1. 解析opencv自带人脸识别源码（……/opencv-3.1.0/samples/cpp/facedetect.cpp）
@ 操作系统：Ubuntu 15.04
OpenCV版本：3.1.0
#include "opencv2/objdetect.hpp"#include "opencv2/highgui.hpp"#include "opencv2/imgproc.hpp"#include <iostream>
using namespace std;using namespace cv;
static void help(){ cout << "\nThis program demonstrates the cascade recognizer. Now you can use Haar or LBP features.\n" "This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n" "It's most known use is for faces.\n" "Usage:\n" "./facedetect [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n" " [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]\n" " [--scale=<image scale greater or equal to 1, try 1.3 for example>]\n" " [--try-flip]\n" " [filename|camera_index]\n\n" "see facedetect.cmd for one call:\n" "./facedetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml\" --scale=1.3\n\n" "During execution:\n\tHit any key to quit.\n" "\tUsing OpenCV version " << CV_VERSION << "\n" << endl;}
void detectAndDraw( Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip );
string cascadeName;string nestedCascadeName;
int main( int argc, const char** argv ){ VideoCapture capture; Mat frame, image; string inputName; bool tryflip;
// CascadeClassifier是Opencv中做人脸检测的时候的一个级联分类器，现在有两种选择：一是使用老版本的CvHaarClassifierCascade函数，一是使用新版本的CascadeClassifier类。老版本的分类器只支持类Haar特征，而新版本的分类器既可以使用Haar，也可以使用LBP特征。 CascadeClassifier cascade, nestedCascade; double scale;
cv::CommandLineParser parser(argc, argv, "{help h||}" "{cascade|../../data/haarcascades/haarcascade_frontalface_alt.xml|}" "{nested-cascade|../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}" "{scale|1|}{try-flip||}{@filename||}" ); if (parser.has("help")) { help(); return 0; }
// 问题1：不用定义返回类型? cascadeName = parser.get<string>("cascade"); nestedCascadeName = parser.get<string>("nested-cascade"); scale = parser.get<double>("scale"); if (scale < 1) scale = 1; tryflip = parser.has("try-flip"); inputName = parser.get<string>("@filename"); std::cout << inputName << std::endl; // test if (!parser.check()) { parser.printErrors(); return 0; } // 加载模型 if ( !nestedCascade.load( nestedCascadeName ) ) cerr << "WARNING: Could not load classifier cascade for nested objects" << endl; if( !cascade.load( cascadeName ) ) { cerr << "ERROR: Could not load classifier cascade" << endl; help(); return -1; } // 读取摄像头 // isdigit检测字符是否为阿拉伯数字 if( inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1) ) { int c = inputName.empty() ? 0 : inputName[0] - '0'; // 此处若系统在虚拟机上，需在虚拟机中设置接管摄像头：虚拟机(M)-> 可移动设备 -> 摄像头名称 -> 连接(断开与主机连接) if(!capture.open(c)) cout << "Capture from camera #" << c << " didn't work" << endl; else { capture.set(CV_CAP_PROP_FRAME_WIDTH, 640); capture.set(CV_CAP_PROP_FRAME_HEIGHT, 480); } } else if( inputName.size() ) { image = imread( inputName, 1 ); if( image.empty() ) { if(!capture.open( inputName )) cout << "Could not read " << inputName << endl; } } else { image = imread( "../data/lena.jpg", 1 ); if(image.empty()) cout << "Couldn't read ../data/lena.jpg" << endl; }
if( capture.isOpened() ) { cout << "Video capturing has been started ..." << endl;
for(;;) { std::cout << "capturing..." << std::endl; // test capture >> frame; if( frame.empty() ) break;
Mat frame1 = frame.clone(); std::cout << "Start to detect..." << std::endl; // test detectAndDraw( frame1, cascade, nestedCascade, scale, tryflip );
int c = waitKey(10); if( c == 27 || c == 'q' || c == 'Q' ) break; } } else { cout << "Detecting face(s) in " << inputName << endl; if( !image.empty() ) { detectAndDraw( image, cascade, nestedCascade, scale, tryflip ); waitKey(0); } else if( !inputName.empty() ) { /* assume it is a text file containing the list of the image filenames to be processed - one per line */ FILE* f = fopen( inputName.c_str(), "rt" ); if( f ) { char buf[1000+1]; while( fgets( buf, 1000, f ) ) { int len = (int)strlen(buf), c; while( len > 0 && isspace(buf[len-1]) ) len--; buf[len] = ''; cout << "file " << buf << endl; image = imread( buf, 1 ); if( !image.empty() ) { detectAndDraw( image, cascade, nestedCascade, scale, tryflip ); c = waitKey(0); if( c == 27 || c == 'q' || c == 'Q' ) break; } else { cerr << "Aw snap, couldn't read image " << buf << endl; } } fclose(f); } } }
return 0;}
void 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)cvGetTickCount(); cascade.detectMultiScale( smallImg, faces, 1.1, 2, 0 //|CASCADE_FIND_BIGGEST_OBJECT //|CASCADE_DO_ROUGH_SEARCH |CASCADE_SCALE_IMAGE, Size(30, 30) ); if( tryflip ) { flip(smallImg, smallImg, 1); cascade.detectMultiScale( smallImg, faces2, 1.1, 2, 0 //|CASCADE_FIND_BIGGEST_OBJECT //|CASCADE_DO_ROUGH_SEARCH |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)cvGetTickCount() - t; printf( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) ); 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_FIND_BIGGEST_OBJECT //|CASCADE_DO_ROUGH_SEARCH //|CASCADE_DO_CANNY_PRUNING |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 ); } } imshow( "result", img );}
问题未解决：
运行到capture>>frame;时出现select timeout的错误；
@ 操作系统：Windows 10
OpenCV版本：3.1.0
代码与Linux版本基本相同，未出现错误；
OpenCV官方教程中文版（For Python） PDF http://www.xuebuyuan.com/Linux/2015-08/121400.htm
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[翻译]Ubuntu 14.04, 13.10 下安装 OpenCV 2.4.9 http://www.xuebuyuan.com/Linux/2014-12/110045.htm
OpenCV的详细介绍：请点这里OpenCV的下载地址：请点这里
本文永久更新链接地址：http://www.xuebuyuan.com/Linux/2016-11/137099.htm
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