


'''simple docstring'''





from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[Any] ):






			"""simple docstring"""




			return {key.lstrip('-' ): value for key, value in zip(unknown_args[::2],unknown_args[1::2] )}






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			__A=	ArgumentParser(
			    'HuggingFace Datasets CLI tool',usage='datasets-cli <command> [<args>]',allow_abbrev=SCREAMING_SNAKE_CASE_ )
			__A=	parser.add_subparsers(help='datasets-cli command helpers' )
			set_verbosity_info()

			# Register commands
			ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )
			EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )
			TestCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )
			RunBeamCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )
			DummyDataCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )

			# Parse args
			__A,	__A=	parser.parse_known_args()
			if not hasattr(SCREAMING_SNAKE_CASE_,'func' ):
						parser.print_help()
						exit(1 )
			__A=	parse_unknown_args(SCREAMING_SNAKE_CASE_ )

			# Run
			__A=	args.func(SCREAMING_SNAKE_CASE_,**SCREAMING_SNAKE_CASE_ )
			service.run()


if __name__ == "__main__":
					main()





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     np.ndarray:

						# prepare kernel
						# the kernel size have to be odd
						if (ksize % 2) == 0:
												_lowercase					       =					ksize + 1
						_lowercase					       =					np.zeros((ksize, ksize)		,   dtype=np.floataa       )

						# each value
						for y in range(SCREAMING_SNAKE_CASE_       ):
												for x in range(SCREAMING_SNAKE_CASE_       ):
																		# distance from center
																		_lowercase					       =					x - ksize // 2
																		_lowercase					       =					y - ksize // 2

																		# degree to radiant
																		_lowercase					       =					theta / 1_80 * np.pi
																		_lowercase					       =					np.cos(_theta       )
																		_lowercase					       =					np.sin(_theta       )

																		# get kernel x
																		_lowercase					       =					cos_theta * px + sin_theta * py

																		# get kernel y
																		_lowercase					       =					-sin_theta * px + cos_theta * py

																		# fill kernel
																		_lowercase					       =					np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)       ) * np.cos(2 * np.pi * _x / lambd + psi       )

						return gabor


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# read original image
		A		: Optional[Any]								=     imread('''../image_data/lena.jpg''')
		# turn image in gray scale value
		A		: Optional[int]								=     cvtColor(img, COLOR_BGR2GRAY)

		# Apply multiple Kernel to detect edges
		A		: Optional[int]								=     np.zeros(gray.shape[:2])
		for theta in [0, 30, 60, 90, 120, 150]:
				A		: Optional[int]								=     gabor_filter_kernel(10, 8, theta, 10, 0, 0)
				out += filteraD(gray, CV_8UC3, kernel_aa)
		A		: int								=     out / out.max() * 255
		A		: str								=     out.astype(np.uinta)

		imshow('''Original''', gray)
		imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

		waitKey(0)