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spatial.py
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spatial.py
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"""
Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import torch
from torch import Tensor
from typing import Sequence, List
from nndet.io.transforms.base import AbstractTransform
class Mirror(AbstractTransform):
def __init__(self, keys: Sequence[str], dims: Sequence[int],
point_keys: Sequence[str] = (), box_keys: Sequence[str] = (),
grad: bool = False):
"""
Mirror Transform
Args:
keys: keys to mirror (first key must correspond to data for
shape information) expected shape [N, C, dims]
dims: dimensions to mirror (starting from the first spatial
dimension)
point_keys: keys where points for transformation are located
[N, dims]
box_keys: keys where boxes are located; following format
needs to be used (x1, y1, x2, y2, (z1, z2)) [N, dims * 2]
grad: enable gradient computation inside transformation
"""
super().__init__(grad=grad)
self.dims = dims
self.keys = keys
self.point_keys = point_keys
self.box_keys = box_keys
def forward(self, **data) -> dict:
"""
Implement transform functionality here
Args
data: dict with data
Returns
dict: dict with transformed data
"""
for key in self.keys:
data[key] = mirror(data[key], self.dims)
data_shape = data[self.keys[0]].shape
data_shapes = [tuple(data_shape[2:])] * data_shape[0]
for key in self.box_keys:
points = [boxes2points(b) for b in data[key]]
points = mirror_points(points, self.dims, data_shapes)
data[key] = [points2boxes(p) for p in points]
for key in self.point_keys:
data[key] = mirror_points(data[key], self.dims, data_shapes)
return data
def invert(self, **data) -> dict:
"""
Revert mirroring
Args:
**data: dict with data
Returns:
dict with re-transformed data
"""
return self(**data)
def mirror(data: torch.Tensor, dims: Sequence[int]) -> torch.Tensor:
"""
Mirror data at dims
Args
data: input data [N, C, spatial dims]
dims: dimensions to mirror starting from spatial dims
e.g. dim=(0,) mirror the first spatial dimension
Returns
torch.Tensor: tensor with mirrored dimensions
"""
dims = [d + 2 for d in dims]
return data.flip(dims)
def mirror_points(points: Sequence[torch.Tensor], dims: Sequence[int],
data_shapes: Sequence[Sequence[int]]) -> List[torch.Tensor]:
"""
Mirror points along given dimensions
Args:
points: points per batch element [N, dims]
dims: dimensions to mirror
data_shapes: shape of data
Returns:
Tensor: transformed points [N, dims]
"""
cartesian_dims = points[0].shape[1]
homogeneous_points = points_to_homogeneous(points)
transformed = []
for points_per_image, data_shape in zip(homogeneous_points, data_shapes):
matrix = nd_mirror_matrix(cartesian_dims, dims, data_shape).to(points_per_image)
transformed.append(points_per_image @ matrix.transpose(0, 1))
return points_to_cartesian(transformed)
def nd_mirror_matrix(cartesian_dims: int, mirror_dims: Sequence[int],
data_shape: Sequence[int]) -> torch.Tensor:
"""
Create n dimensional matrix to for mirroring
Args:
cartesian_dims: number of cartesian dimensions
mirror_dims: dimensions to mirror
data_shape: shape of image
Returns:
Tensor: matrix for mirroring in homogeneous coordinated,
[cartesian_dims + 1, cartesian_dims + 1]
"""
mirror_dims = tuple(mirror_dims)
data_shape = list(data_shape)
homogeneous_dims = cartesian_dims + 1
mat = torch.eye(homogeneous_dims, dtype=torch.float)
# reflection
mat[[mirror_dims] * 2] = -1
# add data shape to axis which were reflected
self_tensor = torch.zeros(cartesian_dims, dtype=torch.float)
index_tensor = torch.Tensor(mirror_dims).long()
src_tensor = torch.tensor([1] * len(mirror_dims), dtype=torch.float)
offset_mask = self_tensor.scatter_(0, index_tensor, src_tensor)
mat[:-1, -1] = offset_mask * torch.tensor(data_shape)
return mat
def points_to_homogeneous(points: Sequence[torch.Tensor]) -> List[torch.Tensor]:
"""
Transforms points from cartesian to homogeneous coordinates
Args:
points: list of points to transform [N, dims] where N is the number
of points and dims is the number of spatial dimensions
Returns
torch.Tensor: the batch of points in homogeneous coordinates [N, dim + 1]
"""
return [torch.cat([p, torch.ones(p.shape[0], 1).to(p)], dim=1) for p in points]
def points_to_cartesian(points: Sequence[torch.Tensor]) -> List[torch.Tensor]:
"""
Transforms points in homogeneous coordinates back to cartesian
coordinates.
Args:
points: homogeneous points [N, in_dims], N number of points,
in_dims number of input dimensions (spatial dimensions + 1)
Returns:
List[Tensor]]: cartesian points [N, in_dims] = [N, dims]
"""
return [p[..., :-1] / p[..., -1][:, None] for p in points]
def boxes2points(boxes: Tensor) -> Tensor:
"""
Convert boxes to points
Args:
boxes: (x1, y1, x2, y2, (z1, z2))[N, dims *2]
Returns:
Tensor: points [N * 2, dims]
"""
if boxes.shape[1] == 4:
idx0 = [0, 1]
idx1 = [2, 3]
else:
idx0 = [0, 1, 4]
idx1 = [2, 3, 5]
points0 = boxes[:, idx0]
points1 = boxes[:, idx1]
return torch.cat([points0, points1], dim=0)
def points2boxes(points: Tensor) -> Tensor:
"""
Convert points to boxes
Args:
points: boxes need to be order as specified
order: [point_box_0, ... point_box_N/2] * 4
format of points: (x, y(, z)))[N, dims]
Returns:
Tensor: bounding boxes [N / 2, dims * 2]
"""
if points.nelement() > 0:
points0, points1 = points.split(points.shape[0] // 2)
boxes = torch.zeros(points.shape[0] // 2, points.shape[1] * 2).to(
device=points.device, dtype=points.dtype)
boxes[:, 0] = torch.min(points0[:, 0], points1[:, 0])
boxes[:, 1] = torch.min(points0[:, 1], points1[:, 1])
boxes[:, 2] = torch.max(points0[:, 0], points1[:, 0])
boxes[:, 3] = torch.max(points0[:, 1], points1[:, 1])
if boxes.shape[1] == 6:
boxes[:, 4] = torch.min(points0[:, 2], points1[:, 2])
boxes[:, 5] = torch.max(points0[:, 2], points1[:, 2])
return boxes
else:
return torch.tensor([]).view(-1, points.shape[1] * 2).to(points)