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;