ipfml/ipfml/metrics.py

# module file which contains all image metrics used in project

from numpy.linalg import svd
from scipy import misc

import numpy as np
from sklearn import preprocessing
from skimage import io, color

import cv2

def get_SVD(image):
    """
    @brief Transforms Image into SVD
    @param image to convert
    @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)

def get_SVD_s(image):
    """
    @brief Transforms Image into SVD and returns only 's' part
    @param image - image to convert
    @return s 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

def get_SVD_U(image):
    """
    @brief Transforms Image into SVD and returns only 'U' part
    @param image - image to convert
    @return 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

def get_SVD_V(image):
    """
    @brief Transforms Image into SVD and returns only 'V' part
    @param image - image to convert
    @return 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

def get_LAB(image):
    """
    @brief Transforms RGB  Image into Lab
    @param image - image to convert
    @return 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)

def get_LAB_L(image):
    """
    @brief Transforms RGB Image into Lab and returns L
    @param image - image to convert
    @return 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]

def get_LAB_a(image):
    """
    @brief Transforms RGB Image into LAB and returns a
    @param image - image to convert
    @return 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]

def get_LAB_b(image):
    """
    @brief Transforms RGB Image into LAB and returns b
    @param image - image to convert
    @return 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]

def get_XYZ(image):
    """
    @brief Transforms RGB Image into XYZ
    @param image - image to convert
    @return 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)

def get_XYZ_X(image):
    """
    @brief Transforms RGB Image into XYZ and returns X
    @param image - image to convert
    @return 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]

def get_XYZ_Y(image):
    """
    @brief Transforms RGB Image into XYZ and returns Y
    @param image - image to convert
    @return 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]

def get_XYZ_Z(image):
    """
    @brief Transforms RGB Image into XYZ and returns Z
    @param image - image to convert
    @return Z chanel from XYZ information

    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]

def get_low_bits_img(image, nb_bits=4):
    """
    @brief Returns Image or Numpy array with data information reduced using only low bits
    @param image, image to convert
    @param nb_bits, optional parameter which indicates the number of bits to keep
    @return 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

def get_bits_img(image, interval):
    """
    @brief Returns only bits specified into the interval
    @param image to convert using this interval of bits value to keep
    @param interval (begin, end) of bits values
    @return Numpy array with reduced values

    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. Minimum value of interval needs to be >= 1.")

    if end > 8:
        raise ValueError("Unexpected value of interval. Maximum value of interval 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


def gray_to_mscn(image):
    """
    @brief Convert Grayscale Image into Mean Subtracted Contrast Normalized (MSCN)
    @param 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)
    """

    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