Metadata-Version: 1.1
Name: IPFML
Version: 0.0.8
Summary: Image Processing For Machine Learning
Home-page: https://gogs.univ-littoral.fr/jerome.buisine/IPFML
Author: Jérôme BUISINE
Author-email: jerome.buisine@univ-littoral.fr
License: MIT
Description: IPFML
        =====
        
        Image Processing For Machine Learning package.
        
        How to use ?
        ------------
        
        To use, simply do :
        
            >>> from PIL import Image
            >>> from ipfml import image_processing
            >>> img = Image.open('path/to/image.png')
            >>> s = image_processing.get_LAB_L_SVD_s(img)
        
        
        Modules
        -------
        
        This project contains modules.
        
        - **img_processing** : *PIL image processing part*
            - fig2data(fig): *Convert a Matplotlib figure to a 3D numpy array with RGB channels and return it*
            - fig2img(fig): *Convert a Matplotlib figure to a PIL Image in RGB format and return it*
            - get_LAB_L_SVD_U(image): *Returns U SVD from L of LAB Image information*
            - get_LAB_L_SVD_s(image): *Returns s (Singular values) SVD from L of LAB Image information*
            - get_LAB_L_SVD_V(image): *Returns V SVD from L of LAB Image information*
            - divide_in_blocks(image, block_size): Divide image into equal size blocks
        
        - **metrics** : *Metrics computation of PIL image*
            - get_SVD(image): *Transforms PIL Image into SVD*
            - get_SVD_U(image): *Transforms PIL Image into SVD and returns only 'U' part*
            - get_SVD_s(image): *Transforms PIL Image into SVD and returns only 's' part*
            - get_SVD_V(image): *Transforms PIL Image into SVD and returns only 'V' part*
            - get_LAB(image): *Transforms PIL Image into LAB*
            - get_LAB_L(image): *Transforms PIL Image into LAB and returns only 'L' part*
            - get_LAB_A(image): *Transforms PIL Image into LAB and returns only 'A' part*
            - get_LAB_B(image): *Transforms PIL Image into LAB and returns only 'B' part*
            - get_XYZ(image): *Transforms PIL Image into XYZ*
            - get_XYZ_X(image): *Transforms PIL Image into XYZ and returns only 'X' part*
            - get_XYZ_Y(image): *Transforms PIL Image into XYZ and returns only 'Y' part*
            - get_XYZ_Z(image): *Transforms PIL Image into XYZ and returns only 'Z' part*
        
        - **ts_model_helper** : *contains helpful function to save or display model information and performance of tensorflow model*
            - save(history, filename): *Function which saves data from neural network model*
            - show(history, filename): *Function which shows data from neural network model*
        
        All these modules will be enhanced during development of the project
        
        How to contribute
        -----------------
        
        This git project uses git-flow_ implementation. You are free to contribute to it.
        
        .. _git-flow : https://danielkummer.github.io/git-flow-cheatsheet/
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
