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name: visualization-and-demo description: "Render T-Rex2 detections, filter results, and operate the optional Gradio visual-prompt demo workflow." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Visualization and Demo

Use this sub-skill when a task already has T-Rex2 detection results or asks about the optional local Gradio demo. It covers drawing boxes/points, filtering by score, JSON detection schemas, and UI prompt-mode constraints.

Use this when

• Rendering a T-Rex2 detection dictionary with trex.visualize.
• Filtering scores, labels, and boxes before saving an annotated image.
• Converting raw API output into a simple image/count/annotation artifact.
• Debugging visualization errors, malformed detection JSON, or score/box length mismatches.
• Explaining or launching the local Gradio demo with gradio-image-prompter.
• Diagnosing Gradio prompt errors such as point-only input, both interactive and generic prompts, or missing target image.

Do not use this for

• Creating T-Rex2 visual prompt or embedding API payloads. Route to cloud-api-workflows.
• Live DeepDataSpace API calls, token handling, or API polling. Route to cloud-api-workflows.
• Package installation and setup-time torch/Gradio dependency repair. Start from the root skill and root troubleshooting reference.

Key boundaries

• trex.visualize expects a target dictionary with boxes, scores, and labels of equal length.
• Convert scores to NumPy or torch scalars before drawing; the renderer calls score.item(), so plain Python float scores can fail.
• Boxes are pixel coordinates [x1, y1, x2, y2], not normalized center-width-height boxes.
• The Gradio demo is a UI/server workflow. It requires optional UI dependencies and a cloud API token for live inference; do not launch it as a bounded smoke test unless the user explicitly asks.
• The Gradio prompt packers support rectangle prompts in practice; point-only prompts raise an error in the repo code.

Entry points

• Rendering schema and options: references/visualization-reference.md
• Local Gradio demo workflow: references/gradio-demo.md
• Visualization/UI failures: references/troubleshooting.md
• Reusable renderer: scripts/render_detections.py

Fast routing

1. If detections came from a bundled cloud script, pass the script's output JSON directly to render_detections.py; it reads either top-level detections or raw scores/labels/boxes.
2. If the user has Python lists from TRex2APIWrapper.postprocess, convert them to NumPy arrays or use render_detections.py, which does the conversion.
3. If the user needs a Gradio UI, read references/gradio-demo.md before launching; confirm dependencies, token, and prompt mode first.
4. If the user still needs detections, route to cloud-api-workflows before rendering.

Minimal rendering command

From the generated t-rex skill directory:

python sub-skills/visualization-and-demo/scripts/render_detections.py \
  --image images/target.jpg \
  --detections-json outputs/detections.json \
  --output-image outputs/annotated.jpg \
  --box-threshold 0.3 \
  --draw-score

Use --demo-fixture for a no-input smoke check that creates a tiny synthetic image and renders one box to the requested output path.