#### Local Binary Pattern

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import cv2
import numpy as np

def get_neighbors_pixel(
image: np.ndarray, x_coordinate: int, y_coordinate: int, center: int
) -> int:
"""
Comparing local neighborhood pixel value with threshold value of centre pixel.
Exception is required when neighborhood value of a center pixel value is null.
i.e. values present at boundaries.

:param image: The image we're working with
:param x_coordinate: x-coordinate of the  pixel
:param y_coordinate: The y coordinate of the pixel
:param center: center pixel value
:return: The value of the pixel is being returned.
"""

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

def local_binary_value(image: np.ndarray, x_coordinate: int, y_coordinate: int) -> int:
"""
It takes an image, an x and y coordinate, and returns the
decimal value of the local binary patternof the pixel
at that coordinate

:param image: the image to be processed
:param x_coordinate: x coordinate of the pixel
:param y_coordinate: the y coordinate of the pixel
:return: The decimal value of the binary value of the pixels
around the center pixel.
"""
center = image[x_coordinate][y_coordinate]
powers = [1, 2, 4, 8, 16, 32, 64, 128]

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

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

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

if __name__ == "__main__":
# Reading the image and converting it to grayscale.
)

# Create a numpy array as the same height and width of read image
lbp_image = 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(image.shape[0]):
for j in range(image.shape[1]):
lbp_image[i][j] = local_binary_value(image, i, j)

cv2.imshow("local binary pattern", lbp_image)
cv2.waitKey(0)
cv2.destroyAllWindows()