ipfml.processing.transform

Functions which can be used to extract information from image or reduce it

Functions

get_LAB(image)	Transforms RGB Image into Lab
get_LAB_L(image)	Transforms RGB Image into Lab and returns L
get_LAB_L_SVD(image)	Returns Singular values from LAB L Image information
get_LAB_L_SVD_U(image)	Returns U SVD from L of LAB Image information
get_LAB_L_SVD_V(image)	Returns V 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_a(image)	Transforms RGB Image into LAB and returns a
get_LAB_b(image)	Transforms RGB Image into LAB and returns b
get_XYZ(image)	Transforms RGB Image into XYZ
get_XYZ_X(image)	Transforms RGB Image into XYZ and returns X
get_XYZ_Y(image)	Transforms RGB Image into XYZ and returns Y
get_XYZ_Z(image)	Transforms RGB Image into XYZ and returns Z
get_bits_img(image, interval)	Returns only bits specified into the interval
get_low_bits_img(image[, nb_bits])	Returns Image or Numpy array with data information reduced using only low bits
get_mscn_coefficients(image)	Compute the Mean Substracted Constrast Normalized coefficients of an image
gray_to_mscn(image)	Convert Grayscale Image into Mean Subtracted Contrast Normalized (MSCN)
rgb_to_LAB_L_bits(image, interval)	Returns only bits from LAB L canal specified into the interval
rgb_to_LAB_L_low_bits(image[, nb_bits])	Convert RGB Image into Lab L channel image using only 4 low bits values
rgb_to_grey_low_bits(image[, nb_bits])	Convert RGB Image into grey image using only 4 low bits values
rgb_to_mscn(image)	Convert RGB Image into Mean Subtracted Contrast Normalized (MSCN)

ipfml.processing.transform.get_LAB(image)

Transforms RGB Image into Lab

Parameters:image – image to convert Returns:Lab information

Usage:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> Lab = transform.get_LAB(img)
>>> Lab.shape
(200, 200, 3)

ipfml.processing.transform.get_LAB_L(image)

Transforms RGB Image into Lab and returns L

Parameters:image – image to convert Returns:The L chanel from Lab information

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> L = transform.get_LAB_L(img)
>>> L.shape
(200, 200)

ipfml.processing.transform.get_LAB_L_SVD(image)

Returns Singular values from LAB L Image information

Parameters:image – PIL Image or Numpy array Returns:U, s, V information obtained from SVD compression using Lab

Example:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> U, s, V = transform.get_LAB_L_SVD(img)
>>> U.shape
(200, 200)
>>> len(s)
200
>>> V.shape
(200, 200)

ipfml.processing.transform.get_LAB_L_SVD_U(image)

Returns U SVD from L of LAB Image information

Parameters:image – PIL Image or Numpy array Returns:U matrix of SVD compression

Example:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> U = transform.get_LAB_L_SVD_U(img)
>>> U.shape
(200, 200)

ipfml.processing.transform.get_LAB_L_SVD_V(image)

Returns V SVD from L of LAB Image information

Parameters:image – PIL Image or Numpy array Returns:V matrix of SVD compression

Example:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> V = transform.get_LAB_L_SVD_V(img)
>>> V.shape
(200, 200)

ipfml.processing.transform.get_LAB_L_SVD_s(image)

Returns s (Singular values) SVD from L of LAB Image information

Parameters:image – PIL Image or Numpy array Returns:vector of singular values

Example:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> s = transform.get_LAB_L_SVD_s(img)
>>> len(s)
200

ipfml.processing.transform.get_LAB_a(image)

Transforms RGB Image into LAB and returns a

Parameters:image – image to convert Returns:The a chanel from Lab information

Usage:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> a = transform.get_LAB_a(img)
>>> a.shape
(200, 200)

ipfml.processing.transform.get_LAB_b(image)

Transforms RGB Image into LAB and returns b

Parameters:image – image to convert Returns:The b chanel from Lab information

Usage :

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> b = transform.get_LAB_b(img)
>>> b.shape
(200, 200)

ipfml.processing.transform.get_XYZ(image)

Transforms RGB Image into XYZ

Parameters:image – image to convert Returns:XYZ information obtained from transformation

Usage:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> transform.get_XYZ(img).shape
(200, 200, 3)

ipfml.processing.transform.get_XYZ_X(image)

Transforms RGB Image into XYZ and returns X

Parameters:image – image to convert Returns:The X chanel from XYZ information

Usage:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> x = transform.get_XYZ_X(img)
>>> x.shape
(200, 200)

ipfml.processing.transform.get_XYZ_Y(image)

Transforms RGB Image into XYZ and returns Y

Parameters:image – image to convert Returns:The Y chanel from XYZ information

Usage:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> y = transform.get_XYZ_Y(img)
>>> y.shape
(200, 200)

ipfml.processing.transform.get_XYZ_Z(image)

Transforms RGB Image into XYZ and returns Z

Parameters:image – image to convert Returns:The Z chanel from XYZ information Raises:ValueError – If nb_bits has unexpected value. nb_bits needs to be in interval [1, 8].

Usage:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> z = transform.get_XYZ_Z(img)
>>> z.shape
(200, 200)

ipfml.processing.transform.get_bits_img(image, interval)

Returns only bits specified into the interval

Parameters:

• image – image to convert using this interval of bits value to keep
• interval – (begin, end) of bits values

Returns:

Numpy array with reduced values

Raises:

• ValueError – If min value from interval is not >= 1.
• ValueError – If max value from interval is not <= 8.
• ValueError – If min value from interval >= max value.

Usage:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> bits_img = transform.get_bits_img(img, (2, 5))
>>> bits_img.shape
(200, 200, 3)

ipfml.processing.transform.get_low_bits_img(image, nb_bits=4)

Returns Image or Numpy array with data information reduced using only low bits

Parameters:

• image – image to convert
• nb_bits – optional parameter which indicates the number of bits to keep

Returns:

Numpy array with reduced values

Usage:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> low_bits_img = transform.get_low_bits_img(img, 5)
>>> low_bits_img.shape
(200, 200, 3)

ipfml.processing.transform.get_mscn_coefficients(image)

Compute the Mean Substracted Constrast Normalized coefficients of an image

Parameters:

image – PIL Image, Numpy array or path of image

Returns:

MSCN coefficients

Raises:

• FileNotFoundError – If image is set as str path and image was not found
• ValueError – If image numpy shape are not correct

Example:

>>> from PIL import Image
>>> import numpy as np
>>> from ipfml.processing import transform
>>> image_values = Image.open('./images/test_img.png')
>>> mscn_coefficients = transform.get_mscn_coefficients(image_values)
>>> mscn_coefficients.shape
(200, 200)

ipfml.processing.transform.gray_to_mscn(image)

Convert Grayscale Image into Mean Subtracted Contrast Normalized (MSCN)

Parameters:image – grayscale image Returns:MSCN matrix obtained from transformation

Usage:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> img = transform.get_LAB_L(img)
>>> img_mscn = transform.gray_to_mscn(img)
>>> img_mscn.shape
(200, 200)

ipfml.processing.transform.rgb_to_LAB_L_bits(image, interval)

Returns only bits from LAB L canal specified into the interval

Parameters:

• image – image to convert using this interval of bits value to keep
• interval – (begin, end) of bits values

Returns:

2D Numpy array with reduced values

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> bits_Lab_l_img = transform.rgb_to_LAB_L_bits(img, (2, 6))
>>> bits_Lab_l_img.shape
(200, 200)

ipfml.processing.transform.rgb_to_LAB_L_low_bits(image, nb_bits=4)

Convert RGB Image into Lab L channel image using only 4 low bits values

Parameters:

• image – 3D RGB image Numpy array or PIL RGB image
• nb_bits – optional parameter which indicates the number of bits to keep (default 4)

Returns:

2D Numpy array with low bits information kept

Example:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> low_bits_Lab_l_img = transform.rgb_to_LAB_L_low_bits(img, 5)
>>> low_bits_Lab_l_img.shape
(200, 200)

ipfml.processing.transform.rgb_to_grey_low_bits(image, nb_bits=4)

Convert RGB Image into grey image using only 4 low bits values

Parameters:

• image – 3D RGB image Numpy array or PIL RGB image
• nb_bits – optional parameter which indicates the number of bits to keep (default 4)

Returns:

2D Numpy array with low bits information kept

Example:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> low_bits_grey_img = transform.rgb_to_grey_low_bits(img, 5)
>>> low_bits_grey_img.shape
(200, 200)

ipfml.processing.transform.rgb_to_mscn(image)

Convert RGB Image into Mean Subtracted Contrast Normalized (MSCN)

Parameters:image – 3D RGB image Numpy array or PIL RGB image Returns:2D Numpy array with MSCN information

Example:

>>> from PIL import Image
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> img_mscn = transform.rgb_to_mscn(img)
>>> img_mscn.shape
(200, 200)