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import torch
from functions.make_mask import make_mask
from functions.preprocess_camera_sequence import preprocess_camera_sequence
from functions.gauss_smear import gauss_smear
from functions.regression import regression
@torch.no_grad()
def preprocessing(
cameras: list[str],
camera_sequence: list[torch.Tensor],
filename_mask: str,
device: torch.device,
first_none_ramp_frame: int,
spatial_width: float,
temporal_width: float,
target_camera: list[str],
regressor_cameras: list[str],
donor_correction_factor: torch.Tensor,
acceptor_correction_factor: torch.Tensor,
dtype: torch.dtype = torch.float32,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
mask: torch.Tensor = make_mask(
filename_mask=filename_mask,
camera_sequence=camera_sequence,
device=device,
dtype=dtype,
)
for num_cams in range(len(camera_sequence)):
camera_sequence[num_cams], mask = preprocess_camera_sequence(
camera_sequence=camera_sequence[num_cams],
mask=mask,
first_none_ramp_frame=first_none_ramp_frame,
device=device,
dtype=dtype,
)
camera_sequence_filtered: list[torch.Tensor] = []
for id in range(0, len(camera_sequence)):
camera_sequence_filtered.append(camera_sequence[id].clone())
camera_sequence_filtered = gauss_smear(
camera_sequence_filtered,
mask.type(dtype=dtype),
spatial_width=spatial_width,
temporal_width=temporal_width,
)
regressor_camera_ids: list[int] = []
for cam in regressor_cameras:
regressor_camera_ids.append(cameras.index(cam))
results: list[torch.Tensor] = []
for channel_position in range(0, len(target_camera)):
print(f"channel position: {channel_position}")
target_camera_selected = target_camera[channel_position]
target_camera_id: int = cameras.index(target_camera_selected)
output = regression(
target_camera_id=target_camera_id,
regressor_camera_ids=regressor_camera_ids,
mask=mask,
camera_sequence=camera_sequence,
camera_sequence_filtered=camera_sequence_filtered,
first_none_ramp_frame=first_none_ramp_frame,
)
results.append(output)
donor_factor: torch.Tensor = (
donor_correction_factor + acceptor_correction_factor
) / (2 * donor_correction_factor)
acceptor_factor: torch.Tensor = (
donor_correction_factor + acceptor_correction_factor
) / (2 * acceptor_correction_factor)
results[0] *= acceptor_factor * mask.unsqueeze(-1)
results[1] *= donor_factor * mask.unsqueeze(-1)
return results[0], results[1], mask