blur.py

import cv2 as cv
import numpy as np

filename = "img.jpg"
img = cv.imread(filename)

def preview_img(img):
    '''
        Helper function which displays given
        np array img to user.
    '''
    cv.imshow('img',img)
    cv.waitKey(0)
    cv.destroyAllWindows()

def tadd(a, b):
    '''
        Add two tuples together.
    '''
    return (a[0]+b[0], a[1]+b[1], a[2]+b[2])

def tmult(a, val):
    '''
        Multiply tuple by a scalar.
    '''
    return (a[0]*val, a[1]*val, a[2]*val)
    
xlow = 100
xhigh = 300
ylow = 0
yhigh = 200
img = img[xlow:xhigh, ylow:yhigh]

def apply_kernel(img, px, py):
    gaussian5x5 = np.matrix([
                            [0.003765, 0.015019, 0.023792, 0.015019, 0.003765],
                            [0.015019, 0.059912, 0.094907, 0.059912, 0.015019],
                            [0.023792, 0.094907, 0.150342, 0.094907, 0.023792],
                            [0.015019, 0.059912, 0.094907, 0.059912, 0.015019],
                            [0.003765, 0.015019, 0.023792, 0.015019, 0.003765]
    ])
    ix, iy = img.shape[:2]
    col = (0.0, 0.0, 0.0)
    gsum = 0.0

    for i in range(0, 5):
        for j in range(0, 5):
            nx = px + i - 2
            ny = py + j - 2
            if nx >= 0 and nx <= ix-1 and ny >= 0 and ny <= iy-1:
                elem_val = gaussian5x5[i,j] * 255.0
                col = tadd(col, tmult(img[nx, ny], elem_val))
                gsum += elem_val

    return ( col[0]/gsum, col[1]/gsum, col[2]/gsum )

def gaussian_blur(img):
    x, y = img.shape[:2]
    nimg = img

    for i in range (0, x):
        for j in range(0, y):
            nimg[i, j] = apply_kernel(img, i, j)

    return img

import copy

original_img = copy.deepcopy(img)
original_img_2 = copy.deepcopy(img)

our_blurred_img = gaussian_blur(original_img)
cv_blurred_img = cv.GaussianBlur(original_img_2,(5,5),0)

imgs = np.concatenate((img, our_blurred_img), axis=1)
imgs = np.concatenate((imgs, cv_blurred_img), axis=1)

preview_img(imgs)