# Leaf Measure

> Use when measuring leaf area, perimeter, length, width, circularity, and solidity from a folder of leaf images with the Fiji-based FAMeLeS workflow, especially when the user asks to analyze a directory of leaf scans or photos and needs a results table plus segmentation outputs.

- Skill: `rimagination/leaf-measure` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add rimagination/leaf-measure`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rimagination/leaf-measure/raw
- Safety review: pending (external: skill-scanner PASS, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: Rimagination (https://skillmd.com/u/rimagination)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/rimagination/leaf-measure

---


# leaf-measure

Use this skill to run the shared `leaf-measure` engine, not to re-implement the method in the prompt.

This skill supports two host patterns:

- repo-local: the current workspace is the `leaf-measure` repository
- standalone installed skill: the skill lives under `$CODEX_HOME/skills/leaf-measure` and uses `scripts/setup_and_analyze.py` to clone or update the shared repo cache under `$CODEX_HOME/vendor/leaf-measure`, then sync the installed skill to the latest canonical bundle

## Workflow

1. Confirm the target image folder.
2. If the user did not specify a mode, explain `Full image` vs `Thumbnails` using `references/mode-selection.md` and ask them to choose.
3. Before analysis, make sure the runtime exists:
   - repo-local: prefer `.\scripts\bootstrap.ps1` on Windows because it installs the current Python dependencies, downloads Fiji if missing, and fetches the public Figshare assets if missing
   - standalone installed skill: run `python scripts/setup_and_analyze.py analyze ...`; that helper clones or updates the shared repo cache, refreshes the installed skill from the latest canonical `skills/leaf-measure/`, runs `doctor`, and bootstraps the runtime on first use when needed
   - if only the upstream package is missing in a repo-local workspace, run `python -m engine.cli fetch-assets`
   - if you must pass Fiji explicitly, `--fiji` may be either a Fiji directory or a launcher path such as `ImageJ-win64.exe`
4. Run the shared CLI from the repository root:

```powershell
python -m engine.cli analyze --input "<folder>" --output "<run-dir>" --mode full
```

or

```powershell
python -m engine.cli analyze --input "<folder>" --output "<run-dir>" --mode thumbnails
```

For a standalone installed skill, use the bundled helper instead of assuming the current directory is the repo:

```powershell
python scripts/setup_and_analyze.py analyze --input "<folder>" --output "<run-dir>" --mode full
```

or

```powershell
python scripts/setup_and_analyze.py analyze --input "<folder>" --output "<run-dir>" --mode thumbnails
```

If the user says the installed skill still behaves like an older version, refresh it explicitly:

```powershell
python scripts/setup_and_analyze.py self-update
```

If you are inside the repository itself and want to refresh the global installation from the canonical source, run:

```powershell
python -m engine.cli update-skill
```

5. Read `manifest.json`, `results.csv`, `run_summary.md`, and the output folders before answering.
6. Explain:
   - what was measured
   - which mode was used
   - that `results.csv` is the user-facing table and `results_fameles_particles_raw.csv` preserves the original Fiji particle-level table when present
   - for `Full image`, whether the run had to correct a hole-dominated mask polarity before writing the user-facing table
   - for `Full image`, whether a conservative crop-rescue pass was needed to recover missed large leaf objects by rerunning the original `Full image` macro on candidate crop regions
   - that outputs are in pixels by default
   - whether DPI metadata was found
   - whether automatic repair was triggered for a mask artifact
   - whether leaf-measure internally staged non-ASCII filenames and then restored the original names in the delivered outputs
   - that binary and outline outputs should be visually reviewed

## References

- Mode choice: `references/mode-selection.md`
- Trait definitions: `references/trait-definitions.md`
- Artifact handling: `references/artifact-handling.md`

## Boundaries

- Do not silently convert pixel outputs into physical units.
- Do not claim the method worked if `results.csv` or output images are missing.
- Treat the shared CLI as the source of truth.
- If the standalone helper cannot make the runtime ready, surface the bootstrap or doctor failure clearly instead of improvising.