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haar.cpp
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/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// 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 Intel Corporation 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 */
#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/objdetect/objdetect_c.h"
#include <stdio.h>
#include "haar.hpp"
#if CV_HAAR_FEATURE_MAX_LOCAL != CV_HAAR_FEATURE_MAX
#error CV_HAAR_FEATURE_MAX definition changed. Adjust CV_HAAR_FEATURE_MAX_LOCAL value please.
#endif
#if CV_SSE2
# if 1 /*!CV_SSE4_1 && !CV_SSE4_2*/
# 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
#endif
#if CV_HAAR_USE_AVX
# if defined _MSC_VER
# pragma warning( disable : 4752 )
# endif
#else
# if CV_SSE2
# define CV_HAAR_USE_SSE 1
# endif
#endif
/* these settings affect the quality of detection: change with care */
#define CV_ADJUST_FEATURES 1
#define CV_ADJUST_WEIGHTS 0
typedef struct CvHidHaarStageClassifier
{
int count;
float threshold;
CvHidHaarClassifier* classifier;
int two_rects;
struct CvHidHaarStageClassifier* next;
struct CvHidHaarStageClassifier* child;
struct CvHidHaarStageClassifier* parent;
} CvHidHaarStageClassifier;
typedef struct CvHidHaarClassifierCascade
{
int count;
int has_tilted_features;
double inv_window_area;
CvMat sum, sqsum, tilted;
CvHidHaarStageClassifier* stage_classifier;
sqsumtype *pq0, *pq1, *pq2, *pq3;
sumtype *p0, *p1, *p2, *p3;
void** ipp_stages;
bool is_tree;
bool isStumpBased;
} CvHidHaarClassifierCascade;
const int icv_object_win_border = 1;
const float icv_stage_threshold_bias = 0.0001f;
static CvHaarClassifierCascade*
icvCreateHaarClassifierCascade( 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 void
icvReleaseHidHaarClassifierCascade( CvHidHaarClassifierCascade** _cascade )
{
if( _cascade && *_cascade )
{
#ifdef HAVE_IPP
CvHidHaarClassifierCascade* cascade = *_cascade;
if( CV_IPP_CHECK_COND && cascade->ipp_stages )
{
int i;
for( i = 0; i < cascade->count; i++ )
{
if( cascade->ipp_stages[i] )
#if IPP_VERSION_X100 < 900 && !IPP_DISABLE_HAAR
ippiHaarClassifierFree_32f( (IppiHaarClassifier_32f*)cascade->ipp_stages[i] );
#else
cvFree(&cascade->ipp_stages[i]);
#endif
}
}
cvFree( &cascade->ipp_stages );
#endif
cvFree( _cascade );
}
}
/* create more efficient internal representation of haar classifier cascade */
static CvHidHaarClassifierCascade*
icvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade )
{
CvRect* ipp_features = 0;
float *ipp_weights = 0, *ipp_thresholds = 0, *ipp_val1 = 0, *ipp_val2 = 0;
int* ipp_counts = 0;
CvHidHaarClassifierCascade* out = 0;
int i, j, k, l;
int datasize;
int total_classifiers = 0;
int total_nodes = 0;
char errorstr[1000];
CvHidHaarClassifier* haar_classifier_ptr;
CvHidHaarTreeNode* haar_node_ptr;
CvSize orig_window_size;
bool has_tilted_features = false;
int max_count = 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 );
}
max_count = MAX( max_count, stage_classifier->count );
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 = 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(CvHidHaarClassifierCascade) +
sizeof(CvHidHaarStageClassifier)*cascade->count +
sizeof(CvHidHaarClassifier) * total_classifiers +
sizeof(CvHidHaarTreeNode) * total_nodes +
sizeof(void*)*(total_nodes + total_classifiers);
out = (CvHidHaarClassifierCascade*)cvAlloc( datasize );
memset( out, 0, sizeof(*out) );
/* init header */
out->count = cascade->count;
out->stage_classifier = (CvHidHaarStageClassifier*)(out + 1);
haar_classifier_ptr = (CvHidHaarClassifier*)(out->stage_classifier + cascade->count);
haar_node_ptr = (CvHidHaarTreeNode*)(haar_classifier_ptr + total_classifiers);
out->isStumpBased = true;
out->has_tilted_features = has_tilted_features;
out->is_tree = false;
/* initialize internal representation */
for( i = 0; i < cascade->count; i++ )
{
CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;
CvHidHaarStageClassifier* hid_stage_classifier = out->stage_classifier + 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 : out->stage_classifier + stage_classifier->parent;
hid_stage_classifier->next = (stage_classifier->next == -1)
? NULL : out->stage_classifier + stage_classifier->next;
hid_stage_classifier->child = (stage_classifier->child == -1)
? NULL : out->stage_classifier + stage_classifier->child;
out->is_tree = out->is_tree || (hid_stage_classifier->next != NULL);
for( j = 0; j < stage_classifier->count; j++ )
{
CvHaarClassifier* classifier = stage_classifier->classifier + j;
CvHidHaarClassifier* hid_classifier = hid_stage_classifier->classifier + j;
int node_count = classifier->count;
float* alpha_ptr = (float*)(haar_node_ptr + node_count);
hid_classifier->count = node_count;
hid_classifier->node = haar_node_ptr;
hid_classifier->alpha = alpha_ptr;
for( l = 0; l < node_count; l++ )
{
CvHidHaarTreeNode* 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 )
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 =
(CvHidHaarTreeNode*)cvAlignPtr(alpha_ptr+node_count+1, sizeof(void*));
out->isStumpBased = out->isStumpBased && (node_count == 1);
}
}
#if defined HAVE_IPP && !IPP_DISABLE_HAAR
int can_use_ipp = CV_IPP_CHECK_COND && (!out->has_tilted_features && !out->is_tree && out->isStumpBased);
if( can_use_ipp )
{
int ipp_datasize = cascade->count*sizeof(out->ipp_stages[0]);
float ipp_weight_scale=(float)(1./((orig_window_size.width-icv_object_win_border*2)*
(orig_window_size.height-icv_object_win_border*2)));
out->ipp_stages = (void**)cvAlloc( ipp_datasize );
memset( out->ipp_stages, 0, ipp_datasize );
ipp_features = (CvRect*)cvAlloc( max_count*3*sizeof(ipp_features[0]) );
ipp_weights = (float*)cvAlloc( max_count*3*sizeof(ipp_weights[0]) );
ipp_thresholds = (float*)cvAlloc( max_count*sizeof(ipp_thresholds[0]) );
ipp_val1 = (float*)cvAlloc( max_count*sizeof(ipp_val1[0]) );
ipp_val2 = (float*)cvAlloc( max_count*sizeof(ipp_val2[0]) );
ipp_counts = (int*)cvAlloc( max_count*sizeof(ipp_counts[0]) );
for( i = 0; i < cascade->count; i++ )
{
CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;
for( j = 0, k = 0; j < stage_classifier->count; j++ )
{
CvHaarClassifier* classifier = stage_classifier->classifier + j;
int rect_count = 2 + (classifier->haar_feature->rect[2].r.width != 0);
ipp_thresholds[j] = classifier->threshold[0];
ipp_val1[j] = classifier->alpha[0];
ipp_val2[j] = classifier->alpha[1];
ipp_counts[j] = rect_count;
for( l = 0; l < rect_count; l++, k++ )
{
ipp_features[k] = classifier->haar_feature->rect[l].r;
//ipp_features[k].y = orig_window_size.height - ipp_features[k].y - ipp_features[k].height;
ipp_weights[k] = classifier->haar_feature->rect[l].weight*ipp_weight_scale;
}
}
if( ippiHaarClassifierInitAlloc_32f( (IppiHaarClassifier_32f**)&out->ipp_stages[i],
(const IppiRect*)ipp_features, ipp_weights, ipp_thresholds,
ipp_val1, ipp_val2, ipp_counts, stage_classifier->count ) < 0 )
break;
}
if( i < cascade->count )
{
for( j = 0; j < i; j++ )
if( out->ipp_stages[i] )
ippiHaarClassifierFree_32f( (IppiHaarClassifier_32f*)out->ipp_stages[i] );
cvFree( &out->ipp_stages );
}
}
#endif
cascade->hid_cascade = out;
assert( (char*)haar_node_ptr - (char*)out <= datasize );
cvFree( &ipp_features );
cvFree( &ipp_weights );
cvFree( &ipp_thresholds );
cvFree( &ipp_val1 );
cvFree( &ipp_val2 );
cvFree( &ipp_counts );
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
cvSetImagesForHaarClassifierCascade( 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;
CvHidHaarClassifierCascade* cascade;
int coi0 = 0, coi1 = 0;
int i;
CvRect equRect;
double weight_scale;
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 )
icvCreateHidHaarClassifierCascade(_cascade);
cascade = _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 );
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];
/* CvHidHaarClassifier* classifier =
cascade->stage_classifier[i].classifier + j; */
CvHidHaarFeature* 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 = sum_elem_ptr(*sum, tr.y, tr.x);
hidfeature->rect[k].p1 = sum_elem_ptr(*sum, tr.y, tr.x + tr.width);
hidfeature->rect[k].p2 = sum_elem_ptr(*sum, tr.y + tr.height, tr.x);
hidfeature->rect[k].p3 = sum_elem_ptr(*sum, tr.y + tr.height, tr.x + tr.width);
}
else
{
hidfeature->rect[k].p2 = sum_elem_ptr(*tilted, tr.y + tr.width, tr.x + tr.width);
hidfeature->rect[k].p3 = sum_elem_ptr(*tilted, tr.y + tr.width + tr.height,
tr.x + tr.width - tr.height);
hidfeature->rect[k].p0 = sum_elem_ptr(*tilted, tr.y, tr.x);
hidfeature->rect[k].p1 = sum_elem_ptr(*tilted, tr.y + tr.height, 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 icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier,
double variance_norm_factor,
size_t p_offset )
{
int idx = 0;
/*#if CV_HAAR_USE_SSE && !CV_HAAR_USE_AVX
if(cv::checkHardwareSupport(CV_CPU_SSE2))//based on old SSE variant. Works slow
{
double CV_DECL_ALIGNED(16) temp[2];
__m128d zero = _mm_setzero_pd();
do
{
CvHidHaarTreeNode* node = classifier->node + idx;
__m128d t = _mm_set1_pd((node->threshold)*variance_norm_factor);
__m128d left = _mm_set1_pd(node->left);
__m128d right = _mm_set1_pd(node->right);
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;
__m128d sum = _mm_set1_pd(_sum);
t = _mm_cmplt_sd(sum, t);
sum = _mm_blendv_pd(right, left, t);
_mm_store_pd(temp, sum);
idx = (int)temp[0];
}
while(idx > 0 );
}
else
#endif*/
{
do
{
CvHidHaarTreeNode* 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];
}
static int
cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
CvPoint pt, double& stage_sum, int start_stage )
{
#if CV_HAAR_USE_AVX
bool haveAVX = CV_CPU_HAS_SUPPORT_AVX;
#else
# ifdef CV_HAAR_USE_SSE
bool haveSSE2 = cv::checkHardwareSupport(CV_CPU_SSE2);
# endif
#endif
int p_offset, pq_offset;
int i, j;
double mean, variance_norm_factor;
CvHidHaarClassifierCascade* cascade;
if( !CV_IS_HAAR_CLASSIFIER(_cascade) )
CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid cascade pointer" );
cascade = _cascade->hid_cascade;
if( !cascade )
CV_Error( CV_StsNullPtr, "Hidden cascade has not been created.\n"
"Use cvSetImagesForHaarClassifierCascade" );
if( pt.x < 0 || pt.y < 0 ||
pt.x + _cascade->real_window_size.width >= cascade->sum.width ||
pt.y + _cascade->real_window_size.height >= cascade->sum.height )
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 = std::sqrt(variance_norm_factor);
else
variance_norm_factor = 1.;
if( cascade->is_tree )
{
CvHidHaarStageClassifier* ptr = cascade->stage_classifier;
assert( start_stage == 0 );
while( ptr )
{
stage_sum = 0.0;
j = 0;
#if CV_HAAR_USE_AVX
if(haveAVX)
{
for( ; j <= ptr->count - 8; j += 8 )
{
stage_sum += cv_haar_avx::icvEvalHidHaarClassifierAVX(
ptr->classifier + j,
variance_norm_factor, p_offset );
}
}
#endif
for( ; j < ptr->count; j++ )
{
stage_sum += icvEvalHidHaarClassifier( ptr->classifier + j, variance_norm_factor, p_offset );
}
if( stage_sum >= ptr->threshold )
{
ptr = ptr->child;
}
else
{
while( ptr && ptr->next == NULL ) ptr = ptr->parent;
if( ptr == NULL )
return 0;
ptr = ptr->next;
}
}
}
else if( cascade->isStumpBased )
{
#if CV_HAAR_USE_AVX
if(haveAVX)
{
for( i = start_stage; i < cascade->count; i++ )
{
stage_sum = 0.0;
j = 0;
if( cascade->stage_classifier[i].two_rects )
{
for( ; j <= cascade->stage_classifier[i].count - 8; j += 8 )
{
stage_sum += cv_haar_avx::icvEvalHidHaarStumpClassifierTwoRectAVX(
cascade->stage_classifier[i].classifier + j,
variance_norm_factor, p_offset);
}
for( ; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* 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 <= (cascade->stage_classifier[i].count)-8; j+=8 )
{
stage_sum += cv_haar_avx::icvEvalHidHaarStumpClassifierAVX(
cascade->stage_classifier[i].classifier + j,
variance_norm_factor, p_offset);
}
for( ; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* 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;
}
}
else
#elif defined CV_HAAR_USE_SSE //old SSE optimization
if(haveSSE2)
{
for( i = start_stage; i < cascade->count; i++ )
{
__m128d vstage_sum = _mm_setzero_pd();
if( cascade->stage_classifier[i].two_rects )
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* node = classifier->node;
// ayasin - NHM perf optim. Avoid use of costly flaky jcc
__m128d t = _mm_set_sd(node->threshold*variance_norm_factor);
__m128d a = _mm_set_sd(classifier->alpha[0]);
__m128d b = _mm_set_sd(classifier->alpha[1]);
__m128d sum = _mm_set_sd(calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight +
calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight);
t = _mm_cmpgt_sd(t, sum);
vstage_sum = _mm_add_sd(vstage_sum, _mm_blendv_pd(b, a, t));
}
}
else
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* node = classifier->node;
// ayasin - NHM perf optim. Avoid use of costly flaky jcc
__m128d t = _mm_set_sd(node->threshold*variance_norm_factor);
__m128d a = _mm_set_sd(classifier->alpha[0]);
__m128d b = _mm_set_sd(classifier->alpha[1]);
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;
__m128d sum = _mm_set_sd(_sum);
t = _mm_cmpgt_sd(t, sum);
vstage_sum = _mm_add_sd(vstage_sum, _mm_blendv_pd(b, a, t));
}
}
__m128d i_threshold = _mm_set1_pd(cascade->stage_classifier[i].threshold);
if( _mm_comilt_sd(vstage_sum, i_threshold) )
return -i;
}
}
else
#endif // AVX or SSE
{
for( i = start_stage; i < cascade->count; i++ )
{
stage_sum = 0.0;
if( cascade->stage_classifier[i].two_rects )
{
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* 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++ )
{
CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
CvHidHaarTreeNode* 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;
}
}
}
else
{
for( i = start_stage; i < cascade->count; i++ )
{
stage_sum = 0.0;
int k = 0;
#if CV_HAAR_USE_AVX
if(haveAVX)
{
for( ; k < cascade->stage_classifier[i].count - 8; k += 8 )
{
stage_sum += cv_haar_avx::icvEvalHidHaarClassifierAVX(
cascade->stage_classifier[i].classifier + k,
variance_norm_factor, p_offset );
}
}
#endif
for(; k < cascade->stage_classifier[i].count; k++ )
{
stage_sum += icvEvalHidHaarClassifier(
cascade->stage_classifier[i].classifier + k,
variance_norm_factor, p_offset );
}
if( stage_sum < cascade->stage_classifier[i].threshold )
return -i;
}
}
return 1;
}
CV_IMPL int
cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
CvPoint pt, int start_stage )
{
CV_INSTRUMENT_REGION()
double stage_sum;
return cvRunHaarClassifierCascadeSum(_cascade, pt, stage_sum, start_stage);
}
namespace cv
{
const size_t PARALLEL_LOOP_BATCH_SIZE = 100;
class HaarDetectObjects_ScaleImage_Invoker : public ParallelLoopBody
{
public:
HaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade* _cascade,
int _stripSize, double _factor,
const Mat& _sum1, const Mat& _sqsum1, Mat* _norm1,
Mat* _mask1, Rect _equRect, std::vector<Rect>& _vec,
std::vector<int>& _levels, std::vector<double>& _weights,
bool _outputLevels, Mutex *_mtx )
{
cascade = _cascade;
stripSize = _stripSize;
factor = _factor;
sum1 = _sum1;
sqsum1 = _sqsum1;
norm1 = _norm1;
mask1 = _mask1;
equRect = _equRect;
vec = &_vec;
rejectLevels = _outputLevels ? &_levels : 0;
levelWeights = _outputLevels ? &_weights : 0;
mtx = _mtx;
}
void operator()( const Range& range ) const
{
CV_INSTRUMENT_REGION()
Size winSize0 = cascade->orig_window_size;
Size winSize(cvRound(winSize0.width*factor), cvRound(winSize0.height*factor));
int y1 = range.start*stripSize, y2 = std::min(range.end*stripSize, sum1.rows - 1 - winSize0.height);
if (y2 <= y1 || sum1.cols <= 1 + winSize0.width)
return;
Size ssz(sum1.cols - 1 - winSize0.width, y2 - y1);
int x, y, ystep = factor > 2 ? 1 : 2;
std::vector<Rect> vecLocal;
std::vector<int> rejectLevelsLocal;
std::vector<double> levelWeightsLocal;
#ifdef HAVE_IPP
if(CV_IPP_CHECK_COND && cascade->hid_cascade->ipp_stages )
{
IppiRect iequRect = {equRect.x, equRect.y, equRect.width, equRect.height};
CV_INSTRUMENT_FUN_IPP(ippiRectStdDev_32f_C1R, sum1.ptr<float>(y1), (int)sum1.step,
sqsum1.ptr<double>(y1), (int)sqsum1.step,
norm1->ptr<float>(y1), (int)norm1->step,
ippiSize(ssz.width, ssz.height), iequRect);
int positive = (ssz.width/ystep)*((ssz.height + ystep-1)/ystep);
if( ystep == 1 )
(*mask1) = Scalar::all(1);
else
for( y = y1; y < y2; y++ )
{
uchar* mask1row = mask1->ptr(y);
memset( mask1row, 0, ssz.width );
if( y % ystep == 0 )
for( x = 0; x < ssz.width; x += ystep )
mask1row[x] = (uchar)1;
}