📚 Python Image Manipulation Cheat Sheets #
Welcome to the comprehensive collection of cheat sheets for Python image manipulation. This repository provides a series of guides ranging from a general overview to advanced, in-depth documentation for the most popular Python imaging libraries.
Whether you’re a beginner looking to get started or an experienced developer seeking advanced techniques and best practices, these guides are designed to be a valuable resource.
📖 Available Guides #
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🐍 Python Image Manipulation Overview
- A high-level introduction to the three major imaging libraries: Pillow, OpenCV, and scikit-image.
- Covers installation, core concepts, and common use cases for each.
- Best for: A quick start or a side-by-side comparison of the libraries.
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- An in-depth guide to the Pillow library (PIL Fork).
- Focuses on best practices for file handling, advanced color space manipulation, masking, image enhancement, and creating animated GIFs.
- Best for: Developers focused on image file I/O, manipulation, and web-related image tasks.
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- A deep dive into OpenCV-Python for advanced computer vision.
- Covers performance optimization, advanced filtering, morphological transformations, feature detection, video analysis, and object detection with Haar Cascades.
- Best for: Real-time computer vision, video processing, and machine learning applications.
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- A comprehensive guide to scikit-image for scientific image analysis.
- Explores its NumPy-centric design, advanced segmentation algorithms (like Watershed), feature detection, and robust methods for measuring region properties.
- Best for: Scientific research, academic use, and complex image analysis tasks.
🛠️ Setup #
To run the examples in these cheat sheets, you will need to install the required libraries. A requirements.txt file is provided in the root directory of this project for easy installation:
pip install -r ../requirements.txt