143 lines
5.4 KiB
Python
143 lines
5.4 KiB
Python
import torch
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import torchvision as tv # type: ignore
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import logging
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from functions.calculate_translation import calculate_translation
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from functions.ImageAlignment import ImageAlignment
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@torch.no_grad()
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def perform_donor_volume_translation(
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mylogger: logging.Logger,
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acceptor: torch.Tensor,
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donor: torch.Tensor,
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oxygenation: torch.Tensor,
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volume: torch.Tensor,
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ref_image_donor: torch.Tensor,
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ref_image_volume: torch.Tensor,
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image_alignment: ImageAlignment,
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batch_size: int,
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config: dict,
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fill_value: float = 0,
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) -> tuple[
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torch.Tensor,
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torch.Tensor,
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torch.Tensor,
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torch.Tensor,
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torch.Tensor,
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]:
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mylogger.info("Calculate translation between donor data and donor ref image")
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tvec_donor = calculate_translation(
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input=donor,
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reference_image=ref_image_donor,
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image_alignment=image_alignment,
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batch_size=batch_size,
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)
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mylogger.info("Calculate translation between volume data and volume ref image")
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tvec_volume = calculate_translation(
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input=volume,
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reference_image=ref_image_volume,
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image_alignment=image_alignment,
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batch_size=batch_size,
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)
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mylogger.info("Average over both translations")
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for i in range(0, 2):
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mylogger.info(f"Processing dimension {i}")
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donor_threshold: torch.Tensor = torch.sort(torch.abs(tvec_donor[:, i]))[0]
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donor_threshold = donor_threshold[
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int(
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donor_threshold.shape[0]
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* float(config["rotation_stabilization_threshold_border"])
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)
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] * float(config["rotation_stabilization_threshold_factor"])
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volume_threshold: torch.Tensor = torch.sort(torch.abs(tvec_volume[:, i]))[0]
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volume_threshold = volume_threshold[
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int(
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volume_threshold.shape[0]
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* float(config["rotation_stabilization_threshold_border"])
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)
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] * float(config["rotation_stabilization_threshold_factor"])
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donor_idx = torch.where(torch.abs(tvec_donor[:, i]) > donor_threshold)[0]
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volume_idx = torch.where(torch.abs(tvec_volume[:, i]) > volume_threshold)[0]
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mylogger.info(
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f"Border: {config['rotation_stabilization_threshold_border']}, "
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f"factor {config['rotation_stabilization_threshold_factor']} "
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)
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mylogger.info(
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f"Donor threshold: {donor_threshold:.3e}, volume threshold: {volume_threshold:.3e}"
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)
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mylogger.info(
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f"Found broken rotation values: "
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f"donor {int(donor_idx.shape[0])}, "
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f"volume {int(volume_idx.shape[0])}"
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)
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tvec_donor[donor_idx, i] = tvec_volume[donor_idx, i]
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tvec_volume[volume_idx, i] = tvec_donor[volume_idx, i]
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donor_idx = torch.where(torch.abs(tvec_donor[:, i]) > donor_threshold)[0]
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volume_idx = torch.where(torch.abs(tvec_volume[:, i]) > volume_threshold)[0]
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mylogger.info(
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f"After fill in these broken rotation values remain: "
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f"donor {int(donor_idx.shape[0])}, "
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f"volume {int(volume_idx.shape[0])}"
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)
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tvec_donor[donor_idx, i] = 0.0
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tvec_volume[volume_idx, i] = 0.0
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tvec_donor_volume = (tvec_donor + tvec_volume) / 2.0
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mylogger.info("Translate acceptor data based on the average translation vector")
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for frame_id in range(0, tvec_donor_volume.shape[0]):
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acceptor[frame_id, ...] = tv.transforms.functional.affine(
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img=acceptor[frame_id, ...].unsqueeze(0),
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angle=0,
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translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
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scale=1.0,
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shear=0,
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interpolation=tv.transforms.InterpolationMode.BILINEAR,
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fill=fill_value,
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).squeeze(0)
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mylogger.info("Translate donor data based on the average translation vector")
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for frame_id in range(0, tvec_donor_volume.shape[0]):
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donor[frame_id, ...] = tv.transforms.functional.affine(
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img=donor[frame_id, ...].unsqueeze(0),
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angle=0,
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translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
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scale=1.0,
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shear=0,
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interpolation=tv.transforms.InterpolationMode.BILINEAR,
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fill=fill_value,
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).squeeze(0)
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mylogger.info("Translate oxygenation data based on the average translation vector")
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for frame_id in range(0, tvec_donor_volume.shape[0]):
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oxygenation[frame_id, ...] = tv.transforms.functional.affine(
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img=oxygenation[frame_id, ...].unsqueeze(0),
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angle=0,
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translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
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scale=1.0,
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shear=0,
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interpolation=tv.transforms.InterpolationMode.BILINEAR,
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fill=fill_value,
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).squeeze(0)
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mylogger.info("Translate volume data based on the average translation vector")
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for frame_id in range(0, tvec_donor_volume.shape[0]):
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volume[frame_id, ...] = tv.transforms.functional.affine(
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img=volume[frame_id, ...].unsqueeze(0),
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angle=0,
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translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
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scale=1.0,
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shear=0,
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interpolation=tv.transforms.InterpolationMode.BILINEAR,
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fill=fill_value,
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).squeeze(0)
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return (acceptor, donor, oxygenation, volume, tvec_donor_volume)
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