An open API service indexing awesome lists of open source software.

https://github.com/t4vexx/image-processing

This repository contains a collection of Jupyter notebooks and scripts designed to explore various techniques in digital image processing. The techniques covered include image equalization, noise operations, edge detection, and more.
https://github.com/t4vexx/image-processing

cv2 image-processing python

Last synced: 2 months ago
JSON representation

This repository contains a collection of Jupyter notebooks and scripts designed to explore various techniques in digital image processing. The techniques covered include image equalization, noise operations, edge detection, and more.

Awesome Lists containing this project

README

          

# Digital Image Processing

This repository contains a collection of notebooks and scripts to explore different techniques in digital image processing. The techniques covered include image equalization, noise operations, edge detection, and more.

## Contents

### Notebooks

1. **ImageEqualize.ipynb**
- Image equalization methods.
- Implementation of contrast stretching and histogram equalization.

2. **ImageOperation.ipynb**
- Basic image operations, such as image multiplication and division.
- Normalization of results for visualization.

3. **FixNoise.ipynb**
- Noise models for images.
- Implementation of uniform, Gaussian, and salt-and-pepper noise.

4. **BorderDetection.ipynb**
- Introduction to edge detection in images.
- 4-neighborhood and 8-neighborhood verification for object detection.

### Main Functions

- **salt_and_pepper_noise**: Adds salt and pepper noise to an image.
- **uniform_noise**: Adds uniform noise to an image.
- **gaussian_noise**: Adds Gaussian noise to an image.
- **verifica4neighborhood**: Verifies objects using 4-neighborhood.
- **verifica8neighborhood**: Verifies objects using 8-neighborhood.

## Requirements

- Python 3.6 or higher
- Libraries: `numpy`, `matplotlib`, `opencv-python`

## How to Use

1. Clone the repository:
```bash
git clone https://github.com/your-username/your-repository.git
```

2. Install the dependencies:
```bash
pip install -r requirements.txt
```

3. Run the notebooks using Jupyter:
```bash
jupyter notebook
```

## Contributions

Contributions are welcome! Feel free to open issues or submit pull requests.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.