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

static CvHidHaarClassifierCascade* icvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade )
{
CvRect* ipp_features = 0;//定义一个矩形框指针
float *ipp_weights = 0, *ipp_thresholds = 0, *ipp_val1 = 0, *ipp_val2 = 0;//单精度浮点数指针4个
int* ipp_counts = 0;//整形指针1个

CvHidHaarClassifierCascade* out = 0;//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 = 0;//总的分类器数目
int total_nodes = 0;
char errorstr[1000];//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 = 0;
int max_count = 0;

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 <= 0 )//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 = 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
{
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 <= 0 )
{
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 = 0; 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 = 0; 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 = 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
{
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 != 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
if( r.width < 0 || r.height < 0 || r.y < 0 ||
r.x + r.width > orig_window_size.width
||
(!tilted &&
(r.x < 0 || r.y + r.height > orig_window_size.height))
||
(tilted && (r.x - r.height < 0 ||
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, 0, 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 + 1);//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 = 1;//布尔类型，true
out->has_tilted_features = has_tilted_features;
out->is_tree = 0;

/* initialize internal representation */
for( i = 0; 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 = 1;
haar_classifier_ptr += stage_classifier->count;

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;

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

for( j = 0; 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 = 0; l < node_count; l++ )
{
CvHidHaarTreeNode* node = hid_classifier->node + l;
CvHaarFeature* 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];

if( fabs(feature->rect[2].weight) < DBL_EPSILON ||
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;
}

memcpy( alpha_ptr, classifier->alpha, (node_count+1)*sizeof(alpha_ptr[0]));
haar_node_ptr =
(CvHidHaarTreeNode*)cvAlignPtr(alpha_ptr+node_count+1, sizeof(void*));

out->isStumpBased &= node_count == 1;
}
}
/*
#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;
}