文章目录
1.关于Javacv~~2. [官网下载最新OpenCV4.8](https://opencv.org/releases/),并解压~~ *不一定要安装opencv*~~3. 将opencv的jar包及动态库dll文件引入项目~~4.pom引入javacv库5.测试5.1 图片美颜5.2 图片人脸检测5.3 提取视频中的语音5.4 音视频剪辑5.5 录屏5.6 推流与流媒体播放 [参考](https://xinchen.blog.csdn.net/article/details/121434969)5.7 摄像头的几个案例 [参考](https://xinchen.blog.csdn.net/article/details/121572093)5.7.1 保存摄像头视频为mp45.7.2 摄像头抓图5.7.3 摄像头推流
5.8 人脸识别训练及预测5.8.1 使用Javacv训练人脸识别模型5.8.2 使用模型预测人脸照片5.8.3 只需要将图片读取人脸改为摄像头抓取即可实现人脸检测并识别
1.关于Javacv
基于opencv实现,用于实现图片、音视频处理,视频捕捉处理;多媒体RTMP、HLS拉流推流; 机器学习如图像识别、人脸识别等业务实现。这些特性可能在python实现得可能更好或更适合,但Javacv感觉还是不错的。
2. 官网下载最新OpenCV4.8,并解压 不一定要安装opencv
3. 将opencv的jar包及动态库dll文件引入项目
① E:\opencv\build\java\opencv-480.jar可以通过maven命令直接安装到本地maven仓库,也可以IDEA settings->project Structure->Libraries-> “+” 入该jar ②E:\opencv\build\java\x64\opencv_java480.dll可以直接copy到动态库搜索路径如C:\Windows\System32或通过环境变量设置或直接在代码中加载该库 System.load("E:\\opencv\\build\\java\\x64\\opencv_java480.dll");
4.pom引入javacv库
5.测试
5.1 图片美颜
package cv;
import org.bytedeco.opencv.opencv_core.Mat;
import java.io.File;
import static org.bytedeco.opencv.global.opencv_imgcodecs.imread;
import static org.bytedeco.opencv.global.opencv_imgcodecs.imwrite;
import static org.bytedeco.opencv.global.opencv_imgproc.bilateralFilter;
public class Meiyan {
public static void main(String[] args) {
Mat result = new Mat();
Mat image = imread("D:\\dayun.jpg");
int level = 18;// 值越大,过滤强度越大
bilateralFilter(image, result, level, level * 2, level / 2);
File out = new File("out.png");
imwrite(out.getPath(), result);
}
}
5.2 图片人脸检测
注意,检查到的人脸会圈出,有些人脸可能检测不到;这里加载人脸检测CascadeClassifier文件是来自opencv安装包或其他地方找一个即可
package cv;
import org.bytedeco.opencv.opencv_core.*;
import org.bytedeco.opencv.opencv_objdetect.CascadeClassifier;
import static org.bytedeco.opencv.global.opencv_imgcodecs.imread;
import static org.bytedeco.opencv.global.opencv_imgcodecs.imwrite;
import static org.bytedeco.opencv.global.opencv_imgproc.LINE_8;
import static org.bytedeco.opencv.global.opencv_imgproc.rectangle;
public class FaceDetector {
public static void main(String[] args) {
// Load the image
Mat image = imread("D://meinv.jpeg");
// Load the face cascade classifier
CascadeClassifier faceCascade = new CascadeClassifier("E:\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml");
// Detect faces in the image
RectVector faceDetections = new RectVector();
faceCascade.detectMultiScale(image, faceDetections);
// Draw a rectangle around each detected face
for (Rect rect : faceDetections.get()) {
rectangle(image, new Point(rect.x(), rect.y()), new Point(rect.x() + rect.width(), rect.y() + rect.height()),
new Scalar(0, 255, 0, 0), 2, LINE_8, 0);
}
// Save the image with the detected faces
imwrite("face.jpg", image);
}
}
5.3 提取视频中的语音
package cv;
import org.bytedeco.javacv.FFmpegFrameGrabber;
import org.bytedeco.javacv.FFmpegFrameRecorder;
import org.bytedeco.javacv.Frame;
import java.io.File;
import java.util.UUID;
public class MP4ToAudio {
public static void mp4ToAudio(String sourceFilePath) {
System.out.println("提取音频文件");
File file = new File(sourceFilePath);
//抓取资源
FFmpegFrameGrabber frameGrabber1 = new FFmpegFrameGrabber(sourceFilePath);
Frame frame = null;
FFmpegFrameRecorder recorder = null;
String fileName = null;
try {
frameGrabber1.start();
fileName = file.getAbsolutePath() + UUID.randomUUID() + ".mp3";
System.out.println("--文件名-->>" + fileName);
recorder = new FFmpegFrameRecorder(fileName, frameGrabber1.getAudioChannels());
recorder.setFormat("mp3");
recorder.setSampleRate(frameGrabber1.getSampleRate());
recorder.setTimestamp(frameGrabber1.getTimestamp());
recorder.setAudioQuality(0);
recorder.start();
int index = 0;
while (true) {
frame = frameGrabber1.grab();
if (frame == null) {
System.out.println("视频处理完成");
break;
}
if (frame.samples != null) {
recorder.recordSamples(frame.sampleRate, frame.audioChannels, frame.samples);
}
System.out.println("帧值=" + index);
index++;
}
recorder.stop();
recorder.release();
frameGrabber1.stop();
} catch (Exception e) {
e.printStackTrace();
}
}
public static void main(String[] args) {
String sourceFilePath = "D://test.mp4";
mp4ToAudio(sourceFilePath);
}
}
5.4 音视频剪辑
下面使用第三方工具ffmpeg.exe来处理音视频,如果安装了剪映等工具,可以直接找到它的ffmpeg.exe(非Javacv)
package cv;
import java.io.File;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
public class CvCutter {
private static String ffmpegEXE = "F:\\JianYing\\bin\\ffmpeg.exe";//上篇文章视频转换为MP4的云盘有可以直接下载的
private static List
private static List
public static Boolean cutVideoOrAudio(String src, String start, String end, String dest) throws Exception {
File file = new File(dest);
if (file.exists()) {
return false;
}
if (!file.getParentFile().isDirectory()) {
file.getParentFile().mkdirs();
}
List
ProcessBuilder builder = new ProcessBuilder();
Process process = builder.command(command).redirectErrorStream(true).start();
process.waitFor();
process.destroy();
return true;
}
public static List
String suffix = src.substring(src.lastIndexOf(".") + 1);
List
if (VIDEO_LIST.contains(suffix)) {
command.add(ffmpegEXE);
command.add("-ss");
command.add(start);
command.add("-to");
command.add(end);
command.add("-i");
command.add(src);
command.add("-c:v");
command.add("libx264");
command.add("-c:a");
command.add("aac");
command.add("-strict");
command.add("experimental");
command.add("-b:a");
command.add("98k");
command.add(dest);
command.add("-y");
} else if (AUDIO_LIST.contains(suffix)) {
command.add(ffmpegEXE);
command.add("-i");
command.add(src);
command.add("-ss");
command.add(start);
command.add("-to");
command.add(end);
command.add(dest);
command.add("-y");
} else {
throw new RuntimeException("unknown format");
}
return command;
}
public static void main(String[] args) throws Exception {
String input = "D:\\test.mp3";
String out = "D:\\part.mp3";
String suffix = input.substring(input.lastIndexOf(".") + 1);
System.out.println(suffix);
String start = "00:00:10";
String end = "00:00:20";
CvCutter.cutVideoOrAudio(input, start, end, out);
}
}
5.5 录屏
package cv;
import org.bytedeco.ffmpeg.global.avcodec;
import org.bytedeco.javacv.FFmpegFrameRecorder;
import org.bytedeco.javacv.Java2DFrameConverter;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.time.LocalDateTime;
import java.time.temporal.ChronoUnit;
/**
* TODO
*
* @author majun
* @version 1.0
* @since 2023-10-11 20:40
*/
public class ScreenRecord {
/**
* 录屏
* @param filename 文件名称
* @param seconds 时长
*/
public static void recordScreen(String filename, int seconds) {
final int FRAME_RATE = 30;
final Dimension SCREEN_SIZE = Toolkit.getDefaultToolkit().getScreenSize();
// 创建录屏对象,并设置相关属性
FFmpegFrameRecorder recorder = new FFmpegFrameRecorder(filename, SCREEN_SIZE.width, SCREEN_SIZE.height);
recorder.setVideoCodec(avcodec.AV_CODEC_ID_H264);
recorder.setFormat("mp4");
recorder.setFrameRate(FRAME_RATE);
Java2DFrameConverter converter = new Java2DFrameConverter();
try {
// 初始化录屏对象
recorder.start();
Robot robot = new Robot();
BufferedImage screenShot;
// 系统当前时间
LocalDateTime now = LocalDateTime.now();
System.out.println(now);
// 30秒后
LocalDateTime plus = now.plus(seconds, ChronoUnit.SECONDS);
System.out.println(plus);
// 开始录制
while (true) {
// 获取屏幕截图并写入文件
screenShot = robot.createScreenCapture(new Rectangle(SCREEN_SIZE));
recorder.record(converter.getFrame(screenShot));
// 停止时间
LocalDateTime time = LocalDateTime.now();
if(plus.isBefore(time)){
System.out.println(time);
break;
}
}
} catch (Exception e) {
e.printStackTrace();
} finally {
// 关闭录制器
try {
recorder.stop();
} catch (Exception e) {
e.printStackTrace();
}
}
}
public static void main(String[] args) {
recordScreen("screen.mp4",10);
}
}
5.6 推流与流媒体播放 参考
首先启动一个流媒体服务器SRS docker run -p 1935:1935 -p 1985:1985 -p 8080:8080 ossrs/srs,然后运行推流代码,最后用VLC流媒体播放器ctrl+N访问rtmp://192.168.72.126:1935/live/livestream(同推流地址)从SRS拉流播放
package cv;
import lombok.extern.slf4j.Slf4j;
import org.bytedeco.ffmpeg.avcodec.AVCodecParameters;
import org.bytedeco.ffmpeg.avformat.AVFormatContext;
import org.bytedeco.ffmpeg.avformat.AVStream;
import org.bytedeco.ffmpeg.global.avcodec;
import org.bytedeco.ffmpeg.global.avutil;
import org.bytedeco.javacv.FFmpegFrameGrabber;
import org.bytedeco.javacv.FFmpegFrameRecorder;
import org.bytedeco.javacv.FFmpegLogCallback;
import org.bytedeco.javacv.Frame;
/**
* @author willzhao
* @version 1.0
* @description 读取指定的mp4文件,推送到SRS服务器
* @date 2021/11/19 8:49
*/
@Slf4j
public class PushMp4 {
private static final String MP4_FILE_PATH = "D://test.mp4";
/**
* SRS的推流地址
*/
private static final String SRS_PUSH_ADDRESS = "rtmp://192.168.72.126:1935/live/livestream";
/**
* 读取指定的mp4文件,推送到SRS服务器
* @param sourceFilePath 视频文件的绝对路径
* @param PUSH_ADDRESS 推流地址
* @throws Exception
*/
private static void grabAndPush(String sourceFilePath, String PUSH_ADDRESS) throws Exception {
// ffmepg日志级别
avutil.av_log_set_level(avutil.AV_LOG_INFO);
FFmpegLogCallback.set();
// 实例化帧抓取器对象,将文件路径传入
FFmpegFrameGrabber grabber = new FFmpegFrameGrabber(MP4_FILE_PATH);
long startTime = System.currentTimeMillis();
log.info("开始初始化帧抓取器");
// 初始化帧抓取器,例如数据结构(时间戳、编码器上下文、帧对象等),
// 如果入参等于true,还会调用avformat_find_stream_info方法获取流的信息,放入AVFormatContext类型的成员变量oc中
grabber.start(true);
log.info("帧抓取器初始化完成,耗时[{}]毫秒", System.currentTimeMillis()-startTime);
// grabber.start方法中,初始化的解码器信息存在放在grabber的成员变量oc中
AVFormatContext avFormatContext = grabber.getFormatContext();
// 文件内有几个媒体流(一般是视频流+音频流)
int streamNum = avFormatContext.nb_streams();
// 没有媒体流就不用继续了
if (streamNum<1) {
log.error("文件内不存在媒体流");
return;
}
// 取得视频的帧率
int frameRate = (int)grabber.getVideoFrameRate();
log.info("视频帧率[{}],视频时长[{}]秒,媒体流数量[{}]",
frameRate,
avFormatContext.duration()/1000000,
avFormatContext.nb_streams());
// 遍历每一个流,检查其类型
for (int i=0; i< streamNum; i++) {
AVStream avStream = avFormatContext.streams(i);
AVCodecParameters avCodecParameters = avStream.codecpar();
log.info("流的索引[{}],编码器类型[{}],编码器ID[{}]", i, avCodecParameters.codec_type(), avCodecParameters.codec_id());
}
// 视频宽度
int frameWidth = grabber.getImageWidth();
// 视频高度
int frameHeight = grabber.getImageHeight();
// 音频通道数量
int audioChannels = grabber.getAudioChannels();
log.info("视频宽度[{}],视频高度[{}],音频通道数[{}]",
frameWidth,
frameHeight,
audioChannels);
// 实例化FFmpegFrameRecorder,将SRS的推送地址传入
FFmpegFrameRecorder recorder = new FFmpegFrameRecorder(SRS_PUSH_ADDRESS,
frameWidth,
frameHeight,
audioChannels);
// 设置编码格式
recorder.setVideoCodec(avcodec.AV_CODEC_ID_H264);
// 设置封装格式
recorder.setFormat("flv");
// 一秒内的帧数
recorder.setFrameRate(frameRate);
// 两个关键帧之间的帧数
recorder.setGopSize(frameRate);
// 设置音频通道数,与视频源的通道数相等
recorder.setAudioChannels(grabber.getAudioChannels());
startTime = System.currentTimeMillis();
log.info("开始初始化帧抓取器");
// 初始化帧录制器,例如数据结构(音频流、视频流指针,编码器),
// 调用av_guess_format方法,确定视频输出时的封装方式,
// 媒体上下文对象的内存分配,
// 编码器的各项参数设置
recorder.start();
log.info("帧录制初始化完成,耗时[{}]毫秒", System.currentTimeMillis()-startTime);
Frame frame;
startTime = System.currentTimeMillis();
log.info("开始推流");
long videoTS = 0;
int videoFrameNum = 0;
int audioFrameNum = 0;
int dataFrameNum = 0;
// 假设一秒钟15帧,那么两帧间隔就是(1000/15)毫秒
int interVal = 1000/frameRate;
// 发送完一帧后sleep的时间,不能完全等于(1000/frameRate),不然会卡顿,
// 要更小一些,这里取八分之一
interVal/=8;
// 持续从视频源取帧
while (null!=(frame=grabber.grab())) {
videoTS = 1000 * (System.currentTimeMillis() - startTime);
// 时间戳
recorder.setTimestamp(videoTS);
// 有图像,就把视频帧加一
if (null!=frame.image) {
videoFrameNum++;
}
// 有声音,就把音频帧加一
if (null!=frame.samples) {
audioFrameNum++;
}
// 有数据,就把数据帧加一
if (null!=frame.data) {
dataFrameNum++;
}
// 取出的每一帧,都推送到SRS
recorder.record(frame);
// 停顿一下再推送
Thread.sleep(interVal);
}
log.info("推送完成,视频帧[{}],音频帧[{}],数据帧[{}],耗时[{}]秒",
videoFrameNum,
audioFrameNum,
dataFrameNum,
(System.currentTimeMillis()-startTime)/1000);
// 关闭帧录制器
recorder.close();
// 关闭帧抓取器
grabber.close();
}
public static void main(String[] args) throws Exception {
grabAndPush(MP4_FILE_PATH, SRS_PUSH_ADDRESS);
}
}
5.7 摄像头的几个案例 参考
如果没有摄像头,可以使用手机做摄像头,大致方法是手机安装无他相机,PC安装无他伴侣;手机“关于手机”->狂点系统版本区域打开开发者模式->打开USB调试模式->连接数据线选择“打开文件”,然后手机无法相机进入直播助手,PC无他伴侣选择探测到的手机并点击同步即可。之后的几个案例继承如下抽象基类进行实现
package com.bolingcavalry.grabpush.camera;
import lombok.Getter;
import lombok.extern.slf4j.Slf4j;
import org.bytedeco.ffmpeg.global.avutil;
import org.bytedeco.javacv.*;
import org.bytedeco.opencv.global.opencv_imgproc;
import org.bytedeco.opencv.opencv_core.Mat;
import org.bytedeco.opencv.opencv_core.Scalar;
import java.text.SimpleDateFormat;
import java.util.Date;
/**
* @author will
* @email zq2599@gmail.com
* @date 2021/11/19 8:07 上午
* @description 摄像头应用的基础类,这里面定义了拉流和推流的基本流程,子类只需实现具体的业务方法即可
*/
@Slf4j
public abstract class AbstractCameraApplication {
/**
* 摄像头序号,如果只有一个摄像头,那就是0
*/
protected static final int CAMERA_INDEX = 0;
/**
* 帧抓取器
*/
protected FrameGrabber grabber;
/**
* 输出帧率
*/
@Getter
private final double frameRate = 30;
/**
* 摄像头视频的宽
*/
@Getter
private final int cameraImageWidth = 1280;
/**
* 摄像头视频的高
*/
@Getter
private final int cameraImageHeight = 720;
/**
* 转换器
*/
private final OpenCVFrameConverter.ToIplImage openCVConverter = new OpenCVFrameConverter.ToIplImage();
/**
* 实例化、初始化输出操作相关的资源
*/
protected abstract void initOutput() throws Exception;
/**
* 输出
*/
protected abstract void output(Frame frame) throws Exception;
/**
* 释放输出操作相关的资源
*/
protected abstract void releaseOutputResource() throws Exception;
/**
* 两帧之间的间隔时间
* @return
*/
protected int getInterval() {
// 假设一秒钟15帧,那么两帧间隔就是(1000/15)毫秒
return (int)(1000/ frameRate);
}
/**
* 实例化帧抓取器,默认OpenCVFrameGrabber对象,
* 子类可按需要自行覆盖
* @throws FFmpegFrameGrabber.Exception
*/
protected void instanceGrabber() throws FrameGrabber.Exception {
grabber = new OpenCVFrameGrabber(CAMERA_INDEX);
}
/**
* 用帧抓取器抓取一帧,默认调用grab()方法,
* 子类可以按需求自行覆盖
* @return
*/
protected Frame grabFrame() throws FrameGrabber.Exception {
return grabber.grab();
}
/**
* 初始化帧抓取器
* @throws Exception
*/
protected void initGrabber() throws Exception {
// 实例化帧抓取器
instanceGrabber();
// 摄像头有可能有多个分辨率,这里指定
// 可以指定宽高,也可以不指定反而调用grabber.getImageWidth去获取,
grabber.setImageWidth(cameraImageWidth);
grabber.setImageHeight(cameraImageHeight);
// 开启抓取器
grabber.start();
}
/**
* 预览和输出
* @param grabSeconds 持续时长
* @throws Exception
*/
private void grabAndOutput(int grabSeconds) throws Exception {
// 添加水印时用到的时间工具
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
long endTime = System.currentTimeMillis() + 1000L *grabSeconds;
// 两帧输出之间的间隔时间,默认是1000除以帧率,子类可酌情修改
int interVal = getInterval();
// 水印在图片上的位置
org.bytedeco.opencv.opencv_core.Point point = new org.bytedeco.opencv.opencv_core.Point(15, 35);
Frame captureFrame;
Mat mat;
// 超过指定时间就结束循环
while (System.currentTimeMillis() // 取一帧 captureFrame = grabFrame(); if (null==captureFrame) { log.error("帧对象为空"); break; } // 将帧对象转为mat对象 mat = openCVConverter.convertToMat(captureFrame); // 在图片上添加水印,水印内容是当前时间,位置是左上角 opencv_imgproc.putText(mat, simpleDateFormat.format(new Date()), point, opencv_imgproc.CV_FONT_VECTOR0, 0.8, new Scalar(0, 200, 255, 0), 1, 0, false); // 子类输出 output(openCVConverter.convert(mat)); // 适当间隔,让肉感感受不到闪屏即可 if(interVal>0) { Thread.sleep(interVal); } } log.info("输出结束"); } /** * 释放所有资源 */ private void safeRelease() { try { // 子类需要释放的资源 releaseOutputResource(); } catch (Exception exception) { log.error("do releaseOutputResource error", exception); } if (null!=grabber) { try { grabber.close(); } catch (Exception exception) { log.error("close grabber error", exception); } } } /** * 整合了所有初始化操作 * @throws Exception */ private void init() throws Exception { long startTime = System.currentTimeMillis(); // 设置ffmepg日志级别 avutil.av_log_set_level(avutil.AV_LOG_INFO); FFmpegLogCallback.set(); // 实例化、初始化帧抓取器 initGrabber(); // 实例化、初始化输出操作相关的资源, // 具体怎么输出由子类决定,例如窗口预览、存视频文件等 initOutput(); log.info("初始化完成,耗时[{}]毫秒,帧率[{}],图像宽度[{}],图像高度[{}]", System.currentTimeMillis()-startTime, frameRate, cameraImageWidth, cameraImageHeight); } /** * 执行抓取和输出的操作 */ public void action(int grabSeconds) { try { // 初始化操作 init(); // 持续拉取和推送 grabAndOutput(grabSeconds); } catch (Exception exception) { log.error("execute action error", exception); } finally { // 无论如何都要释放资源 safeRelease(); } } } 5.7.1 保存摄像头视频为mp4 package cv; import org.bytedeco.ffmpeg.global.avcodec; import org.bytedeco.javacv.FFmpegFrameRecorder; import org.bytedeco.javacv.Frame; import org.bytedeco.javacv.FrameRecorder; import static org.bytedeco.ffmpeg.global.avutil.AV_PIX_FMT_YUV420P; /** * TODO * * @author majun * @version 1.0 * @since 2023-10-11 22:13 */ public class CameraMp4Recorder extends AbstractCameraApplication{ protected FrameRecorder recorder; @Override protected void initOutput() throws Exception { // 实例化FFmpegFrameRecorder recorder = new FFmpegFrameRecorder("CameraMp4Recorder.mp4", // 存放文件的位置 getCameraImageWidth(), // 分辨率的宽,与视频源一致 getCameraImageHeight(), // 分辨率的高,与视频源一致 0); // 音频通道,0表示无 // 文件格式 recorder.setFormat("mp4"); // 帧率与抓取器一致 recorder.setFrameRate(getFrameRate()); // 编码格式 recorder.setPixelFormat(AV_PIX_FMT_YUV420P); // 编码器类型 recorder.setVideoCodec(avcodec.AV_CODEC_ID_MPEG4); // 视频质量,0表示无损 recorder.setVideoQuality(0); // 初始化 recorder.start(); } @Override protected void output(Frame frame) throws Exception { recorder.record(frame); } @Override protected void releaseOutputResource() throws Exception { recorder.close(); } public static void main(String[] args) { new CameraMp4Recorder().action(10); } } 5.7.2 摄像头抓图 package cv; import lombok.extern.slf4j.Slf4j; import org.bytedeco.javacv.Frame; import org.bytedeco.javacv.Java2DFrameConverter; import javax.imageio.ImageIO; import java.awt.image.BufferedImage; import java.io.FileOutputStream; /** * TODO * * @author majun * @version 1.0 * @since 2023-10-11 22:35 */ @Slf4j public class CameraImageGraber extends AbstractCameraApplication{ private Java2DFrameConverter converter = new Java2DFrameConverter(); @Override protected void initOutput() throws Exception { } @Override protected void output(Frame frame) throws Exception { // 把帧对象转为Image对象 BufferedImage bufferedImage = converter.getBufferedImage(frame); ImageIO.write(bufferedImage, "jpg", new FileOutputStream(System.currentTimeMillis()+".jpg")); } @Override protected void releaseOutputResource() throws Exception { } @Override protected int getInterval() { // 每秒1抓 return 1000; } public static void main(String[] args) { // 连续十秒执行抓图操作 new CameraImageGraber().action(10); } } 5.7.3 摄像头推流 类似之前的本地mp4推流到SRS package cv; import org.bytedeco.ffmpeg.global.avcodec; import org.bytedeco.javacv.Frame; import org.bytedeco.javacv.FrameRecorder; /** * TODO * * @author majun * @version 1.0 * @since 2023-10-11 22:52 */ public class CameraPushSRS extends AbstractCameraApplication{ private static final String RECORD_ADDRESS = "rtmp://192.168.72.126:1935/hls/camera"; protected FrameRecorder recorder; protected long startRecordTime = 0L; @Override protected void initOutput() throws Exception { // 实例化FFmpegFrameRecorder,将SRS的推送地址传入 recorder = FrameRecorder.createDefault(RECORD_ADDRESS, getCameraImageWidth(), getCameraImageHeight()); // 降低启动时的延时,参考 // https://trac.ffmpeg.org/wiki/StreamingGuide) recorder.setVideoOption("tune", "zerolatency"); // 在视频质量和编码速度之间选择适合自己的方案,包括这些选项: // ultrafast,superfast, veryfast, faster, fast, medium, slow, slower, veryslow // ultrafast offers us the least amount of compression (lower encoder // CPU) at the cost of a larger stream size // at the other end, veryslow provides the best compression (high // encoder CPU) while lowering the stream size // (see: https://trac.ffmpeg.org/wiki/Encode/H.264) // ultrafast对CPU消耗最低 recorder.setVideoOption("preset", "ultrafast"); // Constant Rate Factor (see: https://trac.ffmpeg.org/wiki/Encode/H.264) recorder.setVideoOption("crf", "28"); // 2000 kb/s, reasonable "sane" area for 720 recorder.setVideoBitrate(2000000); // 设置编码格式 recorder.setVideoCodec(avcodec.AV_CODEC_ID_H264); // 设置封装格式 recorder.setFormat("flv"); // FPS (frames per second) // 一秒内的帧数 recorder.setFrameRate(getFrameRate()); // Key frame interval, in our case every 2 seconds -> 30 (fps) * 2 = 60 // 关键帧间隔 recorder.setGopSize((int)getFrameRate()*2); // 帧录制器开始初始化 recorder.start(); } @Override protected void output(Frame frame) throws Exception { if (0L==startRecordTime) { startRecordTime = System.currentTimeMillis(); } recorder.setTimestamp(1000 * (System.currentTimeMillis()-startRecordTime)); recorder.record(frame); } @Override protected void releaseOutputResource() throws Exception { recorder.close(); } @Override protected int getInterval() { // 相比本地预览,推流时两帧间隔时间更短 return super.getInterval()/4; } public static void main(String[] args) { new CameraPushSRS().action(10); } } 5.8 人脸识别训练及预测 常见的场景就是公司的门禁系统实现:javacv训练员工人脸图片得到模型,摄像头采集到人脸后使用模型进行预测判断是否为公司员工。 xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd"> 5.8.1 使用Javacv训练人脸识别模型 package cv; import lombok.SneakyThrows; import org.bytedeco.opencv.global.opencv_imgcodecs; import org.bytedeco.opencv.opencv_core.Mat; import org.bytedeco.opencv.opencv_core.MatVector; import org.bytedeco.opencv.opencv_core.Size; import org.bytedeco.opencv.opencv_face.FisherFaceRecognizer; import java.nio.IntBuffer; import java.nio.file.Files; import java.nio.file.Paths; import java.util.Arrays; import java.util.concurrent.atomic.AtomicInteger; import static org.bytedeco.opencv.global.opencv_core.CV_32SC1; import static org.bytedeco.opencv.global.opencv_imgproc.*; public class Training { @SneakyThrows public static void main(String[] args) { // 网上找的30张刘德华存到D:\\1 30张刘亦菲存到D:\\2 ,图片尽量找质量好一点,找多一些或直接找开源人脸检测人脸识别的数据集 int imageNum = 60; // 用于存放60张图片矩阵 MatVector images = new MatVector(imageNum); Mat lables = new Mat(imageNum, 1, CV_32SC1); IntBuffer lablesBuf = lables.createBuffer(); AtomicInteger counter = new AtomicInteger(0); // 读取两个文件夹图片矩阵,调整shape,图片灰度化。文件夹名就是训练 for (String dir : Arrays.asList("D:\\1", "D:\\2")) { Files.list(Paths.get(dir)).map(path -> opencv_imgcodecs.imread(path.toFile().getAbsolutePath(), 1)).forEachOrdered( mat -> { Mat resizedMat = new Mat(); resize(mat, resizedMat, new Size(300, 400));// 调整shape,百度图片另存为的那些图片大概就300*400 Mat grayMat = new Mat(); cvtColor(resizedMat, grayMat, COLOR_RGB2GRAY);//灰度 int currentIndex = counter.getAndIncrement(); images.put(currentIndex, grayMat); lablesBuf.put(currentIndex, Integer.parseInt(dir.substring(dir.length() - 1))); }); } //创建人脸分类器,有Fisher、Eigen、LBPH FisherFaceRecognizer fr = FisherFaceRecognizer.create(); //训练人脸模型 fr.train(images, lables); //保存训练结果 fr.save("faceRecognize.xml"); fr.close(); } } 5.8.2 使用模型预测人脸照片 package cv; import lombok.SneakyThrows; import org.bytedeco.javacpp.DoublePointer; import org.bytedeco.javacpp.IntPointer; import org.bytedeco.javacv.CanvasFrame; import org.bytedeco.javacv.Frame; import org.bytedeco.javacv.Java2DFrameConverter; import org.bytedeco.javacv.OpenCVFrameConverter; import org.bytedeco.opencv.opencv_core.*; import org.bytedeco.opencv.opencv_face.FisherFaceRecognizer; import org.bytedeco.opencv.opencv_objdetect.CascadeClassifier; import javax.imageio.ImageIO; import javax.swing.*; import java.awt.image.BufferedImage; import java.io.File; import java.nio.file.Files; import java.nio.file.Paths; import java.util.List; import java.util.Random; import java.util.stream.Collectors; import static org.bytedeco.opencv.global.opencv_imgproc.*; public class Inference { @SneakyThrows public static void main(String[] args) { // 加载模型 FisherFaceRecognizer faceRecognizer = FisherFaceRecognizer.create(); faceRecognizer.read("faceRecognize.xml"); //输入人脸与模型中的人脸(这里是1、2)的欧氏距离?小于设定的阈值才会被判断为该人脸 faceRecognizer.setThreshold(1300.0); // 新建一个窗口 CanvasFrame canvas = new CanvasFrame("人脸检测"); canvas.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); OpenCVFrameConverter.ToMat convertor = new OpenCVFrameConverter.ToMat();//用于类型转换 while (canvas.isEnabled()) { Mat grayImage = new Mat(); Mat face = new Mat(); List File file = new File(toTests.get(new Random().nextInt(toTests.size()))); BufferedImage image = ImageIO.read(file); Java2DFrameConverter imageConverter = new Java2DFrameConverter(); Frame imgFrame = imageConverter.convert(image); //类型转换 OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat(); Mat scr = converter.convertToMat(imgFrame); cvtColor(scr, grayImage, COLOR_RGB2GRAY);//摄像头是彩色图像,所以先灰度化下 //读取opencv人脸检测器,参考我的路径改为自己的路径 CascadeClassifier cascade = new CascadeClassifier( "E:\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml"); //检测人脸 RectVector faces = new RectVector(); cascade.detectMultiScale(grayImage, faces); IntPointer label = new IntPointer(1); DoublePointer confidence = new DoublePointer(1); //识别人脸,一张图可能多个人脸 for (int i = 0; i < faces.size(); i++) { Rect rect = faces.get(i); rectangle(scr, rect, new Scalar(0, 255, 0, 1)); // 带框选的灰度图 Mat grayImageWithRectangle = new Mat(grayImage, rect); resize(grayImageWithRectangle, face, new Size(300, 400));//同训练模型的设定 faceRecognizer.predict(face, label, confidence); int predictedLabel = label.get(0);//预测结果 System.out.println(predictedLabel); System.gc(); // 内存使用飙升 //判断预测结果 int pos_x = Math.max(rect.tl().x() - 10, 0); int pos_y = Math.max(rect.tl().y() - 10, 0); putText(scr, predictedLabel == 1 ? "LDF" : predictedLabel == 2 ? "LYF" : "Unknown", new Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, new Scalar(0, 255, 0, 2.0)); } //显示 Frame frame = convertor.convert(scr); canvas.showImage(frame);// 显示有框选及判断Text的图片到窗口 Thread.sleep(100);//100毫秒刷新一次图像 } } } 5.8.3 只需要将图片读取人脸改为摄像头抓取即可实现人脸检测并识别 OpenCVFrameGrabber grabber = new OpenCVFrameGrabber(0); grabber.setImageWidth(300); grabber.setImageHeight(400); grabber.start(); Frame frame=grabber.grab(); 好文链接
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