OpenCV 人脸检测自学笔记(8)_读trainCascade的训练结果的代码笔记

读trainCascade的训练结果的代码笔记

这个是OpenCV提供的人脸检测调用trainCascade训练结果的代码。最近一直在用，不过才发现里面用到的接口不是trainCascade里的，用的是modules\objdetect\里的objdetect.hpp & cascadedetect.cpp。

#include "opencv2/objdetect/objdetect.hpp"
 #include "opencv2/highgui/highgui.hpp"
 #include "opencv2/imgproc/imgproc.hpp"


 #include <iostream>
 #include <stdio.h>


 using namespace std;
 using namespace cv;


 /** 函数声明 */
 void detectAndDisplay( Mat frame );


 /** 全局变量 */
 string face_cascade_name = "haarcascade_frontalface_alt.xml";
 string eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
 CascadeClassifier face_cascade;
 CascadeClassifier eyes_cascade;
 string window_name = "Capture - Face detection";
 RNG rng(12345);


 /** @主函数 */
 int main( int argc, const char** argv )
 {
   CvCapture* capture;
   Mat frame;


   //-- 1. 加载级联分类器文件
   if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
   if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };


   //-- 2. 打开内置摄像头视频流
   capture = cvCaptureFromCAM( -1 );
   if( capture )
   {
     while( true )
     {
   frame = cvQueryFrame( capture );


   //-- 3. 对当前帧使用分类器进行检测
       if( !frame.empty() )
       { detectAndDisplay( frame ); }
       else
       { printf(" --(!) No captured frame -- Break!"); break; }


       int c = waitKey(10);
       if( (char)c == 'c' ) { break; }
      }
   }
   return 0;
 }


/** @函数 detectAndDisplay */
void detectAndDisplay( Mat frame )
{
  std::vector<Rect> faces;
  Mat frame_gray;


  cvtColor( frame, frame_gray, CV_BGR2GRAY );
  equalizeHist( frame_gray, frame_gray );


  //-- 多尺寸检测人脸
  face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );


  for( int i = 0; i < faces.size(); i++ )
  {
    Point center( faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5 );
    ellipse( frame, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );


    Mat faceROI = frame_gray( faces[i] );
    std::vector<Rect> eyes;


    //-- 在每张人脸上检测双眼
    eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );


    for( int j = 0; j < eyes.size(); j++ )
     {
       Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );
       int radius = cvRound( (eyes[j].width + eyes[i].height)*0.25 );
       circle( frame, center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
     }
  }
  //-- 显示结果图像
  imshow( window_name, frame );
 }

 
 里面用到的load和detectMultiScale函数都是OpenCV的CascadeClassifier类的内容，而不是训练的时候用的CvCascadeClassifier类。以load为例：

 

bool CascadeClassifier::load(const string& filename)
{
    oldCascade.release();
    data = Data();
    featureEvaluator.release();


    FileStorage fs(filename, FileStorage::READ);
    if( !fs.isOpened() )
        return false;


    if( read(fs.getFirstTopLevelNode()) )//开始读xml文件
        return true;


    fs.release();


    oldCascade = Ptr<CvHaarClassifierCascade>((CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0));//旧版本的xml文件
    return !oldCascade.empty();
}

bool CascadeClassifier::read(const FileNode& root)
{
    if( !data.read(root) )//Step 1:这里应该是读xml开头的params以及stage里的信息
        return false;


    // load features
    featureEvaluator = FeatureEvaluator::create(data.featureType);
    FileNode fn = root[CC_FEATURES];//Step 2:这里开始应该是读xml下面那块<features>开始的rect
    if( fn.empty() )
        return false;


    return featureEvaluator->read(fn);
}

这里step 1 和 2 就是之前分析xml格式的时候的信息了。

//Step 1:
bool CascadeClassifier::Data::read(const FileNode &root)
{
    static const float THRESHOLD_EPS = 1e-5f;


    // load stage params
    string stageTypeStr = (string)root[CC_STAGE_TYPE];
    if( stageTypeStr == CC_BOOST )
        stageType = BOOST;
    else
        return false;


    string featureTypeStr = (string)root[CC_FEATURE_TYPE];
    if( featureTypeStr == CC_HAAR )
        featureType = FeatureEvaluator::HAAR;
    else if( featureTypeStr == CC_LBP )
        featureType = FeatureEvaluator::LBP;
    else if( featureTypeStr == CC_HOG )
        featureType = FeatureEvaluator::HOG;


    else
        return false;


    origWinSize.width = (int)root[CC_WIDTH];
    origWinSize.height = (int)root[CC_HEIGHT];
    CV_Assert( origWinSize.height > 0 && origWinSize.width > 0 );


    isStumpBased = (int)(root[CC_STAGE_PARAMS][CC_MAX_DEPTH]) == 1 ? true : false;


    // load feature params
    FileNode fn = root[CC_FEATURE_PARAMS];
    if( fn.empty() )
        return false;


    ncategories = fn[CC_MAX_CAT_COUNT];
    int subsetSize = (ncategories + 31)/32,
        nodeStep = 3 + ( ncategories>0 ? subsetSize : 1 );


    // load stages
    fn = root[CC_STAGES];
    if( fn.empty() )
        return false;


    stages.reserve(fn.size());
    classifiers.clear();
    nodes.clear();


    FileNodeIterator it = fn.begin(), it_end = fn.end();


    for( int si = 0; it != it_end; si++, ++it )
    {
        FileNode fns = *it;
        Stage stage;
        stage.threshold = (float)fns[CC_STAGE_THRESHOLD] - THRESHOLD_EPS;
        fns = fns[CC_WEAK_CLASSIFIERS];
        if(fns.empty())
            return false;
        stage.ntrees = (int)fns.size();
        stage.first = (int)classifiers.size();
        stages.push_back(stage);
        classifiers.reserve(stages[si].first + stages[si].ntrees);


        FileNodeIterator it1 = fns.begin(), it1_end = fns.end();
        for( ; it1 != it1_end; ++it1 ) // weak trees
        {
            FileNode fnw = *it1;
            FileNode internalNodes = fnw[CC_INTERNAL_NODES];//internal nodes
            FileNode leafValues = fnw[CC_LEAF_VALUES];//leaf value
            if( internalNodes.empty() || leafValues.empty() )
                return false;


            DTree tree;
            tree.nodeCount = (int)internalNodes.size()/nodeStep;
            classifiers.push_back(tree);


            nodes.reserve(nodes.size() + tree.nodeCount);
            leaves.reserve(leaves.size() + leafValues.size());
            if( subsetSize > 0 )
                subsets.reserve(subsets.size() + tree.nodeCount*subsetSize);


            FileNodeIterator internalNodesIter = internalNodes.begin(), internalNodesEnd = internalNodes.end();


            for( ; internalNodesIter != internalNodesEnd; ) // nodes
            {
                DTreeNode node;
                node.left = (int)*internalNodesIter; ++internalNodesIter;
                node.right = (int)*internalNodesIter; ++internalNodesIter;
                node.featureIdx = (int)*internalNodesIter; ++internalNodesIter;
                if( subsetSize > 0 )
                {
                    for( int j = 0; j < subsetSize; j++, ++internalNodesIter )
                        subsets.push_back((int)*internalNodesIter);
                    node.threshold = 0.f;
                }
                else
                {
                    node.threshold = (float)*internalNodesIter; ++internalNodesIter;
                }
                nodes.push_back(node);
            }


            internalNodesIter = leafValues.begin(), internalNodesEnd = leafValues.end();


            for( ; internalNodesIter != internalNodesEnd; ++internalNodesIter ) // leaves
                leaves.push_back((float)*internalNodesIter);
        }
    }


    return true;
}

//Step 2:以LBP为例
bool LBPEvaluator::read( const FileNode& node )
{
    features->resize(node.size());
    featuresPtr = &(*features)[0];
    FileNodeIterator it = node.begin(), it_end = node.end();
    for(int i = 0; it != it_end; ++it, i++)
    {
        if(!featuresPtr[i].read(*it))
            return false;
    }
    return true;
}
bool LBPEvaluator::Feature :: read(const FileNode& node )
{
    FileNode rnode = node[CC_RECT];
    FileNodeIterator it = rnode.begin();
    it >> rect.x >> rect.y >> rect.width >> rect.height;
    return true;
}

My Q:至今不太明白为啥有trainCascade和CvtrainCascade，FeatureEvaluator和CvFeatureEvaluator等等，怎么感觉很多是重复的东西呢。

补充：

关于FeatureEvaluator 和 CvFeatureEvaluator的关系：

区别来看的话：
1. 首先FeatureEvaluator是在modules\objdetect\下，而CvFeatureEvaluator定义在apps\trainCascade\下。
2. FeatureEvaluator应该是去调用训练好的结果。而CvFeatureEvaluator应该是用来去训练而设计的。因为在FeatureEvaluator里只有read, 没有write。相反在CvFeatureEvaluator中只有write没有read。再有就是FeatureEvaluator里没有关于class Label和sample index的参数接口，而CvFeatureEvaluator里面有。
3. CvFeatureEvaluator把有些部分分出去作为Params

两者是有很多共同的地方。比如计算图像的积分图,创建函数create( int featureType )。因为这部分不管是训练还是调用训练后的结果都要用到。