

In [1]:

    
import neukrill_net.utils
import PIL.Image
import skimage.transform



In [6]:

    
%matplotlib inline
import matplotlib.pyplot as plt



In [3]:

    
cd ..









    



/home/gavin/repositories/neukrill-net-work

Doing what the train.py script does to load images. First loads a the settings into its own object.



In [5]:

    
settings = neukrill_net.utils.Settings("settings.json")

Then get the dictionary of all the images:



In [12]:

    
c = next(iter(settings.image_fnames['train'].keys()))
example = settings.image_fnames['train'][c][0]



In [19]:

    
from IPython.display import display
from IPython.display import Image
from IPython.display import HTML
import matplotlib.cm as cm



In [16]:

    
e = Image(example)
display(e)



In [17]:

    
example_array = skimage.io.imread(example)



In [21]:

    
plt.imshow(example_array, cmap=cm.Greys_r)









    Out[21]:





<matplotlib.image.AxesImage at 0x7f5668caa710>

Interesting, so the cropping applied to the data is unreliable. We're going to need that cropping data augmentation. Should get an idea of how much cropping should be applied by these images where the plankton overlaps the edges. Should search the data for these examples before working on that.

skimage resize

Easiest way to do this is probably to pad it to a square and then resize.



In [32]:

    
import skimage.util



In [49]:

    
d = abs(example_array.shape[1]-example_array.shape[0])
if d%2 > 0:
    x = 1
    d = int((d-1)/2)
else:
    x = 0
    d = int(d/2)
if example_array.shape[1] > example_array.shape[0]:
    t = skimage.util.pad(example_array,((d,d+x),(0,0)), 'edge')
else:
    t = skimage.util.pad(example_array,((0,0),(d+x,d)), 'edge')
print(t.shape)
plt.imshow(t, cmap=cm.Greys_r)









    



(52, 52)






    Out[49]:





<matplotlib.image.AxesImage at 0x7f566892f160>



In [44]:

    
r = skimage.transform.resize(t,(48,48))
plt.imshow(r, cmap=cm.Greys_r)









    Out[44]:





<matplotlib.image.AxesImage at 0x7f566898e048>

Have now written this into the augmentation wrapper. Testing this out here:



In [67]:

    
import neukrill_net.augment



In [72]:

    
augment_settings = {"rotate":10,"resize":(20,20)}
processing = neukrill_net.augment.augmentation_wrapper(augment_settings)



In [83]:

    
import imp



In [93]:

    
imp.reload(neukrill_net.image_processing)









    Out[93]:





<module 'neukrill_net.image_processing' from '/home/gavin/repositories/neukrill-net-tools/neukrill_net/image_processing.py'>



In [94]:

    
imp.reload(neukrill_net.augment)









    Out[94]:





<module 'neukrill_net.augment' from '/home/gavin/repositories/neukrill-net-tools/neukrill_net/augment.py'>



In [95]:

    
processed = processing(example_array)



In [96]:

    
for ea in processed:
    print(ea.shape)
    plt.imshow(ea, cmap=cm.Greys_r)
    plt.show()

Looks like that's working now.



In [97]:

    
x = np.ones((2,2))



In [101]:

    
x - 2*np.mean(x)









    Out[101]:





array([[-1., -1.],
       [-1., -1.]])