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name: segmentation-and-shapes description: "Generate masks and synthetic shapes, clean and label them, and segment images with morphology, watershed, random walker, superpixels, graph helpers, and active contours." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Segmentation and Shapes

Use this sub-skill when the task is about drawing masks, synthesizing labeled scenes, cleaning binary regions, or partitioning an image into segments. It owns the stable skimage.draw, skimage.morphology, skimage.segmentation, and skimage.graph workflows that turn pixels into regions. This route stops before feature extraction or geometric registration.

Route Here For

• Drawing lines, polygons, disks, ellipses, rectangles, Bezier curves, and perimeters.
• Painting or filling masks with set_color and polygon2mask.
• Generating synthetic scenes with random_shapes, including overlap control, intensity ranges, and grayscale via channel_axis=None.
• Cleaning masks with binary morphology, small-object/hole removal, flooding, reconstruction, and skeletonization.
• Creating markers and segments with watershed, random_walker, slic, felzenszwalb, quickshift, expand_labels, join_segmentations, clear_border, and relabel_sequential.
• Building or visualizing region adjacency graphs with RAG, rag_mean_color, rag_boundary, show_rag, cut_threshold, cut_normalized, and merge_hierarchical.
• Using active contours and snakes with chan_vese, active_contour, morphological_chan_vese, morphological_geodesic_active_contour, inverse_gaussian_gradient, checkerboard_level_set, and disk_level_set.
• Handling legacy or experimental segmentation helpers from skimage.future, including manual and trainable segmentation.

Use Other Sub-skills For

• Image loading, dtype conversion, and sample data: ../data-io/SKILL.md.
• Denoising, threshold selection, and intensity/color prep: ../enhancement/SKILL.md.
• Feature extraction, region measurement, and metrics: ../analysis/SKILL.md.
• Geometric warps, transforms, and registration: ../transform-registration/SKILL.md.

Start Here

• Read references/workflows.md for concrete recipes and API selection.
• Read references/troubleshooting.md for markers, connectivity, channel_axis, and legacy-API caveats.
• Prefer tiny synthetic arrays and CPU-only smoke checks when validating a segmentation path.

Safe Defaults

• Treat 0 as background or unlabeled unless the API says otherwise; keep positive integers as seeds or region IDs.
• Use channel_axis=None for grayscale images and an explicit channel axis for multichannel SLIC or random walker workflows.
• Seed synthetic examples with rng=0 or another fixed value when reproducibility matters.
• Prefer expand_labels over dilation when growth must not overlap.
• Run relabel_sequential after joins, cuts, or manual edits when downstream code expects dense labels.
• Treat skimage.future helpers as legacy and expect warnings or missing support in skimage2.

Acceptance Checks

• Can a future agent build a mask, clean it, segment it, and inspect boundaries without reopening the source docs?
• Can a future agent choose between watershed, random walker, SLIC, graph cuts, and active contours?
• Are legacy or interactive helpers clearly separated from the stable segmentation route?