openCV学习笔记（十六） —— 人脸识别 —— 程序 —— PCA特征脸Eigenfaces、重建人脸

此程序主要来源于F:\personal\data\openCV\opencv_contrib_3.4.1\modules\face\samples\facerec_eigenfaces.cpp

流程

1. 读人脸数据文件
2. 创建特征脸识别与训练
3. 识别人脸
4. 输出中间平均脸、特征脸、重建结果 

相关图示 

程序 

/*
	此程序主要来源于F:\personal\data\openCV\opencv_contrib_3.4.1\modules\face\samples\facerec_eigenfaces.cpp
*/
#include 
#include 

//#include 
//#include 

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

Mat normal(Mat& src, Mat& dst)
{
	if (src.channels() == 1)
	{
		normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
	}
	else
	{
		normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
	}

	return dst;
}

void read_csv(const string& filename, vector& images, vector& labels, char separator = ';')
{
	std::ifstream file(filename.c_str(), ifstream::in);
	if (!file) {
		string error_message = "No valid input file was given, please check the given filename.";
		CV_Error(Error::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));
			labels.push_back(atoi(classlabel.c_str()));
		}
	}
}

int main()
{
	string filename = ".\\face_recg\\face\\at.txt";

	vector images;
	vector labels;

	//从文件中读取图像保存到向量中
	try {
		read_csv(filename, images, labels);
	}
	catch (Exception& e)
	{
		cerr << "Error opening file \n" << filename << "\". Reason: " << e.msg << endl;
		exit(1);
	}

	if (images.size() <= 1)
	{
		cerr << "图像太少，请至少添加两个图片！" << endl;
		exit(1);
	}

	//从第一个图像获取高度，在后面的代码里面需要用到它来重塑原来的图片大小
	int height = images[0].rows;
	int width = images[0].cols;
	printf("height: %d, width: %d\n", height, width);

	//最后一个人为测试样本
	Mat testSample = images[images.size() - 1];
	int testLabel = labels[labels.size() - 1];
	images.pop_back();
	labels.pop_back();

	//训练
	Ptr model = EigenFaceRecognizer::create();
	model->train(images, labels);

	//识别
	int predictedLabel = model->predict(testSample);
	printf("actual label: %d, predict label: %d\n", testLabel, predictedLabel);

	//获得特征值，特征向量，均值，平均脸
	Mat eigenValues = model->getEigenValues();
	Mat eigenVector = model->getEigenVectors();
	Mat mean = model->getMean();
	Mat meanFace = mean.reshape(1, height);

	Mat dst;
	dst = normal(meanFace, dst);
	imshow("MeanFace", dst);

	//特征脸
	for (int i = 0; i < min(10, eigenVector.cols); i++)
	{
		Mat ev = eigenVector.col(i).clone();
		Mat eigenFace = ev.reshape(1, height);

		Mat grayScale;
		grayScale = normal(eigenFace, grayScale);

		Mat colorFace;
		applyColorMap(grayScale, colorFace, COLORMAP_BONE);

		imshow(format("eigenFace_%d", i), colorFace);
	}

	//重建人脸
	for (int i = min(10, eigenVector.cols); i < min(300, eigenVector.cols); i += 15)
	{
		Mat evs = Mat(eigenVector, Range::all(), Range(0, i));
		Mat projection = LDA::subspaceProject(evs, mean, images[0].reshape(1, 1));
		Mat reconstruction = LDA::subspaceReconstruct(evs, mean, projection);

		Mat result = reconstruction.reshape(1, height);
		reconstruction = normal(result, reconstruction);
		imshow(format("reco_face_%d", i), reconstruction);
	}

	waitKey(0);
	destroyAllWindows();

	return 0;
}