Java+OpenCV實現(xiàn)圖片中的人臉識別

更新時間：2024年04月08日 11:49:50 作者：TGITCIC

這篇文章主要介紹了如何利用java?opencv實現(xiàn)人臉識別功能,本文給大家介紹的非常詳細，對大家的學(xué)習(xí)或工作具有一定的參考借鑒價值，需要的朋友可以參考下

MatOfRect.detectMultiScale函數(shù)

OpenCV用的是detectMultiScale來認出這是一個臉的。記得，這只是認出這是一個臉，而不是這個臉是誰。

這個臉是誰我們會逐步展開，前面勿求夯實基礎(chǔ)。

detectMultiScale需要兩個參數(shù)(Mat src, MatOfRect objDetections);

第一個函數(shù)，是傳入的圖片，帶有人臉的圖片；
第二個函數(shù)，是把所有的這個圖片里的人臉得到并輸出到MatOfRect對象里；

實現(xiàn)代碼

ImageViewer.java

再上一遍

package org.mk.opencv;
 
import org.mk.opencv.util.OpenCVUtil;
import org.opencv.core.Mat;
import javax.swing.*;
import java.awt.*;
 
public class ImageViewer {
    private JLabel imageView;
    private Mat image;
    private String windowName;
 
    private JFrame frame = null;
 
    public ImageViewer() {
        frame = createJFrame(windowName, 800, 600);
    }
 
    public ImageViewer(Mat image) {
        this.image = image;
    }
 
    /**
     * @param image      要顯示的mat
     * @param windowName 窗口標題
     */
    public ImageViewer(Mat image, String windowName) {
        frame = createJFrame(windowName, 1024, 768);
        this.image = image;
        this.windowName = windowName;
    }
 
    public void setTitle(String windowName) {
        this.windowName = windowName;
    }
 
    public void setImage(Mat image) {
        this.image = image;
    }
 
    /**
     * 圖片顯示
     */
    public void imshow() {
        setSystemLookAndFeel();
        frame.pack();
        frame.setLocationRelativeTo(null);
        frame.setVisible(true);
        frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);// 用戶點擊窗口關(guān)閉
        if (image != null) {
            Image loadedImage = OpenCVUtil.matToImage(image);
            // JFrame frame = createJFrame(windowName, image.width(), image.height());
            imageView.setIcon(new ImageIcon(loadedImage));
            frame.pack();
            // frame.setLocationRelativeTo(null);
            // frame.setVisible(true);
            // frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);// 用戶點擊窗口關(guān)閉
        }
    }
 
    private void setSystemLookAndFeel() {
        try {
            UIManager.setLookAndFeel(UIManager.getSystemLookAndFeelClassName());
        } catch (ClassNotFoundException e) {
            e.printStackTrace();
        } catch (InstantiationException e) {
            e.printStackTrace();
        } catch (IllegalAccessException e) {
            e.printStackTrace();
        } catch (UnsupportedLookAndFeelException e) {
            e.printStackTrace();
        }
    }
 
    private JFrame createJFrame(String windowName, int width, int height) {
        JFrame frame = new JFrame(windowName);
        imageView = new JLabel();
        final JScrollPane imageScrollPane = new JScrollPane(imageView);
        imageScrollPane.setPreferredSize(new Dimension(width, height));
        frame.add(imageScrollPane, BorderLayout.CENTER);
        frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
        return frame;
    }
 
}

DetectFace.java

這個是主類。

老三樣：

1.加載opencv_java343.dll；

2.加載人臉分揀器；

3.創(chuàng)建Mat對象；

然后我們開始把臉識別出來：

1.使用detectMultiScale把傳入的Mat對象中含有臉的那些全部識別出來；

2.識別出來后我們可以使用for (Rect rect : objDetections.toArray())把所有的臉枚舉出來；

3.使用Imgproc.rectangle在每個識別出來的臉上用“綠”色把它們一個個框出來；

4.使用ImageViewer的.imgShow顯示標識出來的臉；

package org.mk.opencv;
 
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
 
public class DetectFace {
    public static void main(String[] args) {
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        //Mat src = Imgcodecs.imread("/Users/chrishu123126.com/opt/img/detect-face-4.jpg");
        Mat src = Imgcodecs.imread("D:\\opencv-demo\\green-arrow.jpg");
        if (src.empty()) {
            System.out.println("圖片路徑不正確");
            return;
        }
        Mat dst = dobj(src);
        ImageViewer imageViewer = new ImageViewer(dst, "識臉");
        imageViewer.imshow();
    }
 
    private static Mat dobj(Mat src) {
        Mat dst = src.clone();
 
        CascadeClassifier objDetector = new CascadeClassifier(
                "D:\\opencvinstall\\build\\install\\etc\\lbpcascades\\lbpcascade_frontalface.xml");
 
        MatOfRect objDetections = new MatOfRect();
 
        objDetector.detectMultiScale(dst, objDetections);
 
        if (objDetections.toArray().length <= 0) {
            return src;
        }
 
        for (Rect rect : objDetections.toArray()) {
            Imgproc.rectangle(dst, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.width),
                    new Scalar(0, 255, 0), 1); //new Scalar(0, 255, 0), 1)綠 //new Scalar(0, 0, 255), 1)紅 //new Scalar(255, 0, 0), 1)藍
        }
        return dst;
    }
}

把識別出來的臉存成文件

我們現(xiàn)在把識別出來的5張臉存成5個jpg圖片。

制作一個寫盤函數(shù)，很簡單。

    private static void outputFace(String outputDir, Mat face) {
        long millSecs = System.currentTimeMillis();
        int temp = (int) (Math.random() * 10000);
        StringBuffer outputImgName = new StringBuffer();
        outputImgName.append(outputDir).append("/").append(millSecs).append(temp).append(".jpg");
        if (face != null) {
            Imgcodecs.imwrite(outputImgName.toString(), face);
            logger.info(">>>>>>write image into->" + outputDir);
        }
    }

然后我們在我們的原來的代碼中加入這個函數(shù)

package org.mk.opencv;
 
import org.apache.log4j.Logger;
import org.mk.opencv.face.FaceRecogFromFiles;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
 
public class DetectFace {
 
    private static Logger logger = Logger.getLogger(DetectFace.class);
    private final static String faceOutPutDir = "d://opencv-demo/face";
 
    public static void main(String[] args) {
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
 
        // Mat src =
        // Imgcodecs.imread("/Users/chrishu123126.com/opt/img/detect-face-4.jpg");
        Mat src = Imgcodecs.imread("D:\\opencv-demo\\green-arrow.jpg");
        if (src.empty()) {
            System.out.println("圖片路徑不正確");
            return;
        }
        Mat dst = dobj(src);
        ImageViewer imageViewer = new ImageViewer(dst, "識臉");
        imageViewer.imshow();
    }
 
    private static Mat dobj(Mat src) {
        Mat dst = src.clone();
 
        CascadeClassifier objDetector = new CascadeClassifier(
                "D:\\opencvinstall\\build\\install\\etc\\lbpcascades\\lbpcascade_frontalface.xml");
 
        MatOfRect objDetections = new MatOfRect();
 
        objDetector.detectMultiScale(dst, objDetections);
 
        if (objDetections.toArray().length <= 0) {
            return src;
        }
 
        for (Rect rect : objDetections.toArray()) {
            Imgproc.rectangle(dst, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.width),
                    new Scalar(0, 255, 0), 1); // new Scalar(0, 255, 0), 1)綠 //new Scalar(0, 0, 255), 1)紅 //new
                                                // Scalar(255, 0, 0), 1)藍
            outputFace(faceOutPutDir, src.submat(rect));
        }
        return dst;
    }
 
    private static void outputFace(String outputDir, Mat face) {
        long millSecs = System.currentTimeMillis();
        int temp = (int) (Math.random() * 10000);
        StringBuffer outputImgName = new StringBuffer();
        outputImgName.append(outputDir).append("/").append(millSecs).append(temp).append(".jpg");
        if (face != null) {
            Imgcodecs.imwrite(outputImgName.toString(), face);
            logger.info(">>>>>>write image into->" + outputDir);
        }
    }
}

運行DetectFace.java

到此這篇關(guān)于Java+OpenCV實現(xiàn)圖片中的人臉識別的文章就介紹到這了,更多相關(guān)Java OpenCV人臉識別內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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