
'''simple docstring'''





import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					180




def 				__magic_name__				(     __lowerCAmelCase			:		Union[str, Any]	,						__lowerCAmelCase			:		Optional[Any]							)    ->     str:
				__lowerCamelCase					      =			k_size // 2
				__lowerCamelCase      ,					__lowerCamelCase					      =			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				__lowerCamelCase					      =			(
				    1
				    / (2 * np.pi * sigma)
				    * np.exp(-(np.square(snake_case__							) + np.square(snake_case__							)) / (2 * np.square(snake_case__							))							)
				)
				return g




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Optional[Any]							)    ->     Union[str, Any]:
				__lowerCamelCase					      =			np.zeros(snake_case__							)

				for row in range(1	,						image_shape[0] - 1							):
								for col in range(1	,						image_shape[1] - 1							):
												__lowerCamelCase					      =			gradient_direction[row, col]

												if (
												    0 <= direction < PI / 8
												    or 15 * PI / 8 <= direction <= 2 * PI
												    or 7 * PI / 8 <= direction <= 9 * PI / 8
												):
																__lowerCamelCase					      =			sobel_grad[row, col - 1]
																__lowerCamelCase					      =			sobel_grad[row, col + 1]
																if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																				__lowerCamelCase					      =			sobel_grad[row, col]

												elif (
												    PI / 8 <= direction < 3 * PI / 8
												    or 9 * PI / 8 <= direction < 11 * PI / 8
												):
																__lowerCamelCase					      =			sobel_grad[row + 1, col - 1]
																__lowerCamelCase					      =			sobel_grad[row - 1, col + 1]
																if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																				__lowerCamelCase					      =			sobel_grad[row, col]

												elif (
												    3 * PI / 8 <= direction < 5 * PI / 8
												    or 11 * PI / 8 <= direction < 13 * PI / 8
												):
																__lowerCamelCase					      =			sobel_grad[row - 1, col]
																__lowerCamelCase					      =			sobel_grad[row + 1, col]
																if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																				__lowerCamelCase					      =			sobel_grad[row, col]

												elif (
												    5 * PI / 8 <= direction < 7 * PI / 8
												    or 13 * PI / 8 <= direction < 15 * PI / 8
												):
																__lowerCamelCase					      =			sobel_grad[row - 1, col - 1]
																__lowerCamelCase					      =			sobel_grad[row + 1, col + 1]
																if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																				__lowerCamelCase					      =			sobel_grad[row, col]

				return destination




def 				__magic_name__				(     __lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		Tuple	,						__lowerCAmelCase			:		List[Any]							)    ->     Optional[int]:
				for row in range(1	,						image_shape[0] - 1							):
								for col in range(1	,						image_shape[1] - 1							):
												if destination[row, col] >= threshold_high:
																__lowerCamelCase					      =			strong
												elif destination[row, col] <= threshold_low:
																__lowerCamelCase					      =			0
												else:
																__lowerCamelCase					      =			weak




def 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		List[str]	,						__lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Union[str, Any]							)    ->     Union[str, Any]:
				for row in range(1	,						image_shape[0]							):
								for col in range(1	,						image_shape[1]							):
												if destination[row, col] == weak:
																if 255 in (
																    destination[row, col + 1],
																    destination[row, col - 1],
																    destination[row - 1, col],
																    destination[row + 1, col],
																    destination[row - 1, col - 1],
																    destination[row + 1, col - 1],
																    destination[row - 1, col + 1],
																    destination[row + 1, col + 1],
																):
																				__lowerCamelCase					      =			strong
																else:
																				__lowerCamelCase					      =			0




def 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		List[str]=15	,						__lowerCAmelCase			:		List[str]=30	,						__lowerCAmelCase			:		Union[str, Any]=128	,						__lowerCAmelCase			:		Optional[int]=255							)    ->     str:
				# gaussian_filter
				__lowerCamelCase					      =			img_convolve(snake_case__	,						gen_gaussian_kernel(9	,						sigma=1.4							)							)
				# get the gradient and degree by sobel_filter
				__lowerCamelCase      ,					__lowerCamelCase					      =			sobel_filter(snake_case__							)
				__lowerCamelCase					      =			PI + np.radadeg(snake_case__							)

				__lowerCamelCase					      =			suppress_non_maximum(image.shape	,						snake_case__	,						snake_case__							)

				detect_high_low_threshold(
				    image.shape	,						snake_case__	,						snake_case__	,						snake_case__	,						snake_case__	,						snake_case__							)

				track_edge(image.shape	,						snake_case__	,						snake_case__	,						snake_case__							)

				return destination


if __name__ == "__main__":
					# read original image in gray mode
					SCREAMING_SNAKE_CASE__     :    str      							=					cva.imread(r"../image_data/lena.jpg", 0)
					# canny edge detection
					SCREAMING_SNAKE_CASE__     :    int      							=					canny(lena)
					cva.imshow("canny", canny_destination)
					cva.waitKey(0)





import bsa
import requests




def 				__magic_name__				(     __lowerCAmelCase			:		bsa.element.ResultSet							)    ->     dict[str, str]:
				return {
				    "name": soup.ha.a.text,
				    "genre": soup.find('''span'''	,						class_='''genre'''							).text.strip(),
				    "rating": soup.strong.text,
				    "page_link": f'''https://www.imdb.com{soup.a.get('href'							)}''',
				}




def 				__magic_name__				(     __lowerCAmelCase			:		int = 5							)    ->     tuple:
				__lowerCamelCase					      =			int(float(__lowerCAmelCase							)							)
				if num_movies < 1:
								return ()
				__lowerCamelCase					      =			(
				    '''https://www.imdb.com/search/title?title_type='''
				    f'''feature&sort=num_votes,desc&count={num_movies}'''
				)
				__lowerCamelCase					      =			bsa.BeautifulSoup(requests.get(__lowerCAmelCase							).content	,						'''html.parser'''							)
				return tuple(
				    get_movie_data_from_soup(__lowerCAmelCase							)
				    for movie in source.find_all('''div'''	,						class_='''lister-item mode-advanced'''							)							)


if __name__ == "__main__":
					import json

					SCREAMING_SNAKE_CASE__     :    str      							=					int(input("How many movies would you like to see? "))
					print(
					    ", ".join(
					        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
					    )
					)



