【Java】人流量统计-动态版之视频转图识别请访问 http://ai.baidu.com/forum/topic/show/940413
本文是基于上一篇进行迭代的。本文主要是以摄像头画面进行人流量统计。并对返回图像进行展示。需要额外了解JavaCV OpenCV swing awt等
也许JavaCV OpenCV 不需要也可以实现效果。但是小帅丶就先用这样的方式实现了。别的方式大家就自己尝试吧
有可能显示的in out不对。请设置帧率试试。鄙人不是专业的。所以对帧率也不是很懂。以下代码加入也没有明显的变化。
代码语言：javascript复制grabber.setFrameRate(10);
grabber.setFrameNumber(10);项目代码地址 https://gitee.com/xshuai/bodyTrack
- 注意的问题
1.动态识别的area参数为矩阵的4个顶点的xy坐标(即像素) 顺序是 上左下右 也就是顺时针一圈4个点的坐标点
2.case_id 为int 请不要给大于int范围的值。或非int类型的值 即正整数就行
3.area的值不要大于图片本身的宽高- 需要用到的jar 通过maven引入(下载的jar较多。需要等待较长时间)
<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工具类
HttpUtil https://ai.baidu.com/file/544D677F5D4E4F17B4122FBD60DB82B3- 调用接口示例代码(需要自己的电脑有摄像头哦)
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;
/**
* 获取摄像头画面进行处理并回显图片在画面中
* 人流量统计（动态版）JavaAPI示例代码
* @author 小帅丶
*
*/
public class JavavcCameraTest {
static OpenCVFrameConverter.ToIplImage converter = new OpenCVFrameConverter.ToIplImage();
//人流量统计（动态版）接口地址
private static String BODY_TRACKING_URL="https://aip.baidubce.com/rest/2.0/image-classify/v1/body_tracking";
private static String ACCESS_TOKEN ="";//接口的token
/**
* 每个case的初始化信号，为true时对该case下的跟踪算法进行初始化，为false时重载该case的跟踪状态。当为false且读取不到相应case的信息时，直接重新初始化
* caseId=0 第一次请求 case_init=true caseId>0 非第一次请求 case_init=false
*/
static int caseId = 0;
public static void main(String[] args) throws Exception,
InterruptedException {
OpenCVFrameGrabber grabber = new OpenCVFrameGrabber(0);
grabber.start(); // 开始获取摄像头数据
CanvasFrame canvas = new CanvasFrame("人流量实时统计");// 新建一个窗口
canvas.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
canvas.setAlwaysOnTop(true);
int ex = 0;
while (true) {
if (!canvas.isDisplayable()) {// 窗口是否关闭
grabber.stop();// 停止抓取
System.exit(2);// 退出
grabber.close();
}
// canvas.showImage(grabber.grab());//显示摄像头抓取的画面
Java2DFrameConverter java2dFrameConverter = new Java2DFrameConverter();
// 摄像头抓取的画面转BufferedImage
BufferedImage bufferedImage = java2dFrameConverter.getBufferedImage(grabber.grabFrame());
// bufferedImage 请求API接口 检测人流量
String result = getBodyTrack(bufferedImage);
BufferedImage bufferedImageAPI = getAPIResult(result);
// 如果识别为空 则显示摄像头抓取的画面
if (null == bufferedImageAPI) {
canvas.showImage(grabber.grab());
} else {
// BufferedImage转IplImage
IplImage iplImageAPI = BufImgToIplData(bufferedImageAPI);
// 将IplImage转为Frame 并显示在窗口中
Frame convertFrame = converter.convert(iplImageAPI);
canvas.showImage(convertFrame);
}
ex ;
// Thread.sleep(100);// 100毫秒刷新一次图像.因为接口返回需要时间。所以看到的画面还是会有一定的延迟
}
}
/**
* BufferedImage转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 转 BufferedImage
* @param mat
* @return BufferedImage
*/
public static BufferedImage iplToBufImgData(IplImage mat) {
if (mat.height() > 0 && mat.width() > 0) {
//TYPE_3BYTE_BGR 表示一个具有 8 位 RGB 颜色分量的图像，对应于 Windows 风格的 BGR 颜色模型，具有用 3 字节存储的 Blue、Green 和 Red 三种颜色。
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;
}
/**
* 接口结果转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解码
for (int i = 0; i < imageInByte.length; i) {
if (imageInByte[i] < 0) {// 调整异常数据
imageInByte[i] = 256;
}
}
OutputStream out = new FileOutputStream("G:/testimg/xiaoshuairesult.jpg");//接口返回的渲染图
out.write(imageInByte);
out.flush();
out.close();
return bufferedImage;
}else{
return null;
}
}
/**
* 获取接口处理结果图
* @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解码
for (int i = 0; i < imageInByte.length; i) {
if (imageInByte[i] < 0) {// 调整异常数据
imageInByte[i] = 256;
}
}
// 生成jpeg图片
OutputStream out = new FileOutputStream("G:/testimg/xiaoshuai.jpg");// 新生成的图片
out.write(imageInByte);
out.flush();
out.close();
System.out.println("保存成功");
baos.flush();
baos.close();
String access_token = ACCESS_TOKEN;
String case_id = "2018";
String case_init = "";
String area = "10,10,630,10,630,470,10,469";
String params = "";
if(caseId==0){
case_init = "true";
params = "image=" URLEncoder.encode(imageBase64, "utf-8")
"&dynamic=true&show=true&case_id=" case_id
"&case_init=" case_init "&area=" area;
}else{
case_init = "false";
params = "image=" URLEncoder.encode(imageBase64, "utf-8")
"&dynamic=true&show=true&case_id=" case_id
"&case_init=" case_init "&area=" area;
}
//静态识别
// 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("接口内容==>" result);
return result;
}
/**
* IplImage 转 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;
}
}- 摄像头中的内容截图示意(本人头像就不直接显示了。万一吓着大家呢) 也不要用去马赛克的技术还原图片哦。
还是很好玩的、不需要自己去整OpenCV一套就能实现统计摄像头中的人数。
