def outputRestoredImage(self):
self.predicted_array = croppingForNumpy(
self.predicted_array, self.lower_pad_size[1].tolist(),
self.upper_pad_size[1].tolist())
predicted = getImageWithMeta(self.predicted_array, self.org_image)
return predicted
def main(args):
printArgs(args)
image = sitk.ReadImage(args.image_path)
ref = sitk.ReadImage(args.ref_path)
image_array = sitk.GetArrayFromImage(image)
unified_image = getImageWithMeta(image_array[::-1, ::-1, :], ref)
Path(args.save_path).parent.mkdir(exist_ok=True, parents=True)
sitk.WriteImage(unified_image, args.save_path)
def outputRestoredImage(self):
""" Usually, this method is used after all of predicted patch array is insert to self.predicted_array with insertToPredictedArray. """
""" Address division by zero. """
self.counter_array = np.where(self.counter_array == 0, 1,
self.counter_array)
self.predicted_array /= self.counter_array
self.predicted_array = np.argmax(self.predicted_array,
axis=self.class_axis)
self.predicted_array = croppingForNumpy(
self.predicted_array, self.lower_pad_size[1].tolist(),
self.upper_pad_size[1].tolist())
predicted = getImageWithMeta(self.predicted_array, self.org)
return predicted
def outputRestoredImage(self):
predicted_array = croppingForNumpy(self.predicted_array,
self.lower_pad_size[1].tolist(),
self.upper_pad_size[1].tolist())
lower_size = (abs(self.diff) // 2).tolist()
upper_size = ((abs(self.diff) + 1) // 2).tolist()
if (self.diff < 0).any():
predicted_array = paddingForNumpy(predicted_array, lower_size,
upper_size)
else:
predicted_array = croppingForNumpy(predicted_array, lower_size,
upper_size)
predicted = getImageWithMeta(predicted_array, self.org)
return predicted
def outputRestoredImage(self):
""" Usually, this method is used after all of predicted patch array is insert to self.predicted_array with insertToPredictedArray. """
""" Address division by zero. """
self.counter_array = np.where(self.counter_array == 0, 1,
self.counter_array)
self.predicted_array /= self.counter_array
if self.num_class == 1:
self.predicted_array = (self.predicted_array > 0.5).astype(
np.uint8)
else:
self.predicted_array = np.argmax(self.predicted_array,
axis=self.class_axis)
predicted = getImageWithMeta(self.predicted_array, self.label)
predicted = cropping(predicted, self.lower_pad_size[1].tolist(),
self.upper_pad_size[1].tolist())
return predicted
def main(args):
printArgs(args)
label = sitk.ReadImage(args.label_path)
label_array = sitk.GetArrayFromImage(label)
if args.mask_number < 0:
mask_array = (label_array > 0).astype(np.int)
else:
mask_array = (label_array == args.mask_number).astype(np.int)
mask = getImageWithMeta(mask_array, label)
save_path = Path(args.save_path)
save_path.parent.mkdir(parents=True, exist_ok=True)
print("Saving mask image to {} ...".format(str(save_path)))
sitk.WriteImage(mask, str(save_path), True)
print("Done")
def main(args):
printArgs(args)
label = sitk.ReadImage(args.label_path)
label_array = sitk.GetArrayFromImage(label)
cnt = 0
for c in range(args.num_class):
if c in args.ignore_classes:
label_array = np.where(label_array == c, 0, label_array)
else:
if args.squeeze:
label_array = np.where(label_array == c, cnt, label_array)
cnt += 1
else:
label_array = np.where(label_array == c, c, label_array)
print("Max_num_class: ", label_array.max())
re_label = getImageWithMeta(label_array, label)
Path(args.save_path).parent.mkdir(parents=True, exist_ok=True)
sitk.WriteImage(re_label, args.save_path, True)