tfg.datasets.features.Camera

FeatureConnector for camera calibration (extrinsic and intrinsic).

tfg.datasets.features.Camera()

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.

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.

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 (np.int32,...) and dicts will automatically be converted into FeatureConnector.
doc Documentation of this feature (e.g. description).

ValueError If one of the given features is not recognized

doc

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

flat_sequence_ranks

flat_serialized_info

np_dtype

numpy_dtype

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

tf_example_spec Returns the tf.Example proto structure.

Methods

catalog_documentation

catalog_documentation() -> List[feature_lib.CatalogFeatureDocumentation]

Returns the feature documentation to be shown in the catalog.

cls_from_name

@classmethod
cls_from_name(
    python_class_name: str
) -> Type['FeatureConnector']

Returns the feature class for the given Python class.

decode_batch_example

decode_batch_example(
    tfexample_data
)

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_example(
    serialized_example, *, decoders=None
)

Decode the feature dict to TF compatible input.

Args
tfexample_data Data or dictionary of data, as read by the tf-example reader. It correspond to the tf.Tensor() (or dict of tf.Tensor()) extracted from the tf.train.Example, matching the info defined in get_serialized_info().

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

decode_example_np

decode_example_np(
    serialized_example, *, decoders=None
)

Encode the feature dict into NumPy-compatible input.

Args
example_data Value to convert to NumPy.

Returns
np_data Data as NumPy-compatible type: either a Python primitive (bytes, int, etc) or a NumPy array.

decode_ragged_example

decode_ragged_example(
    tfexample_data
)

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

deserialize_example

deserialize_example(
    serialized_example: Union[tf.Tensor, bytes], *, decoders=None
) -> utils.TensorDict

Decodes the tf.train.Example data into tf.Tensor.

See serialize_example to encode the data into proto.

Args
serialized_example The tensor-like object containing the serialized tf.train.Example proto.
decoders Eventual decoders to apply (see documentation)

Returns
The decoded features tensors.

deserialize_example_np

deserialize_example_np(
    serialized_example: Union[tf.Tensor, bytes], *, decoders=None
) -> utils.NpArrayOrScalarDict

encode_example

View source

encode_example(
    example_dict
)

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

from_config

@classmethod
from_config(
    root_dir: str
) -> FeatureConnector

Reconstructs the FeatureConnector from the config file.

Usage:

features = FeatureConnector.from_config('path/to/dir')

Args
root_dir Directory containing the features.json file.

Returns
The reconstructed feature instance.

from_json

@classmethod
from_json(
    value: Json
) -> FeatureConnector

FeatureConnector factory.

This function should be called from the tfds.features.FeatureConnector base class. Subclass should implement the from_json_content.

Example:

feature = tfds.features.FeatureConnector.from_json(
    {'type': 'Image', 'content': {'shape': [32, 32, 3], 'dtype': 'uint8'} }
)
assert isinstance(feature, tfds.features.Image)

Args
value dict(type=, content=) containing the feature to restore. Match dict returned by to_json.

Returns
The reconstructed FeatureConnector.

from_json_content

View source

@classmethod
from_json_content(
    value
) -> 'Camera'

FeatureConnector factory (to overwrite).

Subclasses should overwrite this method. This method is used when importing the feature connector from the config.

This function should not be called directly. FeatureConnector.from_json should be called instead.

See existing FeatureConnectors for implementation examples.

Args
value FeatureConnector information represented as either Json or a Feature proto. The content must match what is returned by to_json_content.
doc Documentation of this feature (e.g. description).

Returns
The reconstructed FeatureConnector.

from_proto

@classmethod
from_proto(
    feature_proto: feature_pb2.Feature
) -> T

Instantiates a feature from its proto representation.

get_serialized_info

get_serialized_info()

See base class for details.

get_tensor_info

get_tensor_info()

See base class for details.

get_tensor_spec

get_tensor_spec() -> TreeDict[tf.TensorSpec]

Returns the tf.TensorSpec of this feature (not the element spec!).

Note that the output of this method may not correspond to the element spec of the dataset. For example, currently this method does not support RaggedTensorSpec.

items

items()

keys

keys()

load_metadata

load_metadata(
    data_dir, feature_name=None
)

See base class for details.

repr_html

repr_html(
    ex: np.ndarray
) -> str

Returns the HTML str representation of the object.

repr_html_batch

repr_html_batch(
    ex: np.ndarray
) -> str

Returns the HTML str representation of the object (Sequence).

repr_html_ragged

repr_html_ragged(
    ex: np.ndarray
) -> str

Returns the HTML str representation of the object (Nested sequence).

save_config

save_config(
    root_dir: str
) -> None

Exports the FeatureConnector to a file.

Args
root_dir path/to/dir containing the features.json

save_metadata

save_metadata(
    data_dir, feature_name=None
)

See base class for details.

serialize_example

serialize_example(
    example_data
) -> bytes

Encodes nested data values into tf.train.Example bytes.

See deserialize_example to decode the proto into tf.Tensor.

Args
example_data Example data to encode (numpy-like nested dict)

Returns
The serialized tf.train.Example.

to_json

to_json() -> Json

Exports the FeatureConnector to Json.

Each feature is serialized as a dict(type=..., content=...).

  • type: The cannonical name of the feature (module.FeatureName).
  • content: is specific to each feature connector and defined in to_json_content. Can contain nested sub-features (like for tfds.features.FeaturesDict and tfds.features.Sequence).

For example:

tfds.features.FeaturesDict({
    'input': tfds.features.Image(),
    'target': tfds.features.ClassLabel(num_classes=10),
})

Is serialized as:

{
    "type": "tensorflow_datasets.core.features.features_dict.FeaturesDict",
    "content": {
        "input": {
            "type": "tensorflow_datasets.core.features.image_feature.Image",
            "content": {
                "shape": [null, null, 3],
                "dtype": "uint8",
                "encoding_format": "png"
            }
        },
        "target": {
            "type":
            "tensorflow_datasets.core.features.class_label_feature.ClassLabel",
            "content": {
              "num_classes": 10
            }
        }
    }
}

Returns
A dict(type=, content=). Will be forwarded to from_json when reconstructing the feature.

to_json_content

View source

to_json_content()

FeatureConnector factory (to overwrite).

This function should be overwritten by the subclass to allow re-importing the feature connector from the config. See existing FeatureConnector for example of implementation.

Returns
The FeatureConnector metadata in either a dict, or a Feature proto. This output is used in from_json_content when reconstructing the feature.

to_proto

to_proto() -> feature_pb2.Feature

Exports the FeatureConnector to the Feature proto.

For features that have a specific schema defined in a proto, this function needs to be overriden. If there's no specific proto schema, then the feature will be represented using JSON.

Returns
The feature proto describing this feature.

values

values()

__contains__

__contains__(
    k
)

__getitem__

__getitem__(
    key
)

Return the feature associated with the key.

__iter__

__iter__()

__len__

__len__()

ALIASES []