1 /*M///////////////////////////////////////////////////////////////////////////////////////
3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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.
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
25 // Redistribution and use in source and binary forms, with or without modification,
26 // are permitted provided that the following conditions are met:
28 // * Redistribution's of source code must retain the above copyright notice,
29 // this list of conditions and the following disclaimer.
31 // * Redistribution's in binary form must reproduce the above copyright notice,
32 // this list of conditions and the following disclaimer in the documentation
33 // and/or other materials provided with the distribution.
35 // * The name of the copyright holders may not be used to endorse or promote products
36 // derived from this software without specific prior written permission.
38 // This software is provided by the copyright holders and contributors "as is" and
39 // any express or implied warranties, including, but not limited to, the implied
40 // warranties of merchantability and fitness for a particular purpose are disclaimed.
41 // In no event shall the Intel Corporation or contributors be liable for any direct,
42 // indirect, incidental, special, exemplary, or consequential damages
43 // (including, but not limited to, procurement of substitute goods or services;
44 // loss of use, data, or profits; or business interruption) however caused
45 // and on any theory of liability, whether in contract, strict liability,
46 // or tort (including negligence or otherwise) arising in any way out of
47 // the use of this software, even if advised of the possibility of such damage.
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);
752 cl_mem candidatebuffer;
753 cl_mem scaleinfobuffer;
755 cv::Mat imgroi, imgroisq;
756 cv::ocl::oclMat resizeroi, gimgroi, gimgroisq;
758 if(gsqsum.clCxt->supportsFeature(ocl::FEATURE_CL_DOUBLE))
762 sdepth = CV_MAT_DEPTH(sdepth);
763 int type = CV_MAKE_TYPE(sdepth, 1);
765 cv::ocl::oclMat gsqsum_t(gsqsum.size(), type);
768 size_t blocksize = 8;
769 size_t localThreads[3] = { blocksize, blocksize , 1 };
770 size_t globalThreads[3] = { grp_per_CU *(gsum.clCxt->getDeviceInfo().maxComputeUnits) *localThreads[0],
773 int outputsz = 256 * globalThreads[0] / localThreads[0];
774 int loopcount = sizev.size();
775 detect_piramid_info *scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
777 for( int i = 0; i < loopcount; i++ )
781 roi = Rect(0, indexy, sz.width, sz.height);
782 roi2 = Rect(0, 0, sz.width - 1, sz.height - 1);
783 resizeroi = gimg1(roi2);
785 gimgroisq = gsqsum_t(roi);
786 int width = gimgroi.cols - 1 - cascade->orig_window_size.width;
787 int height = gimgroi.rows - 1 - cascade->orig_window_size.height;
788 scaleinfo[i].width_height = (width << 16) | height;
791 int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
792 int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
794 scaleinfo[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
795 scaleinfo[i].imgoff = gimgroi.offset >> 2;
796 scaleinfo[i].factor = factor;
797 cv::ocl::resize(gimg, resizeroi, Size(sz.width - 1, sz.height - 1), 0, 0, INTER_LINEAR);
798 cv::ocl::integral(resizeroi, gimgroi, gimgroisq);
802 gsqsum_t.convertTo(gsqsum, CV_32FC1);
804 gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
805 stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
806 classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
807 node = (GpuHidHaarTreeNode *)(classifier->node);
809 int nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) -
810 sizeof(GpuHidHaarStageClassifier) * gcascade->count - sizeof(GpuHidHaarClassifier) * totalclassifier) / sizeof(GpuHidHaarTreeNode);
812 candidate = (int *)malloc(4 * sizeof(int) * outputsz);
814 gpuSetImagesForHaarClassifierCascade( cascade, 1., gsum.step / 4 );
816 stagebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(GpuHidHaarStageClassifier) * gcascade->count);
817 openCLSafeCall(clEnqueueWriteBuffer(qu, stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
819 nodebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, nodenum * sizeof(GpuHidHaarTreeNode));
821 openCLSafeCall(clEnqueueWriteBuffer(qu, nodebuffer, 1, 0, nodenum * sizeof(GpuHidHaarTreeNode),
822 node, 0, NULL, NULL));
823 candidatebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_WRITE_ONLY, 4 * sizeof(int) * outputsz);
825 scaleinfobuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
826 openCLSafeCall(clEnqueueWriteBuffer(qu, scaleinfobuffer, 1, 0, sizeof(detect_piramid_info)*loopcount, scaleinfo, 0, NULL, NULL));
829 int endstage = gcascade->count;
831 int pixelstep = gsum.step / 4;
833 int splitnode = stage[0].count + stage[1].count + stage[2].count;
835 p.s[0] = gcascade->p0;
836 p.s[1] = gcascade->p1;
837 p.s[2] = gcascade->p2;
838 p.s[3] = gcascade->p3;
839 pq.s[0] = gcascade->pq0;
840 pq.s[1] = gcascade->pq1;
841 pq.s[2] = gcascade->pq2;
842 pq.s[3] = gcascade->pq3;
843 float correction = gcascade->inv_window_area;
845 vector<pair<size_t, const void *> > args;
846 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&stagebuffer ));
847 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&scaleinfobuffer ));
848 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&nodebuffer ));
849 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
850 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
851 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&candidatebuffer ));
852 args.push_back ( make_pair(sizeof(cl_int) , (void *)&pixelstep ));
853 args.push_back ( make_pair(sizeof(cl_int) , (void *)&loopcount ));
854 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
855 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
856 args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
857 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
858 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitnode ));
859 args.push_back ( make_pair(sizeof(cl_int4) , (void *)&p ));
860 args.push_back ( make_pair(sizeof(cl_int4) , (void *)&pq ));
861 args.push_back ( make_pair(sizeof(cl_float) , (void *)&correction ));
863 if(gcascade->is_stump_based && gsum.clCxt->supportsFeature(FEATURE_CL_INTEL_DEVICE))
865 //setup local group size
867 localThreads[1] = 16;
870 //init maximal number of workgroups
871 int WGNumX = 1+(sizev[0].width /(localThreads[0]));
872 int WGNumY = 1+(sizev[0].height/(localThreads[1]));
873 int WGNumZ = loopcount;
874 int WGNum = 0; //accurate number of non -empty workgroups
875 oclMat oclWGInfo(1,sizeof(cl_int4) * WGNumX*WGNumY*WGNumZ,CV_8U);
877 cl_int4* pWGInfo = (cl_int4*)clEnqueueMapBuffer(getClCommandQueue(oclWGInfo.clCxt),(cl_mem)oclWGInfo.datastart,true,CL_MAP_WRITE, 0, oclWGInfo.step, 0,0,0,&status);
878 openCLVerifyCall(status);
879 for(int z=0;z<WGNumZ;++z)
881 int Width = (scaleinfo[z].width_height >> 16)&0xFFFF;
882 int Height = (scaleinfo[z].width_height >> 0 )& 0xFFFF;
883 for(int y=0;y<WGNumY;++y)
885 int gy = y*localThreads[1];
886 if(gy>=(Height-cascade->orig_window_size.height))
887 continue; // no data to process
888 for(int x=0;x<WGNumX;++x)
890 int gx = x*localThreads[0];
891 if(gx>=(Width-cascade->orig_window_size.width))
892 continue; // no data to process
894 // save no-empty workgroup info into array
895 pWGInfo[WGNum].s[0] = scaleinfo[z].width_height;
896 pWGInfo[WGNum].s[1] = (gx << 16) | gy;
897 pWGInfo[WGNum].s[2] = scaleinfo[z].imgoff;
898 memcpy(&(pWGInfo[WGNum].s[3]),&(scaleinfo[z].factor),sizeof(float));
903 openCLSafeCall(clEnqueueUnmapMemObject(getClCommandQueue(oclWGInfo.clCxt),(cl_mem)oclWGInfo.datastart,pWGInfo,0,0,0));
907 // setup global sizes to have linear array of workgroups with WGNum size
908 globalThreads[0] = localThreads[0]*WGNum;
909 globalThreads[1] = localThreads[1];
910 globalThreads[2] = 1;
913 // pack node info to have less memory loads
914 oclMat oclNodesPK(1,sizeof(cl_int) * NODE_SIZE * nodenum,CV_8U);
917 cl_int* pNodesPK = (cl_int*)clEnqueueMapBuffer(getClCommandQueue(oclNodesPK.clCxt),(cl_mem)oclNodesPK.datastart,true,CL_MAP_WRITE, 0, oclNodesPK.step, 0,0,0,&status);
918 openCLVerifyCall(status);
919 //use known local data stride to precalulate indexes
920 int DATA_SIZE_X = (localThreads[0]+cascade->orig_window_size.width);
921 // check that maximal value is less than maximal unsigned short
922 assert(DATA_SIZE_X*cascade->orig_window_size.height+cascade->orig_window_size.width < USHRT_MAX);
923 for(int i = 0;i<nodenum;++i)
924 {//process each node from classifier
927 unsigned short slm_index[3][4];
932 struct NodePK * pOut = (struct NodePK *)(pNodesPK + NODE_SIZE*i);
934 {// calc 4 short indexes in shared local mem for each rectangle instead of 2 (x,y) pair.
935 int* p = &(node[i].p[k][0]);
936 pOut->slm_index[k][0] = (unsigned short)(p[1]*DATA_SIZE_X+p[0]);
937 pOut->slm_index[k][1] = (unsigned short)(p[1]*DATA_SIZE_X+p[2]);
938 pOut->slm_index[k][2] = (unsigned short)(p[3]*DATA_SIZE_X+p[0]);
939 pOut->slm_index[k][3] = (unsigned short)(p[3]*DATA_SIZE_X+p[2]);
941 //store used float point values for each node
942 pOut->weight[0] = node[i].weight[0];
943 pOut->weight[1] = node[i].weight[1];
944 pOut->weight[2] = node[i].weight[2];
945 pOut->threshold = node[i].threshold;
946 pOut->alpha[0] = node[i].alpha[0];
947 pOut->alpha[1] = node[i].alpha[1];
949 openCLSafeCall(clEnqueueUnmapMemObject(getClCommandQueue(oclNodesPK.clCxt),(cl_mem)oclNodesPK.datastart,pNodesPK,0,0,0));
952 // add 2 additional buffers (WGinfo and packed nodes) as 2 last args
953 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&oclNodesPK.datastart ));
954 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&oclWGInfo.datastart ));
956 //form build options for kernel
957 string options = "-D PACKED_CLASSIFIER";
958 options += format(" -D NODE_SIZE=%d",NODE_SIZE);
959 options += format(" -D WND_SIZE_X=%d",cascade->orig_window_size.width);
960 options += format(" -D WND_SIZE_Y=%d",cascade->orig_window_size.height);
961 options += format(" -D STUMP_BASED=%d",gcascade->is_stump_based);
962 options += format(" -D LSx=%d",localThreads[0]);
963 options += format(" -D LSy=%d",localThreads[1]);
964 options += format(" -D SPLITNODE=%d",splitnode);
965 options += format(" -D SPLITSTAGE=%d",splitstage);
966 options += format(" -D OUTPUTSZ=%d",outputsz);
968 // init candiate global count by 0
970 openCLSafeCall(clEnqueueWriteBuffer(qu, candidatebuffer, 1, 0, 1 * sizeof(pattern),&pattern, 0, NULL, NULL));
971 // execute face detector
972 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascadePacked", globalThreads, localThreads, args, -1, -1, options.c_str());
973 //read candidate buffer back and put it into host list
974 openCLReadBuffer( gsum.clCxt, candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
975 assert(candidate[0]<outputsz);
976 //printf("candidate[0]=%d\n",candidate[0]);
977 for(int i = 1; i <= candidate[0]; i++)
979 allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],candidate[4 * i + 2], candidate[4 * i + 3]));
984 const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
986 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascade", globalThreads, localThreads, args, -1, -1, build_options);
988 openCLReadBuffer( gsum.clCxt, candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
990 for(int i = 0; i < outputsz; i++)
991 if(candidate[4 * i + 2] != 0)
992 allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
993 candidate[4 * i + 2], candidate[4 * i + 3]));
998 openCLSafeCall(clReleaseMemObject(stagebuffer));
999 openCLSafeCall(clReleaseMemObject(scaleinfobuffer));
1000 openCLSafeCall(clReleaseMemObject(nodebuffer));
1001 openCLSafeCall(clReleaseMemObject(candidatebuffer));
1006 CvSize winsize0 = cascade->orig_window_size;
1010 cv::ocl::oclMat gsqsum_t;
1011 cv::ocl::integral(gimg, gsum, gsqsum_t);
1012 gsqsum_t.convertTo(gsqsum, CV_32FC1);
1014 vector<CvSize> sizev;
1015 vector<float> scalev;
1016 gpuSetHaarClassifierCascade(cascade);
1017 gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
1018 stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
1019 classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
1020 node = (GpuHidHaarTreeNode *)(classifier->node);
1023 cl_mem candidatebuffer;
1024 cl_mem scaleinfobuffer;
1026 cl_mem correctionbuffer;
1027 for( n_factors = 0, factor = 1;
1028 cvRound(factor * winsize0.width) < gimg.cols - 10 &&
1029 cvRound(factor * winsize0.height) < gimg.rows - 10;
1030 n_factors++, factor *= scaleFactor )
1032 CvSize winSize = { cvRound( winsize0.width * factor ),
1033 cvRound( winsize0.height * factor )
1035 if( winSize.width < minSize.width || winSize.height < minSize.height )
1039 sizev.push_back(winSize);
1040 scalev.push_back(factor);
1042 int loopcount = scalev.size();
1047 sizev.push_back(minSize);
1048 scalev.push_back( std::min(cvRound(minSize.width / winsize0.width), cvRound(minSize.height / winsize0.height)) );
1051 detect_piramid_info *scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
1052 cl_int4 *p = (cl_int4 *)malloc(sizeof(cl_int4) * loopcount);
1053 float *correction = (float *)malloc(sizeof(float) * loopcount);
1054 int grp_per_CU = 12;
1055 size_t blocksize = 8;
1056 size_t localThreads[3] = { blocksize, blocksize , 1 };
1057 size_t globalThreads[3] = { grp_per_CU *gsum.clCxt->getDeviceInfo().maxComputeUnits *localThreads[0],
1058 localThreads[1], 1 };
1059 int outputsz = 256 * globalThreads[0] / localThreads[0];
1060 int nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) -
1061 sizeof(GpuHidHaarStageClassifier) * gcascade->count - sizeof(GpuHidHaarClassifier) * totalclassifier) / sizeof(GpuHidHaarTreeNode);
1062 nodebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY,
1063 nodenum * sizeof(GpuHidHaarTreeNode));
1064 openCLSafeCall(clEnqueueWriteBuffer(qu, nodebuffer, 1, 0,
1065 nodenum * sizeof(GpuHidHaarTreeNode),
1066 node, 0, NULL, NULL));
1067 cl_mem newnodebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_WRITE,
1068 loopcount * nodenum * sizeof(GpuHidHaarTreeNode));
1070 int endstage = gcascade->count;
1071 for(int i = 0; i < loopcount; i++)
1075 double ystep = std::max(2., factor);
1076 int equRect_x = cvRound(factor * gcascade->p0);
1077 int equRect_y = cvRound(factor * gcascade->p1);
1078 int equRect_w = cvRound(factor * gcascade->p3);
1079 int equRect_h = cvRound(factor * gcascade->p2);
1080 p[i].s[0] = equRect_x;
1081 p[i].s[1] = equRect_y;
1082 p[i].s[2] = equRect_x + equRect_w;
1083 p[i].s[3] = equRect_y + equRect_h;
1084 correction[i] = 1. / (equRect_w * equRect_h);
1085 int width = (gsum.cols - 1 - sz.width + ystep - 1) / ystep;
1086 int height = (gsum.rows - 1 - sz.height + ystep - 1) / ystep;
1087 int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
1088 int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
1090 scaleinfo[i].width_height = (width << 16) | height;
1091 scaleinfo[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
1092 scaleinfo[i].imgoff = 0;
1093 scaleinfo[i].factor = factor;
1094 int startnodenum = nodenum * i;
1095 float factor2 = (float)factor;
1097 vector<pair<size_t, const void *> > args1;
1098 args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&nodebuffer ));
1099 args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&newnodebuffer ));
1100 args1.push_back ( make_pair(sizeof(cl_float) , (void *)&factor2 ));
1101 args1.push_back ( make_pair(sizeof(cl_float) , (void *)&correction[i] ));
1102 args1.push_back ( make_pair(sizeof(cl_int) , (void *)&startnodenum ));
1104 size_t globalThreads2[3] = {nodenum, 1, 1};
1105 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuscaleclassifier", globalThreads2, NULL/*localThreads2*/, args1, -1, -1);
1108 int step = gsum.step / 4;
1111 stagebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(GpuHidHaarStageClassifier) * gcascade->count);
1112 openCLSafeCall(clEnqueueWriteBuffer(qu, stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
1113 candidatebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_WRITE_ONLY | CL_MEM_ALLOC_HOST_PTR, 4 * sizeof(int) * outputsz);
1114 scaleinfobuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
1115 openCLSafeCall(clEnqueueWriteBuffer(qu, scaleinfobuffer, 1, 0, sizeof(detect_piramid_info)*loopcount, scaleinfo, 0, NULL, NULL));
1116 pbuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(cl_int4) * loopcount);
1117 openCLSafeCall(clEnqueueWriteBuffer(qu, pbuffer, 1, 0, sizeof(cl_int4)*loopcount, p, 0, NULL, NULL));
1118 correctionbuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(cl_float) * loopcount);
1119 openCLSafeCall(clEnqueueWriteBuffer(qu, correctionbuffer, 1, 0, sizeof(cl_float)*loopcount, correction, 0, NULL, NULL));
1121 vector<pair<size_t, const void *> > args;
1122 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&stagebuffer ));
1123 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&scaleinfobuffer ));
1124 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&newnodebuffer ));
1125 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
1126 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
1127 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&candidatebuffer ));
1128 args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.rows ));
1129 args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.cols ));
1130 args.push_back ( make_pair(sizeof(cl_int) , (void *)&step ));
1131 args.push_back ( make_pair(sizeof(cl_int) , (void *)&loopcount ));
1132 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
1133 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
1134 args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
1135 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
1136 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&pbuffer ));
1137 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&correctionbuffer ));
1138 args.push_back ( make_pair(sizeof(cl_int) , (void *)&nodenum ));
1139 const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
1140 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuRunHaarClassifierCascade_scaled2", globalThreads, localThreads, args, -1, -1, build_options);
1142 candidate = (int *)clEnqueueMapBuffer(qu, candidatebuffer, 1, CL_MAP_READ, 0, 4 * sizeof(int) * outputsz, 0, 0, 0, &status);
1144 for(int i = 0; i < outputsz; i++)
1146 if(candidate[4 * i + 2] != 0)
1147 allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1], candidate[4 * i + 2], candidate[4 * i + 3]));
1153 clEnqueueUnmapMemObject(qu, candidatebuffer, candidate, 0, 0, 0);
1154 openCLSafeCall(clReleaseMemObject(stagebuffer));
1155 openCLSafeCall(clReleaseMemObject(scaleinfobuffer));
1156 openCLSafeCall(clReleaseMemObject(nodebuffer));
1157 openCLSafeCall(clReleaseMemObject(newnodebuffer));
1158 openCLSafeCall(clReleaseMemObject(candidatebuffer));
1159 openCLSafeCall(clReleaseMemObject(pbuffer));
1160 openCLSafeCall(clReleaseMemObject(correctionbuffer));
1163 cvFree(&cascade->hid_cascade);
1164 rectList.resize(allCandidates.size());
1165 if(!allCandidates.empty())
1166 std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
1168 if( minNeighbors != 0 || findBiggestObject )
1169 groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
1171 rweights.resize(rectList.size(), 0);
1173 if( findBiggestObject && rectList.size() )
1175 CvAvgComp result_comp = {{0, 0, 0, 0}, 0};
1177 for( size_t i = 0; i < rectList.size(); i++ )
1179 cv::Rect r = rectList[i];
1180 if( r.area() > cv::Rect(result_comp.rect).area() )
1182 result_comp.rect = r;
1183 result_comp.neighbors = rweights[i];
1186 cvSeqPush( result_seq, &result_comp );
1190 for( size_t i = 0; i < rectList.size(); i++ )
1193 c.rect = rectList[i];
1194 c.neighbors = rweights[i];
1195 cvSeqPush( result_seq, &c );
1205 Rect operator()(const CvAvgComp &e) const
1211 void cv::ocl::OclCascadeClassifier::detectMultiScale(oclMat &gimg, CV_OUT std::vector<cv::Rect>& faces,
1212 double scaleFactor, int minNeighbors, int flags,
1213 Size minSize, Size maxSize)
1216 MemStorage storage(cvCreateMemStorage(0));
1217 _objects = oclHaarDetectObjects(gimg, storage, scaleFactor, minNeighbors, flags, minSize, maxSize);
1218 vector<CvAvgComp> vecAvgComp;
1219 Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
1220 faces.resize(vecAvgComp.size());
1221 std::transform(vecAvgComp.begin(), vecAvgComp.end(), faces.begin(), getRect());
1228 cl_mem candidatebuffer;
1229 cl_mem scaleinfobuffer;
1231 cl_mem correctionbuffer;
1232 cl_mem newnodebuffer;
1236 void cv::ocl::OclCascadeClassifierBuf::detectMultiScale(oclMat &gimg, CV_OUT std::vector<cv::Rect>& faces,
1237 double scaleFactor, int minNeighbors, int flags,
1238 Size minSize, Size maxSize)
1241 int grp_per_CU = 12;
1242 size_t localThreads[3] = { blocksize, blocksize, 1 };
1243 size_t globalThreads[3] = { grp_per_CU * cv::ocl::Context::getContext()->getDeviceInfo().maxComputeUnits *localThreads[0],
1246 int outputsz = 256 * globalThreads[0] / localThreads[0];
1248 Init(gimg.rows, gimg.cols, scaleFactor, flags, outputsz, localThreads, minSize, maxSize);
1250 const double GROUP_EPS = 0.2;
1252 cv::ConcurrentRectVector allCandidates;
1253 std::vector<cv::Rect> rectList;
1254 std::vector<int> rweights;
1256 CvHaarClassifierCascade *cascade = oldCascade;
1257 GpuHidHaarClassifierCascade *gcascade;
1258 GpuHidHaarStageClassifier *stage;
1260 if( CV_MAT_DEPTH(gimg.type()) != CV_8U )
1261 CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" );
1263 if( CV_MAT_CN(gimg.type()) > 1 )
1266 cvtColor( gimg, gtemp, CV_BGR2GRAY );
1271 cl_command_queue qu = getClCommandQueue(Context::getContext());
1272 if( (flags & CV_HAAR_SCALE_IMAGE) )
1279 cv::ocl::oclMat resizeroi, gimgroi, gimgroisq;
1280 if(gsqsum.clCxt->supportsFeature(ocl::FEATURE_CL_DOUBLE))
1284 sdepth = CV_MAT_DEPTH(sdepth);
1285 int type = CV_MAKE_TYPE(sdepth, 1);
1287 cv::ocl::oclMat gsqsum_t(gsqsum.size(), type);
1289 for( int i = 0; i < m_loopcount; i++ )
1292 roi = Rect(0, indexy, sz.width, sz.height);
1293 roi2 = Rect(0, 0, sz.width - 1, sz.height - 1);
1294 resizeroi = gimg1(roi2);
1295 gimgroi = gsum(roi);
1296 gimgroisq = gsqsum_t(roi);
1298 cv::ocl::resize(gimg, resizeroi, Size(sz.width - 1, sz.height - 1), 0, 0, INTER_LINEAR);
1299 cv::ocl::integral(resizeroi, gimgroi, gimgroisq);
1300 indexy += sz.height;
1302 gsqsum_t.convertTo(gsqsum, CV_32FC1);
1303 gcascade = (GpuHidHaarClassifierCascade *)(cascade->hid_cascade);
1304 stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
1307 int endstage = gcascade->count;
1309 int pixelstep = gsum.step / 4;
1311 int splitnode = stage[0].count + stage[1].count + stage[2].count;
1313 p.s[0] = gcascade->p0;
1314 p.s[1] = gcascade->p1;
1315 p.s[2] = gcascade->p2;
1316 p.s[3] = gcascade->p3;
1317 pq.s[0] = gcascade->pq0;
1318 pq.s[1] = gcascade->pq1;
1319 pq.s[2] = gcascade->pq2;
1320 pq.s[3] = gcascade->pq3;
1321 float correction = gcascade->inv_window_area;
1323 vector<pair<size_t, const void *> > args;
1324 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->stagebuffer ));
1325 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->scaleinfobuffer ));
1326 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->nodebuffer ));
1327 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
1328 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
1329 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->candidatebuffer ));
1330 args.push_back ( make_pair(sizeof(cl_int) , (void *)&pixelstep ));
1331 args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_loopcount ));
1332 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
1333 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
1334 args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
1335 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
1336 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitnode ));
1337 args.push_back ( make_pair(sizeof(cl_int4) , (void *)&p ));
1338 args.push_back ( make_pair(sizeof(cl_int4) , (void *)&pq ));
1339 args.push_back ( make_pair(sizeof(cl_float) , (void *)&correction ));
1341 const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
1343 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascade", globalThreads, localThreads, args, -1, -1, build_options);
1345 candidate = (int *)malloc(4 * sizeof(int) * outputsz);
1346 memset(candidate, 0, 4 * sizeof(int) * outputsz);
1348 openCLReadBuffer( gsum.clCxt, ((OclBuffers *)buffers)->candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
1350 for(int i = 0; i < outputsz; i++)
1352 if(candidate[4 * i + 2] != 0)
1354 allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
1355 candidate[4 * i + 2], candidate[4 * i + 3]));
1358 free((void *)candidate);
1363 cv::ocl::oclMat gsqsum_t;
1364 cv::ocl::integral(gimg, gsum, gsqsum_t);
1365 gsqsum_t.convertTo(gsqsum, CV_32FC1);
1367 gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
1369 int step = gsum.step / 4;
1374 int endstage = gcascade->count;
1376 vector<pair<size_t, const void *> > args;
1377 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->stagebuffer ));
1378 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->scaleinfobuffer ));
1379 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->newnodebuffer ));
1380 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
1381 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
1382 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->candidatebuffer ));
1383 args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.rows ));
1384 args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.cols ));
1385 args.push_back ( make_pair(sizeof(cl_int) , (void *)&step ));
1386 args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_loopcount ));
1387 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
1388 args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
1389 args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
1390 args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
1391 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->pbuffer ));
1392 args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->correctionbuffer ));
1393 args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_nodenum ));
1395 const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
1396 openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuRunHaarClassifierCascade_scaled2", globalThreads, localThreads, args, -1, -1, build_options);
1398 candidate = (int *)clEnqueueMapBuffer(qu, ((OclBuffers *)buffers)->candidatebuffer, 1, CL_MAP_READ, 0, 4 * sizeof(int) * outputsz, 0, 0, 0, NULL);
1400 for(int i = 0; i < outputsz; i++)
1402 if(candidate[4 * i + 2] != 0)
1403 allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
1404 candidate[4 * i + 2], candidate[4 * i + 3]));
1406 clEnqueueUnmapMemObject(qu, ((OclBuffers *)buffers)->candidatebuffer, candidate, 0, 0, 0);
1408 rectList.resize(allCandidates.size());
1409 if(!allCandidates.empty())
1410 std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
1412 if( minNeighbors != 0 || findBiggestObject )
1413 groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
1415 rweights.resize(rectList.size(), 0);
1417 GenResult(faces, rectList, rweights);
1420 void cv::ocl::OclCascadeClassifierBuf::Init(const int rows, const int cols,
1421 double scaleFactor, int flags,
1422 const int outputsz, const size_t localThreads[],
1423 CvSize minSize, CvSize maxSize)
1427 return; // we only allow one time initialization
1429 CvHaarClassifierCascade *cascade = oldCascade;
1431 if( !CV_IS_HAAR_CLASSIFIER(cascade) )
1432 CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" );
1434 if( scaleFactor <= 1 )
1435 CV_Error( CV_StsOutOfRange, "scale factor must be > 1" );
1437 if( cols < minSize.width || rows < minSize.height )
1438 CV_Error(CV_StsError, "Image too small");
1441 int totalclassifier=0;
1443 if( !cascade->hid_cascade )
1445 gpuCreateHidHaarClassifierCascade(cascade, &datasize, &totalclassifier);
1448 if( maxSize.height == 0 || maxSize.width == 0 )
1450 maxSize.height = rows;
1451 maxSize.width = cols;
1454 findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0;
1455 if( findBiggestObject )
1456 flags &= ~(CV_HAAR_SCALE_IMAGE | CV_HAAR_DO_CANNY_PRUNING);
1458 CreateBaseBufs(datasize, totalclassifier, flags, outputsz);
1459 CreateFactorRelatedBufs(rows, cols, flags, scaleFactor, localThreads, minSize, maxSize);
1461 m_scaleFactor = scaleFactor;
1465 m_minSize = minSize;
1466 m_maxSize = maxSize;
1469 GpuHidHaarClassifierCascade *gcascade;
1470 GpuHidHaarStageClassifier *stage;
1471 GpuHidHaarClassifier *classifier;
1472 GpuHidHaarTreeNode *node;
1473 cl_command_queue qu = getClCommandQueue(Context::getContext());
1474 if( (flags & CV_HAAR_SCALE_IMAGE) )
1476 gcascade = (GpuHidHaarClassifierCascade *)(cascade->hid_cascade);
1477 stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
1478 classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
1479 node = (GpuHidHaarTreeNode *)(classifier->node);
1481 gpuSetImagesForHaarClassifierCascade( cascade, 1., gsum.step / 4 );
1483 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->stagebuffer, 1, 0,
1484 sizeof(GpuHidHaarStageClassifier) * gcascade->count,
1485 stage, 0, NULL, NULL));
1487 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->nodebuffer, 1, 0,
1488 m_nodenum * sizeof(GpuHidHaarTreeNode),
1489 node, 0, NULL, NULL));
1493 gpuSetHaarClassifierCascade(cascade);
1495 gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
1496 stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
1497 classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
1498 node = (GpuHidHaarTreeNode *)(classifier->node);
1500 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->nodebuffer, 1, 0,
1501 m_nodenum * sizeof(GpuHidHaarTreeNode),
1502 node, 0, NULL, NULL));
1504 cl_int4 *p = (cl_int4 *)malloc(sizeof(cl_int4) * m_loopcount);
1505 float *correction = (float *)malloc(sizeof(float) * m_loopcount);
1507 for(int i = 0; i < m_loopcount; i++)
1510 int equRect_x = (int)(factor * gcascade->p0 + 0.5);
1511 int equRect_y = (int)(factor * gcascade->p1 + 0.5);
1512 int equRect_w = (int)(factor * gcascade->p3 + 0.5);
1513 int equRect_h = (int)(factor * gcascade->p2 + 0.5);
1514 p[i].s[0] = equRect_x;
1515 p[i].s[1] = equRect_y;
1516 p[i].s[2] = equRect_x + equRect_w;
1517 p[i].s[3] = equRect_y + equRect_h;
1518 correction[i] = 1. / (equRect_w * equRect_h);
1519 int startnodenum = m_nodenum * i;
1520 float factor2 = (float)factor;
1522 vector<pair<size_t, const void *> > args1;
1523 args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->nodebuffer ));
1524 args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->newnodebuffer ));
1525 args1.push_back ( make_pair(sizeof(cl_float) , (void *)&factor2 ));
1526 args1.push_back ( make_pair(sizeof(cl_float) , (void *)&correction[i] ));
1527 args1.push_back ( make_pair(sizeof(cl_int) , (void *)&startnodenum ));
1529 size_t globalThreads2[3] = {m_nodenum, 1, 1};
1531 openCLExecuteKernel(Context::getContext(), &haarobjectdetect_scaled2, "gpuscaleclassifier", globalThreads2, NULL/*localThreads2*/, args1, -1, -1);
1533 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
1534 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->pbuffer, 1, 0, sizeof(cl_int4)*m_loopcount, p, 0, NULL, NULL));
1535 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->correctionbuffer, 1, 0, sizeof(cl_float)*m_loopcount, correction, 0, NULL, NULL));
1543 void cv::ocl::OclCascadeClassifierBuf::CreateBaseBufs(const int datasize, const int totalclassifier,
1544 const int flags, const int outputsz)
1548 buffers = malloc(sizeof(OclBuffers));
1551 sizeof(GpuHidHaarStageClassifier) * ((GpuHidHaarClassifierCascade *)oldCascade->hid_cascade)->count;
1552 m_nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) - tempSize - sizeof(GpuHidHaarClassifier) * totalclassifier)
1553 / sizeof(GpuHidHaarTreeNode);
1555 ((OclBuffers *)buffers)->stagebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, tempSize);
1556 ((OclBuffers *)buffers)->nodebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, m_nodenum * sizeof(GpuHidHaarTreeNode));
1560 && ((m_flags & CV_HAAR_SCALE_IMAGE) ^ (flags & CV_HAAR_SCALE_IMAGE)))
1562 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->candidatebuffer));
1565 if (flags & CV_HAAR_SCALE_IMAGE)
1567 ((OclBuffers *)buffers)->candidatebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(),
1569 4 * sizeof(int) * outputsz);
1573 ((OclBuffers *)buffers)->candidatebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(),
1574 CL_MEM_WRITE_ONLY | CL_MEM_ALLOC_HOST_PTR,
1575 4 * sizeof(int) * outputsz);
1579 void cv::ocl::OclCascadeClassifierBuf::CreateFactorRelatedBufs(
1580 const int rows, const int cols, const int flags,
1581 const double scaleFactor, const size_t localThreads[],
1582 CvSize minSize, CvSize maxSize)
1586 if ((m_flags & CV_HAAR_SCALE_IMAGE) && !(flags & CV_HAAR_SCALE_IMAGE))
1592 else if (!(m_flags & CV_HAAR_SCALE_IMAGE) && (flags & CV_HAAR_SCALE_IMAGE))
1594 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
1595 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
1596 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
1598 else if ((m_flags & CV_HAAR_SCALE_IMAGE) && (flags & CV_HAAR_SCALE_IMAGE))
1600 if (fabs(m_scaleFactor - scaleFactor) < 1e-6
1601 && (rows == m_rows && cols == m_cols)
1602 && (minSize.width == m_minSize.width)
1603 && (minSize.height == m_minSize.height)
1604 && (maxSize.width == m_maxSize.width)
1605 && (maxSize.height == m_maxSize.height))
1612 if (fabs(m_scaleFactor - scaleFactor) < 1e-6
1613 && (rows == m_rows && cols == m_cols)
1614 && (minSize.width == m_minSize.width)
1615 && (minSize.height == m_minSize.height)
1616 && (maxSize.width == m_maxSize.width)
1617 && (maxSize.height == m_maxSize.height))
1623 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
1624 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
1625 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
1632 int totalheight = 0;
1636 CvSize winSize0 = oldCascade->orig_window_size;
1637 detect_piramid_info *scaleinfo;
1638 cl_command_queue qu = getClCommandQueue(Context::getContext());
1639 if (flags & CV_HAAR_SCALE_IMAGE)
1641 for(factor = 1.f;; factor *= scaleFactor)
1643 CvSize winSize = { cvRound(winSize0.width * factor), cvRound(winSize0.height * factor) };
1644 sz.width = cvRound( cols / factor ) + 1;
1645 sz.height = cvRound( rows / factor ) + 1;
1646 CvSize sz1 = { sz.width - winSize0.width - 1, sz.height - winSize0.height - 1 };
1648 if( sz1.width <= 0 || sz1.height <= 0 )
1650 if( winSize.width > maxSize.width || winSize.height > maxSize.height )
1652 if( winSize.width < minSize.width || winSize.height < minSize.height )
1655 totalheight += sz.height;
1656 sizev.push_back(sz);
1657 scalev.push_back(static_cast<float>(factor));
1660 loopcount = sizev.size();
1661 gimg1.create(rows, cols, CV_8UC1);
1662 gsum.create(totalheight + 4, cols + 1, CV_32SC1);
1663 gsqsum.create(totalheight + 4, cols + 1, CV_32FC1);
1665 scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
1666 for( int i = 0; i < loopcount; i++ )
1669 roi = Rect(0, indexy, sz.width, sz.height);
1670 int width = sz.width - 1 - oldCascade->orig_window_size.width;
1671 int height = sz.height - 1 - oldCascade->orig_window_size.height;
1672 int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
1673 int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
1675 ((detect_piramid_info *)scaleinfo)[i].width_height = (width << 16) | height;
1676 ((detect_piramid_info *)scaleinfo)[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
1677 ((detect_piramid_info *)scaleinfo)[i].imgoff = gsum(roi).offset >> 2;
1678 ((detect_piramid_info *)scaleinfo)[i].factor = scalev[i];
1680 indexy += sz.height;
1686 cvRound(factor * winSize0.width) < cols - 10 && cvRound(factor * winSize0.height) < rows - 10;
1687 factor *= scaleFactor)
1689 CvSize winSize = { cvRound( winSize0.width * factor ), cvRound( winSize0.height * factor ) };
1690 if( winSize.width < minSize.width || winSize.height < minSize.height )
1694 sizev.push_back(winSize);
1695 scalev.push_back(factor);
1698 loopcount = scalev.size();
1702 sizev.push_back(minSize);
1703 scalev.push_back( std::min(cvRound(minSize.width / winSize0.width), cvRound(minSize.height / winSize0.height)) );
1706 ((OclBuffers *)buffers)->pbuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY,
1707 sizeof(cl_int4) * loopcount);
1708 ((OclBuffers *)buffers)->correctionbuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY,
1709 sizeof(cl_float) * loopcount);
1710 ((OclBuffers *)buffers)->newnodebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_WRITE,
1711 loopcount * m_nodenum * sizeof(GpuHidHaarTreeNode));
1713 scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
1714 for( int i = 0; i < loopcount; i++ )
1718 double ystep = cv::max(2.,factor);
1719 int width = cvRound((cols - 1 - sz.width + ystep - 1) / ystep);
1720 int height = cvRound((rows - 1 - sz.height + ystep - 1) / ystep);
1721 int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
1722 int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
1724 ((detect_piramid_info *)scaleinfo)[i].width_height = (width << 16) | height;
1725 ((detect_piramid_info *)scaleinfo)[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
1726 ((detect_piramid_info *)scaleinfo)[i].imgoff = 0;
1727 ((detect_piramid_info *)scaleinfo)[i].factor = factor;
1731 if (loopcount != m_loopcount)
1735 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->scaleinfobuffer));
1737 ((OclBuffers *)buffers)->scaleinfobuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
1740 openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->scaleinfobuffer, 1, 0,
1741 sizeof(detect_piramid_info)*loopcount,
1742 scaleinfo, 0, NULL, NULL));
1745 m_loopcount = loopcount;
1748 void cv::ocl::OclCascadeClassifierBuf::GenResult(CV_OUT std::vector<cv::Rect>& faces,
1749 const std::vector<cv::Rect> &rectList,
1750 const std::vector<int> &rweights)
1752 MemStorage tempStorage(cvCreateMemStorage(0));
1753 CvSeq *result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), tempStorage );
1755 if( findBiggestObject && rectList.size() )
1757 CvAvgComp result_comp = {{0, 0, 0, 0}, 0};
1759 for( size_t i = 0; i < rectList.size(); i++ )
1761 cv::Rect r = rectList[i];
1762 if( r.area() > cv::Rect(result_comp.rect).area() )
1764 result_comp.rect = r;
1765 result_comp.neighbors = rweights[i];
1768 cvSeqPush( result_seq, &result_comp );
1772 for( size_t i = 0; i < rectList.size(); i++ )
1775 c.rect = rectList[i];
1776 c.neighbors = rweights[i];
1777 cvSeqPush( result_seq, &c );
1781 vector<CvAvgComp> vecAvgComp;
1782 Seq<CvAvgComp>(result_seq).copyTo(vecAvgComp);
1783 faces.resize(vecAvgComp.size());
1784 std::transform(vecAvgComp.begin(), vecAvgComp.end(), faces.begin(), getRect());
1787 void cv::ocl::OclCascadeClassifierBuf::release()
1791 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->stagebuffer));
1792 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->scaleinfobuffer));
1793 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->nodebuffer));
1794 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->candidatebuffer));
1796 if( (m_flags & CV_HAAR_SCALE_IMAGE) )
1798 cvFree(&oldCascade->hid_cascade);
1802 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
1803 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
1804 openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
1809 initialized = false;
1814 #define _MAX_PATH 1024