/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Niko Li, newlife20080214@gmail.com
// Wang Weiyan, wangweiyanster@gmail.com
// Jia Haipeng, jiahaipeng95@gmail.com
// Wu Xinglong, wxl370@126.com
// Wang Yao, bitwangyaoyao@gmail.com
// Sen Liu, swjtuls1987@126.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
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//M*/
#include "precomp.hpp"
#include "opencl_kernels.hpp"
using namespace cv;
using namespace cv::ocl;
/* these settings affect the quality of detection: change with care */
#define CV_ADJUST_FEATURES 1
#define CV_ADJUST_WEIGHTS 0
typedef int sumtype;
typedef double sqsumtype;
typedef struct CvHidHaarFeature
{
struct
{
sumtype *p0, *p1, *p2, *p3;
float weight;
}
rect[CV_HAAR_FEATURE_MAX];
}
CvHidHaarFeature;
typedef struct CvHidHaarTreeNode
{
CvHidHaarFeature feature;
float threshold;
int left;
int right;
}
CvHidHaarTreeNode;
typedef struct CvHidHaarClassifier
{
int count;
//CvHaarFeature* orig_feature;
CvHidHaarTreeNode *node;
float *alpha;
}
CvHidHaarClassifier;
typedef struct CvHidHaarStageClassifier
{
int count;
float threshold;
CvHidHaarClassifier *classifier;
int two_rects;
struct CvHidHaarStageClassifier *next;
struct CvHidHaarStageClassifier *child;
struct CvHidHaarStageClassifier *parent;
}
CvHidHaarStageClassifier;
struct CvHidHaarClassifierCascade
{
int count;
int is_stump_based;
int has_tilted_features;
int is_tree;
double inv_window_area;
CvMat sum, sqsum, tilted;
CvHidHaarStageClassifier *stage_classifier;
sqsumtype *pq0, *pq1, *pq2, *pq3;
sumtype *p0, *p1, *p2, *p3;
void **ipp_stages;
};
typedef struct
{
int width_height;
int grpnumperline_totalgrp;
int imgoff;
float factor;
} detect_piramid_info;
#ifdef _MSC_VER
#define _ALIGNED_ON(_ALIGNMENT) __declspec(align(_ALIGNMENT))
typedef _ALIGNED_ON(128) struct GpuHidHaarTreeNode
{
_ALIGNED_ON(64) int p[CV_HAAR_FEATURE_MAX][4];
float weight[CV_HAAR_FEATURE_MAX] ;
float threshold ;
_ALIGNED_ON(16) float alpha[3] ;
_ALIGNED_ON(4) int left ;
_ALIGNED_ON(4) int right ;
}
GpuHidHaarTreeNode;
typedef _ALIGNED_ON(32) struct GpuHidHaarClassifier
{
_ALIGNED_ON(4) int count;
_ALIGNED_ON(8) GpuHidHaarTreeNode *node ;
_ALIGNED_ON(8) float *alpha ;
}
GpuHidHaarClassifier;
typedef _ALIGNED_ON(64) struct GpuHidHaarStageClassifier
{
_ALIGNED_ON(4) int count ;
_ALIGNED_ON(4) float threshold ;
_ALIGNED_ON(4) int two_rects ;
_ALIGNED_ON(8) GpuHidHaarClassifier *classifier ;
_ALIGNED_ON(8) struct GpuHidHaarStageClassifier *next;
_ALIGNED_ON(8) struct GpuHidHaarStageClassifier *child ;
_ALIGNED_ON(8) struct GpuHidHaarStageClassifier *parent ;
}
GpuHidHaarStageClassifier;
typedef _ALIGNED_ON(64) struct GpuHidHaarClassifierCascade
{
_ALIGNED_ON(4) int count ;
_ALIGNED_ON(4) int is_stump_based ;
_ALIGNED_ON(4) int has_tilted_features ;
_ALIGNED_ON(4) int is_tree ;
_ALIGNED_ON(4) int pq0 ;
_ALIGNED_ON(4) int pq1 ;
_ALIGNED_ON(4) int pq2 ;
_ALIGNED_ON(4) int pq3 ;
_ALIGNED_ON(4) int p0 ;
_ALIGNED_ON(4) int p1 ;
_ALIGNED_ON(4) int p2 ;
_ALIGNED_ON(4) int p3 ;
_ALIGNED_ON(4) float inv_window_area ;
} GpuHidHaarClassifierCascade;
#else
#define _ALIGNED_ON(_ALIGNMENT) __attribute__((aligned(_ALIGNMENT) ))
typedef struct _ALIGNED_ON(128) GpuHidHaarTreeNode
{
int p[CV_HAAR_FEATURE_MAX][4] _ALIGNED_ON(64);
float weight[CV_HAAR_FEATURE_MAX];// _ALIGNED_ON(16);
float threshold;// _ALIGNED_ON(4);
float alpha[3] _ALIGNED_ON(16);
int left _ALIGNED_ON(4);
int right _ALIGNED_ON(4);
}
GpuHidHaarTreeNode;
typedef struct _ALIGNED_ON(32) GpuHidHaarClassifier
{
int count _ALIGNED_ON(4);
GpuHidHaarTreeNode *node _ALIGNED_ON(8);
float *alpha _ALIGNED_ON(8);
}
GpuHidHaarClassifier;
typedef struct _ALIGNED_ON(64) GpuHidHaarStageClassifier
{
int count _ALIGNED_ON(4);
float threshold _ALIGNED_ON(4);
int two_rects _ALIGNED_ON(4);
GpuHidHaarClassifier *classifier _ALIGNED_ON(8);
struct GpuHidHaarStageClassifier *next _ALIGNED_ON(8);
struct GpuHidHaarStageClassifier *child _ALIGNED_ON(8);
struct GpuHidHaarStageClassifier *parent _ALIGNED_ON(8);
}
GpuHidHaarStageClassifier;
typedef struct _ALIGNED_ON(64) GpuHidHaarClassifierCascade
{
int count _ALIGNED_ON(4);
int is_stump_based _ALIGNED_ON(4);
int has_tilted_features _ALIGNED_ON(4);
int is_tree _ALIGNED_ON(4);
int pq0 _ALIGNED_ON(4);
int pq1 _ALIGNED_ON(4);
int pq2 _ALIGNED_ON(4);
int pq3 _ALIGNED_ON(4);
int p0 _ALIGNED_ON(4);
int p1 _ALIGNED_ON(4);
int p2 _ALIGNED_ON(4);
int p3 _ALIGNED_ON(4);
float inv_window_area _ALIGNED_ON(4);
} GpuHidHaarClassifierCascade;
#endif
const int icv_object_win_border = 1;
const float icv_stage_threshold_bias = 0.0001f;
double globaltime = 0;
/* create more efficient internal representation of haar classifier cascade */
static GpuHidHaarClassifierCascade * gpuCreateHidHaarClassifierCascade( CvHaarClassifierCascade *cascade, int *size, int *totalclassifier)
{
GpuHidHaarClassifierCascade *out = 0;
int i, j, k, l;
int datasize;
int total_classifiers = 0;
int total_nodes = 0;
char errorstr[256];
GpuHidHaarStageClassifier *stage_classifier_ptr;
GpuHidHaarClassifier *haar_classifier_ptr;
GpuHidHaarTreeNode *haar_node_ptr;
CvSize orig_window_size;
int has_tilted_features = 0;
if( !CV_IS_HAAR_CLASSIFIER(cascade) )
CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );
if( cascade->hid_cascade )
CV_Error( CV_StsError, "hid_cascade has been already created" );
if( !cascade->stage_classifier )
CV_Error( CV_StsNullPtr, "" );
if( cascade->count <= 0 )
CV_Error( CV_StsOutOfRange, "Negative number of cascade stages" );
orig_window_size = cascade->orig_window_size;
/* check input structure correctness and calculate total memory size needed for
internal representation of the classifier cascade */
for( i = 0; i < cascade->count; i++ )
{
CvHaarStageClassifier *stage_classifier = cascade->stage_classifier + i;
if( !stage_classifier->classifier ||
stage_classifier->count <= 0 )
{
sprintf( errorstr, "header of the stage classifier #%d is invalid "
"(has null pointers or non-positive classfier count)", i );
CV_Error( CV_StsError, errorstr );
}
total_classifiers += stage_classifier->count;
for( j = 0; j < stage_classifier->count; j++ )
{
CvHaarClassifier *classifier = stage_classifier->classifier + j;
total_nodes += classifier->count;
for( l = 0; l < classifier->count; l++ )
{
for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
{
if( classifier->haar_feature[l].rect[k].r.width )
{
CvRect r = classifier->haar_feature[l].rect[k].r;
int tilted = classifier->haar_feature[l].tilted;
has_tilted_features |= tilted != 0;
if( r.width < 0 || r.height < 0 || r.y < 0 ||
r.x + r.width > orig_window_size.width
||
(!tilted &&
(r.x < 0 || r.y + r.height > orig_window_size.height))
||
(tilted && (r.x - r.height < 0 ||
r.y + r.width + r.height > orig_window_size.height)))
{
sprintf( errorstr, "rectangle #%d of the classifier #%d of "
"the stage classifier #%d is not inside "
"the reference (original) cascade window", k, j, i );
CV_Error( CV_StsNullPtr, errorstr );
}
}
}
}
}
}
// this is an upper boundary for the whole hidden cascade size
datasize = sizeof(GpuHidHaarClassifierCascade) +
sizeof(GpuHidHaarStageClassifier) * cascade->count +
sizeof(GpuHidHaarClassifier) * total_classifiers +
sizeof(GpuHidHaarTreeNode) * total_nodes;
*totalclassifier = total_classifiers;
*size = datasize;
out = (GpuHidHaarClassifierCascade *)cvAlloc( datasize );
memset( out, 0, sizeof(*out) );
/* init header */
out->count = cascade->count;
stage_classifier_ptr = (GpuHidHaarStageClassifier *)(out + 1);
haar_classifier_ptr = (GpuHidHaarClassifier *)(stage_classifier_ptr + cascade->count);
haar_node_ptr = (GpuHidHaarTreeNode *)(haar_classifier_ptr + total_classifiers);
out->is_stump_based = 1;
out->has_tilted_features = has_tilted_features;
out->is_tree = 0;
/* initialize internal representation */
for( i = 0; i < cascade->count; i++ )
{
CvHaarStageClassifier *stage_classifier = cascade->stage_classifier + i;
GpuHidHaarStageClassifier *hid_stage_classifier = stage_classifier_ptr + i;
hid_stage_classifier->count = stage_classifier->count;
hid_stage_classifier->threshold = stage_classifier->threshold - icv_stage_threshold_bias;
hid_stage_classifier->classifier = haar_classifier_ptr;
hid_stage_classifier->two_rects = 1;
haar_classifier_ptr += stage_classifier->count;
for( j = 0; j < stage_classifier->count; j++ )
{
CvHaarClassifier *classifier = stage_classifier->classifier + j;
GpuHidHaarClassifier *hid_classifier = hid_stage_classifier->classifier + j;
int node_count = classifier->count;
float *alpha_ptr = &haar_node_ptr->alpha[0];
hid_classifier->count = node_count;
hid_classifier->node = haar_node_ptr;
hid_classifier->alpha = alpha_ptr;
for( l = 0; l < node_count; l++ )
{
GpuHidHaarTreeNode *node = hid_classifier->node + l;
CvHaarFeature *feature = classifier->haar_feature + l;
memset( node, -1, sizeof(*node) );
node->threshold = classifier->threshold[l];
node->left = classifier->left[l];
node->right = classifier->right[l];
if( fabs(feature->rect[2].weight) < DBL_EPSILON ||
feature->rect[2].r.width == 0 ||
feature->rect[2].r.height == 0 )
{
node->p[2][0] = 0;
node->p[2][1] = 0;
node->p[2][2] = 0;
node->p[2][3] = 0;
node->weight[2] = 0;
}
else
hid_stage_classifier->two_rects = 0;
memcpy( node->alpha, classifier->alpha, (node_count + 1)*sizeof(alpha_ptr[0]));
haar_node_ptr = haar_node_ptr + 1;
}
out->is_stump_based &= node_count == 1;
}
}
cascade->hid_cascade = (CvHidHaarClassifierCascade *)out;
assert( (char *)haar_node_ptr - (char *)out <= datasize );
return out;
}
#define sum_elem_ptr(sum,row,col) \
((sumtype*)CV_MAT_ELEM_PTR_FAST((sum),(row),(col),sizeof(sumtype)))
#define sqsum_elem_ptr(sqsum,row,col) \
((sqsumtype*)CV_MAT_ELEM_PTR_FAST((sqsum),(row),(col),sizeof(sqsumtype)))
#define calc_sum(rect,offset) \
((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])
static void gpuSetImagesForHaarClassifierCascade( CvHaarClassifierCascade *_cascade,
double scale,
int step)
{
GpuHidHaarClassifierCascade *cascade;
int coi0 = 0, coi1 = 0;
int i;
int datasize;
int total;
CvRect equRect;
double weight_scale;
GpuHidHaarStageClassifier *stage_classifier;
if( !CV_IS_HAAR_CLASSIFIER(_cascade) )
CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );
if( scale <= 0 )
CV_Error( CV_StsOutOfRange, "Scale must be positive" );
if( coi0 || coi1 )
CV_Error( CV_BadCOI, "COI is not supported" );
if( !_cascade->hid_cascade )
gpuCreateHidHaarClassifierCascade(_cascade, &datasize, &total);
cascade = (GpuHidHaarClassifierCascade *) _cascade->hid_cascade;
stage_classifier = (GpuHidHaarStageClassifier *) (cascade + 1);
_cascade->scale = scale;
_cascade->real_window_size.width = cvRound( _cascade->orig_window_size.width * scale );
_cascade->real_window_size.height = cvRound( _cascade->orig_window_size.height * scale );
equRect.x = equRect.y = cvRound(scale);
equRect.width = cvRound((_cascade->orig_window_size.width - 2) * scale);
equRect.height = cvRound((_cascade->orig_window_size.height - 2) * scale);
weight_scale = 1. / (equRect.width * equRect.height);
cascade->inv_window_area = weight_scale;
cascade->pq0 = equRect.x;
cascade->pq1 = equRect.y;
cascade->pq2 = equRect.x + equRect.width;
cascade->pq3 = equRect.y + equRect.height;
cascade->p0 = equRect.x;
cascade->p1 = equRect.y;
cascade->p2 = equRect.x + equRect.width;
cascade->p3 = equRect.y + equRect.height;
/* init pointers in haar features according to real window size and
given image pointers */
for( i = 0; i < _cascade->count; i++ )
{
int j, k, l;
for( j = 0; j < stage_classifier[i].count; j++ )
{
for( l = 0; l < stage_classifier[i].classifier[j].count; l++ )
{
CvHaarFeature *feature =
&_cascade->stage_classifier[i].classifier[j].haar_feature[l];
GpuHidHaarTreeNode *hidnode = &stage_classifier[i].classifier[j].node[l];
double sum0 = 0, area0 = 0;
CvRect r[3];
int base_w = -1, base_h = -1;
int new_base_w = 0, new_base_h = 0;
int kx, ky;
int flagx = 0, flagy = 0;
int x0 = 0, y0 = 0;
int nr;
/* align blocks */
for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
{
if(!hidnode->p[k][0])
break;
r[k] = feature->rect[k].r;
base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].width - 1) );
base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].x - r[0].x - 1) );
base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].height - 1) );
base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].y - r[0].y - 1) );
}
nr = k;
base_w += 1;
base_h += 1;
if(base_w == 0)
base_w = 1;
kx = r[0].width / base_w;
if(base_h == 0)
base_h = 1;
ky = r[0].height / base_h;
if( kx <= 0 )
{
flagx = 1;
new_base_w = cvRound( r[0].width * scale ) / kx;
x0 = cvRound( r[0].x * scale );
}
if( ky <= 0 )
{
flagy = 1;
new_base_h = cvRound( r[0].height * scale ) / ky;
y0 = cvRound( r[0].y * scale );
}
for( k = 0; k < nr; k++ )
{
CvRect tr;
double correction_ratio;
if( flagx )
{
tr.x = (r[k].x - r[0].x) * new_base_w / base_w + x0;
tr.width = r[k].width * new_base_w / base_w;
}
else
{
tr.x = cvRound( r[k].x * scale );
tr.width = cvRound( r[k].width * scale );
}
if( flagy )
{
tr.y = (r[k].y - r[0].y) * new_base_h / base_h + y0;
tr.height = r[k].height * new_base_h / base_h;
}
else
{
tr.y = cvRound( r[k].y * scale );
tr.height = cvRound( r[k].height * scale );
}
#if CV_ADJUST_WEIGHTS
{
// RAINER START
const float orig_feature_size = (float)(feature->rect[k].r.width) * feature->rect[k].r.height;
const float orig_norm_size = (float)(_cascade->orig_window_size.width) * (_cascade->orig_window_size.height);
const float feature_size = float(tr.width * tr.height);
//const float normSize = float(equRect.width*equRect.height);
float target_ratio = orig_feature_size / orig_norm_size;
//float isRatio = featureSize / normSize;
//correctionRatio = targetRatio / isRatio / normSize;
correction_ratio = target_ratio / feature_size;
// RAINER END
}
#else
correction_ratio = weight_scale * (!feature->tilted ? 1 : 0.5);
#endif
if( !feature->tilted )
{
hidnode->p[k][0] = tr.x;
hidnode->p[k][1] = tr.y;
hidnode->p[k][2] = tr.x + tr.width;
hidnode->p[k][3] = tr.y + tr.height;
}
else
{
hidnode->p[k][2] = (tr.y + tr.width) * step + tr.x + tr.width;
hidnode->p[k][3] = (tr.y + tr.width + tr.height) * step + tr.x + tr.width - tr.height;
hidnode->p[k][0] = tr.y * step + tr.x;
hidnode->p[k][1] = (tr.y + tr.height) * step + tr.x - tr.height;
}
hidnode->weight[k] = (float)(feature->rect[k].weight * correction_ratio);
if( k == 0 )
area0 = tr.width * tr.height;
else
sum0 += hidnode->weight[k] * tr.width * tr.height;
}
hidnode->weight[0] = (float)(-sum0 / area0);
} /* l */
} /* j */
}
}
static void gpuSetHaarClassifierCascade( CvHaarClassifierCascade *_cascade)
{
GpuHidHaarClassifierCascade *cascade;
int i;
int datasize;
int total;
CvRect equRect;
double weight_scale;
GpuHidHaarStageClassifier *stage_classifier;
if( !CV_IS_HAAR_CLASSIFIER(_cascade) )
CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );
if( !_cascade->hid_cascade )
gpuCreateHidHaarClassifierCascade(_cascade, &datasize, &total);
cascade = (GpuHidHaarClassifierCascade *) _cascade->hid_cascade;
stage_classifier = (GpuHidHaarStageClassifier *) cascade + 1;
_cascade->scale = 1.0;
_cascade->real_window_size.width = _cascade->orig_window_size.width ;
_cascade->real_window_size.height = _cascade->orig_window_size.height;
equRect.x = equRect.y = 1;
equRect.width = _cascade->orig_window_size.width - 2;
equRect.height = _cascade->orig_window_size.height - 2;
weight_scale = 1;
cascade->inv_window_area = weight_scale;
cascade->p0 = equRect.x;
cascade->p1 = equRect.y;
cascade->p2 = equRect.height;
cascade->p3 = equRect.width ;
for( i = 0; i < _cascade->count; i++ )
{
int j, l;
for( j = 0; j < stage_classifier[i].count; j++ )
{
for( l = 0; l < stage_classifier[i].classifier[j].count; l++ )
{
const CvHaarFeature *feature =
&_cascade->stage_classifier[i].classifier[j].haar_feature[l];
GpuHidHaarTreeNode *hidnode = &stage_classifier[i].classifier[j].node[l];
for( int k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
{
const CvRect tr = feature->rect[k].r;
if (tr.width == 0)
break;
double correction_ratio = weight_scale * (!feature->tilted ? 1 : 0.5);
hidnode->p[k][0] = tr.x;
hidnode->p[k][1] = tr.y;
hidnode->p[k][2] = tr.width;
hidnode->p[k][3] = tr.height;
hidnode->weight[k] = (float)(feature->rect[k].weight * correction_ratio);
}
} /* l */
} /* j */
}
}
CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemStorage *storage, double scaleFactor,
int minNeighbors, int flags, CvSize minSize, CvSize maxSize)
{
CvHaarClassifierCascade *cascade = oldCascade;
const double GROUP_EPS = 0.2;
CvSeq *result_seq = 0;
cv::ConcurrentRectVector allCandidates;
std::vector<cv::Rect> rectList;
std::vector<int> rweights;
double factor;
int datasize=0;
int totalclassifier=0;
GpuHidHaarClassifierCascade *gcascade;
GpuHidHaarStageClassifier *stage;
GpuHidHaarClassifier *classifier;
GpuHidHaarTreeNode *node;
int *candidate;
cl_int status;
bool findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0;
if( maxSize.height == 0 || maxSize.width == 0 )
{
maxSize.height = gimg.rows;
maxSize.width = gimg.cols;
}
if( !CV_IS_HAAR_CLASSIFIER(cascade) )
CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" );
if( !storage )
CV_Error( CV_StsNullPtr, "Null storage pointer" );
if( CV_MAT_DEPTH(gimg.type()) != CV_8U )
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" );
if( scaleFactor <= 1 )
CV_Error( CV_StsOutOfRange, "scale factor must be > 1" );
if( findBiggestObject )
flags &= ~CV_HAAR_SCALE_IMAGE;
if( !cascade->hid_cascade )
gpuCreateHidHaarClassifierCascade(cascade, &datasize, &totalclassifier);
result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage );
if( CV_MAT_CN(gimg.type()) > 1 )
{
oclMat gtemp;
cvtColor( gimg, gtemp, CV_BGR2GRAY );
gimg = gtemp;
}
if( findBiggestObject )
flags &= ~(CV_HAAR_SCALE_IMAGE | CV_HAAR_DO_CANNY_PRUNING);
if( gimg.cols < minSize.width || gimg.rows < minSize.height )
CV_Error(CV_StsError, "Image too small");
cl_command_queue qu = getClCommandQueue(Context::getContext());
if( (flags & CV_HAAR_SCALE_IMAGE) )
{
CvSize winSize0 = cascade->orig_window_size;
int totalheight = 0;
int indexy = 0;
CvSize sz;
vector<CvSize> sizev;
vector<float> scalev;
for(factor = 1.f;; factor *= scaleFactor)
{
CvSize winSize = { cvRound(winSize0.width * factor), cvRound(winSize0.height * factor) };
sz.width = cvRound( gimg.cols / factor ) + 1;
sz.height = cvRound( gimg.rows / factor ) + 1;
CvSize sz1 = { sz.width - winSize0.width - 1, sz.height - winSize0.height - 1 };
if( sz1.width <= 0 || sz1.height <= 0 )
break;
if( winSize.width > maxSize.width || winSize.height > maxSize.height )
break;
if( winSize.width < minSize.width || winSize.height < minSize.height )
continue;
totalheight += sz.height;
sizev.push_back(sz);
scalev.push_back(factor);
}
oclMat gimg1(gimg.rows, gimg.cols, CV_8UC1);
oclMat gsum(totalheight + 4, gimg.cols + 1, CV_32SC1);
oclMat gsqsum(totalheight + 4, gimg.cols + 1, CV_32FC1);
cl_mem stagebuffer;
cl_mem nodebuffer;
cl_mem candidatebuffer;
cl_mem scaleinfobuffer;
cv::Rect roi, roi2;
cv::Mat imgroi, imgroisq;
cv::ocl::oclMat resizeroi, gimgroi, gimgroisq;
int grp_per_CU = 12;
size_t blocksize = 8;
size_t localThreads[3] = { blocksize, blocksize , 1 };
size_t globalThreads[3] = { grp_per_CU *(gsum.clCxt->getDeviceInfo().maxComputeUnits) *localThreads[0],
localThreads[1], 1
};
int outputsz = 256 * globalThreads[0] / localThreads[0];
int loopcount = sizev.size();
detect_piramid_info *scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
for( int i = 0; i < loopcount; i++ )
{
sz = sizev[i];
factor = scalev[i];
roi = Rect(0, indexy, sz.width, sz.height);
roi2 = Rect(0, 0, sz.width - 1, sz.height - 1);
resizeroi = gimg1(roi2);
gimgroi = gsum(roi);
gimgroisq = gsqsum(roi);
int width = gimgroi.cols - 1 - cascade->orig_window_size.width;
int height = gimgroi.rows - 1 - cascade->orig_window_size.height;
scaleinfo[i].width_height = (width << 16) | height;
int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
scaleinfo[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
scaleinfo[i].imgoff = gimgroi.offset >> 2;
scaleinfo[i].factor = factor;
cv::ocl::resize(gimg, resizeroi, Size(sz.width - 1, sz.height - 1), 0, 0, INTER_LINEAR);
cv::ocl::integral(resizeroi, gimgroi, gimgroisq);
indexy += sz.height;
}
gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
node = (GpuHidHaarTreeNode *)(classifier->node);
int nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) -
sizeof(GpuHidHaarStageClassifier) * gcascade->count - sizeof(GpuHidHaarClassifier) * totalclassifier) / sizeof(GpuHidHaarTreeNode);
candidate = (int *)malloc(4 * sizeof(int) * outputsz);
gpuSetImagesForHaarClassifierCascade( cascade, 1., gsum.step / 4 );
stagebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(GpuHidHaarStageClassifier) * gcascade->count);
openCLSafeCall(clEnqueueWriteBuffer(qu, stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
nodebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, nodenum * sizeof(GpuHidHaarTreeNode));
openCLSafeCall(clEnqueueWriteBuffer(qu, nodebuffer, 1, 0, nodenum * sizeof(GpuHidHaarTreeNode),
node, 0, NULL, NULL));
candidatebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_WRITE_ONLY, 4 * sizeof(int) * outputsz);
scaleinfobuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
openCLSafeCall(clEnqueueWriteBuffer(qu, scaleinfobuffer, 1, 0, sizeof(detect_piramid_info)*loopcount, scaleinfo, 0, NULL, NULL));
int startstage = 0;
int endstage = gcascade->count;
int startnode = 0;
int pixelstep = gsum.step / 4;
int splitstage = 3;
int splitnode = stage[0].count + stage[1].count + stage[2].count;
cl_int4 p, pq;
p.s[0] = gcascade->p0;
p.s[1] = gcascade->p1;
p.s[2] = gcascade->p2;
p.s[3] = gcascade->p3;
pq.s[0] = gcascade->pq0;
pq.s[1] = gcascade->pq1;
pq.s[2] = gcascade->pq2;
pq.s[3] = gcascade->pq3;
float correction = gcascade->inv_window_area;
vector<pair<size_t, const void *> > args;
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&stagebuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&scaleinfobuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&nodebuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&candidatebuffer ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&pixelstep ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&loopcount ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitnode ));
args.push_back ( make_pair(sizeof(cl_int4) , (void *)&p ));
args.push_back ( make_pair(sizeof(cl_int4) , (void *)&pq ));
args.push_back ( make_pair(sizeof(cl_float) , (void *)&correction ));
if(gcascade->is_stump_based && gsum.clCxt->supportsFeature(FEATURE_CL_INTEL_DEVICE))
{
//setup local group size for "pixel step" = 1
localThreads[0] = 16;
localThreads[1] = 32;
localThreads[2] = 1;
//calc maximal number of workgroups
int WGNumX = 1+(sizev[0].width /(localThreads[0]));
int WGNumY = 1+(sizev[0].height/(localThreads[1]));
int WGNumZ = loopcount;
int WGNumTotal = 0; //accurate number of non-empty workgroups
int WGNumSampled = 0; //accurate number of workgroups processed only 1/4 part of all pixels. it is made for large images with scale <= 2
oclMat oclWGInfo(1,sizeof(cl_int4) * WGNumX*WGNumY*WGNumZ,CV_8U);
{
cl_int4* pWGInfo = (cl_int4*)clEnqueueMapBuffer(getClCommandQueue(oclWGInfo.clCxt),(cl_mem)oclWGInfo.datastart,true,CL_MAP_WRITE, 0, oclWGInfo.step, 0,0,0,&status);
openCLVerifyCall(status);
for(int z=0;z<WGNumZ;++z)
{
int Width = (scaleinfo[z].width_height >> 16)&0xFFFF;
int Height = (scaleinfo[z].width_height >> 0 )& 0xFFFF;
for(int y=0;y<WGNumY;++y)
{
int gy = y*localThreads[1];
if(gy>=Height)
continue; // no data to process
for(int x=0;x<WGNumX;++x)
{
int gx = x*localThreads[0];
if(gx>=Width)
continue; // no data to process
if(scaleinfo[z].factor<=2)
{
WGNumSampled++;
}
// save no-empty workgroup info into array
pWGInfo[WGNumTotal].s[0] = scaleinfo[z].width_height;
pWGInfo[WGNumTotal].s[1] = (gx << 16) | gy;
pWGInfo[WGNumTotal].s[2] = scaleinfo[z].imgoff;
memcpy(&(pWGInfo[WGNumTotal].s[3]),&(scaleinfo[z].factor),sizeof(float));
WGNumTotal++;
}
}
}
openCLSafeCall(clEnqueueUnmapMemObject(getClCommandQueue(oclWGInfo.clCxt),(cl_mem)oclWGInfo.datastart,pWGInfo,0,0,0));
pWGInfo = NULL;
}
#define NODE_SIZE 12
// pack node info to have less memory loads on the device side
oclMat oclNodesPK(1,sizeof(cl_int) * NODE_SIZE * nodenum,CV_8U);
{
cl_int status;
cl_int* pNodesPK = (cl_int*)clEnqueueMapBuffer(getClCommandQueue(oclNodesPK.clCxt),(cl_mem)oclNodesPK.datastart,true,CL_MAP_WRITE, 0, oclNodesPK.step, 0,0,0,&status);
openCLVerifyCall(status);
//use known local data stride to precalulate indexes
int DATA_SIZE_X = (localThreads[0]+cascade->orig_window_size.width);
// check that maximal value is less than maximal unsigned short
assert(DATA_SIZE_X*cascade->orig_window_size.height+cascade->orig_window_size.width < (int)USHRT_MAX);
for(int i = 0;i<nodenum;++i)
{//process each node from classifier
struct NodePK
{
unsigned short slm_index[3][4];
float weight[3];
float threshold;
float alpha[2];
};
struct NodePK * pOut = (struct NodePK *)(pNodesPK + NODE_SIZE*i);
for(int k=0;k<3;++k)
{// calc 4 short indexes in shared local mem for each rectangle instead of 2 (x,y) pair.
int* p = &(node[i].p[k][0]);
pOut->slm_index[k][0] = (unsigned short)(p[1]*DATA_SIZE_X+p[0]);
pOut->slm_index[k][1] = (unsigned short)(p[1]*DATA_SIZE_X+p[2]);
pOut->slm_index[k][2] = (unsigned short)(p[3]*DATA_SIZE_X+p[0]);
pOut->slm_index[k][3] = (unsigned short)(p[3]*DATA_SIZE_X+p[2]);
}
//store used float point values for each node
pOut->weight[0] = node[i].weight[0];
pOut->weight[1] = node[i].weight[1];
pOut->weight[2] = node[i].weight[2];
pOut->threshold = node[i].threshold;
pOut->alpha[0] = node[i].alpha[0];
pOut->alpha[1] = node[i].alpha[1];
}
openCLSafeCall(clEnqueueUnmapMemObject(getClCommandQueue(oclNodesPK.clCxt),(cl_mem)oclNodesPK.datastart,pNodesPK,0,0,0));
pNodesPK = NULL;
}
// add 2 additional buffers (WGinfo and packed nodes) as 2 last args
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&oclNodesPK.datastart ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&oclWGInfo.datastart ));
//form build options for kernel
string options = "-D PACKED_CLASSIFIER";
options += format(" -D NODE_SIZE=%d",NODE_SIZE);
options += format(" -D WND_SIZE_X=%d",cascade->orig_window_size.width);
options += format(" -D WND_SIZE_Y=%d",cascade->orig_window_size.height);
options += format(" -D STUMP_BASED=%d",gcascade->is_stump_based);
options += format(" -D SPLITNODE=%d",splitnode);
options += format(" -D SPLITSTAGE=%d",splitstage);
options += format(" -D OUTPUTSZ=%d",outputsz);
// init candiate global count by 0
int pattern = 0;
openCLSafeCall(clEnqueueWriteBuffer(qu, candidatebuffer, 1, 0, 1 * sizeof(pattern),&pattern, 0, NULL, NULL));
if(WGNumTotal>WGNumSampled)
{// small images and each pixel is processed
// setup global sizes to have linear array of workgroups with WGNum size
int pixelstep = 1;
size_t LS[3]={localThreads[0]/pixelstep,localThreads[1]/pixelstep,1};
globalThreads[0] = LS[0]*(WGNumTotal-WGNumSampled);
globalThreads[1] = LS[1];
globalThreads[2] = 1;
string options1 = options;
options1 += format(" -D PIXEL_STEP=%d",pixelstep);
options1 += format(" -D WGSTART=%d",WGNumSampled);
options1 += format(" -D LSx=%d",LS[0]);
options1 += format(" -D LSy=%d",LS[1]);
// execute face detector
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascadePacked", globalThreads, LS, args, -1, -1, options1.c_str());
}
if(WGNumSampled>0)
{// large images each 4th pixel is processed
// setup global sizes to have linear array of workgroups with WGNum size
int pixelstep = 2;
size_t LS[3]={localThreads[0]/pixelstep,localThreads[1]/pixelstep,1};
globalThreads[0] = LS[0]*WGNumSampled;
globalThreads[1] = LS[1];
globalThreads[2] = 1;
string options2 = options;
options2 += format(" -D PIXEL_STEP=%d",pixelstep);
options2 += format(" -D WGSTART=%d",0);
options2 += format(" -D LSx=%d",LS[0]);
options2 += format(" -D LSy=%d",LS[1]);
// execute face detector
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascadePacked", globalThreads, LS, args, -1, -1, options2.c_str());
}
//read candidate buffer back and put it into host list
openCLReadBuffer( gsum.clCxt, candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
assert(candidate[0]<outputsz);
//printf("candidate[0]=%d\n",candidate[0]);
for(int i = 1; i <= candidate[0]; i++)
{
allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],candidate[4 * i + 2], candidate[4 * i + 3]));
}
}
else
{
const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascade", globalThreads, localThreads, args, -1, -1, build_options);
openCLReadBuffer( gsum.clCxt, candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
for(int i = 0; i < outputsz; i++)
if(candidate[4 * i + 2] != 0)
allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
candidate[4 * i + 2], candidate[4 * i + 3]));
}
free(scaleinfo);
free(candidate);
openCLSafeCall(clReleaseMemObject(stagebuffer));
openCLSafeCall(clReleaseMemObject(scaleinfobuffer));
openCLSafeCall(clReleaseMemObject(nodebuffer));
openCLSafeCall(clReleaseMemObject(candidatebuffer));
}
else
{
CvSize winsize0 = cascade->orig_window_size;
int n_factors = 0;
oclMat gsum;
oclMat gsqsum;
cv::ocl::integral(gimg, gsum, gsqsum);
CvSize sz;
vector<CvSize> sizev;
vector<float> scalev;
gpuSetHaarClassifierCascade(cascade);
gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
node = (GpuHidHaarTreeNode *)(classifier->node);
cl_mem stagebuffer;
cl_mem nodebuffer;
cl_mem candidatebuffer;
cl_mem scaleinfobuffer;
cl_mem pbuffer;
cl_mem correctionbuffer;
for( n_factors = 0, factor = 1;
cvRound(factor * winsize0.width) < gimg.cols - 10 &&
cvRound(factor * winsize0.height) < gimg.rows - 10;
n_factors++, factor *= scaleFactor )
{
CvSize winSize = { cvRound( winsize0.width * factor ),
cvRound( winsize0.height * factor )
};
if( winSize.width < minSize.width || winSize.height < minSize.height )
{
continue;
}
sizev.push_back(winSize);
scalev.push_back(factor);
}
int loopcount = scalev.size();
if(loopcount == 0)
{
loopcount = 1;
n_factors = 1;
sizev.push_back(minSize);
scalev.push_back( std::min(cvRound(minSize.width / winsize0.width), cvRound(minSize.height / winsize0.height)) );
}
detect_piramid_info *scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
cl_int4 *p = (cl_int4 *)malloc(sizeof(cl_int4) * loopcount);
float *correction = (float *)malloc(sizeof(float) * loopcount);
int grp_per_CU = 12;
size_t blocksize = 8;
size_t localThreads[3] = { blocksize, blocksize , 1 };
size_t globalThreads[3] = { grp_per_CU *gsum.clCxt->getDeviceInfo().maxComputeUnits *localThreads[0],
localThreads[1], 1 };
int outputsz = 256 * globalThreads[0] / localThreads[0];
int nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) -
sizeof(GpuHidHaarStageClassifier) * gcascade->count - sizeof(GpuHidHaarClassifier) * totalclassifier) / sizeof(GpuHidHaarTreeNode);
nodebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY,
nodenum * sizeof(GpuHidHaarTreeNode));
openCLSafeCall(clEnqueueWriteBuffer(qu, nodebuffer, 1, 0,
nodenum * sizeof(GpuHidHaarTreeNode),
node, 0, NULL, NULL));
cl_mem newnodebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_WRITE,
loopcount * nodenum * sizeof(GpuHidHaarTreeNode));
int startstage = 0;
int endstage = gcascade->count;
for(int i = 0; i < loopcount; i++)
{
sz = sizev[i];
factor = scalev[i];
double ystep = std::max(2., factor);
int equRect_x = cvRound(factor * gcascade->p0);
int equRect_y = cvRound(factor * gcascade->p1);
int equRect_w = cvRound(factor * gcascade->p3);
int equRect_h = cvRound(factor * gcascade->p2);
p[i].s[0] = equRect_x;
p[i].s[1] = equRect_y;
p[i].s[2] = equRect_x + equRect_w;
p[i].s[3] = equRect_y + equRect_h;
correction[i] = 1. / (equRect_w * equRect_h);
int width = (gsum.cols - 1 - sz.width + ystep - 1) / ystep;
int height = (gsum.rows - 1 - sz.height + ystep - 1) / ystep;
int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
scaleinfo[i].width_height = (width << 16) | height;
scaleinfo[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
scaleinfo[i].imgoff = 0;
scaleinfo[i].factor = factor;
int startnodenum = nodenum * i;
float factor2 = (float)factor;
vector<pair<size_t, const void *> > args1;
args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&nodebuffer ));
args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&newnodebuffer ));
args1.push_back ( make_pair(sizeof(cl_float) , (void *)&factor2 ));
args1.push_back ( make_pair(sizeof(cl_float) , (void *)&correction[i] ));
args1.push_back ( make_pair(sizeof(cl_int) , (void *)&startnodenum ));
size_t globalThreads2[3] = {nodenum, 1, 1};
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuscaleclassifier", globalThreads2, NULL/*localThreads2*/, args1, -1, -1);
}
int step = gsum.step / 4;
int startnode = 0;
int splitstage = 3;
stagebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(GpuHidHaarStageClassifier) * gcascade->count);
openCLSafeCall(clEnqueueWriteBuffer(qu, stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
candidatebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_WRITE_ONLY | CL_MEM_ALLOC_HOST_PTR, 4 * sizeof(int) * outputsz);
scaleinfobuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
openCLSafeCall(clEnqueueWriteBuffer(qu, scaleinfobuffer, 1, 0, sizeof(detect_piramid_info)*loopcount, scaleinfo, 0, NULL, NULL));
pbuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(cl_int4) * loopcount);
openCLSafeCall(clEnqueueWriteBuffer(qu, pbuffer, 1, 0, sizeof(cl_int4)*loopcount, p, 0, NULL, NULL));
correctionbuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(cl_float) * loopcount);
openCLSafeCall(clEnqueueWriteBuffer(qu, correctionbuffer, 1, 0, sizeof(cl_float)*loopcount, correction, 0, NULL, NULL));
vector<pair<size_t, const void *> > args;
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&stagebuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&scaleinfobuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&newnodebuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&candidatebuffer ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.rows ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.cols ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&step ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&loopcount ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&pbuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&correctionbuffer ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&nodenum ));
const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuRunHaarClassifierCascade_scaled2", globalThreads, localThreads, args, -1, -1, build_options);
candidate = (int *)clEnqueueMapBuffer(qu, candidatebuffer, 1, CL_MAP_READ, 0, 4 * sizeof(int) * outputsz, 0, 0, 0, &status);
for(int i = 0; i < outputsz; i++)
{
if(candidate[4 * i + 2] != 0)
allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1], candidate[4 * i + 2], candidate[4 * i + 3]));
}
free(scaleinfo);
free(p);
free(correction);
clEnqueueUnmapMemObject(qu, candidatebuffer, candidate, 0, 0, 0);
openCLSafeCall(clReleaseMemObject(stagebuffer));
openCLSafeCall(clReleaseMemObject(scaleinfobuffer));
openCLSafeCall(clReleaseMemObject(nodebuffer));
openCLSafeCall(clReleaseMemObject(newnodebuffer));
openCLSafeCall(clReleaseMemObject(candidatebuffer));
openCLSafeCall(clReleaseMemObject(pbuffer));
openCLSafeCall(clReleaseMemObject(correctionbuffer));
}
cvFree(&cascade->hid_cascade);
rectList.resize(allCandidates.size());
if(!allCandidates.empty())
std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
if( minNeighbors != 0 || findBiggestObject )
groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
else
rweights.resize(rectList.size(), 0);
if( findBiggestObject && rectList.size() )
{
CvAvgComp result_comp = {{0, 0, 0, 0}, 0};
for( size_t i = 0; i < rectList.size(); i++ )
{
cv::Rect r = rectList[i];
if( r.area() > cv::Rect(result_comp.rect).area() )
{
result_comp.rect = r;
result_comp.neighbors = rweights[i];
}
}
cvSeqPush( result_seq, &result_comp );
}
else
{
for( size_t i = 0; i < rectList.size(); i++ )
{
CvAvgComp c;
c.rect = rectList[i];
c.neighbors = rweights[i];
cvSeqPush( result_seq, &c );
}
}
return result_seq;
}
struct getRect
{
Rect operator()(const CvAvgComp &e) const
{
return e.rect;
}
};
void cv::ocl::OclCascadeClassifier::detectMultiScale(oclMat &gimg, CV_OUT std::vector<cv::Rect>& faces,
double scaleFactor, int minNeighbors, int flags,
Size minSize, Size maxSize)
{
CvSeq* _objects;
MemStorage storage(cvCreateMemStorage(0));
_objects = oclHaarDetectObjects(gimg, storage, scaleFactor, minNeighbors, flags, minSize, maxSize);
vector<CvAvgComp> vecAvgComp;
Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
faces.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), faces.begin(), getRect());
}
struct OclBuffers
{
cl_mem stagebuffer;
cl_mem nodebuffer;
cl_mem candidatebuffer;
cl_mem scaleinfobuffer;
cl_mem pbuffer;
cl_mem correctionbuffer;
cl_mem newnodebuffer;
};
void cv::ocl::OclCascadeClassifierBuf::detectMultiScale(oclMat &gimg, CV_OUT std::vector<cv::Rect>& faces,
double scaleFactor, int minNeighbors, int flags,
Size minSize, Size maxSize)
{
int blocksize = 8;
int grp_per_CU = 12;
size_t localThreads[3] = { blocksize, blocksize, 1 };
size_t globalThreads[3] = { grp_per_CU * cv::ocl::Context::getContext()->getDeviceInfo().maxComputeUnits *localThreads[0],
localThreads[1],
1 };
int outputsz = 256 * globalThreads[0] / localThreads[0];
Init(gimg.rows, gimg.cols, scaleFactor, flags, outputsz, localThreads, minSize, maxSize);
const double GROUP_EPS = 0.2;
cv::ConcurrentRectVector allCandidates;
std::vector<cv::Rect> rectList;
std::vector<int> rweights;
CvHaarClassifierCascade *cascade = oldCascade;
GpuHidHaarClassifierCascade *gcascade;
GpuHidHaarStageClassifier *stage;
if( CV_MAT_DEPTH(gimg.type()) != CV_8U )
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" );
if( CV_MAT_CN(gimg.type()) > 1 )
{
oclMat gtemp;
cvtColor( gimg, gtemp, CV_BGR2GRAY );
gimg = gtemp;
}
int *candidate;
cl_command_queue qu = getClCommandQueue(Context::getContext());
if( (flags & CV_HAAR_SCALE_IMAGE) )
{
int indexy = 0;
CvSize sz;
cv::Rect roi, roi2;
cv::ocl::oclMat resizeroi, gimgroi, gimgroisq;
for( int i = 0; i < m_loopcount; i++ )
{
sz = sizev[i];
roi = Rect(0, indexy, sz.width, sz.height);
roi2 = Rect(0, 0, sz.width - 1, sz.height - 1);
resizeroi = gimg1(roi2);
gimgroi = gsum(roi);
gimgroisq = gsqsum(roi);
cv::ocl::resize(gimg, resizeroi, Size(sz.width - 1, sz.height - 1), 0, 0, INTER_LINEAR);
cv::ocl::integral(resizeroi, gimgroi, gimgroisq);
indexy += sz.height;
}
gcascade = (GpuHidHaarClassifierCascade *)(cascade->hid_cascade);
stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
int startstage = 0;
int endstage = gcascade->count;
int startnode = 0;
int pixelstep = gsum.step / 4;
int splitstage = 3;
int splitnode = stage[0].count + stage[1].count + stage[2].count;
cl_int4 p, pq;
p.s[0] = gcascade->p0;
p.s[1] = gcascade->p1;
p.s[2] = gcascade->p2;
p.s[3] = gcascade->p3;
pq.s[0] = gcascade->pq0;
pq.s[1] = gcascade->pq1;
pq.s[2] = gcascade->pq2;
pq.s[3] = gcascade->pq3;
float correction = gcascade->inv_window_area;
vector<pair<size_t, const void *> > args;
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->stagebuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->scaleinfobuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->nodebuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->candidatebuffer ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&pixelstep ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_loopcount ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitnode ));
args.push_back ( make_pair(sizeof(cl_int4) , (void *)&p ));
args.push_back ( make_pair(sizeof(cl_int4) , (void *)&pq ));
args.push_back ( make_pair(sizeof(cl_float) , (void *)&correction ));
const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascade", globalThreads, localThreads, args, -1, -1, build_options);
candidate = (int *)malloc(4 * sizeof(int) * outputsz);
memset(candidate, 0, 4 * sizeof(int) * outputsz);
openCLReadBuffer( gsum.clCxt, ((OclBuffers *)buffers)->candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
for(int i = 0; i < outputsz; i++)
{
if(candidate[4 * i + 2] != 0)
{
allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
candidate[4 * i + 2], candidate[4 * i + 3]));
}
}
free((void *)candidate);
candidate = NULL;
}
else
{
cv::ocl::integral(gimg, gsum, gsqsum);
gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
int step = gsum.step / 4;
int startnode = 0;
int splitstage = 3;
int startstage = 0;
int endstage = gcascade->count;
vector<pair<size_t, const void *> > args;
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->stagebuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->scaleinfobuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->newnodebuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsum.data ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&gsqsum.data ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->candidatebuffer ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.rows ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&gsum.cols ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&step ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_loopcount ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&startstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&splitstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&endstage ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&startnode ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->pbuffer ));
args.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->correctionbuffer ));
args.push_back ( make_pair(sizeof(cl_int) , (void *)&m_nodenum ));
const char * build_options = gcascade->is_stump_based ? "-D STUMP_BASED=1" : "-D STUMP_BASED=0";
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuRunHaarClassifierCascade_scaled2", globalThreads, localThreads, args, -1, -1, build_options);
candidate = (int *)clEnqueueMapBuffer(qu, ((OclBuffers *)buffers)->candidatebuffer, 1, CL_MAP_READ, 0, 4 * sizeof(int) * outputsz, 0, 0, 0, NULL);
for(int i = 0; i < outputsz; i++)
{
if(candidate[4 * i + 2] != 0)
allCandidates.push_back(Rect(candidate[4 * i], candidate[4 * i + 1],
candidate[4 * i + 2], candidate[4 * i + 3]));
}
clEnqueueUnmapMemObject(qu, ((OclBuffers *)buffers)->candidatebuffer, candidate, 0, 0, 0);
}
rectList.resize(allCandidates.size());
if(!allCandidates.empty())
std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
if( minNeighbors != 0 || findBiggestObject )
groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
else
rweights.resize(rectList.size(), 0);
GenResult(faces, rectList, rweights);
}
void cv::ocl::OclCascadeClassifierBuf::Init(const int rows, const int cols,
double scaleFactor, int flags,
const int outputsz, const size_t localThreads[],
CvSize minSize, CvSize maxSize)
{
if(initialized)
{
return; // we only allow one time initialization
}
CvHaarClassifierCascade *cascade = oldCascade;
if( !CV_IS_HAAR_CLASSIFIER(cascade) )
CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" );
if( scaleFactor <= 1 )
CV_Error( CV_StsOutOfRange, "scale factor must be > 1" );
if( cols < minSize.width || rows < minSize.height )
CV_Error(CV_StsError, "Image too small");
int datasize=0;
int totalclassifier=0;
if( !cascade->hid_cascade )
{
gpuCreateHidHaarClassifierCascade(cascade, &datasize, &totalclassifier);
}
if( maxSize.height == 0 || maxSize.width == 0 )
{
maxSize.height = rows;
maxSize.width = cols;
}
findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0;
if( findBiggestObject )
flags &= ~(CV_HAAR_SCALE_IMAGE | CV_HAAR_DO_CANNY_PRUNING);
CreateBaseBufs(datasize, totalclassifier, flags, outputsz);
CreateFactorRelatedBufs(rows, cols, flags, scaleFactor, localThreads, minSize, maxSize);
m_scaleFactor = scaleFactor;
m_rows = rows;
m_cols = cols;
m_flags = flags;
m_minSize = minSize;
m_maxSize = maxSize;
// initialize nodes
GpuHidHaarClassifierCascade *gcascade;
GpuHidHaarStageClassifier *stage;
GpuHidHaarClassifier *classifier;
GpuHidHaarTreeNode *node;
cl_command_queue qu = getClCommandQueue(Context::getContext());
if( (flags & CV_HAAR_SCALE_IMAGE) )
{
gcascade = (GpuHidHaarClassifierCascade *)(cascade->hid_cascade);
stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
node = (GpuHidHaarTreeNode *)(classifier->node);
gpuSetImagesForHaarClassifierCascade( cascade, 1., gsum.step / 4 );
openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->stagebuffer, 1, 0,
sizeof(GpuHidHaarStageClassifier) * gcascade->count,
stage, 0, NULL, NULL));
openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->nodebuffer, 1, 0,
m_nodenum * sizeof(GpuHidHaarTreeNode),
node, 0, NULL, NULL));
}
else
{
gpuSetHaarClassifierCascade(cascade);
gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
node = (GpuHidHaarTreeNode *)(classifier->node);
openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->nodebuffer, 1, 0,
m_nodenum * sizeof(GpuHidHaarTreeNode),
node, 0, NULL, NULL));
cl_int4 *p = (cl_int4 *)malloc(sizeof(cl_int4) * m_loopcount);
float *correction = (float *)malloc(sizeof(float) * m_loopcount);
double factor;
for(int i = 0; i < m_loopcount; i++)
{
factor = scalev[i];
int equRect_x = (int)(factor * gcascade->p0 + 0.5);
int equRect_y = (int)(factor * gcascade->p1 + 0.5);
int equRect_w = (int)(factor * gcascade->p3 + 0.5);
int equRect_h = (int)(factor * gcascade->p2 + 0.5);
p[i].s[0] = equRect_x;
p[i].s[1] = equRect_y;
p[i].s[2] = equRect_x + equRect_w;
p[i].s[3] = equRect_y + equRect_h;
correction[i] = 1. / (equRect_w * equRect_h);
int startnodenum = m_nodenum * i;
float factor2 = (float)factor;
vector<pair<size_t, const void *> > args1;
args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->nodebuffer ));
args1.push_back ( make_pair(sizeof(cl_mem) , (void *)&((OclBuffers *)buffers)->newnodebuffer ));
args1.push_back ( make_pair(sizeof(cl_float) , (void *)&factor2 ));
args1.push_back ( make_pair(sizeof(cl_float) , (void *)&correction[i] ));
args1.push_back ( make_pair(sizeof(cl_int) , (void *)&startnodenum ));
size_t globalThreads2[3] = {m_nodenum, 1, 1};
openCLExecuteKernel(Context::getContext(), &haarobjectdetect_scaled2, "gpuscaleclassifier", globalThreads2, NULL/*localThreads2*/, args1, -1, -1);
}
openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->pbuffer, 1, 0, sizeof(cl_int4)*m_loopcount, p, 0, NULL, NULL));
openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->correctionbuffer, 1, 0, sizeof(cl_float)*m_loopcount, correction, 0, NULL, NULL));
free(p);
free(correction);
}
initialized = true;
}
void cv::ocl::OclCascadeClassifierBuf::CreateBaseBufs(const int datasize, const int totalclassifier,
const int flags, const int outputsz)
{
if (!initialized)
{
buffers = malloc(sizeof(OclBuffers));
size_t tempSize =
sizeof(GpuHidHaarStageClassifier) * ((GpuHidHaarClassifierCascade *)oldCascade->hid_cascade)->count;
m_nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) - tempSize - sizeof(GpuHidHaarClassifier) * totalclassifier)
/ sizeof(GpuHidHaarTreeNode);
((OclBuffers *)buffers)->stagebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, tempSize);
((OclBuffers *)buffers)->nodebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, m_nodenum * sizeof(GpuHidHaarTreeNode));
}
if (initialized
&& ((m_flags & CV_HAAR_SCALE_IMAGE) ^ (flags & CV_HAAR_SCALE_IMAGE)))
{
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->candidatebuffer));
}
if (flags & CV_HAAR_SCALE_IMAGE)
{
((OclBuffers *)buffers)->candidatebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(),
CL_MEM_WRITE_ONLY,
4 * sizeof(int) * outputsz);
}
else
{
((OclBuffers *)buffers)->candidatebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(),
CL_MEM_WRITE_ONLY | CL_MEM_ALLOC_HOST_PTR,
4 * sizeof(int) * outputsz);
}
}
void cv::ocl::OclCascadeClassifierBuf::CreateFactorRelatedBufs(
const int rows, const int cols, const int flags,
const double scaleFactor, const size_t localThreads[],
CvSize minSize, CvSize maxSize)
{
if (initialized)
{
if ((m_flags & CV_HAAR_SCALE_IMAGE) && !(flags & CV_HAAR_SCALE_IMAGE))
{
gimg1.release();
gsum.release();
gsqsum.release();
}
else if (!(m_flags & CV_HAAR_SCALE_IMAGE) && (flags & CV_HAAR_SCALE_IMAGE))
{
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
}
else if ((m_flags & CV_HAAR_SCALE_IMAGE) && (flags & CV_HAAR_SCALE_IMAGE))
{
if (fabs(m_scaleFactor - scaleFactor) < 1e-6
&& (rows == m_rows && cols == m_cols)
&& (minSize.width == m_minSize.width)
&& (minSize.height == m_minSize.height)
&& (maxSize.width == m_maxSize.width)
&& (maxSize.height == m_maxSize.height))
{
return;
}
}
else
{
if (fabs(m_scaleFactor - scaleFactor) < 1e-6
&& (rows == m_rows && cols == m_cols)
&& (minSize.width == m_minSize.width)
&& (minSize.height == m_minSize.height)
&& (maxSize.width == m_maxSize.width)
&& (maxSize.height == m_maxSize.height))
{
return;
}
else
{
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
}
}
}
int loopcount;
int indexy = 0;
int totalheight = 0;
double factor;
Rect roi;
CvSize sz;
CvSize winSize0 = oldCascade->orig_window_size;
detect_piramid_info *scaleinfo;
cl_command_queue qu = getClCommandQueue(Context::getContext());
if (flags & CV_HAAR_SCALE_IMAGE)
{
for(factor = 1.f;; factor *= scaleFactor)
{
CvSize winSize = { cvRound(winSize0.width * factor), cvRound(winSize0.height * factor) };
sz.width = cvRound( cols / factor ) + 1;
sz.height = cvRound( rows / factor ) + 1;
CvSize sz1 = { sz.width - winSize0.width - 1, sz.height - winSize0.height - 1 };
if( sz1.width <= 0 || sz1.height <= 0 )
break;
if( winSize.width > maxSize.width || winSize.height > maxSize.height )
break;
if( winSize.width < minSize.width || winSize.height < minSize.height )
continue;
totalheight += sz.height;
sizev.push_back(sz);
scalev.push_back(static_cast<float>(factor));
}
loopcount = sizev.size();
gimg1.create(rows, cols, CV_8UC1);
gsum.create(totalheight + 4, cols + 1, CV_32SC1);
gsqsum.create(totalheight + 4, cols + 1, CV_32FC1);
scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
for( int i = 0; i < loopcount; i++ )
{
sz = sizev[i];
roi = Rect(0, indexy, sz.width, sz.height);
int width = sz.width - 1 - oldCascade->orig_window_size.width;
int height = sz.height - 1 - oldCascade->orig_window_size.height;
int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
((detect_piramid_info *)scaleinfo)[i].width_height = (width << 16) | height;
((detect_piramid_info *)scaleinfo)[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
((detect_piramid_info *)scaleinfo)[i].imgoff = gsum(roi).offset >> 2;
((detect_piramid_info *)scaleinfo)[i].factor = scalev[i];
indexy += sz.height;
}
}
else
{
for(factor = 1;
cvRound(factor * winSize0.width) < cols - 10 && cvRound(factor * winSize0.height) < rows - 10;
factor *= scaleFactor)
{
CvSize winSize = { cvRound( winSize0.width * factor ), cvRound( winSize0.height * factor ) };
if( winSize.width < minSize.width || winSize.height < minSize.height )
{
continue;
}
sizev.push_back(winSize);
scalev.push_back(factor);
}
loopcount = scalev.size();
if(loopcount == 0)
{
loopcount = 1;
sizev.push_back(minSize);
scalev.push_back( std::min(cvRound(minSize.width / winSize0.width), cvRound(minSize.height / winSize0.height)) );
}
((OclBuffers *)buffers)->pbuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY,
sizeof(cl_int4) * loopcount);
((OclBuffers *)buffers)->correctionbuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY,
sizeof(cl_float) * loopcount);
((OclBuffers *)buffers)->newnodebuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_WRITE,
loopcount * m_nodenum * sizeof(GpuHidHaarTreeNode));
scaleinfo = (detect_piramid_info *)malloc(sizeof(detect_piramid_info) * loopcount);
for( int i = 0; i < loopcount; i++ )
{
sz = sizev[i];
factor = scalev[i];
double ystep = cv::max(2.,factor);
int width = cvRound((cols - 1 - sz.width + ystep - 1) / ystep);
int height = cvRound((rows - 1 - sz.height + ystep - 1) / ystep);
int grpnumperline = (width + localThreads[0] - 1) / localThreads[0];
int totalgrp = ((height + localThreads[1] - 1) / localThreads[1]) * grpnumperline;
((detect_piramid_info *)scaleinfo)[i].width_height = (width << 16) | height;
((detect_piramid_info *)scaleinfo)[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
((detect_piramid_info *)scaleinfo)[i].imgoff = 0;
((detect_piramid_info *)scaleinfo)[i].factor = factor;
}
}
if (loopcount != m_loopcount)
{
if (initialized)
{
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->scaleinfobuffer));
}
((OclBuffers *)buffers)->scaleinfobuffer = openCLCreateBuffer(cv::ocl::Context::getContext(), CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
}
openCLSafeCall(clEnqueueWriteBuffer(qu, ((OclBuffers *)buffers)->scaleinfobuffer, 1, 0,
sizeof(detect_piramid_info)*loopcount,
scaleinfo, 0, NULL, NULL));
free(scaleinfo);
m_loopcount = loopcount;
}
void cv::ocl::OclCascadeClassifierBuf::GenResult(CV_OUT std::vector<cv::Rect>& faces,
const std::vector<cv::Rect> &rectList,
const std::vector<int> &rweights)
{
MemStorage tempStorage(cvCreateMemStorage(0));
CvSeq *result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), tempStorage );
if( findBiggestObject && rectList.size() )
{
CvAvgComp result_comp = {{0, 0, 0, 0}, 0};
for( size_t i = 0; i < rectList.size(); i++ )
{
cv::Rect r = rectList[i];
if( r.area() > cv::Rect(result_comp.rect).area() )
{
result_comp.rect = r;
result_comp.neighbors = rweights[i];
}
}
cvSeqPush( result_seq, &result_comp );
}
else
{
for( size_t i = 0; i < rectList.size(); i++ )
{
CvAvgComp c;
c.rect = rectList[i];
c.neighbors = rweights[i];
cvSeqPush( result_seq, &c );
}
}
vector<CvAvgComp> vecAvgComp;
Seq<CvAvgComp>(result_seq).copyTo(vecAvgComp);
faces.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), faces.begin(), getRect());
}
void cv::ocl::OclCascadeClassifierBuf::release()
{
if(initialized)
{
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->stagebuffer));
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->scaleinfobuffer));
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->nodebuffer));
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->candidatebuffer));
if( (m_flags & CV_HAAR_SCALE_IMAGE) )
{
cvFree(&oldCascade->hid_cascade);
}
else
{
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->newnodebuffer));
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->correctionbuffer));
openCLSafeCall(clReleaseMemObject(((OclBuffers *)buffers)->pbuffer));
}
free(buffers);
buffers = NULL;
initialized = false;
}
}
#ifndef _MAX_PATH
#define _MAX_PATH 1024
#endif