def make_pipeline(imageset_path, output_dir):
"""returns an augmentation pipeline for a given image set"""
p = Pipeline(imageset_path, output_dir)
p.random_distortion(probability=0.7,
grid_width=4,
grid_height=4,
magnitude=8)
p.flip_left_right(probability=0.5)
p.flip_top_bottom(probability=0.5)
p.zoom(probability=0.3, min_factor=1.1, max_factor=1.4)
p.rotate(probability=0.5, max_left_rotation=10, max_right_rotation=10)
return p
# In[10]:
print(os.listdir())
# In[11]:
from Augmentor import Pipeline
# In[12]:
augmentor = Pipeline('/home/asherif844/sparkNotebooks/Ch03/MNIST/images')
# In[13]:
augmentor.rotate(probability=0.9, max_left_rotation=25, max_right_rotation=25)
# In[14]:
for i in range(1, 3):
augmentor.sample(10)
# In[15]:
xtrain = data.train.images
ytrain = np.asarray(data.train.labels)
xtest = data.test.images
ytest = np.asarray(data.test.labels)
# In[16]: