scikit-image Regionprops Feature Extraction

Use this skill to extract deterministic morphology and intensity features from a toy image with scikit-image regionprops_table.

What it does

• creates a reproducible grayscale toy image with three bright ellipse-like objects
• thresholds and labels connected components locally
• summarizes per-object morphology and intensity features into compact JSON
• keeps the output stable enough for smoke tests and starter workflows

When to use it

• You need a local feature-extraction starter in the scikit-image ecosystem.
• You want a minimal handoff point before building larger imaging or phenotype-analysis pipelines.

Example

./slurm/envs/scientific-python/bin/python \
  skills/imaging-and-phenotype-analysis/skimage-regionprops-feature-extraction/scripts/run_skimage_regionprops_features.py \
  --threshold 0.35 \
  --out scratch/skimage-regionprops/summary.json

Verification

• Skill-local tests: python3 -m unittest discover -s skills/imaging-and-phenotype-analysis/skimage-regionprops-feature-extraction/tests -p 'test_*.py'
• Asset regeneration: ./slurm/envs/scientific-python/bin/python skills/imaging-and-phenotype-analysis/skimage-regionprops-feature-extraction/scripts/run_skimage_regionprops_features.py --out skills/imaging-and-phenotype-analysis/skimage-regionprops-feature-extraction/assets/toy_regionprops_summary.json

Notes

• This is a starter for the taxonomy leaf feature-extraction.
• The image is synthetic and deterministic; it is intended for scaffolding and verification rather than biological interpretation.