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Commit 089de14e authored by Andrey Kamaev's avatar Andrey Kamaev
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Fix copy-paste bug in AVX optimization of haar

parent f32eb05e
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Showing with 223 additions and 151 deletions
modules/objdetect/src/haar.cpp
View file @ 089de14e
......@@ -45,7 +45,6 @@
#include <stdio.h>
#include "opencv2/core/internal.hpp"
#if CV_SSE2 || CV_SSE3
# if !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))
......@@ -53,13 +52,13 @@
# endif
#endif
# if CV_AVX
#if CV_AVX
# define CV_HAAR_USE_AVX 1
# else
#else
# if CV_SSE2 || CV_SSE3
# define CV_HAAR_USE_SSE 1
# endif
# endif
#endif
/* these settings affect the quality of detection: change with care */
#define CV_ADJUST_FEATURES 1
......@@ -76,8 +75,7 @@ typedef struct CvHidHaarFeature
float weight;
}
rect[CV_HAAR_FEATURE_MAX];
}
CvHidHaarFeature;
} CvHidHaarFeature;
typedef struct CvHidHaarTreeNode
......@@ -86,8 +84,7 @@ typedef struct CvHidHaarTreeNode
float threshold;
int left;
int right;
}
CvHidHaarTreeNode;
} CvHidHaarTreeNode;
typedef struct CvHidHaarClassifier
......@@ -96,8 +93,7 @@ typedef struct CvHidHaarClassifier
//CvHaarFeature* orig_feature;
CvHidHaarTreeNode* node;
float* alpha;
}
CvHidHaarClassifier;
} CvHidHaarClassifier;
typedef struct CvHidHaarStageClassifier
......@@ -110,11 +106,10 @@ typedef struct CvHidHaarStageClassifier
struct CvHidHaarStageClassifier* next;
struct CvHidHaarStageClassifier* child;
struct CvHidHaarStageClassifier* parent;
}
CvHidHaarStageClassifier;
} CvHidHaarStageClassifier;
struct CvHidHaarClassifierCascade
typedef struct CvHidHaarClassifierCascade
{
int count;
int isStumpBased;
......@@ -127,7 +122,7 @@ struct CvHidHaarClassifierCascade
sumtype *p0, *p1, *p2, *p3;
void** ipp_stages;
};
} CvHidHaarClassifierCascade;
const int icv_object_win_border = 1;
......@@ -634,21 +629,21 @@ cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade,
}
//AVX version icvEvalHidHaarClassifier. Process 8 CvHidHaarClassifiers per call. Check AVX support before invocation!!
// AVX version icvEvalHidHaarClassifier. Process 8 CvHidHaarClassifiers per call. Check AVX support before invocation!!
#ifdef CV_HAAR_USE_AVX
CV_INLINE
double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
double variance_norm_factor, size_t p_offset )
{
int CV_DECL_ALIGNED(32) idxV[8] = {0,0,0,0,0,0,0,0};
char flags[8] = {0,0,0,0,0,0,0,0};
uchar flags[8] = {0,0,0,0,0,0,0,0};
CvHidHaarTreeNode* nodes[8];
double res = 0;
char exitConditionFlag = 0;
uchar exitConditionFlag = 0;
for(;;)
{
float CV_DECL_ALIGNED(32) tmp[8] = {0,0,0,0,0,0,0,0};
nodes[0] = classifier ->node + idxV[0];
nodes[0] = (classifier+0)->node + idxV[0];
nodes[1] = (classifier+1)->node + idxV[1];
nodes[2] = (classifier+2)->node + idxV[2];
nodes[3] = (classifier+3)->node + idxV[3];
......@@ -658,46 +653,79 @@ double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
nodes[7] = (classifier+7)->node + idxV[7];
__m256 t = _mm256_set1_ps(variance_norm_factor);
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,nodes[6]->threshold,nodes[5]->threshold,nodes[4]->threshold,nodes[3]->threshold,nodes[2]->threshold,nodes[1]->threshold,nodes[0]->threshold));
__m256 offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[0],p_offset), calc_sum(nodes[6]->feature.rect[0],p_offset), calc_sum(nodes[5]->feature.rect[0],p_offset),
calc_sum(nodes[4]->feature.rect[0],p_offset), calc_sum(nodes[3]->feature.rect[0],p_offset), calc_sum(nodes[2]->feature.rect[0],p_offset), calc_sum(nodes[1]->feature.rect[0],
p_offset),calc_sum(nodes[0]->feature.rect[0],p_offset));
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight, nodes[6]->feature.rect[0].weight, nodes[5]->feature.rect[0].weight,
nodes[4]->feature.rect[0].weight, nodes[3]->feature.rect[0].weight, nodes[2]->feature.rect[0].weight, nodes[1]->feature.rect[0].weight, nodes[0]->feature.rect[0].weight);
__m256 sum = _mm256_mul_ps(offset, weight);
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
nodes[6]->threshold,
nodes[5]->threshold,
nodes[4]->threshold,
nodes[3]->threshold,
nodes[2]->threshold,
nodes[1]->threshold,
nodes[0]->threshold));
__m256 offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[0], p_offset),
calc_sum(nodes[6]->feature.rect[0], p_offset),
calc_sum(nodes[5]->feature.rect[0], p_offset),
calc_sum(nodes[4]->feature.rect[0], p_offset),
calc_sum(nodes[3]->feature.rect[0], p_offset),
calc_sum(nodes[2]->feature.rect[0], p_offset),
calc_sum(nodes[1]->feature.rect[0], p_offset),
calc_sum(nodes[0]->feature.rect[0], p_offset));
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
nodes[6]->feature.rect[0].weight,
nodes[5]->feature.rect[0].weight,
nodes[4]->feature.rect[0].weight,
nodes[3]->feature.rect[0].weight,
nodes[2]->feature.rect[0].weight,
nodes[1]->feature.rect[0].weight,
nodes[0]->feature.rect[0].weight);
offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[1],p_offset),calc_sum(nodes[6]->feature.rect[1],p_offset),calc_sum(nodes[5]->feature.rect[1],p_offset),
calc_sum(nodes[4]->feature.rect[1],p_offset),calc_sum(nodes[3]->feature.rect[1],p_offset),calc_sum(nodes[2]->feature.rect[1],p_offset),calc_sum(nodes[1]->feature.rect[1],p_offset),
calc_sum(nodes[0]->feature.rect[1],p_offset));
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight, nodes[6]->feature.rect[1].weight, nodes[5]->feature.rect[1].weight, nodes[4]->feature.rect[1].weight,
nodes[3]->feature.rect[1].weight, nodes[2]->feature.rect[1].weight, nodes[1]->feature.rect[1].weight, nodes[0]->feature.rect[1].weight);
__m256 sum = _mm256_mul_ps(offset, weight);
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset,weight));
offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[1], p_offset),
calc_sum(nodes[6]->feature.rect[1], p_offset),
calc_sum(nodes[5]->feature.rect[1], p_offset),
calc_sum(nodes[4]->feature.rect[1], p_offset),
calc_sum(nodes[3]->feature.rect[1], p_offset),
calc_sum(nodes[2]->feature.rect[1], p_offset),
calc_sum(nodes[1]->feature.rect[1], p_offset),
calc_sum(nodes[0]->feature.rect[1], p_offset));
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
nodes[6]->feature.rect[1].weight,
nodes[5]->feature.rect[1].weight,
nodes[4]->feature.rect[1].weight,
nodes[3]->feature.rect[1].weight,
nodes[2]->feature.rect[1].weight,
nodes[1]->feature.rect[1].weight,
nodes[0]->feature.rect[1].weight);
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
if( nodes[0]->feature.rect[2].p0 )
tmp[0] = calc_sum(nodes[0]->feature.rect[2],p_offset) * nodes[0]->feature.rect[2].weight;
tmp[0] = calc_sum(nodes[0]->feature.rect[2], p_offset) * nodes[0]->feature.rect[2].weight;
if( nodes[1]->feature.rect[2].p0 )
tmp[1] = calc_sum(nodes[1]->feature.rect[2],p_offset) * nodes[1]->feature.rect[2].weight;
tmp[1] = calc_sum(nodes[1]->feature.rect[2], p_offset) * nodes[1]->feature.rect[2].weight;
if( nodes[2]->feature.rect[2].p0 )
tmp[2] = calc_sum(nodes[2]->feature.rect[2],p_offset) * nodes[2]->feature.rect[2].weight;
tmp[2] = calc_sum(nodes[2]->feature.rect[2], p_offset) * nodes[2]->feature.rect[2].weight;
if( nodes[3]->feature.rect[2].p0 )
tmp[3] = calc_sum(nodes[3]->feature.rect[2],p_offset) * nodes[3]->feature.rect[2].weight;
tmp[3] = calc_sum(nodes[3]->feature.rect[2], p_offset) * nodes[3]->feature.rect[2].weight;
if( nodes[4]->feature.rect[2].p0 )
tmp[4] = calc_sum(nodes[4]->feature.rect[2],p_offset) * nodes[4]->feature.rect[2].weight;
tmp[4] = calc_sum(nodes[4]->feature.rect[2], p_offset) * nodes[4]->feature.rect[2].weight;
if( nodes[5]->feature.rect[2].p0 )
tmp[5] = calc_sum(nodes[5]->feature.rect[2],p_offset) * nodes[5]->feature.rect[2].weight;
tmp[5] = calc_sum(nodes[5]->feature.rect[2], p_offset) * nodes[5]->feature.rect[2].weight;
if( nodes[6]->feature.rect[2].p0 )
tmp[6] = calc_sum(nodes[6]->feature.rect[2],p_offset) * nodes[6]->feature.rect[2].weight;
tmp[6] = calc_sum(nodes[6]->feature.rect[2], p_offset) * nodes[6]->feature.rect[2].weight;
if( nodes[7]->feature.rect[2].p0 )
tmp[7] = calc_sum(nodes[7]->feature.rect[2],p_offset) * nodes[7]->feature.rect[2].weight;
tmp[7] = calc_sum(nodes[7]->feature.rect[2], p_offset) * nodes[7]->feature.rect[2].weight;
sum = _mm256_add_ps(sum,_mm256_load_ps(tmp));
__m256 left = _mm256_set_ps(nodes[7]->left,nodes[6]->left,nodes[5]->left,nodes[4]->left,nodes[3]->left,nodes[2]->left,nodes[1]->left,nodes[0]->left);
__m256 left = _mm256_set_ps(nodes[7]->left, nodes[6]->left, nodes[5]->left, nodes[4]->left, nodes[3]->left, nodes[2]->left, nodes[1]->left, nodes[0]->left );
__m256 right = _mm256_set_ps(nodes[7]->right,nodes[6]->right,nodes[5]->right,nodes[4]->right,nodes[3]->right,nodes[2]->right,nodes[1]->right,nodes[0]->right);
_mm256_store_si256((__m256i*)idxV,_mm256_cvttps_epi32(_mm256_blendv_ps(right, left,_mm256_cmp_ps(sum, t, _CMP_LT_OQ ))));
_mm256_store_si256((__m256i*)idxV, _mm256_cvttps_epi32(_mm256_blendv_ps(right, left, _mm256_cmp_ps(sum, t, _CMP_LT_OQ))));
for(int i = 0; i < 8; i++)
{
......@@ -706,17 +734,17 @@ double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
if(!flags[i])
{
exitConditionFlag++;
flags[i]=1;
res+=((classifier+i)->alpha[-idxV[i]]);
flags[i] = 1;
res += (classifier+i)->alpha[-idxV[i]];
}
idxV[i]=0;
}
}
if(exitConditionFlag==8)
if(exitConditionFlag == 8)
return res;
}
}
#endif
#endif //CV_HAAR_USE_AVX
CV_INLINE
double icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier,
......@@ -778,18 +806,16 @@ static int
cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
CvPoint pt, double& stage_sum, int start_stage )
{
#ifdef CV_HAAR_USE_AVX
#ifdef CV_HAAR_USE_AVX
bool haveAVX = false;
if(cv::checkHardwareSupport(CV_CPU_AVX))
if(__xgetbv()&0x6)// Check if the OS will save the YMM registers
{
haveAVX = true;
}
#else
#ifdef CV_HAAR_USE_SSE
#else
# ifdef CV_HAAR_USE_SSE
bool haveSSE2 = cv::checkHardwareSupport(CV_CPU_SSE2);
#endif
#endif
# endif
#endif
int p_offset, pq_offset;
int i, j;
......@@ -828,19 +854,20 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
while( ptr )
{
stage_sum = 0.0;
j = 0;
#ifdef CV_HAAR_USE_AVX
#ifdef CV_HAAR_USE_AVX
if(haveAVX)
{
for( ; j < cascade->stage_classifier[i].count-8; j+=8 )
for( ; j <= ptr->count - 8; j += 8 )
{
stage_sum += icvEvalHidHaarClassifierAVX(
cascade->stage_classifier[i].classifier+j,
ptr->classifier + j,
variance_norm_factor, p_offset );
}
}
#endif
for( j = 0; j < ptr->count; j++ )
#endif
for( ; j < ptr->count; j++ )
{
stage_sum += icvEvalHidHaarClassifier( ptr->classifier + j, variance_norm_factor, p_offset );
}
......@@ -860,7 +887,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
}
else if( cascade->isStumpBased )
{
#ifdef CV_HAAR_USE_AVX
#ifdef CV_HAAR_USE_AVX
if(haveAVX)
{
CvHidHaarClassifier* classifiers[8];
......@@ -872,15 +899,14 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
float CV_DECL_ALIGNED(32) buf[8];
if( cascade->stage_classifier[i].two_rects )
{
for( ; j <= cascade->stage_classifier[i].count-8; j+=8 )
for( ; j <= cascade->stage_classifier[i].count - 8; j += 8 )
{
//__m256 stage_sumPart = _mm256_setzero_ps();
classifiers[0] = cascade->stage_classifier[i].classifier + j;
nodes[0] = classifiers[0]->node;
classifiers[1] = cascade->stage_classifier[i].classifier + j + 1;
nodes[1] = classifiers[1]->node;
classifiers[2] = cascade->stage_classifier[i].classifier + j + 2;
nodes[2]= classifiers[2]->node;
nodes[2] = classifiers[2]->node;
classifiers[3] = cascade->stage_classifier[i].classifier + j + 3;
nodes[3] = classifiers[3]->node;
classifiers[4] = cascade->stage_classifier[i].classifier + j + 4;
......@@ -893,30 +919,74 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
nodes[7] = classifiers[7]->node;
__m256 t = _mm256_set1_ps(variance_norm_factor);
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,nodes[6]->threshold,nodes[5]->threshold,nodes[4]->threshold,nodes[3]->threshold,nodes[2]->threshold,nodes[1]->threshold,nodes[0]->threshold));
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
nodes[6]->threshold,
nodes[5]->threshold,
nodes[4]->threshold,
nodes[3]->threshold,
nodes[2]->threshold,
nodes[1]->threshold,
nodes[0]->threshold));
__m256 offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[0], p_offset),
calc_sum(nodes[6]->feature.rect[0], p_offset),
calc_sum(nodes[5]->feature.rect[0], p_offset),
calc_sum(nodes[4]->feature.rect[0], p_offset),
calc_sum(nodes[3]->feature.rect[0], p_offset),
calc_sum(nodes[2]->feature.rect[0], p_offset),
calc_sum(nodes[1]->feature.rect[0], p_offset),
calc_sum(nodes[0]->feature.rect[0], p_offset));
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
nodes[6]->feature.rect[0].weight,
nodes[5]->feature.rect[0].weight,
nodes[4]->feature.rect[0].weight,
nodes[3]->feature.rect[0].weight,
nodes[2]->feature.rect[0].weight,
nodes[1]->feature.rect[0].weight,
nodes[0]->feature.rect[0].weight);
__m256 offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[0],p_offset), calc_sum(nodes[6]->feature.rect[0],p_offset), calc_sum(nodes[5]->feature.rect[0],p_offset),
calc_sum(nodes[4]->feature.rect[0],p_offset), calc_sum(nodes[3]->feature.rect[0],p_offset), calc_sum(nodes[2]->feature.rect[0],p_offset), calc_sum(nodes[1]->feature.rect[0],
p_offset),calc_sum(nodes[0]->feature.rect[0],p_offset));
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight, nodes[6]->feature.rect[0].weight, nodes[5]->feature.rect[0].weight,
nodes[4]->feature.rect[0].weight, nodes[3]->feature.rect[0].weight, nodes[2]->feature.rect[0].weight, nodes[1]->feature.rect[0].weight, nodes[0]->feature.rect[0].weight);
__m256 sum = _mm256_mul_ps(offset, weight);
offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[1],p_offset),calc_sum(nodes[6]->feature.rect[1],p_offset),calc_sum(nodes[5]->feature.rect[1],p_offset),
calc_sum(nodes[4]->feature.rect[1],p_offset),calc_sum(nodes[3]->feature.rect[1],p_offset),calc_sum(nodes[2]->feature.rect[1],p_offset),calc_sum(nodes[1]->feature.rect[1],p_offset),
calc_sum(nodes[0]->feature.rect[1],p_offset));
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight, nodes[6]->feature.rect[1].weight, nodes[5]->feature.rect[1].weight, nodes[4]->feature.rect[1].weight,
nodes[3]->feature.rect[1].weight, nodes[2]->feature.rect[1].weight, nodes[1]->feature.rect[1].weight, nodes[0]->feature.rect[1].weight);
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset,weight));
__m256 alpha0 = _mm256_set_ps(classifiers[7]->alpha[0],classifiers[6]->alpha[0],classifiers[5]->alpha[0],classifiers[4]->alpha[0],classifiers[3]->alpha[0],
classifiers[2]->alpha[0],classifiers[1]->alpha[0],classifiers[0]->alpha[0]);
__m256 alpha1 = _mm256_set_ps(classifiers[7]->alpha[1],classifiers[6]->alpha[1],classifiers[5]->alpha[1],classifiers[4]->alpha[1],classifiers[3]->alpha[1],
classifiers[2]->alpha[1],classifiers[1]->alpha[1],classifiers[0]->alpha[1]);
offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[1], p_offset),
calc_sum(nodes[6]->feature.rect[1], p_offset),
calc_sum(nodes[5]->feature.rect[1], p_offset),
calc_sum(nodes[4]->feature.rect[1], p_offset),
calc_sum(nodes[3]->feature.rect[1], p_offset),
calc_sum(nodes[2]->feature.rect[1], p_offset),
calc_sum(nodes[1]->feature.rect[1], p_offset),
calc_sum(nodes[0]->feature.rect[1], p_offset));
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
nodes[6]->feature.rect[1].weight,
nodes[5]->feature.rect[1].weight,
nodes[4]->feature.rect[1].weight,
nodes[3]->feature.rect[1].weight,
nodes[2]->feature.rect[1].weight,
nodes[1]->feature.rect[1].weight,
nodes[0]->feature.rect[1].weight);
_mm256_store_ps(buf, _mm256_blendv_ps(alpha0, alpha1, _mm256_cmp_ps(t, sum, _CMP_LE_OQ )));
stage_sum+=(buf[0]+buf[1]+buf[2]+buf[3]+buf[4]+buf[5]+buf[6]+buf[7]);
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset,weight));
__m256 alpha0 = _mm256_set_ps(classifiers[7]->alpha[0],
classifiers[6]->alpha[0],
classifiers[5]->alpha[0],
classifiers[4]->alpha[0],
classifiers[3]->alpha[0],
classifiers[2]->alpha[0],
classifiers[1]->alpha[0],
classifiers[0]->alpha[0]);
__m256 alpha1 = _mm256_set_ps(classifiers[7]->alpha[1],
classifiers[6]->alpha[1],
classifiers[5]->alpha[1],
classifiers[4]->alpha[1],
classifiers[3]->alpha[1],
classifiers[2]->alpha[1],
classifiers[1]->alpha[1],
classifiers[0]->alpha[1]);
_mm256_store_ps(buf, _mm256_blendv_ps(alpha0, alpha1, _mm256_cmp_ps(t, sum, _CMP_LE_OQ)));
stage_sum += (buf[0]+buf[1]+buf[2]+buf[3]+buf[4]+buf[5]+buf[6]+buf[7]);
}
for( ; j < cascade->stage_classifier[i].count; j++ )
......@@ -941,7 +1011,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
classifiers[1] = cascade->stage_classifier[i].classifier + j + 1;
nodes[1] = classifiers[1]->node;
classifiers[2] = cascade->stage_classifier[i].classifier + j + 2;
nodes[2]= classifiers[2]->node;
nodes[2] = classifiers[2]->node;
classifiers[3] = cascade->stage_classifier[i].classifier + j + 3;
nodes[3] = classifiers[3]->node;
classifiers[4] = cascade->stage_classifier[i].classifier + j + 4;
......@@ -954,22 +1024,55 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
nodes[7] = classifiers[7]->node;
__m256 t = _mm256_set1_ps(variance_norm_factor);
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,nodes[6]->threshold,nodes[5]->threshold,nodes[4]->threshold,nodes[3]->threshold,nodes[2]->threshold,nodes[1]->threshold,nodes[0]->threshold));
__m256 offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[0],p_offset), calc_sum(nodes[6]->feature.rect[0],p_offset), calc_sum(nodes[5]->feature.rect[0],p_offset),
calc_sum(nodes[4]->feature.rect[0],p_offset), calc_sum(nodes[3]->feature.rect[0],p_offset), calc_sum(nodes[2]->feature.rect[0],p_offset), calc_sum(nodes[1]->feature.rect[0],
p_offset),calc_sum(nodes[0]->feature.rect[0],p_offset));
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight, nodes[6]->feature.rect[0].weight, nodes[5]->feature.rect[0].weight,
nodes[4]->feature.rect[0].weight, nodes[3]->feature.rect[0].weight, nodes[2]->feature.rect[0].weight, nodes[1]->feature.rect[0].weight, nodes[0]->feature.rect[0].weight);
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
nodes[6]->threshold,
nodes[5]->threshold,
nodes[4]->threshold,
nodes[3]->threshold,
nodes[2]->threshold,
nodes[1]->threshold,
nodes[0]->threshold));
__m256 offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[0], p_offset),
calc_sum(nodes[6]->feature.rect[0], p_offset),
calc_sum(nodes[5]->feature.rect[0], p_offset),
calc_sum(nodes[4]->feature.rect[0], p_offset),
calc_sum(nodes[3]->feature.rect[0], p_offset),
calc_sum(nodes[2]->feature.rect[0], p_offset),
calc_sum(nodes[1]->feature.rect[0], p_offset),
calc_sum(nodes[0]->feature.rect[0], p_offset));
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
nodes[6]->feature.rect[0].weight,
nodes[5]->feature.rect[0].weight,
nodes[4]->feature.rect[0].weight,
nodes[3]->feature.rect[0].weight,
nodes[2]->feature.rect[0].weight,
nodes[1]->feature.rect[0].weight,
nodes[0]->feature.rect[0].weight);
__m256 sum = _mm256_mul_ps(offset, weight);
offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[1],p_offset),calc_sum(nodes[6]->feature.rect[1],p_offset),calc_sum(nodes[5]->feature.rect[1],p_offset),
calc_sum(nodes[4]->feature.rect[1],p_offset),calc_sum(nodes[3]->feature.rect[1],p_offset),calc_sum(nodes[2]->feature.rect[1],p_offset),calc_sum(nodes[1]->feature.rect[1],p_offset),
calc_sum(nodes[0]->feature.rect[1],p_offset));
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight, nodes[6]->feature.rect[1].weight, nodes[5]->feature.rect[1].weight, nodes[4]->feature.rect[1].weight,
nodes[3]->feature.rect[1].weight, nodes[2]->feature.rect[1].weight, nodes[1]->feature.rect[1].weight, nodes[0]->feature.rect[1].weight);
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset,weight));
offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[1], p_offset),
calc_sum(nodes[6]->feature.rect[1], p_offset),
calc_sum(nodes[5]->feature.rect[1], p_offset),
calc_sum(nodes[4]->feature.rect[1], p_offset),
calc_sum(nodes[3]->feature.rect[1], p_offset),
calc_sum(nodes[2]->feature.rect[1], p_offset),
calc_sum(nodes[1]->feature.rect[1], p_offset),
calc_sum(nodes[0]->feature.rect[1], p_offset));
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
nodes[6]->feature.rect[1].weight,
nodes[5]->feature.rect[1].weight,
nodes[4]->feature.rect[1].weight,
nodes[3]->feature.rect[1].weight,
nodes[2]->feature.rect[1].weight,
nodes[1]->feature.rect[1].weight,
nodes[0]->feature.rect[1].weight);
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
if( nodes[0]->feature.rect[2].p0 )
tmp[0] = calc_sum(nodes[0]->feature.rect[2],p_offset) * nodes[0]->feature.rect[2].weight;
......@@ -990,16 +1093,28 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
sum = _mm256_add_ps(sum, _mm256_load_ps(tmp));
__m256 alpha0 = _mm256_set_ps(classifiers[7]->alpha[0],classifiers[6]->alpha[0],classifiers[5]->alpha[0],classifiers[4]->alpha[0],classifiers[3]->alpha[0],
classifiers[2]->alpha[0],classifiers[1]->alpha[0],classifiers[0]->alpha[0]);
__m256 alpha1 = _mm256_set_ps(classifiers[7]->alpha[1],classifiers[6]->alpha[1],classifiers[5]->alpha[1],classifiers[4]->alpha[1],classifiers[3]->alpha[1],
classifiers[2]->alpha[1],classifiers[1]->alpha[1],classifiers[0]->alpha[1]);
__m256 alpha0 = _mm256_set_ps(classifiers[7]->alpha[0],
classifiers[6]->alpha[0],
classifiers[5]->alpha[0],
classifiers[4]->alpha[0],
classifiers[3]->alpha[0],
classifiers[2]->alpha[0],
classifiers[1]->alpha[0],
classifiers[0]->alpha[0]);
__m256 alpha1 = _mm256_set_ps(classifiers[7]->alpha[1],
classifiers[6]->alpha[1],
classifiers[5]->alpha[1],
classifiers[4]->alpha[1],
classifiers[3]->alpha[1],
classifiers[2]->alpha[1],
classifiers[1]->alpha[1],
classifiers[0]->alpha[1]);
__m256 outBuf = _mm256_blendv_ps(alpha0, alpha1, _mm256_cmp_ps(t, sum, _CMP_LE_OQ ));
outBuf = _mm256_hadd_ps(outBuf, outBuf);
outBuf = _mm256_hadd_ps(outBuf, outBuf);
_mm256_store_ps(buf, outBuf);
stage_sum+=(buf[0]+buf[4]);//(buf[0]+buf[1]+buf[2]+buf[3]+buf[4]+buf[5]+buf[6]+buf[7]);
stage_sum += (buf[0] + buf[4]);
}
for( ; j < cascade->stage_classifier[i].count; j++ )
......@@ -1020,8 +1135,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
}
}
else
#endif
#if defined CV_HAAR_USE_SSE && CV_HAAR_USE_SSE && (!defined CV_HAAR_USE_AVX || !CV_HAAR_USE_AVX) //old SSE optimization
#elif defined CV_HAAR_USE_SSE //old SSE optimization
if(haveSSE2)
{
for( i = start_stage; i < cascade->count; i++ )
......@@ -1070,7 +1184,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
}
}
else
#endif
#endif // AVX or SSE
{
for( i = start_stage; i < cascade->count; i++ )
{
......@@ -1106,24 +1220,24 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
}
}
}
else
{
for( i = start_stage; i < cascade->count; i++ )
{
stage_sum = 0.0;
int k = 0;
#ifdef CV_HAAR_USE_AVX
#ifdef CV_HAAR_USE_AVX
if(haveAVX)
{
for( ; k < cascade->stage_classifier[i].count-8; k+=8 )
for( ; k < cascade->stage_classifier[i].count - 8; k += 8 )
{
stage_sum += icvEvalHidHaarClassifierAVX(
cascade->stage_classifier[i].classifier+k,
cascade->stage_classifier[i].classifier + k,
variance_norm_factor, p_offset );
}
}
#endif
#endif
for(; k < cascade->stage_classifier[i].count; k++ )
{
......@@ -1136,7 +1250,6 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
return -i;
}
}
//_mm256_zeroupper();
return 1;
}
......@@ -1186,7 +1299,7 @@ struct HaarDetectObjects_ScaleImage_Invoker
Size ssz(sum1.cols - 1 - winSize0.width, y2 - y1);
int x, y, ystep = factor > 2 ? 1 : 2;
#ifdef HAVE_IPP
#ifdef HAVE_IPP
if( cascade->hid_cascade->ipp_stages )
{
IppiRect iequRect = {equRect.x, equRect.y, equRect.width, equRect.height};
......@@ -1241,7 +1354,7 @@ struct HaarDetectObjects_ScaleImage_Invoker
}
}
else
#endif
#endif // IPP
for( y = y1; y < y2; y += ystep )
for( x = 0; x < ssz.width; x += ystep )
{
......@@ -2418,45 +2531,4 @@ CvType haar_type( CV_TYPE_NAME_HAAR, icvIsHaarClassifier,
icvReadHaarClassifier, icvWriteHaarClassifier,
icvCloneHaarClassifier );
#if 0
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 = cvHaarDetectObjects( &_image, cascade, storage, scaleFactor,
minNeighbors, flags, minSize );
Seq<Rect>(_objects).copyTo(objects);
}
int HaarClassifierCascade::runAt(Point pt, int startStage, int) const
{
return cvRunHaarClassifierCascade(cascade, pt, startStage);
}
void HaarClassifierCascade::setImages( const Mat& sum, const Mat& sqsum,
const Mat& tilted, double scale )
{
CvMat _sum = sum, _sqsum = sqsum, _tilted = tilted;
cvSetImagesForHaarClassifierCascade( cascade, &_sum, &_sqsum, &_tilted, scale );
}
}
#endif
/* End of file. */
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