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https://github.com/yoch/pykuwahara
Implementation of kuwahara filter in Python (numpy + OpenCV)
https://github.com/yoch/pykuwahara
Last synced: about 2 months ago
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Implementation of kuwahara filter in Python (numpy + OpenCV)
- Host: GitHub
- URL: https://github.com/yoch/pykuwahara
- Owner: yoch
- License: gpl-3.0
- Created: 2021-04-14T11:13:53.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-04-20T16:55:25.000Z (over 3 years ago)
- Last Synced: 2024-08-18T00:25:00.921Z (5 months ago)
- Language: Python
- Size: 960 KB
- Stars: 15
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pykuwahara
Kuwahara filter in Python (numpy + OpenCV).
> The Kuwahara filter is a non-linear smoothing filter used in image processing for adaptive noise reduction. It is able to apply smoothing on the image while preserving the edges.
> Source: [Wikipedia](https://en.wikipedia.org/wiki/Kuwahara_filter)
This implementation provide two variants of the filter:
- The classic one, using a uniform kernel to compute the window mean.
- A gaussian based filter, by computing the window gaussian mean. This is inspired by the [ImageMagick](http://www.fmwconcepts.com/imagemagick/kuwahara/index.php) approach.
## Installation
`pip install pykuwahara`
## Usage
### Simple example
```
import cv2
from pykuwahara import kuwahara
image = cv2.imread('lena_std.jpg')
filt1 = kuwahara(image, method='mean', radius=3)
filt2 = kuwahara(image, method='gaussian', radius=3) # default sigma: computed by OpenCV
cv2.imwrite('lena-kfilt-mean.jpg', filt1)
cv2.imwrite('lena-kfilt-gaus.jpg', filt2)
```
#### Original image
#### Filtered with Kuwahara (mean)
#### Filtered with Kuwahara (gaussian)
### Painting effect
Kuwahara filter can be used to apply a painting effet on pictures.
```
import cv2
from pykuwahara import kuwahara
image = cv2.imread('photo.jpg')
# Set radius according to the image dimensions and the desired effect
filt1 = kuwahara(image, method='mean', radius=4)
# NOTE: with sigma >= radius, this is equivalent to using 'mean' method
# NOTE: with sigma << radius, the radius has no effect
filt2 = kuwahara(image, method='gaussian', radius=4, sigma=1.5)
cv2.imwrite('photo-kfilt-mean.jpg', filt1)
cv2.imwrite('photo-kfilt-gaus.jpg', filt2)
```
#### Original image (source: [wikipedia](https://en.wikipedia.org/wiki/File:Kuwahara_creates_artistic_photo.jpg))
#### Filtered with Kuwahara (mean)
#### Filtered with Kuwahara (gaussian)
### Advanced usage
Color image are supported by grayscaling the source image and using the gray channel to calculate the variance.
The user can provide another channel at his convenience, and alternatively give the right color conversion code (default is `COLOR_BGR2GARY`).
```
import cv2
from pykuwahara import kuwahara
image = cv2.imread('selfie.jpg')
image = (image / 255).astype('float32') # pykuwahara supports float32 as well
lab_image = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)
l, a, b = cv2.split(lab_image)
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv_image)
filt1 = kuwahara(image, method='gaussian', radius=5, sigma=2., image_2d=l)
filt2 = kuwahara(image, method='gaussian', radius=5, sigma=2., image_2d=v)
cv2.imwrite('selfie-kfilt-gaus1.jpg', filt1 * 255)
cv2.imwrite('selfie-kfilt-gaus2.jpg', filt2 * 255)
```
#### Original image ([source](https://stackoverflow.com/questions/47017741/image-filter-to-cartoonize-and-colorize#47087756))
#### Filtered with Kuwahara on L (Lab)
#### Filtered with Kuwahara on V (HSV)
