Java + opencv 实现人脸识别，图片人脸识别、视频人脸识别、摄像头实时人脸识别

<!-- 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>
        <dependency>
            <groupId>commons-io</groupId>
            <artifactId>commons-io</artifactId>
            <version>2.4</version>
        </dependency>


        <!-- 视频摄像头 -->
        <!-- 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>

package com.jack.demo.face;

import org.opencv.core.*;
import org.opencv.highgui.HighGui;
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 java.util.Arrays;

/** * * @Title: Opencv 图片人脸识别、实时摄像头人脸识别、视频文件人脸识别 * @Description: OpenCV-4.1.1 测试文件 * @date: 2019年8月19日 17:17:48 * @version: V-1.0.0 * */
public class FaceVideo { 
   

    // 初始化人脸探测器
    static CascadeClassifier faceDetector;

    static int i=0;

    static { 
   
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        faceDetector = new CascadeClassifier("D:\\Sofeware\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml");
    }

    public static void main(String[] args) { 
   
        // 1- 从摄像头实时人脸识别，识别成功保存图片到本地
        getVideoFromCamera();

        // 2- 从本地视频文件中识别人脸
// getVideoFromFile();

        // 3- 本地图片人脸识别，识别成功并保存人脸图片到本地
        face();

        // 4- 比对本地2张图的人脸相似度 （越接近1越相似）
        String basePicPath = "D:\\Documents\\Pictures\\";
        double compareHist = compare_image(basePicPath + "fc.jpg", basePicPath + "fc_1.jpg");
        System.out.println(compareHist);
        if (compareHist > 0.72) { 
   
            System.out.println("人脸匹配");
        } else { 
   
            System.out.println("人脸不匹配");
        }
    }




    /** * OpenCV-4.1.1 从摄像头实时读取 * @return: void * @date: 2019年8月19日 17:20:13 */
    public static void getVideoFromCamera() { 
   
        //1 如果要从摄像头获取视频 则要在 VideoCapture 的构造方法写 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("实时人脸识别", getFace(video));
                index=HighGui.waitKey(100);
                if (index==27) { 
   
                    capture.release();
                    break;
                }
            }
        }else{ 
   
            System.out.println("摄像头未开启");
        }
        try { 
   
            capture.release();
            Thread.sleep(1000);
            System.exit(0);
        } catch (InterruptedException e) { 
   
            e.printStackTrace();
        }
        return;
    }

    /** * OpenCV-4.1.1 从视频文件中读取 * @return: void * @date: 2019年8月19日 17:20:20 */
    public static void getVideoFromFile() { 
   
        VideoCapture capture=new VideoCapture();
        capture.open("C:\\Users\\Administrator\\Desktop\\1.avi");//1 读取视频文件的路径

        if(!capture.isOpened()){ 
   
            System.out.println("读取视频文件失败！");
            return;
        }
        Mat video=new Mat();
        int index=0;
        while(capture.isOpened()) { 
   
            capture.read(video);//2 视频文件的视频写入 Mat video 中
            HighGui.imshow("本地视频识别人脸", getFace(video));//3 显示图像
            index=HighGui.waitKey(100);//4 获取键盘输入
            if(index==27) { 
   //5 如果是 Esc 则退出
                capture.release();
                return;
            }
        }
    }

    /** * OpenCV-4.1.1 人脸识别 * @date: 2019年8月19日 17:19:36 * @param image 待处理Mat图片(视频中的某一帧) * @return 处理后的图片 */
    public static Mat getFace(Mat image) { 
   
        // 1 读取OpenCV自带的人脸识别特征XML文件(faceDetector)
// CascadeClassifier facebook=new CascadeClassifier("D:\\Sofeware\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml");
        // 2 特征匹配类
        MatOfRect face = new MatOfRect();
        // 3 特征匹配
        faceDetector.detectMultiScale(image, face);
        Rect[] rects=face.toArray();
        System.out.println("匹配到 "+rects.length+" 个人脸");
        if(rects != null && rects.length >= 1) { 
   

            // 4 为每张识别到的人脸画一个圈
            for (int i = 0; i < rects.length; i++) { 
   
                Imgproc.rectangle(image, new Point(rects[i].x, rects[i].y), new Point(rects[i].x + rects[i].width, rects[i].y + rects[i].height), new Scalar(0, 255, 0));
                Imgproc.putText(image, "Human", new Point(rects[i].x, rects[i].y), Imgproc.FONT_HERSHEY_SCRIPT_SIMPLEX, 1.0, new Scalar(0, 255, 0), 1, Imgproc.LINE_AA, false);
                //Mat dst=image.clone();
                //Imgproc.resize(image, image, new Size(300,300));
            }
            i++;
            if(i==3) { 
   // 获取匹配成功第10次的照片
                Imgcodecs.imwrite("D:\\Documents\\Pictures\\" + "face.png", image);
            }
        }
        return image;
    }



    /** * OpenCV-4.1.1 图片人脸识别 * @return: void * @date: 2019年5月7日12:16:55 */
    public static void face() { 
   
        // 1 读取OpenCV自带的人脸识别特征XML文件
        //OpenCV 图像识别库一般位于 opencv\sources\data 下面
// CascadeClassifier facebook=new CascadeClassifier("D:\\Sofeware\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml");
        // 2 读取测试图片
        String imgPath = "D:\\Documents\\Pictures\\he.png";
        Mat image=Imgcodecs.imread(imgPath);
        if(image.empty()){ 
   
            System.out.println("image 内容不存在！");
            return;
        }
        // 3 特征匹配
        MatOfRect face = new MatOfRect();
        faceDetector.detectMultiScale(image, face);
        // 4 匹配 Rect 矩阵 数组
        Rect[] rects=face.toArray();
        System.out.println("匹配到 "+rects.length+" 个人脸");
        // 5 为每张识别到的人脸画一个圈
        int i =1 ;
        for (Rect rect : face.toArray()) { 
   
            Imgproc.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
                    new Scalar(0, 255, 0), 3);
            imageCut(imgPath, "D:\\Documents\\Pictures\\"+i+".jpg", rect.x, rect.y, rect.width, rect.height);// 进行图片裁剪
            i++;
        }
        // 6 展示图片
        HighGui.imshow("人脸识别", image);
        HighGui.waitKey(0);
    }

    /** * 裁剪人脸 * @param imagePath * @param outFile * @param posX * @param posY * @param width * @param height */
    public static void imageCut(String imagePath, String outFile, int posX, int posY, int width, int height) { 
   
        // 原始图像
        Mat image = Imgcodecs.imread(imagePath);
        // 截取的区域：参数,坐标X,坐标Y,截图宽度,截图长度
        Rect rect = new Rect(posX, posY, width, height);
        // 两句效果一样
        Mat sub = image.submat(rect); // Mat sub = new Mat(image,rect);
        Mat mat = new Mat();
        Size size = new Size(width, height);
        Imgproc.resize(sub, mat, size);// 将人脸进行截图并保存
        Imgcodecs.imwrite(outFile, mat);
        System.out.println(String.format("图片裁切成功，裁切后图片文件为： %s", outFile));

    }

    /** * 人脸比对 * @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();

        //颜色范围
        MatOfFloat ranges = new MatOfFloat(0f, 256f);
        //直方图大小， 越大匹配越精确 (越慢)
        MatOfInt histSize = new MatOfInt(1000);

        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 相关系数
        double res = Imgproc.compareHist(hist_1, hist_2, Imgproc.CV_COMP_CORREL);
        return res;
    }

    /** * 灰度化人脸 * @param img * @return */
    public static Mat conv_Mat(String img) { 
   
        Mat image0 = Imgcodecs.imread(img);

        Mat image1 = new Mat();
        // 灰度化
        Imgproc.cvtColor(image0, image1, Imgproc.COLOR_BGR2GRAY);
        // 探测人脸
        MatOfRect faceDetections = new MatOfRect();
        faceDetector.detectMultiScale(image1, faceDetections);
        // rect中人脸图片的范围
        for (Rect rect : faceDetections.toArray()) { 
   
            Mat face = new Mat(image1, rect);
            return face;
        }
        return null;
    }

    /** * OpenCV-4.1.1 将摄像头拍摄的视频写入本地 * @return: void * @date: 2019年8月19日 17:20:48 */
    public static void writeVideo() { 
   
        //1 如果要从摄像头获取视频 则要在 VideoCapture 的构造方法写 0
        VideoCapture capture=new VideoCapture(0);
        Mat video=new Mat();
        int index=0;
        Size size=new Size(capture.get(Videoio.CAP_PROP_FRAME_WIDTH),capture.get(Videoio.CAP_PROP_FRAME_HEIGHT));
        VideoWriter writer=new VideoWriter("D:/a.mp4",VideoWriter.fourcc('D', 'I', 'V', 'X'), 15.0,size, true);
        while(capture.isOpened()) { 
   
            capture.read(video);//2 将摄像头的视频写入 Mat video 中
            writer.write(video);
            HighGui.imshow("像头获取视频", video);//3 显示图像
            index=HighGui.waitKey(100);//4 获取键盘输入
            if(index==27) { 
   //5 如果是 Esc 则退出
                capture.release();
                writer.release();
                return;
            }
        }
    }

}