# Image segmentation and display with TFLite Source: [https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-image-segmentation-and-display.html](https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-image-segmentation-and-display.html) The use case uses the `deeplabv3_resnet50` TFLite model to identify semantic segmentations in a scene from a camera stream, compose the semantics and original video stream using qtivcomposer, and then display the results. Run the use case: gst-launch-1.0 -e --gst-debug=2 \ qtiqmmfsrc name=camsrc ! video/x-raw\(memory:GBM\),format=NV12,width=1280,height=720,framerate=30/1,compression=ubwc ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::alpha=0.5 ! queue ! waylandsink fullscreen=true sync=false \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/opt/deeplabv3_resnet50.tflite ! queue ! qtimlvsegmentation module=deeplab-argmax labels=/opt/deeplabv3_resnet50.labels ! video/x-raw,width=256,height=144 ! queue ! mixer.Copy to clipboard To stop the use case, press CTRL + C. Figure : Pipeline for segmentation with qtivcomposer  The figure shows the flow of the use case execution: 1. Identify scenes from a video stream coming through a camera source. 2. Compose semantic segmentation 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/80-70015-50/topic/qtiqmmfsrc.html) |
The segmentation model uses this tensor stream for
inferencing.
In this use case, qtimlvsegmentation does the
following: