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ISP with Python

This repository is based on the course Python for Signal and Image Processing Master Class by Dr. Zeeshan.
It supports my pivot from fullstack development to ISP, leveraging my background in photography.

About the Course

  • Instructor: Dr. Zeeshan
  • Focus: Python for signal and image processing, with hands-on projects and real-world applications.

Purpose

  • Document my learning and progress through the course
  • Apply ISP techniques to real-world and photography-related problems
  • Build a portfolio of relevant projects and notebooks

Contents

  • main.py: Main script for experiments and code samples
  • Numpy-deep-dive.ipynb: NumPy exercises and explorations
  • Plotting-and-visualization.ipynb: Visualization and plotting techniques
  • Program1.ipynb: Course assignments and hands-on projects
  • readMe.md: This documentation

Key Learning Areas

  • Signal processing fundamentals (filtering, transforms, etc.)
  • Image processing (enhancement, restoration, segmentation)
  • Python libraries: NumPy, Matplotlib, OpenCV, SciPy, and more
  • Visualization and analysis of signals and images
  • Applying ISP concepts to photography

Getting Started

  1. Clone the repository: 
    git clone https://github.com/hbrandon15/ISP-with-Python.git
  2. Create and activate a virtual environment: 
    python -m venv .venv
.\.venv\Scripts\activate
  3. Install dependencies: 
    pip install -r requirements.txt
  4. Open notebooks in VS Code or JupyterLab.

Why ISP?

With a foundation in photography, I am passionate about the science behind digital images and signals.
This course and repository are part of my journey to:

  • Deepen my technical expertise in ISP
  • Transition my career from fullstack to ISP
  • Combine creative and analytical skills

Inspired by the intersection of art and science in digital imaging.