Noise

dataphy.visionpack.tforms.noise

Classes

GaussianNoise(sigma: list = [0.005, 0.03], per_channel: bool = True, **kwargs: Any)

Bases: BaseTransform

Gaussian noise transform for image augmentation.

This transform adds random Gaussian noise to images, simulating sensor noise or other random perturbations. It can apply noise per-channel or globally.

Parameters:

Name	Type	Description	Default
sigma	list	Range of noise standard deviation [min, max]	[0.005, 0.03]
per_channel	bool	Whether to apply different noise to each channel	True
**kwargs	Any	Additional base transform parameters	{}

Source code in src/dataphy/visionpack/tforms/noise.py

def __init__(
    self,
    sigma: list = [0.005, 0.03],
    per_channel: bool = True,
    **kwargs: Any
):
    """Initialize the GaussianNoise transform.

    Args:
        sigma: Range of noise standard deviation [min, max]
        per_channel: Whether to apply different noise to each channel
        **kwargs: Additional base transform parameters
    """
    super().__init__(**kwargs)
    self.sigma_range = sigma
    self.per_channel = per_channel

Attributes

sigma_range = sigma instance-attribute

per_channel = per_channel instance-attribute

p = p instance-attribute

apply_to = apply_to if apply_to is not None else ['rgb'] instance-attribute

sync_views = sync_views instance-attribute

update_intrinsics = update_intrinsics instance-attribute

mask_protect = mask_protect if mask_protect is not None else [] instance-attribute

min_visible_mask_pct = min_visible_mask_pct instance-attribute

resample = resample instance-attribute

border_mode = border_mode instance-attribute

pad_mode = pad_mode instance-attribute

pad_value = pad_value instance-attribute

seed_policy = seed_policy instance-attribute

Functions

forward(batch: Dict[str, Any]) -> Dict[str, Any]

Base forward method that handles cross-cutting logic.

Source code in src/dataphy/visionpack/tforms/base.py

def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]:
    """Base forward method that handles cross-cutting logic."""
    if not self._should_apply():
        return batch

    # Setup seed policy
    episode_id = batch.get("episode_id")
    self._setup_seed(episode_id)

    # Apply transform
    return self._apply_transform(batch)

PoissonNoise(scale: list = [0.5, 1.5], **kwargs: Any)

Bases: BaseTransform

Poisson noise transform for image augmentation.

This transform adds Poisson (shot) noise to images, simulating photon noise in camera sensors. It includes error handling for edge cases and fallback to Gaussian approximation when needed.

Parameters:

Name	Type	Description	Default
scale	list	Range of scaling factors for noise intensity [min, max]	[0.5, 1.5]
**kwargs	Any	Additional base transform parameters	{}

Source code in src/dataphy/visionpack/tforms/noise.py

def __init__(
    self,
    scale: list = [0.5, 1.5],
    **kwargs: Any
):
    """Initialize the PoissonNoise transform.

    Args:
        scale: Range of scaling factors for noise intensity [min, max]
        **kwargs: Additional base transform parameters
    """
    super().__init__(**kwargs)
    self.scale_range = scale

Attributes

scale_range = scale instance-attribute

p = p instance-attribute

apply_to = apply_to if apply_to is not None else ['rgb'] instance-attribute

sync_views = sync_views instance-attribute

update_intrinsics = update_intrinsics instance-attribute

mask_protect = mask_protect if mask_protect is not None else [] instance-attribute

min_visible_mask_pct = min_visible_mask_pct instance-attribute

resample = resample instance-attribute

border_mode = border_mode instance-attribute

pad_mode = pad_mode instance-attribute

pad_value = pad_value instance-attribute

seed_policy = seed_policy instance-attribute

Functions

forward(batch: Dict[str, Any]) -> Dict[str, Any]

Base forward method that handles cross-cutting logic.

Source code in src/dataphy/visionpack/tforms/base.py

def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]:
    """Base forward method that handles cross-cutting logic."""
    if not self._should_apply():
        return batch

    # Setup seed policy
    episode_id = batch.get("episode_id")
    self._setup_seed(episode_id)

    # Apply transform
    return self._apply_transform(batch)

SpeckleNoise(sigma: list = [0.01, 0.05], **kwargs: Any)

Bases: BaseTransform

Source code in src/dataphy/visionpack/tforms/noise.py

def __init__(
    self,
    sigma: list = [0.01, 0.05],
    **kwargs: Any
):
    super().__init__(**kwargs)
    self.sigma_range = sigma

Attributes

sigma_range = sigma instance-attribute

p = p instance-attribute

apply_to = apply_to if apply_to is not None else ['rgb'] instance-attribute

sync_views = sync_views instance-attribute

update_intrinsics = update_intrinsics instance-attribute

mask_protect = mask_protect if mask_protect is not None else [] instance-attribute

min_visible_mask_pct = min_visible_mask_pct instance-attribute

resample = resample instance-attribute

border_mode = border_mode instance-attribute

pad_mode = pad_mode instance-attribute

pad_value = pad_value instance-attribute

seed_policy = seed_policy instance-attribute

Functions

forward(batch: Dict[str, Any]) -> Dict[str, Any]

Base forward method that handles cross-cutting logic.

Source code in src/dataphy/visionpack/tforms/base.py

def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]:
    """Base forward method that handles cross-cutting logic."""
    if not self._should_apply():
        return batch

    # Setup seed policy
    episode_id = batch.get("episode_id")
    self._setup_seed(episode_id)

    # Apply transform
    return self._apply_transform(batch)

SaltPepperNoise(amount: list = [0.001, 0.01], salt_vs_pepper: float = 0.5, **kwargs: Any)

Bases: BaseTransform

Source code in src/dataphy/visionpack/tforms/noise.py

def __init__(
    self,
    amount: list = [0.001, 0.01],
    salt_vs_pepper: float = 0.5,
    **kwargs: Any
):
    super().__init__(**kwargs)
    self.amount_range = amount
    self.salt_vs_pepper = salt_vs_pepper

Attributes

amount_range = amount instance-attribute

salt_vs_pepper = salt_vs_pepper instance-attribute

p = p instance-attribute

apply_to = apply_to if apply_to is not None else ['rgb'] instance-attribute

sync_views = sync_views instance-attribute

update_intrinsics = update_intrinsics instance-attribute

mask_protect = mask_protect if mask_protect is not None else [] instance-attribute

min_visible_mask_pct = min_visible_mask_pct instance-attribute

resample = resample instance-attribute

border_mode = border_mode instance-attribute

pad_mode = pad_mode instance-attribute

pad_value = pad_value instance-attribute

seed_policy = seed_policy instance-attribute

Functions

forward(batch: Dict[str, Any]) -> Dict[str, Any]

Base forward method that handles cross-cutting logic.

Source code in src/dataphy/visionpack/tforms/base.py

def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]:
    """Base forward method that handles cross-cutting logic."""
    if not self._should_apply():
        return batch

    # Setup seed policy
    episode_id = batch.get("episode_id")
    self._setup_seed(episode_id)

    # Apply transform
    return self._apply_transform(batch)