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.

• 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, nb_bits=4): Convert RGB Image into grey image using only 4 low bits values by default
  • rgb_to_LAB_L_low_bits(image, nb_bits=4): Convert RGB Image into LAB L chanel image using only 4 low bits values by default
  • rgb_to_LAB_L_bits(image, interval): Convert RGB Image into LAB L chanel image using specific interval of bits to keep (2, 5) such as example
  • 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, nb_bits=4): Returns Image or Numpy array with data information reduced using only low bits

• filters : Image filter module

  • noise : Noise filters implemented
    • white_noise(image, n, distribution_interval=(-0.5, 0.5)) : *Add white noise to image using the n variable which manages intensity of noise in interval [1, 999] and the distribution_interval variable which manages interval of uniform distribution*

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.