The given code uses NumPy's `np.pad()` function to add padding...

The given code uses NumPy's np.pad() function to add padding to a 2D array (commonly representing an image). Here's a detailed explanation of what the code does:

Code Breakdown:

1. Inputs:

   • image: This is the 2D array (or grayscale image) to which padding is being applied.
   • ((filterWidth, filterWidth), (filterHeight, filterHeight)): This specifies the width of padding to be added along each dimension.
     • (filterWidth, filterWidth): Adds filterWidth rows of padding to both the top and bottom of the image.
     • (filterHeight, filterHeight): Adds filterHeight columns of padding to both the left and right of the image.

2. Padding:

   • The mode='constant' argument indicates that the padding will be filled with a constant value.
   • constant_values=0: Specifies that the constant value for the padding will be 0.

3. Result:

   • The output is a new image (padded_image) that is larger than the original image because it has rows and columns of zeros added as padding around the original image.

Purpose:

This padding operation is often used in image processing tasks (e.g., convolution in deep learning) to ensure that features near the edges of the image are fully processed. Specifically:

• Adds borders around the image to maintain the spatial dimensions of the output after applying a filter.
• Prevents loss of information near the edges when sliding a filter across the image.

Example:

Suppose image is a 5x5 array, filterWidth=1, and filterHeight=1:

Original image:

[[1, 2, 3, 4, 5],
 [6, 7, 8, 9, 10],
 [11, 12, 13, 14, 15],
 [16, 17, 18, 19, 20],
 [21, 22, 23, 24, 25]]

padded_image after applying the code:

[[0, 0, 0, 0, 0, 0, 0],
 [0, 1, 2, 3, 4, 5, 0],
 [0, 6, 7, 8, 9, 10, 0],
 [0, 11, 12, 13, 14, 15, 0],
 [0, 16, 17, 18, 19, 20, 0],
 [0, 21, 22, 23, 24, 25, 0],
 [0, 0, 0, 0, 0, 0, 0]]

Here, a border of zeros was added around the original image.