


from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class   __lowercase						(datasets.BuilderConfig     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	None
							_UpperCAmelCase						     =	None
							_UpperCAmelCase						     =	None






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										assert self.generator is not None, "generator must be specified"

										if self.gen_kwargs is None:
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				{}







class   __lowercase						(datasets.GeneratorBasedBuilder     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	GeneratorConfig






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return datasets.DatasetInfo(features=self.config.features					)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,	gen_kwargs=self.config.gen_kwargs					)]






							def 				UpperCamelCase__    (			self ,	**lowerCAmelCase__					):





										"""simple docstring"""




										for idx, ex in enumerate(self.config.generator(**lowerCAmelCase__					)					):
													yield idx, ex









import cva
import numpy as np






def 	a__ (							A__,			A__,			A__,			A__							):

			try:
						return int(image[x_coordinate][y_coordinate] >= center							)
			except (IndexError, TypeError):
						return 0






def 	a__ (							A__,			A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				image[x_coordinate][y_coordinate]
			SCREAMING_SNAKE_CASE_		:    str 		=				[1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

			# skip get_neighbors_pixel if center is null
			if center is None:
						return 0

			# Starting from the top right, assigning value to pixels clockwise
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				[
			    get_neighbors_pixel(A__,			x_coordinate - 1,			y_coordinate + 1,			A__							),
			    get_neighbors_pixel(A__,			A__,			y_coordinate + 1,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate - 1,			A__,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate + 1,			y_coordinate + 1,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate + 1,			A__,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate + 1,			y_coordinate - 1,			A__							),
			    get_neighbors_pixel(A__,			A__,			y_coordinate - 1,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate - 1,			y_coordinate - 1,			A__							),
			]

			# Converting the binary value to decimal.
			return sum(
			    binary_value * power for binary_value, power in zip(A__,			A__							)							)


if __name__ == "__main__":
				# Reading the image and converting it to grayscale.
				lowerCAmelCase__ :			List[Any]         =cva.imread(
				    'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
				)

				# Create a numpy array as the same height and width of read image
				lowerCAmelCase__ :			List[Any]         =np.zeros((image.shape[0], image.shape[1]))

				# Iterating through the image and calculating the
				# local binary pattern value for each pixel.
				for i in range(0, image.shape[0]):
								for j in range(0, image.shape[1]):
												lowerCAmelCase__ :			Optional[Any]         =local_binary_value(image, i, j)

				cva.imshow('local binary pattern', lbp_image)
				cva.waitKey(0)
				cva.destroyAllWindows()





