java+opencv實(shí)現人臉識別功能

背景：最近需要用到人臉識別，但又不花錢(qián)使用現有的第三方人臉識別接口，為此使用opencv結合java進(jìn)行人臉識別（ps:opencv是開(kāi)源的，使用它來(lái)做人臉識別存在一定的誤差，效果一般）。

1.安裝opencv
官網(wǎng)地址： , 由于官網(wǎng)下載速度是真的慢

百度網(wǎng)盤(pán)：

鏈接:

提取碼: pq7v

如果是官網(wǎng)下載，就無(wú)腦安裝就行了，安裝完畢后。

將圖一的兩個(gè)文件復制到圖二中。

從我網(wǎng)盤(pán)下載的，忽略這些。

2.在項目中引入pom依賴(lài)

<!-- opencv + javacv + ffmpeg-->
        <dependency>
            <groupId>org.bytedeco.javacpp-presets</groupId>
            <artifactId>ffmpeg</artifactId>
            <version>4.1-1.4.4</version>
        </dependency>
        <dependency>
            <groupId>org.bytedeco</groupId>
            <artifactId>javacv</artifactId>
            <version>1.4.4</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.bytedeco.javacpp-presets/ffmpeg-platform -->
        <dependency>
            <groupId>org.bytedeco.javacpp-presets</groupId>
            <artifactId>ffmpeg-platform</artifactId>
            <version>4.1-1.4.4</version>
        </dependency>

        <!-- 視頻攝像頭 -->
        <!-- https://mvnrepository.com/artifact/org.bytedeco/javacv-platform -->
        <dependency>
            <groupId>org.bytedeco</groupId>
            <artifactId>javacv-platform</artifactId>
            <version>1.4.4</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.bytedeco.javacpp-presets/opencv-platform -->
        <dependency>
            <groupId>org.bytedeco.javacpp-presets</groupId>
            <artifactId>opencv-platform</artifactId>
            <version>4.0.1-1.4.4</version>
        </dependency>

1.導入庫依賴(lài)
File --> Project Structure，點(diǎn)擊Modules，選擇需要使用opencv.jar的項目。

選擇直接opencv安裝路徑

2.java代碼demo

package org.Litluecat.utils;

import org.apache.commons.lang.StringUtils;
import org.opencv.core.*;
import org.opencv.highgui.HighGui;
import org.opencv.highgui.ImageWindow;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
import org.opencv.videoio.VideoCapture;
import org.opencv.videoio.VideoWriter;
import org.opencv.videoio.Videoio;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.Arrays;

/**
 * 人臉比對工具類(lèi)
 * @author Litluecat
 * @Title: Opencv 圖片人臉識別、實(shí)時(shí)攝像頭人臉識別
**/
public class FaceVideo {

    private static final Logger log = LoggerFactory.getLogger(FaceVideo.class);

    private static final String endImgUrl = "C:\\Users\\lenovo\\Desktop\\";
    /**
     * opencv的人臉識別xml文件路徑
     */
    private static final String faceDetectorXML2URL = "D:\\Sofeware\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml";
    /**
     * opencv的人眼識別xml文件路徑
     */
    private static final String eyeDetectorXML2URL = "D:\\Sofeware\\opencv\\sources\\data\\haarcascades\\haarcascade_eye.xml";
    /**
     * 直方圖大小，越大精度越高，運行越慢
     */
    private static int Matching_Accuracy = 100000;
    /**
     * 初始化人臉探測器
     */
    private static CascadeClassifier faceDetector;
    /**
     * 初始化人眼探測器
     */
    private static CascadeClassifier eyeDetector;

    private static int i=0;

    static {
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        faceDetector = new CascadeClassifier(faceDetectorXML2URL);
        eyeDetector = new CascadeClassifier(eyeDetectorXML2URL);
    }

    public static void main(String[] args) {
        log.info("開(kāi)始人臉匹配");
        long begin = System.currentTimeMillis();
        // 1- 從攝像頭實(shí)時(shí)人臉識別，識別成功保存圖片到本地
        try{
            getVideoFromCamera(endImgUrl + "2.jpg");
            //僅用于強制拋異常，從而關(guān)閉GUI界面
            Thread.sleep(1000);
            int err = 1/0;
     
            // 2- 比對本地2張圖的人臉相似度 （越接近1越相似）
//            double compareHist = FaceVideo.compare_image(endImgUrl + "test1.jpg" , endImgUrl + "face.jpg");
//            log.info("匹配度：{}",compareHist);
//            if (compareHist > 0.72) {
//                log.info("人臉匹配");
//            } else {
//                log.info("人臉不匹配");
//            }

        }catch (Exception e){
            log.info("開(kāi)始強制關(guān)閉");
            log.info("人臉匹配結束，總耗時(shí)：{}ms",(System.currentTimeMillis()-begin));
            System.exit(0);
        }
    }


    /**
     * OpenCV-4.1.1 從攝像頭實(shí)時(shí)讀取
     * @param targetImgUrl 比對身份證圖片
     * @return: void
     * @date: 2019年8月19日 17:20:13
     */
    public static void getVideoFromCamera(String targetImgUrl) {
        //1 如果要從攝像頭獲取視頻 則要在 VideoCapture 的構造方法寫(xiě) 0
        VideoCapture capture = new VideoCapture(0);
        Mat video = new Mat();
        int index = 0;
        if (capture.isOpened()) {
            while(i<3) {
                // 匹配成功3次退出
                capture.read(video);
                HighGui.imshow("實(shí)時(shí)人臉識別", getFace(video, targetImgUrl));
                //窗口延遲等待100ms，返回退出按鍵
                index = HighGui.waitKey(100);
                //當退出按鍵為Esc時(shí)，退出窗口
                if (index == 27) {
                    break;
                }
            }
        }else{
            log.info("攝像頭未開(kāi)啟");
        }
        //該窗口銷(xiāo)毀不生效，該方法存在問(wèn)題
        HighGui.destroyAllWindows();
        capture.release();
        return;
    }

    /**
     * OpenCV-4.1.0 人臉識別
     * @param image 待處理Mat圖片(視頻中的某一幀)
     * @param targetImgUrl 匹配身份證照片地址
     * @return 處理后的圖片
     */
    public static Mat getFace(Mat image, String targetImgUrl) {
        MatOfRect face = new MatOfRect();
        faceDetector.detectMultiScale(image, face);
        Rect[] rects=face.toArray();
        log.info("匹配到 "+rects.length+" 個(gè)人臉");
        if(rects != null && rects.length >= 1) {
            i++;
            if(i==3) {
                // 獲取匹配成功第3次的照片
                Imgcodecs.imwrite(endImgUrl + "face.jpg", image);
                FaceVideoThread faceVideoThread = new FaceVideoThread(targetImgUrl , endImgUrl + "face.jpg");
                new Thread(faceVideoThread,"人臉比對線(xiàn)程").start();
            }
        }
        return image;
    }

    /**
     * 人臉截圖
     * @param img
     * @return
     */
    public static String face2Img(String img) {
        String faceImg = null;
        Mat image0 = Imgcodecs.imread(img);
        Mat image1 = new Mat();
        // 灰度化
        Imgproc.cvtColor(image0, image1, Imgproc.COLOR_BGR2GRAY);
        // 探測人臉
        MatOfRect faceDetections = new MatOfRect();
        faceDetector.detectMultiScale(image1, faceDetections);
        // rect中人臉圖片的范圍
        for (Rect rect : faceDetections.toArray()) {
            faceImg = img+"_.jpg";
            // 進(jìn)行圖片裁剪
            imageCut(img, faceImg, rect.x, rect.y, rect.width, rect.height);
        }
        if(null == faceImg){
            log.info("face2Img未識別出該圖像中的人臉，img={}",img);
        }
        return faceImg;
    }

    /**
     * 人臉比對
     * @param img_1
     * @param img_2
     * @return
     */
    public static double compare_image(String img_1, String img_2) {
        Mat mat_1 = conv_Mat(img_1);
        Mat mat_2 = conv_Mat(img_2);
        Mat hist_1 = new Mat();
        Mat hist_2 = new Mat();

        //顏色范圍
        MatOfFloat ranges = new MatOfFloat(0f, 256f);
        //直方圖大小， 越大匹配越精確 (越慢)
        MatOfInt histSize = new MatOfInt(Matching_Accuracy);

        Imgproc.calcHist(Arrays.asList(mat_1), new MatOfInt(0), new Mat(), hist_1, histSize, ranges);
        Imgproc.calcHist(Arrays.asList(mat_2), new MatOfInt(0), new Mat(), hist_2, histSize, ranges);

        // CORREL 相關(guān)系數
        double res = Imgproc.compareHist(hist_1, hist_2, Imgproc.CV_COMP_CORREL);
        return res;
    }

    /**
     * 灰度化人臉
     * @param img
     * @return
     */
    public static Mat conv_Mat(String img) {
        if(StringUtils.isBlank(img)){
            return null;
        }
        Mat image0 = Imgcodecs.imread(img);
        Mat image1 = new Mat();
        //Mat image2 = new Mat();
        // 灰度化
        Imgproc.cvtColor(image0, image1, Imgproc.COLOR_BGR2GRAY);
        //直方均勻
        //Imgproc.equalizeHist(image1, image2);

        // 探測人臉
        MatOfRect faceDetections = new MatOfRect();
        faceDetector.detectMultiScale(image1, faceDetections);

        //探測人眼
//        MatOfRect eyeDetections = new MatOfRect();
//        eyeDetector.detectMultiScale(image1, eyeDetections);

        // rect中人臉圖片的范圍
        Mat face = null;
        for (Rect rect : faceDetections.toArray()) {

            //給圖片上畫(huà)框框 參數1是圖片 參數2是矩形 參數3是顏色 參數四是畫(huà)出來(lái)的線(xiàn)條大小
            //Imgproc.rectangle(image0,rect,new Scalar(0,0,255),2);
            //輸出圖片
            //Imgcodecs.imwrite(img+"_.jpg",image0);

            face = new Mat(image1, rect);
        }
        if(null == face){
            log.info("conv_Mat未識別出該圖像中的人臉，img={}",img);
        }
        return face;
    }

}

這邊的人臉識別是另外其線(xiàn)程進(jìn)行比對，代碼如下。

package org.Litluecat.utils;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;


public class FaceVideoThread implements Runnable{
    private static final Logger log = LoggerFactory.getLogger(FaceVideoThread.class);

    private String oneImgUrl = null;
    private String otherImgUrl = null;
    public FaceVideoThread(String oneImgUrl, String otherImgUrl){
        this.oneImgUrl = oneImgUrl;
        this.otherImgUrl = otherImgUrl;
    }
    @Override
    public void run() {
        try {
            double compareHist = FaceVideo.compare_image(oneImgUrl , otherImgUrl);
            log.info("匹配度：{}",compareHist);
            if (compareHist > 0.72) {
                log.info("人臉匹配");
            } else {
                log.info("人臉不匹配");
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

提醒：如果運行異常，請添加你opencv的安裝地址-Djava.library.path=D:\Sofeware\opencv\build\java\x64;

總結：java+opencv做人臉識別的精度不夠，我也是有待學(xué)習，如果大家有更好的方式，能將opencv更好的展現出來(lái)，并達到更精準的人臉識別，請分享給我，謝謝。

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