conv_matrix.py

import numpy as np

green_matrix = np.array(
    [
        [1, 1, 1, 0, 0],
        [0, 1, 1, 1, 0],
        [0, 0, 1, 1, 1],
        [0, 0, 1, 1, 0],
        [0, 1, 1, 0, 0],
    ]
)
# This is our kernel
orange_matrix = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])


def compute_conv_matrix(image_matrix, kernel):
    kernel_height, kernel_width = kernel.shape
    image_height, image_width = image_matrix.shape
    conv_matrix = np.zeros(kernel.shape)
    height_window = kernel_height
    row_start = 0
    while height_window < image_height + 1:
        height_offset = image_height - height_window
        m = image_matrix[row_start : image_height - height_offset]
        column_offset = 0
        while True:
            end_column = column_offset + kernel_width
            if end_column > image_width:
                break
            submatrix = m[:, column_offset:end_column]
            kernel_filter = submatrix * kernel
            conv_matrix[row_start, column_offset] = kernel_filter.sum()
            column_offset += 1
        row_start += 1
        height_window += 1
    return conv_matrix


print(compute_conv_matrix(green_matrix, orange_matrix))