



"""simple docstring"""


import cva
import numpy as np

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		):

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

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					image[x_coordinate][y_coordinate]
						UpperCAmelCase			 =					[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
						UpperCAmelCase			 =					[
						    get_neighbors_pixel(lowercase_						,      x_coordinate - 1						,      y_coordinate + 1						,      lowercase_		),
						    get_neighbors_pixel(lowercase_						,      lowercase_						,      y_coordinate + 1						,      lowercase_		),
						    get_neighbors_pixel(lowercase_						,      x_coordinate - 1						,      lowercase_						,      lowercase_		),
						    get_neighbors_pixel(lowercase_						,      x_coordinate + 1						,      y_coordinate + 1						,      lowercase_		),
						    get_neighbors_pixel(lowercase_						,      x_coordinate + 1						,      lowercase_						,      lowercase_		),
						    get_neighbors_pixel(lowercase_						,      x_coordinate + 1						,      y_coordinate - 1						,      lowercase_		),
						    get_neighbors_pixel(lowercase_						,      lowercase_						,      y_coordinate - 1						,      lowercase_		),
						    get_neighbors_pixel(lowercase_						,      x_coordinate - 1						,      y_coordinate - 1						,      lowercase_		),
						]

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


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

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











'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a__         =     {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CvtForImageClassification''',
		    '''CvtModel''',
		    '''CvtPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCvtForImageClassification''',
		    '''TFCvtModel''',
		    '''TFCvtPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cvt import (
				    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CvtForImageClassification,
				    CvtModel,
				    CvtPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_cvt import (
				    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCvtForImageClassification,
				    TFCvtModel,
				    TFCvtPreTrainedModel,
				)


else:
		import sys

		a__         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)