原 薦 Java基於百度AI+JavaCV+OpenCV 實現攝像頭人數動態統計

【Java】人流量統計-動態版之視訊轉圖識別請訪問 http://ai.baidu.com/forum/topic/show/940413

本文是基於上一篇進行迭代的。本文主要是以攝像頭畫面進行人流量統計。並對返回影象進行展示。需要額外瞭解 JavaCV OpenCV swing awt等 

也許JavaCV OpenCV  不需要也可以實現效果。但是小帥丶就先用這樣的方式實現了。別的方式大家就自己嘗試吧

專案程式碼地址 https://gitee.com/xshuai/bodyTrack

• 注意的問題

1.動態識別的area引數為矩陣的4個頂點的xy座標(即畫素) 順序是 上左下右 也就是順時針一圈4個點的座標點
2.case_init 為int 請不要給大於int範圍的值。或非int型別的值 即正整數就行 
3.area的值不要大於圖片本身的寬高

• 需要用到的jar 通過maven引入(下載的jar較多。需要等待較長時間)

<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<ffmpeg.version>3.2.1-1.3</ffmpeg.version>
<javacv.version>1.4.1</javacv.version>
</properties>

<dependencies>

 <dependency>
<groupId>org.bytedeco.javacpp-presets</groupId>
<artifactId>ffmpeg-platform</artifactId>
<version>${ffmpeg.version}</version>
</dependency>
<!-- fastjson -->
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.35</version>
</dependency>
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>javacv</artifactId>
<version>${javacv.version}</version>
</dependency>

<dependency>
<groupId>org.bytedeco.javacpp-presets</groupId>
<artifactId>opencv-platform</artifactId>
<version>3.4.1-1.4.1</version>
</dependency>
</dependencies>

•  需要用到的Java工具類

HttpUtil https://ai.baidu.com/file/544D677F5D4E4F17B4122FBD60DB82B3

• 呼叫介面示例程式碼(需要自己的電腦有攝像頭哦)

import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.awt.image.WritableRaster;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.FileOutputStream;
import java.io.OutputStream;
import java.net.URLEncoder;
import java.util.Base64;
import java.util.Base64.Decoder;
import java.util.Base64.Encoder;

import javax.imageio.ImageIO;
import javax.swing.JFrame;

import org.bytedeco.javacpp.BytePointer;
import org.bytedeco.javacpp.opencv_core.IplImage;
import org.bytedeco.javacv.CanvasFrame;
import org.bytedeco.javacv.Frame;
import org.bytedeco.javacv.Java2DFrameConverter;
import org.bytedeco.javacv.OpenCVFrameConverter;
import org.bytedeco.javacv.OpenCVFrameConverter.ToIplImage;
import org.bytedeco.javacv.OpenCVFrameGrabber;

import com.alibaba.fastjson.JSONObject;

import cn.xsshome.body.util.HttpUtil;
/**
 * 獲取攝像頭畫面進行處理並回顯圖片在畫面中
 * 人流量統計（動態版）JavaAPI示例程式碼
 * @author 小帥丶
 *
 */
public class JavavcCameraTest {

static OpenCVFrameConverter.ToIplImage converter = new OpenCVFrameConverter.ToIplImage();
//人流量統計（動態版）介面地址
private static String BODY_TRACKING_URL="https://aip.baidubce.com/rest/2.0/image-classify/v1/body_tracking";

private static String ACCESS_TOKEN ="";//介面的token

public static void main(String[] args) throws Exception,
InterruptedException {
OpenCVFrameGrabber grabber = new OpenCVFrameGrabber(0);
grabber.start(); // 開始獲取攝像頭資料
CanvasFrame canvas = new CanvasFrame("人流量實時統計");// 新建一個視窗
canvas.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
canvas.setAlwaysOnTop(true);
int ex = 0;
while (true) {
if (!canvas.isDisplayable()) {// 視窗是否關閉
grabber.stop();// 停止抓取
System.exit(2);// 退出
grabber.close();
}
// canvas.showImage(grabber.grab());//顯示攝像頭抓取的畫面
Java2DFrameConverter java2dFrameConverter = new Java2DFrameConverter();
// 攝像頭抓取的畫面轉BufferedImage
BufferedImage bufferedImage = java2dFrameConverter.getBufferedImage(grabber.grabFrame());
// bufferedImage 請求API介面 檢測人流量
String result = getBodyTrack(bufferedImage);
BufferedImage bufferedImageAPI = getAPIResult(result);
// 如果識別為空 則顯示攝像頭抓取的畫面
if (null == bufferedImageAPI) {
canvas.showImage(grabber.grab());
} else {
// BufferedImage轉IplImage
IplImage iplImageAPI = BufImgToIplData(bufferedImageAPI);
// 將IplImage轉為Frame 並顯示在視窗中
Frame convertFrame = converter.convert(iplImageAPI);
canvas.showImage(convertFrame);
}
ex++;
//Thread.sleep(100);// 100毫秒重新整理一次影象.因為介面返回需要時間。所以看到的畫面還是會有一定的延遲
}
}
/**
* BufferedImage轉IplImage
* @param bufferedImageAPI
* @return
*/
private static IplImage BufImgToIplData(BufferedImage bufferedImageAPI) {
IplImage iplImage = null;
ToIplImage iplConverter = new OpenCVFrameConverter.ToIplImage();
Java2DFrameConverter java2dConverter = new Java2DFrameConverter();
iplImage = iplConverter.convert(java2dConverter.convert(bufferedImageAPI));
return iplImage;
}
/**
* IplImage 轉 BufferedImage
* @param mat
* @return BufferedImage
*/
public static BufferedImage iplToBufImgData(IplImage mat) {
if (mat.height() > 0 && mat.width() > 0) {
//TYPE_3BYTE_BGR 表示一個具有 8 位 RGB 顏色分量的影象，對應於 Windows 風格的 BGR 顏色模型，具有用 3 位元組儲存的 Blue、Green 和 Red 三種顏色。 
BufferedImage image = new BufferedImage(mat.width(), mat.height(),BufferedImage.TYPE_3BYTE_BGR);
WritableRaster raster = image.getRaster();
DataBufferByte dataBuffer = (DataBufferByte) raster.getDataBuffer();
byte[] data = dataBuffer.getData();
BytePointer bytePointer = new BytePointer(data);
mat.imageData(bytePointer);
return image;
}
return null;
}
/**
* 介面結果轉bufferimage
* @param result
* @return BufferedImage
* @throws Exception 
*/
private static BufferedImage getAPIResult(String result) throws Exception {
JSONObject object = JSONObject.parseObject(result);
BufferedImage bufferedImage = null;
if(object.getInteger("person_num")>=1){
Decoder decoder = Base64.getDecoder();
byte [] b = decoder.decode(object.getString("image"));
ByteArrayInputStream in = new ByteArrayInputStream(b);
bufferedImage = ImageIO.read(in); 

ByteArrayOutputStream baos = new ByteArrayOutputStream(); 
ImageIO.write(bufferedImage,"jpg", baos); 
 byte[] imageInByte = baos.toByteArray(); 
// Base64解碼
for (int i = 0; i < imageInByte.length; ++i) {
if (imageInByte[i] < 0) {// 調整異常資料
imageInByte[i] += 256;
}
 }
 OutputStream out = new FileOutputStream("G:/testimg/xiaoshuairesult.jpg");//介面返回的渲染圖
 out.write(imageInByte);
 out.flush();
 out.close();
return bufferedImage;
}else{
return null;
}
}
/**
 * 獲取介面處理結果圖
 * @param bufferedImage
 * @return String
 * @throws Exception
 */
public static String getBodyTrack(BufferedImage bufferedImage) throws Exception{
 ByteArrayOutputStream baos = new ByteArrayOutputStream(); 
ImageIO.write(bufferedImage,"jpg",baos); 
byte[] imageInByte = baos.toByteArray(); 
Encoder base64 = Base64.getEncoder();
String imageBase64 = base64.encodeToString(imageInByte);
// Base64解碼
for (int i = 0; i < imageInByte.length; ++i) {
if (imageInByte[i] < 0) {// 調整異常資料
imageInByte[i] += 256;
}
 }
 // 生成jpeg圖片
 OutputStream out = new FileOutputStream("G:/testimg/xiaoshuai.jpg");// 新生成的圖片
 out.write(imageInByte);
 out.flush();
 out.close();
 System.out.println("儲存成功");
 baos.flush();
 baos.close();
String access_token = ACCESS_TOKEN;
String case_id = "2018";
String case_init = "true";
String area = "10,10,630,10,630,470,10,469";
 String params = "image=" + URLEncoder.encode(imageBase64, "utf-8")
+ "&dynamic=true&show=true&case_id=" + case_id
+ "&case_init="+case_init +"&area="+area;
//靜態識別
// String params = "image=" + URLEncoder.encode(imageBase64, "utf-8")+"&dynamic=false&show=true";
  String result = HttpUtil.post(BODY_TRACKING_URL, access_token, params);
  System.out.println("介面內容==>"+result);
 return result;
}
/**
* IplImage 轉 BufferedImage
* @param mat
* @return BufferedImage
*/
public static BufferedImage bufferimgToBase64(IplImage mat) {
if (mat.height() > 0 && mat.width() > 0) {
BufferedImage image = new BufferedImage(mat.width(), mat.height(),BufferedImage.TYPE_3BYTE_BGR);
WritableRaster raster = image.getRaster();
DataBufferByte dataBuffer = (DataBufferByte) raster.getDataBuffer();
byte[] data = dataBuffer.getData();
BytePointer bytePointer = new BytePointer(data);
mat.imageData(bytePointer);
return image;
}
return null;
}
}

• 攝像頭中的內容截圖示意(本人頭像就不直接顯示了。萬一嚇著大家呢) 也不要用去馬賽克的技術還原圖片哦。

還是很好玩的、不需要自己去整OpenCV一套就能實現統計攝像頭中的人數。