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Commit 302a5adc authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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converted Haar cascades to the new format; now they are handled with C++ code.

parent fdf1996e
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data/haarcascades/haarcascade_eye.xml
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data/haarcascades/haarcascade_eye_tree_eyeglasses.xml
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data/haarcascades/haarcascade_frontalface_alt.xml
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data/haarcascades/haarcascade_frontalface_alt2.xml
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data/haarcascades/haarcascade_frontalface_alt_tree.xml
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data/haarcascades/haarcascade_frontalface_default.xml
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data/haarcascades/haarcascade_fullbody.xml
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data/haarcascades/haarcascade_lefteye_2splits.xml
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data/haarcascades/haarcascade_lowerbody.xml
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data/haarcascades/haarcascade_mcs_eyepair_big.xml
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data/haarcascades/haarcascade_mcs_eyepair_small.xml
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data/haarcascades/haarcascade_mcs_leftear.xml
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data/haarcascades/haarcascade_mcs_lefteye.xml
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data/haarcascades/haarcascade_mcs_mouth.xml
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data/haarcascades/haarcascade_mcs_nose.xml
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data/haarcascades/haarcascade_mcs_rightear.xml
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data/haarcascades/haarcascade_mcs_righteye.xml
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data/haarcascades/haarcascade_mcs_upperbody.xml
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data/haarcascades/haarcascade_profileface.xml
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data/haarcascades/haarcascade_righteye_2splits.xml
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data/haarcascades/haarcascade_smile.xml
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data/haarcascades/haarcascade_upperbody.xml
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modules/objdetect/src/cascadedetect.cpp
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......@@ -474,16 +474,18 @@ HaarEvaluator::~HaarEvaluator()
bool HaarEvaluator::read(const FileNode& node)
{
features->resize(node.size());
featuresPtr = &(*features)[0];
FileNodeIterator it = node.begin(), it_end = node.end();
size_t i, n = node.size();
CV_Assert(n > 0);
features.resize(n);
featuresPtr = &features[0];
FileNodeIterator it = node.begin();
hasTiltedFeatures = false;
for(int i = 0; it != it_end; ++it, i++)
for(i = 0; i < n; i++, ++it)
{
if(!featuresPtr[i].read(*it))
if(!features[i].read(*it))
return false;
if( featuresPtr[i].tilted )
if( features[i].tilted )
hasTiltedFeatures = true;
}
return true;
......@@ -494,7 +496,6 @@ Ptr<FeatureEvaluator> HaarEvaluator::clone() const
Ptr<HaarEvaluator> ret = makePtr<HaarEvaluator>();
ret->origWinSize = origWinSize;
ret->features = features;
ret->featuresPtr = &(*ret->features)[0];
ret->hasTiltedFeatures = hasTiltedFeatures;
ret->sum0 = sum0, ret->sqsum0 = sqsum0, ret->tilted0 = tilted0;
ret->sum = sum, ret->sqsum = sqsum, ret->tilted = tilted;
......@@ -540,10 +541,10 @@ bool HaarEvaluator::setImage( const Mat &image, Size _origWinSize )
CV_SUM_PTRS( p[0], p[1], p[2], p[3], sdata, normrect, sumStep );
CV_SUM_PTRS( pq[0], pq[1], pq[2], pq[3], sqdata, normrect, sqsumStep );
size_t fi, nfeatures = features->size();
size_t fi, nfeatures = features.size();
for( fi = 0; fi < nfeatures; fi++ )
featuresPtr[fi].updatePtrs( !featuresPtr[fi].tilted ? sum : tilted );
optfeaturesPtr[fi].updatePtrs( !featuresPtr[fi].tilted ? sum : tilted );
return true;
}
......
modules/objdetect/src/cascadedetect.hpp
View file @ 302a5adc
......@@ -186,6 +186,32 @@ protected:
#define CALC_SUM(rect,offset) CALC_SUM_((rect)[0], (rect)[1], (rect)[2], (rect)[3], offset)
#define CV_SUM_OFS( p0, p1, p2, p3, sum, rect, step ) \
/* (x, y) */ \
(p0) = sum + (rect).x + (step) * (rect).y, \
/* (x + w, y) */ \
(p1) = sum + (rect).x + (rect).width + (step) * (rect).y, \
/* (x + w, y) */ \
(p2) = sum + (rect).x + (step) * ((rect).y + (rect).height), \
/* (x + w, y + h) */ \
(p3) = sum + (rect).x + (rect).width + (step) * ((rect).y + (rect).height)
#define CV_TILTED_OFS( p0, p1, p2, p3, tilted, rect, step ) \
/* (x, y) */ \
(p0) = tilted + (rect).x + (step) * (rect).y, \
/* (x - h, y + h) */ \
(p1) = tilted + (rect).x - (rect).height + (step) * ((rect).y + (rect).height), \
/* (x + w, y + w) */ \
(p2) = tilted + (rect).x + (rect).width + (step) * ((rect).y + (rect).width), \
/* (x + w - h, y + w + h) */ \
(p3) = tilted + (rect).x + (rect).width - (rect).height \
+ (step) * ((rect).y + (rect).width + (rect).height)
#define CALC_SUM_(p0, p1, p2, p3, offset) \
((p0)[offset] - (p1)[offset] - (p2)[offset] + (p3)[offset])
#define CALC_SUM(rect,offset) CALC_SUM_((rect)[0], (rect)[1], (rect)[2], (rect)[3], offset)
//---------------------------------------------- HaarEvaluator ---------------------------------------
class HaarEvaluator : public FeatureEvaluator
......@@ -195,8 +221,6 @@ public:
{
Feature();
float calc( int offset ) const;
void updatePtrs( const Mat& sum );
bool read( const FileNode& node );
bool tilted;
......@@ -208,8 +232,19 @@ public:
Rect r;
float weight;
} rect[RECT_NUM];
};
struct OptFeature
{
OptFeature();
const int* p[RECT_NUM][4];
enum { RECT_NUM = Feature::RECT_NUM };
float calc( const int* pwin ) const;
void setPtrs( const Mat& sum, const Feature& f );
int ofs[RECT_NUM][4];
float weight[RECT_NUM];
};
HaarEvaluator();
......@@ -223,23 +258,26 @@ public:
virtual bool setWindow(Point pt);
double operator()(int featureIdx) const
{ return featuresPtr[featureIdx].calc(offset) * varianceNormFactor; }
{ return optfeaturesPtr[featureIdx].calc(pwin) * varianceNormFactor; }
virtual double calcOrd(int featureIdx) const
{ return (*this)(featureIdx); }
protected:
Size origWinSize;
Ptr<std::vector<Feature> > features;
Feature* featuresPtr; // optimization
std::vector<Feature> features;
std::vector<OptFeature> optfeatures;
OptFeature* optfeaturesPtr; // optimization
bool hasTiltedFeatures;
Mat sum0, sqsum0, tilted0;
Mat sum, sqsum, tilted;
Rect normrect;
const int *p[4];
const double *pq[4];
int p[4];
int pq[4];
const int* pwin;
const double* pqwin;
int offset;
double varianceNormFactor;
};
......@@ -249,12 +287,18 @@ inline HaarEvaluator::Feature :: Feature()
tilted = false;
rect[0].r = rect[1].r = rect[2].r = Rect();
rect[0].weight = rect[1].weight = rect[2].weight = 0;
p[0][0] = p[0][1] = p[0][2] = p[0][3] =
p[1][0] = p[1][1] = p[1][2] = p[1][3] =
p[2][0] = p[2][1] = p[2][2] = p[2][3] = 0;
}
inline float HaarEvaluator::Feature :: calc( int _offset ) const
inline HaarEvaluator::OptFeature :: OptFeature()
{
weight[0] = weight[1] = weight[2] = 0.f;
ofs[0][0] = ofs[0][1] = ofs[0][2] = ofs[0][3] =
ofs[1][0] = ofs[1][1] = ofs[1][2] = ofs[1][3] =
ofs[2][0] = ofs[2][1] = ofs[2][2] = ofs[2][3] = 0;
}
/*inline float HaarEvaluator::Feature :: calc( int _offset ) const
{
float ret = rect[0].weight * CALC_SUM(p[0], _offset) + rect[1].weight * CALC_SUM(p[1], _offset);
......@@ -262,12 +306,13 @@ inline float HaarEvaluator::Feature :: calc( int _offset ) const
ret += rect[2].weight * CALC_SUM(p[2], _offset);
return ret;
}
}*/
inline void HaarEvaluator::Feature :: updatePtrs( const Mat& _sum )
inline void HaarEvaluator::OptFeature :: setPtrs( const Mat& _sum, const Feature& _f )
{
const int* ptr = (const int*)_sum.data;
size_t step = _sum.step/sizeof(ptr[0]);
size_t tiltedofs =
if (tilted)
{
CV_TILTED_PTRS( p[0][0], p[0][1], p[0][2], p[0][3], ptr, rect[0].r, step );
......
modules/objdetect/src/cascadedetect_convert.cpp
View file @ 302a5adc
......@@ -203,7 +203,8 @@ static bool convert(const String& oldcascade, const String& newcascade)
for( i = 0; i < nstages; i++ )
maxWeakCount = std::max(maxWeakCount, stages[i].weaks.size());
newfs << "stageType" << "BOOST"
newfs << "cascade" << "{:opencv-cascade-classifier"
<< "stageType" << "BOOST"
<< "featureType" << "HAAR"
<< "height" << cascadesize.width
<< "width" << cascadesize.height
......@@ -250,8 +251,8 @@ static bool convert(const String& oldcascade, const String& newcascade)
{
if( j >= 2 && fabs(f.rect[j].weight) < FLT_EPSILON )
break;
newfs << f.rect[j].r.x << f.rect[j].r.y <<
f.rect[j].r.width << f.rect[j].r.height << f.rect[j].weight;
newfs << "[" << f.rect[j].r.x << f.rect[j].r.y <<
f.rect[j].r.width << f.rect[j].r.height << f.rect[j].weight << "]";
}
newfs << "]";
if( f.tilted )
......@@ -259,7 +260,7 @@ static bool convert(const String& oldcascade, const String& newcascade)
newfs << "}";
}
newfs << "]";
newfs << "]" << "}";
return true;
}
......
modules/objdetect/src/cascadedetect_ocl.cpp 0 → 100644
View file @ 302a5adc
/*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.
//
//M*/
#include "precomp.hpp"
#include "opencl_kernels.hpp"
#if 0
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);
int sdepth = 0;
if(Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE))
sdepth = CV_64FC1;
else
sdepth = CV_32FC1;
sdepth = CV_MAT_DEPTH(sdepth);
int type = CV_MAKE_TYPE(sdepth, 1);
oclMat gsqsum_t(totalheight + 4, gimg.cols + 1, type);
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_t(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;
}
if(gsqsum_t.depth() == CV_64F)
gsqsum_t.convertTo(gsqsum, CV_32FC1);
else
gsqsum = gsqsum_t;
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
localThreads[0] = 8;
localThreads[1] = 16;
localThreads[2] = 1;
//init maximal number of workgroups
int WGNumX = 1+(sizev[0].width /(localThreads[0]));
int WGNumY = 1+(sizev[0].height/(localThreads[1]));
int WGNumZ = loopcount;
int WGNum = 0; //accurate number of non -empty workgroups
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-cascade->orig_window_size.height))
continue; // no data to process
for(int x=0;x<WGNumX;++x)
{
int gx = x*localThreads[0];
if(gx>=(Width-cascade->orig_window_size.width))
continue; // no data to process
// save no-empty workgroup info into array
pWGInfo[WGNum].s[0] = scaleinfo[z].width_height;
pWGInfo[WGNum].s[1] = (gx << 16) | gy;
pWGInfo[WGNum].s[2] = scaleinfo[z].imgoff;
memcpy(&(pWGInfo[WGNum].s[3]),&(scaleinfo[z].factor),sizeof(float));
WGNum++;
}
}
}
openCLSafeCall(clEnqueueUnmapMemObject(getClCommandQueue(oclWGInfo.clCxt),(cl_mem)oclWGInfo.datastart,pWGInfo,0,0,0));
pWGInfo = NULL;
}
// setup global sizes to have linear array of workgroups with WGNum size
globalThreads[0] = localThreads[0]*WGNum;
globalThreads[1] = localThreads[1];
globalThreads[2] = 1;
#define NODE_SIZE 12
// pack node info to have less memory loads
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 LSx=%d",localThreads[0]);
options += format(" -D LSy=%d",localThreads[1]);
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));
// execute face detector
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascadePacked", globalThreads, localThreads, args, -1, -1, options.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;
oclMat gsqsum_t;
cv::ocl::integral(gimg, gsum, gsqsum_t);
if(gsqsum_t.depth() == CV_64F)
gsqsum_t.convertTo(gsqsum, CV_32FC1);
else
gsqsum = gsqsum_t;
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_t(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;
}
if(gsqsum_t.depth() == CV_64F)
gsqsum_t.convertTo(gsqsum, CV_32FC1);
else
gsqsum = gsqsum_t;
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_t);
if(gsqsum_t.depth() == CV_64F)
gsqsum_t.convertTo(gsqsum, CV_32FC1);
else
gsqsum = gsqsum_t;
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();
gsqsum_t.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);
int sdepth = 0;
if(Context::getContext()->supportsFeature(FEATURE_CL_DOUBLE))
sdepth = CV_64FC1;
else
sdepth = CV_32FC1;
sdepth = CV_MAT_DEPTH(sdepth);
int type = CV_MAKE_TYPE(sdepth, 1);
gsqsum_t.create(totalheight + 4, cols + 1, type);
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
#endif
modules/objdetect/src/opencl/haarobjectdetect.cl 0 → 100644
View file @ 302a5adc
// 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
// Nathan, liujun@multicorewareinc.com
// Peng Xiao, pengxiao@outlook.com
// Erping Pang, erping@multicorewareinc.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.
//
//
#define CV_HAAR_FEATURE_MAX 3
#define calc_sum(rect,offset) (sum[(rect).p0+offset] - sum[(rect).p1+offset] - sum[(rect).p2+offset] + sum[(rect).p3+offset])
#define calc_sum1(rect,offset,i) (sum[(rect).p0[i]+offset] - sum[(rect).p1[i]+offset] - sum[(rect).p2[i]+offset] + sum[(rect).p3[i]+offset])
typedef int sumtype;
typedef float sqsumtype;
#ifndef STUMP_BASED
#define STUMP_BASED 1
#endif
typedef struct __attribute__((aligned (128) )) GpuHidHaarTreeNode
{
int p[CV_HAAR_FEATURE_MAX][4] __attribute__((aligned (64)));
float weight[CV_HAAR_FEATURE_MAX];
float threshold;
float alpha[3] __attribute__((aligned (16)));
int left __attribute__((aligned (4)));
int right __attribute__((aligned (4)));
}
GpuHidHaarTreeNode;
//typedef struct __attribute__((aligned (32))) GpuHidHaarClassifier
//{
// int count __attribute__((aligned (4)));
// GpuHidHaarTreeNode* node __attribute__((aligned (8)));
// float* alpha __attribute__((aligned (8)));
//}
//GpuHidHaarClassifier;
typedef struct __attribute__((aligned (64))) GpuHidHaarStageClassifier
{
int count __attribute__((aligned (4)));
float threshold __attribute__((aligned (4)));
int two_rects __attribute__((aligned (4)));
int reserved0 __attribute__((aligned (8)));
int reserved1 __attribute__((aligned (8)));
int reserved2 __attribute__((aligned (8)));
int reserved3 __attribute__((aligned (8)));
}
GpuHidHaarStageClassifier;
//typedef struct __attribute__((aligned (64))) GpuHidHaarClassifierCascade
//{
// int count __attribute__((aligned (4)));
// int is_stump_based __attribute__((aligned (4)));
// int has_tilted_features __attribute__((aligned (4)));
// int is_tree __attribute__((aligned (4)));
// int pq0 __attribute__((aligned (4)));
// int pq1 __attribute__((aligned (4)));
// int pq2 __attribute__((aligned (4)));
// int pq3 __attribute__((aligned (4)));
// int p0 __attribute__((aligned (4)));
// int p1 __attribute__((aligned (4)));
// int p2 __attribute__((aligned (4)));
// int p3 __attribute__((aligned (4)));
// float inv_window_area __attribute__((aligned (4)));
//} GpuHidHaarClassifierCascade;
#ifdef PACKED_CLASSIFIER
// this code is scalar, one pixel -> one workitem
__kernel void gpuRunHaarClassifierCascadePacked(
global const GpuHidHaarStageClassifier * stagecascadeptr,
global const int4 * info,
global const GpuHidHaarTreeNode * nodeptr,
global const int * restrict sum,
global const float * restrict sqsum,
volatile global int4 * candidate,
const int pixelstep,
const int loopcount,
const int start_stage,
const int split_stage,
const int end_stage,
const int startnode,
const int splitnode,
const int4 p,
const int4 pq,
const float correction,
global const int* pNodesPK,
global const int4* pWGInfo
)
{
// this version used information provided for each workgroup
// no empty WG
int gid = (int)get_group_id(0);
int lid_x = (int)get_local_id(0);
int lid_y = (int)get_local_id(1);
int lid = lid_y*LSx+lid_x;
int4 WGInfo = pWGInfo[gid];
int GroupX = (WGInfo.y >> 16)&0xFFFF;
int GroupY = (WGInfo.y >> 0 )& 0xFFFF;
int Width = (WGInfo.x >> 16)&0xFFFF;
int Height = (WGInfo.x >> 0 )& 0xFFFF;
int ImgOffset = WGInfo.z;
float ScaleFactor = as_float(WGInfo.w);
#define DATA_SIZE_X (LSx+WND_SIZE_X)
#define DATA_SIZE_Y (LSy+WND_SIZE_Y)
#define DATA_SIZE (DATA_SIZE_X*DATA_SIZE_Y)
local int SumL[DATA_SIZE];
// read input data window into local mem
for(int i = 0; i<DATA_SIZE; i+=(LSx*LSy))
{
int index = i+lid; // index in shared local memory
if(index<DATA_SIZE)
{// calc global x,y coordinat and read data from there
int x = min(GroupX + (index % (DATA_SIZE_X)),Width-1);
int y = min(GroupY + (index / (DATA_SIZE_X)),Height-1);
SumL[index] = sum[ImgOffset+y*pixelstep+x];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
// calc variance_norm_factor for all stages
float variance_norm_factor;
int nodecounter= startnode;
int4 info1 = p;
int4 info2 = pq;
{
int xl = lid_x;
int yl = lid_y;
int OffsetLocal = yl * DATA_SIZE_X + xl;
int OffsetGlobal = (GroupY+yl)* pixelstep + (GroupX+xl);
// add shift to get position on scaled image
OffsetGlobal += ImgOffset;
float mean =
SumL[info1.y*DATA_SIZE_X+info1.x+OffsetLocal] -
SumL[info1.y*DATA_SIZE_X+info1.z+OffsetLocal] -
SumL[info1.w*DATA_SIZE_X+info1.x+OffsetLocal] +
SumL[info1.w*DATA_SIZE_X+info1.z+OffsetLocal];
float sq =
sqsum[info2.y*pixelstep+info2.x+OffsetGlobal] -
sqsum[info2.y*pixelstep+info2.z+OffsetGlobal] -
sqsum[info2.w*pixelstep+info2.x+OffsetGlobal] +
sqsum[info2.w*pixelstep+info2.z+OffsetGlobal];
mean *= correction;
sq *= correction;
variance_norm_factor = sq - mean * mean;
variance_norm_factor = (variance_norm_factor >=0.f) ? sqrt(variance_norm_factor) : 1.f;
}// end calc variance_norm_factor for all stages
int result = (1.0f>0.0f);
for(int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++ )
{// iterate until candidate is exist
float stage_sum = 0.0f;
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
float stagethreshold = stageinfo->threshold;
int lcl_off = (lid_y*DATA_SIZE_X)+(lid_x);
for(int nodeloop = 0; nodeloop < stagecount; nodecounter++,nodeloop++ )
{
// simple macro to extract shorts from int
#define M0(_t) ((_t)&0xFFFF)
#define M1(_t) (((_t)>>16)&0xFFFF)
// load packed node data from global memory (L3) into registers
global const int4* pN = (__global int4*)(pNodesPK+nodecounter*NODE_SIZE);
int4 n0 = pN[0];
int4 n1 = pN[1];
int4 n2 = pN[2];
float nodethreshold = as_float(n2.y) * variance_norm_factor;
// calc sum of intensity pixels according to node information
float classsum =
(SumL[M0(n0.x)+lcl_off] - SumL[M1(n0.x)+lcl_off] - SumL[M0(n0.y)+lcl_off] + SumL[M1(n0.y)+lcl_off]) * as_float(n1.z) +
(SumL[M0(n0.z)+lcl_off] - SumL[M1(n0.z)+lcl_off] - SumL[M0(n0.w)+lcl_off] + SumL[M1(n0.w)+lcl_off]) * as_float(n1.w) +
(SumL[M0(n1.x)+lcl_off] - SumL[M1(n1.x)+lcl_off] - SumL[M0(n1.y)+lcl_off] + SumL[M1(n1.y)+lcl_off]) * as_float(n2.x);
//accumulate stage responce
stage_sum += (classsum >= nodethreshold) ? as_float(n2.w) : as_float(n2.z);
}
result = (stage_sum >= stagethreshold);
}// next stage if needed
if(result)
{// all stages will be passed and there is a detected face on the tested position
int index = 1+atomic_inc((volatile global int*)candidate); //get index to write global data with face info
if(index<OUTPUTSZ)
{
int x = GroupX+lid_x;
int y = GroupY+lid_y;
int4 candidate_result;
candidate_result.x = convert_int_rtn(x*ScaleFactor);
candidate_result.y = convert_int_rtn(y*ScaleFactor);
candidate_result.z = convert_int_rtn(ScaleFactor*WND_SIZE_X);
candidate_result.w = convert_int_rtn(ScaleFactor*WND_SIZE_Y);
candidate[index] = candidate_result;
}
}
}//end gpuRunHaarClassifierCascade
#else
__kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCascade(
global GpuHidHaarStageClassifier * stagecascadeptr,
global int4 * info,
global GpuHidHaarTreeNode * nodeptr,
global const int * restrict sum1,
global const float * restrict sqsum1,
global int4 * candidate,
const int pixelstep,
const int loopcount,
const int start_stage,
const int split_stage,
const int end_stage,
const int startnode,
const int splitnode,
const int4 p,
const int4 pq,
const float correction)
{
int grpszx = get_local_size(0);
int grpszy = get_local_size(1);
int grpnumx = get_num_groups(0);
int grpidx = get_group_id(0);
int lclidx = get_local_id(0);
int lclidy = get_local_id(1);
int lcl_sz = mul24(grpszx,grpszy);
int lcl_id = mad24(lclidy,grpszx,lclidx);
__local int lclshare[1024];
__local int* lcldata = lclshare;//for save win data
__local int* glboutindex = lcldata + 28*28;//for save global out index
__local int* lclcount = glboutindex + 1;//for save the numuber of temp pass pixel
__local int* lcloutindex = lclcount + 1;//for save info of temp pass pixel
__local float* partialsum = (__local float*)(lcloutindex + (lcl_sz<<1));
glboutindex[0]=0;
int outputoff = mul24(grpidx,256);
//assume window size is 20X20
#define WINDOWSIZE 20+1
//make sure readwidth is the multiple of 4
//ystep =1, from host code
int readwidth = ((grpszx-1 + WINDOWSIZE+3)>>2)<<2;
int readheight = grpszy-1+WINDOWSIZE;
int read_horiz_cnt = readwidth >> 2;//each read int4
int total_read = mul24(read_horiz_cnt,readheight);
int read_loop = (total_read + lcl_sz - 1) >> 6;
candidate[outputoff+(lcl_id<<2)] = (int4)0;
candidate[outputoff+(lcl_id<<2)+1] = (int4)0;
candidate[outputoff+(lcl_id<<2)+2] = (int4)0;
candidate[outputoff+(lcl_id<<2)+3] = (int4)0;
for(int scalei = 0; scalei <loopcount; scalei++)
{
int4 scaleinfo1= info[scalei];
int height = scaleinfo1.x & 0xffff;
int grpnumperline =(scaleinfo1.y & 0xffff0000) >> 16;
int totalgrp = scaleinfo1.y & 0xffff;
int imgoff = scaleinfo1.z;
float factor = as_float(scaleinfo1.w);
__global const int * sum = sum1 + imgoff;
__global const float * sqsum = sqsum1 + imgoff;
for(int grploop=grpidx; grploop<totalgrp; grploop+=grpnumx)
{
int grpidy = grploop / grpnumperline;
int grpidx = grploop - mul24(grpidy, grpnumperline);
int x = mad24(grpidx,grpszx,lclidx);
int y = mad24(grpidy,grpszy,lclidy);
int grpoffx = x-lclidx;
int grpoffy = y-lclidy;
for(int i=0; i<read_loop; i++)
{
int pos_id = mad24(i,lcl_sz,lcl_id);
pos_id = pos_id < total_read ? pos_id : 0;
int lcl_y = pos_id / read_horiz_cnt;
int lcl_x = pos_id - mul24(lcl_y, read_horiz_cnt);
int glb_x = grpoffx + (lcl_x<<2);
int glb_y = grpoffy + lcl_y;
int glb_off = mad24(min(glb_y, height + WINDOWSIZE - 1),pixelstep,glb_x);
int4 data = *(__global int4*)&sum[glb_off];
int lcl_off = mad24(lcl_y, readwidth, lcl_x<<2);
vstore4(data, 0, &lcldata[lcl_off]);
}
lcloutindex[lcl_id] = 0;
lclcount[0] = 0;
int result = 1;
int nodecounter= startnode;
float mean, variance_norm_factor;
barrier(CLK_LOCAL_MEM_FENCE);
int lcl_off = mad24(lclidy,readwidth,lclidx);
int4 cascadeinfo1, cascadeinfo2;
cascadeinfo1 = p;
cascadeinfo2 = pq;
cascadeinfo1.x +=lcl_off;
cascadeinfo1.z +=lcl_off;
mean = (lcldata[mad24(cascadeinfo1.y,readwidth,cascadeinfo1.x)] - lcldata[mad24(cascadeinfo1.y,readwidth,cascadeinfo1.z)] -
lcldata[mad24(cascadeinfo1.w,readwidth,cascadeinfo1.x)] + lcldata[mad24(cascadeinfo1.w,readwidth,cascadeinfo1.z)])
*correction;
int p_offset = mad24(y, pixelstep, x);
cascadeinfo2.x +=p_offset;
cascadeinfo2.z +=p_offset;
variance_norm_factor =sqsum[mad24(cascadeinfo2.y, pixelstep, cascadeinfo2.x)] - sqsum[mad24(cascadeinfo2.y, pixelstep, cascadeinfo2.z)] -
sqsum[mad24(cascadeinfo2.w, pixelstep, cascadeinfo2.x)] + sqsum[mad24(cascadeinfo2.w, pixelstep, cascadeinfo2.z)];
variance_norm_factor = variance_norm_factor * correction - mean * mean;
variance_norm_factor = variance_norm_factor >=0.f ? sqrt(variance_norm_factor) : 1.f;
for(int stageloop = start_stage; (stageloop < split_stage) && result; stageloop++ )
{
float stage_sum = 0.f;
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
float stagethreshold = stageinfo->threshold;
for(int nodeloop = 0; nodeloop < stagecount; )
{
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*)
(((__global uchar*)nodeptr) + nodecounter * sizeof(GpuHidHaarTreeNode));
int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4*)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4*)(&(currentnodeptr->weight[0]));
float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0]));
float nodethreshold = w.w * variance_norm_factor;
info1.x +=lcl_off;
info1.z +=lcl_off;
info2.x +=lcl_off;
info2.z +=lcl_off;
float classsum = (lcldata[mad24(info1.y,readwidth,info1.x)] - lcldata[mad24(info1.y,readwidth,info1.z)] -
lcldata[mad24(info1.w,readwidth,info1.x)] + lcldata[mad24(info1.w,readwidth,info1.z)]) * w.x;
classsum += (lcldata[mad24(info2.y,readwidth,info2.x)] - lcldata[mad24(info2.y,readwidth,info2.z)] -
lcldata[mad24(info2.w,readwidth,info2.x)] + lcldata[mad24(info2.w,readwidth,info2.z)]) * w.y;
info3.x +=lcl_off;
info3.z +=lcl_off;
classsum += (lcldata[mad24(info3.y,readwidth,info3.x)] - lcldata[mad24(info3.y,readwidth,info3.z)] -
lcldata[mad24(info3.w,readwidth,info3.x)] + lcldata[mad24(info3.w,readwidth,info3.z)]) * w.z;
bool passThres = classsum >= nodethreshold;
#if STUMP_BASED
stage_sum += passThres ? alpha3.y : alpha3.x;
nodecounter++;
nodeloop++;
#else
bool isRootNode = (nodecounter & 1) == 0;
if(isRootNode)
{
if( (passThres && currentnodeptr->right) ||
(!passThres && currentnodeptr->left))
{
nodecounter ++;
}
else
{
stage_sum += alpha3.x;
nodecounter += 2;
nodeloop ++;
}
}
else
{
stage_sum += passThres ? alpha3.z : alpha3.y;
nodecounter ++;
nodeloop ++;
}
#endif
}
result = (stage_sum >= stagethreshold) ? 1 : 0;
}
if(factor < 2)
{
if(result && lclidx %2 ==0 && lclidy %2 ==0 )
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex<<1] = (lclidy << 16) | lclidx;
lcloutindex[(queueindex<<1)+1] = as_int((float)variance_norm_factor);
}
}
else
{
if(result)
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex<<1] = (lclidy << 16) | lclidx;
lcloutindex[(queueindex<<1)+1] = as_int((float)variance_norm_factor);
}
}
barrier(CLK_LOCAL_MEM_FENCE);
int queuecount = lclcount[0];
barrier(CLK_LOCAL_MEM_FENCE);
nodecounter = splitnode;
for(int stageloop = split_stage; stageloop< end_stage && queuecount>0; stageloop++)
{
lclcount[0]=0;
barrier(CLK_LOCAL_MEM_FENCE);
//int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
float stagethreshold = stageinfo->threshold;
int perfscale = queuecount > 4 ? 3 : 2;
int queuecount_loop = (queuecount + (1<<perfscale)-1) >> perfscale;
int lcl_compute_win = lcl_sz >> perfscale;
int lcl_compute_win_id = (lcl_id >>(6-perfscale));
int lcl_loops = (stagecount + lcl_compute_win -1) >> (6-perfscale);
int lcl_compute_id = lcl_id - (lcl_compute_win_id << (6-perfscale));
for(int queueloop=0; queueloop<queuecount_loop; queueloop++)
{
float stage_sum = 0.f;
int temp_coord = lcloutindex[lcl_compute_win_id<<1];
float variance_norm_factor = as_float(lcloutindex[(lcl_compute_win_id<<1)+1]);
int queue_pixel = mad24(((temp_coord & (int)0xffff0000)>>16),readwidth,temp_coord & 0xffff);
if(lcl_compute_win_id < queuecount)
{
int tempnodecounter = lcl_compute_id;
float part_sum = 0.f;
const int stump_factor = STUMP_BASED ? 1 : 2;
int root_offset = 0;
for(int lcl_loop=0; lcl_loop<lcl_loops && tempnodecounter<stagecount;)
{
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*)
(((__global uchar*)nodeptr) + sizeof(GpuHidHaarTreeNode) * ((nodecounter + tempnodecounter) * stump_factor + root_offset));
int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4*)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4*)(&(currentnodeptr->weight[0]));
float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0]));
float nodethreshold = w.w * variance_norm_factor;
info1.x +=queue_pixel;
info1.z +=queue_pixel;
info2.x +=queue_pixel;
info2.z +=queue_pixel;
float classsum = (lcldata[mad24(info1.y,readwidth,info1.x)] - lcldata[mad24(info1.y,readwidth,info1.z)] -
lcldata[mad24(info1.w,readwidth,info1.x)] + lcldata[mad24(info1.w,readwidth,info1.z)]) * w.x;
classsum += (lcldata[mad24(info2.y,readwidth,info2.x)] - lcldata[mad24(info2.y,readwidth,info2.z)] -
lcldata[mad24(info2.w,readwidth,info2.x)] + lcldata[mad24(info2.w,readwidth,info2.z)]) * w.y;
info3.x +=queue_pixel;
info3.z +=queue_pixel;
classsum += (lcldata[mad24(info3.y,readwidth,info3.x)] - lcldata[mad24(info3.y,readwidth,info3.z)] -
lcldata[mad24(info3.w,readwidth,info3.x)] + lcldata[mad24(info3.w,readwidth,info3.z)]) * w.z;
bool passThres = classsum >= nodethreshold;
#if STUMP_BASED
part_sum += passThres ? alpha3.y : alpha3.x;
tempnodecounter += lcl_compute_win;
lcl_loop++;
#else
if(root_offset == 0)
{
if( (passThres && currentnodeptr->right) ||
(!passThres && currentnodeptr->left))
{
root_offset = 1;
}
else
{
part_sum += alpha3.x;
tempnodecounter += lcl_compute_win;
lcl_loop++;
}
}
else
{
part_sum += passThres ? alpha3.z : alpha3.y;
tempnodecounter += lcl_compute_win;
lcl_loop++;
root_offset = 0;
}
#endif
}//end for(int lcl_loop=0;lcl_loop<lcl_loops;lcl_loop++)
partialsum[lcl_id]=part_sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lcl_compute_win_id < queuecount)
{
for(int i=0; i<lcl_compute_win && (lcl_compute_id==0); i++)
{
stage_sum += partialsum[lcl_id+i];
}
if(stage_sum >= stagethreshold && (lcl_compute_id==0))
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex<<1] = temp_coord;
lcloutindex[(queueindex<<1)+1] = as_int(variance_norm_factor);
}
lcl_compute_win_id +=(1<<perfscale);
}
barrier(CLK_LOCAL_MEM_FENCE);
}//end for(int queueloop=0;queueloop<queuecount_loop;queueloop++)
queuecount = lclcount[0];
barrier(CLK_LOCAL_MEM_FENCE);
nodecounter += stagecount;
}//end for(int stageloop = splitstage; stageloop< endstage && queuecount>0;stageloop++)
if(lcl_id<queuecount)
{
int temp = lcloutindex[lcl_id<<1];
int x = mad24(grpidx,grpszx,temp & 0xffff);
int y = mad24(grpidy,grpszy,((temp & (int)0xffff0000) >> 16));
temp = glboutindex[0];
int4 candidate_result;
candidate_result.zw = (int2)convert_int_rte(factor*20.f);
candidate_result.x = convert_int_rte(x*factor);
candidate_result.y = convert_int_rte(y*factor);
atomic_inc(glboutindex);
int i = outputoff+temp+lcl_id;
if(candidate[i].z == 0)
{
candidate[i] = candidate_result;
}
else
{
for(i=i+1;;i++)
{
if(candidate[i].z == 0)
{
candidate[i] = candidate_result;
break;
}
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}//end for(int grploop=grpidx;grploop<totalgrp;grploop+=grpnumx)
}//end for(int scalei = 0; scalei <loopcount; scalei++)
}
#endif
modules/objdetect/src/opencl/haarobjectdetect_scaled2.cl 0 → 100644
View file @ 302a5adc
/*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
// Wu Xinglong, wxl370@126.com
// Sen Liu, swjtuls1987@126.com
// Peng Xiao, pengxiao@outlook.com
// Erping Pang, erping@multicorewareinc.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.
//
//M*/
#define CV_HAAR_FEATURE_MAX 3
typedef int sumtype;
typedef float sqsumtype;
typedef struct __attribute__((aligned(128))) GpuHidHaarTreeNode
{
int p[CV_HAAR_FEATURE_MAX][4] __attribute__((aligned(64)));
float weight[CV_HAAR_FEATURE_MAX] /*__attribute__((aligned (16)))*/;
float threshold /*__attribute__((aligned (4)))*/;
float alpha[3] __attribute__((aligned(16)));
int left __attribute__((aligned(4)));
int right __attribute__((aligned(4)));
}
GpuHidHaarTreeNode;
//typedef struct __attribute__((aligned(32))) GpuHidHaarClassifier
//{
// int count __attribute__((aligned(4)));
// GpuHidHaarTreeNode *node __attribute__((aligned(8)));
// float *alpha __attribute__((aligned(8)));
//}
//GpuHidHaarClassifier;
typedef struct __attribute__((aligned(64))) GpuHidHaarStageClassifier
{
int count __attribute__((aligned(4)));
float threshold __attribute__((aligned(4)));
int two_rects __attribute__((aligned(4)));
int reserved0 __attribute__((aligned(8)));
int reserved1 __attribute__((aligned(8)));
int reserved2 __attribute__((aligned(8)));
int reserved3 __attribute__((aligned(8)));
}
GpuHidHaarStageClassifier;
//typedef struct __attribute__((aligned(64))) GpuHidHaarClassifierCascade
//{
// int count __attribute__((aligned(4)));
// int is_stump_based __attribute__((aligned(4)));
// int has_tilted_features __attribute__((aligned(4)));
// int is_tree __attribute__((aligned(4)));
// int pq0 __attribute__((aligned(4)));
// int pq1 __attribute__((aligned(4)));
// int pq2 __attribute__((aligned(4)));
// int pq3 __attribute__((aligned(4)));
// int p0 __attribute__((aligned(4)));
// int p1 __attribute__((aligned(4)));
// int p2 __attribute__((aligned(4)));
// int p3 __attribute__((aligned(4)));
// float inv_window_area __attribute__((aligned(4)));
//} GpuHidHaarClassifierCascade;
__kernel void gpuRunHaarClassifierCascade_scaled2(
global GpuHidHaarStageClassifier *stagecascadeptr_,
global int4 *info,
global GpuHidHaarTreeNode *nodeptr_,
global const int *restrict sum,
global const float *restrict sqsum,
global int4 *candidate,
const int rows,
const int cols,
const int step,
const int loopcount,
const int start_stage,
const int split_stage,
const int end_stage,
const int startnode,
global int4 *p,
global float *correction,
const int nodecount)
{
int grpszx = get_local_size(0);
int grpszy = get_local_size(1);
int grpnumx = get_num_groups(0);
int grpidx = get_group_id(0);
int lclidx = get_local_id(0);
int lclidy = get_local_id(1);
int lcl_id = mad24(lclidy, grpszx, lclidx);
__local int glboutindex[1];
__local int lclcount[1];
__local int lcloutindex[64];
glboutindex[0] = 0;
int outputoff = mul24(grpidx, 256);
candidate[outputoff + (lcl_id << 2)] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 1] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 2] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 3] = (int4)0;
int max_idx = rows * cols - 1;
for (int scalei = 0; scalei < loopcount; scalei++)
{
int4 scaleinfo1 = info[scalei];
int grpnumperline = (scaleinfo1.y & 0xffff0000) >> 16;
int totalgrp = scaleinfo1.y & 0xffff;
float factor = as_float(scaleinfo1.w);
float correction_t = correction[scalei];
float ystep = max(2.0f, factor);
for (int grploop = get_group_id(0); grploop < totalgrp; grploop += grpnumx)
{
int4 cascadeinfo = p[scalei];
int grpidy = grploop / grpnumperline;
int grpidx = grploop - mul24(grpidy, grpnumperline);
int ix = mad24(grpidx, grpszx, lclidx);
int iy = mad24(grpidy, grpszy, lclidy);
int x = round(ix * ystep);
int y = round(iy * ystep);
lcloutindex[lcl_id] = 0;
lclcount[0] = 0;
int nodecounter;
float mean, variance_norm_factor;
//if((ix < width) && (iy < height))
{
const int p_offset = mad24(y, step, x);
cascadeinfo.x += p_offset;
cascadeinfo.z += p_offset;
mean = (sum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.x), 0, max_idx)]
- sum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.z), 0, max_idx)] -
sum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.x), 0, max_idx)]
+ sum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.z), 0, max_idx)])
* correction_t;
variance_norm_factor = sqsum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.x), 0, max_idx)]
- sqsum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.z), 0, max_idx)] -
sqsum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.x), 0, max_idx)]
+ sqsum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.z), 0, max_idx)];
variance_norm_factor = variance_norm_factor * correction_t - mean * mean;
variance_norm_factor = variance_norm_factor >= 0.f ? sqrt(variance_norm_factor) : 1.f;
bool result = true;
nodecounter = startnode + nodecount * scalei;
for (int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++)
{
float stage_sum = 0.f;
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
(((__global uchar*)stagecascadeptr_)+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
for (int nodeloop = 0; nodeloop < stagecount;)
{
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*)
(((__global uchar*)nodeptr_) + nodecounter * sizeof(GpuHidHaarTreeNode));
int4 info1 = *(__global int4 *)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4 *)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4 *)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4 *)(&(currentnodeptr->weight[0]));
float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0]));
float nodethreshold = w.w * variance_norm_factor;
info1.x += p_offset;
info1.z += p_offset;
info2.x += p_offset;
info2.z += p_offset;
info3.x += p_offset;
info3.z += p_offset;
float classsum = (sum[clamp(mad24(info1.y, step, info1.x), 0, max_idx)]
- sum[clamp(mad24(info1.y, step, info1.z), 0, max_idx)] -
sum[clamp(mad24(info1.w, step, info1.x), 0, max_idx)]
+ sum[clamp(mad24(info1.w, step, info1.z), 0, max_idx)]) * w.x;
classsum += (sum[clamp(mad24(info2.y, step, info2.x), 0, max_idx)]
- sum[clamp(mad24(info2.y, step, info2.z), 0, max_idx)] -
sum[clamp(mad24(info2.w, step, info2.x), 0, max_idx)]
+ sum[clamp(mad24(info2.w, step, info2.z), 0, max_idx)]) * w.y;
classsum += (sum[clamp(mad24(info3.y, step, info3.x), 0, max_idx)]
- sum[clamp(mad24(info3.y, step, info3.z), 0, max_idx)] -
sum[clamp(mad24(info3.w, step, info3.x), 0, max_idx)]
+ sum[clamp(mad24(info3.w, step, info3.z), 0, max_idx)]) * w.z;
bool passThres = (classsum >= nodethreshold) ? 1 : 0;
#if STUMP_BASED
stage_sum += passThres ? alpha3.y : alpha3.x;
nodecounter++;
nodeloop++;
#else
bool isRootNode = (nodecounter & 1) == 0;
if(isRootNode)
{
if( (passThres && currentnodeptr->right) ||
(!passThres && currentnodeptr->left))
{
nodecounter ++;
}
else
{
stage_sum += alpha3.x;
nodecounter += 2;
nodeloop ++;
}
}
else
{
stage_sum += (passThres ? alpha3.z : alpha3.y);
nodecounter ++;
nodeloop ++;
}
#endif
}
result = (stage_sum >= stageinfo->threshold) ? 1 : 0;
}
barrier(CLK_LOCAL_MEM_FENCE);
if (result)
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex] = (y << 16) | x;
}
barrier(CLK_LOCAL_MEM_FENCE);
int queuecount = lclcount[0];
if (lcl_id < queuecount)
{
int temp = lcloutindex[lcl_id];
int x = temp & 0xffff;
int y = (temp & (int)0xffff0000) >> 16;
temp = atomic_inc(glboutindex);
int4 candidate_result;
candidate_result.zw = (int2)convert_int_rte(factor * 20.f);
candidate_result.x = x;
candidate_result.y = y;
int i = outputoff+temp+lcl_id;
if(candidate[i].z == 0)
{
candidate[i] = candidate_result;
}
else
{
for(i=i+1;;i++)
{
if(candidate[i].z == 0)
{
candidate[i] = candidate_result;
break;
}
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
}
}
__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuHidHaarTreeNode *newnode, float scale, float weight_scale, const int nodenum)
{
const int counter = get_global_id(0);
int tr_x[3], tr_y[3], tr_h[3], tr_w[3], i = 0;
GpuHidHaarTreeNode t1 = *(__global GpuHidHaarTreeNode*)
(((__global uchar*)orinode) + counter * sizeof(GpuHidHaarTreeNode));
__global GpuHidHaarTreeNode* pNew = (__global GpuHidHaarTreeNode*)
(((__global uchar*)newnode) + (counter + nodenum) * sizeof(GpuHidHaarTreeNode));
#pragma unroll
for (i = 0; i < 3; i++)
{
tr_x[i] = (int)(t1.p[i][0] * scale + 0.5f);
tr_y[i] = (int)(t1.p[i][1] * scale + 0.5f);
tr_w[i] = (int)(t1.p[i][2] * scale + 0.5f);
tr_h[i] = (int)(t1.p[i][3] * scale + 0.5f);
}
t1.weight[0] = -(t1.weight[1] * tr_h[1] * tr_w[1] + t1.weight[2] * tr_h[2] * tr_w[2]) / (tr_h[0] * tr_w[0]);
#pragma unroll
for (i = 0; i < 3; i++)
{
pNew->p[i][0] = tr_x[i];
pNew->p[i][1] = tr_y[i];
pNew->p[i][2] = tr_x[i] + tr_w[i];
pNew->p[i][3] = tr_y[i] + tr_h[i];
pNew->weight[i] = t1.weight[i] * weight_scale;
}
pNew->left = t1.left;
pNew->right = t1.right;
pNew->threshold = t1.threshold;
pNew->alpha[0] = t1.alpha[0];
pNew->alpha[1] = t1.alpha[1];
pNew->alpha[2] = t1.alpha[2];
}
samples/cpp/ufacedetect.cpp
View file @ 302a5adc
......@@ -98,6 +98,8 @@ int main( int argc, const char** argv )
return -1;
}
cout << "old cascade: " << (cascade.isOldFormatCascade() ? "TRUE" : "FALSE") << endl;
if( inputName.empty() || (isdigit(inputName.c_str()[0]) && inputName.c_str()[1] == '\0') )
{
int c = inputName.empty() ? 0 : inputName.c_str()[0] - '0';
......
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