# Image classification and display with LiteRT Source: [https://docs.qualcomm.com/doc/80-70018-50/topic/single-camera-stream-with-image-classification-and-display.html](https://docs.qualcomm.com/doc/80-70018-50/topic/single-camera-stream-with-image-classification-and-display.html) The use cases use the Inceptionv3 LiteRT model to classify scenes from a single camera stream and either overlay or compose the classification labels. ## Use qtivoverlay plugin to apply classification overlay Run this use case: gst-launch-1.0 -e --gst-debug=2 qtiqmmfsrc name=camsrc ! video/x-raw,format=NV12_Q08C,width=1280,height=720,framerate=30/1 ! queue ! \ tee name=split split. ! queue ! qtimetamux name=metamux ! queue ! qtivoverlay ! queue ! waylandsink fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/inception_v3_quantized.tflite ! queue ! \ qtimlvclassification threshold=40.0 results=2 module=mobilenet labels=/etc/labels/classification.labels \ constants="Inceptionv3,q-offsets=<38.0>,q-scales=<0.17039915919303894>;" ! text/x-raw ! queue ! metamux.Copy to clipboard To stop the use case, use CTRL + C. Figure : Pipeline for classification overlay ![](data:image/png;base64,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) The figure shows the flow of the use case execution: 1. Classify scenes from a video stream coming through a camera source. 2. Overlay the classification labels using overlaylib. 3. Display the results. The table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70018-50/topic/qtiqmmfsrc.html) |

  1. Collects the video stream (source) and creates two copies of
    the source:


| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing:


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

    The classification model uses this tensor stream
    for inferencing.




| | **Inferencing** | **Inferencing** | | [qtimltflite](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimltflite.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** | | [qtimlvclassification](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimlvclassification.html) |

  1. Receives the inference tensors from a classification 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, qtimlvclassification 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/80-70018-50/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/80-70018-50/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/80-70018-50/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 this use case: gst-launch-1.0 -e --gst-debug=2 qtiqmmfsrc name=camsrc ! video/x-raw,format=NV12_Q08C,width=1280,height=720,framerate=30/1 ! queue ! \ tee name=split split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<480, 480>" ! queue ! waylandsink fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/inception_v3_quantized.tflite ! queue ! \ qtimlvclassification threshold=40.0 results=2 module=mobilenet labels=/etc/labels/classification.labels \ constants="Inceptionv3,q-offsets=<38.0>,q-scales=<0.17039915919303894>;" ! video/x-raw,format=BGRA,width=640,height=480 ! queue ! mixer.Copy to clipboard To stop the use case, select CTRL + C. Figure : Pipeline for classification with qtivcomposer ![](data:image/png;base64,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) The figure shows the flow of the use case execution: 1. Classify scenes from a video stream coming through a camera source. 2. Compose classification labels and video stream using qtivcomposer. 3. Display the results. The table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70018-50/topic/qtiqmmfsrc.html) |

  1. Collects the video stream (source) and creates two copies of
    the source:


| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing:


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

    The classification model uses this tensor stream
    for inferencing.




| | **Inferencing** | **Inferencing** | | [qtimltflite](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimltflite.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** | | [qtimlvclassification](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimlvclassification.html) |

  1. Receives the inference results from a classification 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, qtimlvclassification 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/80-70018-50/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/80-70018-50/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.


| **Parent Topic:** [LiteRT use cases](https://docs.qualcomm.com/doc/80-70018-50/topic/tensorflow-lite-use-cases.html) Last Published: Jan 30, 2026 [Previous Topic LiteRT use cases](https://docs.qualcomm.com/bundle/publicresource/80-70018-50/topics/tensorflow-lite-use-cases.md) [Next Topic Image classification and encode with LiteRT](https://docs.qualcomm.com/bundle/publicresource/80-70018-50/topics/single-camera-stream-with-image-classification-and-encode.md)