def coscia_apply(modelObj: DynamicDLModel, data: dict):
from dl.common.padorcut import padorcut
from dl.common.preprocess_train import split_mirror
from scipy.ndimage import zoom
try:
np
except:
import numpy as np
from dl.labels.thigh import long_labels as LABELS_DICT
MODEL_RESOLUTION = np.array([1.037037, 1.037037])
MODEL_SIZE = (432, 432)
netc = modelObj.model
resolution = np.array(data['resolution'])
zoomFactor = resolution / MODEL_RESOLUTION
img = data['image']
originalShape = img.shape
img = zoom(img, zoomFactor) # resample the image to the model resolution
img = padorcut(img, MODEL_SIZE)
segmentation = netc.predict(
np.expand_dims(np.stack([img, np.zeros(MODEL_SIZE)], axis=-1), axis=0))
labelsMask = np.argmax(np.squeeze(segmentation[0, :, :, :13]), axis=2)
if (data['split_laterality']):
a1, a2, a3, a4, b1, b2 = split_mirror(img)
labelsMask_left = np.zeros(MODEL_SIZE, dtype='float32')
labelsMask_right = np.zeros(MODEL_SIZE, dtype='float32')
labelsMask_left[int(b1):int(b2),
int(a1):int(a2)] = labelsMask[int(b1):int(b2),
int(a1):int(a2)]
labelsMask_right[int(b1):int(b2),
int(a3):int(a4)] = labelsMask[int(b1):int(b2),
int(a3):int(a4)]
labelsMask_left = zoom(labelsMask_left, 1 / zoomFactor, order=0)
labelsMask_left = padorcut(labelsMask_left,
originalShape).astype(np.int8)
labelsMask_right = zoom(labelsMask_right, 1 / zoomFactor, order=0)
labelsMask_right = padorcut(labelsMask_right,
originalShape).astype(np.int8)
outputLabels = {}
for labelValue, labelName in LABELS_DICT.items():
outputLabels[labelName +
'_L'] = (labelsMask_left == labelValue).astype(
np.int8)
outputLabels[labelName +
'_R'] = (labelsMask_right == labelValue).astype(
np.int8)
return outputLabels
else:
labelsMask = zoom(labelsMask, 1 / zoomFactor, order=0)
labelsMask = padorcut(labelsMask, originalShape).astype(np.int8)
outputLabels = {}
for labelValue, labelName in LABELS_DICT.items():
outputLabels[labelName] = (labelsMask == labelValue).astype(
np.int8)
return outputLabels
def leg_incremental(modelObj: DynamicDLModel, trainingdata: dict,
trainingoutputs):
from unused.create_train import create_train_leg
import dl.common.preprocess_train as pretrain
import os
import math
from keras.callbacks import ModelCheckpoint
from keras import optimizers
try:
np
except:
import numpy as np
create_train_leg(trainingdata, trainingoutputs)
path = './train/leg'
pretrain.split_mirror(path, card, 7)
batch_size = 5
card = len(os.listdir(path))
steps = int(math.floor(float(card / batch_size)))
pretrain.input_creation(path, card, 270, 64, 7)
netc = modelObj.model
checkpoint_path = "./Weights_incremental/weights_leg_split-{epoch:02d}-{loss:.2f}.hdf5"
check = ModelCheckpoint(filepath=checkpoint_path,
monitor='loss',
verbose=0,
save_best_only=False,
save_weights_only=True,
mode='auto',
period=5)
adamlr = optimizers.Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
amsgrad=True)
training_generator = DataGenerator(path=path,
list_X=list(
range(1, steps * batch_size + 1)),
batch_size=batch_size)
netc.compile(loss=weighted_loss, optimizer=adamlr)
history = netc.fit_generator(generator=training_generator,
steps_per_epoch=steps,
epochs=5,
callbacks=[check],
verbose=1)
def coscia_apply(modelObj: DynamicDLModel, data: dict):
from dl.common.padorcut import padorcut
import dl.common.biascorrection as biascorrection
import dl.common.preprocess_train as pretrain
from dl.common.preprocess_train import split_mirror
from scipy.ndimage import zoom
try:
np
except:
import numpy as np
from dl.labels.thigh import long_labels as LABELS_DICT
MODEL_RESOLUTION = np.array([1.037037, 1.037037])
MODEL_SIZE = (432, 432)
MODEL_SIZE_SPLIT = (250, 250)
netc = modelObj.model
resolution = np.array(data['resolution'])
zoomFactor = resolution / MODEL_RESOLUTION
img = data['image']
originalShape = img.shape
img = zoom(img, zoomFactor) # resample the image to the model resolution
img = padorcut(img, MODEL_SIZE)
imgbc = biascorrection.biascorrection_image(img)
a1, a2, a3, a4, b1, b2 = split_mirror(imgbc)
left = imgbc[int(b1):int(b2), int(a1):int(a2)]
left = padorcut(left, MODEL_SIZE_SPLIT)
right = imgbc[int(b1):int(b2), int(a3):int(a4)]
right = right[::1, ::-1]
right = padorcut(right, MODEL_SIZE_SPLIT)
segmentationleft = netc.predict(
np.expand_dims(np.stack([left, np.zeros(MODEL_SIZE_SPLIT)], axis=-1),
axis=0))
labelleft = np.argmax(np.squeeze(segmentationleft[0, :, :, :13]), axis=2)
segmentationright = netc.predict(
np.expand_dims(np.stack([right, np.zeros(MODEL_SIZE_SPLIT)], axis=-1),
axis=0))
labelright = np.argmax(np.squeeze(segmentationright[0, :, :, :13]), axis=2)
labelright = labelright[::1, ::-1]
labelsMask_left = np.zeros(MODEL_SIZE, dtype='float32')
labelsMask_right = np.zeros(MODEL_SIZE, dtype='float32')
labelsMask_left[int(b1):int(b2),
int(a1):int(a2)] = padorcut(labelleft, [b2 - b1, a2 - a1])
labelsMask_right[int(b1):int(b2),
int(a3):int(a4)] = padorcut(labelright,
[b2 - b1, a4 - a3])
labelsMask_left = zoom(labelsMask_left, 1 / zoomFactor, order=0)
labelsMask_left = padorcut(labelsMask_left, originalShape).astype(np.int8)
labelsMask_right = zoom(labelsMask_right, 1 / zoomFactor, order=0)
labelsMask_right = padorcut(labelsMask_right,
originalShape).astype(np.int8)
outputLabels = {}
if data['split_laterality']:
for labelValue, labelName in LABELS_DICT.items():
outputLabels[labelName + '_R'] = (
labelsMask_left == labelValue).astype(
np.int8) # left in the image is right in the anatomy
outputLabels[labelName +
'_L'] = (labelsMask_right == labelValue).astype(
np.int8)
return outputLabels
else:
for labelValue, labelName in LABELS_DICT.items():
outputLabels[labelName] = np.logical_or(
labelsMask_left == labelValue,
labelsMask_right == labelValue).astype(np.int8)
return outputLabels
def gamba_apply(modelObj: DynamicDLModel, data: dict):
from dl.common.padorcut import padorcut
import dl.common.biascorrection as biascorrection
import dl.common.preprocess_train as pretrain
from dl.common.preprocess_train import split_mirror
from scipy.ndimage import zoom
try:
np
except:
import numpy as np
from dl.labels.leg import long_labels as LABELS_DICT
MODEL_RESOLUTION = np.array([1.037037, 1.037037])
MODEL_SIZE = (432, 432)
MODEL_SIZE_SPLIT = (216, 216)
classification = data.get('classification', '')
single_side = False
swap = False
# Note: this is anatomical right, which means it's image left! It's the image right that is swapped
if classification.lower().strip().endswith('right'):
single_side = True
swap = False
elif classification.lower().strip().endswith('left'):
single_side = True
swap = True
# otherwise: double sided
netc = modelObj.model
resolution = np.array(data['resolution'])
zoomFactor = resolution / MODEL_RESOLUTION
img = data['image']
originalShape = img.shape
img = zoom(img, zoomFactor) # resample the image to the model resolution
if single_side:
img = padorcut(img, MODEL_SIZE_SPLIT)
if swap:
img = img[::1, ::-1]
segmentation = netc.predict(np.expand_dims(np.stack([img, np.zeros(MODEL_SIZE_SPLIT)], axis=-1), axis=0))
label = np.argmax(np.squeeze(segmentation[0, :, :, :13]), axis=2)
if swap:
label = label[::1, ::-1]
labelsMask = zoom(label, 1 / zoomFactor, order=0)
labelsMask = padorcut(labelsMask, originalShape).astype(np.int8)
outputLabels = {}
suffix = ''
if data['split_laterality']:
suffix = '_L' if swap else '_R'
for labelValue, labelName in LABELS_DICT.items():
outputLabels[labelName + suffix] = (labelsMask == labelValue).astype(
np.int8) # left in the image is right in the anatomy
return outputLabels
# two sides
img = padorcut(img, MODEL_SIZE)
imgbc=biascorrection.biascorrection_image(img)
a1,a2,a3,a4,b1,b2=split_mirror(imgbc)
left=imgbc[int(b1):int(b2),int(a1):int(a2)]
left=padorcut(left, MODEL_SIZE_SPLIT)
right=imgbc[int(b1):int(b2),int(a3):int(a4)]
right=right[::1,::-1]
right=padorcut(right, MODEL_SIZE_SPLIT)
segmentationleft=netc.predict(np.expand_dims(np.stack([left,np.zeros(MODEL_SIZE_SPLIT)],axis=-1),axis=0))
labelleft=np.argmax(np.squeeze(segmentationleft[0,:,:,:7]), axis=2)
segmentationright=netc.predict(np.expand_dims(np.stack([right,np.zeros(MODEL_SIZE_SPLIT)],axis=-1),axis=0))
labelright=np.argmax(np.squeeze(segmentationright[0,:,:,:7]), axis=2)
labelright=labelright[::1,::-1]
labelsMask_left=np.zeros(MODEL_SIZE,dtype='float32')
labelsMask_right=np.zeros(MODEL_SIZE,dtype='float32')
labelsMask_left[int(b1):int(b2),int(a1):int(a2)]=padorcut(labelleft, [b2-b1, a2-a1])
labelsMask_right[int(b1):int(b2),int(a3):int(a4)]=padorcut(labelright, [b2-b1, a4-a3])
labelsMask_left = zoom(labelsMask_left, 1/zoomFactor, order=0)
labelsMask_left = padorcut(labelsMask_left, originalShape).astype(np.int8)
labelsMask_right = zoom(labelsMask_right, 1/zoomFactor, order=0)
labelsMask_right = padorcut(labelsMask_right, originalShape).astype(np.int8)
outputLabels = {}
if data['split_laterality']:
for labelValue, labelName in LABELS_DICT.items():
outputLabels[labelName+'_R'] = (labelsMask_left == labelValue).astype(np.int8)
outputLabels[labelName+'_L'] = (labelsMask_right == labelValue).astype(np.int8)
return outputLabels
else:
for labelValue, labelName in LABELS_DICT.items():
outputLabels[labelName] = np.logical_or(labelsMask_left == labelValue, labelsMask_right == labelValue).astype(np.int8)
return outputLabels