python 3.x - LabVIEW: How to implement bilateral filter over a image

Question

I have an image of 360x360 matrix

I want to Implement a bilateral filter over this IMG

I tried to implement this on python without any built-in function

This is the python code for this

I tried to implement it in LabView but failed

import numpy as np
import cv2
import sys
import math    
def distance(x, y, i, j):
    return np.sqrt((x-i)**2 + (y-j)**2)

def gaussian(x, sigma):
    return (1.0 / (2 * math.pi * (sigma ** 2))) * math.exp(- (x ** 2) / (2 * sigma ** 2))

def apply_bilateral_filter(source, filtered_image, x, y, diameter, sigma_i, sigma_s):
    hl = diameter/2
    i_filtered = 0
    Wp = 0
    i = 0
    while i < diameter:
        j = 0
        while j < diameter:
            neighbour_x = x - (hl - i)
            neighbour_y = y - (hl - j)
            if neighbour_x >= len(source):
                neighbour_x -= len(source)
            if neighbour_y >= len(source[0]):
                neighbour_y -= len(source[0])
            gi = gaussian(source[neighbour_x][neighbour_y] - source[x][y], sigma_i)
            gs = gaussian(distance(neighbour_x, neighbour_y, x, y), sigma_s)
            w = gi * gs
            i_filtered += source[neighbour_x][neighbour_y] * w
            Wp += w
            j += 1
        i += 1
    i_filtered = i_filtered / Wp
    filtered_image[x][y] = int(round(i_filtered))

def bilateral_filter_own(source, filter_diameter, sigma_i, sigma_s):
    filtered_image = np.zeros(source.shape)
    i = 0
    while i < len(source):
        j = 0
        while j < len(source[0]):
            apply_bilateral_filter(source, filtered_image, i, j, filter_diameter, sigma_i, sigma_s)
            j += 1
        i += 1
    return filtered_image

if __name__ == "__main__":
    src = cv2.imread(str(sys.argv[1]), 0)
    filtered_image_own = bilateral_filter_own(src, 5, 12.0, 16.0)
    cv2.imwrite("filtered_image_own.png", filtered_image_own)

I tried to implement this in Labview but failed

Answer 1

Finally attempted and DONE this! it was too tedious to make but made it!