Neural art check-point viewer

Simple notebook to load the images saved during an nstyle training run and view them together.



In [1]:

    
import glob
dir_name = 'output/'
prefix = dir_name + 'e_'
files = glob.glob(prefix+'*.jpg')



In [2]:

    
# Sort by numeric part
files.sort(key=lambda x: int(x[len(prefix):-4]))
files.append(dir_name+'final.jpg')
files[:5]









    Out[2]:





['output/e_50.jpg', 'output/e_100.jpg', 'output/e_150.jpg', 'output/final.jpg']



In [3]:

    
# Set up some code to display the images in a notebook
import IPython.display
def show_img(fn):
    h = IPython.display.HTML("<img src='"+fn+"'>")
    IPython.display.display(h)

Animated display

Flip through the frames fairly quickly for a movie-like experience.
NOTE: this tends to work much better the second time through after the browser has cached all the images.



In [4]:

    
# Animated Display
import time
for img in files:
    IPython.display.clear_output()
    print(img)
    show_img(img)
    time.sleep(1)









    



output/final.jpg



In [5]:

    
%matplotlib inline
import matplotlib.pyplot as plt
from skimage import io

for img_fn in files:
    print(img_fn)
    im = io.imread(img_fn)
    plt.figure(figsize=(3,2))
    plt.axis('off')
    plt.imshow(im)
    plt.show()









    



output/e_50.jpg






    












    



output/e_100.jpg






    












    



output/e_150.jpg






    












    



output/final.jpg



In [ ]: