saim1309/Cell_Segmentation

1from CellAware import BoundaryExclusion, IntensityDiversification2from LoadImage import CustomLoadImaged,CustomLoadImageD,CustomLoadImageDict,CustomLoadImage3from NormalizeImage import CustomNormalizeImage,CustomNormalizeImageD,CustomNormalizeImageDict ,CustomNormalizeImaged4 5from monai.transforms import *6 7__all__ = [8    "train_transforms",9    "public_transforms",10    "valid_transforms",11    "tuning_transforms",12    "unlabeled_transforms",13]14 15train_transforms = Compose(16    [17        # >>> Load and refine data --- img: (H, W, 3); label: (H, W)18        CustomLoadImaged(keys=["img", "label"], image_only=True),19        CustomNormalizeImaged(20            keys=["img"],21            allow_missing_keys=True,22            channel_wise=False,23            percentiles=[0.0, 99.5],24        ),25        EnsureChannelFirstd(keys=["img", "label"], channel_dim=-1),26        RemoveRepeatedChanneld(keys=["label"], repeats=3),  # label: (H, W)27        ScaleIntensityd(keys=["img"], allow_missing_keys=True),  # Do not scale label28        # >>> Spatial transforms29        RandZoomd(30            keys=["img", "label"],31            prob=0.5,32            min_zoom=0.25,33            max_zoom=1.5,34            mode=["area", "nearest"],35            keep_size=False,36        ),37        SpatialPadd(keys=["img", "label"], spatial_size=512),38        RandSpatialCropd(keys=["img", "label"], roi_size=512, random_size=False),39        RandAxisFlipd(keys=["img", "label"], prob=0.5),40        RandRotate90d(keys=["img", "label"], prob=0.5, spatial_axes=[0, 1]),41        IntensityDiversification(keys=["img", "label"], allow_missing_keys=True),42        # # >>> Intensity transforms43        RandGaussianNoised(keys=["img"], prob=0.25, mean=0, std=0.1),44        RandAdjustContrastd(keys=["img"], prob=0.25, gamma=(1, 2)),45        RandGaussianSmoothd(keys=["img"], prob=0.25, sigma_x=(1, 2)),46        RandHistogramShiftd(keys=["img"], prob=0.25, num_control_points=3),47        RandGaussianSharpend(keys=["img"], prob=0.25),48        EnsureTyped(keys=["img", "label"]),49    ]50)51 52 53public_transforms = Compose(54    [55        CustomLoadImaged(keys=["img", "label"], image_only=True),56        BoundaryExclusion(keys=["label"]),57        CustomNormalizeImaged(58            keys=["img"],59            allow_missing_keys=True,60            channel_wise=False,61            percentiles=[0.0, 99.5],62        ),63        EnsureChannelFirstd(keys=["img", "label"], channel_dim=-1),64        RemoveRepeatedChanneld(keys=["label"], repeats=3),  # label: (H, W)65        ScaleIntensityd(keys=["img"], allow_missing_keys=True),  # Do not scale label66        # >>> Spatial transforms67        SpatialPadd(keys=["img", "label"], spatial_size=512),68        RandSpatialCropd(keys=["img", "label"], roi_size=512, random_size=False),69        RandAxisFlipd(keys=["img", "label"], prob=0.5),70        RandRotate90d(keys=["img", "label"], prob=0.5, spatial_axes=[0, 1]),71        Rotate90d(k=1, keys=["label"], spatial_axes=(0, 1)),72        Flipd(keys=["label"], spatial_axis=0),73        EnsureTyped(keys=["img", "label"]),74    ]75)76 77 78valid_transforms = Compose(79    [80        CustomLoadImaged(keys=["img", "label"], allow_missing_keys=True, image_only=True),81        CustomNormalizeImaged(82            keys=["img"],83            allow_missing_keys=True,84            channel_wise=False,85            percentiles=[0.0, 99.5],86        ),87        EnsureChannelFirstd(keys=["img", "label"], allow_missing_keys=True, channel_dim=-1),88        RemoveRepeatedChanneld(keys=["label"], repeats=3),89        ScaleIntensityd(keys=["img"], allow_missing_keys=True),90        EnsureTyped(keys=["img", "label"], allow_missing_keys=True),91    ]92)93 94tuning_transforms = Compose(95    [96        CustomLoadImaged(keys=["img"], image_only=True),97        CustomNormalizeImaged(98            keys=["img"],99            allow_missing_keys=True,100            channel_wise=False,101            percentiles=[0.0, 99.5],102        ),103        EnsureChannelFirstd(keys=["img"], channel_dim=-1),104        ScaleIntensityd(keys=["img"]),105        EnsureTyped(keys=["img"]),106    ]107)108 109unlabeled_transforms = Compose(110    [111        # >>> Load and refine data --- img: (H, W, 3); label: (H, W)112        CustomLoadImaged(keys=["img"], image_only=True),113        CustomNormalizeImaged(114            keys=["img"],115            allow_missing_keys=True,116            channel_wise=False,117            percentiles=[0.0, 99.5],118        ),119        EnsureChannelFirstd(keys=["img"], channel_dim=-1),120        RandZoomd(121            keys=["img"],122            prob=0.5,123            min_zoom=0.25,124            max_zoom=1.25,125            mode=["area"],126            keep_size=False,127        ),128        ScaleIntensityd(keys=["img"], allow_missing_keys=True),  # Do not scale label129        # >>> Spatial transforms130        SpatialPadd(keys=["img"], spatial_size=512),131        RandSpatialCropd(keys=["img"], roi_size=512, random_size=False),132        EnsureTyped(keys=["img"]),133    ]134)135 136 137def get_pred_transforms():138    """Prediction preprocessing"""139    pred_transforms = Compose(140        [141            # >>> Load and refine data142            CustomLoadImage(image_only=True),143            CustomNormalizeImage(channel_wise=False, percentiles=[0.0, 99.5]),144            EnsureChannelFirst(channel_dim=-1),  # image: (3, H, W)145            ScaleIntensity(),146            EnsureType(data_type="tensor"),147        ]148    )149 150    return pred_transforms151