ipfml/ipfml/utils.py

import numpy as np


"""
Utils functions of ipfml package
"""

def normalize_arr(arr):
    '''Normalize data of 1D array shape

    Args:
        arr: array data of 1D shape

    Returns:
        Normalized 1D array

    Example:

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

    output_arr = []
    max_value = max(arr)
    min_value = min(arr)

    for v in arr:
        output_arr.append((v - min_value) / (max_value - min_value))

    return output_arr


def normalize_arr_with_range(arr, min, max):
    '''Normalize data of 1D array shape

    Args:
        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
    '''

    output_arr = []

    for v in arr:
        output_arr.append((v - min) / (max - min))

    return output_arr


def normalize_2D_arr(arr):
    """Return array normalize from its min and max values

    Args:
        arr: 2D Numpy array

    Returns:
        Normalized 2D Numpy array

    Example:

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

    # getting min and max value from 2D array
    max_value = arr.max(axis=1).max()
    min_value = arr.min(axis=1).min()

    # normalize each row
    output_array = []
    width, height = arr.shape

    for row_index in range(0, height):
        values = arr[row_index, :]
        output_array.append(
            normalize_arr_with_range(values, min_value, max_value))

    return np.asarray(output_array)