Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity
Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
6238 Commits
7 Branches
126 Tags
3.0 GiB
 
 
 
 
 
 
Tag: Branch: Tree: 673aa91bac
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '673aa91bac'
${ noResults }
opencv/modules/ocl/src/haar.cpp

2767 lines
108 KiB
Raw Blame History

/*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
//
// 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 oclMaterials 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*/
/* Haar features calculation */
//#define EMU
#include "precomp.hpp"
#include <stdio.h>
#include <string>
#ifdef EMU
#include "runCL.h"
#endif
using namespace cv;
using namespace cv::ocl;
using namespace std;
namespace cv
{
namespace ocl
{
///////////////////////////OpenCL kernel strings///////////////////////////
extern const char *haarobjectdetect;
extern const char *haarobjectdetectbackup;
extern const char *haarobjectdetect_scaled2;
}
}
/* 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 rows;
//int ystep;
int width_height;
//int height;
int grpnumperline_totalgrp;
//int totalgrp;
int imgoff;
float factor;
} detect_piramid_info;
#if WIN32
#define _ALIGNED_ON(_ALIGNMENT) __declspec(align(_ALIGNMENT))
typedef _ALIGNED_ON(128) struct GpuHidHaarFeature
{
_ALIGNED_ON(32) struct
{
_ALIGNED_ON(4) int p0 ;
_ALIGNED_ON(4) int p1 ;
_ALIGNED_ON(4) int p2 ;
_ALIGNED_ON(4) int p3 ;
_ALIGNED_ON(4) float weight ;
}
_ALIGNED_ON(32) rect[CV_HAAR_FEATURE_MAX] ;
}
GpuHidHaarFeature;
typedef _ALIGNED_ON(128) struct GpuHidHaarTreeNode
{
_ALIGNED_ON(64) int p[CV_HAAR_FEATURE_MAX][4];
//_ALIGNED_ON(16) int p1[CV_HAAR_FEATURE_MAX] ;
//_ALIGNED_ON(16) int p2[CV_HAAR_FEATURE_MAX] ;
//_ALIGNED_ON(16) int p3[CV_HAAR_FEATURE_MAX] ;
/*_ALIGNED_ON(16)*/
float weight[CV_HAAR_FEATURE_MAX] ;
/*_ALIGNED_ON(4)*/
float threshold ;
_ALIGNED_ON(8) float alpha[2] ;
_ALIGNED_ON(4) int left ;
_ALIGNED_ON(4) int right ;
// GpuHidHaarFeature feature __attribute__((aligned (128)));
}
GpuHidHaarTreeNode;
typedef _ALIGNED_ON(32) struct GpuHidHaarClassifier
{
_ALIGNED_ON(4) int count;
//CvHaarFeature* orig_feature;
_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 ;
// GpuHidHaarStageClassifier* stage_classifier __attribute__((aligned (8)));
} GpuHidHaarClassifierCascade;
#else
#define _ALIGNED_ON(_ALIGNMENT) __attribute__((aligned(_ALIGNMENT) ))
typedef struct _ALIGNED_ON(128) GpuHidHaarFeature
{
struct _ALIGNED_ON(32)
{
int p0 _ALIGNED_ON(4);
int p1 _ALIGNED_ON(4);
int p2 _ALIGNED_ON(4);
int p3 _ALIGNED_ON(4);
float weight _ALIGNED_ON(4);
}
rect[CV_HAAR_FEATURE_MAX] _ALIGNED_ON(32);
}
GpuHidHaarFeature;
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[2] _ALIGNED_ON(8);
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);
// GpuHidHaarStageClassifier* stage_classifier __attribute__((aligned (8)));
} GpuHidHaarClassifierCascade;
#endif
const int icv_object_win_border = 1;
const float icv_stage_threshold_bias = 0.0001f;
double globaltime = 0;
CvHaarClassifierCascade *
gpuCreateHaarClassifierCascade( int stage_count )
{
CvHaarClassifierCascade *cascade = 0;
int block_size = sizeof(*cascade) + stage_count * sizeof(*cascade->stage_classifier);
if( stage_count <= 0 )
CV_Error( CV_StsOutOfRange, "Number of stages should be positive" );
cascade = (CvHaarClassifierCascade *)cvAlloc( block_size );
memset( cascade, 0, block_size );
cascade->stage_classifier = (CvHaarStageClassifier *)(cascade + 1);
cascade->flags = CV_HAAR_MAGIC_VAL;
cascade->count = stage_count;
return cascade;
}
//static int globalcounter = 0;
void
gpuReleaseHidHaarClassifierCascade( GpuHidHaarClassifierCascade **_cascade )
{
if( _cascade && *_cascade )
{
cvFree( _cascade );
}
}
/* create more efficient internal representation of haar classifier cascade */
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[100];
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;
/*
hid_stage_classifier->parent = (stage_classifier->parent == -1)
? NULL : stage_classifier_ptr + stage_classifier->parent;
hid_stage_classifier->next = (stage_classifier->next == -1)
? NULL : stage_classifier_ptr + stage_classifier->next;
hid_stage_classifier->child = (stage_classifier->child == -1)
? NULL : stage_classifier_ptr + stage_classifier->child;
out->is_tree |= hid_stage_classifier->next != NULL;
*/
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 = (float*)(haar_node_ptr + node_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;
}
// memset( &(node->feature.rect[2]), 0, sizeof(node->feature.rect[2]) );
else
hid_stage_classifier->two_rects = 0;
}
memcpy( alpha_ptr, classifier->alpha, (node_count + 1)*sizeof(alpha_ptr[0]));
haar_node_ptr = haar_node_ptr + 1;
// (GpuHidHaarTreeNode*)cvAlignPtr(alpha_ptr+node_count+1, sizeof(void*));
// (GpuHidHaarTreeNode*)(alpha_ptr+node_count+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])
CV_IMPL void
gpuSetImagesForHaarClassifierCascade( CvHaarClassifierCascade *_cascade,
/* const CvArr* _sum,
const CvArr* _sqsum,
const CvArr* _tilted_sum,*/
double scale,
int step)
{
// CvMat sum_stub, *sum = (CvMat*)_sum;
// CvMat sqsum_stub, *sqsum = (CvMat*)_sqsum;
// CvMat tilted_stub, *tilted = (CvMat*)_tilted_sum;
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" );
// sum = cvGetMat( sum, &sum_stub, &coi0 );
// sqsum = cvGetMat( sqsum, &sqsum_stub, &coi1 );
if( coi0 || coi1 )
CV_Error( CV_BadCOI, "COI is not supported" );
// if( !CV_ARE_SIZES_EQ( sum, sqsum ))
// CV_Error( CV_StsUnmatchedSizes, "All integral images must have the same size" );
// if( CV_MAT_TYPE(sqsum->type) != CV_64FC1 ||
// CV_MAT_TYPE(sum->type) != CV_32SC1 )
// CV_Error( CV_StsUnsupportedFormat,
// "Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" );
if( !_cascade->hid_cascade )
gpuCreateHidHaarClassifierCascade(_cascade, &datasize, &total);
cascade = (GpuHidHaarClassifierCascade *) _cascade->hid_cascade;
stage_classifier = (GpuHidHaarStageClassifier *) (cascade + 1);
if( cascade->has_tilted_features )
{
// tilted = cvGetMat( tilted, &tilted_stub, &coi1 );
// if( CV_MAT_TYPE(tilted->type) != CV_32SC1 )
// CV_Error( CV_StsUnsupportedFormat,
// "Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" );
// if( sum->step != tilted->step )
// CV_Error( CV_StsUnmatchedSizes,
// "Sum and tilted_sum must have the same stride (step, widthStep)" );
// if( !CV_ARE_SIZES_EQ( sum, tilted ))
// CV_Error( CV_StsUnmatchedSizes, "All integral images must have the same size" );
// cascade->tilted = *tilted;
}
_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 );
//cascade->sum = *sum;
//cascade->sqsum = *sqsum;
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.y * step + equRect.x;
// cascade->pq1 = equRect.y * step + equRect.x + equRect.width ;
// cascade->pq2 = (equRect.y + equRect.height)*step + equRect.x;
// cascade->pq3 = (equRect.y + equRect.height)*step + equRect.x + equRect.width ;
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];
/* GpuHidHaarClassifier* classifier =
cascade->stage_classifier[i].classifier + j; */
//GpuHidHaarFeature* hidfeature =
// &cascade->stage_classifier[i].classifier[j].node[l].feature;
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( !hidfeature->rect[k].p0 )
// break;
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 )
{
/* hidfeature->rect[k].p0 = tr.y * sum->cols + tr.x;
hidfeature->rect[k].p1 = tr.y * sum->cols + tr.x + tr.width;
hidfeature->rect[k].p2 = (tr.y + tr.height) * sum->cols + tr.x;
hidfeature->rect[k].p3 = (tr.y + tr.height) * sum->cols + tr.x + tr.width;
*/
/*hidnode->p0[k] = tr.y * step + tr.x;
hidnode->p1[k] = tr.y * step + tr.x + tr.width;
hidnode->p2[k] = (tr.y + tr.height) * step + tr.x;
hidnode->p3[k] = (tr.y + tr.height) * step + tr.x + tr.width;*/
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
{
/* hidfeature->rect[k].p2 = (tr.y + tr.width) * tilted->cols + tr.x + tr.width;
hidfeature->rect[k].p3 = (tr.y + tr.width + tr.height) * tilted->cols + tr.x + tr.width - tr.height;
hidfeature->rect[k].p0 = tr.y * tilted->cols + tr.x;
hidfeature->rect[k].p1 = (tr.y + tr.height) * tilted->cols + tr.x - tr.height;
*/
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;
}
//hidfeature->rect[k].weight = (float)(feature->rect[k].weight * correction_ratio);
hidnode->weight[k] = (float)(feature->rect[k].weight * correction_ratio);
if( k == 0 )
area0 = tr.width * tr.height;
else
//sum0 += hidfeature->rect[k].weight * tr.width * tr.height;
sum0 += hidnode->weight[k] * tr.width * tr.height;
}
// hidfeature->rect[0].weight = (float)(-sum0/area0);
hidnode->weight[0] = (float)(-sum0 / area0);
} /* l */
} /* j */
}
}
CV_IMPL void
gpuSetHaarClassifierCascade( CvHaarClassifierCascade *_cascade
/*double scale=0.0,*/
/*int step*/)
{
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, 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];
CvRect r[3];
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;
for( k = 0; k < nr; k++ )
{
CvRect tr;
double correction_ratio;
tr.x = r[k].x;
tr.width = r[k].width;
tr.y = r[k].y ;
tr.height = r[k].height;
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);
}
//hidnode->weight[0]=(float)(-sum0/area0);
} /* l */
} /* j */
}
}
CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemStorage *storage, double scaleFactor,
int minNeighbors, int flags, CvSize minSize, CvSize maxSize)
{
CvHaarClassifierCascade *cascade = oldCascade;
//double alltime = (double)cvGetTickCount();
//double t = (double)cvGetTickCount();
const double GROUP_EPS = 0.2;
oclMat gtemp, gsum1, gtilted1, gsqsum1, gnormImg, gsumcanny;
CvSeq *result_seq = 0;
cv::Ptr<CvMemStorage> temp_storage;
cv::ConcurrentRectVector allCandidates;
std::vector<cv::Rect> rectList;
std::vector<int> rweights;
double factor;
int datasize;
int totalclassifier;
void *out;
GpuHidHaarClassifierCascade *gcascade;
GpuHidHaarStageClassifier *stage;
GpuHidHaarClassifier *classifier;
GpuHidHaarTreeNode *node;
int *candidate;
cl_int status;
// bool doCannyPruning = (flags & CV_HAAR_DO_CANNY_PRUNING) != 0;
bool findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0;
// bool roughSearch = (flags & CV_HAAR_DO_ROUGH_SEARCH) != 0;
//the Intel HD Graphics is unsupported
if (gimg.clCxt->impl->devName.find("Intel(R) HD Graphics") != string::npos)
{
cout << " Intel HD GPU device unsupported " << endl;
return NULL;
}
//double t = 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;
//gtemp = oclMat( gimg.rows, gimg.cols, CV_8UC1);
//gsum1 = oclMat( gimg.rows + 1, gimg.cols + 1, CV_32SC1 );
//gsqsum1 = oclMat( gimg.rows + 1, gimg.cols + 1, CV_32FC1 );
if( !cascade->hid_cascade )
out = (void *)gpuCreateHidHaarClassifierCascade(cascade, &datasize, &totalclassifier);
if( cascade->hid_cascade->has_tilted_features )
gtilted1 = oclMat( gimg.rows + 1, gimg.cols + 1, CV_32SC1 );
result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage );
if( CV_MAT_CN(gimg.type()) > 1 )
{
cvtColor( gimg, gtemp, CV_BGR2GRAY );
gimg = gtemp;
}
if( findBiggestObject )
flags &= ~(CV_HAAR_SCALE_IMAGE | CV_HAAR_DO_CANNY_PRUNING);
//t = (double)cvGetTickCount() - t;
//printf( "before if time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
if( gimg.cols < minSize.width || gimg.rows < minSize.height )
CV_Error(CV_StsError, "Image too small");
if( flags & CV_HAAR_SCALE_IMAGE )
{
CvSize winSize0 = cascade->orig_window_size;
//float scalefactor = 1.1f;
//float factor = 1.f;
int totalheight = 0;
int indexy = 0;
CvSize sz;
//t = (double)cvGetTickCount();
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);
}
//int flag = 0;
oclMat gimg1(gimg.rows, gimg.cols, CV_8UC1);
oclMat gsum(totalheight, gimg.cols + 1, CV_32SC1);
oclMat gsqsum(totalheight, gimg.cols + 1, CV_32FC1);
//cl_mem cascadebuffer;
cl_mem stagebuffer;
//cl_mem classifierbuffer;
cl_mem nodebuffer;
cl_mem candidatebuffer;
cl_mem scaleinfobuffer;
//cl_kernel kernel;
//kernel = openCLGetKernelFromSource(gimg.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascade");
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)->impl->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);
//t = (double)cvGetTickCount() - t;
// printf( "pre time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
//int *it =scaleinfo;
// t = (double)cvGetTickCount();
for( int i = 0; i < loopcount; i++ )
{
sz = sizev[i];
factor = scalev[i];
roi = Rect(0, indexy, sz.width, sz.height);
roi2 = Rect(0, 0, sz.width - 1, sz.height - 1);
resizeroi = gimg1(roi2);
gimgroi = gsum(roi);
gimgroisq = gsqsum(roi);
//scaleinfo[i].rows = gimgroi.rows;
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;
//outputsz +=width*height;
scaleinfo[i].grpnumperline_totalgrp = (grpnumperline << 16) | totalgrp;
scaleinfo[i].imgoff = gimgroi.offset >> 2;
scaleinfo[i].factor = factor;
//printf("rows = %d,ystep = %d,width = %d,height = %d,grpnumperline = %d,totalgrp = %d,imgoff = %d,factor = %f\n",
// scaleinfo[i].rows,scaleinfo[i].ystep,scaleinfo[i].width,scaleinfo[i].height,scaleinfo[i].grpnumperline,
// scaleinfo[i].totalgrp,scaleinfo[i].imgoff,scaleinfo[i].factor);
cv::ocl::resize(gimg, resizeroi, Size(sz.width - 1, sz.height - 1), 0, 0, INTER_LINEAR);
//cv::imwrite("D:\\1.jpg",gimg1);
cv::ocl::integral(resizeroi, gimgroi, gimgroisq);
//cv::ocl::oclMat chk(sz.height,sz.width,CV_32SC1),chksq(sz.height,sz.width,CV_32FC1);
//cv::ocl::integral(gimg1, chk, chksq);
//double r = cv::norm(chk,gimgroi,NORM_INF);
//if(r > std::numeric_limits<double>::epsilon())
//{
// printf("failed");
//}
indexy += sz.height;
}
//int ystep = factor > 2 ? 1 : 2;
// t = (double)cvGetTickCount() - t;
//printf( "resize integral time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
//t = (double)cvGetTickCount();
gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
node = (GpuHidHaarTreeNode *)(classifier->node);
//int m,n;
//m = (gsum.cols - 1 - cascade->orig_window_size.width + ystep - 1)/ystep;
//n = (gsum.rows - 1 - cascade->orig_window_size.height + ystep - 1)/ystep;
//int counter = m*n;
int nodenum = (datasize - sizeof(GpuHidHaarClassifierCascade) -
sizeof(GpuHidHaarStageClassifier) * gcascade->count - sizeof(GpuHidHaarClassifier) * totalclassifier) / sizeof(GpuHidHaarTreeNode);
//if(flag == 0){
candidate = (int *)malloc(4 * sizeof(int) * outputsz);
//memset((char*)candidate,0,4*sizeof(int)*outputsz);
gpuSetImagesForHaarClassifierCascade( cascade,/* &sum1, &sqsum1, _tilted,*/ 1., gsum.step / 4 );
//cascadebuffer = clCreateBuffer(gsum.clCxt->clContext,CL_MEM_READ_ONLY,sizeof(GpuHidHaarClassifierCascade),NULL,&status);
//openCLVerifyCall(status);
//openCLSafeCall(clEnqueueWriteBuffer(gsum.clCxt->clCmdQueue,cascadebuffer,1,0,sizeof(GpuHidHaarClassifierCascade),gcascade,0,NULL,NULL));
stagebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(GpuHidHaarStageClassifier) * gcascade->count);
//openCLVerifyCall(status);
openCLSafeCall(clEnqueueWriteBuffer(gsum.clCxt->impl->clCmdQueue, stagebuffer, 1, 0, sizeof(GpuHidHaarStageClassifier)*gcascade->count, stage, 0, NULL, NULL));
//classifierbuffer = clCreateBuffer(gsum.clCxt->clContext,CL_MEM_READ_ONLY,sizeof(GpuHidHaarClassifier)*totalclassifier,NULL,&status);
//status = clEnqueueWriteBuffer(gsum.clCxt->clCmdQueue,classifierbuffer,1,0,sizeof(GpuHidHaarClassifier)*totalclassifier,classifier,0,NULL,NULL);
nodebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, nodenum * sizeof(GpuHidHaarTreeNode));
//openCLVerifyCall(status);
openCLSafeCall(clEnqueueWriteBuffer(gsum.clCxt->impl->clCmdQueue, nodebuffer, 1, 0,
nodenum * sizeof(GpuHidHaarTreeNode),
node, 0, NULL, NULL));
candidatebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_WRITE_ONLY, 4 * sizeof(int) * outputsz);
//openCLVerifyCall(status);
scaleinfobuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
//openCLVerifyCall(status);
openCLSafeCall(clEnqueueWriteBuffer(gsum.clCxt->impl->clCmdQueue, scaleinfobuffer, 1, 0, sizeof(detect_piramid_info)*loopcount, scaleinfo, 0, NULL, NULL));
//flag = 1;
//}
//t = (double)cvGetTickCount() - t;
//printf( "update time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
//size_t globalThreads[3] = { counter+blocksize*blocksize-counter%(blocksize*blocksize),1,1};
//t = (double)cvGetTickCount();
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;
//int grpnumperline = ((m + localThreads[0] - 1) / localThreads[0]);
//int totalgrp = ((n + localThreads[1] - 1) / localThreads[1])*grpnumperline;
// openCLVerifyKernel(gsum.clCxt, kernel, &blocksize, globalThreads, localThreads);
//openCLSafeCall(clSetKernelArg(kernel,argcount++,sizeof(cl_mem),(void*)&cascadebuffer));
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 ));
/*
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&stagebuffer));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&scaleinfobuffer));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&nodebuffer));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&gsum.data));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&gsqsum.data));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&candidatebuffer));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&pixelstep));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&loopcount));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&startstage));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&splitstage));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&endstage));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&startnode));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&splitnode));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int4), (void *)&p));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int4), (void *)&pq));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_float), (void *)&correction));*/
//openCLSafeCall(clSetKernelArg(kernel,argcount++,sizeof(cl_int),(void*)&n));
//openCLSafeCall(clSetKernelArg(kernel,argcount++,sizeof(cl_int),(void*)&grpnumperline));
//openCLSafeCall(clSetKernelArg(kernel,argcount++,sizeof(cl_int),(void*)&totalgrp));
// openCLSafeCall(clEnqueueNDRangeKernel(gsum.clCxt->impl->clCmdQueue, kernel, 2, NULL, globalThreads, localThreads, 0, NULL, NULL));
// openCLSafeCall(clFinish(gsum.clCxt->impl->clCmdQueue));
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascade", globalThreads, localThreads, args, -1, -1);
//t = (double)cvGetTickCount() - t;
//printf( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
//t = (double)cvGetTickCount();
//openCLSafeCall(clEnqueueReadBuffer(gsum.clCxt->impl->clCmdQueue, candidatebuffer, 1, 0, 4 * sizeof(int)*outputsz, candidate, 0, NULL, NULL));
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]));
// t = (double)cvGetTickCount() - t;
//printf( "post time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
//t = (double)cvGetTickCount();
free(scaleinfo);
free(candidate);
//openCLSafeCall(clReleaseMemObject(cascadebuffer));
openCLSafeCall(clReleaseMemObject(stagebuffer));
openCLSafeCall(clReleaseMemObject(scaleinfobuffer));
openCLSafeCall(clReleaseMemObject(nodebuffer));
openCLSafeCall(clReleaseMemObject(candidatebuffer));
// openCLSafeCall(clReleaseKernel(kernel));
//t = (double)cvGetTickCount() - t;
//printf( "release time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
}
else
{
CvSize winsize0 = cascade->orig_window_size;
int n_factors = 0;
oclMat gsum;
oclMat gsqsum;
cv::ocl::integral(gimg, gsum, gsqsum);
CvSize sz;
vector<CvSize> sizev;
vector<float> scalev;
gpuSetHaarClassifierCascade(cascade);
gcascade = (GpuHidHaarClassifierCascade *)cascade->hid_cascade;
stage = (GpuHidHaarStageClassifier *)(gcascade + 1);
classifier = (GpuHidHaarClassifier *)(stage + gcascade->count);
node = (GpuHidHaarTreeNode *)(classifier->node);
cl_mem stagebuffer;
//cl_mem classifierbuffer;
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( 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->impl->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));
//openCLVerifyCall(status);
openCLSafeCall(clEnqueueWriteBuffer(gsum.clCxt->impl->clCmdQueue, 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;
//cl_kernel kernel;
//kernel = openCLGetKernelFromSource(gsum.clCxt, &haarobjectdetect_scaled2, "gpuRunHaarClassifierCascade_scaled2");
//cl_kernel kernel2 = openCLGetKernelFromSource(gimg.clCxt, &haarobjectdetect_scaled2, "gpuscaleclassifier");
for(int i = 0; i < loopcount; i++)
{
sz = sizev[i];
factor = scalev[i];
int ystep = cvRound(std::max(2., factor));
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 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;
//outputsz +=width*height;
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;
/*
openCLSafeCall(clSetKernelArg(kernel2, argcounts++, sizeof(cl_mem), (void *)&nodebuffer));
openCLSafeCall(clSetKernelArg(kernel2, argcounts++, sizeof(cl_mem), (void *)&newnodebuffer));
openCLSafeCall(clSetKernelArg(kernel2, argcounts++, sizeof(cl_float), (void *)&factor2));
openCLSafeCall(clSetKernelArg(kernel2, argcounts++, sizeof(cl_float), (void *)&correction[i]));
openCLSafeCall(clSetKernelArg(kernel2, argcounts++, sizeof(cl_int), (void *)&startnodenum));
*/
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);
//clEnqueueNDRangeKernel(gsum.clCxt->impl->clCmdQueue, kernel2, 1, NULL, globalThreads2, 0, 0, NULL, NULL);
//clFinish(gsum.clCxt->impl->clCmdQueue);
}
//clReleaseKernel(kernel2);
int step = gsum.step / 4;
int startnode = 0;
int splitstage = 3;
int splitnode = stage[0].count + stage[1].count + stage[2].count;
stagebuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(GpuHidHaarStageClassifier) * gcascade->count);
//openCLVerifyCall(status);
openCLSafeCall(clEnqueueWriteBuffer(gsum.clCxt->impl->clCmdQueue, 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);
//openCLVerifyCall(status);
scaleinfobuffer = openCLCreateBuffer(gsum.clCxt, CL_MEM_READ_ONLY, sizeof(detect_piramid_info) * loopcount);
//openCLVerifyCall(status);
openCLSafeCall(clEnqueueWriteBuffer(gsum.clCxt->impl->clCmdQueue, 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(gsum.clCxt->impl->clCmdQueue, 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(gsum.clCxt->impl->clCmdQueue, correctionbuffer, 1, 0, sizeof(cl_float)*loopcount, correction, 0, NULL, NULL));
//int argcount = 0;
/*openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&stagebuffer));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&scaleinfobuffer));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&newnodebuffer));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&gsum.data));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&gsqsum.data));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&candidatebuffer));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&step));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&loopcount));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&startstage));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&splitstage));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&endstage));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&startnode));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&splitnode));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&pbuffer));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_mem), (void *)&correctionbuffer));
openCLSafeCall(clSetKernelArg(kernel, argcount++, sizeof(cl_int), (void *)&nodenum));*/
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 *)&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_int) , (void *)&splitnode ));
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 ));
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect_scaled2, "gpuRunHaarClassifierCascade_scaled2", globalThreads, localThreads, args, -1, -1);
//openCLSafeCall(clEnqueueNDRangeKernel(gsum.clCxt->impl->clCmdQueue, kernel, 2, NULL, globalThreads, localThreads, 0, NULL, NULL));
//openCLSafeCall(clFinish(gsum.clCxt->impl->clCmdQueue));
//openCLSafeCall(clEnqueueReadBuffer(gsum.clCxt->clCmdQueue,candidatebuffer,1,0,4*sizeof(int)*outputsz,candidate,0,NULL,NULL));
candidate = (int *)clEnqueueMapBuffer(gsum.clCxt->impl->clCmdQueue, candidatebuffer, 1, CL_MAP_READ, 0, 4 * sizeof(int), 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(gsum.clCxt->impl->clCmdQueue, 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));
}
//t = (double)cvGetTickCount() ;
cvFree(&cascade->hid_cascade);
// printf("%d\n",globalcounter);
rectList.resize(allCandidates.size());
if(!allCandidates.empty())
std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
//cout << "count = " << rectList.size()<< endl;
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 );
}
}
//t = (double)cvGetTickCount() - t;
//printf( "get face time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
//alltime = (double)cvGetTickCount() - alltime;
//printf( "all time = %g ms\n", alltime/((double)cvGetTickFrequency()*1000.) );
return result_seq;
}
CvHaarClassifierCascade *
gpuLoadCascadeCART( const char **input_cascade, int n, CvSize orig_window_size )
{
int i;
CvHaarClassifierCascade *cascade = gpuCreateHaarClassifierCascade(n);
cascade->orig_window_size = orig_window_size;
for( i = 0; i < n; i++ )
{
int j, count, l;
float threshold = 0;
const char *stage = input_cascade[i];
int dl = 0;
/* tree links */
int parent = -1;
int next = -1;
sscanf( stage, "%d%n", &count, &dl );
stage += dl;
assert( count > 0 );
cascade->stage_classifier[i].count = count;
cascade->stage_classifier[i].classifier =
(CvHaarClassifier *)cvAlloc( count * sizeof(cascade->stage_classifier[i].classifier[0]));
for( j = 0; j < count; j++ )
{
CvHaarClassifier *classifier = cascade->stage_classifier[i].classifier + j;
int k, rects = 0;
char str[100];
sscanf( stage, "%d%n", &classifier->count, &dl );
stage += dl;
classifier->haar_feature = (CvHaarFeature *) cvAlloc(
classifier->count * ( sizeof( *classifier->haar_feature ) +
sizeof( *classifier->threshold ) +
sizeof( *classifier->left ) +
sizeof( *classifier->right ) ) +
(classifier->count + 1) * sizeof( *classifier->alpha ) );
classifier->threshold = (float *) (classifier->haar_feature + classifier->count);
classifier->left = (int *) (classifier->threshold + classifier->count);
classifier->right = (int *) (classifier->left + classifier->count);
classifier->alpha = (float *) (classifier->right + classifier->count);
for( l = 0; l < classifier->count; l++ )
{
sscanf( stage, "%d%n", &rects, &dl );
stage += dl;
assert( rects >= 2 && rects <= CV_HAAR_FEATURE_MAX );
for( k = 0; k < rects; k++ )
{
CvRect r;
int band = 0;
sscanf( stage, "%d%d%d%d%d%f%n",
&r.x, &r.y, &r.width, &r.height, &band,
&(classifier->haar_feature[l].rect[k].weight), &dl );
stage += dl;
classifier->haar_feature[l].rect[k].r = r;
}
sscanf( stage, "%s%n", str, &dl );
stage += dl;
classifier->haar_feature[l].tilted = strncmp( str, "tilted", 6 ) == 0;
for( k = rects; k < CV_HAAR_FEATURE_MAX; k++ )
{
memset( classifier->haar_feature[l].rect + k, 0,
sizeof(classifier->haar_feature[l].rect[k]) );
}
sscanf( stage, "%f%d%d%n", &(classifier->threshold[l]),
&(classifier->left[l]),
&(classifier->right[l]), &dl );
stage += dl;
}
for( l = 0; l <= classifier->count; l++ )
{
sscanf( stage, "%f%n", &(classifier->alpha[l]), &dl );
stage += dl;
}
}
sscanf( stage, "%f%n", &threshold, &dl );
stage += dl;
cascade->stage_classifier[i].threshold = threshold;
/* load tree links */
if( sscanf( stage, "%d%d%n", &parent, &next, &dl ) != 2 )
{
parent = i - 1;
next = -1;
}
stage += dl;
cascade->stage_classifier[i].parent = parent;
cascade->stage_classifier[i].next = next;
cascade->stage_classifier[i].child = -1;
if( parent != -1 && cascade->stage_classifier[parent].child == -1 )
{
cascade->stage_classifier[parent].child = i;
}
}
return cascade;
}
#ifndef _MAX_PATH
#define _MAX_PATH 1024
#endif
CV_IMPL CvHaarClassifierCascade *
gpuLoadHaarClassifierCascade( const char *directory, CvSize orig_window_size )
{
const char **input_cascade = 0;
CvHaarClassifierCascade *cascade = 0;
int i, n;
const char *slash;
char name[_MAX_PATH];
int size = 0;
char *ptr = 0;
if( !directory )
CV_Error( CV_StsNullPtr, "Null path is passed" );
n = (int)strlen(directory) - 1;
slash = directory[n] == '\\' || directory[n] == '/' ? "" : "/";
/* try to read the classifier from directory */
for( n = 0; ; n++ )
{
sprintf( name, "%s%s%d/AdaBoostCARTHaarClassifier.txt", directory, slash, n );
FILE *f = fopen( name, "rb" );
if( !f )
break;
fseek( f, 0, SEEK_END );
size += ftell( f ) + 1;
fclose(f);
}
if( n == 0 && slash[0] )
return (CvHaarClassifierCascade *)cvLoad( directory );
if( n == 0 )
CV_Error( CV_StsBadArg, "Invalid path" );
size += (n + 1) * sizeof(char *);
input_cascade = (const char **)cvAlloc( size );
ptr = (char *)(input_cascade + n + 1);
for( i = 0; i < n; i++ )
{
sprintf( name, "%s/%d/AdaBoostCARTHaarClassifier.txt", directory, i );
FILE *f = fopen( name, "rb" );
if( !f )
CV_Error( CV_StsError, "" );
fseek( f, 0, SEEK_END );
size = ftell( f );
fseek( f, 0, SEEK_SET );
fread( ptr, 1, size, f );
fclose(f);
input_cascade[i] = ptr;
ptr += size;
*ptr++ = '\0';
}
input_cascade[n] = 0;
cascade = gpuLoadCascadeCART( input_cascade, n, orig_window_size );
if( input_cascade )
cvFree( &input_cascade );
return cascade;
}
CV_IMPL void
gpuReleaseHaarClassifierCascade( CvHaarClassifierCascade **_cascade )
{
if( _cascade && *_cascade )
{
int i, j;
CvHaarClassifierCascade *cascade = *_cascade;
for( i = 0; i < cascade->count; i++ )
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
cvFree( &cascade->stage_classifier[i].classifier[j].haar_feature );
cvFree( &cascade->stage_classifier[i].classifier );
}
gpuReleaseHidHaarClassifierCascade( (GpuHidHaarClassifierCascade **)&cascade->hid_cascade );
cvFree( _cascade );
}
}
/****************************************************************************************\
* Persistence functions *
\****************************************************************************************/
/* field names */
#define ICV_HAAR_SIZE_NAME "size"
#define ICV_HAAR_STAGES_NAME "stages"
#define ICV_HAAR_TREES_NAME "trees"
#define ICV_HAAR_FEATURE_NAME "feature"
#define ICV_HAAR_RECTS_NAME "rects"
#define ICV_HAAR_TILTED_NAME "tilted"
#define ICV_HAAR_THRESHOLD_NAME "threshold"
#define ICV_HAAR_LEFT_NODE_NAME "left_node"
#define ICV_HAAR_LEFT_VAL_NAME "left_val"
#define ICV_HAAR_RIGHT_NODE_NAME "right_node"
#define ICV_HAAR_RIGHT_VAL_NAME "right_val"
#define ICV_HAAR_STAGE_THRESHOLD_NAME "stage_threshold"
#define ICV_HAAR_PARENT_NAME "parent"
#define ICV_HAAR_NEXT_NAME "next"
int
gpuIsHaarClassifier( const void *struct_ptr )
{
return CV_IS_HAAR_CLASSIFIER( struct_ptr );
}
void *
gpuReadHaarClassifier( CvFileStorage *fs, CvFileNode *node )
{
CvHaarClassifierCascade *cascade = NULL;
char buf[256];
CvFileNode *seq_fn = NULL; /* sequence */
CvFileNode *fn = NULL;
CvFileNode *stages_fn = NULL;
CvSeqReader stages_reader;
int n;
int i, j, k, l;
int parent, next;
stages_fn = cvGetFileNodeByName( fs, node, ICV_HAAR_STAGES_NAME );
if( !stages_fn || !CV_NODE_IS_SEQ( stages_fn->tag) )
CV_Error( CV_StsError, "Invalid stages node" );
n = stages_fn->data.seq->total;
cascade = gpuCreateHaarClassifierCascade(n);
/* read size */
seq_fn = cvGetFileNodeByName( fs, node, ICV_HAAR_SIZE_NAME );
if( !seq_fn || !CV_NODE_IS_SEQ( seq_fn->tag ) || seq_fn->data.seq->total != 2 )
CV_Error( CV_StsError, "size node is not a valid sequence." );
fn = (CvFileNode *) cvGetSeqElem( seq_fn->data.seq, 0 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0 )
CV_Error( CV_StsError, "Invalid size node: width must be positive integer" );
cascade->orig_window_size.width = fn->data.i;
fn = (CvFileNode *) cvGetSeqElem( seq_fn->data.seq, 1 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0 )
CV_Error( CV_StsError, "Invalid size node: height must be positive integer" );
cascade->orig_window_size.height = fn->data.i;
cvStartReadSeq( stages_fn->data.seq, &stages_reader );
for( i = 0; i < n; ++i )
{
CvFileNode *stage_fn;
CvFileNode *trees_fn;
CvSeqReader trees_reader;
stage_fn = (CvFileNode *) stages_reader.ptr;
if( !CV_NODE_IS_MAP( stage_fn->tag ) )
{
sprintf( buf, "Invalid stage %d", i );
CV_Error( CV_StsError, buf );
}
trees_fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_TREES_NAME );
if( !trees_fn || !CV_NODE_IS_SEQ( trees_fn->tag )
|| trees_fn->data.seq->total <= 0 )
{
sprintf( buf, "Trees node is not a valid sequence. (stage %d)", i );
CV_Error( CV_StsError, buf );
}
cascade->stage_classifier[i].classifier =
(CvHaarClassifier *) cvAlloc( trees_fn->data.seq->total
* sizeof( cascade->stage_classifier[i].classifier[0] ) );
for( j = 0; j < trees_fn->data.seq->total; ++j )
{
cascade->stage_classifier[i].classifier[j].haar_feature = NULL;
}
cascade->stage_classifier[i].count = trees_fn->data.seq->total;
cvStartReadSeq( trees_fn->data.seq, &trees_reader );
for( j = 0; j < trees_fn->data.seq->total; ++j )
{
CvFileNode *tree_fn;
CvSeqReader tree_reader;
CvHaarClassifier *classifier;
int last_idx;
classifier = &cascade->stage_classifier[i].classifier[j];
tree_fn = (CvFileNode *) trees_reader.ptr;
if( !CV_NODE_IS_SEQ( tree_fn->tag ) || tree_fn->data.seq->total <= 0 )
{
sprintf( buf, "Tree node is not a valid sequence."
" (stage %d, tree %d)", i, j );
CV_Error( CV_StsError, buf );
}
classifier->count = tree_fn->data.seq->total;
classifier->haar_feature = (CvHaarFeature *) cvAlloc(
classifier->count * ( sizeof( *classifier->haar_feature ) +
sizeof( *classifier->threshold ) +
sizeof( *classifier->left ) +
sizeof( *classifier->right ) ) +
(classifier->count + 1) * sizeof( *classifier->alpha ) );
classifier->threshold = (float *) (classifier->haar_feature + classifier->count);
classifier->left = (int *) (classifier->threshold + classifier->count);
classifier->right = (int *) (classifier->left + classifier->count);
classifier->alpha = (float *) (classifier->right + classifier->count);
cvStartReadSeq( tree_fn->data.seq, &tree_reader );
for( k = 0, last_idx = 0; k < tree_fn->data.seq->total; ++k )
{
CvFileNode *node_fn;
CvFileNode *feature_fn;
CvFileNode *rects_fn;
CvSeqReader rects_reader;
node_fn = (CvFileNode *) tree_reader.ptr;
if( !CV_NODE_IS_MAP( node_fn->tag ) )
{
sprintf( buf, "Tree node %d is not a valid map. (stage %d, tree %d)",
k, i, j );
CV_Error( CV_StsError, buf );
}
feature_fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_FEATURE_NAME );
if( !feature_fn || !CV_NODE_IS_MAP( feature_fn->tag ) )
{
sprintf( buf, "Feature node is not a valid map. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
rects_fn = cvGetFileNodeByName( fs, feature_fn, ICV_HAAR_RECTS_NAME );
if( !rects_fn || !CV_NODE_IS_SEQ( rects_fn->tag )
|| rects_fn->data.seq->total < 1
|| rects_fn->data.seq->total > CV_HAAR_FEATURE_MAX )
{
sprintf( buf, "Rects node is not a valid sequence. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
cvStartReadSeq( rects_fn->data.seq, &rects_reader );
for( l = 0; l < rects_fn->data.seq->total; ++l )
{
CvFileNode *rect_fn;
CvRect r;
rect_fn = (CvFileNode *) rects_reader.ptr;
if( !CV_NODE_IS_SEQ( rect_fn->tag ) || rect_fn->data.seq->total != 5 )
{
sprintf( buf, "Rect %d is not a valid sequence. "
"(stage %d, tree %d, node %d)", l, i, j, k );
CV_Error( CV_StsError, buf );
}
fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 0 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i < 0 )
{
sprintf( buf, "x coordinate must be non-negative integer. "
"(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
CV_Error( CV_StsError, buf );
}
r.x = fn->data.i;
fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 1 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i < 0 )
{
sprintf( buf, "y coordinate must be non-negative integer. "
"(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
CV_Error( CV_StsError, buf );
}
r.y = fn->data.i;
fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 2 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0
|| r.x + fn->data.i > cascade->orig_window_size.width )
{
sprintf( buf, "width must be positive integer and "
"(x + width) must not exceed window width. "
"(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
CV_Error( CV_StsError, buf );
}
r.width = fn->data.i;
fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 3 );
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0
|| r.y + fn->data.i > cascade->orig_window_size.height )
{
sprintf( buf, "height must be positive integer and "
"(y + height) must not exceed window height. "
"(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
CV_Error( CV_StsError, buf );
}
r.height = fn->data.i;
fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 4 );
if( !CV_NODE_IS_REAL( fn->tag ) )
{
sprintf( buf, "weight must be real number. "
"(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
CV_Error( CV_StsError, buf );
}
classifier->haar_feature[k].rect[l].weight = (float) fn->data.f;
classifier->haar_feature[k].rect[l].r = r;
CV_NEXT_SEQ_ELEM( sizeof( *rect_fn ), rects_reader );
} /* for each rect */
for( l = rects_fn->data.seq->total; l < CV_HAAR_FEATURE_MAX; ++l )
{
classifier->haar_feature[k].rect[l].weight = 0;
classifier->haar_feature[k].rect[l].r = cvRect( 0, 0, 0, 0 );
}
fn = cvGetFileNodeByName( fs, feature_fn, ICV_HAAR_TILTED_NAME);
if( !fn || !CV_NODE_IS_INT( fn->tag ) )
{
sprintf( buf, "tilted must be 0 or 1. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
classifier->haar_feature[k].tilted = ( fn->data.i != 0 );
fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_THRESHOLD_NAME);
if( !fn || !CV_NODE_IS_REAL( fn->tag ) )
{
sprintf( buf, "threshold must be real number. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
classifier->threshold[k] = (float) fn->data.f;
fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_LEFT_NODE_NAME);
if( fn )
{
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= k
|| fn->data.i >= tree_fn->data.seq->total )
{
sprintf( buf, "left node must be valid node number. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
/* left node */
classifier->left[k] = fn->data.i;
}
else
{
fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_LEFT_VAL_NAME );
if( !fn )
{
sprintf( buf, "left node or left value must be specified. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
if( !CV_NODE_IS_REAL( fn->tag ) )
{
sprintf( buf, "left value must be real number. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
/* left value */
if( last_idx >= classifier->count + 1 )
{
sprintf( buf, "Tree structure is broken: too many values. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
classifier->left[k] = -last_idx;
classifier->alpha[last_idx++] = (float) fn->data.f;
}
fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_RIGHT_NODE_NAME);
if( fn )
{
if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= k
|| fn->data.i >= tree_fn->data.seq->total )
{
sprintf( buf, "right node must be valid node number. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
/* right node */
classifier->right[k] = fn->data.i;
}
else
{
fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_RIGHT_VAL_NAME );
if( !fn )
{
sprintf( buf, "right node or right value must be specified. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
if( !CV_NODE_IS_REAL( fn->tag ) )
{
sprintf( buf, "right value must be real number. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
/* right value */
if( last_idx >= classifier->count + 1 )
{
sprintf( buf, "Tree structure is broken: too many values. "
"(stage %d, tree %d, node %d)", i, j, k );
CV_Error( CV_StsError, buf );
}
classifier->right[k] = -last_idx;
classifier->alpha[last_idx++] = (float) fn->data.f;
}
CV_NEXT_SEQ_ELEM( sizeof( *node_fn ), tree_reader );
} /* for each node */
if( last_idx != classifier->count + 1 )
{
sprintf( buf, "Tree structure is broken: too few values. "
"(stage %d, tree %d)", i, j );
CV_Error( CV_StsError, buf );
}
CV_NEXT_SEQ_ELEM( sizeof( *tree_fn ), trees_reader );
} /* for each tree */
fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_STAGE_THRESHOLD_NAME);
if( !fn || !CV_NODE_IS_REAL( fn->tag ) )
{
sprintf( buf, "stage threshold must be real number. (stage %d)", i );
CV_Error( CV_StsError, buf );
}
cascade->stage_classifier[i].threshold = (float) fn->data.f;
parent = i - 1;
next = -1;
fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_PARENT_NAME );
if( !fn || !CV_NODE_IS_INT( fn->tag )
|| fn->data.i < -1 || fn->data.i >= cascade->count )
{
sprintf( buf, "parent must be integer number. (stage %d)", i );
CV_Error( CV_StsError, buf );
}
parent = fn->data.i;
fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_NEXT_NAME );
if( !fn || !CV_NODE_IS_INT( fn->tag )
|| fn->data.i < -1 || fn->data.i >= cascade->count )
{
sprintf( buf, "next must be integer number. (stage %d)", i );
CV_Error( CV_StsError, buf );
}
next = fn->data.i;
cascade->stage_classifier[i].parent = parent;
cascade->stage_classifier[i].next = next;
cascade->stage_classifier[i].child = -1;
if( parent != -1 && cascade->stage_classifier[parent].child == -1 )
{
cascade->stage_classifier[parent].child = i;
}
CV_NEXT_SEQ_ELEM( sizeof( *stage_fn ), stages_reader );
} /* for each stage */
return cascade;
}
void
gpuWriteHaarClassifier( CvFileStorage *fs, const char *name, const void *struct_ptr,
CvAttrList attributes )
{
int i, j, k, l;
char buf[256];
const CvHaarClassifierCascade *cascade = (const CvHaarClassifierCascade *) struct_ptr;
/* TODO: parameters check */
cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_HAAR, attributes );
cvStartWriteStruct( fs, ICV_HAAR_SIZE_NAME, CV_NODE_SEQ | CV_NODE_FLOW );
cvWriteInt( fs, NULL, cascade->orig_window_size.width );
cvWriteInt( fs, NULL, cascade->orig_window_size.height );
cvEndWriteStruct( fs ); /* size */
cvStartWriteStruct( fs, ICV_HAAR_STAGES_NAME, CV_NODE_SEQ );
for( i = 0; i < cascade->count; ++i )
{
cvStartWriteStruct( fs, NULL, CV_NODE_MAP );
sprintf( buf, "stage %d", i );
cvWriteComment( fs, buf, 1 );
cvStartWriteStruct( fs, ICV_HAAR_TREES_NAME, CV_NODE_SEQ );
for( j = 0; j < cascade->stage_classifier[i].count; ++j )
{
CvHaarClassifier *tree = &cascade->stage_classifier[i].classifier[j];
cvStartWriteStruct( fs, NULL, CV_NODE_SEQ );
sprintf( buf, "tree %d", j );
cvWriteComment( fs, buf, 1 );
for( k = 0; k < tree->count; ++k )
{
CvHaarFeature *feature = &tree->haar_feature[k];
cvStartWriteStruct( fs, NULL, CV_NODE_MAP );
if( k )
{
sprintf( buf, "node %d", k );
}
else
{
sprintf( buf, "root node" );
}
cvWriteComment( fs, buf, 1 );
cvStartWriteStruct( fs, ICV_HAAR_FEATURE_NAME, CV_NODE_MAP );
cvStartWriteStruct( fs, ICV_HAAR_RECTS_NAME, CV_NODE_SEQ );
for( l = 0; l < CV_HAAR_FEATURE_MAX && feature->rect[l].r.width != 0; ++l )
{
cvStartWriteStruct( fs, NULL, CV_NODE_SEQ | CV_NODE_FLOW );
cvWriteInt( fs, NULL, feature->rect[l].r.x );
cvWriteInt( fs, NULL, feature->rect[l].r.y );
cvWriteInt( fs, NULL, feature->rect[l].r.width );
cvWriteInt( fs, NULL, feature->rect[l].r.height );
cvWriteReal( fs, NULL, feature->rect[l].weight );
cvEndWriteStruct( fs ); /* rect */
}
cvEndWriteStruct( fs ); /* rects */
cvWriteInt( fs, ICV_HAAR_TILTED_NAME, feature->tilted );
cvEndWriteStruct( fs ); /* feature */
cvWriteReal( fs, ICV_HAAR_THRESHOLD_NAME, tree->threshold[k]);
if( tree->left[k] > 0 )
{
cvWriteInt( fs, ICV_HAAR_LEFT_NODE_NAME, tree->left[k] );
}
else
{
cvWriteReal( fs, ICV_HAAR_LEFT_VAL_NAME,
tree->alpha[-tree->left[k]] );
}
if( tree->right[k] > 0 )
{
cvWriteInt( fs, ICV_HAAR_RIGHT_NODE_NAME, tree->right[k] );
}
else
{
cvWriteReal( fs, ICV_HAAR_RIGHT_VAL_NAME,
tree->alpha[-tree->right[k]] );
}
cvEndWriteStruct( fs ); /* split */
}
cvEndWriteStruct( fs ); /* tree */
}
cvEndWriteStruct( fs ); /* trees */
cvWriteReal( fs, ICV_HAAR_STAGE_THRESHOLD_NAME, cascade->stage_classifier[i].threshold);
cvWriteInt( fs, ICV_HAAR_PARENT_NAME, cascade->stage_classifier[i].parent );
cvWriteInt( fs, ICV_HAAR_NEXT_NAME, cascade->stage_classifier[i].next );
cvEndWriteStruct( fs ); /* stage */
} /* for each stage */
cvEndWriteStruct( fs ); /* stages */
cvEndWriteStruct( fs ); /* root */
}
void *
gpuCloneHaarClassifier( const void *struct_ptr )
{
CvHaarClassifierCascade *cascade = NULL;
int i, j, k, n;
const CvHaarClassifierCascade *cascade_src =
(const CvHaarClassifierCascade *) struct_ptr;
n = cascade_src->count;
cascade = gpuCreateHaarClassifierCascade(n);
cascade->orig_window_size = cascade_src->orig_window_size;
for( i = 0; i < n; ++i )
{
cascade->stage_classifier[i].parent = cascade_src->stage_classifier[i].parent;
cascade->stage_classifier[i].next = cascade_src->stage_classifier[i].next;
cascade->stage_classifier[i].child = cascade_src->stage_classifier[i].child;
cascade->stage_classifier[i].threshold = cascade_src->stage_classifier[i].threshold;
cascade->stage_classifier[i].count = 0;
cascade->stage_classifier[i].classifier =
(CvHaarClassifier *) cvAlloc( cascade_src->stage_classifier[i].count
* sizeof( cascade->stage_classifier[i].classifier[0] ) );
cascade->stage_classifier[i].count = cascade_src->stage_classifier[i].count;
for( j = 0; j < cascade->stage_classifier[i].count; ++j )
cascade->stage_classifier[i].classifier[j].haar_feature = NULL;
for( j = 0; j < cascade->stage_classifier[i].count; ++j )
{
const CvHaarClassifier *classifier_src =
&cascade_src->stage_classifier[i].classifier[j];
CvHaarClassifier *classifier =
&cascade->stage_classifier[i].classifier[j];
classifier->count = classifier_src->count;
classifier->haar_feature = (CvHaarFeature *) cvAlloc(
classifier->count * ( sizeof( *classifier->haar_feature ) +
sizeof( *classifier->threshold ) +
sizeof( *classifier->left ) +
sizeof( *classifier->right ) ) +
(classifier->count + 1) * sizeof( *classifier->alpha ) );
classifier->threshold = (float *) (classifier->haar_feature + classifier->count);
classifier->left = (int *) (classifier->threshold + classifier->count);
classifier->right = (int *) (classifier->left + classifier->count);
classifier->alpha = (float *) (classifier->right + classifier->count);
for( k = 0; k < classifier->count; ++k )
{
classifier->haar_feature[k] = classifier_src->haar_feature[k];
classifier->threshold[k] = classifier_src->threshold[k];
classifier->left[k] = classifier_src->left[k];
classifier->right[k] = classifier_src->right[k];
classifier->alpha[k] = classifier_src->alpha[k];
}
classifier->alpha[classifier->count] =
classifier_src->alpha[classifier->count];
}
}
return cascade;
}
#if 0
CvType haar_type( CV_TYPE_NAME_HAAR, gpuIsHaarClassifier,
(CvReleaseFunc)gpuReleaseHaarClassifierCascade,
gpuReadHaarClassifier, gpuWriteHaarClassifier,
gpuCloneHaarClassifier );
namespace cv
{
HaarClassifierCascade::HaarClassifierCascade() {}
HaarClassifierCascade::HaarClassifierCascade(const String &filename)
{
load(filename);
}
bool HaarClassifierCascade::load(const String &filename)
{
cascade = Ptr<CvHaarClassifierCascade>((CvHaarClassifierCascade *)cvLoad(filename.c_str(), 0, 0, 0));
return (CvHaarClassifierCascade *)cascade != 0;
}
void HaarClassifierCascade::detectMultiScale( const Mat &image,
Vector<Rect> &objects, double scaleFactor,
int minNeighbors, int flags,
Size minSize )
{
MemStorage storage(cvCreateMemStorage(0));
CvMat _image = image;
CvSeq *_objects = gpuHaarDetectObjects( &_image, cascade, storage, scaleFactor,
minNeighbors, flags, minSize );
Seq<Rect>(_objects).copyTo(objects);
}
int HaarClassifierCascade::runAt(Point pt, int startStage, int) const
{
return gpuRunHaarClassifierCascade(cascade, pt, startStage);
}
void HaarClassifierCascade::setImages( const Mat &sum, const Mat &sqsum,
const Mat &tilted, double scale )
{
CvMat _sum = sum, _sqsum = sqsum, _tilted = tilted;
gpuSetImagesForHaarClassifierCascade( cascade, &_sum, &_sqsum, &_tilted, scale );
}
}
#endif
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////reserved functios//////////////////////////////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/*#if CV_SSE2
# if CV_SSE4 || defined __SSE4__
# include <smmintrin.h>
# else
# define _mm_blendv_pd(a, b, m) _mm_xor_pd(a, _mm_and_pd(_mm_xor_pd(b, a), m))
# define _mm_blendv_ps(a, b, m) _mm_xor_ps(a, _mm_and_ps(_mm_xor_ps(b, a), m))
# endif
#if defined CV_ICC
# define CV_HAAR_USE_SSE 1
#endif
#endif*/
/*
CV_IMPL void
gpuSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade,
const CvArr* _sum,
const CvArr* _sqsum,
const CvArr* _tilted_sum,
double scale )
{
CvMat sum_stub, *sum = (CvMat*)_sum;
CvMat sqsum_stub, *sqsum = (CvMat*)_sqsum;
CvMat tilted_stub, *tilted = (CvMat*)_tilted_sum;
GpuHidHaarClassifierCascade* cascade;
int coi0 = 0, coi1 = 0;
int i;
int datasize;
int totalclassifier;
CvRect equRect;
double weight_scale;
int rows,cols;
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" );
sum = cvGetMat( sum, &sum_stub, &coi0 );
sqsum = cvGetMat( sqsum, &sqsum_stub, &coi1 );
if( coi0 || coi1 )
CV_Error( CV_BadCOI, "COI is not supported" );
if( !CV_ARE_SIZES_EQ( sum, sqsum ))
CV_Error( CV_StsUnmatchedSizes, "All integral images must have the same size" );
if( CV_MAT_TYPE(sqsum->type) != CV_64FC1 ||
CV_MAT_TYPE(sum->type) != CV_32SC1 )
CV_Error( CV_StsUnsupportedFormat,
"Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" );
if( !_cascade->hid_cascade )
gpuCreateHidHaarClassifierCascade(_cascade,&datasize,&totalclassifier);
cascade =(GpuHidHaarClassifierCascade *)_cascade->hid_cascade;
if( cascade->has_tilted_features )
{
tilted = cvGetMat( tilted, &tilted_stub, &coi1 );
if( CV_MAT_TYPE(tilted->type) != CV_32SC1 )
CV_Error( CV_StsUnsupportedFormat,
"Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" );
if( sum->step != tilted->step )
CV_Error( CV_StsUnmatchedSizes,
"Sum and tilted_sum must have the same stride (step, widthStep)" );
if( !CV_ARE_SIZES_EQ( sum, tilted ))
CV_Error( CV_StsUnmatchedSizes, "All integral images must have the same size" );
//cascade->tilted = *tilted;
}
_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 );
//cascade->sum = *sum;
//cascade->sqsum = *sqsum;
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->p0 = sum_elem_ptr(*sum, equRect.y, equRect.x);
cascade->p1 = sum_elem_ptr(*sum, equRect.y, equRect.x + equRect.width );
cascade->p2 = sum_elem_ptr(*sum, equRect.y + equRect.height, equRect.x );
cascade->p3 = sum_elem_ptr(*sum, equRect.y + equRect.height,
equRect.x + equRect.width );
*/
/* rows=sum->rows;
cols=sum->cols;
cascade->p0 = equRect.y*cols + equRect.x;
cascade->p1 = equRect.y*cols + equRect.x + equRect.width;
cascade->p2 = (equRect.y + equRect.height) * cols + equRect.x;
cascade->p3 = (equRect.y + equRect.height) * cols + equRect.x + equRect.width ;
*/
/*
cascade->pq0 = sqsum_elem_ptr(*sqsum, equRect.y, equRect.x);
cascade->pq1 = sqsum_elem_ptr(*sqsum, equRect.y, equRect.x + equRect.width );
cascade->pq2 = sqsum_elem_ptr(*sqsum, equRect.y + equRect.height, equRect.x );
cascade->pq3 = sqsum_elem_ptr(*sqsum, equRect.y + equRect.height,
equRect.x + equRect.width );
*/
/* 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 < cascade->stage_classifier[i].count; j++ )
{
for( l = 0; l < cascade->stage_classifier[i].classifier[j].count; l++ )
{
CvHaarFeature* feature =
&_cascade->stage_classifier[i].classifier[j].haar_feature[l];
*/ /* GpuHidHaarClassifier* classifier =
cascade->stage_classifier[i].classifier + j; */
//GpuHidHaarFeature* hidfeature =
// &cascade->stage_classifier[i].classifier[j].node[l].feature;
/* 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( !hidfeature->rect[k].p0 )
// 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;
kx = r[0].width / base_w;
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 )
{
hidfeature->rect[k].p0 = tr.y * rows + tr.x;
hidfeature->rect[k].p1 = tr.y * rows + tr.x + tr.width;
hidfeature->rect[k].p2 = (tr.y + tr.height) * rows + tr.x;
hidfeature->rect[k].p3 = (tr.y + tr.height) * rows + tr.x + tr.width;
}
else
{
hidfeature->rect[k].p2 = (tr.y + tr.width) * rows + tr.x + tr.width;
hidfeature->rect[k].p3 = (tr.y + tr.width + tr.height) * rows + tr.x + tr.width - tr.height;
hidfeature->rect[k].p0 = tr.y*rows + tr.x;
hidfeature->rect[k].p1 = (tr.y + tr.height) * rows + tr.x - tr.height;
}
//hidfeature->rect[k].weight = (float)(feature->rect[k].weight * correction_ratio);
if( k == 0 )
area0 = tr.width * tr.height;
else
;// sum0 += hidfeature->rect[k].weight * tr.width * tr.height;
}
//hidfeature->rect[0].weight = (float)(-sum0/area0);*/
// } /* l */
// } /* j */
// }
//}
/*
CV_INLINE
double gpuEvalHidHaarClassifier( GpuHidHaarClassifier *classifier,
double variance_norm_factor,
size_t p_offset )
{
int idx = 0;
do
{
GpuHidHaarTreeNode* node = classifier->node + idx;
double t = node->threshold * variance_norm_factor;
double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
if( node->feature.rect[2].p0 )
sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
idx = sum < t ? node->left : node->right;
}
while( idx > 0 );
return classifier->alpha[-idx];
return 0.;
}
*/
CV_IMPL int
gpuRunHaarClassifierCascade( /*const CvHaarClassifierCascade *_cascade,
CvPoint pt, int start_stage */)
{
/*
int result = -1;
int p_offset, pq_offset;
int i, j;
double mean, variance_norm_factor;
GpuHidHaarClassifierCascade* cascade;
if( !CV_IS_HAAR_CLASSIFIER(_cascade) )
CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid cascade pointer" );
cascade = (GpuHidHaarClassifierCascade*) _cascade->hid_cascade;
if( !cascade )
CV_Error( CV_StsNullPtr, "Hidden cascade has not been created.\n"
"Use gpuSetImagesForHaarClassifierCascade" );
if( pt.x < 0 || pt.y < 0 ||
pt.x + _cascade->real_window_size.width >= cascade->sum.width-2 ||
pt.y + _cascade->real_window_size.height >= cascade->sum.height-2 )
return -1;
p_offset = pt.y * (cascade->sum.step/sizeof(sumtype)) + pt.x;
pq_offset = pt.y * (cascade->sqsum.step/sizeof(sqsumtype)) + pt.x;
mean = calc_sum(*cascade,p_offset)*cascade->inv_window_area;
variance_norm_factor = cascade->pq0[pq_offset] - cascade->pq1[pq_offset] -
cascade->pq2[pq_offset] + cascade->pq3[pq_offset];
variance_norm_factor = variance_norm_factor*cascade->inv_window_area - mean*mean;
if( variance_norm_factor >= 0. )
variance_norm_factor = sqrt(variance_norm_factor);
else
variance_norm_factor = 1.;
if( cascade->is_stump_based )
{
for( i = start_stage; i < cascade->count; i++ )
{
double stage_sum = 0;
if( cascade->stage_classifier[i].two_rects )
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
GpuHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
GpuHidHaarTreeNode* node = classifier->node;
double t = node->threshold*variance_norm_factor;
double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
stage_sum += classifier->alpha[sum >= t];
}
}
else
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
GpuHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
GpuHidHaarTreeNode* node = classifier->node;
double t = node->threshold*variance_norm_factor;
double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
if( node->feature.rect[2].p0 )
sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
stage_sum += classifier->alpha[sum >= t];
}
}
if( stage_sum < cascade->stage_classifier[i].threshold )
return -i;
}
}
*/
return 1;
}
namespace cv
{
namespace ocl
{
struct gpuHaarDetectObjects_ScaleImage_Invoker
{
gpuHaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade *_cascade,
int _stripSize, double _factor,
const Mat &_sum1, const Mat &_sqsum1, Mat *_norm1,
Mat *_mask1, Rect _equRect, ConcurrentRectVector &_vec )
{
cascade = _cascade;
stripSize = _stripSize;
factor = _factor;
sum1 = _sum1;
sqsum1 = _sqsum1;
norm1 = _norm1;
mask1 = _mask1;
equRect = _equRect;
vec = &_vec;
}
void operator()( const BlockedRange &range ) const
{
Size winSize0 = cascade->orig_window_size;
Size winSize(cvRound(winSize0.width * factor), cvRound(winSize0.height * factor));
int y1 = range.begin() * stripSize, y2 = min(range.end() * stripSize, sum1.rows - 1 - winSize0.height);
Size ssz(sum1.cols - 1 - winSize0.width, y2 - y1);
int x, y, ystep = factor > 2 ? 1 : 2;
for( y = y1; y < y2; y += ystep )
for( x = 0; x < ssz.width; x += ystep )
{
if( gpuRunHaarClassifierCascade( /*cascade, cvPoint(x, y), 0*/ ) > 0 )
vec->push_back(Rect(cvRound(x * factor), cvRound(y * factor),
winSize.width, winSize.height));
}
}
const CvHaarClassifierCascade *cascade;
int stripSize;
double factor;
Mat sum1, sqsum1, *norm1, *mask1;
Rect equRect;
ConcurrentRectVector *vec;
};
struct gpuHaarDetectObjects_ScaleCascade_Invoker
{
gpuHaarDetectObjects_ScaleCascade_Invoker( const CvHaarClassifierCascade *_cascade,
Size _winsize, const Range &_xrange, double _ystep,
size_t _sumstep, const int **_p, const int **_pq,
ConcurrentRectVector &_vec )
{
cascade = _cascade;
winsize = _winsize;
xrange = _xrange;
ystep = _ystep;
sumstep = _sumstep;
p = _p;
pq = _pq;
vec = &_vec;
}
void operator()( const BlockedRange &range ) const
{
int iy, startY = range.begin(), endY = range.end();
const int *p0 = p[0], *p1 = p[1], *p2 = p[2], *p3 = p[3];
const int *pq0 = pq[0], *pq1 = pq[1], *pq2 = pq[2], *pq3 = pq[3];
bool doCannyPruning = p0 != 0;
int sstep = (int)(sumstep / sizeof(p0[0]));
for( iy = startY; iy < endY; iy++ )
{
int ix, y = cvRound(iy * ystep), ixstep = 1;
for( ix = xrange.start; ix < xrange.end; ix += ixstep )
{
int x = cvRound(ix * ystep); // it should really be ystep, not ixstep
if( doCannyPruning )
{
int offset = y * sstep + x;
int s = p0[offset] - p1[offset] - p2[offset] + p3[offset];
int sq = pq0[offset] - pq1[offset] - pq2[offset] + pq3[offset];
if( s < 100 || sq < 20 )
{
ixstep = 2;
continue;
}
}
int result = gpuRunHaarClassifierCascade(/* cascade, cvPoint(x, y), 0 */);
if( result > 0 )
vec->push_back(Rect(x, y, winsize.width, winsize.height));
ixstep = result != 0 ? 1 : 2;
}
}
}
const CvHaarClassifierCascade *cascade;
double ystep;
size_t sumstep;
Size winsize;
Range xrange;
const int **p;
const int **pq;
ConcurrentRectVector *vec;
};
}
}
/*
typedef struct _ALIGNED_ON(128) GpuHidHaarFeature
{
struct _ALIGNED_ON(32)
{
int p0 _ALIGNED_ON(4);
int p1 _ALIGNED_ON(4);
int p2 _ALIGNED_ON(4);
int p3 _ALIGNED_ON(4);
float weight _ALIGNED_ON(4);
}
rect[CV_HAAR_FEATURE_MAX] _ALIGNED_ON(32);
}
GpuHidHaarFeature;
typedef struct _ALIGNED_ON(128) GpuHidHaarTreeNode
{
int left _ALIGNED_ON(4);
int right _ALIGNED_ON(4);
float threshold _ALIGNED_ON(4);
int p0[CV_HAAR_FEATURE_MAX] _ALIGNED_ON(16);
int p1[CV_HAAR_FEATURE_MAX] _ALIGNED_ON(16);
int p2[CV_HAAR_FEATURE_MAX] _ALIGNED_ON(16);
int p3[CV_HAAR_FEATURE_MAX] _ALIGNED_ON(16);
float weight[CV_HAAR_FEATURE_MAX] _ALIGNED_ON(16);
float alpha[2] _ALIGNED_ON(8);
// GpuHidHaarFeature feature __attribute__((aligned (128)));
}
GpuHidHaarTreeNode;
typedef struct _ALIGNED_ON(32) GpuHidHaarClassifier
{
int count _ALIGNED_ON(4);
//CvHaarFeature* orig_feature;
GpuHidHaarTreeNode* node _ALIGNED_ON(8);
float* alpha _ALIGNED_ON(8);
}
GpuHidHaarClassifier;
typedef struct _ALIGNED_ON(64) __attribute__((aligned (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);
// GpuHidHaarStageClassifier* stage_classifier __attribute__((aligned (8)));
}GpuHidHaarClassifierCascade;
*/
/* End of file. */