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Scikit-image - Notes

Table of Contents (ToC)

Introduction

Scikit-image is a Python library designed for image processing.

What's Scikit-image?

  • A collection of algorithms for image processing
  • Part of the Scipy ecosystem
  • Open-source and widely used in academia and industry

Key Concepts and Terminology

  • Image Processing: Techniques for analyzing and modifying images
  • Numpy: Underlying data structure for images in Scikit-image
  • Filters: Methods to enhance or extract features from images
  • Segmentation: Process of partitioning an image into segments

Applications

  • Medical imaging analysis
  • Computer vision tasks
  • Image enhancement and restoration
  • Feature extraction for machine learning models

Fundamentals

Scikit-image Architecture Pipeline

  • Image Input: Loading images using Scikit-image
  • Preprocessing: Applying filters and transformations
  • Analysis: Extracting features and information
  • Output: Saving or displaying processed images

How Scikit-image works?

  • Uses Numpy arrays for image representation
  • Provides a comprehensive set of functions for image manipulation
  • Efficient and easy-to-use API for various image processing tasks

Some hands-on examples

  • Image filtering with Gaussian filters
  • Edge detection using Canny edge detector
  • Segmentation using watershed algorithm
  • Feature extraction using local binary patterns

Tools & Frameworks

  • Scipy: Core library for scientific computing
  • Matplotlib: Visualization library for plotting images
  • OpenCV: Another popular library for computer vision tasks
  • PIL/Pillow: Image processing capabilities in Python

Hello World!

import skimage.io as io
import skimage.filters as filters

# Load an image from file
image = io.imread('path/to/your/image.jpg')

# Apply a Gaussian filter to the image
filtered_image = filters.gaussian(image, sigma=1)

# Save the processed image
io.imsave('path/to/save/filtered_image.jpg', filtered_image)

Lab: Zero to Hero Projects

  • Project 1: Building an image enhancement tool
  • Load and display images
  • Apply filters and transformations
  • Save the enhanced images
  • Project 2: Creating a basic image segmentation application
  • Implement edge detection
  • Use segmentation algorithms to partition images
  • Visualize segmented regions
  • Project 3: Developing a feature extraction pipeline for machine learning
  • Extract features using Scikit-image functions
  • Train a simple classifier on extracted features
  • Evaluate the performance of the classifier

References