This repository provides a structured, beginner-friendly guide to image preprocessing
using Python’s PIL (Pillow) library with Matplotlib for visualization.
The notebook demonstrates how essential preprocessing operations transform raw images into clean, analysis-ready inputs — a critical step for building Computer Vision and Machine Learning applications.
- Resizing – Normalize image dimensions for consistent input to models.
- Cropping – Extract regions of interest to reduce noise and improve focus.
- Grayscale Conversion – Simplify data by reducing color channels, lowering computational cost.
- Brightness Adjustment – Improve feature visibility under varying lighting conditions.
- Contrast & Sharpness Tuning – Highlight edges and patterns critical for recognition tasks.
- Color Enhancement – Balance vibrance and saturation to improve feature extraction.
- Comparative Visualization – Validate transformations by comparing original vs. processed outputs.
These preprocessing techniques are essential in Computer Vision pipelines. They transform raw, unstructured image data into clean, standardized, and information-rich inputs. This leads to:
- Better feature extraction
- Improved model training efficiency
- Higher accuracy in classification, detection, and segmentation tasks
This repository also includes an OpenCV-based notebook to introduce learners to industry-standard image preprocessing workflows.
The OpenCV file is added to demonstrate how similar preprocessing operations can be performed using
cv2, which is widely used in real-world computer vision systems.
This addition is intended to help learners:
- Understand the difference between PIL and OpenCV
- Explore production-oriented image processing tools
- Build a strong foundation for advanced computer vision projects
Note: The OpenCV notebook is provided for learning and reference purposes to explain workflow and concepts.
A complete explanation of concepts, workflows, and real-world applications of image preprocessing is documented in my blog: