import numpy as np
from PIL import Image





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							np.array(_UpperCamelCase							)
				if arr.shape[0] != arr.shape[1]:
								raise ValueError('''The input array is not a square matrix'''							)
				__lowercase 			=							0
				__lowercase 			=							0
				__lowercase 			=							0
				__lowercase 			=							0

				# compute the shape of the output matrix
				__lowercase 			=							(arr.shape[0] - size) // stride + 1
				# initialize the output matrix with zeros of shape maxpool_shape
				__lowercase 			=							np.zeros((maxpool_shape, maxpool_shape)							)

				while i < arr.shape[0]:
								if i + size > arr.shape[0]:
												# if the end of the matrix is reached, break
												break
								while j < arr.shape[1]:
												# if the end of the matrix is reached, break
												if j + size > arr.shape[1]:
																break
												# compute the maximum of the pooling matrix
												__lowercase 			=							np.max(arr[i : i + size, j : j + size]							)
												# shift the pooling matrix by stride of column pixels
												j += stride
												mat_j += 1

								# shift the pooling matrix by stride of row pixels
								i += stride
								mat_i += 1

								# reset the column index to 0
								__lowercase 			=							0
								__lowercase 			=							0

				return updated_arr





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							np.array(_UpperCamelCase							)
				if arr.shape[0] != arr.shape[1]:
								raise ValueError('''The input array is not a square matrix'''							)
				__lowercase 			=							0
				__lowercase 			=							0
				__lowercase 			=							0
				__lowercase 			=							0

				# compute the shape of the output matrix
				__lowercase 			=							(arr.shape[0] - size) // stride + 1
				# initialize the output matrix with zeros of shape avgpool_shape
				__lowercase 			=							np.zeros((avgpool_shape, avgpool_shape)							)

				while i < arr.shape[0]:
								# if the end of the matrix is reached, break
								if i + size > arr.shape[0]:
												break
								while j < arr.shape[1]:
												# if the end of the matrix is reached, break
												if j + size > arr.shape[1]:
																break
												# compute the average of the pooling matrix
												__lowercase 			=							int(np.average(arr[i : i + size, j : j + size]							)							)
												# shift the pooling matrix by stride of column pixels
												j += stride
												mat_j += 1

								# shift the pooling matrix by stride of row pixels
								i += stride
								mat_i += 1
								# reset the column index to 0
								__lowercase 			=							0
								__lowercase 			=							0

				return updated_arr


# Main Function
if __name__ == "__main__":
		from doctest import testmod

		testmod(name="""avgpooling""", verbose=True)

		# Loading the image
		__a  :     str       =  Image.open("""path_to_image""")

		# Converting the image to numpy array and maxpooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

		# Converting the image to numpy array and averagepooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()



"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A     :   Union[str, Any]			    =			imread(R"digital_image_processing/image_data/lena_small.jpg")
A     :   Optional[Any]			    =			cvtColor(img, COLOR_BGR2GRAY)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			cn.convert_to_negative(_UpperCamelCase      )
			# assert negative_img array for at least one True
			assert negative_img.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			with Image.open("digital_image_processing/image_data/lena_small.jpg"      ) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(_UpperCamelCase    , 110      )      ).startswith(
						    "<PIL.Image.Image image mode=RGB size=100x100 at"      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			canny.gen_gaussian_kernel(9    , sigma=1.4      )
			# Assert ambiguous array
			assert resp.all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			imread("digital_image_processing/image_data/lena_small.jpg"    , 0      )
			# assert ambiguous array for all == True
			assert canny_img.all()
			__lowerCAmelCase				    =			canny.canny(_UpperCamelCase      )
			# assert canny array for at least one True
			assert canny_array.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert gg.gaussian_filter(_UpperCamelCase    , 5    , sigma=0.9      ).all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]      )
			__lowerCAmelCase				    =			conv.img_convolve(_UpperCamelCase    , _UpperCamelCase      ).astype(_UpperCamelCase      )
			assert res.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert med.median_filter(_UpperCamelCase    , 3      ).any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase				    =			sob.sobel_filter(_UpperCamelCase      )
			assert grad.any() and theta.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			sp.make_sepia(_UpperCamelCase    , 20      )
			assert sepia.all()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			bs.Burkes(imread(_UpperCamelCase    , 1      )    , 120      )
			burkes.process()
			assert burkes.output_img.any()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    , ):

			'''simple docstring'''




			__lowerCAmelCase				    =			rs.NearestNeighbour(imread(_UpperCamelCase    , 1      )    , 400    , 200      )
			nn.process()
			assert nn.output.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"digital_image_processing/image_data/lena.jpg"

			# Reading the image and converting it to grayscale.
			__lowerCAmelCase				    =			imread(_UpperCamelCase    , 0      )

			# Test for get_neighbors_pixel function() return not None
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			image[x_coordinate][y_coordinate]

			__lowerCAmelCase				    =			lbp.get_neighbors_pixel(
			    _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			__lowerCAmelCase				    =			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]      ):
									__lowerCAmelCase				    =			lbp.local_binary_value(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert lbp_image.any()




