"""
Functions which can be used to extract information from image
"""
from numpy.linalg import svd
from scipy import misc
import numpy as np
from sklearn import preprocessing
from skimage import io, color
import cv2
[docs]def get_SVD(image):
"""Transforms Image using SVD compression
Args:
image: image to convert into SVD compression
Return:
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)
"""
return svd(image, full_matrices=False)
[docs]def get_SVD_s(image):
"""Transforms Image into SVD and returns only 's' part
Args:
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
"""
U, s, V = svd(image, full_matrices=False)
return s
[docs]def get_SVD_U(image):
"""Transforms Image into SVD and returns only 'U' part
Args:
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)
"""
U, s, V = svd(image, full_matrices=False)
return U
[docs]def get_SVD_V(image):
"""Transforms Image into SVD and returns only 'V' part
Args:
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)
"""
U, s, V = svd(image, full_matrices=False)
return V
[docs]def get_LAB(image):
"""Transforms RGB Image into Lab
Args:
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)
"""
return color.rgb2lab(image)
[docs]def get_LAB_L(image):
"""Transforms RGB Image into Lab and returns L
Args:
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)
"""
lab = get_LAB(image)
return lab[:, :, 0]
[docs]def get_LAB_a(image):
"""Transforms RGB Image into LAB and returns a
Args:
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)
"""
lab = get_LAB(image)
return lab[:, :, 1]
[docs]def get_LAB_b(image):
"""Transforms RGB Image into LAB and returns b
Args:
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)
"""
lab = get_LAB(image)
return lab[:, :, 2]
[docs]def get_XYZ(image):
"""Transforms RGB Image into XYZ
Args:
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)
"""
return color.rgb2xyz(image)
[docs]def get_XYZ_X(image):
"""Transforms RGB Image into XYZ and returns X
Args:
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)
"""
xyz = color.rgb2xyz(image)
return xyz[:, :, 0]
[docs]def get_XYZ_Y(image):
"""Transforms RGB Image into XYZ and returns Y
Args:
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)
"""
xyz = color.rgb2xyz(image)
return xyz[:, :, 1]
[docs]def get_XYZ_Z(image):
"""Transforms RGB Image into XYZ and returns Z
Args:
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)
"""
xyz = color.rgb2xyz(image)
return xyz[:, :, 2]
[docs]def get_low_bits_img(image, nb_bits=4):
"""Returns Image or Numpy array with data information reduced using only low bits
Args:
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)
"""
if nb_bits <= 0:
raise ValueError(
"unexpected value of number of bits to keep. @nb_bits needs to be positive and greater than 0."
)
if nb_bits > 8:
raise ValueError(
"Unexpected value of number of bits to keep. @nb_bits needs to be in interval [1, 8]."
)
img_arr = np.array(image)
bits_values = sum([pow(2, i - 1) for i in range(1, nb_bits + 1)])
return img_arr & bits_values
[docs]def get_bits_img(image, interval):
"""Returns only bits specified into the interval
Args:
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)
"""
img_arr = np.array(image)
begin, end = interval
if begin < 1:
raise ValueError(
"Unexpected value of interval. Interval min value needs to be >= 1."
)
if end > 8:
raise ValueError(
"Unexpected value of interval. Interval min value needs to be <= 8."
)
if begin >= end:
raise ValueError("Unexpected interval values order.")
bits_values = sum([pow(2, i - 1) for i in range(begin, end + 1)])
return img_arr & bits_values
[docs]def gray_to_mscn(image):
"""Convert Grayscale Image into Mean Subtracted Contrast Normalized (MSCN)
Args:
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)
"""
s = 7 / 6
blurred = cv2.GaussianBlur(image, (7, 7),
s) # apply gaussian blur to the image
blurred_sq = blurred * blurred
sigma = cv2.GaussianBlur(image * image, (7, 7), s)
sigma = abs(sigma - blurred_sq)**0.5
sigma = sigma + 1.0 / 255 # avoid DivideByZero Exception
mscn = (image - blurred) / sigma # MSCN(i, j) image
return mscn