1 static CvHidHaarClassifierCascade* icvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade )  2 {  3     CvRect* ipp_features = 0;//定义一个矩形框指针  4     float *ipp_weights = 0, *ipp_thresholds = 0, *ipp_val1 = 0, *ipp_val2 = 0;//单精度浮点数指针4个  5     int* ipp_counts = 0;//整形指针1个  6   7     CvHidHaarClassifierCascade* out = 0;//最终返回的值  8   9     int i, j, k, l;//for循环的控制变量 10     int datasize;//数据大小 11     int total_classifiers = 0;//总的分类器数目 12     int total_nodes = 0; 13     char errorstr[1000];//错误信息数组 14     CvHidHaarClassifier* haar_classifier_ptr;//级联分类器指针 15     CvHidHaarTreeNode* haar_node_ptr; 16     CvSize orig_window_size;//提取窗口的大小 17     int has_tilted_features = 0; 18     int max_count = 0; 19  20     if( !CV_IS_HAAR_CLASSIFIER(cascade) )//判断传进来的分类器文件是否真正确 21         CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" ); 22  23     if( cascade->hid_cascade )//判断改分类器xml文件是否已经被初始化了 24         CV_Error( CV_StsError, "hid_cascade has been already created" ); 25  26     if( !cascade->stage_classifier )//如果没有阶级分类器，报错 27         CV_Error( CV_StsNullPtr, "" ); 28  29     if( cascade->count <= 0 )//如果分类器的阶级数<=0，报错 30         CV_Error( CV_StsOutOfRange, "Negative number of cascade stages" ); 31  32     orig_window_size = cascade->orig_window_size;//获取识别窗口的大小 33  34     /* check input structure correctness and calculate total memory size needed for 35        internal representation of the classifier cascade */ 36  37     for( i = 0; i < cascade->count; i++ )//对xml文件里面的每阶段的stage进行循环提取相关数据 38     { 39         CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i; 40         //获取每次进入循环的后阶段的子分类器，以haarcascade_upperbody.xml 为例子，count是30，stage_classifier的count是20 41  42         if( !stage_classifier->classifier ||//判断阶段分类器、子分类器及其stage 层数 是否合法 43             stage_classifier->count <= 0 ) 44         { 45             sprintf( errorstr, "header of the stage classifier #%d is invalid " 46                      "(has null pointers or non-positive classfier count)", i ); 47             CV_Error( CV_StsError, errorstr ); 48         } 49  50         max_count = MAX( max_count, stage_classifier->count );//获取子分类器stage的数目，以haarcascade_upperbody.xml为例，是20 51         total_classifiers += stage_classifier->count;//统计出总的子分类器的stage数目，即tree，再统计 52  53         for( j = 0; j < stage_classifier->count; j++ ) 54         //这个for循环主要是进入到子分类器tree里面的数据提取并且对其正确性的判断， 55         //循环条件为字stage数目，以haarcascade_upperbody.xml为例，为20 56         { 57             CvHaarClassifier* classifier = stage_classifier->classifier + j;//同上，找到此时循环的tree 58  59             total_nodes += classifier->count;//计算出此时循环的tree子分类器的root node 数目，再统计。以haarcascade_upperbody.xml为例，每个tree的node是1 60             for( l = 0; l < classifier->count; l++ ) 61                 //这个是关键循环，主数据的获取 62                 //以haarcascade_upperbody.xml为例，此时classifier->count=1，循环一次，进入里面获取关键数据 63             { 64                 for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ )//CV_HAAR_FEATURE_MAX = 3，循环三次，feature的最大数目，以haarcascade_upperbody.xml为例，只有1个 65                 { 66                     if( classifier->haar_feature[l].rect[k].r.width ) 67                         //逐层递归，先找feature，再找它里面的rect标签里面的矩阵row，行的宽度 68                         //以haarcascade_upperbody.xml为例，是2 69                     { 70                         CvRect r = classifier->haar_feature[l].rect[k].r;//把此时row矩阵框赋给r 71                         int tilted = classifier->haar_feature[l].tilted;//获取xml标签tited的值 72                         has_tilted_features |= tilted != 0;//|是位运算，例如0|1=1,这行的作用是判断has和tilted那个是1，还不知道其意义何在 73                         if( r.width < 0 || r.height < 0 || r.y < 0 || 74                             r.x + r.width > orig_window_size.width 75                             || 76                             (!tilted && 77                             (r.x < 0 || r.y + r.height > orig_window_size.height)) 78                             || 79                             (tilted && (r.x - r.height < 0 || 80                             r.y + r.width + r.height > orig_window_size.height))) 81                             //这个if语句是对feature里面的数据矩形的各方面判断，包括矩形的宽、高、等 82                             //矩形# %d的分类器# %d”“级分类器# %d是不是在里面”“参考（原创）级联窗口” 83                         { 84                             sprintf( errorstr, "rectangle #%d of the classifier #%d of " 85                                      "the stage classifier #%d is not inside " 86                                      "the reference (original) cascade window", k, j, i ); 87                             CV_Error( CV_StsNullPtr, errorstr ); 88                         } 89                     } 90                 } 91             } 92         } 93     } 94     //上面数据的判断结束后，到这里 95  96     datasize = sizeof(CvHidHaarClassifierCascade) +//获取整个分类器，xml文件的数据大小 97                sizeof(CvHidHaarStageClassifier)*cascade->count + 98                sizeof(CvHidHaarClassifier) * total_classifiers + 99                sizeof(CvHidHaarTreeNode) * total_nodes +100                sizeof(void*)*(total_nodes + total_classifiers);101 102     out = (CvHidHaarClassifierCascade*)cvAlloc( datasize );//给最终返回的变量分配内存大小103     memset( out, 0, sizeof(*out) );//对变量初始化，全部填充0104 105     //下面是逐个赋值,初始化头部106     /* init header */107     out->count = cascade->count;//新分类器out的stage数目108     out->stage_classifier = (CvHidHaarStageClassifier*)(out + 1);//子分类器tree的数目109     haar_classifier_ptr = (CvHidHaarClassifier*)(out->stage_classifier + cascade->count);//tree指针110     haar_node_ptr = (CvHidHaarTreeNode*)(haar_classifier_ptr + total_classifiers);//tree里面node的指针111 112     out->isStumpBased = 1;//布尔类型，true113     out->has_tilted_features = has_tilted_features;114     out->is_tree = 0;115 116     /* initialize internal representation */117     for( i = 0; i < cascade->count; i++ )118     {119         CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;120         CvHidHaarStageClassifier* hid_stage_classifier = out->stage_classifier + i;121 122         hid_stage_classifier->count = stage_classifier->count;123         hid_stage_classifier->threshold = stage_classifier->threshold - icv_stage_threshold_bias;124         hid_stage_classifier->classifier = haar_classifier_ptr;125         hid_stage_classifier->two_rects = 1;126         haar_classifier_ptr += stage_classifier->count;127 128         hid_stage_classifier->parent = (stage_classifier->parent == -1)129             ? NULL : out->stage_classifier + stage_classifier->parent;130         hid_stage_classifier->next = (stage_classifier->next == -1)131             ? NULL : out->stage_classifier + stage_classifier->next;132         hid_stage_classifier->child = (stage_classifier->child == -1)133             ? NULL : out->stage_classifier + stage_classifier->child;134 135         out->is_tree |= hid_stage_classifier->next != NULL;136 137         for( j = 0; j < stage_classifier->count; j++ )138         {139             CvHaarClassifier* classifier = stage_classifier->classifier + j;140             CvHidHaarClassifier* hid_classifier = hid_stage_classifier->classifier + j;141             int node_count = classifier->count;142             float* alpha_ptr = (float*)(haar_node_ptr + node_count);143 144             hid_classifier->count = node_count;145             hid_classifier->node = haar_node_ptr;146             hid_classifier->alpha = alpha_ptr;147 148             for( l = 0; l < node_count; l++ )149             {150                 CvHidHaarTreeNode* node = hid_classifier->node + l;151                 CvHaarFeature* feature = classifier->haar_feature + l;152                 memset( node, -1, sizeof(*node) );153                 node->threshold = classifier->threshold[l];154                 node->left = classifier->left[l];155                 node->right = classifier->right[l];156 157                 if( fabs(feature->rect[2].weight) < DBL_EPSILON ||158                     feature->rect[2].r.width == 0 ||159                     feature->rect[2].r.height == 0 )160                     memset( &(node->feature.rect[2]), 0, sizeof(node->feature.rect[2]) );161                 else162                     hid_stage_classifier->two_rects = 0;163             }164 165             memcpy( alpha_ptr, classifier->alpha, (node_count+1)*sizeof(alpha_ptr[0]));166             haar_node_ptr =167                 (CvHidHaarTreeNode*)cvAlignPtr(alpha_ptr+node_count+1, sizeof(void*));168 169             out->isStumpBased &= node_count == 1;170         }171     }172 /*173 #ifdef HAVE_IPP174     int can_use_ipp = !out->has_tilted_features && !out->is_tree && out->isStumpBased;175 176     if( can_use_ipp )177     {178         int ipp_datasize = cascade->count*sizeof(out->ipp_stages[0]);179         float ipp_weight_scale=(float)(1./((orig_window_size.width-icv_object_win_border*2)*180             (orig_window_size.height-icv_object_win_border*2)));181 182         out->ipp_stages = (void**)cvAlloc( ipp_datasize );183         memset( out->ipp_stages, 0, ipp_datasize );184 185         ipp_features = (CvRect*)cvAlloc( max_count*3*sizeof(ipp_features[0]) );186         ipp_weights = (float*)cvAlloc( max_count*3*sizeof(ipp_weights[0]) );187         ipp_thresholds = (float*)cvAlloc( max_count*sizeof(ipp_thresholds[0]) );188         ipp_val1 = (float*)cvAlloc( max_count*sizeof(ipp_val1[0]) );189         ipp_val2 = (float*)cvAlloc( max_count*sizeof(ipp_val2[0]) );190         ipp_counts = (int*)cvAlloc( max_count*sizeof(ipp_counts[0]) );191 192         for( i = 0; i < cascade->count; i++ )193         {194             CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;195             for( j = 0, k = 0; j < stage_classifier->count; j++ )196             {197                 CvHaarClassifier* classifier = stage_classifier->classifier + j;198                 int rect_count = 2 + (classifier->haar_feature->rect[2].r.width != 0);199 200                 ipp_thresholds[j] = classifier->threshold[0];201                 ipp_val1[j] = classifier->alpha[0];202                 ipp_val2[j] = classifier->alpha[1];203                 ipp_counts[j] = rect_count;204 205                 for( l = 0; l < rect_count; l++, k++ )206                 {207                     ipp_features[k] = classifier->haar_feature->rect[l].r;208                     //ipp_features[k].y = orig_window_size.height - ipp_features[k].y - ipp_features[k].height;209                     ipp_weights[k] = classifier->haar_feature->rect[l].weight*ipp_weight_scale;210                 }211             }212 213             if( ippiHaarClassifierInitAlloc_32f( (IppiHaarClassifier_32f**)&out->ipp_stages[i],214                 (const IppiRect*)ipp_features, ipp_weights, ipp_thresholds,215                 ipp_val1, ipp_val2, ipp_counts, stage_classifier->count ) < 0 )216                 break;217         }218 219         if( i < cascade->count )220         {221             for( j = 0; j < i; j++ )222                 if( out->ipp_stages[i] )223                     ippiHaarClassifierFree_32f( (IppiHaarClassifier_32f*)out->ipp_stages[i] );224             cvFree( &out->ipp_stages );225         }226     }227 #endif228 */229     cascade->hid_cascade = out;230     assert( (char*)haar_node_ptr - (char*)out <= datasize );231 232     cvFree( &ipp_features );233     cvFree( &ipp_weights );234     cvFree( &ipp_thresholds );235     cvFree( &ipp_val1 );236     cvFree( &ipp_val2 );237     cvFree( &ipp_counts );238 239     return out;240 }