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David Rotermund 2024-02-03 19:19:24 +01:00 committed by GitHub
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3 changed files with 169 additions and 1 deletions
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2
reproduction_effort/functions/align_refref.py
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@ -12,7 +12,7 @@ def align_refref(
ref_image_donor: torch.Tensor,
image_alignment: ImageAlignment,
batch_size: int,
fill_value: int = 0,
fill_value: float = 0,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
angle_refref = calculate_rotation(

84
reproduction_effort/functions/perform_donor_volume_rotation.py Normal file
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@ -0,0 +1,84 @@
import torch
import torchvision as tv # type: ignore
from functions.calculate_rotation import calculate_rotation
from functions.ImageAlignment import ImageAlignment
@torch.no_grad()
def perform_donor_volume_rotation(
acceptor: torch.Tensor,
donor: torch.Tensor,
oxygenation: torch.Tensor,
volume: torch.Tensor,
ref_image_donor: torch.Tensor,
ref_image_volume: torch.Tensor,
image_alignment: ImageAlignment,
batch_size: int,
fill_value: float = 0,
) -> tuple[
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
]:
angle_donor = calculate_rotation(
input=donor,
reference_image=ref_image_donor,
image_alignment=image_alignment,
batch_size=batch_size,
)
angle_volume = calculate_rotation(
input=volume,
reference_image=ref_image_volume,
image_alignment=image_alignment,
batch_size=batch_size,
)
angle_donor_volume = (angle_donor + angle_volume) / 2.0
for frame_id in range(0, angle_donor_volume.shape[0]):
acceptor[frame_id, ...] = tv.transforms.functional.affine(
img=acceptor[frame_id, ...].unsqueeze(0),
angle=-float(angle_donor_volume[frame_id]),
translate=[0, 0],
scale=1.0,
shear=0,
interpolation=tv.transforms.InterpolationMode.BILINEAR,
fill=fill_value,
).squeeze(0)
donor[frame_id, ...] = tv.transforms.functional.affine(
img=donor[frame_id, ...].unsqueeze(0),
angle=-float(angle_donor_volume[frame_id]),
translate=[0, 0],
scale=1.0,
shear=0,
interpolation=tv.transforms.InterpolationMode.BILINEAR,
fill=fill_value,
).squeeze(0)
oxygenation[frame_id, ...] = tv.transforms.functional.affine(
img=oxygenation[frame_id, ...].unsqueeze(0),
angle=-float(angle_donor_volume[frame_id]),
translate=[0, 0],
scale=1.0,
shear=0,
interpolation=tv.transforms.InterpolationMode.BILINEAR,
fill=fill_value,
).squeeze(0)
volume[frame_id, ...] = tv.transforms.functional.affine(
img=volume[frame_id, ...].unsqueeze(0),
angle=-float(angle_donor_volume[frame_id]),
translate=[0, 0],
scale=1.0,
shear=0,
interpolation=tv.transforms.InterpolationMode.BILINEAR,
fill=fill_value,
).squeeze(0)
return (acceptor, donor, oxygenation, volume, angle_donor_volume)

84
reproduction_effort/functions/perform_donor_volume_translation.py Normal file
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@ -0,0 +1,84 @@
import torch
import torchvision as tv # type: ignore
from functions.calculate_translation import calculate_translation
from functions.ImageAlignment import ImageAlignment
@torch.no_grad()
def perform_donor_volume_translation(
acceptor: torch.Tensor,
donor: torch.Tensor,
oxygenation: torch.Tensor,
volume: torch.Tensor,
ref_image_donor: torch.Tensor,
ref_image_volume: torch.Tensor,
image_alignment: ImageAlignment,
batch_size: int,
fill_value: float = 0,
) -> tuple[
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
]:
tvec_donor = calculate_translation(
input=donor,
reference_image=ref_image_donor,
image_alignment=image_alignment,
batch_size=batch_size,
)
tvec_volume = calculate_translation(
input=volume,
reference_image=ref_image_volume,
image_alignment=image_alignment,
batch_size=batch_size,
)
tvec_donor_volume = (tvec_donor + tvec_volume) / 2.0
for frame_id in range(0, tvec_donor_volume.shape[0]):
acceptor[frame_id, ...] = tv.transforms.functional.affine(
img=acceptor[frame_id, ...].unsqueeze(0),
angle=0,
translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
scale=1.0,
shear=0,
interpolation=tv.transforms.InterpolationMode.BILINEAR,
fill=fill_value,
).squeeze(0)
donor[frame_id, ...] = tv.transforms.functional.affine(
img=donor[frame_id, ...].unsqueeze(0),
angle=0,
translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
scale=1.0,
shear=0,
interpolation=tv.transforms.InterpolationMode.BILINEAR,
fill=fill_value,
).squeeze(0)
oxygenation[frame_id, ...] = tv.transforms.functional.affine(
img=oxygenation[frame_id, ...].unsqueeze(0),
angle=0,
translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
scale=1.0,
shear=0,
interpolation=tv.transforms.InterpolationMode.BILINEAR,
fill=fill_value,
).squeeze(0)
volume[frame_id, ...] = tv.transforms.functional.affine(
img=volume[frame_id, ...].unsqueeze(0),
angle=0,
translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
scale=1.0,
shear=0,
interpolation=tv.transforms.InterpolationMode.BILINEAR,
fill=fill_value,
).squeeze(0)
return (acceptor, donor, oxygenation, volume, tvec_donor_volume)