之前简答得实现了一下人脸检测，不过使用的opencv自带的摄像头操作函数，然而那个并不能使用在嵌入式设备上，为了通用性，我将让opencv使用v4l2获得的数据帧进行检测。
说明
v4l2其实并不难，只不过大家容易被网上那动辄几百行的代码唬住，那些代码大多都是在检错，因为操作硬件这种事情出错的概率确实很高，每一步都可能出错，但是核心的语句也就那几行，这里推荐这几篇文章（点我跳转），我都已经仔细读过，含金量比较高，当然英语好直接都自带文档也是极好的。
opencv与v4l2结合的问题就是数据格式，因为我使用的是Qt，v4l2获得的数据帧会转换为QImage类型，OpenCV则主要使用Mat类型，不过不用担心，简单转换一下就可以了，这篇文章给出了转换方式，由于是一个工程，所以只上OpenCV部分的代码了。
代码
需要下载整个项目的（点我下载）
#include "facedetect.h"FaceDetect::FaceDetect(){ this->tryflip = false; this->cascade.load("/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml"); this->nestedCascade.load("/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml"); this->scale = 1.5;}void FaceDetect::setMatImageFromQImage(QImage img){ this->image = QImage2cvMat(img).clone();}QImage FaceDetect::getQImage(){ return cvMat2QImage(detectAndDraw(image, cascade, nestedCascade, scale, tryflip));}QImage FaceDetect::cvMat2QImage(const cv::Mat& mat){ if(mat.type() == CV_8UC1) { QImage image(mat.cols, mat.rows, QImage::Format_Indexed8); image.setNumColors(256); for(int i = 0; i < 256; i++) { image.setColor(i, qRgb(i, i, i)); } uchar *pSrc = mat.data; for(int row = 0; row < mat.rows; row ++) { uchar *pDest = image.scanLine(row); memcpy(pDest, pSrc, mat.cols); pSrc += mat.step; } return image; } else if(mat.type() == CV_8UC3) { const uchar *pSrc = (const uchar*)mat.data; QImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_RGB888); return image.rgbSwapped(); } else if(mat.type() == CV_8UC4) { const uchar *pSrc = (const uchar*)mat.data; QImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_ARGB32); return image.copy(); } else { return QImage(); }}Mat FaceDetect::QImage2cvMat(QImage image){ cv::Mat mat; switch(image.format()) { case QImage::Format_ARGB32: case QImage::Format_RGB32: case QImage::Format_ARGB32_Premultiplied: mat = cv::Mat(image.height(), image.width(), CV_8UC4, (void*)image.bits(), image.bytesPerLine()); break; case QImage::Format_RGB888: mat = cv::Mat(image.height(), image.width(), CV_8UC3, (void*)image.bits(), image.bytesPerLine()); cv::cvtColor(mat, mat, CV_BGR2RGB); break; case QImage::Format_Indexed8: mat = cv::Mat(image.height(), image.width(), CV_8UC1, (void*)image.bits(), image.bytesPerLine()); break; } return mat;}Mat FaceDetect::detectAndDraw( Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip ){ double t = 0; vector<Rect> faces, faces2; const static Scalar colors[] = { Scalar(255,0,0), Scalar(255,128,0), Scalar(255,255,0), Scalar(0,255,0), Scalar(0,128,255), Scalar(0,255,255), Scalar(0,0,255), Scalar(255,0,255) }; Mat gray, smallImg; cvtColor( img, gray, COLOR_BGR2GRAY ); double fx = 1 / scale; resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR ); equalizeHist( smallImg, smallImg ); t = (double)getTickCount(); cascade.detectMultiScale( smallImg, faces, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) ); if( tryflip ) { flip(smallImg, smallImg, 1); cascade.detectMultiScale( smallImg, faces2, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) ); for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); ++r ) { faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height)); } } t = (double)getTickCount() - t; for ( size_t i = 0; i < faces.size(); i++ ) { Rect r = faces[i]; Mat smallImgROI; vector<Rect> nestedObjects; Point center; Scalar color = colors[i%8]; int radius; double aspect_ratio = (double)r.width/r.height; if( 0.75 < aspect_ratio && aspect_ratio < 1.3 ) { center.x = cvRound((r.x + r.width*0.5)*scale); center.y = cvRound((r.y + r.height*0.5)*scale); radius = cvRound((r.width + r.height)*0.25*scale); circle( img, center, radius, color, 3, 8, 0 ); } else rectangle( img, cvPoint(cvRound(r.x*scale), cvRound(r.y*scale)), cvPoint(cvRound((r.x + r.width-1)*scale), cvRound((r.y + r.height-1)*scale)), color, 3, 8, 0); if( nestedCascade.empty() ) continue; smallImgROI = smallImg( r ); nestedCascade.detectMultiScale( smallImgROI, nestedObjects, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) ); for ( size_t j = 0; j < nestedObjects.size(); j++ ) { Rect nr = nestedObjects[j]; center.x = cvRound((r.x + nr.x + nr.width*0.5)*scale); center.y = cvRound((r.y + nr.y + nr.height*0.5)*scale); radius = cvRound((nr.width + nr.height)*0.25*scale); circle( img, center, radius, color, 3, 8, 0 ); } } return img;}点击打开链接
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