# scikit-image

A Cursor rule that pins down how a team writes scikit-image code: immutable inputs, float range [0, 1], vectorized NumPy over pixel loops.

- rule · `type=rule` · Cursor · ★ 3579
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/scikit-image.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/scikit-image

## What it does

Drop `.cursor/rules/scikit-image.mdc` into your project and Cursor applies it to every `**/*.py` file. The rule lists mandatory conventions for image pipelines: functions take and return plain `ndarray`, input images stay untouched, and larger tasks get split into small functions with PEP 257 docstrings and PEP 484 type hints using `numpy.typing.NDArray`.

A second block covers data handling. Process in `np.float64` with values scaled to `[0, 1]`, convert to `uint8` or `uint16` only for display or saving, and keep color images in `(Height, Width, Channels)` order. Loading and writing go through `skimage.io.imread` and `skimage.io.imsave`. Private functions with a leading underscore are off limits, because they break on library upgrades.

The rule assumes several people editing the same codebase. On a throwaway script of twenty lines, the docstring and function-splitting requirements cost more than they return.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/scikit-image.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/scikit-image.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/scikit-image.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/scikit-image.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/scikit-image.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/scikit-image.mdc

## When to use it
- agree on one image-processing style across a team
- stop functions from mutating input arrays
- move computation to float and avoid uint8 clipping
- replace pixel-wise Python loops with vectorized NumPy
- keep color channels in HWC order

## Example prompts
- Split this image processing function into separate steps following the scikit-image rule
- Check my code for places where I modify the input array in place
- Rewrite this pixel loop as a vectorized NumPy operation

## Source text · scikit-image.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: d72f1369c618431d.

BEGIN THIRD-PARTY FILE d72f1369c618431d
````text
---
description: This guide defines the definitive best practices for developing with scikit-image, ensuring maintainable, performant, and robust image processing code.
globs: **/*.py
---
# scikit-image Best Practices

This document outlines the mandatory guidelines for all `scikit-image` development within our team. Adhering to these rules ensures consistency, performance, and long-term maintainability of our image processing pipelines.

## 1. Core Principles

`scikit-image` is built on NumPy. All functions must accept and return plain `ndarray` objects.

### 1.1. Immutability of Input Images
**Always** treat input images as immutable. Functions must return new arrays, never modify inputs in-place. This prevents unexpected side effects and simplifies debugging.

❌ BAD: In-place modification
```python
import numpy as np
from skimage import exposure

def normalize_image_bad(image: np.ndarray) -> None:
    """❌ BAD: Modifies the input image directly."""
    image[:] = exposure.rescale_intensity(image, out_range=(0, 1))

img = np.array([[0, 100], [50, 200]], dtype=np.uint8)
original_img_id = id(img)
normalize_image_bad(img)
print(f"Image ID changed? {id(img) != original_img_id}") # False, same object modified
```

✅ GOOD: Return a new array
```python
import numpy as np
from skimage import exposure
from numpy.typing import NDArray, Any

def normalize_image_good(image: NDArray[Any, Any]) -> NDArray[Any, Any]:
    """✅ GOOD: Returns a new, processed image array."""
    return exposure.rescale_intensity(image, out_range=(0, 1))

img = np.array([[0, 100], [50, 200]], dtype=np.uint8)
original_img_id = id(img)
processed_img = normalize_image_good(img)
print(f"Image ID changed? {id(processed_img) != original_img_id}") # True, new object returned
```

### 1.2. Public API Usage
**Always** use the documented public API. Avoid internal or private functions (prefixed with `_`) to ensure forward compatibility and stability.

## 2. Code Organization and Structure

### 2.1. Modular Functions
Break down complex image processing tasks into small, focused, and reusable functions. Each function should do one thing well.

❌ BAD: Monolithic function
```python
import numpy as np
from skimage import io, filters, exposure

def process_image_bad(path: str) -> np.ndarray:
    """❌ BAD: Combines loading, filtering, and normalization."""
    image = io.imread(path, as_gray=True)
    blurred = filters.gaussian(image, sigma=1)
    normalized = exposure.rescale_intensity(blurred, out_range=(0, 1))
    return normalized
```

✅ GOOD: Chained, focused functions
```python
import numpy as np
from skimage import io, filters, exposure
from numpy.typing import NDArray, Any

def load_grayscale_image(path: str) -> NDArray[Any, Any]:
    """Loads an image from path as grayscale."""
    return io.imread(path, as_gray=True)

def apply_gaussian_blur(image: NDArray[Any, Any], sigma: float) -> NDArray[Any, Any]:
    """Applies a Gaussian blur to the image."""
    return filters.gaussian(image, sigma=sigma)

def normalize_intensity(image: NDArray[Any, Any]) -> NDArray[Any, Any]:
    """Rescales image intensity to [0, 1]."""
    return exposure.rescale_intensity(image, out_range=(0, 1))

# Usage:
# image_path = "path/to/your/image.png"
# img = load_grayscale_image(image_path)
# blurred_img = apply_gaussian_blur(img, sigma=1.5)
# final_img = normalize_intensity(blurred_img)
```

### 2.2. Docstrings and Type Hints
**Every** function, class, and method must have a comprehensive docstring following PEP 257 and explicit type hints following PEP 484. Use `numpy.typing.NDArray` for NumPy arrays.

```python
from typing import Tuple, Literal
import numpy as np
from numpy.typing import NDArray, DTypeLike

def convert_to_float(
    image: NDArray[DTypeLike, Any],
````
END THIRD-PARTY FILE d72f1369c618431d

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