In [1]:

    

%load_ext autoreload

%autoreload 2


    

In [2]:

    

%pylab inline

import os
import sys
sys.path.append('..')

from PIL import Image

from utils.filters import *


    

    




Populating the interactive namespace from numpy and matplotlib

In [3]:

    

image = Image.open('../utils/tests/lena.jpg')
imshow(image)


    

    
Out[3]:





<matplotlib.image.AxesImage at 0x7f3a2409d198>






    

In [4]:

    

blurred = GaussianBlur(15).apply(image)
imshow(blurred)


    

    
Out[4]:





<matplotlib.image.AxesImage at 0x7f3a224c5ef0>






    

In [5]:

    

noised1 = GaussianNoise(alpha=0.66, loc=128, scale=128).apply(image)
imshow(noised1)


    

    
Out[5]:





<matplotlib.image.AxesImage at 0x7f3a2232ecc0>






    

In [6]:

    

noised2 = UniformNoise(alpha=0.66).apply(image)
imshow(noised2)


    

    
Out[6]:





<matplotlib.image.AxesImage at 0x7f3a22313278>






    

In [7]:

    

vignette = Vignette().apply(image)
imshow(vignette)


    

    
Out[7]:





<matplotlib.image.AxesImage at 0x7f3a222737f0>






    

In [8]:

    

gotham = Gotham().apply(image)
imshow(gotham)


    

    
Out[8]:





<matplotlib.image.AxesImage at 0x7f3a22256390>






    

In [9]:

    

lomo = Lomo().apply(image)
imshow(lomo)


    

    
Out[9]:





<matplotlib.image.AxesImage at 0x7f3a221b6ef0>






    

In [10]:

    

toaster = Toaster().apply(image)
imshow(toaster)


    

    
Out[10]:





<matplotlib.image.AxesImage at 0x7f3a2211ba90>






    

In [11]:

    

kelvin = Kelvin().apply(image)
imshow(kelvin)


    

    
Out[11]:





<matplotlib.image.AxesImage at 0x7f3a220ff5f8>