🖼️ Scikit-Image Journey: From Basics to Mastery 🌌

Welcome to the Scikit-Image Journey repository! 🚀 This repo takes you from fundamental image processing techniques 🖥️ all the way to advanced scikit-image applications 🎨. If you're looking to master the scikit-image library, you've come to the right place!

📖 Table of Contents

• 🔍 Introduction
• ⚙️ Getting Started
  • 💻 Installation
  • 📁 Repository Structure
• 🔧 Fundamentals of Image Processing
  • 🖼️ Images & Arrays
  • ✂️ Basic Image Operations
  • 🎨 Image Filtering
  • 🔲 Morphological Operations
• 🔄 Intermediate Topics
  • 🧩 Image Segmentation
  • 📍 Feature Detection & Extraction
  • 🔄 Image Transformations
  • ✨ Image Enhancement Techniques
• 🧠 Advanced Topics
  • 🤖 Machine Learning with scikit-image
  • 🧊 Working with 3D Images
  • 🔗 Image Registration
  • 🔬 Advanced Feature Detection
• 🤝 Contributing
• 📜 License

🔍 Introduction

scikit-image is a Python library for image processing that includes a variety of algorithms for transforming, filtering, and analyzing images. This repository is designed for anyone who wants to get hands-on experience with scikit-image, from beginners 🤓 to advanced users 👩‍💻.

⚙️ Getting Started

💻 Installation

To get started, you'll need Python (version 3.7 or later) and a few essential Python packages. You can install them by running: pip install scikit-image numpy matplotlib 📁 Repository Structure Here's how this repository is organized:

notebooks/ 📚 - Jupyter notebooks with tutorials on each topic. scripts/ 📝 - Python scripts for various scikit-image functionalities. data/ 🖼️ - Sample images and datasets used in tutorials. 🔧 Fundamentals of Image Processing 🖼️ Images & Arrays Learn about the relationship between images and arrays, using NumPy and scikit-image to manipulate pixels:

📷 Converting images to arrays 🧮 Basic array operations ✂️ Basic Image Operations Dive into basic image manipulations, such as:

🔍 Cropping & Resizing 🎨 Color manipulation and channel operations 🎨 Image Filtering Explore filters to enhance or transform images:

🌫️ Gaussian blur and other smoothing techniques ⚡ Edge detection (e.g., Sobel, Canny) ✨ Sharpening filters 🔲 Morphological Operations Learn the basics of morphological operations:

🧱 Erosion and Dilation 🔗 Opening and Closing 🧼 Cleaning up binary images 🔄 Intermediate Topics 🧩 Image Segmentation Identify distinct objects and regions in images:

🔲 Thresholding (Otsu, adaptive) 💧 Watershed segmentation 🌱 Region growing and labeling 📍 Feature Detection & Extraction Extract features for pattern recognition:

🌄 Edge and corner detection 🧬 Texture analysis 🔍 Blob and contour detection 🔄 Image Transformations Manipulate images with transformations:

🔄 Rotation and Scaling 📐 Affine and perspective transformations 🌀 Warping techniques ✨ Image Enhancement Techniques Enhance image quality for better analysis:

🔋 Contrast enhancement (e.g., Histogram Equalization) 🎭 Denoising (Gaussian, Median) 🖌️ Color adjustment 🧠 Advanced Topics 🤖 Machine Learning with scikit-image Integrate scikit-image with scikit-learn for machine learning:

📊 Classification with image data 🔢 Clustering pixels and regions 🔎 Feature engineering 🧊 Working with 3D Images Explore advanced techniques for 3D image processing:

🧽 3D filtering and denoising 🔍 3D segmentation and volume rendering 🌈 Visualizing 3D data 🔗 Image Registration Align multiple images of the same scene:

🎯 Rigid and non-rigid transformations 📐 Image alignment techniques 🧩 Template matching 🔬 Advanced Feature Detection Detect advanced image features and analyze them:

🌌 Keypoint detection for image matching 📷 Object tracking in video 🧩 Descriptors for feature matching 🤝 Contributing We'd love to have you contribute to this project! 🤗 Feel free to fork the repository and submit a pull request. For more details, check out our CONTRIBUTING.md file.

📜 License This repository is licensed under the MIT License. For more information, refer to the LICENSE file.