In [ ]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [12]:

    

import numpy as np
import imtools
import imtools.misc
import imtools.sample_data
import imtools.image_manipulation as imma
import sed3


    

In [13]:

    

datap = imtools.sample_data.generate()
data3d = datap["data3d"]
segmentation = datap["segmentation"]

print(data3d.shape, data3d.dtype)


    

    




(100, 100, 100) int64

In [14]:

    

sed3.show_slices(data3d, slice_step=20)


    

In [15]:

    

plt.imshow(data3d[30,:,:])


    

    
Out[15]:





<matplotlib.image.AxesImage at 0x7f123ff55d10>






    

In [16]:

    

crinfo = [[10, 56], [14, 64], [39, 78]]
data3d_cropped = imma.crop(data3d, crinfo)

print(data3d.shape, data3d.dtype)
#sed3.show_slices(data3d_cropped, slice_step=20)
plt.imshow(data3d_cropped[30,:,:])


    

    




(100, 100, 100) int64






    
Out[16]:





<matplotlib.image.AxesImage at 0x7f123fec5fd0>






    

In [17]:

    

plt.imshow(segmentation[30,:,:])


    

    
Out[17]:





<matplotlib.image.AxesImage at 0x7f12440c3cd0>






    

In [18]:

    

crinfo_auto = imma.crinfo_from_specific_data(segmentation, 5)
crinfo_auto


    

    
Out[18]:





[[0, 99], [20, 99], [45, 99]]

In [19]:

    

crinfo_auto = imma.crinfo_from_specific_data(segmentation, [5])
crinfo_auto


    

    
Out[19]:





[[0, 99], [20, 99], [45, 99]]

In [20]:

    

crinfo_auto = imma.crinfo_from_specific_data(segmentation, [5, 5, 5])
crinfo_auto


    

    
Out[20]:





[[0, 99], [20, 99], [45, 99]]

In [21]:

    

data3d_cropped_auto = imma.crop(data3d, crinfo_auto)
plt.imshow(data3d_cropped_auto[30,:,:])


    

    
Out[21]:





<matplotlib.image.AxesImage at 0x7f1256b61250>






    

In [22]:

    

data3d_uncropped = imma.uncrop(
    data3d_cropped_auto, 
    crinfo_auto, 
    [100, 100, 100])
plt.imshow(data3d_uncropped[30,:,:])


    

    
Out[22]:





<matplotlib.image.AxesImage at 0x7f123fdc1dd0>