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Monocular Depth Estimation

gst-ai-monodepth performs monocular depth estimation on each frame of a video stream, generating a depth map rendered as a heatmap overlay. Warm colors (red/orange) indicate closer distances; cool colors (blue) indicate farther distances.

Uses the MiDaS V2 model.

Prerequisites​

Steps​

1. Verify Model and Labels​

radxa@airbox$
ls -l /etc/models/midas_quantized.tflite
ls -l /etc/labels/monodepth.json

2. View Configuration​

radxa@airbox$
cat /etc/configs/config_monodepth.json

Key fields:

FieldDefaultDescription
file-path/etc/media/video.mp4Input video path
ml-frameworktfliteInference framework
model/etc/models/midas_quantized.tfliteModel file
labels/etc/labels/monodepth.jsonColor mapping file
runtimedspInference hardware

3. Run​

radxa@airbox$
gst-ai-monodepth --config-file=/etc/configs/config_monodepth.json

Press Ctrl + C to stop.

Expected Output​

Terminal output:

Running app with model: /etc/models/midas_quantized.tflite and labels: /etc/labels/monodepth.json
Using DSP Delegate
VERBOSE: Replacing 140 out of 140 node(s) with delegate (TfLiteQnnDelegate) node, yielding 1 partitions for the whole graph.
Pipeline state changed from PAUSED to PLAYING

The display shows the test video overlaid with a depth heatmap. Warm colors indicate nearby objects; cool colors indicate distant background.

Validation​

  • Using DSP Delegate: Inference running on NPU
  • Replacing 140 out of 140 node(s): All 140 operators delegated to DSP
  • Pipeline reaches PLAYING state
  • Display correctly shows depth heatmap

How It Works​

MiDaS (Monocular Depth Estimation) takes a single RGB image as input and outputs relative depth values for each pixel. The GStreamer pipeline:

filesrc → qtdemux → h264parse → v4l2h264dec
↓ ↓
(tee split) qtimlvconverter (preprocess)
↓
qtimltflite (DSP inference)
↓
post-process (depth → heatmap)
↓
qtivcomposer
↓
waylandsink

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