📷 Image Understanding and Processing with OpenCV-Python

🚀 Introduction

This repository provides an introduction to image processing and understanding using OpenCV with Python. OpenCV is an open-source computer vision library that allows for real-time image and video processing, making it widely used in fields such as robotics, AI, and medical imaging.

🔧 Prerequisites

Ensure you have Python installed on your system before proceeding. It is recommended to use Python 3.6 or later.

---

🖼️ Image Processing Topics Covered

1. Image Smoothing / Blurring Filters using OpenCV

   • Averaging (Lowpass) filter with cv2.filter2D
   • Box filter with cv2.boxFilter
   • Simple blur with cv2.blur
   • Median filter with cv2.medianBlur (good for salt-and-pepper noise)
   • Gaussian filter with cv2.GaussianBlur

2. Noise Removal Techniques

   • Salt noise removal using Min Filter (PIL)
   • Pepper noise removal using Max Filter (PIL)
   • Both Salt & Pepper noise removal using Median Filter

3. PIL-Based Image Processing

   • ImageFilter.MinFilter and ImageFilter.MaxFilter
   • Grayscale conversion using ImageOps.grayscale
   • Edge detection using ImageFilter.FIND_EDGES

4. Edge Detection

   • Sobel operator using cv2.Sobel (X and Y derivatives)
   • Laplacian operator using cv2.Laplacian
   • Laplacian of Gaussian (LoG): combining Gaussian blur + Laplacian

5. Histogram Equalization

   • Improve contrast of:
     • Dark images
     • Bright images
     • Low contrast images

6. Image Transformations

   • Negative Transformation (invert pixel values)
   • Power-Law (Gamma) Transformation for brightness correction

---

📥 Installation

To set up your environment, install the required dependencies using pip:

pip install opencv-python
pip install matplotlib
python -m pip install jupyter

▶️ Running Jupyter Notebook

To start working with Jupyter Notebook, run the following command:

python -m notebook

This will open Jupyter Notebook in your web browser, allowing you to execute and visualize OpenCV-based image processing scripts.

⌨️ Jupyter Notebook Shortcuts

Here are some essential Jupyter Notebook keyboard shortcuts to improve efficiency:

General Shortcuts

• Shift + Enter → Run the current cell and move to the next
• Ctrl + Enter → Run the current cell but stay on it
• Alt + Enter → Run the current cell and insert a new one below
• Esc + A → Insert a new cell above
• Esc + B → Insert a new cell below
• Esc + D + D → Delete the selected cell
• Esc + M → Convert cell to Markdown
• Esc + Y → Convert cell to Code
• Esc + L → Toggle line numbers in cell
• Esc + H → Show help menu

Navigation

• Up/Down Arrow → Move between cells
• Ctrl + Shift + - → Split a cell at the cursor
• Shift + Tab → Show tooltip for functions
• Ctrl + Shift + P → Open command palette

🤝 Contributing

Contributions are welcome! Feel free to submit issues or pull requests to enhance this repository.

📜 License

This project is licensed under the MIT License. See the LICENSE file for more details.