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.