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- """
- Utils functions of ipfml package (array normalization)
- """
- import numpy as np
- 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)
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