This repository provides polarimetric image augmentations such as an SO(2) rotation.
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|---|---|---|
| spatial-only rotation | spatial + polarimetric rotation | ground truth |
@ARTICLE{11202388, author={Hahne, Christopher and Rodríguez-Núñez, Omar and Gros, Éléa and Lucas, Théotim and Hewer, Ekkehard and Novikova, Tatiana and Maragkou, Theoni and Schucht, Philippe and McKinley, Richard}, journal={IEEE Transactions on Image Processing}, title={Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetry}, year={2025}, volume={34}, number={}, pages={6953-6962}, keywords={Imaging;Polarimetry;Deep learning;Data augmentation;Training;Optical polarization;Optical imaging;Vectors;Interpolation;Standards;Augmentation;polarimetry;Mueller matrix;tumor;classification}, doi={10.1109/TIP.2025.3618390} } $ git clone github.com/hahnec/polar_augment $ cd polar_augment $ bash install.shThe provided transforms expect the image dimensions to be in PyTorch style CxHxW.
import torch from polar_augment.padding import mirror_rotate # direct application from polar_augment.rotation_mm import RandomMuellerRotation rotate = RandomMuellerRotation(degrees=45, p=float('inf'), pad_rotate=mirror_rotate) mm_img = torch.randn([128, 128, 4, 4]).flatten(2, 3).permute(2, 0, 1) mm_img_rotated = rotate(mm_img) print(mm_img_rotated.shape) # application for calibration matrices (dataloader-friendly for raw data) from polar_augment.rotation_raw import RandomPolarRotation rotate = RandomPolarRotation(degrees=45, p=float('inf'), pad_rotate=mirror_rotate) mm_img = torch.randn([128, 128, 4*3, 4]).flatten(2, 3).permute(2, 0, 1) mm_img_rotated = rotate(mm_img) print(mm_img_rotated.shape)Alternatively, the transforms can be integrated during dataloading as for example by
from torchvision.transforms import ToTensor from polar_augment.flip_raw import RandomPolarFlip from polar_augment.rotation_raw import RandomPolarRotation from polar_augment.padding import mirror_rotate from polar_dataset import PolarimetryDataset # define list of transforms transforms = [ ToTensor(), RandomPolarRotation(degrees=180, p=.5, pad_rotate=mirror_rotate), # rotation RandomPolarFlip(orientation=0, p=.5), # horizontal flip RandomPolarFlip(orientation=1, p=.5), # vertical flip RandomPolarFlip(orientation=2, p=.5), # combined horizontal and vertical flip ] # pass transforms to your dataset PolarimetryDataset(some_file_path, transforms=transforms)where the augmentation can then be applied to frames and labels within the PolarimetryDataset
class PolarimetryDataset(Dataset): def __init__( self, path, transforms=[], ): self.load_file_paths(path) self.transforms = transforms def __getitem__(self, i): frame = load_file(self.frame_paths[i]) label = load_file(self.label_paths[i]) for transform in self.transforms: frame, label = transform(frame, label=label)