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David Rotermund 2024-02-27 21:55:51 +01:00 committed by GitHub
parent a1ae933c0a
commit 53cfe8110d
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2 changed files with 91 additions and 3 deletions
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45
new_pipeline/functions/perform_donor_volume_rotation.py
View file

@ -16,6 +16,7 @@ def perform_donor_volume_rotation(
ref_image_volume: torch.Tensor,
image_alignment: ImageAlignment,
batch_size: int,
config: dict,
fill_value: float = 0,
) -> tuple[
torch.Tensor,
@ -43,8 +44,50 @@ def perform_donor_volume_rotation(
)
mylogger.info("Average over both rotations")
donor_threshold: torch.Tensor = torch.sort(torch.abs(angle_donor))[0]
donor_threshold = donor_threshold[
int(
donor_threshold.shape[0]
* float(config["rotation_stabilization_threshold_border"])
)
] * float(config["rotation_stabilization_threshold_factor"])
volume_threshold: torch.Tensor = torch.sort(torch.abs(angle_volume))[0]
volume_threshold = volume_threshold[
int(
volume_threshold.shape[0]
* float(config["rotation_stabilization_threshold_border"])
)
] * float(config["rotation_stabilization_threshold_factor"])
donor_idx = torch.where(torch.abs(angle_donor) > donor_threshold)[0]
volume_idx = torch.where(torch.abs(angle_volume) > volume_threshold)[0]
mylogger.info(
f"Border: {config['rotation_stabilization_threshold_border']}, "
f"factor {config['rotation_stabilization_threshold_factor']} "
)
mylogger.info(
f"Donor threshold: {donor_threshold:.3e}, volume threshold: {volume_threshold:.3e}"
)
mylogger.info(
f"Found broken rotation values: "
f"donor {int(donor_idx.shape[0])}, "
f"volume {int(volume_idx.shape[0])}"
)
angle_donor[donor_idx] = angle_volume[donor_idx]
angle_volume[volume_idx] = angle_donor[volume_idx]
donor_idx = torch.where(torch.abs(angle_donor) > donor_threshold)[0]
volume_idx = torch.where(torch.abs(angle_volume) > volume_threshold)[0]
mylogger.info(
f"After fill in these broken rotation values remain: "
f"donor {int(donor_idx.shape[0])}, "
f"volume {int(volume_idx.shape[0])}"
)
angle_donor[donor_idx] = 0.0
angle_volume[volume_idx] = 0.0
angle_donor_volume = (angle_donor + angle_volume) / 2.0
angle_donor_volume *= 0.0
mylogger.info("Rotate acceptor data based on the average rotation")
for frame_id in range(0, angle_donor_volume.shape[0]):

49
new_pipeline/functions/perform_donor_volume_translation.py
View file

@ -17,6 +17,7 @@ def perform_donor_volume_translation(
ref_image_volume: torch.Tensor,
image_alignment: ImageAlignment,
batch_size: int,
config: dict,
fill_value: float = 0,
) -> tuple[
torch.Tensor,
@ -43,9 +44,53 @@ def perform_donor_volume_translation(
)
mylogger.info("Average over both translations")
tvec_donor_volume = (tvec_donor + tvec_volume) / 2.0
tvec_donor_volume *= 0.0
for i in range(0, 2):
mylogger.info(f"Processing dimension {i}")
donor_threshold: torch.Tensor = torch.sort(torch.abs(tvec_donor[:, i]))[0]
donor_threshold = donor_threshold[
int(
donor_threshold.shape[0]
* float(config["rotation_stabilization_threshold_border"])
)
] * float(config["rotation_stabilization_threshold_factor"])
volume_threshold: torch.Tensor = torch.sort(torch.abs(tvec_volume[:, i]))[0]
volume_threshold = volume_threshold[
int(
volume_threshold.shape[0]
* float(config["rotation_stabilization_threshold_border"])
)
] * float(config["rotation_stabilization_threshold_factor"])
donor_idx = torch.where(torch.abs(tvec_donor[:, i]) > donor_threshold)[0]
volume_idx = torch.where(torch.abs(tvec_volume[:, i]) > volume_threshold)[0]
mylogger.info(
f"Border: {config['rotation_stabilization_threshold_border']}, "
f"factor {config['rotation_stabilization_threshold_factor']} "
)
mylogger.info(
f"Donor threshold: {donor_threshold:.3e}, volume threshold: {volume_threshold:.3e}"
)
mylogger.info(
f"Found broken rotation values: "
f"donor {int(donor_idx.shape[0])}, "
f"volume {int(volume_idx.shape[0])}"
)
tvec_donor[donor_idx, i] = tvec_volume[donor_idx, i]
tvec_volume[volume_idx, i] = tvec_donor[volume_idx, i]
donor_idx = torch.where(torch.abs(tvec_donor[:, i]) > donor_threshold)[0]
volume_idx = torch.where(torch.abs(tvec_volume[:, i]) > volume_threshold)[0]
mylogger.info(
f"After fill in these broken rotation values remain: "
f"donor {int(donor_idx.shape[0])}, "
f"volume {int(volume_idx.shape[0])}"
)
tvec_donor[donor_idx, i] = 0.0
tvec_volume[volume_idx, i] = 0.0
tvec_donor_volume = (tvec_donor + tvec_volume) / 2.0
mylogger.info("Translate acceptor data based on the average translation vector")
for frame_id in range(0, tvec_donor_volume.shape[0]):