pyosv.utils.printer

  1import numpy as np
  2
  3def dict_disp(dictionary : dict) -> None:
  4    '''
  5        Print a dictionary in a more readable way
  6
  7        Parameter:
  8        ----------
  9            - dictionary : dict
 10                python dictionary to print
 11        
 12        Returns:
 13        --------
 14        Nothing, it will print the dictionary
 15
 16        Usage:
 17        ------
 18
 19        ```python
 20        dictionary = {'Name' : 'John', 'Surname' : 'Wick'}
 21        disp_dict(dictionary)
 22        ```
 23
 24        Output:
 25        -------
 26
 27        ```
 28        -----------Dictionary------------  
 29            [*] Name : Johnny
 30            [*] Surname : Wick
 31        ```
 32
 33    '''
 34
 35    l_k = len([k for k in dictionary.keys() if len(str(k))==max([len(str(n)) for n in dictionary.keys()])][0])
 36    l_v = len([k for k in dictionary.values() if len(str(k))==max([len(str(n)) for n in dictionary.values()])][0])
 37
 38    n =  20 + l_k + l_v
 39    print('{:-^{len}}'.format('Dictionary', len=n))
 40
 41    for key, value in dictionary.items():
 42        print('\t [*] {} : {}'.format(str(key), str(value)))
 43
 44
 45def print_stats(img : np.ndarray, bandwise : bool = False) -> None:
 46    '''
 47        Print some statistics for the input image
 48
 49        Parameters:
 50        -----------
 51            - image: a WxHxB image, with width W, height H and B bands
 52            - bandwise : bool 
 53                if true the function will print the statistics for each band separately (default : False)
 54        
 55        Returns:
 56        --------
 57        Nothing, it will print image statistics
 58
 59        Usage:
 60        ------
 61        ```python
 62        import numpy as np
 63
 64        img         = np.array(  
 65            [[
 66                [0.1, 0.2, 0.3],  
 67                [0.4, 0.5, 0.6],  
 68                [0.7, 0.8, 0.9]
 69             ],
 70             [
 71                [1.1, 1.2, 1.3],  
 72                [1.4, 1.5, 1.6],  
 73                [1.7, 1.8, 1.9]
 74             ]
 75            ]  
 76        ) 
 77
 78        # Making channels last
 79        img = np.moveaxis(img, 0, -1)
 80
 81        print_stats(img, bandwise = True)
 82
 83        ```
 84
 85        Output:
 86        -------
 87
 88        ```
 89        ```
 90    '''
 91
 92    if len(img.shape) != 3:
 93        raise Exception('Error: lenght of image shape must be 3 - (space, space, channels)')
 94
 95    print('{:-^{len}}'.format('Image Statistics', len=50))
 96    print('{:<20s}{}'.format('[*] Shape', img.shape))
 97
 98    if bandwise:
 99        for i in range(img.shape[-1]):
100            print('{:<20s}'.format('Band '+str(i)))
101            print('\t{:<20s}{:<30s}'.format('[*] Max',  str(np.max(img[:, :, i]))))
102            print('\t{:<20s}{:<30s}'.format('[*] Min',  str(np.min(img[:, :, i]))))
103            print('\t{:<20s}{:<30s}'.format('[*] Mean', str(np.mean(img[:, :, i]))))
104            print('\t{:<20s}{:<30s}'.format('[*] Std',  str(np.std(img[:, :, i]))))
105            print('\t{:<20s}{:<30s}'.format('[*] Med',  str(np.median(img[:, :, i]))))
106    else:
107        print('{:<20s}{:<30s}'.format('[*] Max',  str(np.max(img))))
108        print('{:<20s}{:<30s}'.format('[*] Min',  str(np.min(img))))
109        print('{:<20s}{:<30s}'.format('[*] Mean', str(np.mean(img))))
110        print('{:<20s}{:<30s}'.format('[*] Std',  str(np.std(img))))
111        print('{:<20s}{:<30s}'.format('[*] Med',  str(np.median(img))))