Can I use scikit-image for object counting?

Yes, the skill provides patterns for using measure.label and measure.regionprops to identify connected components and calculate properties like area, perimeter, and centroid.

How does this skill handle different image data types?

It utilizes specific utility functions like img_as_float and img_as_ubyte to handle rescaling automatically, ensuring data integrity when moving between 0-255 integers and 0-1 float ranges.

How does it optimize performance for large datasets?

The skill encourages vectorized NumPy operations over pixel loops and provides examples for using view_as_windows for memory-efficient patch processing.

Does it support RGB and multichannel image filtering?

While many algorithms expect grayscale, the skill provides specific strategies for per-channel filtering and color space conversion to handle complex RGB data.

Scikit-Image Assistant

Name: Scikit-Image Assistant
Author: tondevrel

bytondevrel

•

Data Science & ML

Simplifies scientific image processing and analysis using Python-based algorithms and NumPy-compatible workflows.

The Scikit-Image skill provides comprehensive guidance for researchers and developers working on scientific image analysis. It streamlines complex tasks such as image segmentation, feature extraction, and geometric transformations by treating images as multi-dimensional NumPy arrays. By offering best practices for denoising, morphological operations, and object measurement, it ensures that users can build robust, research-grade image processing pipelines with mathematical rigor and performance optimization, moving seamlessly from raw sensor data to quantifiable insights.

Key Features

01Morphological operations for skeletonization and object cleaning.

02Optimized vectorized operations for high-performance pixel manipulation.

03Safe data type conversion utilities to prevent rescaling errors.

04Automated image segmentation and marker-based watershed analysis.

05Advanced feature detection including edges, blobs, and textures.

061 GitHub stars

Use Cases

01Detecting structural changes and motion between temporal image sets.

02Identifying and counting particles or nuclei in scientific micrographs.

03Preprocessing and normalizing images for computer vision pipelines.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add tondevrel/scientific-agent-skills scikit-image

For use in Claude.ai and ChatGPT

Download Skill