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anomalib.post_processing.normalization.cdf

Tools for CDF normalization.

Module Contents

Functions

standardize(targets: Union[numpy.ndarray, torch.Tensor], mean: Union[numpy.ndarray, torch.Tensor, float], std: Union[numpy.ndarray, torch.Tensor, float], center_at: Optional[float] = None) → Union[numpy.ndarray, torch.Tensor]	Standardize the targets to the z-domain.
normalize(targets: Union[numpy.ndarray, torch.Tensor], threshold: Union[numpy.ndarray, torch.Tensor, float]) → Union[numpy.ndarray, torch.Tensor]	Normalize the targets by using the cumulative density function.
normalize_torch(targets: torch.Tensor, threshold: torch.Tensor) → torch.Tensor	Normalize the targets by using the cumulative density function, PyTorch version.
normalize_numpy(targets: numpy.ndarray, threshold: Union[numpy.ndarray, float]) → numpy.ndarray	Normalize the targets by using the cumulative density function, Numpy version.

anomalib.post_processing.normalization.cdf.standardize(targets: Union[numpy.ndarray, torch.Tensor], mean: Union[numpy.ndarray, torch.Tensor, float], std: Union[numpy.ndarray, torch.Tensor, float], center_at: Optional[float] = None) → Union[numpy.ndarray, torch.Tensor]

Standardize the targets to the z-domain.

anomalib.post_processing.normalization.cdf.normalize(targets: Union[numpy.ndarray, torch.Tensor], threshold: Union[numpy.ndarray, torch.Tensor, float]) → Union[numpy.ndarray, torch.Tensor]

Normalize the targets by using the cumulative density function.

anomalib.post_processing.normalization.cdf.normalize_torch(targets: torch.Tensor, threshold: torch.Tensor) → torch.Tensor

Normalize the targets by using the cumulative density function, PyTorch version.

anomalib.post_processing.normalization.cdf.normalize_numpy(targets: numpy.ndarray, threshold: Union[numpy.ndarray, float]) → numpy.ndarray

Normalize the targets by using the cumulative density function, Numpy version.