convolution2d.py

import numpy as np

#pure python implementation of convolutions of 2d arrays
def convolve2D(a1,a2,s):
    '''
        Calculating convolution for 2d array
        Asumming all inputs a1,a2 nd-arrays of square shape (n*n)
        Return:
            convolution of inputs, nd-array
        Input:
            a1: input image
            a2: kernal 
            s: stride
    '''
    n1,m1 = a1.shape
    n2,m2 = a2.shape
    
    if(n1 != m1 ): raise ValueError("expect square matrix")
    if(n2 != m2): raise ValueError("expect square matrix")
    if(n2>n1): raise ValueError("expect kernel shape lower than input")
    
    outputSize = int(((n1 - n2)/s) + 1)
    convMatrix = np.zeros((outputSize,outputSize))
    
    outrow = 0
    for row in range(0,n1-n2+1,s):
        outcol = 0
        for col in range(0,m1-m2+1,s):
            #print("here\n",a1[row:row+n2,col:col+m2,:])
            conv = np.sum(a1[row:row+n2,col:col+m2] * a2)
            convMatrix[outrow,outcol] = conv
            outcol = outcol + 1
        outrow = outrow + 1
    return convMatrix