实现监控摄像头视频遮挡异常检测需要以下步骤：
1、预处理视频：将视频分成若干个帧，每个帧使用OpenCV进行图像处理，去除噪声和伪影，同时增强对比度。
2、检测遮挡：使用OpenCV的图像阈值化处理，分析每个像素点的变化，判断遮挡情况。
3、异常诊断：对于检测到的遮挡，利用统计方法（如基于检测帧数的百分比）计算识别概率，并设定阈值，超出阈值则发送警报或提示等操作。
4、作出响应：根据判断结果，作出相应响应，如警报、通知管理员等。
下面是Java代码的实现示例：
package com.ghzn.helmet.test;
import org.opencv.core.*;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.videoio.VideoCapture;
import javax.imageio.ImageIO;
import javax.swing.*;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.ByteArrayInputStream;
/**
* 视频遮挡报警
*/
public class CameraMonitor {
// 阈值，超过该值则认定为遮挡
private static final double THRESHOLD = 0.3;
static {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
}
public static void main(String[] args) {
// 打开摄像头
VideoCapture cap = new VideoCapture();
cap.open(0);
// 初始化帧计数器、监听器
int frames = 0;
double occlusionPercent = 0;
VideoFrameListener listener = new VideoFrameListener();
Mat frame = new Mat();
while (cap.read(frame)) {
// 帧处理逻辑
frames++;
if (frames>1){
occlusionPercent = calculateOcclusionPercent(frame);
if (occlusionPercent > THRESHOLD) {
listener.actionPerformed(null);
System.out.println("遮挡异常！遮挡率为：" + occlusionPercent);
} else {
System.out.println("当前遮挡率为：" + occlusionPercent);
}
}
// 显示处理后的帧
BufferedImage img = matToBufferedImage(frame);
if (img != null) {
listener.updateImage(img);
}
}
cap.release();
System.exit(0);
}
/**
* BufferedImage转Mat
* @param image
* @return
*/
public static Mat convertBufToMat(BufferedImage image) {
byte[] pixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
Mat mat = new Mat(image.getHeight(), image.getWidth(), CvType.CV_8UC3);
mat.put(0, 0, pixels);
Mat matBGR = new Mat();
Core.convertScaleAbs(mat, matBGR, 1.0, 0);
return matBGR;
}
// 计算遮挡率
private static double calculateOcclusionPercent(Mat frame) {
Mat grayFrame = new Mat();
Imgproc.cvtColor(frame, grayFrame, Imgproc.COLOR_BGR2GRAY);
Mat lastFrame = new Mat();
Mat lastImage =convertBufToMat(VideoFrameListener.getLastImage());
Imgproc.cvtColor(lastImage, lastFrame, Imgproc.COLOR_BGR2GRAY);
Mat diff = new Mat();
Core.subtract(grayFrame, lastFrame, diff);
Mat threshold = new Mat();
Imgproc.threshold(diff, threshold, 50, 255, Imgproc.THRESH_BINARY);
double allPixel = threshold.total();
double whitePixel = Core.countNonZero(threshold);
return whitePixel / allPixel;
}
// Mat转BufferedImage
private static BufferedImage matToBufferedImage(Mat mat) {
MatOfByte bytemat = new MatOfByte();
Imgcodecs.imencode(".jpg", mat, bytemat);
byte[] bytes = bytemat.toArray();
BufferedImage img = null;
try {
img = ImageIO.read(new ByteArrayInputStream(bytes));
} catch (Exception e) {
e.printStackTrace();
}
return img;
}
// 实时视频帧监听器
private static class VideoFrameListener implements ActionListener {
private static BufferedImage lastImage;
@Override
public void actionPerformed(ActionEvent e) {
JOptionPane.showMessageDialog(null, "视频遮挡！", "警报", JOptionPane.WARNING_MESSAGE);
}
public void updateImage(BufferedImage image) {
lastImage = image;
JFrame frame = new JFrame();
JLabel label = new JLabel();
label.setIcon(new ImageIcon(lastImage));
frame.add(label);
frame.setSize(lastImage.getWidth(), lastImage.getHeight());
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
frame.setVisible(true);
}
public static BufferedImage getLastImage() {
return lastImage;
}
}
}
