
"""simple docstring"""




from pathlib import Path

import numpy as np
from PIL import Image



def 							a__ (				lowerCAmelCase	:       np.ndarray		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.2989 * r + 0.5870 * g + 0.1140 * b



def 							a__ (				lowerCAmelCase	:       np.ndarray		):






     '''simple docstring'''
     return (gray > 127) & (gray <= 255)



def 							a__ (				lowerCAmelCase	:       np.ndarray       ,    lowerCAmelCase	:       np.ndarray		):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						np.zeros_like(lowerCamelCase_		)
     UpperCAmelCase__							: Any 							=						np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)		)

     # Copy image to padded image
     UpperCAmelCase__							: List[Any] 							=						image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]		):
          for y in range(image.shape[0]		):
               UpperCAmelCase__							: Union[str, Any] 							=						(
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               UpperCAmelCase__							: Tuple 							=						int(summation > 0		)
     return output


if __name__ == "__main__":
    # read original image
    A__      :	Union[str, Any]          =    Path(__file__).resolve().parent / 'image_data' / 'lena.jpg'
    A__      :	Optional[Any]          =    np.array(Image.open(lena_path))
    # kernel to be applied
    A__      :	int          =    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
    A__      :	Union[str, Any]          =    dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
    # Save the output image
    A__      :	List[str]          =    Image.fromarray(output).convert("""RGB""")
    pil_img.save("""result_dilation.png""")





"""simple docstring"""




def 							a__ (				lowerCAmelCase	:       list       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     _validate_point(lowerCAmelCase		)
     _validate_point(lowerCAmelCase		)
     if len(lowerCAmelCase		) != len(lowerCAmelCase		):
          raise ValueError("Both points must be in the same n-dimensional space"		)

     return float(sum(abs(a - b		) for a, b in zip(lowerCAmelCase       ,    lowerCAmelCase		)		)		)



def 							a__ (				lowerCAmelCase	:       list[float]		):






     '''simple docstring'''
     if point:
          if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
               for item in point:
                    if not isinstance(lowerCAmelCase       ,    (int, float)		):
                         UpperCAmelCase__							: Tuple 							=						(
                             "Expected a list of numbers as input, found "
                             F"{type(lowerCAmelCase		).__name__}"
                         )
                         raise TypeError(lowerCAmelCase		)
          else:
               UpperCAmelCase__							: Dict 							=						F"Expected a list of numbers as input, found {type(lowerCAmelCase		).__name__}"
               raise TypeError(lowerCAmelCase		)
     else:
          raise ValueError("Missing an input"		)



def 							a__ (				lowerCAmelCase	:       list       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     _validate_point(lowerCAmelCase		)
     _validate_point(lowerCAmelCase		)
     if len(lowerCAmelCase		) != len(lowerCAmelCase		):
          raise ValueError("Both points must be in the same n-dimensional space"		)

     return float(sum(abs(x - y		) for x, y in zip(lowerCAmelCase       ,    lowerCAmelCase		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



