How to blur red color in image with python opencv so that its not clearly visible?

Question

I want to blur red color in image ("1.png" is attached) so that its not clearly visible. I tried below code where I can change red color to black color but how can I blur it? Please help.

import cv2
import numpy as np

frame = cv2.imread("1.png")

hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of red color in HSV
lower = np.array([0,50,50])
upper = np.array([10,255,255])

# define range of blue color in HSV
# lower = np.array([38, 86, 0])
# upper = np.array([121, 255, 255])

# define range of pink color in HSV
# http://www.workwithcolor.com/pink-color-hue-range-01.htm
# lower = np.array([158, 127, 0])
# upper = np.array([179, 255, 255])

# Threshold the HSV image to get only red colors
mask = cv2.inRange(hsv, lower, upper)
color_only = cv2.bitwise_and(frame, frame, mask = mask)

# convert mask to 3-channel image to perform subtract
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
res = cv2.subtract(frame, mask) #negative values become 0 -> black
cv2.imshow("frame", frame)
# cv2.imshow("mask", mask)
# cv2.imshow("color_only", color_only)
cv2.imshow("res", res)
cv2.waitKey()
cv2.destroyAllWindows()

1.png

Answer 1

You need to blur the segmented image and then use alpha blending to composite the blurred ROI with the background image. This code takes you through all the steps:

Read image and segment the color of interest:

import cv2
import numpy as np
frame = cv2.imread("/home/stephen/Desktop/1.png")
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of red color in HSV
lower = np.array([0,50,50])
upper = np.array([10,255,255])
mask = cv2.inRange(hsv, lower, upper)
color_only = cv2.bitwise_and(frame, frame, mask = mask)
###  THE BACKGROUND MUST BE MADE WHITE, NOT BLACK  ###
color_only[np.where((color_only==[0,0,0]).all(axis=2))] = [255,255,255]
cv2.imshow("color_only", color_only)

Next, blur the segmented image. Note, I am using a 13,13 kernel to blur the image:

blur = cv2.blur(color_only, (13,13))
cv2.imshow('blur', blur)

Next, blur the mask of the segmented image. We are going to use this to combine the images later. It's easy to combine images using a bitwise function, but that approach will not work here because the blurred image no longer occupies the same space as the segmented image:

maskForAlphaBlending = blur
## BLACK OUT THE WHITE BACKGROUND OF THE ALPHA MASK
maskForAlphaBlending[np.where((maskForAlphaBlending==[255,255,255]).all(axis=2))] = [0,0,0]
maskForAlphaBlending = cv2.cvtColor(maskForAlphaBlending, cv2.COLOR_BGR2GRAY)
cv2.imshow('maskForAlphaBlending', maskForAlphaBlending)

Finally alpha blending can be used to composite the blurred segmented image and the green and white background image:

foreground = blur
background = frame
alpha = cv2.cvtColor(maskForAlphaBlending, cv2.COLOR_GRAY2BGR)
foreground = foreground.astype(float)
background = background.astype(float)
alpha = alpha.astype(float)/100
foreground = cv2.multiply(alpha, foreground)
background = cv2.multiply(1.0 - alpha, background)
outImage = cv2.add(foreground, background)
cv2.imshow("outImg", outImage/255)

Note how the red lines are blurred, but the green and white border is not: