# Image classification and display with LiteRT Source: [https://docs.qualcomm.com/doc/80-70022-50/topic/single-camera-stream-with-image-classification-and-display-with-litert.html](https://docs.qualcomm.com/doc/80-70022-50/topic/single-camera-stream-with-image-classification-and-display-with-litert.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 on the target device: 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 ! \ 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: 1. Classify scenes from a video stream coming through a camera source. 2. Overlay the classification labels using overlaylib. 3. Display the results. Figure : Pipeline for classification overlay Qualcomm Open source camsrc tee qtimlvconverter qtimltflite qtimlpostprocess qtivcomposer waylandsink The following table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70022-50/topic/qtiqmmfsrc.html) |

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


| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70022-50/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/80-70022-50/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** | | qtimlpostprocess |

  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, 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/80-70022-50/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/80-70022-50/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/80-70022-50/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 on the target device: 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 ! \ qtimlpostprocess settings="{\"confidence\": 40.0}" results=2 module=mobilenet-softmax labels=/etc/labels/classification.json ! \ video/x-raw,format=BGRA,width=640,height=480 ! queue ! mixer.Copy to clipboard To stop the use case, select CTRL + C. The following 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. Figure : Pipeline for classification with qtivcomposer Qualcomm Open source camsrc tee qtimlvconverter qtimltflite qtimlpostprocess qtivcomposer waylandsink The following table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70022-50/topic/qtiqmmfsrc.html) |

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


| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70022-50/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/80-70022-50/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** | | qtimlpostprocess |

  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, 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/80-70022-50/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/80-70022-50/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/80-70022-50/topic/tensorflow-lite-use-cases.html) Last Published: Feb 20, 2026 [Previous Topic LiteRT use cases](https://docs.qualcomm.com/bundle/publicresource/80-70022-50/topics/tensorflow-lite-use-cases.md) [Next Topic Image classification and encode with LiteRT](https://docs.qualcomm.com/bundle/publicresource/80-70022-50/topics/single-camera-stream-with-image-classification-and-encode.md)