<pre name="code" class="cpp">#include "Haar.h" #include "loadCascade.h" #include "Util.h" #include "stdio.h" #include "string.h" #include <math.h> #include <stdint.h> #include <c6x.h> /*******************Global************************************/ HaarClassifierCascade *cascade ; //HidHaarClassifierCascade hid_cascade; //32bits cell Mat int MatPool32[MaxMatNum][MAXROWS][MAXCOLS]; //8bits cell unsigned char MatPool8[MaxMatNum][MAXROWS][MAXCOLS]; //8bits*3 cell unsigned char ImgRGBPool8[MaxMatNum][RGBCHANNEL][MAXROWS][MAXCOLS]; //64bits cell _int64 MatPool64[MaxMatNum][MAXROWS][MAXCOLS]; //候选区域坐标节点并查集 PTreeNode PTreeNodes[MAXPTREENODES]; char HidCascade[MAXHIDCASCADE]; //分类器检测结果区域序列 Sequence result_seq; //================================================================== //函数名： IsEqual //作者： qiurenbo //日期： 2014-10-1 //功能： 判断两个矩形是否邻接 //输入参数：_r1 _r2 候选区域矩形 //返回值： 返回相似性（是否是邻接的矩形） //修改记录： //================================================================== int IsEqual( const void* _r1, const void* _r2) { const Rect* r1 = (const Rect*)_r1; const Rect* r2 = (const Rect*)_r2; int distance5x = r1->width ;//int distance = cvRound(r1->width*0.2); return r2->x*5 <= r1->x*5 + distance5x && r2->x*5 >= r1->x*5 - distance5x && r2->y*5 <= r1->y*5 + distance5x && r2->y*5 >= r1->y*5 - distance5x && r2->width*5 <= r1->width * 6 && r2->width * 6 >= r1->width*5; } //================================================================== //函数名： ReadFaceCascade //作者： qiurenbo //日期： 2014-10-1 //功能： 根据候选区域的相似性（IsEqual函数）建立并查集 //输入参数：seq 候选目标区域序列 //返回值： 返回分类后的类别数 //修改记录： //================================================================== int SeqPartition( const Sequence* seq ) { Sequence* result = 0; //CvMemStorage* temp_storage = 0; int class_idx = 0; memset(PTreeNodes, 0, MAXPTREENODES*sizeof(PTreeNode)); int i, j; //建立以seq中元素为根节点的森林 for( i = 0; i < seq->total; i++ ) PTreeNodes[i].element = (char*)&seq->rectQueue[i]; //遍历所有根节点 for( i = 0; i < seq->total; i++ ) { PTreeNode* node = &PTreeNodes[i]; PTreeNode* root = node; //确保node中元素指针不为空 if( !node->element ) continue; //找到元素在树中的根结点 while( root->parent ) root = root->parent; for( j = 0; j < seq->total; j++ ) { PTreeNode* node2 = &PTreeNodes[j]; //确保1.node中元素指针不为空 // 2.且不是同一个node结点 // 3.且是相似区域 // 若是相似区域，则合并元素 if( node2->element && node2 != node && IsEqual( node->element, node2->element)) { PTreeNode* root2 = node2; //找到元素在树中的根结点 while( root2->parent ) root2 = root2->parent; //合并的前提是不在一颗树中 if( root2 != root ) { //秩小的树归入秩大的树中 if( root->rank > root2->rank ) root2->parent = root; //秩相等的时候才改变树的秩 else { root->parent = root2; root2->rank += root->rank == root2->rank; root = root2; } //assert( root->parent == 0 ); // 路径压缩，子节点node2直接指向根节点 while( node2->parent ) { PTreeNode* temp = node2; node2 = node2->parent; temp->parent = root; } // 路径压缩，子节点node直接指向根节点 node2 = node; while( node2->parent ) { PTreeNode* temp = node2; node2 = node2->parent; temp->parent = root; } } } } } for( i = 0; i < seq->total; i++ ) { PTreeNode* node = &PTreeNodes[i]; int idx = -1; if( node->element ) { while( node->parent ) node = node->parent; //计算有几棵并查树，巧妙地利用取反避免重复计算 if( node->rank >= 0 ) node->rank = ~class_idx++; idx = ~node->rank; } } return class_idx; } //================================================================== //函数名： ReadFaceCascade //作者： qiurenbo //日期： 2014-09-30 //功能： 读取Cascade文件 //输入参数：void //返回值： void //修改记录： //================================================================== void ReadFaceCascade() { int i; //load cascade cascade = (HaarClassifierCascade*)HaarClassifierCascade_face; //load stages int stage_size = StageClassifier_face[0]; HaarStageClassifier *stages ; stages = (HaarStageClassifier *)(StageClassifier_face+1); //load classifier int classifier_size = Classifier_face[0]; HaarClassifier *cls ; cls = (HaarClassifier*) (Classifier_face+1); int class_info_size = class_info[0]; int * cls_info ; cls_info = (int*)(class_info+1); //link cascade with stages cascade->stage_classifier = stages; //link stages，classifiers int offset=0; int offset_t=(sizeof(HaarFeature)/sizeof(int)); int offset_l=offset_t+1; int offset_r=offset_t+2; int offset_a=offset_t+3; int offset_total=0; for(i=0;i<stage_size;++i) { (stages+i)->classifier = (cls+offset); offset +=(stages+i)->count; } offset_total = 5+ (sizeof(HaarFeature)/sizeof(int)); //link classifiers and haar_featrue; for(i=0;i<classifier_size;++i) { HaarClassifier *cs= cls+i; cs->haar_feature = (HaarFeature*)(cls_info+i*offset_total); cs->threshold = (int*)(cls_info+i*offset_total+offset_t); cs->left =(int*)(cls_info+i*offset_total+offset_l); cs->right=(int*)(cls_info+i*offset_total+offset_r); cs->alpha=(int*)(cls_info+i*offset_total+offset_a); } } //================================================================== //函数名： IntegralImage //作者： qiurenbo //日期： 2014-09-26 //功能： 从矩阵池中获取rows * cols的矩阵 //输入参数：mat 矩阵结构体地址 // rows 待分配的行数 // cols 待分配的列数 // type 待分配的矩阵类型 // matIndex 从矩阵池中分配的矩阵序列（手动指定..） //返回值： void //修改记录： //================================================================== void GetMat(void* mat, int rows, int cols, int type, int matIndex) { switch(type) { case BITS8: ((Mat8*)mat)->rows = rows; ((Mat8*)mat)->cols = cols; ((Mat8*)mat)->mat8Ptr = (Mat8Ptr)&MatPool8[matIndex]; break; case BITS32: ((Mat32*)mat)->rows = rows; ((Mat32*)mat)->cols = cols; ((Mat32*)mat)->mat32Ptr = (Mat32Ptr)&MatPool32[matIndex]; break; case BITS64: ((Mat64*)mat)->rows = rows; ((Mat64*)mat)->cols = cols; ((Mat64*)mat)->mat64Ptr = (Mat64Ptr)&MatPool64[matIndex]; break; } } //================================================================== //函数名： IntegralImage //作者： qiurenbo //日期： 2014-09-26 //功能： 计算目标检测区域的积分图 //输入参数：src 待检测目标所在矩阵起始 // srcstep 待检测区域列数 // sum 积分图矩阵 (W+1)*(H+1) // sumstep 积分图矩阵列数 // sqsum 平方和图矩阵 (W+1)*(H+1) // sqsumstep 平方和图矩阵列数 // size 待检测区域大小 W*H // // //返回值： void //修改记录： //================================================================== void IntegralImage(ImgPtr src, int srcstep, Mat32Ptr sum, int sumstep, Mat64Ptr sqsum, int sqsumstep, Size size) { int s = 0; _int64 sq = 0; //移动指针到积分图的下一行,第一行全为0 sum += sumstep + 1; sqsum += sqsumstep + 1; //y代表相对于输入检测矩阵起始第几行 for(int y = 0; y < size.height; y++, src += srcstep, sum += sumstep, sqsum += sqsumstep ) { //sum和sqsum为(W+1)*(H+1)大小矩阵，故将第一列置为0 sum[-1] = 0; sqsum[-1] = 0; for(int x = 0 ; x < size.width; x++ ) { int it = src[x]; int t = (it); //查表计算平方 _int64 tq = CV_8TO16U_SQR(it); //s代表行上的累加和 s += t; //sq代表行上的累加和 sq += tq; t = sum[x - sumstep] + s; tq = sqsum[x - sqsumstep] + sq; sum[x] = t; sqsum[x] = (_int64)tq; } } } //================================================================== //函数名： Integral //作者： qiurenbo //日期： 2014-09-26 //功能： 计算目标检测区域的积分图 //输入参数：image 图像 // sumImage 积分图指针 // sumSqImage 平方和图指针 //返回值： void //修改记录： //================================================================== void Integral(Image* image, Mat32* sumImage, Mat64* sumSqImage) { //取保地址空间已经分配，从数组中 if (image == NULL || sumImage == NULL || sumSqImage == NULL) return; Image*src = (Image*)image; Mat32 *sum = (Mat32*)sumImage; Mat64 *sqsum = (Mat64*)sumSqImage; Size size; size.height = src->rows; size.width = src->cols; IntegralImage(src->imgPtr, src->cols, sum->mat32Ptr, sum->cols, sqsum->mat64Ptr, sqsum->cols,size); } //================================================================== //函数名： AlignPtr //作者： qiurenbo //日期： 2014-10-03 //功能： 按algin字节对齐 //输入参数：ptr 要对齐的指针 // align 对齐的字节数 //返回值： void* //修改记录： //================================================================== void* AlignPtr( const void* ptr, int align) { return (void*)( ((unsigned int)ptr + align - 1) & ~(align-1) ); } //================================================================== //函数名： CreateHidHaarClassifierCascade //作者： qiurenbo //日期： 2014-09-28 //功能： 创建隐式积分图加快计算速度 //输入参数：cascade 级联分类器指针 //返回值： static HidHaarClassifierCascade* 返回一个隐式级联分类器指针 //修改记录： //================================================================== static HidHaarClassifierCascade* CreateHidHaarClassifierCascade(HaarClassifierCascade* cascade) { cascade->hid_cascade = (struct HidHaarClassifierCascade *)HidCascade; //分配栈空间 HidHaarClassifierCascade* out = (struct HidHaarClassifierCascade *)HidCascade; const int icv_stage_threshold_bias = 419; //0.0001*(2^22)=419.4304 HidHaarClassifier* haar_classifier_ptr; HidHaarTreeNode* haar_node_ptr; int i, j, l; int total_classifiers = 2135; int total_nodes = 0; int has_tilted_features = 0; int max_count = 0; /* 初始化HidCascade头 */ out->count = cascade->count; out->stage_classifier = (HidHaarStageClassifier*)(out + 1); //out->stage_classifier = (HidHaarStageClassifier*)AlignPtr(out + 1, 4); //classifier起始地址 haar_classifier_ptr = (HidHaarClassifier*)(out->stage_classifier + cascade->count); //haar_classifier_ptr = (HidHaarClassifier*)AlignPtr(out->stage_classifier + cascade->count, 4); //node起始地址 //haar_node_ptr = (HidHaarTreeNode*)AlignPtr(haar_classifier_ptr + total_classifiers, 4); haar_node_ptr = (HidHaarTreeNode*)(haar_classifier_ptr + total_classifiers); out->is_stump_based = 1; out->is_tree = 0; // 用cascade初始化HidCascade for( i = 0; i < cascade->count; i++ ) { //用cascades Stage初始化HidCascade的Stage HaarStageClassifier* stage_classifier = cascade->stage_classifier + i; HidHaarStageClassifier* 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 = (struct HidHaarClassifier *)&HidClassifiers[i]; hid_stage_classifier->classifier = haar_classifier_ptr; //初始化为二特征，下面会根据真实的特征数至1或0（三特征） hid_stage_classifier->two_rects = 1; haar_classifier_ptr += stage_classifier->count; //Stage构成一颗退化的二叉树（单分支），每个结点最多只有一个孩子 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 ; //判断该stage是否为树状结构（多分枝） out->is_tree |= hid_stage_classifier->next != NULL; //赋值classifer属性 for( j = 0; j < stage_classifier->count; j++ ) { HaarClassifier* classifier = stage_classifier->classifier + j; HidHaarClassifier* hid_classifier = hid_stage_classifier->classifier + j; int node_count = classifier->count; int* alpha_ptr = (int*)(haar_node_ptr + node_count); hid_classifier->count = node_count; hid_classifier->node = haar_node_ptr; hid_classifier->alpha = alpha_ptr; //赋值node属性 for( l = 0; l < node_count; l++ ) { HidHaarTreeNode* node = hid_classifier->node + l; HaarFeature* 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]; //对特征数目进行判断,若是三特征，则至two_rects为0 if( (feature->rect[2].weight) == 0 || 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; } //赋值alpha memcpy( hid_classifier->alpha, classifier->alpha, (node_count+1)*sizeof(hid_classifier->alpha[0])); haar_node_ptr = (HidHaarTreeNode*)(alpha_ptr+node_count + 1); //判断cascade中的分类器是否是树桩分类器，只有根结点的决策树 out->is_stump_based &= node_count == 1; } } //cascade->hid_cascade = out; //assert( (char*)haar_node_ptr - (char*)out <= datasize ); return out; } //================================================================== //函数名： SetImagesForHaarClassifierCascade //作者： qiurenbo //日期： 2014-09-29 //功能： 根据尺度调整Haar特征的大小和权重 //输入参数：cascade 级联分类器指针 // sum 积分图 // sqsum 平方和积分图 // scale32x 尺度 //返回值： 无 //修改记录： //================================================================== void SetImagesForHaarClassifierCascade(HaarClassifierCascade* _cascade, Mat32* sum, Mat64* sqsum, int scale32x) { HidHaarClassifierCascade* hidCascade; int coi0 = 0, coi1 = 0; int i; Rect equ_rect; int weight_scale; HaarFeature* feature; HidHaarFeature* hidfeature; int sum0 = 0, area0 = 0; Rect r[3]; Rect tr; int correction_ratio; //根据尺度获取窗口大小 _cascade->scale32x = scale32x; _cascade->real_window_size.width = (_cascade->orig_window_size.width * scale32x + 16)>>5 ; _cascade->real_window_size.height = (_cascade->orig_window_size.height * scale32x +16) >> 5; //设置隐式级联分类器的积分图 hidCascade = _cascade->hid_cascade; hidCascade->sum = sum; hidCascade->sqsum = sqsum; //根据尺度设置积分图起始矩阵的位置 equ_rect.x = equ_rect.y = (scale32x+16)>>5; equ_rect.width = ((_cascade->orig_window_size.width-2)*scale32x + 16 ) >> 5; //+0.5是为了四舍五入 equ_rect.height = ((_cascade->orig_window_size.height-2)*scale32x + 16 ) >> 5; weight_scale = equ_rect.width*equ_rect.height; hidCascade->window_area = weight_scale; //矩形面积 //获取积分图上起始矩阵四个像素的坐标 hidCascade->p0 = sum->mat32Ptr + (equ_rect.y) * sum->cols+ equ_rect.x; hidCascade->p1 = sum->mat32Ptr + (equ_rect.y) * sum->cols + equ_rect.x + equ_rect.width; hidCascade->p2 = sum->mat32Ptr + (equ_rect.y + equ_rect.height) * sum->cols + equ_rect.x; hidCascade->p3 = sum->mat32Ptr + (equ_rect.y + equ_rect.height) * sum->cols + equ_rect.x + equ_rect.width; //获取平方和积分图上起始矩阵四个像素的坐标 hidCascade->pq0 = sqsum->mat64Ptr + (equ_rect.y) * sqsum->cols+ equ_rect.x; hidCascade->pq1 = sqsum->mat64Ptr + (equ_rect.y) * sqsum->cols+ equ_rect.x + equ_rect.width; hidCascade->pq2 = sqsum->mat64Ptr + (equ_rect.y + equ_rect.height) * sqsum->cols+ equ_rect.x; hidCascade->pq3 = sqsum->mat64Ptr + (equ_rect.y + equ_rect.height) * sqsum->cols+ equ_rect.x + equ_rect.width; //遍历每个Classifer所使用的特征，对它们进行尺度放大，并将改变的值赋给HidCascade，隐式级联分类器 for( i = 0; i < hidCascade->count; i++ ) { int j, k, l; for( j = 0; j < hidCascade->stage_classifier[i].count; j++ ) { for( l = 0; l < hidCascade->stage_classifier[i].classifier[j].count; l++ ) { feature = &_cascade->stage_classifier[i].classifier[j].haar_feature[l]; hidfeature = &hidCascade->stage_classifier[i].classifier[j].node[l].feature; sum0 = 0; area0 = 0; for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ ) { if( !hidfeature->rect[k].p0 ) break; r[k] = feature->rect[k].r; //左上角坐标和矩阵长宽都按尺度放大 tr.x = (r[k].x * scale32x + 16) >> 5; tr.width = (r[k].width * scale32x + 16) >> 5; tr.y = ( r[k].y * scale32x + 16 ) >> 5; tr.height = ( r[k].height * scale32x +16 ) >> 5; correction_ratio = weight_scale; //设置矩阵四个顶点在积分图中的位置（为了计算特征方便） hidfeature->rect[k].p0 = sum->mat32Ptr + tr.y * sum->cols + tr.x; hidfeature->rect[k].p1 = sum->mat32Ptr + tr.y * sum->cols + tr.x + tr.width; hidfeature->rect[k].p2 = sum->mat32Ptr + (tr.y + tr.height) *sum->cols + tr.x; hidfeature->rect[k].p3 = sum->mat32Ptr + (tr.y + tr.height) *sum->cols + tr.x + tr.width; //rect[1] = weight/area， 左移22位是为了避免浮点计算，将权值/检测窗口面积（不断扩大），降低权值 hidfeature->rect[k].weight = ((feature->rect[k].weight)<< NODE_THRESHOLD_SHIFT)/(correction_ratio); if( k == 0 ) area0 = tr.width * tr.height; else sum0 += hidfeature->rect[k].weight * tr.width * tr.height; } //rect[0].weight ,权重和特征矩形面积成反比 hidfeature->rect[0].weight = (int)(-sum0/area0); } /* l */ } /* j */ } }; uint64_t block1 = 0; //uint64_t block2 = 0; //================================================================== //函数名： RunHaarClassifierCascade //作者： qiurenbo //日期： 2014-09-30 //功能： 在指定窗口范围计算特征 //输入参数：_cascade 级联分类器指针 // pt 检测窗口左上角坐标 // start_stage 起始stage下标 //返回值： <=0 未检测到目标或参数有问题 // 1 成功检测到目标 //修改记录： //==================================================================== int RunHaarClassifierCascade( HaarClassifierCascade* _cascade, Point& pt, int start_stage ) { int result = -1; int p_offset, pq_offset; int i, j; _int64 rectsum, variance_factor; int variance_norm_factor; HidHaarClassifier* classifier; HidHaarTreeNode* node; int sum, t, a, b; int stage_sum; /* uint64_t start_time, end_time, overhead, cyclecountSet=0, cyclecountRun=0; //In the initialization portion of the code: TSCL = 0; //enable TSC start_time = _itoll(TSCH, TSCL); end_time = _itoll(TSCH, TSCL); overhead = end_time-start_time; //Calculating the overhead of the method.*/ HidHaarClassifierCascade* hidCascade; if (_cascade == NULL) return -1; hidCascade = _cascade->hid_cascade; if( !hidCascade ) return -1; //确保矩形的有效性，并防止计算窗口出边界 if( pt.x < 0 || pt.y < 0 || pt.x + _cascade->real_window_size.width >= hidCascade->sum->cols-2 || pt.y + _cascade->real_window_size.height >= hidCascade->sum->rows-2 ) return -1; //计算特征点在积分图中的偏移，相当于移动窗口 p_offset = pt.y * (hidCascade->sum->cols) + pt.x; pq_offset = pt.y * (hidCascade->sqsum->cols) + pt.x; //计算移动后整个窗口的特征值 rectsum = calc_sum(*hidCascade,p_offset);//*cascade->inv_window_area; variance_factor = hidCascade->pq0[pq_offset] - hidCascade->pq1[pq_offset] - hidCascade->pq2[pq_offset] + hidCascade->pq3[pq_offset]; variance_factor = (variance_factor - ((rectsum*rectsum*windowArea[hidCascade->window_area-324])>>16))*windowArea[hidCascade->window_area-324]>>16; //variance_norm_factor = int(sqrt(float(variance_factor))+0.5f);//qmath variance_norm_factor = shortSqrtTable[variance_factor]; if( variance_norm_factor < 0 ) variance_norm_factor = 1; //计算每个classifier的用到的特征区域的特征值 for( i = start_stage; i < hidCascade->count; i++ ) //for( i = start_stage; i < hidCascade->count; i++ ) { stage_sum = 0; node = hidCascade->stage_classifier[i].classifier->node; classifier = hidCascade->stage_classifier[i].classifier; //if( hidCascade->stage_classifier[i].two_rects ) //{ for( j = 0; j < hidCascade->stage_classifier[i].count; j++ ) { //start_time = _itoll(TSCH, TSCL); //classifier = hidCascade->stage_classifier[i].classifier + j; //start_time = _itoll(TSCH, TSCL); t = node->threshold*variance_norm_factor >> 10; //end_time = _itoll(TSCH, TSCL); // block1 += end_time - start_time - overhead; //start_time = _itoll(TSCH, TSCL); //计算Haar特征 sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight >> 10; sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight >> 10; //两特征和三特征分开处理 if( node->feature.rect[2].p0 ) sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight >> 10; //end_time = _itoll(TSCH, TSCL); //block1 += end_time - start_time - overhead; // //a = classifier->alpha[0]; //b = classifier->alpha[1]; //start_time = _itoll(TSCH, TSCL); stage_sum += sum < t ? classifier->alpha[0] : classifier->alpha[1]; // end_time = _itoll(TSCH, TSCL); // block2 += end_time - start_time - overhead node = (HidHaarTreeNode*)((char*)(node) + 80); classifier++; } if( stage_sum < hidCascade->stage_classifier[i].threshold ) { return -i; } } //QueryPerformanceCounter(&t2); //printf("FeatureDetectTime:%fms ",(t2.QuadPart - t1.QuadPart)*1000.0/tc.QuadPart); return 1; } //================================================================== //函数名： HaarDetectObjects //作者： qiurenbo //日期： 2014-09-30 //功能： 在指定图片中查找目标 //输入参数： _img 图片指针 // cascade 级联分类器指针 // start_stage 起始stage下标 // scale_factor32x 窗口变化尺度倍数 /32 // min_neighbors 最小临界目标（min_neighbors个以上的候选目标的区域才是最后的目标区域） // minSize 目标最小的大小 //返回值： <=0 未检测到目标或参数有问题 // 1 成功检测到目标 //修改记录： //==================================================================== void HaarDetectObjects(Image* _img, HaarClassifierCascade* cascade, //训练好的级联分类器 char* storage, int scale_factor32x, int min_neighbors, int flags, Size minSize) { //第一次分类用到的最大stage //第二次分类用到的起始stage int split_stage = 2; // ImgPtr stub, *img = _img; Mat32 sum ; Mat64 sqsum; Image tmp; //检测区域候选队列 Sequence seq; //结果候选恿? Sequence seq2; //并查集合并序列 Sequence comps; Rect r1; PTreeNode* node; int r1_neighbor; int j, flag = 1; Rect r2 ; int r2_neighbor; int distance;//cvRound( r2.rect.width * 0.2 ); memset(&seq, 0, sizeof(Sequence)); memset(&comps, 0, sizeof(Sequence)); memset(&seq2, 0, sizeof(Sequence)); memset(&result_seq, 0, sizeof(result_seq)); int i; int factor32x; int npass = 2; if( !cascade ) return ; //获取积分图和平方和积分图的矩阵 GetMat(&sum , _img->rows + 1, _img->cols + 1, BITS32, 0); GetMat(&sqsum, _img->rows + 1, _img->cols + 1, BITS64, 0); GetMat(&tmp, _img->rows, _img->cols, BITS8, 1); //若不存在隐式积分图（用于加速计算），则创建一个 if( !cascade->hid_cascade ) CreateHidHaarClassifierCascade(cascade); //计算积分图 Integral(_img, &sum, &sqsum); int count = 0; int count2 = 0; // In the variable declaration portion of the code: /*uint64_t start_time, end_time, overhead, cyclecountSet=0, cyclecountRun=0; // In the initialization portion of the code: TSCL = 0; //enable TSC start_time = _itoll(TSCH, TSCL); end_time = _itoll(TSCH, TSCL); overhead = end_time-start_time; //Calculating the overhead of the method.*/ //不断调整窗口尺度，直到到达图像边缘(_img->cols-10) ||(_img->rows - 10) //并且确保尺度小于3倍(96) for( factor32x = 32; factor32x*cascade->orig_window_size.width < (_img->cols - 10)<<5 && factor32x*cascade->orig_window_size.height < (_img->rows - 10)<<5 &&factor32x<96; factor32x = (factor32x*scale_factor32x+16)>>5 ) { const int ystep32x = MAX(64, factor32x); //调整搜索窗口尺度 Size win_size; win_size.height = (cascade->orig_window_size.height * factor32x + 16)>>5; win_size.width = (cascade->orig_window_size.width * factor32x + 16 )>>5; //pass指扫描次数，stage_offset指第二次扫描时从第几个stage开始 int pass, stage_offset = 0; //确保搜索窗口在尺度放大后仍然在图像中 int stop_height = ( ((_img->rows - win_size.height)<<5)+ (ystep32x>>1) ) / ystep32x; //确保搜索窗口大于目标的最小尺寸 if( win_size.width < minSize.width || win_size.height < minSize.height ) continue; //QueryPerformanceFrequency(&tc); //QueryPerformanceCounter(&t1); //根据尺度设置隐式级联分类器中的特征和权重，并设置这些特征在积分图中的位置，以加速运算 // Code to be profiled //start_time = _itoll(TSCH, TSCL); SetImagesForHaarClassifierCascade(cascade, &sum, &sqsum, factor32x ); //end_time = _itoll(TSCH, TSCL); //cyclecountSet = end_time-start_time-overhead; //QueryPerformanceCounter(&t2); //printf("SetImageFeatureRunTime:%fms ",(t2.QuadPart - t1.QuadPart)*1000.0/tc.QuadPart); //设置粗检测所使用的起始分类器 cascade->hid_cascade->count = split_stage; //用检测窗口扫描两遍图像: //第一遍通过级联两个stage粗略定位目标大致区域，对候选区域进行标定（利用tmp矩阵） //第二遍对标定的候选区域进行完整筛选，将候选区域放置到队列中 for( pass = 0; pass < npass; pass++ ) { for( int _iy = 0; _iy < stop_height; _iy++ ) { //检测窗口纵坐标步长为2，保持不变 int iy = (_iy*ystep32x+16)>>5; int _ix, _xstep = 1; //stop_width是指_ix迭代的上限，_ix还要*ystep32x才是真正的窗口坐标 int stop_width =( ((_img->cols - win_size.width)<<5) +ystep32x/2) / ystep32x; unsigned char* mask_row = tmp.imgPtr + tmp.cols* iy; for( _ix = 0; _ix < stop_width; _ix += _xstep ) { //检测窗口横坐标按步长为4开始移动，若没有检测到目标，则改变下一次步长为2 int ix = (_ix*ystep32x+16)>>5; // it really should be ystep //当前检测窗口左上角坐标 Point pt; pt.x = ix; pt.y = iy; //粗略检测 if( pass == 0 ) { int result = 0; _xstep = 2; //start_time = _itoll(TSCH, TSCL); result = RunHaarClassifierCascade( cascade, pt, 0 ); //end_time = _itoll(TSCH, TSCL); //cyclecountRun += end_time-start_time-overhead; if( result > 0 ) { if( pass < npass - 1 ) mask_row[ix] = 1; } //没有检测到改变步长为2（看ix的值） if( result < 0 ) _xstep = 1; } //第二次检测先前粗定位的坐标 else if( mask_row[ix] ) { //start_time = _itoll(TSCH, TSCL); int result = RunHaarClassifierCascade(cascade, pt, stage_offset); // end_time = _itoll(TSCH, TSCL); // cyclecountRun += end_time-start_time-overhead; //count2++; //int result = 0; if( result > 0 ) { seq.rectQueue[seq.tail].height = win_size.height; seq.rectQueue[seq.tail].width = win_size.width; seq.rectQueue[seq.tail].x = ix; seq.rectQueue[seq.tail].y = iy; seq.total++; seq.tail++; } else mask_row[ix] = 0; } } } //因为前两个stage在第一次检测的时候已经用过； //第二次检测的时候，从第3个stage开始进行完整的检测 stage_offset = cascade->hid_cascade->count; cascade->hid_cascade->count = cascade->count; //cascade->hid_cascade->count = 15; } } //printf("The SetImage section took: %lld CPU cycles ", cyclecountSet); // printf("The RunImage section took: %lld CPU cycles ", cyclecountRun); // printf("The Block1 section took: %lld CPU cycles ", block1); // printf("The Block2 section took: %lld CPU cycles ", block2); if( min_neighbors != 0 ) { //将候选目标按相似度构成并查集 //返回值代表并查集树的个数 int ncomp = SeqPartition(&seq); //对相邻候选区域进行累加，为计算平均边界做准备 for( i = 0; i < seq.total; i++ ) { r1 = seq.rectQueue[i]; node = &PTreeNodes[i]; while(node->parent) node = node->parent; int idx = (node - PTreeNodes); comps.neighbors[idx]++; comps.rectQueue[idx].x += r1.x; comps.rectQueue[idx].y += r1.y; comps.rectQueue[idx].width += r1.width; comps.rectQueue[idx].height += r1.height; } // 计算平均目标边界 for( i = 0; i < seq.total; i++ ) { int n = comps.neighbors[i]; //只有满足最小临接的结果才是最终结果 if( n >= min_neighbors ) { Rect* rect = &seq2.rectQueue[seq2.tail]; rect->x = (comps.rectQueue[i].x*2 + n)/(2*n); rect->y = (comps.rectQueue[i].y*2 + n)/(2*n); rect->width = (comps.rectQueue[i].width*2 + n)/(2*n); rect->height = (comps.rectQueue[i].height*2 + n)/(2*n); seq2.neighbors[seq2.tail] = comps.neighbors[i]; seq2.tail++; seq2.total++; } } //从候选矩形中得到最大的矩形 for( i = 0; i < seq2.total; i++ ) { r1 = seq2.rectQueue[i]; r1_neighbor = seq2.neighbors[i]; flag = 1; for( j = 0; j < seq2.total; j++ ) { r2 = seq2.rectQueue[j]; r2_neighbor = seq2.neighbors[j]; distance = (r2.width *2+5)/10;//cvRound( r2.rect.width * 0.2 ); if( i != j && r1.x >= r2.x - distance && r1.y >= r2.y - distance && r1.x + r1.width <= r2.x + r2.width + distance && r1.y + r1.height <= r2.y + r2.height + distance && (r2_neighbor > MAX( 3, r1_neighbor ) || r1_neighbor < 3) ) { flag = 0; break; } } if( flag ) { result_seq.rectQueue[result_seq.tail] = r1; result_seq.tail++; result_seq.total++; } } } } void DownSample(Image* pImage, int factor) { int i = 0; int j = 0; int counti = 0; int countj = 0; int step = pImage->cols / factor; for (i =0; i < pImage->rows; i+= factor) { countj++; for (j =0; j < pImage->cols; j += factor) { *(pImage->imgPtr + i*step/factor + j/factor) = *(pImage->imgPtr + i*pImage->cols + j); counti++; } counti = 0; } pImage->cols /= factor; pImage->rows /= factor; }