ipfml 0.1.1

Image Processing For Machine Learning

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

  • normalize_arr(arr): Normalize array values

  • normalize_arr_with_range(arr, min, max): Normalize array values with specific min and max values

  • rgb_to_mscn(image): Convert RGB Image into Mean Subtracted Contrast Normalized (MSCN) using only gray level

• 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.