# fast_skimage Library - Image Class

Welcome to the Image Processing Library. This powerful library offers a wide range of tools for advanced image manipulation and analysis, wrapped up in the accessible `Image` class.

## Features
- **Advanced Manipulation**: Apply complex operations like adding watermarks, noise detection, auto-enhancement, and saturation increase with simple method calls.
- **Filtering and Thresholding**: Includes mean, median filtering, Otsu's thresholding, and custom thresholding methods for image segmentation and noise reduction.
- **Fourier Transforms**: Utilize Fourier-based methods for reducing image dithering and other artifacts.
- **Histogram Operations**: Equalize and stretch image histograms to improve contrast and visibility.
- **Texture Analysis**: Perform texture segmentation using a variety of descriptors.

## Getting Started
1. **Installation**: Clone the repository or download the `Image` class module to your project.
2. **Dependencies**: Ensure all dependencies such as `numpy`, `matplotlib`, `scikit-image`, and `PyWavelets` are installed.
3. **Usage**: Import the `Image` class from the module and instantiate it with the path to your image or a NumPy array.

## Example
```python
from fast_skimage import Image
from skimage.data import immunohistochemistry

# Load an image with path...
img = Image("Pictures/camera.jpg")
# ... or numpy array
img2 = Image(immunohistochemistry())

# Apply auto-enhancement
img.auto_enhance()
img2.auto_enhance()

# Display the result
img.show(subplots=(1, 2, 1), size=12)
img2.show(subplots=(1, 2, 2), title='Immunochemistry Image')

# Plot histogram
img.show(size=(12, 6), type_of_plot='hist', axis=True)
```

## Documentation
Refer to the in-line comments and method docstrings for detailed usage of each feature.

## Contribution
Contributions are welcome! Feel free to submit pull requests, suggest features, or report bugs.

## License
This library is distributed under the MIT license. See `LICENSE` for more information.

## Contact
- **Author**: Alexandre Le Mercier
- **Date**: November 21, 2023
- **Email**: [alexandre.le.mercier@ulb.be](mailto:alexandre.le.mercier@ulb.be)

Happy Image Processing!