allenact.embodiedai.sensors.vision_sensors

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VisionSensor

class VisionSensor(Sensor[EnvType, SubTaskType])

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VisionSensor.__init__

 | __init__(mean: Optional[np.ndarray] = None, stdev: Optional[np.ndarray] = None, height: Optional[int] = None, width: Optional[int] = None, uuid: str = "vision", output_shape: Optional[Tuple[int, ...]] = None, output_channels: Optional[int] = None, unnormalized_infimum: float = -np.inf, unnormalized_supremum: float = np.inf, scale_first: bool = True, **kwargs: Any)

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Initializer.

Parameters

• mean : The images will be normalized with the given mean
• stdev : The images will be normalized with the given standard deviations.
• height : If it's a non-negative integer and width is also non-negative integer, the image returned from the environment will be rescaled to have height rows and width columns using bilinear sampling.
• width : If it's a non-negative integer and height is also non-negative integer, the image returned from the environment will be rescaled to have height rows and width columns using bilinear sampling.
• uuid : The universally unique identifier for the sensor.
• output_shape : Optional observation space shape (alternative to output_channels).
• output_channels : Optional observation space number of channels (alternative to output_shape).
• unnormalized_infimum : Lower limit(s) for the observation space range.
• unnormalized_supremum : Upper limit(s) for the observation space range.
• scale_first : Whether to scale image before normalization (if needed).
• kwargs : Extra kwargs. Currently unused.

VisionSensor.height

 | @property
 | height() -> Optional[int]

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Height that input image will be rescale to have.

Returns

The height as a non-negative integer or None if no rescaling is done.

VisionSensor.width

 | @property
 | width() -> Optional[int]

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Width that input image will be rescale to have.

Returns

The width as a non-negative integer or None if no rescaling is done.

RGBSensor

class RGBSensor(VisionSensor[EnvType, SubTaskType],  ABC)

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RGBSensor.__init__

 | __init__(use_resnet_normalization: bool = False, mean: Optional[Union[np.ndarray, Sequence[float]]] = (0.485, 0.456, 0.406), stdev: Optional[Union[np.ndarray, Sequence[float]]] = (0.229, 0.224, 0.225), height: Optional[int] = None, width: Optional[int] = None, uuid: str = "rgb", output_shape: Optional[Tuple[int, ...]] = None, output_channels: int = 3, unnormalized_infimum: float = 0.0, unnormalized_supremum: float = 1.0, scale_first: bool = True, **kwargs: Any)

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Initializer.

Parameters

• use_resnet_normalization : Whether to apply image normalization with the given mean and stdev.
• mean : The images will be normalized with the given mean if use_resnet_normalization is True (default [0.485, 0.456, 0.406], i.e. the standard resnet normalization mean).
• stdev : The images will be normalized with the given standard deviation if use_resnet_normalization is True (default [0.229, 0.224, 0.225], i.e. the standard resnet normalization standard deviation).
• height: If it's a non-negative integer and width is also non-negative integer, the image returned from the environment will be rescaled to have height rows and width columns using bilinear sampling.
• width: If it's a non-negative integer and height is also non-negative integer, the image returned from the environment will be rescaled to have height rows and width columns using bilinear sampling.
• uuid: The universally unique identifier for the sensor.
• output_shape: Optional observation space shape (alternative to output_channels).
• output_channels: Optional observation space number of channels (alternative to output_shape).
• unnormalized_infimum: Lower limit(s) for the observation space range.
• unnormalized_supremum: Upper limit(s) for the observation space range.
• scale_first: Whether to scale image before normalization (if needed).
• kwargs : Extra kwargs. Currently unused.

DepthSensor

class DepthSensor(VisionSensor[EnvType, SubTaskType],  ABC)

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DepthSensor.__init__

 | __init__(use_normalization: bool = False, mean: Optional[Union[np.ndarray, float]] = 0.5, stdev: Optional[Union[np.ndarray, float]] = 0.25, height: Optional[int] = None, width: Optional[int] = None, uuid: str = "depth", output_shape: Optional[Tuple[int, ...]] = None, output_channels: int = 1, unnormalized_infimum: float = 0.0, unnormalized_supremum: float = 5.0, scale_first: bool = True, **kwargs: Any)

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Initializer.

Parameters

• config : If config["use_normalization"] is True then the depth images will be normalized with mean 0.5 and standard deviation 0.25. If both config["height"] and config["width"] are non-negative integers then the depth image returned from the environment will be rescaled to have shape (config["height"], config["width"]) using bilinear sampling.
• use_normalization : Whether to apply image normalization with the given mean and stdev.
• mean : The images will be normalized with the given mean if use_normalization is True (default 0.5).
• stdev : The images will be normalized with the given standard deviation if use_normalization is True (default 0.25).
• height: If it's a non-negative integer and width is also non-negative integer, the image returned from the environment will be rescaled to have height rows and width columns using bilinear sampling.
• width: If it's a non-negative integer and height is also non-negative integer, the image returned from the environment will be rescaled to have height rows and width columns using bilinear sampling.
• uuid: The universally unique identifier for the sensor.
• output_shape: Optional observation space shape (alternative to output_channels).
• output_channels: Optional observation space number of channels (alternative to output_shape).
• unnormalized_infimum: Lower limit(s) for the observation space range.
• unnormalized_supremum: Upper limit(s) for the observation space range.
• scale_first: Whether to scale image before normalization (if needed).
• kwargs : Extra kwargs. Currently unused.