diff --git a/modules/objdetect/src/haar.cpp b/modules/objdetect/src/haar.cpp index 5e2d5324dc..e9eda9dc89 100644 --- a/modules/objdetect/src/haar.cpp +++ b/modules/objdetect/src/haar.cpp @@ -45,7 +45,6 @@ #include #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 -# define CV_HAAR_USE_AVX 1 -# else -# if CV_SSE2 || CV_SSE3 -# define CV_HAAR_USE_SSE 1 -# endif -# endif +#if CV_AVX +# define CV_HAAR_USE_AVX 1 +#else +# if CV_SSE2 || CV_SSE3 +# define CV_HAAR_USE_SSE 1 +# 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]; + float CV_DECL_ALIGNED(32) tmp[8] = {0,0,0,0,0,0,0,0}; + 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 - 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 - bool haveSSE2 = cv::checkHardwareSupport(CV_CPU_SSE2); - #endif - #endif +#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 + bool haveSSE2 = cv::checkHardwareSupport(CV_CPU_SSE2); +# 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 ); } @@ -860,283 +887,369 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade, } else if( cascade->isStumpBased ) { - #ifdef CV_HAAR_USE_AVX - if(haveAVX) +#ifdef CV_HAAR_USE_AVX + if(haveAVX) + { + CvHidHaarClassifier* classifiers[8]; + CvHidHaarTreeNode* nodes[8]; + for( i = start_stage; i < cascade->count; i++ ) { - CvHidHaarClassifier* classifiers[8]; - CvHidHaarTreeNode* nodes[8]; - for( i = start_stage; i < cascade->count; i++ ) + stage_sum = 0.0; + j = 0; + float CV_DECL_ALIGNED(32) buf[8]; + if( cascade->stage_classifier[i].two_rects ) { - stage_sum = 0.0; - j = 0; - 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; - classifiers[3] = cascade->stage_classifier[i].classifier + j + 3; - nodes[3] = classifiers[3]->node; - classifiers[4] = cascade->stage_classifier[i].classifier + j + 4; - nodes[4] = classifiers[4]->node; - classifiers[5] = cascade->stage_classifier[i].classifier + j + 5; - nodes[5] = classifiers[5]->node; - classifiers[6] = cascade->stage_classifier[i].classifier + j + 6; - nodes[6] = classifiers[6]->node; - classifiers[7] = cascade->stage_classifier[i].classifier + j + 7; - 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); - __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]); - - _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]); - - } + 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; + classifiers[3] = cascade->stage_classifier[i].classifier + j + 3; + nodes[3] = classifiers[3]->node; + classifiers[4] = cascade->stage_classifier[i].classifier + j + 4; + nodes[4] = classifiers[4]->node; + classifiers[5] = cascade->stage_classifier[i].classifier + j + 5; + nodes[5] = classifiers[5]->node; + classifiers[6] = cascade->stage_classifier[i].classifier + j + 6; + nodes[6] = classifiers[6]->node; + classifiers[7] = cascade->stage_classifier[i].classifier + j + 7; + 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); + + __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]); + + _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++ ) - { - CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j; - CvHidHaarTreeNode* node = classifier->node; + 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]; - } + 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 + } + else + { + for( ; j <= (cascade->stage_classifier[i].count)-8; j+=8 ) { - for( ; j <= (cascade->stage_classifier[i].count)-8; j+=8 ) - { - float CV_DECL_ALIGNED(32) tmp[8] = {0,0,0,0,0,0,0,0}; - - 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; - classifiers[3] = cascade->stage_classifier[i].classifier + j + 3; - nodes[3] = classifiers[3]->node; - classifiers[4] = cascade->stage_classifier[i].classifier + j + 4; - nodes[4] = classifiers[4]->node; - classifiers[5] = cascade->stage_classifier[i].classifier + j + 5; - nodes[5] = classifiers[5]->node; - classifiers[6] = cascade->stage_classifier[i].classifier + j + 6; - nodes[6] = classifiers[6]->node; - classifiers[7] = cascade->stage_classifier[i].classifier + j + 7; - 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); - __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)); - - if( nodes[0]->feature.rect[2].p0 ) - 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; - if( nodes[2]->feature.rect[2].p0 ) - 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; - if( nodes[4]->feature.rect[2].p0 ) - 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; - if( nodes[6]->feature.rect[2].p0 ) - 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; - - 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 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]); - } + float CV_DECL_ALIGNED(32) tmp[8] = {0,0,0,0,0,0,0,0}; + + 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; + classifiers[3] = cascade->stage_classifier[i].classifier + j + 3; + nodes[3] = classifiers[3]->node; + classifiers[4] = cascade->stage_classifier[i].classifier + j + 4; + nodes[4] = classifiers[4]->node; + classifiers[5] = cascade->stage_classifier[i].classifier + j + 5; + nodes[5] = classifiers[5]->node; + classifiers[6] = cascade->stage_classifier[i].classifier + j + 6; + nodes[6] = classifiers[6]->node; + classifiers[7] = cascade->stage_classifier[i].classifier + j + 7; + 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); + + __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)); + + if( nodes[0]->feature.rect[2].p0 ) + 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; + if( nodes[2]->feature.rect[2].p0 ) + 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; + if( nodes[4]->feature.rect[2].p0 ) + 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; + if( nodes[6]->feature.rect[2].p0 ) + 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; + + 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 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]); + } - 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]; - } + 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; } + if( stage_sum < cascade->stage_classifier[i].threshold ) + return -i; } - else - #endif - #if defined CV_HAAR_USE_SSE && CV_HAAR_USE_SSE && (!defined CV_HAAR_USE_AVX || !CV_HAAR_USE_AVX) //old SSE optimization - if(haveSSE2) + } + else +#elif defined CV_HAAR_USE_SSE //old SSE optimization + if(haveSSE2) + { + for( i = start_stage; i < cascade->count; i++ ) { - for( i = start_stage; i < cascade->count; i++ ) + __m128d vstage_sum = _mm_setzero_pd(); + if( cascade->stage_classifier[i].two_rects ) { - __m128d vstage_sum = _mm_setzero_pd(); - if( cascade->stage_classifier[i].two_rects ) + for( j = 0; j < cascade->stage_classifier[i].count; j++ ) { - 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)); - } + 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 + } + else + { + for( j = 0; j < cascade->stage_classifier[i].count; j++ ) { - 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)); - } + 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; } + __m128d i_threshold = _mm_set1_pd(cascade->stage_classifier[i].threshold); + if( _mm_comilt_sd(vstage_sum, i_threshold) ) + return -i; } - else - #endif + } + else +#endif // AVX or SSE + { + for( i = start_stage; i < cascade->count; i++ ) { - for( i = start_stage; i < cascade->count; i++ ) + stage_sum = 0.0; + if( cascade->stage_classifier[i].two_rects ) { - stage_sum = 0.0; - if( cascade->stage_classifier[i].two_rects ) + for( j = 0; j < cascade->stage_classifier[i].count; j++ ) { - 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]; - } + 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 + } + else + { + for( j = 0; j < cascade->stage_classifier[i].count; j++ ) { - 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]; - } + 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; } + 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; - #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 - for(; k < cascade->stage_classifier[i].count; k++ ) - { +#endif + for(; k < cascade->stage_classifier[i].count; k++ ) + { - stage_sum += icvEvalHidHaarClassifier( - cascade->stage_classifier[i].classifier + k, - variance_norm_factor, p_offset ); - } + stage_sum += icvEvalHidHaarClassifier( + cascade->stage_classifier[i].classifier + k, + variance_norm_factor, p_offset ); + } if( stage_sum < cascade->stage_classifier[i].threshold ) 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 ) { @@ -1880,18 +1993,18 @@ cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** _cascade ) #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" +#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" static int icvIsHaarClassifier( const void* struct_ptr ) @@ -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*)cvLoad(filename.c_str(), 0, 0, 0)); - return (CvHaarClassifierCascade*)cascade != 0; -} - -void HaarClassifierCascade::detectMultiScale( const Mat& image, - Vector& 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(_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. */