fast-skimage 0.1.1

Demo library

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

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: [e-mail]

Happy Image Processing!