In [1]:

    

%matplotlib inline

path = "data/galaxy/sample/"
#path = "data/galaxy/"

train_path = path + 'train/'
valid_path = path + 'valid/'
test_path = path + 'test/'
results_path = path + 'results/'
model_path = path + 'model/'


    

In [2]:

    

from utils import *


    

    




Using Theano backend.

In [88]:

    

g = glob(train_path+'images/*.jpg')

img = np.expand_dims(misc.imresize(ndimage.imread(g[12]), (64,64)),axis=0)
img.shape


    

    
Out[88]:





(1, 64, 64, 3)

In [89]:

    

plt.imshow(img[0])


    

    
Out[89]:





<matplotlib.image.AxesImage at 0x7fa684895910>






    

In [90]:

    

gen = image.ImageDataGenerator(samplewise_center=0,
                               rotation_range=360,
                               width_shift_range=0.05,
                               height_shift_range=0.05,
                               zoom_range=[0.9,1.3],
                               horizontal_flip=True,
                               channel_shift_range=0.5,
                               dim_ordering='tf')


    

In [91]:

    

aug_iter = gen.flow(img, batch_size=1)
aug_imgs = [next(aug_iter)[0] for i in range(8)]
plots(aug_imgs, (20,7), 2)


    

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