def finalize_data(layers):
layers = np.array(layers)
# Get in right order (x,y,z) and
if HP.SLICE_DIRECTION == "x":
layers = layers.transpose(0, 1, 2, 3)
elif HP.SLICE_DIRECTION == "y":
layers = layers.transpose(1, 0, 2, 3)
elif HP.SLICE_DIRECTION == "z":
layers = layers.transpose(1, 2, 0, 3)
if scale_to_world_shape:
layers = DatasetUtils.scale_input_to_world_shape(layers, HP.DATASET, HP.RESOLUTION)
return layers.astype(np.float32)
def get_seg_single_img(self, HP, probs=False, scale_to_world_shape=True):
'''
Returns layers for one image (batch manager is only allowed to return batches for one image)
:param HP:
:return: ([146, 174, 146, nrClasses], [146, 174, 146, nrClasses]) (Prediction, Groundtruth)
'''
#Test Time DAug
for i in range(1):
# segs = []
# ys = []
layers_seg = []
layers_y = []
batch_generator = self.dataManager.get_batches(batch_size=1)
batch_generator = list(batch_generator)
for j in tqdm(list(range(len(batch_generator)))):
batch = batch_generator[j]
x = batch["data"] # (bs, nr_of_channels, x, y)
y = batch["seg"] # (bs, x, y, nr_of_classes)
y = y.astype(HP.LABELS_TYPE)
y = np.squeeze(
y
) # remove bs dimension which is only 1 -> (x, y, nrClasses)
#For normal prediction
layer_probs = self.model.get_probs(x) # (bs, x, y, nrClasses)
layer_probs = np.squeeze(
layer_probs
) # remove bs dimension which is only 1 -> (x, y, nrClasses)
#For Dropout Sampling (must set Deterministic=False in model)
# NR_SAMPLING = 30
# samples = []
# for i in range(NR_SAMPLING):
# layer_probs = self.model.get_probs(x) # (bs, x, y, nrClasses)
# samples.append(layer_probs)
#
# samples = np.array(samples) # (NR_SAMPLING, bs, x, y, nrClasses)
# samples = np.squeeze(samples) # (NR_SAMPLING, x, y, nrClasses)
# layer_probs = np.mean(samples, axis=0)
# #layer_probs = np.std(samples, axis=0) #use std
if probs:
seg = layer_probs # (x, y, nrClasses)
else:
seg = layer_probs
seg[seg >= HP.THRESHOLD] = 1
seg[seg < HP.THRESHOLD] = 0
seg = seg.astype(np.int16)
layers_seg.append(seg)
layers_y.append(y)
layers_seg = np.array(layers_seg)
layers_y = np.array(layers_y)
#Get in right order (x,y,z) and
if HP.SLICE_DIRECTION == "x":
layers_seg = layers_seg.transpose(0, 1, 2, 3)
layers_y = layers_y.transpose(0, 1, 2, 3)
elif HP.SLICE_DIRECTION == "y":
layers_seg = layers_seg.transpose(1, 0, 2, 3)
layers_y = layers_y.transpose(1, 0, 2, 3)
elif HP.SLICE_DIRECTION == "z":
layers_seg = layers_seg.transpose(1, 2, 0, 3)
layers_y = layers_y.transpose(1, 2, 0, 3)
if scale_to_world_shape:
layers_seg = DatasetUtils.scale_input_to_world_shape(
layers_seg, HP.DATASET, HP.RESOLUTION)
layers_y = DatasetUtils.scale_input_to_world_shape(
layers_y, HP.DATASET, HP.RESOLUTION)
layers_seg = layers_seg.astype(np.float32)
layers_y = layers_y.astype(np.float32)
return layers_seg, layers_y # (Prediction, Groundtruth)