From d848bfbf3229f2a3240a298a583f6b80a9efc1fd Mon Sep 17 00:00:00 2001 From: Navaneeth Sharma <63489382+Navaneeth-Sharma@users.noreply.github.com> Date: Wed, 17 Nov 2021 04:28:47 +0530 Subject: [PATCH] Adding Pooling Algorithms (#5826) * adding pooling algorithms * pooling.py: Adding pooling algorithms to computer vision pull_number= * pooling.py: Adding pooling algorithms to computer vision * pooling_functions.py: Adding pooling algorithms to computer vision * pooling.py: Adding Pooling Algorithms * pooling_functions.py Add and Update * Update pooling_functions.py * Update computer_vision/pooling_functions.py Co-authored-by: Christian Clauss * Update computer_vision/pooling_functions.py Co-authored-by: Christian Clauss * Update computer_vision/pooling_functions.py Co-authored-by: Christian Clauss * Update computer_vision/pooling_functions.py Co-authored-by: Christian Clauss * Update pooling_functions.py * Formatting pooling.py Co-authored-by: Christian Clauss --- computer_vision/pooling_functions.py | 135 +++++++++++++++++++++++++++ 1 file changed, 135 insertions(+) create mode 100644 computer_vision/pooling_functions.py diff --git a/computer_vision/pooling_functions.py b/computer_vision/pooling_functions.py new file mode 100644 index 000000000..09beabcba --- /dev/null +++ b/computer_vision/pooling_functions.py @@ -0,0 +1,135 @@ +# 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()