Source Part Segmentation

MCP backend

Use when: Overlapping part extraction, component masks, marker/watershed segmentation, contour hierarchy analysis, atlas part isolation, manual seed fallback for source-locked Blender reconstruction.

Before the first Blender call, read ../text-to-blender/references/mcp-backends.md. Prefer official MCP for shared capabilities; preserve the selected backend for the whole task. Tool names in recipes are client/legacy examples: map them through that reference and its agent-host guide.

Source Part Segmentation

Use this before contour-to-mesh when a source image contains overlapping or touching designed parts. The output is not “nice masks”; it is a source-of-truth part inventory that downstream geometry must obey.

Inputs

• source image, wireframe, decal, or texture atlas;
• optional manual seed manifest with named parts, polygons, seed points, rough rectangles, or HSV/color ranges;
• source manifest with structural/decorative/context classification and expected part count.

Workflow

1. Choose the cleanest modality: alpha, edge, dark-line, bright-on-dark, color-band, or atlas region.
2. Extract contours and hierarchy to identify candidate objects, holes, nested details, and strokes.
3. If components touch, run distance-transform marker watershed first.
4. If watershed over/under-splits, switch to seeded segmentation:
   • create named part seeds (bbox, polygon, or seed_point + optional flood/HSV tolerance);
   • save one mask per named structural part;
   • mark ambiguous overlaps explicitly instead of merging them.
5. Classify masks as structural, decorative, face_feature, aura_context, or validation_only.
6. Pass structural masks to contour-to-mesh; pass feature masks/landmarks to landmark-fit-repair; pass atlas regions to atlas-uv-fitting.

Hard rules

• Do not infer repeated parts from symmetry; segment what the source shows.
• Do not merge overlapping components if the manifest expects separate structural meshes.
• Do not proceed to final modeling when part count differs between source images; write a conflict report or canonical policy.
• If automatic segmentation is ambiguous, write an ambiguity report and require or create manual seed rectangles/points.
• Keep stroke/line masks separate from filled-part masks; wireframe strokes are guides unless explicitly used as the contour boundary.

Seed manifest schema

jsonCopy code
{
  "schema": "source_part_seed_manifest.v1",
  "image": "path/to/source.png",
  "parts": [
    {"name":"leaf_top", "class":"structural", "bbox":[x,y,w,h], "mode":"non_background"},
    {"name":"face_shell", "class":"structural", "polygon":[[x,y],[x,y],...], "mode":"polygon"}
  ]
}

Allowed mode values: polygon, bbox, non_background, dark_lines, bright_on_dark, hsv_range.

Scripts

• scripts/segment_source_parts.py produces component masks and a JSON report from an image, with optional watershed.
• scripts/seeded_part_masks.py converts a named seed manifest into deterministic named masks and a part inventory.

Sources distilled

• OpenCV contours/hierarchy/moments are the base measurement layer.
• OpenCV distance transform + marker watershed is the first automated split method for touching components.
• Active contour refinement can improve a rough mask boundary after segmentation.