OpenCV超简单且有趣的人脸识别

拖更好久了，因为懒么。其实主要是不知道写什么。。。

想了想写了挺多机器视觉里特征识别的代码。总感觉少了人脸识别有些怪。那么就进入正题吧。

直接引用opencv里面的demo

#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 cascade, nestedCascade;//级联分类器
	double scale;

	cv::CommandLineParser parser(argc, argv,
		"{help h||}"
		"{cascade|haarcascade_frontalface_alt.xml|}"//脸部识别xml
		"{nested-cascade|haarcascade_eye.xml|}"//眼睛识别xml
		"{scale|1|}{try-flip||}{@filename|test.jpg|}"
	);
	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");
	if (!parser.check())
	{
		parser.printErrors();
		return 0;
	}
	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;
	}
	if (inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1))
	{
		int camera = inputName.empty() ? 0 : inputName[0] - '0';
		if (!capture.open(camera))
			cout << "Capture from camera #" << camera << " didn't work" << endl;
	}
	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 (;;)
		{
			capture >> frame;
			if (frame.empty())
				break;

			Mat frame1 = frame.clone();
			detectAndDraw(frame1, cascade, nestedCascade, scale, tryflip);

			char c = (char)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);
					while (len > 0 && isspace(buf[len - 1]))
						len--;
					buf[len] = '\0';
					cout << "file " << buf << endl;
					image = imread(buf, 1);
					if (!image.empty())
					{
						detectAndDraw(image, cascade, nestedCascade, scale, tryflip);
						char c = (char)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)getTickCount();
	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)getTickCount() - t;
	printf("detection time = %g ms\n", t * 1000 / getTickFrequency());
	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);
}

核心的在于已经训练过的脸部识别xml和眼睛识别xml，一般地，选择

haarcascade_frontalface_alt.xml和haarcascade_eye.xml

使用的级联分类器cascadeclassifier的load方法加载，检测利用detectMultiScale方法

 

识别结果：

 以上基本上是Opencv最基本的人脸识别的demo了，可以过过瘾，体验一下。