Skip to main content

#Pyblur ##Python image blurring routines.

Just a few fixes to port the unmaintained original https://github.com/lospooky/pyblur to python3.

Pyblur is a collection of simple image blurring routines.<br> 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](http://www.fit.vutbr.cz/~ihradis/CNN-Deblur/).<br> Functions receive a PIL image as input, and return another as output.<br> Kernel sizes can either be specified as input, or randomized.<br> Finally, there’s a RandomizedBlur function that applies a random type of blurring kernel with a random width/strength.

##Installation From Pip: pip install pyblur3`<br> Or alternatively `git clone this repo and run locally

##Usage

from pyblur import *

###Gaussian Blur Blurs image using a Gaussian Kernel

blurred = GaussianBlur(img, bandwidth)

Randomized kernel bandwidth (between 0.5 and 3.5)

blurred = GaussianBlur_random(img)

###Defocus (Disk) Blur Blurs image using a Disk Kernel

blurred = DefocusBlur(img, kernelsize)

Randomized kernel size (between 3 and 9)

blurred = DefocusBlur_random(img)

###Box Blur Blurs image using a Box Kernel

blurred = BoxBlur(img, kernelsize)

Randomized kernel size (between 3 and 9)

blurred = BoxBlur_random(img)

###Linear Motion Blur Blurs image using a Line Kernel

blurred = LinearMotionBlur(img, dim, angle, linetype)

####Parameters * dim Kernel Size: {3,5,7,9} <br> * angle Angle of the line of motion. Will be floored to the closest one available for the given kernel size. <br> * linetype = {left, right, full} Controls whether the blur kernel will be applied in full or only the left/right halves of it. <br>

Randomized kernel size, angle, and line type

blurred = LinearMotionBlur_random(img)

### PSF Blur Blurs image using one of the Point Spread Functions (Kernels) used in:<br> [Convolutional Neural Networks for Direct Text Deblurring](http://www.fit.vutbr.cz/~ihradis/CNN-Deblur/)

blurred = PsfBlur(img, psfid)

####Parameters * psfid Id of the Point Spread Function to apply [0, 99] <br>

Randomized kernel size, angle, and line type

blurred = PsfBlur_random(img)

###Random Blur Randomly applies one of the supported blur types, with a randomized bandwidth/strenght.

blurred = RandomizedBlur(img)

Metadata

Release files for pyblur3 0.1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for pyblur3 0.1.2
File Interpreter ABI Platform Actions
pyblur3-0.1.2-py3-none-any.whl Python 3 none any Details for pyblur3-0.1.2-py3-none-any.whl

Release files / pyblur3-0.1.2-py3-none-any.whl

Download URL pyblur3-0.1.2-py3-none-any.whl
Size 27.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
7e056dce2c048746382c8f87435cc5cae138ac7843f891821c612864c5e7087c
BLAKE2b-256 checksum
How to use checksums
c2c35c22642db74cad825ac55bc97e759b300e0dcaab9e96b40a7c9e2a496abe
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.1.1 pkginfo/1.5.0.1 requests/2.25.1 setuptools/45.2.0 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.10

Security for pyblur3 0.1.2

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

0.1.2 This release

1 release file No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page