How to demonstrate the Gaussian Kernal used in opencv GausssianBlurr, using the instruction `GaussianBlur()` from OpenCV?

Question

I want to demonstrate the Gaussian Kernel used in openCV. cv2.GaussianBlurr(img, kernel_size, sigma) for explanation purposes.
I know how to demonstrate the image which results after applying the blur, and that is not my objective here.
My objective is to demonstrate the kernel automatically for any used sigma, and any used kernel size!
I have seen a code(mentioned down) but I prefer to use something more related to instruction used in OpenCV, rather than just a general mathematical dependent approach.
The expected output kernel is something like this:

import cv2
import numpy as np

# Read Image
img_path = 'image.jpg'
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Gaussian Blurr
Kernel = np.ones((15,15))
sigma = 2

Blurred_Image = cv2.GaussianBlur(img, (Kernel.shape[0], Kernel.shape[1]), sigma)

Gaussian Kernel Manual Code:

def dnorm(x, mu, sd):
    return 1 / (np.sqrt(2 * np.pi) * sd) * np.e ** (-np.power((x - mu) / sd, 2) / 2)

def gaussian_kernel(size, sigma=1, verbose=False):
 
    kernel_1D = np.linspace(-(size // 2), size // 2, size)
    for i in range(size):
        kernel_1D[i] = dnorm(kernel_1D[i], 0, sigma)
    kernel_2D = np.outer(kernel_1D.T, kernel_1D.T)
 
    kernel_2D *= 1.0 / kernel_2D.max()
 
    if verbose:
        plt.imshow(kernel_2D, interpolation='none',cmap='gray')
        plt.title("Image")
        plt.show()
 
    return kernel_2D

@YvesDaoust I don't like this manual procedure to demonstrate the kernel, I prefer to derive the kernel from the OpenCV definition of the blur directly. — Bilal, Nov 04 '20 at 19:21
@YvesDaoust I used the word manual to describe the mathematical approach used up without any connection to the OpenCV library and the resultant image from the instruction `cv2.GaussianBlur()` — Bilal, Nov 04 '20 at 20:31
Does this answer your question? [How do I get to show Gaussian Kernel for 2d? (opencv)](https://stackoverflow.com/questions/61394826/how-do-i-get-to-show-gaussian-kernel-for-2d-opencv) — Cris Luengo, Nov 05 '20 at 03:00
@CrisLuengo Actually not, because my problem isn't about the theoretical background of the Gaussian Blur, that is already demonstrated on the code up, the difficulty was how to demonstrate that using OpenCV in **code**, and that's what the answer down does. — Bilal, Nov 05 '20 at 06:37
My answer there outlines the exact same process as the answer below. — Cris Luengo, Nov 05 '20 at 06:41
@CrisLuengo my problem is technical, how to use the code to demonstrate that, and your answer there doesn't implement a code, the answer down contains a code, and that code was the solution to my problem. — Bilal, Nov 05 '20 at 06:44

score 1 · Accepted Answer · answered Nov 04 '20 at 20:10

Here is one way in Python/OpenCV.

 - Read the input
 - Create a delta image (one white pixel in the center of a black background)
 - Blur the image
 - List item
 - Resize the image to enlarge it
 - Stretch the image to full dynamic range
 - Save the result

import cv2
import numpy as np
import skimage.exposure as exposure

# create delta image
dims = 30
dims2 = 30 // 2
delta = np.zeros((dims,dims,3), dtype=np.float32)
delta[dims2:dims2+1, dims2:dims2+1] = (255,255,255)

# blur image
blur = cv2.GaussianBlur(delta, (0,0), sigmaX=5, sigmaY=5)

# resize 16x
dims4x = dims * 16
resized = cv2.resize(blur, (dims4x,dims4x), interpolation = cv2.INTER_AREA)

# stretch to full dynamic range
result = exposure.rescale_intensity(resized, in_range='image', out_range=(0,255)).astype(np.uint8)

# save image
cv2.imwrite('delta.png',delta)
cv2.imwrite('gaussian_blur_view.png',result)

# show the images
cv2.imshow("delta", delta)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Delta image:

Result:

How to demonstrate the Gaussian Kernal used in opencv GausssianBlurr, using the instruction `GaussianBlur()` from OpenCV?

Gaussian Kernel Manual Code:

1 Answers1