Digital Image Processing with OpenCV

A comprehensive image processing project developed for the Digital Image Processing course at An-Najah National University.

Authors

• Ameed Othman (12220692)
• Yahya Musmar (12112501)

Description

This project demonstrates various digital image processing techniques using OpenCV and Python. It's divided into two main parts:

1. Part 1: A sequence of image processing operations applied to a static image
2. Part 2: A real-time camera filter application with multiple effect options

Requirements

• Python 3.x
• OpenCV (cv2)
• NumPy
• Matplotlib

Project Structure

Part 1: Basic Image Processing Pipeline

• main.py - Image loading and watermarking
• step1_grayscale_stats.py - Grayscale conversion and statistics
• step2_brightness_modification.py - Brightness adjustment
• step3_brightness_correction.py - Histogram equalization
• step4_add_noise.py - Salt-and-pepper noise addition
• step5_noise_filtering.py - Mean and median filtering
• step6_sharpening.py - Image detail enhancement

Part 2: Real-time Camera Filter Application

• main.py - Camera filter application with multiple modes
• edge_detection.py - Canny edge detection implementation
• grayscale_quantization.py - Intensity level reduction
• contrast_enhancement.py - Histogram-based contrast improvement
• soft_appearance.py - Bilateral filtering for soft appearance
• cartoon_filter.py - Stylized cartoon effect

Features

• Grayscale conversion and statistical analysis
• Brightness modification with parameter control
• Histogram equalization for contrast enhancement
• Salt-and-pepper noise simulation and removal
• Comparison of mean and median filters
• Image sharpening with custom kernels
• Real-time camera effects with 6 different filter modes

How to Run

Part 1

Run each script in sequence (1-6) to see the step-by-step image processing:

python part1/main.py
python part1/step1_grayscale_stats.py
...

Part 2

Run the camera application:

python part2/main.py

Controls:

• Press 0-5 to switch between filter modes
• Press Q to quit

Available filters:

• Normal view (0)
• Edge detection (1)
• Grayscale quantization (2)
• Contrast enhancement (3)
• Soft appearance (4)
• Cartoon filter (5)

Project Report

For detailed information about the implementation, results, and analysis, refer to the project report PDF.