Pyblur is a collection of simple image blurring routines.
It supports Gaussian, Disk, Box, and Linear Motion Blur Kernels as well as the Point Spread Functions
used in Convolutional Neural Networks for Direct Text Deblurring.
Functions receive a PIL image as input, and return another as output.
Kernel sizes can either be specified as input, or randomized.
Finally, there's a RandomizedBlur function that applies a random type of blurring kernel with a random width/strength.
pypi: https://pypi.python.org/pypi?:action=display&name=pyblur&version=0.2.3
- numpy
- scipy
- pillow
- scikit-image
From Pip: pip install pyblur
Or alternatively git clone
this repo and run locally
from pyblur import *
Blurs image using a Gaussian Kernel
blurred = GaussianBlur(img, bandwidth)
Randomized kernel bandwidth (between 0.5 and 3.5)
blurred = GaussianBlur_random(img)
Blurs image using a Disk Kernel
blurred = DefocusBlur(img, kernelsize)
Randomized kernel size (between 3 and 9)
blurred = DefocusBlur_random(img)
Blurs image using a Box Kernel
blurred = BoxBlur(img, kernelsize)
Randomized kernel size (between 3 and 9)
blurred = BoxBlur_random(img)
Blurs image using a Line Kernel
blurred = LinearMotionBlur(img, dim, angle, linetype)
dim
Kernel Size: {3,5,7,9}angle
Angle of the line of motion. Will be floored to the closest one available for the given kernel size.linetype = {left, right, full}
Controls whether the blur kernel will be applied in full or only the left/right halves of it.
Randomized kernel size, angle, and line type
blurred = LinearMotionBlur_random(img)
Blurs image using one of the Point Spread Functions (Kernels) used in:
Convolutional Neural Networks for Direct Text Deblurring
blurred = PsfBlur(img, psfid)
psfid
Id of the Point Spread Function to apply [0, 99]
Randomized kernel size, angle, and line type
blurred = PsfBlur_random(img)
Randomly applies one of the supported blur types, with a randomized bandwidth/strenght.
blurred = RandomizedBlur(img)