def adjust_transforms(transforms, seg_pair, length=None, stride=None):
"""This function adapts the transforms by adding the BoundingBoxCrop transform according the specific parameters of
an image. The dimensions of the crop are also adapted to fit the length and stride parameters if the 3D loader is
used.
Args:
transforms (Compose): Prepreocessing transforms.
seg_pair (dict): Segmentation pair (input, gt and metadata).
length (list or tuple): Patch size of the 3D loader.
stride (list or tuple): Stride value of the 3D loader.
Returns:
Compose: Modified transforms.
"""
resample_idx = [-1, -1, -1]
if transforms is None:
transforms = imed_transforms.Compose({})
for i, img_type in enumerate(transforms.transform):
for idx, transfo in enumerate(
transforms.transform[img_type].transforms):
if "BoundingBoxCrop" == transfo.__class__.__name__:
transforms.transform[img_type].transforms.pop(idx)
if "Resample" == transfo.__class__.__name__:
resample_idx[i] = idx
resample_bounding_box(seg_pair, transforms)
index_shape = []
for i, img_type in enumerate(transforms.transform):
x_min, x_max, y_min, y_max, z_min, z_max = seg_pair['input_metadata'][
0]['bounding_box']
size = [x_max - x_min, y_max - y_min, z_max - z_min]
if length is not None and stride is not None:
for idx, dim in enumerate(size):
if dim < length[idx]:
size[idx] = length[idx]
# Adjust size according to stride to avoid dimension mismatch
size = resize_to_multiple(size, stride, length)
index_shape.append(tuple(size))
transform_obj = imed_transforms.BoundingBoxCrop(size=size)
transforms.transform[img_type].transforms.insert(
resample_idx[i] + 1, transform_obj)
for metadata in seg_pair['input_metadata']:
assert len(set(index_shape)) == 1
metadata['index_shape'] = index_shape[0]
return transforms
def adjust_undo_transforms(transforms, seg_pair, index=0):
"""This function adapts the undo transforms by adding the BoundingBoxCrop to undo transform according the specific
parameters of an image.
Args:
transforms (Compose): Transforms.
seg_pair (dict): Segmentation pair (input, gt and metadata).
index (int): Batch index of the seg_pair.
"""
for img_type in transforms.transform:
resample_idx = -1
for idx, transfo in enumerate(transforms.transform[img_type].transforms):
if "Resample" == transfo.__class__.__name__:
resample_idx = idx
if "BoundingBoxCrop" == transfo.__class__.__name__:
transforms.transform[img_type].transforms.pop(idx)
if "bounding_box" in seg_pair['input_metadata'][index][0]:
size = list(seg_pair['input_metadata'][index][0]['index_shape'])
transform_obj = imed_transforms.BoundingBoxCrop(size=size)
transforms.transform[img_type].transforms.insert(resample_idx + 1, transform_obj)
