Image Processing Project

Overview

This project focuses on implementing various image processing techniques to analyze, enhance, and manipulate images. These techniques are widely used in applications such as computer vision, machine learning, and artificial intelligence.

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Features

• Image Loading and Display: Load images in various formats and display them for visualization.
• Filtering: Apply filters such as Gaussian, Median, and Sobel to enhance image quality.
• Edge Detection: Identify and highlight edges in images using algorithms like Canny and Laplacian.
• Histogram Equalization: Improve image contrast for better visualization and analysis.
• Thresholding: Segment images using binary, adaptive, or Otsu's thresholding techniques.
• Morphological Operations: Perform dilation, erosion, opening, and closing for shape analysis.
• Color Space Conversion: Convert between RGB, Grayscale, HSV, and other color models.
• Feature Detection: Detect keypoints using methods like SIFT, SURF, or ORB.
• Image Transformation: Perform geometric transformations, such as rotation, translation, and scaling.

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Prerequisites

Ensure the following tools and libraries are installed:

1. Python (>=3.8)
2. Libraries:
   • OpenCV
   • NumPy
   • Matplotlib
   • Pillow

Install them using the following command:

pip install opencv-python numpy matplotlib pillow

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Installation

1. Clone the repository:

git clone https://github.com/your-username/image-processing-project.git
cd image-processing-project

2. Install required dependencies:

pip install -r requirements.txt

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Usage

Run the main script to execute the project:

python main.py

Follow the prompts to select the desired image processing technique. Alternatively, modify the script to automate specific tasks.

Example Code Snippet

Edge Detection Example:

import cv2

# Load image
gray_image = cv2.imread('example.jpg', cv2.IMREAD_GRAYSCALE)

# Apply Canny Edge Detection
edges = cv2.Canny(gray_image, 100, 200)

# Display results
cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

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Folder Structure

image-processing-project/
|— data/                # Sample images
|— outputs/             # Processed images
|— src/                 # Source code
|   — main.py          # Main script
|— README.md            # Documentation
|— requirements.txt     # Dependency list

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Future Enhancements

• Implement advanced techniques like Deep Learning for object detection.
• Add GUI support for interactive image manipulation.
• Extend support for video processing.

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Contributing

Contributions are welcome! Please fork this repository and submit a pull request with your improvements.

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License

This project is licensed under the MIT License.

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Contact

For any queries or suggestions, please contact:

• Your Name: [email protected]
• GitHub Profile

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