convolutions_and_blurring.py

import cv2
import numpy as np

image = cv2.imread('images/elephant.jpg')
cv2.imshow('Original Image', image)
cv2.waitKey(0)

# Creating our 3 x 3 kernel
kernel_3x3 = np.ones((3, 3), np.float32) / 9

# We use the cv2.fitler2D to conovlve the kernal with an image 
blurred = cv2.filter2D(image, -1, kernel_3x3)
cv2.imshow('3x3 Kernel Blurring', blurred)
cv2.waitKey(0)

# Creating our 7 x 7 kernel
kernel_7x7 = np.ones((7, 7), np.float32) / 49

blurred2 = cv2.filter2D(image, -1, kernel_7x7)
cv2.imshow('7x7 Kernel Blurring', blurred2)
cv2.waitKey(0)

cv2.destroyAllWindows()



import cv2
import numpy as np

image = cv2.imread('images/elephant.jpg')

# Averaging done by convolving the image with a normalized box filter. 
# This takes the pixels under the box and replaces the central element
# Box size needs to odd and positive 
blur = cv2.blur(image, (3,3))
cv2.imshow('Averaging', blur)
cv2.waitKey(0)

# Instead of box filter, gaussian kernel
Gaussian = cv2.GaussianBlur(image, (7,7), 0)
cv2.imshow('Gaussian Blurring', Gaussian)
cv2.waitKey(0)

# Takes median of all the pixels under kernel area and central 
# element is replaced with this median value
median = cv2.medianBlur(image, 5)
cv2.imshow('Median Blurring', median)
cv2.waitKey(0)

# Bilateral is very effective in noise removal while keeping edges sharp
bilateral = cv2.bilateralFilter(image, 9, 75, 75)
cv2.imshow('Bilateral Blurring', bilateral)
cv2.waitKey(0)
cv2.destroyAllWindows()



import numpy as np
import cv2

image = cv2.imread('images/elephant.jpg')

# Parameters, after None are - the filter strength 'h' (5-10 is a good range)
# Next is hForColorComponents, set as same value as h again
# 
dst = cv2.fastNlMeansDenoisingColored(image, None, 6, 6, 7, 21)

cv2.imshow('Fast Means Denoising', dst)
cv2.waitKey(0)

cv2.destroyAllWindows()