def predict_autocontext( self,
img,
mask,
nb_labels,
nb_autocontext,
debug=False,
return_all=False ):
proba = np.ones((nb_labels,img.shape[0],img.shape[1],img.shape[2]),dtype='float32')
proba /= nb_labels
header = img.get_header()
header['dim'][3] = nb_labels
proba = irtk.Image(proba,header,squeeze=False)
all_steps = []
for k in xrange(nb_autocontext):
knowledge = self.get_knowledge(img,proba,mask=mask)
if debug:
irtk.imwrite("knowledge_"+str(k)+".nii.gz", knowledge)
forest = integralForest( folder=self.folder(k),
test=self.params['test'],
parallel=self.params['parallel'],
nb_knowledge_layers=knowledge.shape[0] )
proba = forest.predict_autocontext( img,
knowledge,
mask,
self.params['ksampling'] )
proba = irtk.Image(proba,header,squeeze=False)
if return_all:
all_steps.append( proba.copy() )
if debug:
irtk.imwrite("debug_"+str(k)+".nii.gz", proba)
if k < 1:
for i in xrange(proba.shape[0]):
if i == 0:
proba[i] = 0
else:
proba[i] = self.get_center(proba[i])
if debug:
print "done autocontext", k
# irtk.imwrite("debug_rescaled_"+str(k)+".nii.gz", proba)
if not return_all:
return proba
else:
return all_steps
def fit( self,
patient_ids,
n_validation=5,
min_dice_score=0.7,
max_autocontext=10,
start=0 ):
"""
Train the classifier.
"""
nb_labels = len(self.params['labels'])+1
## Preprocess data only once to speed up training
## (requires more memory)
# split patients to get validation set
np.random.shuffle(patient_ids)
n_validation = min(len(patient_ids)/2,n_validation)
training_patients = patient_ids[:-n_validation]
validation_patients = patient_ids[-n_validation:]
self.info['training_patients'] = training_patients
self.info['validation_patients'] = validation_patients
if self.params['verbose']:
print "fitting with", len(training_patients), "training patients and", \
len(validation_patients), "validation patients"
print "doing preprocessing..."
gc.collect()
training_data = Parallel(n_jobs=self.params['n_jobs'])(delayed(self.params['training_preprocessing_function'])( patient_id,
self.params['img_folder'],
self.params['seg_folder'],
self.params["resample"],
online=False )
for patient_id in training_patients )
if self.params['verbose']:
print "learning"
i = start
dice_score = 0
previous_dice_score = -1
while ( i < max_autocontext and
dice_score < min_dice_score and
dice_score > previous_dice_score ):
forest = integralForest( ntrees=self.params['n_estimators'],
bagging=self.params['bootstrap'],
max_depth=self.params['max_depth'],
min_items=self.params['min_items'],
nb_tests=self.params['nb_tests'],
parallel=self.params['parallel'],
test=self.params['test'],
cx=self.params['cx'], cy=self.params['cy'], cz=self.params['cz'],
dx=self.params['dx'], dy=self.params['dy'], dz=self.params['dz'],
nb_labels=nb_labels,
nb_knowledge_layers=2*(nb_labels-1),
ksampling=self.params['ksampling'],
verbose=False,
nfeatures=len(self.feature_mapping())
)
print "predicting training data"
tmp_probas = Parallel(n_jobs=self.params['n_jobs'])(delayed(predict_autocontext)( self,
data['img'],
data['mask'],
nb_labels,
i )
for data in training_data )
# tmp_probas = []
# for data in training_data:
# tmp_probas.append( predict_autocontext( self,
# data['img'],
# data['mask'],
# data['extra_layers'],
# data['metadata'],
# nb_labels,
# all_ga[data['patient_id']],
# i ) )
for data,proba in zip(training_data,tmp_probas):
img = data['img']
mask = data['mask']
seg = data['seg'].copy()
# kind of bootstrapping
for l in range(proba.shape[0]):
correct = np.logical_and( proba[l] > 0.5, seg == l )
# remove half of the correctly classified voxels
points = np.transpose(np.nonzero(correct))
if len(points) > 10:
np.random.shuffle(points)
points = points[:len(points)/2]
seg[points[:,0],
points[:,1],
points[:,2]] = 255
knowledge = self.get_knowledge(img,proba,mask=mask)
forest.add_image_autocontext(img,seg,knowledge)
# irtk.imwrite( "debug/"+data['patient_id']+"_knowledge"+str(i)+".nii.gz",
# knowledge )
print "starting to learn autocontext",i
forest.grow( self.params['nb_samples'],
self.params['nb_background_samples'] )
print "writing"
forest.write(self.folder(i))
print "done", i
feature_importance = forest.get_feature_importance()
mapping = self.feature_mapping()
if len(feature_importance) != len(mapping):
print "ERROR: forest.get_feature_importance() returns", len(feature_importance), "features"
print " feature_mapping() expects", len(mapping), "features"
feature_importance = dict( zip(mapping,
feature_importance) )
self.info['feature_importance'].append( feature_importance )
print feature_importance
i += 1
# release memory
del forest
gc.collect()
previous_dice_score = dice_score
print "scoring"
dice_score = self.score(validation_patients,nb_autocontext=i)
improvement = dice_score - previous_dice_score
self.info['validation_scores'].append(dice_score)
self.info['improvements'].append(improvement)
if self.params['verbose']:
print "Validation score:", dice_score
print "improvement:", improvement
self.save()
#!/usr/bin/python
import cv, cv2
import numpy as np
import matplotlib.pyplot as plt
from lib.integralforest import integralForest
from glob import glob
import re
import irtk
forest = integralForest( ntrees=5,
bagging=0.5,
max_depth=7,
min_items=10,
nb_tests=1000,
parallel=-1,
test="patch",
cx=50, cy=50, cz=0,
dx=50, dy=50, dz=0 )
images = []
segmentations = []
raw_data_folder = "/home/kevin/Imperial/PhD/gitlab/ess/brain_test/raw_data/"
ground_truth = glob("/home/kevin/Imperial/PhD/gitlab/ess/brain_test/ground_truth/*")#[:50]
segmentations = []
for f in ground_truth:
pattern = r'/(?P<raw_file>[^/]+)_(?P<x>\d+)_(?P<y>\d+)_(?P<w>\d+)_(?P<h>\d+)\.png$'
match = re.search( pattern, f )
import cv, cv2
import numpy as np
import matplotlib.pyplot as plt
from lib.integralforest import integralForest
from glob import glob
import re
import irtk
forest = integralForest(ntrees=5,
bagging=0.5,
max_depth=7,
min_items=10,
nb_tests=1000,
parallel=-1,
test="patch",
cx=50,
cy=50,
cz=0,
dx=50,
dy=50,
dz=0)
images = []
segmentations = []
raw_data_folder = "/home/kevin/Imperial/PhD/gitlab/ess/brain_test/raw_data/"
ground_truth = glob(
"/home/kevin/Imperial/PhD/gitlab/ess/brain_test/ground_truth/*") #[:50]
segmentations = []
def predict_autocontext( self,
img,
mask,
extra_layers,
metadata,
nb_labels,
ga,
nb_autocontext,
debug=False,
return_all=False ):
proba = np.ones((nb_labels,img.shape[0],img.shape[1],img.shape[2]),dtype='float32')
proba /= nb_labels
header = img.get_header()
header['dim'][3] = nb_labels
proba = irtk.Image(proba,header)
all_steps = []
for k in xrange(nb_autocontext):
metadata = self.get_center_axis(proba,k)
knowledge = self.get_knowledge(img,proba,extra_layers,mask=mask)
if debug:
irtk.imwrite("knowledge_"+str(k)+".nii.gz", knowledge)
forest = integralForest( folder=self.folder(k),
test=self.params['test'],
parallel=self.params['parallel'],
nb_knowledge_layers=knowledge.shape[0] )
proba = forest.predict_autocontext( img,
knowledge,
mask,
self.params['ksampling'],
metadata )
proba = irtk.Image(proba,header)
if return_all:
all_steps.append( proba.copy() )
if debug:
irtk.imwrite("debug_"+str(k)+".nii.gz", proba)
if k < 1:
for i in xrange(proba.shape[0]):
if i == 0:
proba[i] = 0
else:
proba[i] = self.groups[i-1].get_center(proba[i])
# # volume constraint
# # set not ventricule to 0
# tmp_proba = proba[1]
# for i in xrange(proba.shape[0]):
# if i == 1:
# continue
# proba[i] = 0
# # rescale ventricule
# target_volume = 182950.0*0.001**3
# #target_volume = 151807.0*0.001**3
# if k == 0:
# target_volume *= 0.5
# # elif k == 1:
# # target_volume *= 0.25
# # elif k == 2:
# # target_volume *= 0.5
# box_volume = float(proba.shape[1])*proba.header['pixelSize'][2]*float(proba.shape[2])*proba.header['pixelSize'][1]*float(proba.shape[3])*proba.header['pixelSize'][0]
# ratio = float(target_volume) / float(box_volume)
# #print "ratio", ratio
# q0 = mquantiles( tmp_proba.flatten(), prob=[1.0-ratio] )
# tmp_proba[proba[1]<q0] = q0
# tmp_proba -= tmp_proba.min()
# tmp_proba /= tmp_proba.max()
# lcc = irtk.largest_connected_component(tmp_proba,fill_holes=False)
# tmp_proba[lcc==0] = 0
# proba[1] = tmp_proba
if debug:
print "done autocontext", k
# irtk.imwrite("debug_rescaled_"+str(k)+".nii.gz", proba)
if not return_all:
return proba
else:
return all_steps
def predict_autocontext(self,
img,
mask,
extra_layers,
metadata,
nb_labels,
ga,
nb_autocontext,
debug=False,
return_all=False):
proba = np.ones((nb_labels, img.shape[0], img.shape[1], img.shape[2]),
dtype='float32')
proba /= nb_labels
header = img.get_header()
header['dim'][3] = nb_labels
proba = irtk.Image(proba, header)
all_steps = []
for k in xrange(nb_autocontext):
metadata = self.get_center_axis(proba, k)
knowledge = self.get_knowledge(img, proba, extra_layers, mask=mask)
if debug:
irtk.imwrite("knowledge_" + str(k) + ".nii.gz", knowledge)
forest = integralForest(folder=self.folder(k),
test=self.params['test'],
parallel=self.params['parallel'],
nb_knowledge_layers=knowledge.shape[0])
proba = forest.predict_autocontext(img, knowledge, mask,
self.params['ksampling'], metadata)
proba = irtk.Image(proba, header)
if return_all:
all_steps.append(proba.copy())
if debug:
irtk.imwrite("debug_" + str(k) + ".nii.gz", proba)
if k < 1:
for i in xrange(proba.shape[0]):
if i == 0:
proba[i] = 0
else:
proba[i] = self.groups[i - 1].get_center(proba[i])
# # volume constraint
# # set not ventricule to 0
# tmp_proba = proba[1]
# for i in xrange(proba.shape[0]):
# if i == 1:
# continue
# proba[i] = 0
# # rescale ventricule
# target_volume = 182950.0*0.001**3
# #target_volume = 151807.0*0.001**3
# if k == 0:
# target_volume *= 0.5
# # elif k == 1:
# # target_volume *= 0.25
# # elif k == 2:
# # target_volume *= 0.5
# box_volume = float(proba.shape[1])*proba.header['pixelSize'][2]*float(proba.shape[2])*proba.header['pixelSize'][1]*float(proba.shape[3])*proba.header['pixelSize'][0]
# ratio = float(target_volume) / float(box_volume)
# #print "ratio", ratio
# q0 = mquantiles( tmp_proba.flatten(), prob=[1.0-ratio] )
# tmp_proba[proba[1]<q0] = q0
# tmp_proba -= tmp_proba.min()
# tmp_proba /= tmp_proba.max()
# lcc = irtk.largest_connected_component(tmp_proba,fill_holes=False)
# tmp_proba[lcc==0] = 0
# proba[1] = tmp_proba
if debug:
print "done autocontext", k
# irtk.imwrite("debug_rescaled_"+str(k)+".nii.gz", proba)
if not return_all:
return proba
else:
return all_steps
