tfg.datasets.features.Camera

FeatureConnector for camera calibration (extrinsic and intrinsic).

During _generate_examples, the feature connector accepts as input:

  • parameter_dict: A dictionary containing the extrinsic and instrinsic parameters of the camera as:
    • 'pose': Dictionary containing
      • Either 3x3 rotation matrix and translation vector: { 'R': A float32 tensor with shape [3, 3] denoting the 3D rotation matrix. 't': A float32 tensor with shape [3,] denoting the translation vector. } OR
      • look_at, position and up-vector: { 'look_at': float32 vector of shape (3,). 'position': float32 vector of shape (3,). 'up': float32 vector of shape (3,). }
    • 'f': focal length of the camera in pixel (either single float32 value or tuple of float32 as (f_x, f_y).
    • 'optical_center': Optical center of the camera in pixel coordinates as tuple (c_x, c_y) Optional parameters:
    • 'skew': float32 denoting the skew of the camera axes.
    • 'aspect_ratio': float32 denoting the aspect_ratio, if single fixed focal length is provided.

Output:

A dictionary containing:

  • 'pose': A tensorflow_graphics.datasets.features.Pose FeatureConnector representing the 3D pose of the camera.
  • 'intrinsics': A float32 tensor with shape [3,3] denoting the intrinsic matrix.

Example:

Default values for skew (s) and aspect_ratio(a) are 0 and 1, respectively.

Full calibration matrix: K = [[ f_x, s, c_x ], [ 0, f_y, c_y ], [ 0, 0, 1 ]]

With same focal length: K = [[ f, s, c_x ], [ 0, af, c_y ], [ 0, 0, 1 ]]

feature_dict (dict): Dictionary containing the feature connectors of a example. The keys should correspond to the data dict as returned by tf.data.Dataset(). Types (tf.int32,...) and dicts will automatically be converted into FeatureConnector.

ValueError If one of the given features is not recognized

dtype Return the dtype (or dict of dtype) of this FeatureConnector.
shape Return the shape (or dict of shape) of this FeatureConnector.

Methods

decode_batch_example

Decode multiple features batched in a single tf.Tensor.

This function is used to decode features wrapped in tfds.features.Sequence(). By default, this function apply decode_example on each individual elements using tf.map_fn. However, for optimization, features can overwrite this method to apply a custom batch decoding.

Args
tfexample_data Same tf.Tensor inputs as decode_example, but with and additional first dimension for the sequence length.

Returns
tensor_data Tensor or dictionary of tensor, output of the tf.data.Dataset object

decode_example

Decode the serialize examples.

Args
serialized_example Nested dict of tf.Tensor
decoders Nested dict of Decoder objects which allow to customize the decoding. The structure should match the feature structure, but only customized feature keys need to be present. See the guide for more info.

Returns
example Nested dict containing the decoded nested examples.

decode_ragged_example

Decode nested features from a tf.RaggedTensor.

This function is used to decode features wrapped in nested tfds.features.Sequence(). By default, this function apply decode_batch_example on the flat values of the ragged tensor. For optimization, features can overwrite this method to apply a custom batch decoding.

Args
tfexample_data tf.RaggedTensor inputs containing the nested encoded examples.

Returns
tensor_data The decoded tf.RaggedTensor or dictionary of tensor, output of the tf.data.Dataset object

encode_example

View source

Convert the given parameters into a dict convertible to tf example.

from_json_content

View source

get_serialized_info

See base class for details.

get_tensor_info

See base class for details.

items

keys

load_metadata

See base class for details.

save_metadata

See base class for details.

to_json_content

View source

values

__contains__

__getitem__

Return the feature associated with the key.

__iter__

__len__