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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)