前段时间看的OpenCV，其实有很多的例子程序，参考代码值得我们学习，对图像特征提取三大法宝：HOG特征，LBP特征，Haar特征有一定了解后。
对本文中的例子程序刚开始没有调通，今晚上调通了，试了试效果还可以，还需要深入理解。值得大家动手试试，还是很有成就感的，虽然是现成的例子.......
环境：OpenCV3.1+VS2013+WIN10
/*!
* \file Capture.cpp
*
* \author ranjiewen
* \date 十一月 2016
*
解析opencv自带人脸识别源码（……/opencv-3.1.0/samples/cpp/facedetect.cpp）
*/
#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|D:/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml|}" //默认路径实在安装路径下sample，修改了路径，以便加载load成功
"{nested-cascade|D:/opencv/sources/data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}" //修改路径
"{scale|1|}{try-flip||}{@filename||}" //文件为空时，设置摄像头，实时检测人脸
);
if (parser.has("help"))
{
help();
return 0;
}
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;