

"""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()








lowerCAmelCase__							=			float("inf")






class     __magic_name__     :


 def __init__(    self		:			List[str]      ,    lowerCAmelCase__		:			Dict     )     ->   List[Any]:
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[[] for _ in range(lowerCAmelCase__     )]


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Optional[Any]=0     )     ->   Any:
   self.adj[a].append([b, len(self.adj[b]     ), c, 0]     )
   self.adj[b].append([a, len(self.adj[a]     ) - 1, rcap, 0]     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Any     )     ->   List[str]:
   if vertex == sink or not flow:
     return flow

   for i in range(self.ptr[vertex]      ,    len(self.adj[vertex]     )     ):
     UpperCAmelCase          =	self.adj[vertex][i]
     if self.lvl[e[0]] == self.lvl[vertex] + 1:
       UpperCAmelCase          =	self.depth_first_search(e[0]      ,    lowerCAmelCase__      ,    min(lowerCAmelCase__      ,    e[2] - e[3]     )     )
       if p:
         self.adj[vertex][i][3] += p
         self.adj[e[0]][e[1]][3] -= p
         return p
     UpperCAmelCase          =	self.ptr[vertex] + 1
   return 0


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Tuple     )     ->   int:
   UpperCAmelCase   ,						UpperCAmelCase          =	0, source
   for l in range(3_1     ):  # noqa: E741  l = 30 maybe faster for random data
     while True:
       UpperCAmelCase   ,						UpperCAmelCase          =	[0] * len(self.q     ), [0] * len(self.q     )
       UpperCAmelCase   ,						UpperCAmelCase   ,						UpperCAmelCase          =	0, 1, 1
       while qi < qe and not self.lvl[sink]:
         UpperCAmelCase          =	self.q[qi]
         qi += 1
         for e in self.adj[v]:
           if not self.lvl[e[0]] and (e[2] - e[3]) >> (3_0 - l):
             UpperCAmelCase          =	e[0]
             qe += 1
             UpperCAmelCase          =	self.lvl[v] + 1

       UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )
       while p:
         flow += p
         UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )

       if not self.lvl[sink]:
         break

   return flow
lowerCAmelCase__							=			Dinic(10)
lowerCAmelCase__							=			0
lowerCAmelCase__							=			9
for vertex in range(1, 5):
      graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
      graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
      graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))



