基于opencv人脸识别

//这个是生成照片的代码
#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

//cv库，哈尔检测人脸使用的配置文件
string haar_face_datapath = "E:/opencv/install/etc/haarcascades/haarcascade_frontalface_alt_tree.xml";


int main(int argc, char** argv) {
	//打开摄像头
	VideoCapture capture(0); 
	if (!capture.isOpened()) {
		printf("could not open camera...\n");
		return -1;
	}
	Size S = Size((int)capture.get(CV_CAP_PROP_FRAME_WIDTH), (int)capture.get(CV_CAP_PROP_FRAME_HEIGHT));
	int fps = capture.get(CV_CAP_PROP_FPS);

	//人脸检测库
	CascadeClassifier faceDetector;
	//人脸检测函数
	faceDetector.load(haar_face_datapath);


	Mat frame;
	namedWindow("camera-demo", CV_WINDOW_AUTOSIZE);
	vector<Rect> faces;
	int count = 0;
	while (capture.read(frame)) {
		flip(frame, frame, 1);
		faceDetector.detectMultiScale(frame, faces, 1.1, 1, 0, Size(100, 120), Size(380, 400));
		for (int i = 0; i < faces.size(); i++) {
			if (count % 10 == 0) {
				Mat dst;
				//重新把照片大小改成模板的大小
				resize(frame(faces[i]), dst, Size(112, 92));
				//保存照片
				imwrite(format("D:/else//led/face_%d.jpg", count), dst);
			}
			rectangle(frame, faces[i], Scalar(0, 0, 255), 2, 8, 0);
		}
		imshow("camera-demo", frame);
		char c = waitKey(10);
		//按键退出截图
		if (c == 27) {
			break;
		}
		count++;
	}

	capture.release();

	waitKey(0);
	return 0;
}

//这个是生成csv文件的教程，直接用shell命令，不需要写代码


1. 打开cmd


2. 输入 F: (因为我的文件在F盘)  注意一定要先进入F盘才可以后续操作，不然会显示找不到文件


3. 输入cd 文件路径，点回车


4. 输入dir /b/s/p/w *.jpg>at.csv 注意，我的文件格式为.jpg

#include <opencv2/opencv.hpp>
#include <opencv2/face.hpp>
#include <iostream>

using namespace cv;
using namespace cv::face;
using namespace std;

int main(int argc, char** argv) {
	
	//加载自己的csv文件
	string filename = string("D:/else/wyz/at.csv");
	ifstream file(filename.c_str(), ifstream::in);
	if (!file) {
		printf("could not load file correctly...\n");
		return -1;
	}

	string line, path, classlabel;
	vector<Mat> images;
	vector<int> labels;
	char separator = ';';
	while (getline(file, line)) {
		stringstream liness(line);
		getline(liness, path, separator);
		getline(liness, classlabel);
		if (!path.empty() && !classlabel.empty()) {
			//printf("path : %s\n", path.c_str());
			images.push_back(imread(path, 0));
			labels.push_back(atoi(classlabel.c_str()));
		}
	}

	if (images.size() < 1 || labels.size() < 1) {
		printf("invalid image path...\n");
		return -1;
	}

	int height = images[0].rows;
	int width = images[0].cols;
	printf("height : %d, width : %d\n", height, width);

	Mat testSample = images[images.size() - 1];
	int testLabel = labels[labels.size() - 1];
	images.pop_back();
	labels.pop_back();

	// train it，训练自己的模型，到这里已经完成训练操作
	Ptr<BasicFaceRecognizer> model = createEigenFaceRecognizer();
	model->train(images, labels);
	model->save("D:/else/wyz/led_wyz.xml");


	/* 以下两个方法训练模型同样操作
	Ptr<FaceRecognizer> model1 = createFisherFaceRecognizer();
	model1->train(images, labels);
	model1->save("MyFaceFisherModel.xml");

	Ptr<FaceRecognizer> model2 = createLBPHFaceRecognizer();
	model2->train(images, labels);
	model2->save("MyFaceLBPHModel.xml");
	*/



	/* 以下内容用于检测验证自己重建的人脸模，可以直接跳过，本代码全部注释掉了
	// recognition face
	int predictedLabel = model->predict(testSample);
	printf("actual label : %d, predict label :  %d\n", testLabel, predictedLabel);

	Mat eigenvalues = model->getEigenValues();
	Mat W = model->getEigenVectors();
	Mat mean = model->getMean();
	Mat meanFace = mean.reshape(1, height);
	Mat dst;
	if (meanFace.channels() == 1) {
		normalize(meanFace, dst, 0, 255, NORM_MINMAX, CV_8UC1);
	}
	else if (meanFace.channels() == 3) {
		normalize(meanFace, dst, 0, 255, NORM_MINMAX, CV_8UC3);
	}
	imshow("Mean Face", dst);



	// show eigen faces
	for (int i = 0; i < min(25, W.cols); i++) {
		Mat ev = W.col(i).clone();
		Mat grayscale;
		Mat eigenFace = ev.reshape(1, height);
		if (eigenFace.channels() == 1) {
			normalize(eigenFace, grayscale, 0, 255, NORM_MINMAX, CV_8UC1);
		}
		else if (eigenFace.channels() == 3) {
			normalize(eigenFace, grayscale, 0, 255, NORM_MINMAX, CV_8UC3);
		}
		Mat colorface;
		applyColorMap(grayscale, colorface, COLORMAP_JET);
		char* winTitle = new char[128];
		sprintf(winTitle, "eigenface_%d", i);
		imshow(winTitle, colorface);
	}


	// 重建人脸
	for (int num = min(25, W.cols); num < min(W.cols, 300); num += 1) {
		Mat evs = Mat(W, Range::all(), Range(0, num));
		Mat projection = LDA::subspaceProject(evs, mean, images[0].reshape(1, 1));
		Mat reconstruction = LDA::subspaceReconstruct(evs, mean, projection);

		Mat result = reconstruction.reshape(1, height);
		if (result.channels() == 1) {
			normalize(result, reconstruction, 0, 255, NORM_MINMAX, CV_8UC1);
		}
		else if (result.channels() == 3) {
			normalize(result, reconstruction, 0, 255, NORM_MINMAX, CV_8UC3);
		}
		char* winTitle = new char[128];
		sprintf(winTitle, "recon_face_%d", num);
		imshow(winTitle, reconstruction);
	}
	*/

	waitKey(0);
	return 0;
}

// train it
	Ptr<BasicFaceRecognizer> model = createEigenFaceRecognizer();
	model->train(images, labels);
	model->save("D:/else/wyz/led_wyz.xml");

	Ptr<FaceRecognizer> model1 = createFisherFaceRecognizer();
	model1->train(images, labels);
	model1->save("MyFaceFisherModel.xml");

	Ptr<FaceRecognizer> model2 = createLBPHFaceRecognizer();
	model2->train(images, labels);
	model2->save("MyFaceLBPHModel.xml");