source-part-segmentation
Segment overlapping visual parts from source images, wireframes, texture atlases, and decals before mesh reconstruction. Use when a mascot/logo/template contains touching or overlapping components and exact structural part masks are needed before contour-to-mesh, UV fitting, or landmark repair.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/roble3/cc-blender-skill --skill source-part-segmentation- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads source-part-segmentation automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
- Choose the cleanest modality: alpha, edge, dark-line, bright-on-dark, color-band, or atlas region.
- Extract contours and hierarchy to identify candidate objects, holes, nested details, and strokes.
- If components touch, run distance-transform marker watershed first.
- If watershed over/under-splits, switch to seeded segmentation:
- create named part seeds (
bbox,polygon, orseed_point+ optional flood/HSV tolerance); - save one mask per named structural part;
- mark ambiguous overlaps explicitly instead of merging them.
- create named part seeds (
- Classify masks as
structural,decorative,face_feature,aura_context, orvalidation_only. - Pass structural masks to
contour-to-mesh; pass feature masks/landmarks tolandmark-fit-repair; pass atlas regions toatlas-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
{
"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.pyproduces component masks and a JSON report from an image, with optional watershed.scripts/seeded_part_masks.pyconverts 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.
FAQ
What does the source-part-segmentation skill do?
Segment overlapping visual parts from source images, wireframes, texture atlases, and decals before mesh reconstruction. Use when a mascot/logo/template contains touching or overlapping components and exact structural part masks are needed before contour-to-mesh, UV fitting, or landmark repair.
How do I install source-part-segmentation?
Run npx skills add https://github.com/roble3/cc-blender-skill --skill source-part-segmentation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/source-part-segmentation/ (personal) or .claude/skills/source-part-segmentation/ (project).
Which AI agents can use source-part-segmentation?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is source-part-segmentation safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains source-part-segmentation?
It is published by roble3 in roble3/cc-blender-skill. It has 748 recorded installs.
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