

"""simple docstring"""




import cva
import numpy as np
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""

					try:
										return int(image[x_coordinate][y_coordinate] >= center )
					except (IndexError, TypeError):
										return 0
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   image[x_coordinate][y_coordinate]
					A     =   [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
					A     =   [
					    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.
	__A	:       Union[str, 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
	__A	:       Tuple        =						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	:       Optional[int]        =						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	:       Dict        =						{'configuration_cvt': ['CVT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CvtConfig']}


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

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       List[str]        =						[
	    '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	:       str        =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





