# 视频单目深度

Source: [https://docs.qualcomm.com/doc/80-70017-50SC/topic/mono-depth-from-video.html](https://docs.qualcomm.com/doc/80-70017-50SC/topic/mono-depth-from-video.html)

**gst-ai-monodepth** 应用程序可以从实时摄像头流、文件、或 RTSP 流的推断输入流的深度。

该图显示了一个 pipeline，该 pipeline 从接收端采集流、对视频数据进行预处理、并使用 AI 硬件运行推理。有关 pipeline 中使用的插件的信息，请参阅 [Pipeline 流](https://docs.qualcomm.com/doc/80-70017-50SC/topic/mono-depth-from-video.html#mono-depth-from-video__section_w3l_s1t_pbc)。

Figure :  gst-ai-monodepth pipeline
            
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)

## 前提条件

- [下载 Qualcomm Neural Processing SDK 的模型、标签和 config JSON 文件](https://docs.qualcomm.com/doc/80-70017-50SC/topic/ai-ml-sample-applications.html#ai-ml-sample-applications__section_chl_dgz_scc).
- [下载 LiteRT 和 Qualcomm AI Engine Direct 的模型、标签和 config JSON 文件](https://docs.qualcomm.com/doc/80-70017-50SC/topic/ai-ml-sample-applications.html#ai-ml-sample-applications__section_fsl_lgz_scc).
- 将模型和标签文件推送到设备以运行应用程序。

        scp midas_quantized.tflite midas_quantized.bin root@<ip-addr of the target device>:/opt/Copy to clipboard

    重命名设备上的 midas\_quantized.tflite 文件名：

        cp midas_quantized.tflite Midas-V2-Quantized.tfliteCopy to clipboard
- 要访问您的主机设备，请启用 SSH。相关说明，可参见[使用 SSH 登录](https://docs.qualcomm.com/bundle/publicresource/topics/80-70017-254/how_to.html#use-ssh)。 
注释： 如果 SSH 已启用，则可以跳过此步骤。

    进入 SSH shell 并运行用例：

        ssh root@<ip-addr of the target device>Copy to clipboard
- 启用显示屏：

        export XDG_RUNTIME_DIR=/dev/socket/weston && export WAYLAND_DISPLAY=wayland-1Copy to clipboard

## 运行应用程序

示例应用程序使用 /opt/config\_monodepth.json 文件读取输入参数。

要创建自己的 config JSON 文件，请使用 [config_monodepth.json](https://git.codelinaro.org/clo/le/platform/vendor/qcom-opensource/gst-plugins-qti-oss/-/blob/imsdk.lnx.2.0.0.r2-rel/gst-sample-apps/gst-ai-monodepth/config_monodepth.json?ref_type=heads) 作为参考。

要运行该应用程序，请使用以下格式的 config\_monodepth.json 文件：

    {
      "file-path": "<input video path>",
      "ml-framework": "<snpe, tflite, or qnn framework>",
      "model": "<path-to-model-file>",
      "labels": "<path-to-label-file>",
      "constants": "<model-constants-for-quantized-LiteRT-model>",
      "runtime": "<dsp, gpu, or cpu runtime>"
    }Copy to clipboard

    gst-ai-monodepth --config-file=/opt/config_monodepth.jsonCopy to clipboard

| 字段 | 值/描述 |
| --- | --- |
| **ml-framework** | **ml-framework** |
| `snpe` | 使用 Qualcomm Neural Processing SDK 模型。 |
| `tflite` | 使用 LiteRT 模型。 |
| `qnn` | 使用 Qualcomm AI Engine Direct 模型。 |
| **runtime** | **runtime** |
| `cpu` | 在 CPU 上运行。 |
| `gpu` | 在 GPU 上运行。 |
| `dsp` | 在数字信号处理器 (DSP) 上运行。 |
| **输入源** | **输入源** |
| `camera` | <ul class="ul" id="mono-depth-from-video__ul_o4w_v4k_pdc"><br>                                    <li class="li">0 – 主摄像头</li><br><br>                                    <li class="li">1 – 辅助摄像头</li><br><br>                                </ul> |
| `file-path` | 视频文件的路径。 |
| `rtsp-ip-port` | RTSP 流的地址，格式为 <u class="ph u"><var class="keyword varname">rtsp://&lt;ip&gt;:&lt;port&gt;/&lt;stream&gt;</var></u>。 |

关于模型和标签文件，请参阅 [示例模型和标签文件](https://docs.qualcomm.com/doc/80-70017-50SC/topic/mono-depth-from-video.html#mono-depth-from-video__section_ugx_n5g_4dc)。

使用 LiteRT 模型和 DSP runtime 运行应用程序，输入来自视频文件以及自定义模型和标签路径：

    {
        "file-path": "/opt/video.mp4",
        "ml-framework": "tflite",
        "model": "/opt/Midas-V2-Quantized.tflite",
        "labels": "/opt/monodepth.labels",
        "constants": "Midas,q-offsets=<0.0>,q-scales=<4.716535568237305>;",
        "runtime": "dsp"
      }Copy to clipboard

    gst-ai-monodepth --config-file=/opt/config_monodepth.jsonCopy to clipboard

要显示可用的帮助选项，请在 SSH shell 中运行以下命令：

    gst-ai-monodepth -hCopy to clipboard

如需停止用例，可按下 CTRL + C。

## 预期输出

叠加的模型输出流与实时流并排显示。

Figure : gst-ai-monodepth 应用程序的预期输出
                
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)

## Pipeline 流

该表列出了单目深度 pipeline 中使用的插件：

| 插件 | 说明 |
| --- | --- |
| 摄像头源：[qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtiqmmfsrc.html) | <ul class="ul" id="mono-depth-from-video__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="mono-depth-from-video__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="mono-depth-from-video__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/80-70017-50SC/topic/v4l2h264dec.html) | 解码视频 |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimlvconverter.html) | AI 处理流将其用于预处理：<ol class="ol" id="mono-depth-from-video__ol_j34_ddg_q1c"><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="mono-depth-from-video__ol_m5z_cpr_lbc"><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>                                    <li class="li">将预处理的视频流转换为其发送端上的张量数据流。</li><br><br>                                </ol><br><br>张量数据流用于 pipeline 后期的推理。 |
| 推理插件：[qtimlsnpe](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimlsnpe.html)、[qtimltflite](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimltflite.html) 和 [qtimlqnn](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimlqnn.html) | 使用 Midasv2 模型实现单目深度估计。<ol class="ol" id="mono-depth-from-video__ol_pyh_4jh_4dc"><br>                                    <li class="li">推理 runtime 在其接收端上接收张量数据。</li><br><br>                                    <li class="li">Runtime 执行推理。</li><br><br>                                    <li class="li">生成一个张量数据流，并在其发送端上显示推理结果。</li><br><br>                                </ol><br>用于处理推理的后处理插件来自 Midasv2 模型。 |
| [qtimlvsegmentation](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimlvsegmentation.html) | 将接收端上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。 |
| [qtivtransform](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtivtransform.html) | 将 Qualcomm 通用带宽压缩缓存转换为其发送端上的非通用带宽压缩缓存。这些缓存用于 Waylandsink 上的合成。 |
| [Waylandsink](https://docs.qualcomm.com/doc/80-70017-50SC/topic/waylandsink.html) | <ol class="ol" id="mono-depth-from-video__ol_kjr_fvr_lbc"><br>                                    <li class="li">Waylandsink 将其接收端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> |

## 示例模型和标签文件

| Runtime | 模型文件 | 标签文件 |
| --- | --- | --- |
| Qualcomm Neural Processing SDK | <var class="keyword varname">midasv2.dlc</var> | <var class="keyword varname">monodepth.labels</var> |
| LiteRT | <var class="keyword varname">midas_quantized.tflite</var> | <var class="keyword varname">monodepth.labels</var> |
| Qualcomm AI Engine Direct | <var class="keyword varname">midas_quantized.bin</var> | <var class="keyword varname">monodepth.labels</var> |
|  |  |  |
|  |  |  |

## 已知问题

Qualcomm Neural Processing 模型中观察到准确率问题。

**上一级主题：** [AI/ML 示例应用程序](https://docs.qualcomm.com/doc/80-70017-50SC/topic/ai-ml-sample-applications.html)

Last Published: Nov 11, 2025

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