::min(cvRound(minSize.width / winSize0.width), cvRound(minSize.height / winSize0.height)) );
1721 ((OclBuffers *)buffers)->pbuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY,
1722 sizeof(cl_int4) * loopcount);
1723 ((OclBuffers *)buffers)->correctionbuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY,
1724 sizeof(cl_float) * loopcount);
1725 ((OclBuffers *)buffers)->newnodebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_WRITE,
1726 loopcount * m_nodenum * sizeof(GpuHidHaarTreeNode));
1728 scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
1729 for( int i = 0; i < loopcount; i++ )
1733 double ystep = cv::max(2.,factor);
1734 int width = cvRound((cols - 1 - sz.width + ystep - 1) / ystep);
1735 int height = cvRound((rows - 1 - sz.height + ystep - 1) / ystep);
1736 int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
1737 int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
1739 ((detect_piramid_info *)scaleinfo)[i].width_height = (width << 16) | height;
1740 ((detect_piramid_info *)scaleinfo)[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
1741 ((detect_piramid_info *)scaleinfo)[i].imgoff = 0;
1742 ((detect_piramid_info *)scaleinfo)[i].factor = factor;
1746 if (loopcount != m_loopcount)
1750 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->scaleinfobuffer));
1752 ((OclBuffers *)buffers)->scaleinfobuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
1755 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->scaleinfobuffer, 1, 0,
1756 sizeof(detect_piramid_info)*loopcount,
1757 scaleinfo, 0, NULL, NULL));
1760 m_loopcount = loopcount;
1763 void cv::ocl::OclCascadeClassifierBuf::GenResult(CV_OUT std::vector<cv::Rect>& faces,
1764 const std::vector<cv::Rect> &rectList,
1765 const std::vector<int> &rweights)
1767 MemStorage tempStorage(cvCreateMemStorage(0));
1768 CvSeq *result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), tempStorage );
1770 if( findBiggestObject && rectList.size() )
1772 CvAvgComp result_comp = {{0, 0, 0, 0}, 0};
1774 for( size_t i = 0; i < rectList.size(); i++ )
1776 cv::Rect r = rectList[i];
1777 if( r.area() > cv::Rect(result_comp.rect).area() )
1779 result_comp.rect = r;
1780 result_comp.neighbors = rweights[i];
1783 cvSeqPush( result_seq, &result_comp );
1787 for( size_t i = 0; i < rectList.size(); i++ )
1790 c.rect = rectList[i];
1791 c.neighbors = rweights[i];
1792 cvSeqPush( result_seq, &c );
1796 vector<CvAvgComp> vecAvgComp;
1797 Seq<CvAvgComp>(result_seq).copyTo(vecAvgComp);
1798 faces.resize(vecAvgComp.size());
1799 std::transform(vecAvgComp.begin(), vecAvgComp.end(), faces.begin(), getRect());
1802 void cv::ocl::OclCascadeClassifierBuf::release()
1806 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->stagebuffer));
1807 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->scaleinfobuffer));
1808 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->nodebuffer));
1809 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->candidatebuffer));
1811 if( (m_flags & CV_HAAR_SCALE_IMAGE) )
1813 cvFree(&oldCascade->hid_cascade);
1817 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
1818 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
1819 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
1824 initialized = false;
1829 #define _MAX_PATH 1024