In [7]:

    

import tensorflow as tf
import os
import random
import pylab
import numpy as np
import matplotlib.pyplot as plt


    

In [8]:

    

DATA_FOLDER = '/home/ankdesh/explore/DeepLearning-UdacityCapston/data/train_sample'


    

In [9]:

    

import matplotlib.pyplot as plt
%matplotlib inline
from PIL import Image


    

In [10]:

    

imgs = []
for i in range(5):
    imgs.append(os.path.join(DATA_FOLDER,random.choice(os.listdir(DATA_FOLDER))))


    

In [11]:

    

f,ax = plt.subplots(1,5, figsize=(15,15))
for i in range(5):
    ax[i].imshow(Image.open(imgs[i]))
plt.show() # or display.display(plt.gcf()) if you prefer


    

In [12]:

    

print [Image.open(x).size for x in imgs]


    

    




[(58, 24), (87, 45), (191, 87), (267, 135), (169, 65)]

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