openCV java（JFrame） 实现人脸识别，人脸自动检测，自动保存裁剪后人脸

基于Win10，调用系统摄像头

下载opencv

下载地址

本文版本为opencv-2413, IDE为idea ;

安装之后将{$opencv}/build/java 下的jar包及对应的dll加载到library

将{$opencv}/sources/data/haarcascades 下面的人脸识别文件放到resources目录下面

下面是具体实现：

import java.awt.EventQueue;

import javax.swing.ImageIcon;
import javax.swing.JFrame;
import javax.swing.JLabel;

import org.opencv.core.*;
import org.opencv.highgui.Highgui;
import org.opencv.highgui.VideoCapture;
import org.opencv.objdetect.CascadeClassifier;

public class CameraBasic {
static {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
}

private JFrame frame;
private static JLabel label;
private static int faceSerialCount = 0;
private static int flag = 0;

public static void main(String[] args) {
EventQueue.invokeLater(new Runnable() {
@Override
public void run() {
try {
CameraBasic window = new CameraBasic();
window.frame.setVisible(true);
} catch (Exception e) {
e.printStackTrace();
}
}
});

VideoCapture camera = new VideoCapture();//创建Opencv中的视频捕捉对象
camera.open(0);//open函数中的0代表当前计算机中索引为0的摄像头，如果你的计算机有多个摄像头，那么一次1,2,3……
if (!camera.isOpened()) {//isOpened函数用来判断摄像头调用是否成功
System.out.println("Camera Error");//如果摄像头调用失败，输出错误信息
} else {
Mat frame = new Mat();//创建一个输出帧
while (flag == 0) {
camera.read(frame);//read方法读取摄像头的当前帧
CascadeClassifier faceDetector = new CascadeClassifier("src/main/resources/lbpcascade_frontalface.xml");
MatOfRect faceDetections = new MatOfRect();
faceDetector.detectMultiScale(frame, faceDetections);
if (faceDetections.toArray().length > 0) {
System.out.println(String.format("Detected %s faces ", faceDetections.toArray().length));
}
Rect rectCrop = null;
for (Rect rect : faceDetections.toArray()) {
Core.rectangle(frame, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(255, 255, 0));
rectCrop = new Rect(rect.x, rect.y, rect.width, rect.height);
if (rect.width + rect.height > rectCrop.height + rectCrop.width) {
rectCrop = new Rect(rect.x, rect.y, rect.width, rect.height);
}
}

//转换图像格式并输出
label.setIcon(new ImageIcon(mat2BufferedImage.matToBufferedImage(frame)));

try {
Thread.sleep(500);//线程暂停500ms
} catch (InterruptedException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}

int faceCount = faceDetections.toArray().length;
if (faceCount > 0) {
faceSerialCount++;
System.out.println(faceSerialCount);
} else {
faceSerialCount = 0;
}

if (faceSerialCount > 6) {
Mat imageRoi = new Mat(frame, rectCrop);
Highgui.imwrite("haha.png", imageRoi);
faceSerialCount = 0;
}
}
}
}

private CameraBasic() {
initialize();
}

private void initialize() {
frame = new JFrame();
frame.setBounds(100, 100, 800, 450);
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
frame.getContentPane().setLayout(null);
label = new JLabel("");
label.setBounds(0, 0, 800, 450);
frame.getContentPane().add(label);
}
}

mat2BufferedImage.java

import org.opencv.core.Mat;

import java.awt.image.BufferedImage;

public class mat2BufferedImage {
public static BufferedImage matToBufferedImage(Mat matrix) {
int cols = matrix.cols();
int rows = matrix.rows();
int elemSize = (int) matrix.elemSize();
byte[] data = new byte[cols * rows * elemSize];
int type;
matrix.get(0, 0, data);
switch (matrix.channels()) {
case 1:
type = BufferedImage.TYPE_BYTE_GRAY;
break;
case 3:
type = BufferedImage.TYPE_3BYTE_BGR;
// bgr to rgb
byte b;
for (int i = 0; i < data.length; i = i + 3) {
b = data[i];
data[i] = data[i + 2];
data[i + 2] = b;
}
break;
default:
return null;
}
BufferedImage image2 = new BufferedImage(cols, rows, type);
image2.getRaster().setDataElements(0, 0, cols, rows, data);
return image2;
}
}