Metadata-Version: 2.1
Name: fast_skimage
Version: 0.3.0
Summary: Fast and easy image processing using an Image class based on the scikit-image, numpy and matplotlib libraries.
Home-page: https://github.com/TortueSagace/fast_skimage
Author: Alexandre Le Mercier
Author-email: alexandre.le.mercier@ulb.be
License: MIT
Description: # fast-skimage
        
        Welcome to `fast-skimage`, an image acquisition and 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.
        - **Small Image Library**: 7 various pictures for testing are provided with the package (see section "Image Library" below).
        
        ## 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 fast_skimage import etretat
        from skimage.data import immunohistochemistry
        
        img = Image("Pictures/camera.jpg")  # Load an image with path...
        img2 = Image(immunohistochemistry())  # ... or numpy array ...
        colored_image_array = etretat() # ... or a library image.
        img3 = Image(colored_image_array.get())
        
        img2.auto_enhance()  # Apply auto-enhancement
        img3.auto_enhance()
        
        img3.show(subplots=(1, 2, 1), size=12)  # Display the result
        img2.show(subplots=(1, 2, 2), title='Immunochemistry Image')
        
        img.show(size=(12, 6), type_of_plot='hist', axis=True)  # Plot histogram
        ```
        
        ## Image Library
        A small image library is provided along with the `Image` class. These can be manually extracted with the following lines:
        ```python
        from fast_skimage import image_name
        image_array = image_name()
        image = Image(image_array.get())
        ```
        Note that all images listed below come from the INFO-H500 course of Prof. Olivier Debeir at ULB (Université Libre de Bruxelles).
        
        ### Grayscale Noisy Image
        
        - `fast-skimage.astronaut_noisy` 
        
        ### Grayscale Clean Images
        
        - `fast-skimage.camera`
        - `fast-skimage.walking`
        
        ### Grayscale Clean Watermark
        
        - `fast-skimage.watermark` (the ULB logo)
        
        ### Colored Clean Images
        
        - `fast-skimage.etretat`
        - `fast-skimage.nyc`
        - `fast-skimage.zebra`
        
        ## 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)
        - **LinkedIn**: [Alexandre Le Mercier](https://www.linkedin.com/in/alexandre-le-mercier-7b5594283/details/experience/)
        
        Happy Image Processing!
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
