Documentation¶

ipfml.metrics¶

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 processing
>>> img = Image.open('./images/test_img.png')
>>> img_mscn = processing.rgb_to_mscn(img)
>>> img_mscn.shape
(200, 200)

ipfml.processing¶

ipfml.processing.divide_in_blocks(image, block_size, pil=True)¶

Divide image into equal size blocks

Parameters:	image – PIL Image or Numpy array block – tuple (width, height) representing the size of each dimension of the block pil – block type returned (default True)
Returns:	list containing all 2D Numpy blocks (in RGB or not)
Raises:	`ValueError` – If image_width or image_heigt are not compatible to produce correct block sizes

Example:

>>> import numpy as np
>>> from PIL import Image
>>> from ipfml import processing
>>> from ipfml import metrics
>>> image_values = np.random.randint(255, size=(800, 800, 3))
>>> blocks = divide_in_blocks(image_values, (20, 20))
>>> len(blocks)
1600
>>> blocks[0].width
20
>>> blocks[0].height
20
>>> img_l = Image.open('./images/test_img.png')
>>> L = metrics.get_LAB_L(img_l)
>>> blocks_L = divide_in_blocks(L, (100, 100))
>>> len(blocks_L)
4
>>> blocks_L[0].width
100

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

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

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

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

ipfml.processing.normalize_2D_arr(arr)¶

Return array normalize from its min and max values

Parameters:	arr – 2D Numpy array
Returns:	Normalized 2D Numpy array

Example:

>>> from PIL import Image
>>> from ipfml import processing
>>> img = Image.open('./images/test_img.png')
>>> img_mscn = processing.rgb_to_mscn(img)
>>> img_normalized = processing.normalize_2D_arr(img_mscn)
>>> img_normalized.shape
(200, 200)

ipfml.processing.normalize_arr(arr)¶

Normalize data of 1D array shape

Parameters:	arr – array data of 1D shape
Returns:	Normalized 1D array

Example:

>>> from ipfml import processing
>>> import numpy as np
>>> arr = np.arange(11)
>>> arr_normalized = processing.normalize_arr(arr)
>>> arr_normalized[1]
0.1

ipfml.processing.normalize_arr_with_range(arr, min, max)¶

Normalize data of 1D array shape

Parameters:	arr – array data of 1D shape
Returns:	Normalized 1D Numpy array

Example:

>>> from ipfml import processing
>>> import numpy as np
>>> arr = np.arange(11)
>>> arr_normalized = processing.normalize_arr_with_range(arr, 0, 20)
>>> arr_normalized[1]
0.05

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

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

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

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

ipfml.filters¶

ipfml.filters.noise¶

ipfml.filters.noise.cauchy_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.0002)¶

Cauchy noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (0, 1))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.0002)

Returns:

2D Numpy array with Cauchy noise applied

Example:

>>> from ipfml.filters.noise import cauchy_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = cauchy_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.gaussian_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.1)¶

Gaussian noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (0, 1))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.1)

Returns:

2D Numpy array with gaussian noise applied

Example:

>>> from ipfml.filters.noise import gaussian_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = gaussian_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.laplace_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.1)¶

Laplace noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (0, 1))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.1)

Returns:

2D Numpay array with Laplace noise applied

Example:

>>> from ipfml.filters.noise import laplace_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = laplace_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.log_normal_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.05)¶

Log-normal noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (0, 1))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.05)

Returns:

2D Numpy array with Log-normal noise applied

Example:

>>> from ipfml.filters.noise import log_normal_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = log_normal_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.mut_white_noise(image, n, identical=False, distribution_interval=(-0.5, 0.5), k=0.2)¶

Multiplied White noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (-0.5, 0.5))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.2)

Returns:

2D Numpy array with multiplied white noise applied

Example:

>>> from ipfml.filters.noise import mut_white_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = mut_white_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.salt_pepper_noise(image, n, identical=False, p=0.1, k=0.5)¶

Pepper salt noise filter to apply on image

Parameters:	image – image used as input (2D or 3D image representation) n – used to set importance of noise [1, 999] identical – keep or not identical noise distribution for each canal if RGB Image (default False) p – probability to increase pixel value otherwise decrease it k – variable that specifies the amount of noise to be taken into account in the output image (default 0.5)
Returns:	2D Numpy array with salt and pepper noise applied

Example:

>>> from ipfml.filters.noise import salt_pepper_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = salt_pepper_noise(image, 10)
>>> noisy_image.shape
(100, 100)

ipfml.filters.noise.white_noise(image, n, identical=False, distribution_interval=(-0.5, 0.5), k=0.2)¶

White noise filter to apply on image

Parameters:

image – image used as input (2D or 3D image representation)
n – used to set importance of noise [1, 999]
identical – keep or not identical noise distribution for each canal if RGB Image (default False)
distribution_interval – set the distribution interval of normal law distribution (default (-0.5, 0.5))
k – variable that specifies the amount of noise to be taken into account in the output image (default 0.2)

Returns:

2D Numpy array with white noise applied

Example:

>>> from ipfml.filters.noise import white_noise
>>> import numpy as np
>>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
>>> noisy_image = white_noise(image, 10)
>>> noisy_image.shape
(100, 100)