
Concept-prompted open-vocabulary segmentation on an Image. Each noun phrase is segmented and the sink resolves the projection: mask-plus-box pairs, masks, boxes, parallel arrays, a merged Mask, a UInt64 count, or a colourised label Image.
Segment a named concept and track the instances across frames.
Segment a concept and crop each instance out of the frame.
prompt must be a non-empty phrase, or a comma-separated list of non-empty phrases, at startup; an empty value fails to start. An invalid runtime update is rejected and the previous prompt is kept.
prompt are segmented one after another, so latency grows with the number of phrases per frame.
prompt_to_class_id assigns a class id per phrase so downstream arms can distinguish them; any phrase absent from the map falls back to class id zero.
model weights slot, so the component runs without runtime network access. Weights load in half precision on GPU to fit a single device; device falls back to CPU (full precision) with a warning when no CUDA runtime is present.
prompt is captured once at startup; prompt is re-read on each frame so it can be steered at runtime, while model and device need a redeploy to change.
Automated release