ipfml.processing

Functions to quickly extract reduced information from image

Functions

divide_in_blocks(image, block_size[, pil])	Divide image into equal size blocks
fast_ica_reconstruction(image, components)	Reconstruct an image from SVD compression using specific number of components to use
fusion_images(images[, pil])	Fusion array of images into single image
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_mscn_coefficients(image)	Compute the Mean Substracted Constrast Normalized coefficients of an image
ipca_reconstruction(image, components[, …])	Reconstruct an image from SVD compression using specific number of components to use and batch size
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)
rotate_image(image[, angle, pil])	Rotate image using specific angle
svd_reconstruction(image, interval)	Reconstruct an image from SVD compression using specific interval of Singular Values

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 as PIL Image (default True)

Returns:

list containing all 2D Numpy blocks (in RGB or not)

Raises:

ValueError – If image_width or image_height 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.fast_ica_reconstruction(image, components)

Reconstruct an image from SVD compression using specific number of components to use

Parameters:

• image – PIL Image, Numpy array or path of 3D image
• components – Number of components used for reconstruction

Returns:

Reconstructed image

Example:

>>> from PIL import Image
>>> import numpy as np
>>> from ipfml import processing
>>> image_values = Image.open('./images/test_img.png')
>>> reconstructed_image = processing.fast_ica_reconstruction(image_values, 25)
>>> reconstructed_image.shape
(200, 200)

ipfml.processing.fusion_images(images, pil=True)

Fusion array of images into single image

Parameters:

• images – array of images (PIL Image or Numpy array)
• pil – block type returned as PIL Image (default True)

Returns:

merged image from array of images

Raises:

• ValueError – if images is not an array or is empty
• NumpyShapeComparisonException – if images array contains images with different shapes

Example:

>>> import numpy as np
>>> from ipfml import processing
>>> image_values_1 = np.random.randint(255, size=(800, 800, 3))
>>> image_values_2 = np.random.randint(255, size=(800, 800, 3))
>>> merged_image = processing.fusion_images([image_values_1, image_values_2], pil=False)
>>> merged_image.shape
(800, 800, 3)

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.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 import processing
>>> image_values = Image.open('./images/test_img.png')
>>> mscn_coefficients = processing.get_mscn_coefficients(image_values)
>>> mscn_coefficients.shape
(200, 200)

ipfml.processing.ipca_reconstruction(image, components, _batch_size=25)

Reconstruct an image from SVD compression using specific number of components to use and batch size

Parameters:

• image – PIL Image, Numpy array or path of 3D image
• components – Number of components used for reconstruction
• batch_size – Batch size used for learn (default 25)

Returns:

Reconstructed image

Example:

>>> from PIL import Image
>>> import numpy as np
>>> from ipfml import processing
>>> image_values = Image.open('./images/test_img.png')
>>> reconstructed_image = processing.ipca_reconstruction(image_values, 20)
>>> reconstructed_image.shape
(200, 200)

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.processing.rotate_image(image, angle=90, pil=True)

Rotate image using specific angle

Parameters:

• image – PIL Image or Numpy array
• angle – Angle value of the rotation
• pil – block type returned as PIL Image (default True)

Returns:

Image with rotation applied

Example:

>>> from PIL import Image
>>> import numpy as np
>>> from ipfml import processing
>>> image_values = Image.open('./images/test_img.png')
>>> rotated_image = processing.rotate_image(image_values, 90, pil=False)
>>> rotated_image.shape
(200, 200, 3)

ipfml.processing.svd_reconstruction(image, interval)

Reconstruct an image from SVD compression using specific interval of Singular Values

Parameters:

• image – PIL Image, Numpy array or path of 3D image
• interval – Interval used for reconstruction

Returns:

Reconstructed image

Example:

>>> from PIL import Image
>>> import numpy as np
>>> from ipfml import processing
>>> image_values = Image.open('./images/test_img.png')
>>> reconstructed_image = processing.svd_reconstruction(image_values, (100, 200))
>>> reconstructed_image.shape
(200, 200)