# 使用 RTSP 流进行解码和目标检测

Source: [https://docs.qualcomm.com/doc/80-70017-50SC/topic/rts-decode-ai-detection-yolo-v8-overlay-display.html](https://docs.qualcomm.com/doc/80-70017-50SC/topic/rts-decode-ai-detection-yolo-v8-overlay-display.html)

**gst-rtspsrc-detection-display.py** 脚本接收 RTSP 流作为源，对其进行解码，使用 [YOLOv8](https://github.com/ultralytics/ultralytics) LiteRT 模型从摄像头流中识别场景中的对象，并将边界框覆盖在检测到的对象上。结果显示在显示屏上。

Figure : 使用 RTSP 流进行解码和目标检测的 pipeline – 预览
            
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)

## 运行应用程序

1. 确保您已完成[前提条件](https://docs.qualcomm.com/doc/80-70017-50SC/topic/python-sample-applications.html#python-sample-applications__section_gm5_s5j_bdc)。
2. 在目标设备上运行目标检测脚本：
    1. 控制台 1：

            gst-launch-1.0 -e qtiqmmfsrc camera=0 ! v4l2h264enc capture-io-mode=5 output-io-mode=5 ! h264parse config-interval=1 ! qtirtspbinCopy to clipboard
    2. 控制台 2：

            python3 /usr/bin/gst-rtspsrc-detection-display.pyCopy to clipboard

Table : 用于使用 RTSP 流进行目标检测的模型和标签文件的默认目录

        | Runtime | 模型 | 标签 |
        | :--- | :--- | :--- |
        | LiteRT | <var class="keyword varname">/opt/data/YoloV8N_Detection_Quantized.tflite</var> | <var class="keyword varname">/opt/data/yolov8n.labels</var> |

    如需显示可用的帮助选项，可运行以下命令：

python3 gst-rtspsrc-detection-display.py --helpCopy to clipboard

## 预期输出

Figure : 使用 RTSP 流进行目标检测和显示的预期输出 – 预览
                
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)

## Pipeline 流

下表列出了使用 RTSP 流 pipeline 进行解码和目标检测中使用的插件：| 插件 | 说明 |
| --- | --- |
| rtspsrc | 从 <var class="keyword varname">rtsp://&lt;ip-address&gt;/live</var> 接收 RTSP 流。 |
| rtph264depay | 从 RTSP 流中提取视频数据。 |
| h264parse | 渲染 H.264 视频。 |
| [v4l2h264dec](https://docs.qualcomm.com/doc/80-70017-50SC/topic/v4l2h264dec.html) | 解码视频然后使用 tee 拆分流进行推理。 |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimlvconverter.html) | <ol class="ol" id="rts-decode-ai-detection-yolo-v8-overlay-display__ol_kgt_hnq_nbc"><br>                                    <li class="li">在其接收端上接收视频流。</li><br><br>                                    <li class="li">对流数据执行以下预处理。当模型需要浮点值作为输入时，会执行此预处理。<ol class="ol" type="a" id="rts-decode-ai-detection-yolo-v8-overlay-display__ol_drd_jnq_nbc"><br>                                            <li class="li">颜色转换</li><br><br>                                            <li class="li">缩放（向上或向下）</li><br><br>                                            <li class="li">归一化</li><br><br>                                        </ol><br></li><br><br>                                </ol><br><br>                                <br>目标检测模型使用张量数据进行推理。 |
| [qtimltflite](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimltflite.html) | <ol class="ol" id="rts-decode-ai-detection-yolo-v8-overlay-display__ol_u1l_cxl_vbc"><br>                                    <li class="li">加载模型。</li><br><br>                                    <li class="li">为选择的 delegate 修改图。</li><br><br>                                    <li class="li">在其接收端上接收张量数据。</li><br><br>                                    <li class="li">执行推理并在其发送端上生成包含推理结果的张量数据。</li><br><br>                                </ol> |
| [qtimlvdetection](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimlvdetection.html) | <ol class="ol" id="rts-decode-ai-detection-yolo-v8-overlay-display__ol_ky5_grn_vbc"><br>                                    <li class="li"> 接收来自目标检测模型的推理张量。 </li><br><br>                                    <li class="li">将接收端上的推理张量转换为视频或文本等格式，稍后可由多媒体插件进行处理。</li><br><br>                                    <li class="li">将阈值应用于所选的结果数。 </li><br><br>                                    <li class="li">加载检测模型的相应模块。 <p class="p">在此用例中，qtimlvdetection 执行以下操作：<br>                                            </p><ol class="ol" type="a" id="rts-decode-ai-detection-yolo-v8-overlay-display__ol_jcd_wnk_5bc"><br>                                            <li class="li">加载 YOLOv8 子模块。 </li><br><br>                                            <li class="li">将结果生成为文本结构。</li><br><br>                                            <li class="li">接着发送到 qtimetamux 的接收端。</li><br><br>                                        </ol><br></li><br><br>                                </ol> |
| [qtimetamux](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimetamux.html) | <ol class="ol" id="rts-decode-ai-detection-yolo-v8-overlay-display__ol_ll3_x5l_vbc"><br>                                    <li class="li">在接收端上接收视频流和文本流，以及与视频流相对应的边框结果。</li><br><br>                                    <li class="li">使用接收端中的视频流内容生成 GST 缓存。</li><br><br>                                    <li class="li">将边框作为 GstVideoRegionOfInterest 从数据接收端添加到其源接插口上的 GST 缓存元数据（元多路复用）。</li><br><br>                                </ol> |
| [qtioverlay](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtioverlay.html) | <ol class="ol" id="rts-decode-ai-detection-yolo-v8-overlay-display__ol_wst_y5l_vbc"><br>                                    <li class="li">接收多路复用流。</li><br><br>                                    <li class="li">使用 CL 将边框叠加在 VideoFrame 上。</li><br><br>                                    <li class="li">在其发送端上生成带有叠加层的 GST 缓存。</li><br><br>                                </ol> |
| [Waylandsink](https://docs.qualcomm.com/doc/80-70017-50SC/topic/waylandsink.html) | <ol class="ol" id="rts-decode-ai-detection-yolo-v8-overlay-display__ol_cgt_mwl_vbc"><br>                                    <li class="li">在其接收端上接收视频</li><br><br>                                    <li class="li">将视频流提交到 Weston。 </li><br><br>                                    <li class="li">Weston 在本地显示器设备上呈现视频流。</li><br><br>                                </ol> |

**上一级主题：** [Python 应用程序](https://docs.qualcomm.com/doc/80-70017-50SC/topic/python-sample-applications.html)

**相关概念**  

- [目标检测](https://docs.qualcomm.com/doc/80-70017-50SC/topic/gst-ai-object-detection.html)

Last Published: Nov 11, 2025

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