In [5]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [6]:

    

import gp


    

    




Using gpu device 0: GeForce GTX TITAN (CNMeM is disabled, CuDNN 4007)
/home/d/nolearn/local/lib/python2.7/site-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.
  "downsample module has been moved to the theano.tensor.signal.pool module.")

In [7]:

    

input_image, input_prob, input_gold, input_rhoana, dojo_bbox = gp.Legacy.read_dojo_data()


    

In [8]:

    

gp.Util.view_labels(input_rhoana[8], [68,70], crop=False)


    

In [9]:

    

binary = gp.Util.view_labels(input_rhoana[8], [68,70], crop=False, return_it=True)
splitted = np.array(binary)


    

In [10]:

    

binary[binary==1] = 2


    

In [11]:

    

imshow(binary)


    

    
Out[11]:





<matplotlib.image.AxesImage at 0x7f1449840bd0>






    

In [12]:

    

imshow(splitted)


    

    
Out[12]:





<matplotlib.image.AxesImage at 0x7f1449779a90>






    

In [13]:

    

gp.Util.vi(binary.astype(np.uint64), input_gold[8].astype(np.uint64))


    

    
Out[13]:





0.9781710987371827

In [14]:

    

gp.Util.vi(splitted.astype(np.uint64), input_gold[8].astype(np.uint64))


    

    
Out[14]:





1.1039548495432179

In [15]:

    

imshow(input_gold[8])


    

    
Out[15]:





<matplotlib.image.AxesImage at 0x7f144972b050>






    

In [16]:

    

gp.Util.view((input_gold[8] + splitted).astype(np.uint64),large=True)


    

In [17]:

    

imshow(input_gold[8] + binary)


    

    
Out[17]:





<matplotlib.image.AxesImage at 0x7f1449587410>






    

In [18]:

    

splitted1 = np.array(splitted)
splitted2 = np.array(splitted)


    

In [19]:

    

splitted1[splitted1==1] = 0


    

In [20]:

    

imshow(splitted1)


    

    
Out[20]:





<matplotlib.image.AxesImage at 0x7f14484ae150>






    

In [21]:

    

splitted2[splitted2==2] = 0


    

In [22]:

    

imshow(splitted2)


    

    
Out[22]:





<matplotlib.image.AxesImage at 0x7f1449757d50>






    

In [23]:

    

gp.Util.vi(splitted1.astype(np.uint64), input_gold[8].astype(np.uint64))


    

    
Out[23]:





0.47895063243286246

In [24]:

    

gp.Util.vi(splitted2.astype(np.uint64), input_gold[8].astype(np.uint64))


    

    
Out[24]:





0.8530532196776562

In [26]:

    

plt.subplot(1,3,1)
imshow(input_gold[8])
plt.subplot(1,3,2)
imshow(splitted)
plt.subplot(1,3,3)
imshow(binary)


    

    
Out[26]:





<matplotlib.image.AxesImage at 0x7f144832c310>






    

In [27]:

    

input_gold.dtype


    

    
Out[27]:





dtype('uint32')

In [28]:

    

splitted.dtype


    

    
Out[28]:





dtype('float64')

In [29]:

    

binary.dtype


    

    
Out[29]:





dtype('float64')

In [36]:

    

gp.Util.vi(splitted.astype(np.uint64).flatten(), input_gold[8].astype(np.uint64).flatten())


    

    
Out[36]:





1.1039548495432179

In [33]:

    

gp.Util.vi(binary.astype(np.uint64).flatten(), input_gold[8].astype(np.uint64).flatten())


    

    
Out[33]:





0.9781710987371827

In [46]:

    

print np.unique(binary)
print np.unique(splitted)
print np.unique(input_gold[8])


    

    




[ 0.  2.]
[ 0.  1.  2.]
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
 50 51 52 53 54 55 56]

In [49]:

    

test = input_gold[8]*(binary>0)
print np.unique(test)


    

    




[ 0 41 43 44 45 50 51 52 53 55]

In [59]:

    

# 43, 52

gt = np.zeros_like(test)
gt[test==43] = 1
gt[test==52] = 2

imshow(gt)


    

    
Out[59]:





<matplotlib.image.AxesImage at 0x7f143b5652d0>






    

In [60]:

    

gp.Util.vi(binary.astype(np.uint64).flatten(), gt.astype(np.uint64).flatten())


    

    
Out[60]:





0.6931450975260867

In [61]:

    

gp.Util.vi(splitted.astype(np.uint64).flatten(), gt.astype(np.uint64).flatten())


    

    
Out[61]:





0.8416733242191282

In [65]:

    

imshow(binary+gt)


    

    
Out[65]:





<matplotlib.image.AxesImage at 0x7f143b295310>






    

In [64]:

    

imshow(splitted+gt)


    

    
Out[64]:





<matplotlib.image.AxesImage at 0x7f143b361dd0>






    

In [74]:

    

test = binary.copy()
test[gt==1] = 5


    

In [75]:

    

imshow(test)


    

    
Out[75]:





<matplotlib.image.AxesImage at 0x7f143ae9e110>






    

In [76]:

    

gp.Util.vi(test.astype(np.uint64).flatten(), gt.astype(np.uint64).flatten())


    

    
Out[76]:





0.0

In [78]:

    

mask = test==5
imshow(mask+binary)


    

    
Out[78]:





<matplotlib.image.AxesImage at 0x7f143ad6cd50>






    

In [79]:

    

gp.Util.vi((mask+binary).astype(np.uint64).flatten(), gt.astype(np.uint64).flatten())


    

    
Out[79]:





0.0

In [80]:

    

import mahotas


    

In [114]:

    

mask = test==5
mask_orig = mask.copy()
values = []

for i in xrange(40):
    mask = mahotas.erode(mask)
    vi = gp.Util.vi((mask+binary).astype(np.uint64).flatten(), gt.astype(np.uint64).flatten())
    #print i, vi
    values.append(vi)
    imshow(mask+binary)
    plt.show()


    

In [115]:

    

plt.plot(values)


    

    
Out[115]:





[<matplotlib.lines.Line2D at 0x7f14394a8890>]






    

In [116]:

    

imshow(gt)


    

    
Out[116]:





<matplotlib.image.AxesImage at 0x7f143a62f150>






    

In [ ]: