Image_Preprocessing.ipynb

Image Processing Techniques: Edge Detection and Frequency Analysis

Original Image:

🚀 Overview

This notebook covers several computer vision tasks using OpenCV and Matplotlib. Here are some key operations included in the notebook:

• Sobel Edge Detection
• Gaussian Blurring
• Gabor Filtering
• Laplacian Edge Detection
• Fourier Transform
• And more...

Goal: To visualize and explore different ways of identifying edges and frequency components in images.

Computer Vision Lab - Edge Detection and Filtering Techniques

This repository provides a Jupyter Notebook showcasing various computer vision techniques using OpenCV and Python. The notebook demonstrates edge detection, image filtering, and texture analysis on grayscale images.

Table of Contents

• Features
• Installation
• Usage
• Examples
• Project Structure
• Contributing
• License
• References

Features

1. Sobel Edge Detection: Detects edges in X and Y directions.
2. Gaussian Blurring: Reduces image noise with a Gaussian filter.
3. Gabor Filtering: Analyzes texture with directional Gabor filters.
4. Laplacian Edge Detection: Applies Laplacian to highlight edges.
5. Fourier Transform: Computes and displays the frequency spectrum.
6. Non-Maximum Suppression: Suppresses non-maximum edges to refine results.
7. Morphological Operations: Performs operations such as dilation and erosion.
8. Bilateral Filtering: Applies bilateral filtering for edge-preserving smoothing.
9. Brightness Adjustment: Adjusts the brightness of the image.
10. Canny Edge Detection: Detects edges using the Canny algorithm.
11. Emboss Filter: Applies an emboss filter to the image.
12. Median Blur: Applies median blur to the image.
13. Motion Blur: Simulates motion blur on the image.
14. Sharpen Filter: Sharpens the image.
15. Add Gaussian Noise: Adds Gaussian noise to the image.
16. Grayscale Conversion: Converts the image to grayscale.
17. Box Filter (Averaging Filter): Applies a box filter for averaging.
18. Unsharp Masking: Enhances the image using unsharp masking.
19. Prewitt Filter: Applies the Prewitt filter for edge detection.
20. Cartoon Effect: Creates a cartoon effect on the image.
21. Pencil Sketch Effect: Converts the image to a pencil sketch.
22. Histogram Equalization: Equalizes the histogram of the image.
23. CLAHE (Contrast Limited Adaptive Histogram Equalization): Applies CLAHE for contrast enhancement.
24. Adaptive Thresholding: Applies adaptive thresholding.
25. Frequency Domain Filtering: Applies high-pass and low-pass filters in the frequency domain.
26. Bilateral Grid Filtering: Applies bilateral grid filtering.
27. Edge-Preserving Filter: Applies edge-preserving filtering.
28. Scharr Filter: Applies the Scharr filter for edge detection.

Installation

1. Clone the repository: Run the following command:

   git clone https://github.com/rixscx/Computer_Vision_Lab.git
   cd Computer_Vision_Lab
   

2. Install Dependencies: Use the following command to install required libraries:

   pip install -r requirements.txt
   

Usage

Open the Jupyter Notebook: Start Jupyter Notebook and open Image_Preprosseing.ipynb:

jupyter notebook 

Image_Preprosseing.ipynb

1. Sobel Edge Detection: Detects edges in the X and Y directions.

Output:

2. Gaussian Blurring: Reduces image noise with a Gaussian filter.

Output:

3. Gabor Filtering: Analyzes texture with directional Gabor filters.

Output:

4. Laplacian Edge Detection: Applies Laplacian to highlight edges.

Output:

5. Fourier Transform: Computes and displays the frequency spectrum.

Output:

6. Non-Maximum Suppression: Suppresses non-maximum edges to refine results.

Output:

7. Morphological Operations: Performs operations such as dilation and erosion.

Output:

8. Bilateral Filtering: Applies bilateral filtering for edge-preserving smoothing.

Output:

9. Brightness Adjustment: Adjusts the brightness of the image.

Output:

10. Canny Edge Detection: Detects edges using the Canny algorithm.

Output:

11. Emboss Filter: Applies an emboss filter to the image.

Output:

12. Median Blur: Applies median blur to the image.

Output:

13. Motion Blur: Simulates motion blur on the image.

Output:

14. Sharpen Filter: Sharpens the image.

Output:

15. Add Gaussian Noise: Adds Gaussian noise to the image.

Output:

16. Grayscale Conversion: Converts the image to grayscale.

Output:

17. Box Filter (Averaging Filter): Applies a box filter for averaging.

Output:

18. Unsharp Masking: Enhances the image using unsharp masking.

Output:

19. Prewitt Filter: Applies the Prewitt filter for edge detection.

Output:

20. Cartoon Effect: Creates a cartoon effect on the image.

Output:

22. Pencil Sketch Effect: Converts the image to a pencil sketch.

Output:

22. Histogram Equalization: Equalizes the histogram of the image.

Output:

23. CLAHE (Contrast Limited Adaptive Histogram Equalization): Applies CLAHE for contrast enhancement.

Output:

24. Adaptive Thresholding: Applies adaptive thresholding.

Output:

25. Frequency Domain Filtering: Applies high-pass and low-pass filters in the frequency domain.

Output:

26. Bilateral Grid Filtering: Applies bilateral grid filtering.

Output:

27. Edge-Preserving Filter: Applies edge-preserving filtering.

Output:

28. Scharr Filter: Applies the Scharr filter for edge detection.

Output:

Project Structure

Image_Preprosseing/
│
├── Image_Preprosseing.ipynb  # Jupyter Notebook with all image processing techniques
├── requirements.txt          # List of dependencies
└── README.md                 # Project documentation

Contributing

Contributions are welcome! Please follow these steps:

1. .Fork the repository.
2. Create a feature branch (git checkout -b feature-branch).
3. Commit changes (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Open a Pull Request.

References

OpenCV Documentation:

https://docs.opencv.org/

Matplotlib Documentation:

https://matplotlib.org/stable/contents.html

Scikit-Image Documentation:

https://scikit-image.org/docs/stable/

License

MIT

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• I would like to express my sincere gratitude to my lecturer, Dr. Agughasi Victor Ikechukwu (https://github.com/Victor-Ikechukwu), for their invaluable guidance and support throughout this project.
• Their expertise and insights have been instrumental in the successful completion of this project on image clustering techniques using OpenCV and Matplotlib. I am deeply grateful for their encouragement and mentorship.
• Thank you for providing the knowledge and resources needed to explore and implement various computer vision tasks.