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Image Processing For Machine Learning

Project description

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_processingPIL 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

  • metricsMetrics 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_helpercontains 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.

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