opencv 之 icvCreateHidHaarClassifierCascade 分类器信息初始化函数部分详细代码注释。

请看注释。这个函数，是人脸识别主函数，里面出现过的函数之一，作用是初始化分类器的数据，就是一个xml文件的数据初始化。

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 static CvHidHaarClassifierCascade* icvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade )
 {
     CvRect* ipp_features = ;//定义一个矩形框指针
     float *ipp_weights = , *ipp_thresholds = , *ipp_val1 = , *ipp_val2 = ;//单精度浮点数指针4个
     int* ipp_counts = ;//整形指针1个

     CvHidHaarClassifierCascade* out = ;//最终返回的值

     int i, j, k, l;//for循环的控制变量
     int datasize;//数据大小
     int total_classifiers = ;//总的分类器数目
     int total_nodes = ;
     char errorstr[];//错误信息数组
     CvHidHaarClassifier* haar_classifier_ptr;//级联分类器指针
     CvHidHaarTreeNode* haar_node_ptr;
     CvSize orig_window_size;//提取窗口的大小
     int has_tilted_features = ;
     int max_count = ;

     if( !CV_IS_HAAR_CLASSIFIER(cascade) )//判断传进来的分类器文件是否真正确
         CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );

     if( cascade->hid_cascade )//判断改分类器xml文件是否已经被初始化了
         CV_Error( CV_StsError, "hid_cascade has been already created" );

     if( !cascade->stage_classifier )//如果没有阶级分类器，报错
         CV_Error( CV_StsNullPtr, "" );

     if( cascade->count <=  )//如果分类器的阶级数<=0，报错
         CV_Error( CV_StsOutOfRange, "Negative number of cascade stages" );

     orig_window_size = cascade->orig_window_size;//获取识别窗口的大小

     /* check input structure correctness and calculate total memory size needed for
        internal representation of the classifier cascade */

     for( i = ; 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
     {
         CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;
         //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

         if( !stage_classifier->classifier ||//判断阶段分类器、子分类器及其stage 层数 是否合法
             stage_classifier->count <=  )
         {
             sprintf( errorstr, "header of the stage classifier #%d is invalid "
                      "(has null pointers or non-positive classfier count)", i );
             CV_Error( CV_StsError, errorstr );
         }

         max_count = MAX( max_count, stage_classifier->count );//获取子分类器stage的数目，以haarcascade_upperbody.xml为例，是20
         total_classifiers += stage_classifier->count;//统计出总的子分类器的stage数目，即tree，再统计

         for( j = ; j < stage_classifier->count; j++ )
         //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，
         //循环条件为字stage数目，以haarcascade_upperbody.xml为例，为20
         {
             CvHaarClassifier* classifier = stage_classifier->classifier + j;//同上，找到此时循环的tree

             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
             for( l = ; l < classifier->count; l++ )
                 //这个是关键循环，主数据的获取
                 //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
             {
                 for( k = ; 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
                 {
                     if( classifier->haar_feature[l].rect[k].r.width )
                         //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
                         //以haarcascade_upperbody.xml为例，是2
                     {
                         CvRect r = classifier->haar_feature[l].rect[k].r;//把此时row矩阵框赋给r
                         int tilted = classifier->haar_feature[l].tilted;//获取xml标签tited的值
                         has_tilted_features |= tilted != ;//|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
                         if( r.width <  || r.height <  || r.y <  ||
                             r.x + r.width > orig_window_size.width
                             ||
                             (!tilted &&
                             (r.x <  || r.y + r.height > orig_window_size.height))
                             ||
                             (tilted && (r.x - r.height <  ||
                             r.y + r.width + r.height > orig_window_size.height)))
                             //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
                             //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”
                         {
                             sprintf( errorstr, "rectangle #%d of the classifier #%d of "
                                      "the stage classifier #%d is not inside "
                                      "the reference (original) cascade window", k, j, i );
                             CV_Error( CV_StsNullPtr, errorstr );
                         }
                     }
                 }
             }
         }
     }
     //上面数据的判断结束后，到这里

     datasize = sizeof(CvHidHaarClassifierCascade) +//获取整个分类器，xml文件的数据大小
                sizeof(CvHidHaarStageClassifier)*cascade->count +
                sizeof(CvHidHaarClassifier) * total_classifiers +
                sizeof(CvHidHaarTreeNode) * total_nodes +
                sizeof(void*)*(total_nodes + total_classifiers);

     out = (CvHidHaarClassifierCascade*)cvAlloc( datasize );//给最终返回的变量分配内存大小
     memset( out, , sizeof(*out) );//对变量初始化，全部填充0

     //下面是逐个赋值,初始化头部
     /* init header */
     out->count = cascade->count;//新分类器out的stage数目
     out->stage_classifier = (CvHidHaarStageClassifier*)(out + );//子分类器tree的数目
     haar_classifier_ptr = (CvHidHaarClassifier*)(out->stage_classifier + cascade->count);//tree指针
     haar_node_ptr = (CvHidHaarTreeNode*)(haar_classifier_ptr + total_classifiers);//tree里面node的指针

     out->isStumpBased = ;//布尔类型，true
     out->has_tilted_features = has_tilted_features;
     out->is_tree = ;

     /* initialize internal representation */
     for( i = ; i < cascade->count; i++ )
     {
         CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;
         CvHidHaarStageClassifier* hid_stage_classifier = out->stage_classifier + i;

         hid_stage_classifier->count = stage_classifier->count;
         hid_stage_classifier->threshold = stage_classifier->threshold - icv_stage_threshold_bias;
         hid_stage_classifier->classifier = haar_classifier_ptr;
         hid_stage_classifier->two_rects = ;
         haar_classifier_ptr += stage_classifier->count;

         hid_stage_classifier->parent = (stage_classifier->parent == -)
             ? NULL : out->stage_classifier + stage_classifier->parent;
         hid_stage_classifier->next = (stage_classifier->next == -)
             ? NULL : out->stage_classifier + stage_classifier->next;
         hid_stage_classifier->child = (stage_classifier->child == -)
             ? NULL : out->stage_classifier + stage_classifier->child;

         out->is_tree |= hid_stage_classifier->next != NULL;

         for( j = ; j < stage_classifier->count; j++ )
         {
             CvHaarClassifier* classifier = stage_classifier->classifier + j;
             CvHidHaarClassifier* hid_classifier = hid_stage_classifier->classifier + j;
             int node_count = classifier->count;
             float* alpha_ptr = (float*)(haar_node_ptr + node_count);

             hid_classifier->count = node_count;
             hid_classifier->node = haar_node_ptr;
             hid_classifier->alpha = alpha_ptr;

             for( l = ; l < node_count; l++ )
             {
                 CvHidHaarTreeNode* node = hid_classifier->node + l;
                 CvHaarFeature* feature = classifier->haar_feature + l;
                 memset( node, -, sizeof(*node) );
                 node->threshold = classifier->threshold[l];
                 node->left = classifier->left[l];
                 node->right = classifier->right[l];

                 if( fabs(feature->rect[].weight) < DBL_EPSILON ||
                     feature->rect[].r.width ==  ||
                     feature->rect[].r.height ==  )
                     memset( &(node->feature.rect[]), , sizeof(node->feature.rect[]) );
                 else
                     hid_stage_classifier->two_rects = ;
             }

             memcpy( alpha_ptr, classifier->alpha, (node_count+)*sizeof(alpha_ptr[]));
             haar_node_ptr =
                 (CvHidHaarTreeNode*)cvAlignPtr(alpha_ptr+node_count+, sizeof(void*));

             out->isStumpBased &= node_count == ;
         }
     }
 /*
 #ifdef HAVE_IPP
     int can_use_ipp = !out->has_tilted_features && !out->is_tree && out->isStumpBased;

     if( can_use_ipp )
     {
         int ipp_datasize = cascade->count*sizeof(out->ipp_stages[0]);
         float ipp_weight_scale=(float)(1./((orig_window_size.width-icv_object_win_border*2)*
             (orig_window_size.height-icv_object_win_border*2)));

         out->ipp_stages = (void**)cvAlloc( ipp_datasize );
         memset( out->ipp_stages, 0, ipp_datasize );

         ipp_features = (CvRect*)cvAlloc( max_count*3*sizeof(ipp_features[0]) );
         ipp_weights = (float*)cvAlloc( max_count*3*sizeof(ipp_weights[0]) );
         ipp_thresholds = (float*)cvAlloc( max_count*sizeof(ipp_thresholds[0]) );
         ipp_val1 = (float*)cvAlloc( max_count*sizeof(ipp_val1[0]) );
         ipp_val2 = (float*)cvAlloc( max_count*sizeof(ipp_val2[0]) );
         ipp_counts = (int*)cvAlloc( max_count*sizeof(ipp_counts[0]) );

         for( i = 0; i < cascade->count; i++ )
         {
             CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;
             for( j = 0, k = 0; j < stage_classifier->count; j++ )
             {
                 CvHaarClassifier* classifier = stage_classifier->classifier + j;
                 int rect_count = 2 + (classifier->haar_feature->rect[2].r.width != 0);

                 ipp_thresholds[j] = classifier->threshold[0];
                 ipp_val1[j] = classifier->alpha[0];
                 ipp_val2[j] = classifier->alpha[1];
                 ipp_counts[j] = rect_count;

                 for( l = 0; l < rect_count; l++, k++ )
                 {
                     ipp_features[k] = classifier->haar_feature->rect[l].r;
                     //ipp_features[k].y = orig_window_size.height - ipp_features[k].y - ipp_features[k].height;
                     ipp_weights[k] = classifier->haar_feature->rect[l].weight*ipp_weight_scale;
                 }
             }

             if( ippiHaarClassifierInitAlloc_32f( (IppiHaarClassifier_32f**)&out->ipp_stages[i],
                 (const IppiRect*)ipp_features, ipp_weights, ipp_thresholds,
                 ipp_val1, ipp_val2, ipp_counts, stage_classifier->count ) < 0 )
                 break;
         }

         if( i < cascade->count )
         {
             for( j = 0; j < i; j++ )
                 if( out->ipp_stages[i] )
                     ippiHaarClassifierFree_32f( (IppiHaarClassifier_32f*)out->ipp_stages[i] );
             cvFree( &out->ipp_stages );
         }
     }
 #endif
 */
     cascade->hid_cascade = out;
     assert( (char*)haar_node_ptr - (char*)out <= datasize );

     cvFree( &ipp_features );
     cvFree( &ipp_weights );
     cvFree( &ipp_thresholds );
     cvFree( &ipp_val1 );
     cvFree( &ipp_val2 );
     cvFree( &ipp_counts );

     return out;
 }