Javacv下人脸识别算法EigenFace

傷城~ 2022-06-16 05:39 449阅读 0赞

这是我当年在安卓上用的一个人脸识别的算法。主要是用了PCA降维的方式，然后用欧式距离比较下最近的人脸。

趁热来一波刚出炉的Javacv写的特征脸算法，写得不是很好，但是也是费了一小番心思做的。我知道还有很多写得不好不对的地方，望请大家指正。不多说了，知道你们想看代码。

import org.bytedeco.javacpp.opencv_core;
    import org.bytedeco.javacpp.opencv_core.*;
    import org.bytedeco.javacv.JavaCV;
    import org.opencv.core.Core;
    
    import android.util.Log;
    import org.opencv.core.CvType;
    import org.opencv.imgproc.Imgproc;
    import static org.bytedeco.javacpp.opencv_core.*;
    import static org.bytedeco.javacpp.opencv_highgui.*;
    import static org.bytedeco.javacpp.opencv_imgproc.equalizeHist;
    /**
     * 特征脸算法
     * @author 火烫火烫的
     *
     */
    public class Eigenface {
    	private double threshold = -1;	//阈值，不设置返回最相似的距离的标签
    	private int num_components = -1;
    	private PCA pca = null;
    	private Mat result;	//经过投影后的矩阵
    	private Mat labels;
    	public double getThreshold() {
    		return threshold;
    	}
    
    	public void setThreshold(double threshold) {
    		this.threshold = threshold;
    	}
    
    	public int getNum_components() {
    		return num_components;
    	}
    
    	public void setNum_components(int num_components) {
    		this.num_components = num_components;
    	}
    
    	
    	public Eigenface() {
    	}
    
    	public Eigenface(int num_components, double threshold) {
    		super();
    		this.threshold = threshold;
    		this.num_components = num_components;
    	}
    
    	/**
    	 * 训练方法
    	 * @param images
    	 * @param labels
    	 */
    	public void train(MatVector images, Mat labels) {
    		this.labels = labels;
    		Mat allsamples = new Mat();	//所有样本以行存放
    		Mat mat;
    		Mat convertMat = new Mat();	//转变后的矩阵
    		Mat placeholder = new Mat();
    		for (int i = 0; i < images.size(); ++i) {	//遍历每一个Mat
    			mat = images.get(i);
    			mat.reshape(1, 1).row(0).convertTo(convertMat, CV_32FC1);
    			allsamples.push_back(convertMat);
    		}
    		if (num_components != -1) {	//说明最大维度为设置过
    			pca = new PCA(allsamples, placeholder, CV_PCA_DATA_AS_ROW, num_components);	//pca
    		} else {
    			pca = new PCA(allsamples, placeholder, CV_PCA_DATA_AS_ROW);	//pca
    		}
    		result = pca.project(allsamples);
    		Log.e("The size of result", result.rows() + "|" + result.cols());
    	}
    	
    	/**
    	 * 进行识别
    	 * @param testImage
    	 * @return 识别的标签,-1代表失败,-2代表没训练
    	 */
    	public int predict(Mat testImage) {
    		if (pca != null) {
    			Mat convertMat = new Mat();
    			testImage.reshape(1, 1).row(0).convertTo(convertMat, CV_32FC1);
    			IntBuffer labelsBuf = labels.getIntBuffer();
    			double[] distance = new double[result.rows()];
    			Mat testResult = pca.project(convertMat);
    			Log.e("The size of testResult", testResult.rows() + "|" + testResult.cols());
    			double min_distance = 0;
    			int min_i = -1;
    			for (int i = 0; i < result.rows(); i++){	//对每个向量进行求欧式距离
    				distance[i] = norm(testResult, result.row(i));
    				Log.e("distance", "" + distance[i]);
    				if (i == 0) {
    					min_distance = distance[0];
    					min_i = i;
    				} else if (min_distance > distance[i]){	//说明新的这个距离更小
    					min_distance = distance[i];
    					min_i = i;
    				}
    			}
    			int label = -1;	//识别标签
    			for (int i = 0; i <= min_i; ++i) {	//对应着取几次
    				if (labelsBuf.hasRemaining()) {
    					label = labelsBuf.get();
    				} else {
    					label = -1;
    				}
    			}
    			if (threshold >= 0) {	//说明阈值被设置过
    				if (threshold > min_distance) {	//是否超过阈值
    					return label;
    				} else {
    					return -1;
    				}
    			} else {
    				return label;
    			}
    		} else {
    			return -2;
    		}
    	}
    	
    	/**
    	 * 用来求欧式距离的
    	 * @param mat1
    	 * @param mat2
    	 * @return
    	 */
    	public double norm(Mat mat1, Mat mat2) {
    		return cvNorm(mat1.asCvMat(), mat2.asCvMat());
    	}
    	
    }