ipfml.metrics

Functions which can be used to extract information from image

Functions

get_LAB(image)	Transforms RGB Image into Lab
get_LAB_L(image)	Transforms RGB Image into Lab and returns L
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_SVD(image)	Transforms Image using SVD compression
get_SVD_U(image)	Transforms Image into SVD and returns only ‘U’ part
get_SVD_V(image)	Transforms Image into SVD and returns only ‘V’ part
get_SVD_s(image)	Transforms Image into SVD and returns only ‘s’ part
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
gray_to_mscn(image)	Convert Grayscale Image into Mean Subtracted Contrast Normalized (MSCN)

ipfml.metrics.get_LAB(image)

Transforms RGB Image into Lab

Parameters:image – image to convert Returns:Lab information

Usage:

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

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

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

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

ipfml.metrics.get_SVD(image)

Transforms Image using SVD compression

Parameters:image – image to convert into SVD compression Returns:U, s, V obtained from SVD compression

Usage:

>>> from PIL import Image
>>> from ipfml import metrics
>>> img = Image.open('./images/test_img.png')
>>> U, s, V = metrics.get_SVD(img)
>>> U.shape
(200, 200, 3)
>>> len(s)
200
>>> V.shape
(200, 3, 3)

ipfml.metrics.get_SVD_U(image)

Transforms Image into SVD and returns only ‘U’ part

Parameters:image – image to convert Returns:U matrix from SVD compression

Usage:

>>> from PIL import Image
>>> from ipfml import metrics
>>> img = Image.open('./images/test_img.png')
>>> U = metrics.get_SVD_U(img)
>>> U.shape
(200, 200, 3)

ipfml.metrics.get_SVD_V(image)

Transforms Image into SVD and returns only ‘V’ part

Parameters:image – image to convert Returns:V matrix obtained from SVD compression

Usage :

>>> from PIL import Image
>>> from ipfml import metrics
>>> img = Image.open('./images/test_img.png')
>>> V = metrics.get_SVD_V(img)
>>> V.shape
(200, 3, 3)

ipfml.metrics.get_SVD_s(image)

Transforms Image into SVD and returns only ‘s’ part

Parameters:image – image to convert Returns:vector of singular values obtained from SVD compression

Usage:

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

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

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

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

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

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

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

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