Myface_Recognition

/*
	步骤：
	1.收集att_faces
	2.收集训练对象——目标人照片10张并建成文件夹s41
	3.对s41的十张图片进行人脸检测ROI分割，并将尺寸设置与前40一致
	4.生成csv文件
	5.检测csv文件，并验证正确性
	6.读取标签，训练模型
	7.训练模型

	常量介绍：



*/


#include<opencv\cv.h>
#include<opencv\highgui.h>
#include<opencv2\opencv.hpp>
#include<opencv\cxcore.h>
#include<opencv2\imgproc\imgproc.hpp>
#include<opencv2\core\core.hpp>
#include<iostream>
#include<fstream>
#include<string>
#include<sstream>
#include<math.h>
using namespace cv;
using namespace std;

string  face_cascade_name = "haarcascade_frontalface_default.xml";
string  eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";

CascadeClassifier face_cascade, eyes_cascade;

//CascadeClassifier face_cascade, eyes_cascade;
//检测学习数据集，并分割ROI





Mat facedetect(Mat frame)
{
	if (!face_cascade.load(face_cascade_name))
	{
		cout << "--(!)Error loading face cascade\n";
		//return -1;
	}
	if (!eyes_cascade.load(eyes_cascade_name))
	{
		cout << "--(!)Error loading face cascade\n";
		//return -1;
	}
	else
	{
		vector<Rect> faces;
		Mat frame_gray;

		cvtColor(frame, frame_gray, COLOR_BGR2GRAY);
		equalizeHist(frame_gray, frame_gray);

		//-- Detect faces  
		face_cascade.detectMultiScale(frame_gray, faces, 1.1, 3, 0/* CV_HAAR_DO_ROUGH_SEARCH, Size(10, 20), Size(2000, 7000)*/);

		for (size_t j = 0; j < faces.size(); j++)
		{
			Mat faceROI = frame(faces[j]);
			Mat MyFace;
			if (faceROI.cols)
			{
				resize(faceROI, MyFace, Size(92, 112));
				//----------Eyes detection
				vector<Rect> eyes;
				eyes_cascade.detectMultiScale(faceROI, eyes, 1.1, 1, CV_HAAR_DO_ROUGH_SEARCH, Size(1, 1));
				
				if (eyes.size()!=NULL) return MyFace;
			}
			else cout << "faceROI error!" << endl;
		}
	}
}

void att_s41_faces_get(void)
{
	for (size_t i = 1; i < 11; i++)
	{
		string frame_names = format("D:\\Shen\\Desktop\\p-s41\\%d.jpg", i);
		Mat frame = imread(frame_names);
		//imshow(frame_names,frame);
		Mat newf = facedetect(frame);

		string  str = format("E:\\Visual studio Projects\\Harr_Face_Recog\\Harr_Face_Recog\\att_faces\\s41\\Myface%d.jpg", i);
		imwrite(str, newf); 
		imshow(str, newf);
		cout << "The Num is " << "  " << i << endl;
		
		/*string  str1 = format("E:\\Visual studio Projects\\Harr_Face_Recog\\Harr_Face_Recog\\att_faces\\s41\\MyfaceA%d.jpg", i);
		imwrite(str1, frame);*/
		frame = NULL;
	}
}


//-----Model Training


//--------------使用csv文件读取图像和标签
static void csv_read(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';')
{
	
	ifstream file(filename/*.c_str*/,ifstream::in);
	if (!file)
	{
		string error_message = "No valid input file was given.";
		CV_Error(CV_StsBadArg,error_message);
	}
	string line, path, classlabel;
	while (getline(file,line))
	{
		stringstream liness(line);
		getline(liness, path, separator);
		getline(liness, classlabel);
		if (!path.empty() && !classlabel.empty())
		{
			images.push_back(imread(path,0));
			int num=atoi(classlabel.c_str());
			cout << num<<endl;
			labels.push_back(num);
			//cout << labels.push_back(num);
		}
	}
	//cout << atoi(classlabel.c_str());
	waitKey(0);
}

//--------------Model train

void model_train()
{
	string fn_csv = "E:\\Visual studio Projects\\Harr_Face_Recog\\Harr_Face_Recog\\att_faces\\at.txt";//csv文件位置
	vector<int> labels;
	vector<Mat> images;//容器，存放图像和标签

	//csv_read(fn_csv, images, labels);


	//读取数据
	try
	{
		csv_read(fn_csv, images, labels);
		cout << "Congratulations! The csv has been readed!" << endl;
	}
	catch (Exception& e)
	{
		cerr << "Error opening file\"" << fn_csv << "\".Reason:" << e.msg << endl;
		exit(1);
	}

	if (images.size()<=1)
	{
		string error_message = "This demo need at least 2 images, please add more images to your data set!";
		CV_Error(CV_StsError,error_message);
	}


	//Mat testSample = images[images.size() - 1];
	//int testLabel = labels[labels.size() - 1];
	/*images.pop_back();
	labels.pop_back();*/
	Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
	model->train(images, labels);
	model->save("MyFacePCAModel.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");

	/*int predictedLabel = model->predict(testSample);
	int predictedLabel1 = model1->predict(testSample);
	int predictedLabel2 = model2->predict(testSample);

	string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
	string result_message1 = format("Predicted class = %d / Actual class = %d.", predictedLabel1, testLabel);
	string result_message2 = format("Predicted class = %d / Actual class = %d.", predictedLabel2, testLabel);
	cout << result_message << endl;
	cout << result_message1 << endl;
	cout << result_message2 << endl;*/

	//waitKey(0);
	//return 0;
}


//----------------大BOSS  Face Recognition
void face_recog()
{
	VideoCapture cap(0);
	Mat frame, edges, gray;
	CascadeClassifier cascade;
	cascade.load("haarcascade_frontalface_alt.xml");
	Ptr<FaceRecognizer>ModelPCA = createEigenFaceRecognizer();
	ModelPCA->load("MyFacePCAModel.xml");
	while (1)
	{
		cap >> frame;
		vector<Rect> faces(0);
		cvtColor(frame, gray, CV_BGR2GRAY);
		equalizeHist(gray, gray);

		cascade.detectMultiScale(gray, faces, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));

		Mat face;
		Point text_lb;

		for (size_t i = 0; i < faces.size(); i++)
		{
			if (faces[i].height>0&&faces[i].width>0)
			{
				face = gray(faces[i]);
				text_lb = Point(faces[i].x,faces[i].y);
				rectangle(frame,faces[i], Scalar(0,255,255),1,8,0);

			}
		}
		Mat face_test;
		int predictPCA = 0;
		if (face.rows > 120) resize(face,face_test,Size(92,112));
		if (!face_test.empty()) predictPCA = ModelPCA->predict(face_test);
		cout << predictPCA << endl;
		if (predictPCA==35)
		{
			string name = "Kris Shen";
			putText(frame,name,text_lb,FONT_HERSHEY_COMPLEX,0.7,Scalar(127,0,255));

		}
		imshow("face",frame);
		if (waitKey(27) >= 0) break;
		//return 0;
	}
}

int main()
{
	//att_s41_faces_get();

	//model_train();
	face_recog();
	waitKey(0);
	return 0;
}