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In [1]:

    
!pip freeze









    



Jinja2==2.7.3
MarkupSafe==0.23
Pillow==2.7.0
PyYAML==3.11
Pygments==2.0.2
Theano==0.6.0
argparse==1.2.1
backports.ssl-match-hostname==3.4.0.2
certifi==14.05.14
holoviews==0.7
ipython==3.0.0
jsonschema==2.4.0
mahotas==1.2.4
matplotlib==1.4.3
mistune==0.5
mock==1.0.1
-e git+git@github.com:Neuroglycerin/neukrill-net-tools.git@fbc052029973dfd05395a5b8601e9f68560f59e2#egg=neukrill_net-master
nose==1.3.4
numpy==1.9.1
param==1.2.1
ptyprocess==0.4
py==1.4.26
-e git+https://github.com/lisa-lab/pylearn2.git@cf3999e7183f8dcaccccf4dfd2a31bbe3a948a97#egg=pylearn2-neukrillnetchosencommit
pyparsing==2.0.3
pytest==2.6.4
python-dateutil==2.4.0
pytz==2014.10
pyzmq==14.5.0
scikit-image==0.10.1
scikit-learn==0.15.2
scipy==0.14.0
six==1.8.0
terminado==0.5
tornado==4.1
wsgiref==0.1.2



In [2]:

    
%pylab









    



Using matplotlib backend: agg
Populating the interactive namespace from numpy and matplotlib



In [3]:

    
%matplotlib inline



In [4]:

    
cd ..









    



/home/scott/Documents/git/neukrill-net-work



In [5]:

    
cd ../neukrill-net-work









    



/home/scott/Documents/git/neukrill-net-work



In [6]:

    
import sys
import numpy as np
import skimage.io



In [7]:

    
import imp



In [8]:

    
import neukrill_net.utils as utils
import neukrill_net.image_processing as image_processing
import neukrill_net.augment as augment



In [9]:

    
from IPython.display import display
from IPython.display import Image
from IPython.display import HTML



In [10]:

    
import matplotlib.pyplot as plt
import matplotlib.image as mpimg



In [11]:

    
img = skimage.io.imread('data/train/acantharia_protist/100224.jpg')



In [14]:

    
type(img)









    Out[14]:





numpy.ndarray



In [15]:

    
img.shape









    Out[15]:





(66, 59)



In [16]:

    
imgplot = plt.imshow(img)
imgplot.set_cmap('gray')



In [17]:

    
imgplot = plt.imshow(image_processing.flip_image(img, flip_x=True))
imgplot.set_cmap('gray')



In [18]:

    
imgplot = plt.imshow(image_processing.rotate_image(img, 360))
imgplot.set_cmap('gray')



In [19]:

    
imgplot = plt.imshow(image_processing.flip_image(image_processing.rotate_image(img, 180), flip_x=True))
imgplot.set_cmap('gray')



In [20]:

    
imgplot = plt.imshow(img)
imgplot.set_cmap('gray')



In [21]:

    
imgplot = plt.imshow(img[10:,:])
imgplot.set_cmap('gray')



In [24]:

    
imp.reload(image_processing)

processingFunction = augment.augmentation_wrapper({'rotate':3,'rotate_is_resizable':True})
imList = processingFunction(img)
for augImg in imList:
    iplot = plt.imshow(augImg)
    iplot.set_cmap('gray')
    show()









    



/home/scott/Documents/git/neukrill-venv2/local/lib/python2.7/site-packages/skimage/util/dtype.py:107: UserWarning: Possible precision loss when converting from float64 to uint8
  "%s to %s" % (dtypeobj_in, dtypeobj))



In [25]:

    
for augImg in imList:
    print(augImg)









    



[[255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 ..., 
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]]
[[255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 ..., 
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]]
[[255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 ..., 
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]]



In [26]:

    
for augImg in imList:
    foo = augImg
    print(foo.dtype)
    print(skimage.dtype_limits(foo))
    print(foo.dtype == np.dtype(np.float64))









    



uint8
(0, 255)
False
uint8
(0, 255)
False
uint8
(0, 255)
False



In [27]:

    
imp.reload(image_processing)

processingFunction = augment.augmentation_wrapper({'rotate':8,'rotate_is_resizable':True,'flip':True,'crop':True,'resize':(50,50)})
imList = processingFunction(img)
print(len(imList))
for augImg in imList:
    print(augImg.dtype)
    iplot = plt.imshow(augImg)
    iplot.set_cmap('gray')
    show()



In [33]:

    
imp.reload(image_processing)
imp.reload(augment)

processingFunction = augment.augmentation_wrapper({'rotate':3,'rotate_is_resizable':True,'flip':False,'traslations':[0,10],'shape':(50,50)})
imList = processingFunction(img)
print(len(imList))
for augImg in imList:
    print(augImg.dtype)
    iplot = plt.imshow(augImg)
    iplot.set_cmap('gray')
    show()



In [14]:

    
reload(image_processing)









    Out[14]:





<module 'neukrill_net.image_processing' from '/home/scott/Documents/git/neukrill-net-tools/neukrill_net/image_processing.py'>



In [28]:

    
augImg = image_processing.shear_image(img, 10)
iplot = plt.imshow(augImg)
iplot.set_cmap('gray')
show()



In [29]:

    
iplot = plt.imshow(skimage.transform.swirl(img,cval=1.0))
iplot.set_cmap('gray')
show()