Merge remote-tracking branch 'origin/master'
This commit is contained in:
43
README.md
43
README.md
@@ -1,40 +1,3 @@
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<p align="center">
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<img alt="logo" src="https://gdhxkj.oss-cn-guangzhou.aliyuncs.com/file/2025/01/16/蒜头王八_20250116174410A005.png" style="width: 80px;">
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</p>
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<h1 align="center" style="margin: 30px 0 30px; font-weight: bold;">rtsp视频分析 v1.0.0</h1>
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<h4 align="center">基于SpringBoot+Vue前后端分离的rtsp视频分析系统</h4>
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## 平台简介
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这是一个基于ruoyi-vue框架改进而来的RTSP视频分析平台，继承ruoyi框架的基本功能。项目集成了虹软SDK，实现了人脸识别、活体检测、3D角度分析、年龄及性别识别等功能；同时，利用JavaCV进行高效的视频处理，将rtsp视频转成http-flv和ws-flv。后端采用SpringBoot框架，前端则运用了Vue3框架，确保系统的稳定与用户体验的流畅。
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* 前端采用Vue3、Element Plus、XgpLayer。
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* 后端采用Spring Boot、Spring Security、Redis 、 javaCv & Jwt。
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加微信联系: chenbai0511
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## 内置功能
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1. 若依全功能。
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2. rtsp视频转http-flv，ws-flv在线播放。
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3. rtsp视频人脸识别，活体检测，3D角度分析，年龄及性别识别。
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## 演示图
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<table>
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<tr>
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<td><img src="https://gdhxkj.oss-cn-guangzhou.aliyuncs.com/file/2025/01/16/g1_20250116174656A007.png"/></td>
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</tr>
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<tr>
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<td><img src="https://gdhxkj.oss-cn-guangzhou.aliyuncs.com/file/2025/01/16/g2_20250116174755A009.png"/></td>
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</tr>
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<tr>
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<td><img src="https://gdhxkj.oss-cn-guangzhou.aliyuncs.com/file/2025/01/16/g3_20250116174816A011.png"/></td>
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</tr>
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</table>
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## 联系我吧
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<img src="https://gdhxkj.oss-cn-guangzhou.aliyuncs.com/file/2025/01/16/17c8bc62cbd5b58c27775e0c2ff83bd_20250116174058A003.jpg" style="width: 500px; display: block; margin: auto;"/>
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qwq
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java 17
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node16 .20
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opencv推测4.10
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2
pom.xml
2
pom.xml
@@ -42,7 +42,7 @@
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<dependency>
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<groupId>org.bytedeco</groupId>
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<artifactId>javacv-platform</artifactId>
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<version>1.5.10</version> <!-- 版本号需与项目兼容 -->
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<version>1.5.12</version> <!-- 版本号需与项目兼容 -->
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</dependency>
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<!-- SpringBoot的依赖配置-->
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<dependency>
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@@ -7,7 +7,7 @@ ruoyi:
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# 版权年份
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copyrightYear: 2025
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# 文件路径 示例（ Windows配置D:/ruoyi/uploadPath，Linux配置 /home/ruoyi/uploadPath）
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profile: /home/wangyu/uploadPath
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profile: D:\temp
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# 获取ip地址开关
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addressEnabled: false
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# 验证码类型 math 数字计算 char 字符验证
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@@ -0,0 +1 @@
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trash
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BIN
ruoyi-admin/src/main/resources/libs/models/garbage/garbage.onnx
Normal file
BIN
ruoyi-admin/src/main/resources/libs/models/garbage/garbage.onnx
Normal file
Binary file not shown.
5
ruoyi-admin/src/main/resources/libs/models/models.json
Normal file
5
ruoyi-admin/src/main/resources/libs/models/models.json
Normal file
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[
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{"name":"garbage","path":"libs/models/garbage","size":[640,640],"backend":"OpenCV"},
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{"name":"smoke","path":"libs/models/smoke","size":[640,640],"backend":"OpenCV"}
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]
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@@ -35,7 +35,12 @@
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<dependency>
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<groupId>org.bytedeco</groupId>
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<artifactId>javacv-platform</artifactId>
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<version>1.5.11</version>
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<version>1.5.12</version>
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</dependency>
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<dependency>
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<groupId>org.bytedeco</groupId>
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<artifactId>opencv-platform</artifactId>
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<version>4.11.0-1.5.12</version>
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</dependency>
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<!-- 解析 models.json-->
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@@ -201,16 +201,15 @@ public class MediaTransferFlvByJavacv extends MediaTransfer implements Runnable
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if (!enableDetection) return;
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modelManager = new ModelManager();
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URL json = getClass().getResource("/models/models.json");
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URL json = getClass().getResource("/libs/models/models.json");
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modelManager.load(json);
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// 你可按需切换单模型或多模型并行
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// detector = modelManager.get("person-helmet");
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detector = new CompositeDetector(
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"all-models",
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java.util.List.of(
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modelManager.get("person-helmet"),
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modelManager.get("vehicle-plate")
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modelManager.get("garbage"),
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modelManager.get("smoke")
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),
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2 // 并行度
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);
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@@ -345,6 +344,7 @@ public class MediaTransferFlvByJavacv extends MediaTransfer implements Runnable
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if (enableDetection) initDetectors();
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} catch (Exception e) {
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log.error("初始化检测模型失败：{}", e.getMessage(), e);
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// 模型失败不影响推流，但不禁用检测
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}
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if (!createGrabber()) return;
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@@ -367,7 +367,7 @@ public class MediaTransferFlvByJavacv extends MediaTransfer implements Runnable
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final long DETECTION_INTERVAL_MS = 3000; // 每3秒检测一次
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long lastDetectionTime = 0;
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List<Detection> currentDetections = Collections.emptyList(); // 当前显示的检测结果
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for (; running && grabberStatus && recorderStatus; ) {
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try {
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if (transferFlag) {
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@@ -395,48 +395,48 @@ public class MediaTransferFlvByJavacv extends MediaTransfer implements Runnable
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closeMedia();
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break;
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}
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if (frame != null && enableDetection) {
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// 将Frame转换为Mat以进行处理
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Mat mat = toMat.convert(frame);
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if (mat != null && !mat.empty()) {
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long currentTime = System.currentTimeMillis();
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// 每隔DETECTION_INTERVAL_MS执行一次检测
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if (currentTime - lastDetectionTime >= DETECTION_INTERVAL_MS) {
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try {
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log.debug("执行新一轮检测，上次检测时间: {}ms前",
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log.debug("执行新一轮检测，上次检测时间: {}ms前",
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currentTime - lastDetectionTime);
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// 创建副本进行检测
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Mat detectionMat = new Mat();
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mat.copyTo(detectionMat);
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// 执行检测
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currentDetections = detector.detect(detectionMat);
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lastDetectionTime = currentTime;
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latestDetections.set(currentDetections);
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// 释放检测Mat
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detectionMat.release();
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// 窗口巡检回调
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if (windowMode && detectionListener != null &&
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if (windowMode && detectionListener != null &&
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currentJobId != null && currentDeviceId != null) {
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detectionListener.onDetections(currentJobId,
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currentDeviceId,
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currentDetections,
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detectionListener.onDetections(currentJobId,
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currentDeviceId,
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currentDetections,
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currentTime);
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}
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log.debug("检测完成，发现 {} 个目标，框将保持3秒",
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log.debug("检测完成，发现 {} 个目标，框将保持3秒",
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currentDetections == null ? 0 : currentDetections.size());
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} catch (Exception e) {
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log.debug("检测异常: {}", e.getMessage());
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}
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}
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// 每一帧都使用最新的检测结果绘制框
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// 这样框会保持在原位置，直到下一次检测更新
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if (currentDetections != null && !currentDetections.isEmpty()) {
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@@ -447,24 +447,24 @@ public class MediaTransferFlvByJavacv extends MediaTransfer implements Runnable
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log.debug("绘制检测框异常: {}", e.getMessage());
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}
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}
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// 更新"最近叠好框的帧"用于存证
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updateLatestAnnotated(mat);
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// 统计（仅窗口巡检时）
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if (windowMode) updateStats(currentDetections);
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// 窗口结束判定
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if (windowMode && System.currentTimeMillis() >= windowEndMs) {
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finishWindow();
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}
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// 将处理后的Mat转换回Frame
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try {
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// 创建新的转换器
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OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat();
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Frame processedFrame = converter.convert(mat);
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if (processedFrame != null) {
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// 使用处理后的帧替换原始帧
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frame = processedFrame;
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@@ -473,7 +473,7 @@ public class MediaTransferFlvByJavacv extends MediaTransfer implements Runnable
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log.debug("Mat转Frame异常: {}", e.getMessage());
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// 如果转换失败，继续使用原始帧
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}
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// 释放Mat
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mat.release();
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}
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@@ -39,7 +39,7 @@ public final class OnnxYoloDetector implements YoloDetector {
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} else {
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this.classes = new String[0];
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}
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System.out.println("CV_VERSION = " + CV_VERSION);
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try {
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// 加载ONNX模型
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this.net = readNetFromONNX(onnx);
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@@ -25,15 +25,9 @@ public final class OpenVinoYoloDetector implements YoloDetector {
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this.input = new Size(inW, inH);
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this.colorBGR = colorBGR;
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// 自动查找模型文件
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String xml = findModelFile(dir, ".xml");
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String bin = findModelFile(dir, ".bin");
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String xml = dir.resolve("model.xml").toString();
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String bin = dir.resolve("model.bin").toString();
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if (xml == null || bin == null) {
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throw new Exception("找不到模型文件，请确保目录中存在 .xml 和 .bin 文件: " + dir);
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}
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// 读取类别文件
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Path clsPath = dir.resolve("classes.txt");
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if (Files.exists(clsPath)) {
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this.classes = Files.readAllLines(clsPath).stream().map(String::trim)
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@@ -42,34 +36,19 @@ public final class OpenVinoYoloDetector implements YoloDetector {
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this.classes = new String[0];
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}
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try {
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// 加载模型，但强制使用OpenCV后端
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this.net = readNetFromModelOptimizer(xml, bin);
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// 强制使用OpenCV后端，避免OpenVINO依赖
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this.net = readNetFromModelOptimizer(xml, bin);
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boolean set = false;
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if ("openvino".equalsIgnoreCase(backend)) {
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try {
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net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
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net.setPreferableTarget(DNN_TARGET_CPU);
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set = true;
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} catch (Throwable ignore) { /* 回退 */ }
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}
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if (!set) {
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(DNN_TARGET_CPU);
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System.out.println("模型加载成功: " + name + " (使用OpenCV后端)");
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} catch (Exception e) {
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throw new Exception("模型加载失败: " + e.getMessage() +
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"\n请确保模型文件完整且格式正确", e);
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}
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}
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/**
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* 在目录中查找指定扩展名的模型文件
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*/
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private String findModelFile(Path dir, String extension) {
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try {
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return Files.list(dir)
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.filter(path -> path.toString().toLowerCase().endsWith(extension.toLowerCase()))
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.map(Path::toString)
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.findFirst()
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.orElse(null);
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} catch (Exception e) {
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return null;
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}
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}
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@@ -248,4 +227,4 @@ public final class OpenVinoYoloDetector implements YoloDetector {
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}
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@Override public void close(){ net.close(); }
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}
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}
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