Wand selective_blur() function in Wand python

Last Updated : 22 Apr, 2020

Another kind of Blur that can be performed using Wand library in pytho is Selective Blur. Selective blur is similar to normal blur. Difference is that it only effect the part of the image that have contrast below a given quantum threshhold. A new attribute named as threshold is introduced in this function.

Syntax :
wand.image.selective_blur(radius= radius_value, sigma= sigma_value,
                          threshold= thrshold_value,
                          channel = "optional_channel_value")

# radius should always be greater than sigma(standard deviation)
Parameters :
Parameter Input Type Description
radius numbers.Real the radius of the, in pixels, not counting the center pixel.
sigma numbers.Real the standard deviation, in pixels
threshold number.Real Only pixels within contrast threshold are effected.

Parameter	Input Type	Description
radius	numbers.Real	the radius of the, in pixels, not counting the center pixel.
sigma	numbers.Real	the standard deviation, in pixels
threshold	number.Real	Only pixels within contrast threshold are effected.

Image Used :

Example #1:

# import display() to show final image
from wand.display import display
  
# import Image from wand.image module
from wand.image import Image
  
# read file using Image function
with Image(filename ="koala.jpeg") as img:
  
    # perform adaptive blur effect using
    # adaptive_blur() function
    img.selective_blur(radius = 8, sigma = 4, 
              threshold = 0.15 * img.quantum_range)
  
    # save final image
    img.save(filename ="mb_koala.jpeg")
  
    # display final image
    display(img)

Output:

Example #2: Increase threshold value to 0.5.

# import display() to show final image
from wand.display import display
  
# import Image from wand.image module
from wand.image import Image
  
# read file using Image function
with Image(filename ="koala.jpeg") as img:
    # perform adaptive blur effect
    # using adaptive_blur() function
    img.selective_blur(radius = 8, sigma = 4, 
              threshold = 0.25 * img.quantum_range)
  
    # save final image
    img.save(filename ="mb_koala.jpeg")
  
    # display final image
    display(img)

Output:

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