级联分类器检测类CascadeClassifier，提供了两个重要的方法：
CascadeClassifier cascade_classifier; cascade_classifier.load( cascade_dir + cascade_name );// 加载 vector<Rect> object_rect; cascade_classifier.detectMultiScale( img1, object_rect, 1.1, min_win, 0|CASCADE_SCALE_IMAGE, Size(32,32) );// 识别
头文件：objdetect.hpp，实现在cascadedetect.cpp中。
CV_WRAP virtual void detectMultiScale( const Mat& image, CV_OUT vector<Rect>& objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size() );
代码的实现：
void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects, double scaleFactor, int minNeighbors, int flags, Size minObjectSize, Size maxObjectSize) { vector<int> fakeLevels; vector<double> fakeWeights; detectMultiScale( image, objects, fakeLevels, fakeWeights, scaleFactor, minNeighbors, flags, minObjectSize, maxObjectSize, false ); }
fakeLevels是检测未通过层的级数，fakeWeights是未通过层的强分类器的输出，不使用时outputRejectLevels=false。
//检测函数 void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects, vector<int>& rejectLevels, vector<double>& levelWeights, double scaleFactor, int minNeighbors, int flags, Size minObjectSize, Size maxObjectSize, bool outputRejectLevels ) { const double GROUP_EPS = 0.2; CV_Assert( scaleFactor > 1 && image.depth() == CV_8U ); if( empty() ) return; if( isOldFormatCascade() ) { MemStorage storage(cvCreateMemStorage(0)); CvMat _image = image; CvSeq* _objects = cvHaarDetectObjectsForROC( &_image, oldCascade, storage, rejectLevels, levelWeights, scaleFactor, minNeighbors, flags, minObjectSize, maxObjectSize, outputRejectLevels ); vector<CvAvgComp> vecAvgComp; Seq<CvAvgComp>(_objects).copyTo(vecAvgComp); objects.resize(vecAvgComp.size()); std::transform(vecAvgComp.begin(), vecAvgComp.end(), objects.begin(), getRect()); return; } objects.clear(); if (!maskGenerator.empty()) { maskGenerator->initializeMask(image); } if( maxObjectSize.height == 0 || maxObjectSize.width == 0 ) maxObjectSize = image.size(); Mat grayImage = image; if( grayImage.channels() > 1 ) { Mat temp; cvtColor(grayImage, temp, CV_BGR2GRAY); grayImage = temp; } Mat imageBuffer(image.rows + 1, image.cols + 1, CV_8U); vector<Rect> candidates; for( double factor = 1; ; factor *= scaleFactor ) { Size originalWindowSize = getOriginalWindowSize(); Size windowSize( cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) ); Size scaledImageSize( cvRound( grayImage.cols/factor ), cvRound( grayImage.rows/factor ) ); Size processingRectSize( scaledImageSize.width - originalWindowSize.width, scaledImageSize.height - originalWindowSize.height ); if( processingRectSize.width <= 0 || processingRectSize.height <= 0 ) break; if( windowSize.width > maxObjectSize.width || windowSize.height > maxObjectSize.height ) break; if( windowSize.width < minObjectSize.width || windowSize.height < minObjectSize.height ) continue; //缩放图片 Mat scaledImage( scaledImageSize, CV_8U, imageBuffer.data ); resize( grayImage, scaledImage, scaledImageSize, 0, 0, CV_INTER_LINEAR ); //计算步长 int yStep; if( getFeatureType() == cv::FeatureEvaluator::HOG ) { yStep = 4; } else { yStep = factor > 2. ? 1 : 2; } int stripCount, stripSize; const int PTS_PER_THREAD = 1000; stripCount = ((processingRectSize.width/yStep)*(processingRectSize.height + yStep-1)/yStep + PTS_PER_THREAD/2)/PTS_PER_THREAD; stripCount = std::min(std::max(stripCount, 1), 100); stripSize = (((processingRectSize.height + stripCount - 1)/stripCount + yStep-1)/yStep)*yStep; // 调用单尺度检测函数进行检测 if( !detectSingleScale( scaledImage, stripCount, processingRectSize, stripSize, yStep, factor, candidates, rejectLevels, levelWeights, outputRejectLevels ) ) break; } objects.resize(candidates.size()); std::copy(candidates.begin(), candidates.end(), objects.begin()); //合并检测结果 if( outputRejectLevels ) { groupRectangles( objects, rejectLevels, levelWeights, minNeighbors, GROUP_EPS ); } else { groupRectangles( objects, minNeighbors, GROUP_EPS ); } }
函数的工作：检测各个尺寸的图片，然后合并检测结果。具体单尺寸的检测见：
bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize, int stripSize, int yStep, double factor, vector<Rect>& candidates, vector<int>& levels, vector<double>& weights, bool outputRejectLevels ) { // 计算当前图像的积分图 if( !featureEvaluator->setImage( image, data.origWinSize ) ) return false; #if defined (LOG_CASCADE_STATISTIC) logger.setImage(image); #endif Mat currentMask; if (!maskGenerator.empty()) { currentMask=maskGenerator->generateMask(image); } vector<Rect> candidatesVector; vector<int> rejectLevels; vector<double> levelWeights; Mutex mtx; if( outputRejectLevels ) { parallel_for_(Range(0, stripCount), CascadeClassifierInvoker( *this, processingRectSize, stripSize, yStep, factor, candidatesVector, rejectLevels, levelWeights, true, currentMask, &mtx)); levels.insert( levels.end(), rejectLevels.begin(), rejectLevels.end() ); weights.insert( weights.end(), levelWeights.begin(), levelWeights.end() ); } else { //CascadeClassifierInvoker函数的operator()实现具体的检测过程 parallel_for_(Range(0, stripCount), CascadeClassifierInvoker( *this, processingRectSize, stripSize, yStep, factor, candidatesVector, rejectLevels, levelWeights, false, currentMask, &mtx)); } candidates.insert( candidates.end(), candidatesVector.begin(), candidatesVector.end() ); #if defined (LOG_CASCADE_STATISTIC) logger.write(); #endif return true; }
进而：
CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor, vector<Rect>& _vec, vector<int>& _levels, vector<double>& _weights, bool outputLevels, const Mat& _mask, Mutex* _mtx) { classifier = &_cc; processingRectSize = _sz1; stripSize = _stripSize; yStep = _yStep; scalingFactor = _factor; rectangles = &_vec; rejectLevels = outputLevels ? &_levels : 0; levelWeights = outputLevels ? &_weights : 0; mask = _mask; mtx = _mtx; } void operator()(const Range& range) const { Ptr<FeatureEvaluator> evaluator = classifier->featureEvaluator->clone(); Size winSize(cvRound(classifier->data.origWinSize.width * scalingFactor), cvRound(classifier->data.origWinSize.height * scalingFactor)); int y1 = range.start * stripSize; int y2 = min(range.end * stripSize, processingRectSize.height); for( int y = y1; y < y2; y += yStep ) { for( int x = 0; x < processingRectSize.width; x += yStep ) { if ( (!mask.empty()) && (mask.at<uchar>(Point(x,y))==0)) { continue; } double gypWeight; int result = classifier->runAt(evaluator, Point(x, y), gypWeight); #if defined (LOG_CASCADE_STATISTIC) logger.setPoint(Point(x, y), result); #endif //是否返回级数 if( rejectLevels ) { if( result == 1 ) result = -(int)classifier->data.stages.size(); if( classifier->data.stages.size() + result < 4 ) { mtx->lock(); rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor), winSize.width, winSize.height)); rejectLevels->push_back(-result); levelWeights->push_back(gypWeight); mtx->unlock(); } } else if( result > 0 ) { mtx->lock(); //添加检测得到的矩形框（还原到原图） rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor), winSize.width, winSize.height)); mtx->unlock(); } // 如果一级都没有通过那么加大搜索步长 if( result == 0 ) x += yStep; } } }
检测框的合并过程：
void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps) { groupRectangles(rectList, groupThreshold, eps, 0, 0); }
进而：
void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights) { if( groupThreshold <= 0 || rectList.empty() ) { if( weights ) { size_t i, sz = rectList.size(); weights->resize(sz); for( i = 0; i < sz; i++ ) (*weights)[i] = 1; } return; } vector<int> labels; //对rectList中的矩形进行分类 int nclasses = partition(rectList, labels, SimilarRects(eps)); vector<Rect> rrects(nclasses); vector<int> rweights(nclasses, 0); vector<int> rejectLevels(nclasses, 0); vector<double> rejectWeights(nclasses, DBL_MIN); int i, j, nlabels = (int)labels.size(); //组合分到同一类别的矩形并保存当前类别下通过stage的最大值以及最大的权重 for( i = 0; i < nlabels; i++ ) { int cls = labels[i]; rrects[cls].x += rectList[i].x; rrects[cls].y += rectList[i].y; rrects[cls].width += rectList[i].width; rrects[cls].height += rectList[i].height; rweights[cls]++; } if ( levelWeights && weights && !weights->empty() && !levelWeights->empty() ) { for( i = 0; i < nlabels; i++ ) { int cls = labels[i]; if( (*weights)[i] > rejectLevels[cls] ) { rejectLevels[cls] = (*weights)[i]; rejectWeights[cls] = (*levelWeights)[i]; } else if( ( (*weights)[i] == rejectLevels[cls] ) && ( (*levelWeights)[i] > rejectWeights[cls] ) ) rejectWeights[cls] = (*levelWeights)[i]; } } for( i = 0; i < nclasses; i++ ) { Rect r = rrects[i]; float s = 1.f/rweights[i]; rrects[i] = Rect(saturate_cast<int>(r.x*s), saturate_cast<int>(r.y*s), saturate_cast<int>(r.width*s), saturate_cast<int>(r.height*s)); } rectList.clear(); if( weights ) weights->clear(); if( levelWeights ) levelWeights->clear(); //按照groupThreshold合并规则，以及是否存在包含关系输出合并后的矩形 for( i = 0; i < nclasses; i++ ) { Rect r1 = rrects[i]; int n1 = levelWeights ? rejectLevels[i] : rweights[i]; double w1 = rejectWeights[i]; if( n1 <= groupThreshold ) continue; // filter out small face rectangles inside large rectangles for( j = 0; j < nclasses; j++ ) { int n2 = rweights[j]; if( j == i || n2 <= groupThreshold ) continue; Rect r2 = rrects[j]; int dx = saturate_cast<int>( r2.width * eps ); int dy = saturate_cast<int>( r2.height * eps ); // 当r1在r2的内部的时候，停止 if( i != j && r1.x >= r2.x - dx && r1.y >= r2.y - dy && r1.x + r1.width <= r2.x + r2.width + dx && r1.y + r1.height <= r2.y + r2.height + dy && (n2 > std::max(3, n1) || n1 < 3) ) break; } if( j == nclasses ) { rectList.push_back(r1); if( weights ) weights->push_back(n1); if( levelWeights ) levelWeights->push_back(w1); } } }
其中：
class CV_EXPORTS SimilarRects { public: SimilarRects(double _eps) : eps(_eps) {} inline bool operator()(const Rect& r1, const Rect& r2) const { double delta = eps*(std::min(r1.width, r2.width) + std::min(r1.height, r2.height))*0.5; return std::abs(r1.x - r2.x) <= delta && std::abs(r1.y - r2.y) <= delta && std::abs(r1.x + r1.width - r2.x - r2.width) <= delta && std::abs(r1.y + r1.height - r2.y - r2.height) <= delta; } double eps; };
参考：http://blog.csdn.net/xidianzhimeng/article/details/41851569
http://blog.csdn.net/xidianzhimeng/article/details/40107763
http://docs.opencv.org/modules/core/doc/clustering.html#partition