notebook.community

Edit and run



In [1]:

    
%load_ext autoreload
%autoreload 2

import cPickle as pickle
import os; import sys; sys.path.append('..')
import gp
import gp.nets as nets

from nolearn.lasagne.visualize import plot_loss
from nolearn.lasagne.visualize import plot_conv_weights
from nolearn.lasagne.visualize import plot_conv_activity
from nolearn.lasagne.visualize import plot_occlusion

from sklearn.metrics import classification_report, accuracy_score, roc_curve, auc, precision_recall_fscore_support, f1_score, precision_recall_curve, average_precision_score, zero_one_loss


from matplotlib.pyplot import imshow
import matplotlib.pyplot as plt

%matplotlib inline









    



/home/d/nolearn/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
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 [39]:

    
%reload_ext autoreload



In [2]:

    
import nolearn.lasagne.visualize



In [2]:

    
NETS = []
NETS.append('../nets/IP_FULL.p') # image + prob
NETS.append('../nets/IPLB_FULL.p') # image + large border
NETS.append('../nets/IPM_FULL.p') # image + prob + binary
NETS.append('../nets/IPMB_FULL.p') # image + prob + binary + border
NETS.append('../nets/IPMLB_FULL.p') # image + prob + binary + large border

network_path = NETS[-1]

with open(network_path, 'rb') as f:
    net = pickle.load(f)



In [ ]:



In [3]:

    
net.train_history_[-52]









    Out[3]:





{'dur': 88.82897090911865,
 'epoch': 261,
 'train_loss': 0.10699970212163648,
 'train_loss_best': False,
 'valid_accuracy': 0.96897836112096492,
 'valid_loss': 0.084532429298247877,
 'valid_loss_best': True}



In [ ]:



In [4]:

    
train_losses = []
valid_losses = []
for h in net.train_history_:
    train_losses.append(h['train_loss'])
    valid_losses.append(h['valid_loss'])



In [22]:

    
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New VI 1.52805374446
--------------------------------------------------------------------------------
Merging for sureness s= 0.0242212433368
merging 38 198
recomputed 98 patches
merged 98 patches into 72
New VI 1.57622281582
--------------------------------------------------------------------------------
Merging for sureness s= 0.022138879227
merging 156 161
recomputed 39 patches
merged 39 patches into 29
New VI 1.57660405418
--------------------------------------------------------------------------------
Merging for sureness s= 0.0221190396696
merging 95 99
recomputed 26 patches
merged 26 patches into 19
New VI 1.57710521394
--------------------------------------------------------------------------------
Merging for sureness s= 0.0217921119183
merging 70 79
recomputed 77 patches
merged 77 patches into 65
New VI 1.57724150281
--------------------------------------------------------------------------------
Merging for sureness s= 0.0185625832528
merging 157 182
recomputed 44 patches
merged 44 patches into 33
New VI 1.58043821909
--------------------------------------------------------------------------------
Merging for sureness s= 0.0178892724216
merging 248 352
recomputed 124 patches
merged 124 patches into 100
New VI 1.60545789717
--------------------------------------------------------------------------------
Merging for sureness s= 0.0177842229605
merging 291 314
recomputed 42 patches
merged 42 patches into 38
New VI 1.60980712565
--------------------------------------------------------------------------------
Merging for sureness s= 0.0212469389662
merging 314 328
recomputed 46 patches
merged 46 patches into 40
New VI 1.63438847466
--------------------------------------------------------------------------------
Merging for sureness s= 0.0521556064487
merging 328 341
recomputed 78 patches
merged 78 patches into 50
New VI 1.63560230716
--------------------------------------------------------------------------------
Merging for sureness s= 0.0171128381044
merging 63 74
recomputed 98 patches
merged 98 patches into 76
New VI 1.63479695415
--------------------------------------------------------------------------------
Merging for sureness s= 0.0127881038934
merging 135 174
recomputed 97 patches
merged 97 patches into 72
New VI 1.63502290577
--------------------------------------------------------------------------------
Merging for sureness s= 0.0117030031979
merging 204 352
recomputed 85 patches
merged 85 patches into 63
New VI 1.66943636197
--------------------------------------------------------------------------------
Merging for sureness s= 0.295977964997
merging 152 352
recomputed 88 patches
merged 88 patches into 64
New VI 1.69671147467
--------------------------------------------------------------------------------
Merging for sureness s= 0.257188727688
merging 182 352
recomputed 67 patches
merged 67 patches into 52
New VI 1.71530917606
--------------------------------------------------------------------------------
Merging for sureness s= 0.810769708455
merging 197 352
recomputed 37 patches
merged 37 patches into 35
New VI 1.71871863148
--------------------------------------------------------------------------------
Merging for sureness s= 0.792244493961
merging 171 352
recomputed 53 patches
merged 53 patches into 42
New VI 1.73258840025
--------------------------------------------------------------------------------
Merging for sureness s= 0.0195108950138
merging 132 352
recomputed 60 patches
merged 60 patches into 50
New VI 1.74611566394
--------------------------------------------------------------------------------
Merging for sureness s= 0.0112796537578
merging 62 97
recomputed 22 patches
merged 22 patches into 18
New VI 1.74927632799
--------------------------------------------------------------------------------
Merging for sureness s= 0.0111202679109
merging 201 202
recomputed 45 patches
merged 45 patches into 38
New VI 1.74912218302
--------------------------------------------------------------------------------
Merging for sureness s= 0.00989824626595
merging 229 352
recomputed 44 patches
merged 44 patches into 38
New VI 1.75378360519
--------------------------------------------------------------------------------
Merging for sureness s= 0.0721650738269
merging 222 352
recomputed 41 patches
merged 41 patches into 35
New VI 1.75677463499
--------------------------------------------------------------------------------
Merging for sureness s= 0.00948217173573
merging 293 326
recomputed 93 patches
merged 93 patches into 73
New VI 1.75512699071
--------------------------------------------------------------------------------
Merging for sureness s= 0.00902084223344
merging 76 99
recomputed 69 patches
merged 69 patches into 55
New VI 1.75387863907
--------------------------------------------------------------------------------
Merging for sureness s= 0.00727092800662
merging 154 160
recomputed 93 patches
merged 93 patches into 70
New VI 1.75398273643
--------------------------------------------------------------------------------
Merging for sureness s= 0.0541511664633
merging 160 198
recomputed 87 patches
merged 87 patches into 68
New VI 1.76527464639
--------------------------------------------------------------------------------
Merging for sureness s= 0.386811276571
merging 174 198
recomputed 76 patches
merged 76 patches into 62
New VI 1.78420368641
--------------------------------------------------------------------------------
Merging for sureness s= 0.0779839847237
merging 180 198
recomputed 98 patches
merged 98 patches into 82
New VI 1.79844640611
--------------------------------------------------------------------------------
Merging for sureness s= 0.00633549457416
merging 242 260
recomputed 59 patches
merged 59 patches into 51
New VI 1.79698328385
--------------------------------------------------------------------------------
Merging for sureness s= 0.0078352605924
merging 216 260
recomputed 59 patches
merged 59 patches into 50
New VI 1.79809087622
--------------------------------------------------------------------------------
Merging for sureness s= 0.215715326369
merging 198 260
recomputed 312 patches
merged 312 patches into 180
New VI 1.88323720079
--------------------------------------------------------------------------------
Merging for sureness s= 0.00825450976845
merging 139 260
recomputed 56 patches
merged 56 patches into 48
New VI 1.8838828531
--------------------------------------------------------------------------------
Merging for sureness s= 0.0082412911579
merging 65 74
recomputed 27 patches
merged 27 patches into 21
New VI 1.88532555561
--------------------------------------------------------------------------------
Merging for sureness s= 0.00824026350165
merging 187 196
recomputed 68 patches
merged 68 patches into 53
New VI 1.8862172932
--------------------------------------------------------------------------------
Merging for sureness s= 0.00623523723334
merging 338 344
recomputed 13 patches
merged 13 patches into 10
New VI 1.88733349144
--------------------------------------------------------------------------------
Merging for sureness s= 0.34743328765
merging 330 344
recomputed 22 patches
merged 22 patches into 16
New VI 1.88885464993
--------------------------------------------------------------------------------
Merging for sureness s= 0.0383177828044
merging 318 344
recomputed 28 patches
merged 28 patches into 24
New VI 1.88697130614
--------------------------------------------------------------------------------
Merging for sureness s= 0.539698168635
merging 307 344
recomputed 47 patches
merged 47 patches into 34
New VI 1.89867505498
--------------------------------------------------------------------------------
Merging for sureness s= 0.363075405359
merging 344 350
recomputed 70 patches
merged 70 patches into 49
New VI 1.89810519373
--------------------------------------------------------------------------------
Merging for sureness s= 0.00876338360831
merging 302 309
recomputed 39 patches
merged 39 patches into 29
New VI 1.89872389374
--------------------------------------------------------------------------------
Merging for sureness s= 0.0287724053487
merging 290 309
recomputed 78 patches
merged 78 patches into 59
New VI 1.9027287832
--------------------------------------------------------------------------------
Merging for sureness s= 0.00579501595348
merging 113 260
recomputed 63 patches
merged 63 patches into 55
New VI 1.90231412774
--------------------------------------------------------------------------------
Merging for sureness s= 0.00558064784855
merging 234 260
recomputed 58 patches
merged 58 patches into 48
New VI 1.91987975101
--------------------------------------------------------------------------------
Merging for sureness s= 0.0865166066214
merging 196 260
recomputed 57 patches
merged 57 patches into 46
New VI 1.93024809688
--------------------------------------------------------------------------------
Merging for sureness s= 0.0572286807001
merging 200 260
recomputed 51 patches
merged 51 patches into 44
New VI 1.93604500232
--------------------------------------------------------------------------------
Merging for sureness s= 0.00509684067219
merging 57 260
recomputed 62 patches
merged 62 patches into 52
New VI 1.93976035579
--------------------------------------------------------------------------------
Merging for sureness s= 0.052419077605
merging 51 260
recomputed 56 patches
merged 56 patches into 50
New VI 1.94157371891
--------------------------------------------------------------------------------
Merging for sureness s= 0.00490943482146
merging 161 260
recomputed 89 patches
merged 89 patches into 74
New VI 1.94921797304
--------------------------------------------------------------------------------
Merging for sureness s= 0.0392993269488
merging 136 260
recomputed 89 patches
merged 89 patches into 74
New VI 1.9594433972
--------------------------------------------------------------------------------
Merging for sureness s= 0.00487307459116
merging 82 89
recomputed 28 patches
merged 28 patches into 23
New VI 1.96012506704
--------------------------------------------------------------------------------
Merging for sureness s= 0.306920390576
merging 89 138
recomputed 26 patches
merged 26 patches into 21
New VI 1.96671961233
--------------------------------------------------------------------------------
Merging for sureness s= 0.998034417629
merging 74 138
recomputed 71 patches
merged 71 patches into 55
New VI 1.97517297463
--------------------------------------------------------------------------------
Merging for sureness s= 0.918484210968
merging 97 138
recomputed 79 patches
merged 79 patches into 53
New VI 2.03259529274
--------------------------------------------------------------------------------
Merging for sureness s= 0.93751180172
merging 83 138
recomputed 62 patches
merged 62 patches into 47
New VI 2.04288126451
--------------------------------------------------------------------------------
Merging for sureness s= 0.00853261724114
merging 98 138
recomputed 14 patches
merged 14 patches into 11
New VI 2.04891184553
--------------------------------------------------------------------------------
Merging for sureness s= 0.0183481732383
merging 107 138
recomputed 59 patches
merged 59 patches into 45
New VI 2.05983770391
--------------------------------------------------------------------------------
Merging for sureness s= 0.830460518599
merging 118 138
recomputed 58 patches
merged 58 patches into 42
New VI 2.06465590897
--------------------------------------------------------------------------------
Merging for sureness s= 0.00494049023837
merging 99 138
recomputed 54 patches
merged 54 patches into 41
New VI 2.06947083425
--------------------------------------------------------------------------------
Merging for sureness s= 0.00993582992086
merging 138 260
recomputed 58 patches
merged 58 patches into 37
New VI 2.28022096064
--------------------------------------------------------------------------------
Merging for sureness s= 0.847376422294
merging 72 260
recomputed 40 patches
merged 40 patches into 33
New VI 2.2884193161
--------------------------------------------------------------------------------
Merging for sureness s= 0.779279434303
merging 123 260
recomputed 38 patches
merged 38 patches into 32
New VI 2.28814496557
--------------------------------------------------------------------------------
Merging for sureness s= 0.761375799333
merging 126 260
recomputed 36 patches
merged 36 patches into 31
New VI 2.28955962971
--------------------------------------------------------------------------------
Merging for sureness s= 0.0325887054205
merging 49 260
recomputed 37 patches
merged 37 patches into 31
New VI 2.28952071901
--------------------------------------------------------------------------------
Merging for sureness s= 0.0114251775667
merging 42 260
recomputed 33 patches
merged 33 patches into 29
New VI 2.31733755916
--------------------------------------------------------------------------------
Merging for sureness s= 0.00462652635473
merging 339 341
recomputed 32 patches
merged 32 patches into 22
New VI 2.33534819251
--------------------------------------------------------------------------------
Merging for sureness s= 0.847836266809
merging 321 341
recomputed 10 patches
merged 10 patches into 10
New VI 2.33641965316
--------------------------------------------------------------------------------
Merging for sureness s= 0.0805548578501
merging 264 341
recomputed 59 patches
merged 59 patches into 46
New VI 2.35702646603
--------------------------------------------------------------------------------
Merging for sureness s= 0.0044182385318
merging 341 346
recomputed 74 patches
merged 74 patches into 49
New VI 2.39153498634
'''



In [44]:

    
surenesses = []
VIs = []
j = 0
for i,a in enumerate(aaa.split('\n')):
    if a.startswith('Merging'):
        surenesses.append(float(a.split('= ')[1]))

        if surenesses[-1] <.95:
            print j
        j+=1            
    if a.startswith('New'):
        VIs.append(float(a.split(' ')[2]))



In [43]:

    
VIs









    Out[43]:





[0.817495412153,
 0.817496076009,
 0.817345725205,
 0.816164002404,
 0.815224805783,
 0.810386772272,
 0.810291085346,
 0.809773543923,
 0.809504817838,
 0.80816184235,
 0.807793095247,
 0.807625196272,
 0.807245717265,
 0.807261826022,
 0.807392229447,
 0.806641613873,
 0.806116165679,
 0.804934332616,
 0.80493479166,
 0.804850008339,
 0.804271064308,
 0.802387523362,
 0.802078369808,
 0.80121195831,
 0.801159926745,
 0.800517738251,
 0.800702906179,
 0.800439730222,
 0.799888828947,
 0.79978407613,
 0.79919900934,
 0.798932407801,
 0.803526469385,
 0.8023368684,
 0.801709608912,
 0.804074938586,
 0.803797318673,
 0.808588417199,
 0.807887142001,
 0.804969089413,
 0.805354670603,
 0.823654087254,
 0.821376001769,
 0.822331558978,
 0.82135711712,
 0.82241327525,
 0.822504403942,
 0.821294228085,
 0.827492814012,
 0.825253403526,
 0.831569005929,
 0.830876774694,
 0.828679256214,
 0.828608363414,
 0.827799443801,
 0.823267001711,
 0.822390767251,
 0.823369878152,
 0.827189516444,
 0.827222396208,
 0.827092241514,
 0.827159366632,
 0.833153247033,
 0.832197862814,
 0.830270649726,
 0.830295609979,
 0.830523710484,
 0.829930003458,
 0.829976519106,
 0.852751152712,
 0.869090723242,
 0.865864846562,
 0.882990521678,
 0.91963623786,
 0.920280101353,
 0.91636859827,
 0.915755789875,
 1.02106859812,
 1.03581159025,
 1.03561717624,
 1.0419603801,
 1.04127446266,
 1.03915038549,
 1.03834048758,
 1.04735341049,
 1.04639257591,
 1.05041536351,
 1.04958100846,
 1.06635072802,
 1.07268386429,
 1.10138602184,
 1.10290143282,
 1.10445155426,
 1.10365196592,
 1.11033269279,
 1.11007618404,
 1.10979814197,
 1.10903647273,
 1.10832002606,
 1.10820729016,
 1.11247123357,
 1.11211664067,
 1.11773634762,
 1.11456329108,
 1.11428757963,
 1.11439789919,
 1.11313575125,
 1.12265676179,
 1.12110309514,
 1.12518941685,
 1.1308750279,
 1.12963248712,
 1.13990205528,
 1.14316340028,
 1.15271915252,
 1.1548299808,
 1.16608462875,
 1.16688025456,
 1.17296865633,
 1.18816776242,
 1.1927351269,
 1.19129248988,
 1.22354082328,
 1.22519066173,
 1.2275241867,
 1.24196690088,
 1.38816305972,
 1.38985690819,
 1.39212520401,
 1.39650878072,
 1.39646042515,
 1.40371438038,
 1.43534704037,
 1.43684816876,
 1.5068488894,
 1.51626529396,
 1.51612711982,
 1.51628265147,
 1.52630287626,
 1.52667337093,
 1.52805374446,
 1.57622281582,
 1.57660405418,
 1.57710521394,
 1.57724150281,
 1.58043821909,
 1.60545789717,
 1.60980712565,
 1.63438847466,
 1.63560230716,
 1.63479695415,
 1.63502290577,
 1.66943636197,
 1.69671147467,
 1.71530917606,
 1.71871863148,
 1.73258840025,
 1.74611566394,
 1.74927632799,
 1.74912218302,
 1.75378360519,
 1.75677463499,
 1.75512699071,
 1.75387863907,
 1.75398273643,
 1.76527464639,
 1.78420368641,
 1.79844640611,
 1.79698328385,
 1.79809087622,
 1.88323720079,
 1.8838828531,
 1.88532555561,
 1.8862172932,
 1.88733349144,
 1.88885464993,
 1.88697130614,
 1.89867505498,
 1.89810519373,
 1.89872389374,
 1.9027287832,
 1.90231412774,
 1.91987975101,
 1.93024809688,
 1.93604500232,
 1.93976035579,
 1.94157371891,
 1.94921797304,
 1.9594433972,
 1.96012506704,
 1.96671961233,
 1.97517297463,
 2.03259529274,
 2.04288126451,
 2.04891184553,
 2.05983770391,
 2.06465590897,
 2.06947083425,
 2.28022096064,
 2.2884193161,
 2.28814496557,
 2.28955962971,
 2.28952071901,
 2.31733755916,
 2.33534819251,
 2.33641965316,
 2.35702646603,
 2.39153498634]



In [57]:

    
fig = plt.figure(figsize=(10,10))

font = {'family' : 'sans-serif',
        'weight' : 'normal',
        'size'   : 32}

plt.rc('font', **font)
plt.axvline(x=26, ymax=0.16, color='red', linewidth=3)
plt.plot(VIs[0:50], label='Training Loss', linewidth=3)

# plt.plot(valid_losses, label='Validation Loss', linewidth=3)
plt.xlabel('Correction')
plt.ylabel('Variation of Information')
# plt.xlim(40)
ax = plt.gca()
plt.rc('legend',**{'fontsize':28})
# legend = ax.legend(loc='upper right')

plt.savefig('/tmp/pt.pdf')



In [21]:

    
fig = plt.figure(figsize=(10,10))

font = {'family' : 'sans-serif',
        'weight' : 'normal',
        'size'   : 32}

plt.rc('font', **font)
plt.plot(train_losses, label='Training Loss', linewidth=3)
plt.plot(valid_losses, label='Validation Loss', linewidth=3)
plt.xlabel('Epoch')
plt.ylabel('Loss')
ax = plt.gca()
plt.rc('legend',**{'fontsize':28})
legend = ax.legend(loc='upper right')

plt.savefig('/tmp/valloss.pdf')



In [ ]:



In [ ]:



In [5]:

    
nolearn.lasagne.visualize.draw_to_notebook(net)









    Out[5]:



In [6]:

    
X_test, y_test = gp.Patch.load_rgba_test_only('ipmlb')









    



Loaded /home/d/patches//ipmlb/ in 0.000455141067505 seconds.



In [7]:

    
len(X_test)









    Out[7]:





17560



In [8]:

    
test_prediction = net.predict(X_test)
test_prediction_prob = net.predict_proba(X_test)
print
print 'Precision/Recall:'
print classification_report(y_test, test_prediction)









    



Precision/Recall:
             precision    recall  f1-score   support

          0       0.96      0.92      0.94      8780
          1       0.92      0.96      0.94      8780

avg / total       0.94      0.94      0.94     17560



In [9]:

    
test_acc = net.score(X_test, y_test)
acc_score = accuracy_score(y_test, test_prediction)
print 'Test Accuracy:', test_acc
print 'Accuracy Score:', acc_score









    



Test Accuracy: 0.940432801822
Accuracy Score: 0.940432801822



In [10]:

    
plot_loss(net)









    Out[10]:





<module 'matplotlib.pyplot' from '/home/d/nolearn/local/lib/python2.7/site-packages/matplotlib/pyplot.pyc'>



In [11]:

    
# ROC/AUC
fpr, tpr, _ = roc_curve(y_test, test_prediction_prob[:,1])
roc_auc = auc(fpr, tpr)
data = {}
data['CNN'] = (fpr, tpr, roc_auc)
gp.Legacy.plot_roc(data)



In [12]:

    
net.uuid = 'IPMLB'



In [17]:

    
image, prob, gold, rhoana, bb = gp.Legacy.read_dojo_data()



In [20]:

    
gp.Util.threshold(rhoana, 583).max()









    Out[20]:





0.0



In [48]:

    
gp.Stats.run_dojo_xp(net)









    



Loading merge errors from file..
10  merge errors found.
Creating dojo bigM..

Correcting merge errors with p < .05
fixing 1.25392163319e-09
fixing 0.0360888727009
fixing 0.036937456578
fixing 0.00606872793287
fixing 1.77100162091e-06
fixing 0.00174562365282
   Mean VI improvement 0.0056644086815
   Median VI improvement 0.0
Correcting split errors with p > .95
   Mean VI improvement 0.0762771864669
   Median VI improvement 0.0784492934953

Correcting merge errors with p < .05
fixing 1.25392163319e-09
fixing 0.0360888727009
fixing 0.036937456578
fixing 0.00606872793287
fixing 1.77100162091e-06
fixing 0.00174562365282
   Mean VI improvement 0.0056644086815
   Median VI improvement 0.0
Correcting split errors with p > .95
30 minutes done bigM_max= 0.592341065407
60 minutes done bigM_max= 0.00225855107419
   Mean VI improvement -3.04422046034
   Median VI improvement -3.10836300705

Correcting merge errors by simulated user (er=0)
   Mean VI improvement 0.00635579286424
   Median VI improvement 0.0
Correcting split errors by simulated user (er=0)
done
   Mean VI improvement 0.159537432967
   Median VI improvement 0.134733432936



In [45]:

    
gp.Stats.run_cylinder_xp(net)









    



Original median VI 0.379410649496
Loading merge errors from file..
151  merge errors found.
Creating cylinder bigM..






    



---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-45-92c57450d604> in <module>()
----> 1 gp.Stats.run_cylinder_xp(net)

/home/d/Projects/gp/gp/stats.py in run_cylinder_xp(cnn)
    564     else:
    565       print 'Creating cylinder bigM..'
--> 566       bigM_cylinder = gp.Legacy.create_bigM_without_mask(cnn, input_image, input_prob, input_rhoana, verbose=True, max=1000000)
    567       with open(bigM_cylinder_file, 'wb') as f:
    568         pickle.dump(bigM_cylinder, f)

/home/d/Projects/gp/gp/legacy.py in create_bigM_without_mask(cnn, volume, volume_prob, volume_segmentation, oversampling, verbose, max)
    819 
    820 
--> 821       patches = Patch.patchify(image, prob, segmentation, oversampling=oversampling, max=max, min_pixels=1)
    822       if verbose:
    823         print len(patches), 'generated in', time.time()-t0, 'seconds.'

/home/d/Projects/gp/gp/patch.pyc in patchify(image, prob, segmentation, sample_rate, min_pixels, max, oversampling, ignore_zero_neighbor)
    212 
    213         # print 'grabbing', l, n
--> 214         p_l, p_n = Patch.grab(image, prob, segmentation, l, n, sample_rate, oversampling=oversampling)
    215 
    216         patches += p_l

/home/d/Projects/gp/gp/patch.pyc in grab(image, prob, segmentation, l, n, sample_rate, mode, oversampling, patch_size)
     32     # analyze both borders
     33     patches_l = Patch.analyze_border(image, prob, binary_l, binary_n, border, l, n, sample_rate=sample_rate, patch_size=patch_size, oversampling=oversampling, mode=mode)
---> 34     patches_n = Patch.analyze_border(image, prob, binary_n, binary_l, border, n, l, sample_rate=sample_rate, patch_size=patch_size, oversampling=oversampling, mode=mode)
     35 
     36     return patches_l, patches_n

/home/d/Projects/gp/gp/patch.pyc in analyze_border(image, prob, binary_mask, binary_mask2, border, l, n, mode, patch_size, sample_rate, oversampling)
    364 
    365       patch_centers = []
--> 366       border_yx = indices = zip(*np.where(border==1))
    367 
    368       if len(border_yx) < 2:

KeyboardInterrupt:



In [29]:

    
image.shape









    Out[29]:





(10, 474, 474)



In [ ]:



In [36]:

    
# load dojo data
input_image, input_prob, input_gold, input_rhoana, dojo_bbox = gp.Legacy.read_dojo_data()



In [40]:

    
input_image[0].max()









    Out[40]:





225



In [38]:

    
imshow(input_prob[2], cmap='gray')









    Out[38]:





<matplotlib.image.AxesImage at 0x7fb6b90e42d0>



In [41]:

    
imshow(input_rhoana[0])









    Out[41]:





<matplotlib.image.AxesImage at 0x7fb72bd079d0>



In [53]:

    
import numpy as np
def seg_to_color(slice):
	colors = np.zeros(slice.shape+(3,),dtype=np.uint8)
	colors[:,:,0] = np.mod(107*slice[:,:],700).astype(np.uint8)
	colors[:,:,1] = np.mod(509*slice[:,:],900).astype(np.uint8)
	colors[:,:,2] = np.mod(200*slice[:,:],777).astype(np.uint8)
	return colors

i_r = input_image[0]
rrr = np.zeros((i_r.shape[0], i_r.shape[1], 3), dtype=np.uint8)
for j in range(rrr.shape[0]):
    for i in range(rrr.shape[1]):
        rrr[j,i] = (i_r[j,i], i_r[j,i], i_r[j,i])
    
rrr[input_prob[0] > 220 ] = (0,255,0)



In [57]:

    
plt.figure()
imshow(rrr)









    Out[57]:





<matplotlib.image.AxesImage at 0x7fb6c687f450>



In [ ]:



In [55]:

    
imshow(input_gold[0], cmap='gray')









    Out[55]:





<matplotlib.image.AxesImage at 0x7fb6c681e9d0>



In [56]:

    
imshow(input_rhoana[0], cmap='gray')









    Out[56]:





<matplotlib.image.AxesImage at 0x7fb72bcf0f50>



In [62]:

    
original_mean_VI, original_median_VI, original_VI_s = gp.Legacy.VI(input_gold, input_rhoana)
original_adapted_rand = gp.metrics.adapted_rand(input_rhoana, input_gold)



In [64]:

    
merge_errors = gp.Legacy.get_top5_merge_errors(net, input_image, input_prob, input_rhoana)









    



---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-64-3647edb73200> in <module>()
----> 1 merge_errors = gp.Legacy.get_top5_merge_errors(net, input_image, input_prob, input_rhoana)

/home/d/Projects/gp/gp/legacy.py in get_top5_merge_errors(cnn, input_image, input_prob, input_rhoana, verbose)
    315                                               dilate=True,
    316                                               border_seeds=True,
--> 317                                               oversampling=False)
    318 
    319             if len(results) > 0:

/home/d/Projects/gp/gp/legacy.py in fix_single_merge(cnn, cropped_image, cropped_prob, cropped_binary, N, invert, dilate, border_seeds, erode, debug, before_merge_error, real_border, oversampling, crop)
    233 
    234           for i in range(5):
--> 235             eroded_ws2 = mh.erode(eroded_ws2)
    236 
    237           # Util.view(eroded_ws, large=True, color=False)

/home/d/nolearn/local/lib/python2.7/site-packages/mahotas/morph.pyc in erode(A, Bc, out, output)
    196     Bc = get_structuring_elem(A,Bc)
    197     output = _get_output(A, out, 'erode', output=output)
--> 198     return _morph.erode(A, Bc, output)
    199 
    200 

KeyboardInterrupt:



In [66]:

    
first_rand = gp.metrics.adapted_rand(input_rhoana[0], input_gold[0])



In [78]:

    
net.uuid = 'IPMLB'



In [80]:

    
M = gp.Legacy.create_bigM_without_mask(net, input_image[0:1], input_prob[0:1], input_rhoana[0:1], verbose=False)



In [85]:

    
newM, new_rhoana, fixes, new_vi_s = gp.Legacy.splits_global_from_M_automatic(net, M, input_image[0:1], input_prob[0:1], 
                                                                             input_rhoana[0:1], input_gold[0:1], sureness_threshold=.95)



In [87]:

    
fixes









    Out[87]:





[(1, 0.99998939037322998),
 (1, 0.99904423952102661),
 (0, 0.99768221378326416),
 (1, 0.99105918407440186),
 (1, 0.98917317390441895),
 (1, 0.97869938611984253),
 (0, 0.97463154792785645),
 (1, 0.96067178249359131),
 (1, 0.96036547422409058)]



In [88]:

    
gp.metrics.adapted_rand(new_rhoana[0], input_gold[0])









    Out[88]:





0.067449536773076524



In [89]:

    
first_rand









    Out[89]:





0.073737703314086689



In [ ]: