def __init__(self, workspace='../', data_type='val'):
self.workspace = workspace
self.csvpath = './' + data_type + '_csv/annotations.csv'
self.data_path = '../nodule_cubes/' + data_type + '_data/'
self.savepath = self.data_path + 'image/'
if data_type == 'val':
self.data = _3D_data(valpath=self.data_path)
self.labels, self.valdata, self.origins, self.new_spacings, self.old_spacings = self.data.get_val(
)
elif data_type == 'test' or data_type == 'new_test':
self.data = _3D_data(testpath=self.data_path)
self.labels, self.valdata, self.origins, self.new_spacings, self.old_spacings = self.data.get_test(
)
elif data_type == 'train':
self.data = _3D_data(trainpath=self.data_path)
self.labels, self.valdata, self.origins, self.new_spacings, self.old_spacings = self.data.get_train(
)
else:
print 'ERROR!'
self.csv_data = pd.read_csv(self.csvpath)
names = self.csv_data['seriesuid']
names = list(names)
self.index = [self.get_data_posotion(name) for name in names]
def __init__(self,
weights_path,
dropout=False,
data_type='val',
data_path=None,
csv=False):
self.weights_path = weights_path
self.dropout = dropout
self.position = []
self.data_type = data_type
self.csv = csv
if data_path is None:
self.data_path = '../nodule_cubes/' + data_type + '_data/'
else:
self.data_path = data_path
if data_type == 'val':
self.data = _3D_data(valpath=self.data_path)
self.labels, self.valdata, self.origins, self.new_spacings, self.old_spacings = self.data.get_val(
)
elif data_type == 'test' or data_type == 'new_test':
self.data = _3D_data(testpath=self.data_path)
self.labels, self.valdata, self.origins, self.new_spacings, self.old_spacings = self.data.get_test(
)
else:
print 'ERROR!'
if csv:
self.csv_data = pd.read_csv(self.data_path + 'csv/annotations.csv')
names = self.csv_data['seriesuid']
names = list(names)
self.index = [self.get_data_posotion(name) for name in names]
else:
self.index = range(len(self.valdata))
def get_data_posotion(labels, key):
for i in range(len(labels)):
if key == labels[i]:
return i
return None
if __name__ == '__main__':
model = _3D_CNN_2(weights_path=weights_path, dropout=True, feature=True)
data_path = '../nodule_cubes/' + data_type + '_data/'
if data_type == 'val':
data = _3D_data(valpath=data_path)
labels, valdata, origins, new_spacings, old_spacings = data.get_val()
elif data_type == 'test' or data_type == 'new_test':
data = _3D_data(testpath=data_path)
labels, valdata, origins, new_spacings, old_spacings = data.get_test()
elif data_type == 'train':
data = _3D_data(trainpath=data_path)
labels, valdata, origins, new_spacings, old_spacings = data.get_train()
else:
print 'ERROR!'
csv_data = pd.read_csv(csv_path)
names = csv_data['seriesuid']
names = list(names)
index = [get_data_posotion(labels, name) for name in names]
data = data[0]
new_datas.append([
data[16 - size[0]:16 + size[0], 16 - size[1]:16 + size[1],
16 - size[2]:16 + size[2]]
])
return np.array(new_datas)
def deal_datas(datas):
datas = np.array(datas, dtype='float32').transpose((0, 2, 3, 4, 1))
datas = (datas + 1000) / 1400
return datas
if __name__ == '__main__':
data = _3D_data(path='../nodule_cubes/')
K.clear_session()
weights_path = './weights/weights3.h5'
model = _3D_CNN_2(dropout=True)
model.setting(optimizer=SGD(lr=0.017,
momentum=0.001,
decay=1e-7,
nesterov=False),
loss=["binary_crossentropy"],
metrics=['accuracy'])
(x_val32, y_val) = data.get_valset()
x_val16 = (deal_datas(get_data2(x_val32, [16, 16, 16])))
x_val32 = (deal_datas(x_val32))
y_val = np.array(y_val, dtype='float32')
tmp['coordX'] = center[2]
tmp['coordY'] = center[1]
tmp['coordZ'] = center[0]
tmp['seriesuid'] = val_labels[k]
tmp['probability'] = y_vals[k, 0]
position.append(tmp)
position_num += 1
else:
negetive_num += 1
print '[%d / %d]' % (position_num, negetive_num)
return position
if __name__ == '__main__':
data = _3D_data(valpath='../nodule_cubes/val_data/')
labels, valdata, origins, new_spacings, old_spacings = data.get_val()
model = _3D_CNN_1(weights_path=weights_path)
index = range(len(valdata))
# random.shuffle(index)
# index = index[0:20]
position = prec(index, [32, 32, 32],
labels,
origins,
new_spacings,
stride=[32, 32, 32])
position = prec(index, [14, 14, 14],
labels,