# 使用 Python 进行并行推理

Source: [https://docs.qualcomm.com/doc/80-70018-50SC/topic/parallel-inference-using-python.html](https://docs.qualcomm.com/doc/80-70018-50SC/topic/parallel-inference-using-python.html)

**gst-parallel-inference.py** 应用程序接收来自摄像头、文件或 RTSP 流的视频输入，并将其发送给 AI 模型执行四通道并行处理（分类、目标检测、姿态检测和分割）。输出显示为带有叠加 AI 模型的预览。

Note: 此应用在 QCS8275 上不支持。

Figure : gst-parallel-inference.py pipeline
            
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)

有关该 pipeline 中使用的插件的信息，请参见 [Pipeline 流](https://docs.qualcomm.com/doc/80-70018-50SC/topic/parallel-inference-using-python.html#parallel-inference-using-python__section_gcg_r3s_lbc)。

## 模型和标签文件

该表列出了运行应用程序之前设备上应可用的模型和标签文件。

Table : gst-parallel-inference.py 的默认模型和标签文件

| 推理 | 模型目录 | 标签目录 |
| --- | --- | --- |
| 目标检测 | /etc/models/YOLOv8-Detection-Quantized.tflite | /etc/labels/yolov8.labels |
| 姿态估计 | /etc/models/hrnet\_pose\_quantized.tflite | etc/labels/posenet\_mobilenet\_v1.labels |
| 分割 | /etc/models/deeplabv3\_plus\_mobilenet\_quantized.tflite | /etc/labels/deeplabv3\_resnet50.labels |
| 分类 | /etc/models/inception\_v3\_quantized.tflite | /etc/labels/classification.labels |

## 运行应用程序

1. 确保您已完成[前提条件](https://docs.qualcomm.com/doc/80-70018-50SC/topic/prerequisites-for-python-sample-applications.html)。
2. 确保[模型和标签文件](https://docs.qualcomm.com/doc/80-70018-50SC/topic/parallel-inference-using-python.html#parallel-inference-using-python__section_sz5_51j_pdc)在设备上可用。
3. 重命名 EVK 上的标签文件：

        cp /etc/labels/yolonas.labels /etc/labels/yolov8.labelsCopy to clipboard
4. 运行用例：
    - 来自摄像头的输入：

            gst-parallel-inference.py --cameraCopy to clipboard
    - 从文件输入：

            gst-parallel-inference.py --file "/etc/media/video.mp4"Copy to clipboard
    - 来自 RTSP 流的输入：

            gst-parallel-inference.py --rtsp "rtsp://<ip>:<port>/<stream>"Copy to clipboard
    - 来自具有目标检测常数的摄像头输入：

            gst-parallel-inference.py --camera --constants-detection "YOLOv8,q-offsets=<21.0, 0.0, 0.0>,q-scales=<3.093529462814331, 0.00390625, 1.0>;"Copy to clipboard
    - 来自具有分类常数的摄像头输入：

            gst-parallel-inference.py --camera --constants-classification "Mobilenet,q-offsets=<38.0>,q-scales=<0.15008972585201263>;"Copy to clipboard
    - 来自具有姿态检测常数的摄像头输入：

            gst-parallel-inference.py --camera --constants-pose "Posenet,q-offsets=<8.0>,q-scales=<0.0040499246679246426>;"Copy to clipboard
    - 来自具有分段常数的摄像头输入：

            gst-parallel-inference.py --camera --constants-segmentation "deeplab,q-offsets=<0.0>,q-scales=<1.0>;"Copy to clipboard

如需显示可用的帮助选项，可运行以下命令：

    gst-parallel-inference.py -hCopy to clipboard

## 预期输出

执行四个并行推理后，结果将并排显示在屏幕上。

Figure : gst-ai-parallel-inference.py 应用程序的预期输出
                
                ![](data:image/png;base64,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)

## Pipeline 流

该表列出了并行推理 pipeline 中使用的插件：| 插件 | 说明 |
| --- | --- |
| 摄像头源：[qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70018-50SC/topic/qtiqmmfsrc.html) | <ul class="ul" id="parallel-inference-using-python__ul_zyl_gj1_mcc"><br>                                    <li class="li">从摄像头采集实时流。</li><br><br>                                    <li class="li">使用 tee 拆分流进行推理。</li><br><br>                                </ul> |
| 文件源：filesrc | <ul class="ul" id="parallel-inference-using-python__ul_z1z_x4f_w1c"><br>                                    <li class="li">使用 filesrc 采集视频流，然后使用 qtdemux 对视频流进行解复用。</li><br><br>                                    <li class="li">使用 tee 拆分流进行推理。</li><br><br>                                </ul> |
| RTSP 源：rtspsrc | <ul class="ul" id="parallel-inference-using-python__ul_vsj_2r4_tbc"><br>                                    <li class="li">使用 rtspsrc 采集 RTSP 流，然后使用 rtph264depay 进行视频提取。</li><br><br>                                    <li class="li">使用 tee 拆分流进行推理。</li><br><br>                                </ul> |
| h264parse | 渲染 H.264 视频。 |
| [v4l2h264dec](https://docs.qualcomm.com/doc/80-70018-50SC/topic/v4l2h264dec.html) | 解码视频 |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70018-50SC/topic/qtimlvconverter.html) | <ol class="ol" id="parallel-inference-using-python__ol_kgt_hnq_nbc"><br>                                    <li class="li">在其接收端口上接收视频流。</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="parallel-inference-using-python__ol_drd_jnq_nbc"><br>                                            <li class="li">颜色转换</li><br><br>                                            <li class="li">缩放（放大或缩小）</li><br><br>                                            <li class="li">归一化</li><br><br>                                        </ol><br></li><br><br>                                </ol><br><br>                                <br>张量数据流用于 pipeline 后期的推理。 |
| [qtimltflite](https://docs.qualcomm.com/doc/80-70018-50SC/topic/qtimltflite.html) | <ol class="ol" id="parallel-inference-using-python__ol_l2x_zjq_nbc"><br>                                    <li class="li">推理 runtime 在其接收端口上接收到张量数据后，会运行推理。</li><br><br>                                    <li class="li">生成一个张量数据流，并在其发送端口上显示推理结果。</li><br><br>                                </ol> |
| 后处理插件 | 处理来自任何目标检测、分类、姿态检测和分割模型的推理结果。<ul class="ul" id="parallel-inference-using-python__ul_pgb_wpq_nbc"><br>                                    <li class="li"><a href="https://docs.qualcomm.com/doc/80-70018-50SC/topic/qtimlvdetection.html">qtimlvdetection</a><ol class="ol" id="parallel-inference-using-python__ol_ol3_dky_kbc"><br>                                            <li class="li">将阈值应用于所选结果数。</li><br><br>                                            <li class="li">加载 YOLOv8 模块。 </li><br><br>                                            <li class="li">生成仅包含边界框的视频帧，这些框可以叠加在对象上，并将它们发送到 qtivcomposer 的接收端口。</li><br><br>                                        </ol><br></li><br><br>                                    <li class="li"><a href="https://docs.qualcomm.com/doc/80-70018-50SC/topic/qtimlvclassification.html">qtimlvclassification</a><ol class="ol" id="parallel-inference-using-python__ol_nt3_jqq_nbc"><br>                                            <li class="li">将阈值应用于所选的结果数。 </li><br><br>                                            <li class="li">加载 MobileNet 模块。</li><br><br>                                            <li class="li">将结果生成为带有分类标签的视频帧，并将它们发送到 qtivcomposer 的接收端口。</li><br><br>                                        </ol><br></li><br><br>                                    <li class="li"><a href="https://docs.qualcomm.com/doc/80-70018-50SC/topic/qtimlvpose.html">qtimlvpose</a><ol class="ol" id="parallel-inference-using-python__ol_znf_mqq_nbc"><br>                                            <li class="li">将阈值应用于所选的结果数。</li><br><br>                                            <li class="li">为各种姿态估计模型加载相应的模块。对于本节中描述的用例，qtimlvpose 加载 PoseNet 模块。 </li><br><br>                                            <li class="li">将结果生成为带绘制姿势的视频帧，并将它们发送到 qtivcomposer 的接收端口。</li><br><br>                                        </ol><br></li><br><br>                                    <li class="li"><a href="https://docs.qualcomm.com/doc/80-70018-50SC/topic/qtimlvsegmentation.html">qtimlvsegmentation</a>：将接收端口上接收到的推理张量转换为视频格式，由多媒体插件进行后续处理。</li><br><br>                                </ul> |
| [qtivcomposer](https://docs.qualcomm.com/doc/80-70018-50SC/topic/qtivcomposer.html) | <ol class="ol" id="parallel-inference-using-python__ol_dmb_2vr_lbc"><br>                                    <li class="li">将接收端口获取的内容组成帧。</li><br><br>                                    <li class="li">将包含这些组合帧的 GStreamer 缓存推送到其发送端口。</li><br><br>                                </ol> |
| [Waylandsink](https://docs.qualcomm.com/doc/80-70018-50SC/topic/waylandsink.html) | <ol class="ol" id="parallel-inference-using-python__ol_kjr_fvr_lbc"><br>                                    <li class="li">Waylandsink 将其接收端口上接收的视频流提交给 Weston。</li><br><br>                                    <li class="li">Weston 在本地显示器上渲染视频流。</li><br><br>                                </ol> |

**Parent Topic:** [Python 应用程序](https://docs.qualcomm.com/doc/80-70018-50SC/topic/python-sample-applications.html)

**Related Resources**  

- [并行推理](https://docs.qualcomm.com/doc/80-70018-50SC/topic/gst-ai-parallel-inference.html)

Last Published: Nov 12, 2025

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