ipfml

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.

• image_processing : Image processing module

  • 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
  • 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

• metrics : Metrics computation of PIL or 2D numpy image

  • get_SVD(image): Transforms Image into SVD
  • get_SVD_U(image): Transforms Image into SVD and returns only 'U' part
  • get_SVD_s(image): Transforms Image into SVD and returns only 's' part
  • get_SVD_V(image): Transforms Image into SVD and returns only 'V' part
  • get_LAB(image): Transforms Image into LAB
  • get_LAB_L(image): Transforms Image into LAB and returns only 'L' part
  • get_LAB_A(image): Transforms Image into LAB and returns only 'A' part
  • get_LAB_B(image): Transforms Image into LAB and returns only 'B' part
  • get_XYZ(image): Transforms Image into XYZ
  • get_XYZ_X(image): Transforms Image into XYZ and returns only 'X' part
  • get_XYZ_Y(image): Transforms Image into XYZ and returns only 'Y' part
  • get_XYZ_Z(image): Transforms 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

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.