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Normalize

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Normalization and denormalization transforms for image patches.

Denormalize

Denormalize an image.

Denormalization is performed expecting a zero mean and unit variance input. This transform expects C(Z)YX dimensions.

Note that an epsilon value of 1e-6 is added to the standard deviation to avoid division by zero during the normalization step, which is taken into account during denormalization.

Parameters:

  • image_means (list or tuple of float) –

    Mean value per channel.

  • image_stds (list or tuple of float) –

    Standard deviation value per channel.

__call__(patch)

Reverse the normalization operation for a batch of patches.

Parameters:

  • patch (NDArray) –

    Patch, 2D or 3D, shape BC(Z)YX.

Returns:

  • NDArray –

    Transformed array.

__init__(image_means, image_stds)

Constructor.

Parameters:

  • image_means (list of float) –

    Mean value per channel.

  • image_stds (list of float) –

    Standard deviation value per channel.

Normalize

Bases: Transform

Normalize an image or image patch.

Normalization is a zero mean and unit variance. This transform expects C(Z)YX dimensions.

Not that an epsilon value of 1e-6 is added to the standard deviation to avoid division by zero and that it returns a float32 image.

Parameters:

  • image_means (list of float) –

    Mean value per channel.

  • image_stds (list of float) –

    Standard deviation value per channel.

  • target_means (list of float, default: None ) –

    Target mean value per channel, by default None.

  • target_stds (list of float, default: None ) –

    Target standard deviation value per channel, by default None.

Attributes:

  • image_means (list of float) –

    Mean value per channel.

  • image_stds (list of float) –

    Standard deviation value per channel.

  • target_means (list of float, optional) –

    Target mean value per channel, by default None.

  • target_stds (list of float, optional) –

    Target standard deviation value per channel, by default None.

__call__(patch, target=None, **additional_arrays)

Apply the transform to the source patch and the target (optional).

Parameters:

  • patch (NDArray) –

    Patch, 2D or 3D, shape C(Z)YX.

  • target (NDArray, default: None ) –

    Target for the patch, by default None.

  • **additional_arrays (NDArray, default: {} ) –

    Additional arrays that will be transformed identically to patch and target.

Returns:

  • tuple of NDArray –

    Transformed patch and target, the target can be returned as None.

__init__(image_means, image_stds, target_means=None, target_stds=None)

Constructor.

Parameters:

  • image_means (list of float) –

    Mean value per channel.

  • image_stds (list of float) –

    Standard deviation value per channel.

  • target_means (list of float, default: None ) –

    Target mean value per channel, by default None.

  • target_stds (list of float, default: None ) –

    Target standard deviation value per channel, by default None.

TrainDenormalize

Denormalize an image tensor for training-time tensors.

This class mirrors Denormalize but operates on torch tensors. It expects the input tensor to have shape BC(Z)YX with the channel dimension at index 1.

Parameters:

  • image_means (list or tuple of float) –

    Mean value per channel.

  • image_stds (list or tuple of float) –

    Standard deviation value per channel.

__call__(patch)

Reverse the normalization operation for a batch of patches.

Parameters:

  • patch (Tensor) –

    Patch, 2D or 3D, shape BC(Z)YX.

Returns:

  • Tensor –

    Denormalized tensor with dtype float32.

__init__(image_means, image_stds)

Initialize Denormalize transform.

Parameters:

  • image_means (list of float) –

    Mean values per channel.

  • image_stds (list of float) –

    Standard deviation values per channel.