
import cva
import numpy as np



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):

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



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  image[x_coordinate][y_coordinate]
				A_       	=  [1, 2, 4, 8, 16, 32, 64, 1_28]

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

				# Starting from the top right, assigning value to pixels clockwise
				A_       	=  [
				    get_neighbors_pixel(__UpperCamelCase ,						x_coordinate - 1 ,						y_coordinate + 1 ,						__UpperCamelCase     ),
				    get_neighbors_pixel(__UpperCamelCase ,						__UpperCamelCase ,						y_coordinate + 1 ,						__UpperCamelCase     ),
				    get_neighbors_pixel(__UpperCamelCase ,						x_coordinate - 1 ,						__UpperCamelCase ,						__UpperCamelCase     ),
				    get_neighbors_pixel(__UpperCamelCase ,						x_coordinate + 1 ,						y_coordinate + 1 ,						__UpperCamelCase     ),
				    get_neighbors_pixel(__UpperCamelCase ,						x_coordinate + 1 ,						__UpperCamelCase ,						__UpperCamelCase     ),
				    get_neighbors_pixel(__UpperCamelCase ,						x_coordinate + 1 ,						y_coordinate - 1 ,						__UpperCamelCase     ),
				    get_neighbors_pixel(__UpperCamelCase ,						__UpperCamelCase ,						y_coordinate - 1 ,						__UpperCamelCase     ),
				    get_neighbors_pixel(__UpperCamelCase ,						x_coordinate - 1 ,						y_coordinate - 1 ,						__UpperCamelCase     ),
				]

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


if __name__ == "__main__":
						# Reading the image and converting it to grayscale.
						SCREAMING_SNAKE_CASE   :		Optional[int]					 =       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
						SCREAMING_SNAKE_CASE   :		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]):
																		SCREAMING_SNAKE_CASE   :		Any					 =       local_binary_value(image, i, j)

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

from cva import destroyAllWindows, imread, imshow, waitKey



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_       	=  img.shape[0], img.shape[1]

				def to_grayscale(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
								return 0.2126 * red + 0.587 * green + 0.114 * blue

				def normalize(__UpperCamelCase     ):
								return min(__UpperCamelCase ,						2_55     )

				for i in range(__UpperCamelCase     ):
								for j in range(__UpperCamelCase     ):
												A_       	=  int(to_grayscale(*img[i][j]     )     )
												A_       	=  [
												    normalize(__UpperCamelCase     ),
												    normalize(greyscale + factor     ),
												    normalize(greyscale + 2 * factor     ),
												]

				return img


if __name__ == "__main__":
						# read original image
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       {
						    percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
						}

						for percentage, img in images.items():
												make_sepia(img, percentage)

						for percentage, img in images.items():
												imshow(f'''Original image with sepia (factor: {percentage})''', img)

						waitKey(0)
						destroyAllWindows()