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# Source : https://computersciencewiki.org/index.php/Max-pooling_/_Pooling

# Importing the libraries

import numpy as np

from PIL import Image

# Maxpooling Function

def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:

"""

This function is used to perform maxpooling on the input array of 2D matrix(image)

Args:

arr: numpy array

size: size of pooling matrix

stride: the number of pixels shifts over the input matrix

Returns:

numpy array of maxpooled matrix

Sample Input Output:

>>> maxpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)

array([[ 6., 8.],

[14., 16.]])

>>> maxpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)

array([[241., 180.],

[241., 157.]])

"""

arr = np.array(arr)

if arr.shape[0] != arr.shape[1]:

raise ValueError("The input array is not a square matrix")

i = 0

j = 0

mat_i = 0

mat_j = 0

# compute the shape of the output matrix

maxpool_shape = (arr.shape[0] - size) // stride + 1

# initialize the output matrix with zeros of shape maxpool_shape

updated_arr = 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

updated_arr[mat_i][mat_j] = 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

j = 0

mat_j = 0

return updated_arr

# Averagepooling Function

def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:

"""

This function is used to perform avgpooling on the input array of 2D matrix(image)

Args:

arr: numpy array

size: size of pooling matrix

stride: the number of pixels shifts over the input matrix

Returns:

numpy array of avgpooled matrix

Sample Input Output:

>>> avgpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)

array([[ 3., 5.],

[11., 13.]])

>>> avgpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)

array([[161., 102.],

[114., 69.]])

"""

arr = np.array(arr)

if arr.shape[0] != arr.shape[1]:

raise ValueError("The input array is not a square matrix")

i = 0

j = 0

mat_i = 0

mat_j = 0

# compute the shape of the output matrix

avgpool_shape = (arr.shape[0] - size) // stride + 1

# initialize the output matrix with zeros of shape avgpool_shape

updated_arr = 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

updated_arr[mat_i][mat_j] = 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

j = 0

mat_j = 0

return updated_arr

# Main Function

if __name__ == "__main__":

from doctest import testmod

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

# Loading the image

image = 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()

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