# Image classification and display with Neural Processing SDK The use cases implement an Inceptionv3 model with Qualcomm Neural Processing SDK to classify scenes, either overlay or compose the classification labels, and then display the results. You can use any publicly available classification model with TensorFlow and convert it to the `.dlc` format as described in [TensorFlow Model Conversion](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-2/model_conv_tensorflow.html). ## Use qtivoverlay plugin to apply classification overlay Run the use case on the target device: gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! queue ! tee name=split \ split. ! queue ! qtimetamux name=metamux ! queue ! qtivoverlay ! queue ! waylandsink fullscreen=true sync=false \ split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp model=/etc/ models/inceptionv3.dlc ! queue ! \ qtimlpostprocess settings="{\"confidence\": 40.0}" results=2 module=mobilenet-softmax labels=/etc/labels/classification.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: - Classify scenes from a video stream coming through a file source. - Overlay classification labels 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 classification 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/80-80021-50/topic/v4l2h264dec.html) | Decodes the video. | | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-80021-50/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 classification model uses this tensor stream for inferencing.



| | **Inferencing** | **Inferencing** | | [qtimlsnpe](https://docs.qualcomm.com/doc/80-80021-50/topic/qtimlsnpe.html) |

  1. Loads the 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 inference results on its source pad.


| | **Postprocessing** | **Postprocessing** | | [qtimlpostprocess](https://docs.qualcomm.com/doc/80-80021-50/topic/qtimlpostprocess.html) |

  1. Receives the inference tensors from the model on its sinkpad.


  2. Converts the tensors into formats such as 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 of the classification models.


    In this use case, qtimlpostprocess does the following:




    1. Loads the submodule of the model.


    2. Produces results as structures of text.


    3. Sends them to the sinkpad of qtimetamux.





| | [qtimetamux](https://docs.qualcomm.com/doc/80-80021-50/topic/qtimetamux.html) |

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


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


  3. Adds classification result from data sinkpad to GST buffer meta (meta muxing) on its source pad.


| | [qtivoverlay](https://docs.qualcomm.com/doc/80-80021-50/topic/qtioverlay.html) |

  1. Receives the multiplexed stream.


  2. Overlays the classification labels on the VideoFrame using CL.


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


| | **Output** | **Output** | | [Waylandsink](https://docs.qualcomm.com/doc/80-80021-50/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 possible classifications generated for that scene on a local display device.


| ## Use qtivcomposer to mix original frame with classification mask Run the use case on the target device: gst-launch-1.0 -e --gst-debug=2 filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! queue ! \ tee name=split split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<320, 320>" ! queue ! waylandsink fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp model=/etc/ models/inceptionv3.dlc ! queue ! \ qtimlpostprocess settings="{\"confidence\": 40.0}" results=2 module=mobilenet-softmax labels=/etc/labels/classification.json ! \ video/x-raw,format=BGRA,width=640,height=360 ! queue ! mixer. Copy to clipboard To stop the use case, use **CTRL + C**. The following figure shows the flow of the use case execution: - Classify scenes from a video stream coming through a file source. - Compose classification labels and video stream together using qtivcomposer. - Display the results to a local display. Qualcomm Open source tee qtimlvconverter qtimlsnpe qtimlpostprocess qtivcomposer waylandsink filesrc qtdemux h264parse v4l2h264dec **Figure : Pipeline for classification using 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/80-80021-50/topic/v4l2h264dec.html) | Decodes the video. | | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-80021-50/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 a model expects the floating point values as input






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


    The classification model uses this tensor stream for inferencing.



| | **Inferencing** | **Inferencing** | | [qtimlsnpe](https://docs.qualcomm.com/doc/80-80021-50/topic/qtimlsnpe.html) |

  1. Loads the 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 inference results on its source pad.


| | **Postprocessing** | **Postprocessing** | | qtimlpostprocess |

  1. Receives the inference results from the model on its sinkpad.


  2. Converts the inference tensors 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 the classification models.


    In this use case, qtimlpostprocess does the following:




    1. Loads the submodule of the model.


    2. Produces results as video frames with classification labels.


    3. Sends them to the sinkpad of qtivcomposer.





| | [qtivcomposer](https://docs.qualcomm.com/doc/80-80021-50/topic/qtivcomposer.html) |

  1. Receives the original video stream with classification results on its sinkpads.


  2. On its sourcepad, produces GST buffers with contents composed of video streams from its sinkpads.


| | **Output** | **Output** | | [Waylandsink](https://docs.qualcomm.com/doc/80-80021-50/topic/waylandsink.html) |

  1. Receives the video in its sinkpad


  2. Submits the video stream to Weston.


  3. Weston renders the video stream and possible classifications generated for that scene on a local display device.


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