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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.