computer_vision/pooling_functions.py · ShuangHao/Algorithms-Python

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Algorithms-Python

computer_vision

pooling_functions.py

pooling_functions.py 4.25 KB

# 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()

一键复制编辑原始数据按行查看历史

Navaneeth Sharma 提交于 2021年11月17日 06:58 +08:00 . Adding Pooling Algorithms (#5826)

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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()