Image Processing For Machine Learning python Package https://pypi.org/project/IPFML/

Jerome Buisine b29b6d64f5 Merge branch 'release/v0.1.4' 5 年 前
images 420f833455 Add of metrics tests 5 年 前
ipfml 53632a2490 Hotfix of unit tests 5 年 前
.gitignore 433801fdfb Initial commit 5 年 前
.python-version d338201ddc First functions added 5 年 前
LICENSE 433801fdfb Initial commit 5 年 前
MANIFEST.in 420f833455 Add of metrics tests 5 年 前
README.md 53632a2490 Hotfix of unit tests 5 年 前
README.rst 53632a2490 Hotfix of unit tests 5 年 前
example.py 139508ebd7 Update of image_processing divide_in_block function 5 年 前
setup.py 4af2a8e833 Update of version 5 年 前

README.md

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
    • 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
  • 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.