# Object detection and display with Neural Processing SDK The use cases implement a `foot_track_net-person-foot-detection.dlc` object detection model with Qualcomm Neural Processing SDK to identify an object from a file stream. The use case is to overlay or compose the bounding boxes over the detected objects, and the display the results. Download [Foot Track Net](https://aihub.qualcomm.com/iot/models/foot_track_net) Qualcomm AI runtime w8a8 precision model from AI hub. ## Use qtivoverlay plugin to apply bounding box overlay Run the use case on the target device: gst-launch-1.0 --gst-debug=2 filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtimetamux name=metamux ! queue ! qtivoverlay ! queue ! waylandsink fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp tensors="" model=/etc/models/foot_track_net-person-foot-detection-w8a8.dlc ! queue ! qtimlpostprocess name=stage_01_postproc results=10 module=qpd labels=/etc/labels/foot_track_net.json settings=/etc/labels/foot_track_net_settings.json ! text/x-raw ! queue ! metamux. Copy to clipboard To stop the use case, use **CTRL + C**. The following figure shows the flow of the use case execution: - Identify an object in a scene from a video stream coming through file source. - Overlay the bounding boxes over the detected objects using overlaylib. - Display the results on a local display. Qualcomm Open source tee qtimlvconverter qtimlsnpe qtimlpostprocess qtimetamux qtivoverlay waylandsink filesrc qtdemux h264parse v4l2h264dec **Figure : Pipeline for bounding box overlay** The following table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | File source: filesrc |

  1. Captures the video stream using filesrc, followed by qtdemux, which demultiplexes the stream.


  2. Uses tee to split the stream for inferencing.


| | h264parse | Parses the H.264 video. | | [v4l2h264dec](https://docs.qualcomm.com/doc/80-80022-50/topic/v4l2h264dec.html) | Decodes the video. | | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing:


    • Color conversion


    • Scaling down/up


    • Normalization on the stream data when the model expects the floating point values as input




  3. Converts the video stream to a tensor stream on its source pad.


    The object detection model uses this tensor stream for inferencing.



| | **Inferencing** | **Inferencing** | | [qtimlsnpe](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlsnpe.html) |

  1. Loads the object detection model.


  2. Modifies the graph for the chosen delegate.


  3. Receives the tensor stream on its sinkpad.


  4. Runs the inference and produces a tensor stream with the object detection results on its source pad.


| | **Postprocessing** | **Postprocessing** | | [qtimlpostprocess](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlpostprocess.html) |

  1. Receives the inference tensors from the object detection model.


  2. Converts the inference tensors on its sinkpad into formats like video or text that the multimedia plugins can process later.


  3. Applies the threshold to the chosen number of results.


  4. Loads the corresponding modules for detection models.


    In this use case, qtimlpostprocess does the following:


    1. Loads YOLOv8 submodule.


    2. Produces results as structures of text.


    3. Sends them to the sinkpad of qtimetamux.




| | [qtimetamux](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimetamux.html) |

  1. Receives video stream and text stream with bounding box results corresponding to the video stream on its sinkpads.


  2. Produces GST buffers with contents of the video stream from its sink pad.


  3. Adds the bounding boxes as GstVideoRegionOfInterest from data sinkpad to GST buffers meta (meta muxing) on its source pad.


| | [qtivoverlay](https://docs.qualcomm.com/doc/80-80022-50/topic/qtioverlay.html) |

  1. Receives the multiplexed stream.


  2. Overlays the bounding boxes on the VideoFrame using CL.


  3. Produces GST buffers with overlays in its source pad.


| | **Output** | **Output** | | [Waylandsink](https://docs.qualcomm.com/doc/80-80022-50/topic/waylandsink.html) |

  1. Receives the video stream on its sinkpad.


  2. Submits the video stream to Weston.


  3. Weston renders the video stream and bounding boxes generated for the objects in that scene on a local display device.


| ## Use qtivcomposer to mix original frame with bounding box mask Run the use case on the target device: gst-launch-1.0 --gst-debug=2 filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split split. ! queue ! qtivcomposer name=mixer ! queue ! waylandsink sync=true fullscreen=true split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp tensors="" model=/etc/models/foot_track_net-person-foot-detection-w8a8.dlc ! queue ! qtimlpostprocess name=stage_01_postproc results=10 module=qpd labels=/etc/labels/foot_track_net.json settings=/etc/labels/foot_track_net_settings.json ! video/x-raw,format=BGRA,width=960,height=540 ! queue ! mixer. Copy to clipboard To stop the use case, use **CTRL + C**. The following figure shows the flow of the use case execution: 1. Identifies object scenes in the scene from a video stream, which is coming through a file source. 2. Composes the following using qtivcomposer: 1. Bounding boxes over objects detected. 2. Original video stream. 3. Display the results. Qualcomm Open source tee qtimlvconverter qtimlsnpe qtimlpostprocess qtivcomposer waylandsink filesrc qtdemux h264parse v4l2h264dec **Figure : Pipeline for bounding box mask with qtivcomposer** The following table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | File source: filesrc |

  1. Captures the video stream using filesrc, followed by qtdemux, which demultiplexes the stream.


  2. Uses tee to split the stream for inferencing.


| | h264parse | Parses the H.264 video. | | [v4l2h264dec](https://docs.qualcomm.com/doc/80-80022-50/topic/v4l2h264dec.html) | Decodes the video. | | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing


    • Color conversion.


    • Scaling down/up.


    • Normalization on the stream data when the model expects the floating point values as input




  3. Converts the video stream to a tensor stream on its source pad.


    The object detection model uses this tensor stream for inferencing.



| | **Inferencing** | **Inferencing** | | [qtimlsnpe](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlsnpe.html) |

  1. Loads the object detection model.


  2. Modifies the graph for the chosen delegate.


  3. Receives the tensor stream on its sinkpad.


  4. Runs the inference and produces a tensor stream with the object detection results on its source pad.


| | **Postprocessing** | **Postprocessing** | | [qtimlpostprocess](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlpostprocess.html) |

  1. Receives the inference tensors from the objection detection model.


  2. Converts the inference tensors on its sinkpad into formats like video or text that the multimedia plugins can process later.


  3. Applies the threshold to the chosen number of results.


  4. Loads the corresponding modules for detection models.


    In this use case, qtimlpostprocess does the following:


    1. Loads the YOLOv8 submodule.


    2. Produces video frames with only bounding boxes that can be overlaid on objects.


    3. Sends them to the sinkpad of qtivcomposer.




| | [qtivcomposer](https://docs.qualcomm.com/doc/80-80022-50/topic/qtivcomposer.html) |

  1. Receives the original video stream and video stream with bounding boxes on its sinkpads.


  2. On its sourcepads, produces content that's composed of the video streams processed from its sinkpads.


| | **Output** | **Output** | | [Waylandsink](https://docs.qualcomm.com/doc/80-80022-50/topic/waylandsink.html) |

  1. Receives the video stream on its sinkpad.


  2. Submits the video stream to Weston.


  3. Weston displays the following on a local display device:


    1. The video stream is captured from the camera.


    2. The bounding boxes are drawn over the allowed number of objects identified in that scene.




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