
"""simple docstring"""



from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			prime_factors(SCREAMING_SNAKE_CASE						)
							if is_square_free(SCREAMING_SNAKE_CASE						):
														return -1 if len(SCREAMING_SNAKE_CASE						) % 2 else 1
							return 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()








"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							return (gray > 127) & (gray <= 255)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray     ,  SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.zeros_like(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

							# Copy image to padded image
							_lowerCAmelCase  	=			image

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


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






