opencv人脸识别

在这里插入代码片
#include<opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc_c.h>
#include <opencv2/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <iostream>

using namespace cv;
using namespace std;

void detechAndDraw(Mat &img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip);

int face_detech()
{
	Mat imag = imread("E:\\Work\\Qt\\1_CStudy\\face_recognition\\face_recognition\\11.png");
	if (!imag.data)
	{
		cout << "no data!" << endl;
		return -1;
	}

	//load trained file
	CascadeClassifier cascade, nestedCascade;
	bool stop = false;
	cascade.load("E:\\Install\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml");
	nestedCascade.load("E:\\Install\\opencv\\sources\\data\\haarcascades\\haarcascade_eye.xml");

	detechAndDraw(imag, cascade, nestedCascade, 2, 0);
	waitKey();
	return 0;
}

void detechAndDraw(Mat &img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip)
{
	int i = 0;
	double t = 0;

	//build save faces container
	vector<Rect> faces, faces2;
	//定义颜色，标注人脸
	const static Scalar colors[] = {
		CV_RGB(0,0,255),
		CV_RGB(0,128,255),
		CV_RGB(0,255,255),
		CV_RGB(0,255,0),
		CV_RGB(255,128,0),
		CV_RGB(255,255,0),
		CV_RGB(255,0,0),
		CV_RGB(255,0,255)
	};
	//建立缩小的图片，加快检测速度
	//nt cvRound (double value) 对一个double型的数进行四舍五入，并返回一个整型数！
	Mat gray, smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1);

	//转成灰度图像，Harr特征基于灰度图
	cvtColor(img, gray, CV_BGR2GRAY);
	imshow("灰度", gray);

	//改变图像大小，使用双线性差值
	resize(gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR);
	imshow("缩小尺寸", smallImg);

	//变换后的图像进行直方图均值化处理
	equalizeHist(smallImg, smallImg);
	imshow("直方图均值处理", smallImg);

	//程序开始和结束插入此函数获取时间，经过计算求得算法执行时间
	t = (double)cvGetTickCount();

	cascade.detectMultiScale(smallImg, faces,
		1.1, 2, 0
		//|CV_HAAR_FIND_BIGGEST_OBJECT
		//|CV_HAAR_DO_ROUGH_SEARCH
		| CV_HAAR_SCALE_IMAGE
		, Size(30, 30));

	//如果使能，翻转图像继续检测
	if (tryflip)
	{
		flip(smallImg, smallImg, 1);
		imshow("反转图像", smallImg);
		cascade.detectMultiScale(smallImg, faces2,
			1.1, 2, 0
			|CV_HAAR_FIND_BIGGEST_OBJECT
			![在这里插入图片描述](https://img-blog.csdnimg.cn/20210630164715577.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDg5ODcyNg==,size_16,color_FFFFFF,t_70#pic_center)
|CV_HAAR_DO_ROUGH_SEARCH
			| CV_HAAR_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;
	for (vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, 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)
		{
			//标示人脸时在缩小之前的图像上标示，所以这里根据缩放比例换算回去
			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);
		//同样方法检测人眼
		nestedCascade.detectMultiScale(smallImgROI, nestedObjects,
			1.1, 2, 0
			|CV_HAAR_FIND_BIGGEST_OBJECT
			|CV_HAAR_DO_ROUGH_SEARCH
			|CV_HAAR_DO_CANNY_PRUNING
			| CV_HAAR_SCALE_IMAGE
			, Size(30, 30));
		for (vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++)
		{
			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("识别结果", img);
}