






import doctest

import projectq
from projectq.ops import H, Measure


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					quantum_engine.allocate_qubit()
					H | qubit
					Measure | qubit
					return int(snake_case    )


if __name__ == "__main__":
	doctest.testmod()

	# initialises a new quantum backend
	A__			   = projectq.MainEngine()

	# Generate a list of 10 random numbers
	A__			   = [get_random_number(quantum_engine) for _ in range(10)]

	# Flushes the quantum engine from memory
	quantum_engine.flush()

	print("""Random numbers""", random_numbers_list)








import cva
import numpy as np


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

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


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					image[x_coordinate][y_coordinate]
					_lowerCAmelCase      			=					[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
					_lowerCAmelCase      			=					[
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		snake_case			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		snake_case			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		snake_case			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		y_coordinate - 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		snake_case			,		y_coordinate - 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		y_coordinate - 1			,		snake_case    ),
					]

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


if __name__ == "__main__":
	# Reading the image and converting it to grayscale.
	A__			   = 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
	A__			   = 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]):
			A__			   = local_binary_value(image, i, j)

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



