ipfml 0.1.4

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.

• image_processingImage processing module

  • 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

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

  • rgb_to_grey_low_bits(image, bind=15): Convert RGB Image into grey image using only 4 low bits values by default

  • rgb_to_LAB_L_low_bits(image, bind=15): Convert RGB Image into LAB L channel image using only 4 low bits values by default

  • normalize_arr(arr): Normalize array values

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

  • normalize_2D_arr(arr): Return 2D array normalize from its min and max values

• metricsMetrics computation of PIL or 2D numpy 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

  • get_low_bits_img(image, bind=15): Returns Image or Numpy array with data information reduced using only low bits (by default 4)

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.