Project - Encord

Project Objects

class Project()

Access project related data and manipulate the project.

project_hash

@property
def project_hash() -> str

Get the project hash (i.e. the Project ID).

title

@property
def title() -> str

Get the title of the project.

description

@property
def description() -> str

Get the description of the project.

created_at

@property
def created_at() -> datetime.datetime

Get the time the project was created at.

last_edited_at

@property
def last_edited_at() -> datetime.datetime

Get the time the project was last edited at.

ontology

@property
@deprecated(version="0.1.95", alternative=".ontology_structure")
def ontology() -> Dict[str, Any]

Get the ontology of the project.

DEPRECATED: Prefer using the :meth:encord.Project.ontology_structure method. This method returns the same structure as :meth:encord.Project.ontology_structure, just in raw python dictionary format.

ontology_hash

@property
def ontology_hash() -> str

Get the ontology hash of the project’s ontology.

ontology_structure

@property
def ontology_structure() -> OntologyStructure

Get the ontology structure of the project’s ontology.

datasets

@property
@deprecated(version="0.1.117", alternative=".list_datasets")
def datasets() -> List[Dict[str, Any]]

DEPRECATED: Prefer using the :meth:encord.project.list_datasets class to work with the data.

Get the info about datasets associated with this project.

project_type

@property
def project_type() -> ProjectType

Get the project type.

label_rows

@property
@deprecated(version="0.1.104", alternative=".list_label_rows_v2")
def label_rows() -> dict

Get the label rows. DEPRECATED: Prefer using :meth:list_label_rows_v2() method and :meth:LabelRowV2 class to work with the data.

.. code::

from encord.orm.label_row import LabelRowMetadata

project = user_client.get_project(“[project_hash]“)

label_rows = LabelRowMetadata.from_list(project.label_rows)

refetch_data

def refetch_data() -> None

The Project class will only fetch its properties once. Use this function if you suspect the state of those properties to be dirty.

refetch_ontology

def refetch_ontology() -> None

Update the ontology for the project to reflect changes on the backend.

get_project

def get_project() -> OrmProject

This function is exposed for convenience. You are encouraged to use the property accessors instead.

workflow

@property
def workflow() -> Workflow

Get the workflow of the project.

Available only for workflow projects.

list_label_rows_v2

def list_label_rows_v2(
        data_hashes: Optional[Union[List[str], List[UUID]]] = None,
        label_hashes: Optional[Union[List[str], List[UUID]]] = None,
        edited_before: Optional[Union[str, datetime.datetime]] = None,
        edited_after: Optional[Union[str, datetime.datetime]] = None,
        label_statuses: Optional[List[AnnotationTaskStatus]] = None,
        shadow_data_state: Optional[ShadowDataState] = None,
        data_title_eq: Optional[str] = None,
        data_title_like: Optional[str] = None,
        workflow_graph_node_title_eq: Optional[str] = None,
        workflow_graph_node_title_like: Optional[str] = None,
        include_workflow_graph_node: bool = True,
        include_client_metadata: bool = False,
        include_images_data: bool = False,
        include_all_label_branches: bool = False,
        branch_name: Optional[str] = None) -> List[LabelRowV2]

List label rows with various filtering options.

Arguments:

data_hashes - List of data hashes to filter by.
label_hashes - List of label hashes to filter by.
edited_before - Optionally filter to only rows last edited before the specified time.
edited_after - Optionally filter to only rows last edited after the specified time.
label_statuses - Optionally filter to only those label rows that have one of the specified :class:~encord.orm.label_row.AnnotationTaskStatuses.
shadow_data_state - Optionally filter by data type in Benchmark QA projects. See :class:~encord.orm.label_row.ShadowDataState.
data_title_eq - Optionally filter by exact title match.
data_title_like - Optionally filter by fuzzy title match; SQL syntax.
workflow_graph_node_title_eq - Optionally filter by exact match with workflow node title.
workflow_graph_node_title_like - Optionally filter by fuzzy match with workflow node title; SQL syntax.
include_workflow_graph_node - Include workflow graph node metadata in all the results. True by default.
include_client_metadata - Optionally include client metadata into the result of this query.
include_images_data - Optionally include image group metadata into the result of this query.
include_all_label_branches - Optionally include all label branches. They will be included as separate label row objects.
branch_name - Optionally specify a branch name. A branch name cannot be specified if include_all_label_branches is set to True

Returns:

A list of :class:~encord.objects.LabelRowV2 instances for all the matching label rows.

add_users

def add_users(user_emails: List[str],
              user_role: ProjectUserRole) -> List[ProjectUser]

Add users to the project.

Arguments:

user_emails - List of user emails to be added.
user_role - The user role to assign to all users.

Returns:

List[ProjectUser] - A list of ProjectUser objects representing the added users.

Raises:

AuthorisationError - If the project API key is invalid.
ResourceNotFoundError - If no project exists by the specified project EntityId.
UnknownError - If an error occurs while adding the users to the project.

list_groups

def list_groups() -> Iterable[ProjectGroup]

List all groups that have access to a particular project.

Returns:

Iterable[ProjectGroup] - An iterable of ProjectGroup objects.

add_group

def add_group(group_hash: Union[List[UUID], UUID], user_role: ProjectUserRole)

Add a group to the project.

Arguments:

group_hash - List of group hashes or a single group hash to be added.
user_role - User role that the group will be given.

Returns:

None

remove_group

def remove_group(group_hash: Union[List[UUID], UUID])

Remove a group from the project.

Arguments:

group_hash - List of group hashes or a single group hash to be removed.

Returns:

None

copy_project

def copy_project(copy_datasets: Union[bool, CopyDatasetOptions] = False,
                 copy_collaborators=False,
                 copy_models=False,
                 *,
                 copy_labels: Optional[CopyLabelsOptions] = None,
                 new_title: Optional[str] = None,
                 new_description: Optional[str] = None) -> str

Copy the current project into a new one with copied contents including settings, datasets, and users. Labels and models are optional.

Arguments:

copy_datasets - If True, the datasets of the existing project are copied over, and new tasks are created from those datasets.
copy_collaborators - If True, all users of the existing project are copied over with their current roles. If label and/or annotator reviewer mapping is set, this will also be copied over.
copy_models - If True, all models with their training information will be copied into the new project.
copy_labels - Options for copying labels, defined in CopyLabelsOptions.
new_title - When provided, will be used as the title for the new project.
new_description - When provided, will be used as the description for the new project.

Returns:

str - The EntityId of the newly created project.

Raises:

AuthorisationError - If the project API key is invalid.
ResourceNotFoundError - If no project exists by the specified project EntityId.
UnknownError - If an error occurs while copying the project.

submit_label_row_for_review

def submit_label_row_for_review(uid: str)

Submit a label row for review.

Note: This method is not supported for workflow-based projects. See the documentation about the workflows.

Arguments:

uid - A label_hash (uid) string.

Returns:

bool - True if the submission was successful, False otherwise.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
UnknownError - If an error occurs while submitting for review.
OperationNotAllowed - If the write operation is not allowed by the API key.

add_datasets

def add_datasets(dataset_hashes: List[str]) -> bool

Add datasets to the project.

Arguments:

dataset_hashes - List of dataset hashes of the datasets to be added.

Returns:

bool - True if the datasets were successfully added, False otherwise.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
ResourceNotFoundError - If one or more datasets don’t exist by the specified dataset_hashes.
UnknownError - If an error occurs while adding the datasets to the project.
OperationNotAllowed - If the write operation is not allowed by the API key.

remove_datasets

def remove_datasets(dataset_hashes: List[str]) -> bool

Remove datasets from the project.

Arguments:

dataset_hashes - List of dataset hashes of the datasets to be removed.

Returns:

bool - True if the datasets were successfully removed, False otherwise.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
ResourceNotFoundError - If no dataset exists by the specified dataset_hash (uid).
UnknownError - If an error occurs while removing the datasets from the project.
OperationNotAllowed - If the operation is not allowed by the API key.

get_project_ontology

@deprecated(version="0.1.95", alternative=".ontology_structure")
def get_project_ontology() -> LegacyOntology

DEPRECATED: Prefer using the ontology_structure property accessor instead.

Returns:

LegacyOntology - The project’s ontology.

add_object

@deprecated("0.1.102", alternative="encord.ontology.Ontology class")
def add_object(name: str, shape: ObjectShape) -> bool

DEPRECATED: Prefer using :class:Ontology [encord.ontology.Ontology] to manipulate ontology.

Add an object to an ontology.

ATTENTION: This legacy method will affect all the projects sharing the same ontology.

Arguments:

name - The name of the object.
shape - The shape of the object. (BOUNDING_BOX, POLYGON, POLYLINE, or KEY_POINT)

Returns:

bool - True if the object was added successfully, False otherwise.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
UnknownError - If an error occurs while adding the object to the project ontology.
OperationNotAllowed - If the operation is not allowed by the API key.
ValueError - If invalid arguments are supplied in the function call.

add_classification

@deprecated("0.1.102", alternative="encord.ontology.Ontology class")
def add_classification(name: str,
                       classification_type: ClassificationType,
                       required: bool,
                       options: Optional[Iterable[str]] = None)

DEPRECATED: Prefer using :class:Ontology encord.ontology.Ontology to manipulate ontology.

Add a classification to an ontology.

ATTENTION: This legacy method will affect all the projects sharing the same ontology.

Arguments:

name - The name of the classification.
classification_type - The classification type (RADIO, TEXT, or CHECKLIST).
required - Whether this classification is required by the annotator.
options - The list of options for the classification (to be set to None for texts).

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
UnknownError - If an error occurs while adding the classification to the project ontology.
OperationNotAllowed - If the operation is not allowed by the API key.
ValueError - If invalid arguments are supplied in the function call.

list_models

def list_models() -> List[ModelConfiguration]

List all models that are associated with the project. Use the :meth:encord.project.Project.get_training_metadata to get more metadata about each training instance.

Example:

.. code::

from encord.utilities.project_utilities import get_all_model_iteration_uids

project = client_instance.get_project([project_hash])

model_configurations = project.list_models() all_model_iteration_uids = get_all_model_iteration_uids(model_configurations) training_metadata = project.get_training_metadata( all_model_iteration_uids, get_model_training_labels=True, )

Returns:

List[ModelConfiguration] - A list of ModelConfiguration objects representing the models associated with the project.

get_training_metadata

def get_training_metadata(
        model_iteration_uids: Iterable[str],
        get_created_at: bool = False,
        get_training_final_loss: bool = False,
        get_model_training_labels: bool = False) -> List[TrainingMetadata]

Given a list of model_iteration_uids, get metadata around each model_iteration.

Arguments:

model_iteration_uids - The model iteration uids.
get_created_at - Whether the created_at field should be retrieved.
get_training_final_loss - Whether the training_final_loss field should be retrieved.
get_model_training_labels - Whether the model_training_labels field should be retrieved.

Returns:

List[TrainingMetadata] - A list of TrainingMetadata objects containing the requested metadata.

create_model_row

def create_model_row(title: str, description: str, features: List[str],
                     model: Union[AutomationModels, str]) -> str

Create a model row.

Arguments:

title - Model title.
description - Model description.
features - List of feature_node_hashes which are IDs of ontology objects or classifications to be included in the model.
model - The model type to be used. For backwards compatibility purposes, strings corresponding to the values of the :class:.AutomationModels Enum are also allowed.

Returns:

str - The uid of the added model row.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
ModelFeaturesInconsistentError - If a feature type is different from what is supported by the model (e.g. if creating a classification model using a bounding box).

model_delete

def model_delete(uid: str) -> bool

Delete a model created on the platform.

Arguments:

uid - A model_hash (uid) string.

Returns:

bool - True if the model was successfully deleted, False otherwise.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
ResourceNotFoundError - If no model exists by the specified model_hash (uid).
UnknownError - If an error occurs during deletion.

model_inference

def model_inference(uid: str,
                    file_paths: Optional[List[str]] = None,
                    base64_strings: Optional[List[bytes]] = None,
                    conf_thresh: float = 0.6,
                    iou_thresh: float = 0.3,
                    device: Device = Device.CUDA,
                    detection_frame_range: Optional[List[int]] = None,
                    allocation_enabled: bool = False,
                    data_hashes: Optional[List[str]] = None,
                    rdp_thresh: float = 0.005)

Run inference with a model trained on the platform.

The image(s)/video(s) can be provided either as local file paths, base64 strings, or as data hashes if the data is already uploaded on the Encord platform.

Arguments:

uid - A model_iteration_hash (uid) string.
file_paths - List of local file paths to image(s) or video(s) - if running inference on files.
base64_strings - List of base64 strings of image(s) or video(s) - if running inference on base64 strings.
conf_thresh - Confidence threshold (default 0.6).
iou_thresh - Intersection over union threshold (default 0.3).
device - Device (CPU or CUDA, default is CUDA).
detection_frame_range - Detection frame range (for videos).
allocation_enabled - Object UID allocation (tracking) enabled (disabled by default).
data_hashes - List of hashes of the videos/image_groups you’d like to run inference on.
rdp_thresh - Parameter specifying the polygon coarseness to be used while running inference. The higher the value, the fewer points in the segmented image.

Returns:

dict - A dictionary of inference results.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
ResourceNotFoundError - If no model exists by the specified model_iteration_hash (uid).
UnknownError - If an error occurs while running inference.
FileTypeNotSupportedError - If the file type is not supported for inference (has to be an image or video).
FileSizeNotSupportedError - If the file size is too big to be supported.
DetectionRangeInvalidError - If a detection range is invalid for video inference.

model_train_start

def model_train_start(model_hash: Union[str, UUID],
                      label_rows: List[Union[str, UUID]],
                      epochs: int,
                      weights: ModelTrainingWeights,
                      batch_size: int = 24,
                      device: Device = Device.CUDA) -> UUID

This method initializes model training in Encord’s backend. Once the training_hash (UUID) is returned, you can exit the terminal while the job continues uninterrupted.

You can check job status at any point using the :meth:model_train_get_result method. This can be done in a separate Python session to the one where the job was initialized.

Arguments:

model_hash - A unique identifier (UUID) for the model. The format is a string.
label_rows - List of label row uids (hashes) for training.
epochs - Number of passes through the training dataset.
weights - Model weights.
batch_size - Number of training examples utilized in one iteration.
device - Device (CPU or CUDA, default is CUDA).

Returns:

UUID - A model iteration training_hash.

Raises:

AuthorisationError - If access to the specified resource is restricted.
ModelWeightsInconsistentError - If the passed model weights are incompatible with the selected model.
ResourceNotFoundError - If no model exists by the specified model_hash (uid).

model_train_get_result

def model_train_get_result(model_hash: Union[str, UUID],
                           training_hash: Union[str, UUID],
                           timeout_seconds: int = 7 * 24 * 60 * 60) -> dict

Fetch model training status, perform long polling process for timeout_seconds.

Arguments:

model_hash: A unique identifier (UUID) for the model. training_hash: A unique identifier(UUID) of the model iteration. This ID enables the user to track the job progress using the SDK or web app. timeout_seconds: Number of seconds the method waits while waiting for a response. If timeout_seconds == 0, only a single checking request is performed. Responses are immediately returned.

Returns:

Response containing details about job status, errors, and progress.

object_interpolation

def object_interpolation(key_frames, objects_to_interpolate)

Run object interpolation algorithm on project labels (requires an editor ontology and feature uids).

Interpolation is supported for bounding box, polygon, and keypoint.

Arguments:

key_frames - Labels for frames to be interpolated. Key frames are consumed in the form::

    {
        "[frame_number]": {
            "objects": [
                {
                    "objectHash": "[object_hash]",
                    "featureHash": "[feature_hash]",
                    "polygon": {
                        "0": { "x": x1, "y": y1, },
                        "1": { "x": x2, "y": y2, },
                        # ...,
                    }
                },
                # ...
            ]
        },
        # ...,
    }

objects_to_interpolate - List of object uids (hashes) of objects to interpolate.

Returns:

dict - Full set of filled frames including interpolated objects.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
UnknownError - If an error occurs while running interpolation.

fitted_bounding_boxes

def fitted_bounding_boxes(frames: dict, video: dict)

Fit bounding boxes to the given frames of a video.

Arguments:

frames - Labels for frames to be fitted. Frames are consumed in the form::

    {
        "[frame_number]": {
            "objects": [
                {
                    "objectHash": "[object_hash]",
                    "featureHash": "[feature_hash]",
                    "polygon": {
                        "0": { "x": x1, "y": y1, },
                        "1": { "x": x2, "y": y2, },
                        # ...,
                    }
                },
                # ...
            ]
        },
        # ...,
    }

video - Metadata of the video for which bounding box fitting needs to be run::

    {
        "width": w,
        "height": h,
    }

Returns:

dict - Full set of filled frames including fitted objects.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
UnknownError - If an error occurs while running interpolation.

get_data

def get_data(
    data_hash: str,
    get_signed_url: bool = False
) -> Tuple[Optional[Video], Optional[List[Image]]]

Retrieve information about a video or image group.

Arguments:

data_hash - The uid of the data object.
get_signed_url - Optionally return signed URLs for timed public access to that resource (default False).

Returns:

A tuple consisting of the video (if it exists) and a list of individual images (if they exist).

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
UnknownError - If an error occurs while retrieving the object.

get_label_logs

def get_label_logs(user_hash: Optional[str] = None,
                   data_hash: Optional[str] = None,
                   from_unix_seconds: Optional[int] = None,
                   to_unix_seconds: Optional[int] = None,
                   after: Optional[datetime.datetime] = None,
                   before: Optional[datetime.datetime] = None,
                   user_email: Optional[str] = None) -> List[LabelLog]

Get label logs, which represent the actions taken in the UI to create labels.

All arguments can be left as None if no filtering should be applied.

Arguments:

user_hash - Filter the label logs by the user.
data_hash - Filter the label logs by the data_hash.
from_unix_seconds - Filter the label logs to only include labels after this timestamp. Deprecated: use parameter after instead.
to_unix_seconds - Filter the label logs to only include labels before this timestamp. Deprecated: use parameter before instead.
after - Filter the label logs to only include labels after the specified time.
before - Filter the label logs to only include labels before the specified time.
user_email - Filter by the annotator email.

Returns:

List of label logs.

get_cloud_integrations

def get_cloud_integrations() -> List[CloudIntegration]

Get the list of cloud integrations.

Returns:

List of CloudIntegration objects.

list_label_rows

@deprecated(version="0.1.104", alternative=".list_label_rows_v2")
def list_label_rows(
        edited_before: Optional[Union[str, datetime.datetime]] = None,
        edited_after: Optional[Union[str, datetime.datetime]] = None,
        label_statuses: Optional[List[AnnotationTaskStatus]] = None,
        shadow_data_state: Optional[ShadowDataState] = None,
        *,
        include_uninitialised_labels=False,
        label_hashes: Optional[List[str]] = None,
        data_hashes: Optional[List[str]] = None) -> List[LabelRowMetadata]

DEPRECATED - use list_label_rows_v2 to manage label rows instead.

Arguments:

edited_before - Optionally filter to only rows last edited before the specified time.
edited_after - Optionally filter to only rows last edited after the specified time.
label_statuses - Optionally filter to only those label rows that have one of the specified AnnotationTaskStatus.
shadow_data_state - Optionally filter by data type in Benchmark QA projects. See ShadowDataState.
include_uninitialised_labels - Whether to return only label rows that are “created” and have a label_hash (default). If set to True, this will return all label rows, including those that do not have a label_hash.
data_hashes - List of data hashes to filter by.
label_hashes - List of label hashes to filter by.

Returns:

A list of LabelRowMetadata instances for all the matching label rows.

Raises:

UnknownError - If an error occurs while retrieving the data.

set_label_status

def set_label_status(label_hash: str, label_status: LabelStatus) -> bool

Set the label status for a label row to a desired value.

Arguments:

label_hash - Unique identifier of the label row whose status is to be updated.
label_status - The new status that needs to be set.

Returns:

True if the label status was successfully updated, False otherwise.

Raises:

AuthorisationError - If the label_hash provided is invalid or not a member of the project.
UnknownError - If an error occurs while updating the status.

get_label_row

@deprecated(version="0.1.123", alternative=".list_label_rows_v2")
def get_label_row(uid: str,
                  get_signed_url: bool = True,
                  *,
                  include_object_feature_hashes: Optional[Set[str]] = None,
                  include_classification_feature_hashes: Optional[
                      Set[str]] = None,
                  include_reviews: bool = False) -> LabelRow

DEPRECATED: Prefer using the list_label_rows_v2 function to interact with label rows.

Retrieve label row. If you need to retrieve multiple label rows, prefer using get_label_rows instead.

Arguments:

uid - A label_hash (uid) string.
get_signed_url - Whether to generate signed urls to the data asset. Generating these should be disabled if the signed urls are not used to speed up the request.
include_object_feature_hashes - If None all the objects will be included. Otherwise, only objects labels will be included of which the feature_hash has been added.
include_classification_feature_hashes - If None all the classifications will be included. Otherwise, only classification labels will be included of which the feature_hash has been added.
include_reviews - Whether to request read only information about the reviews of the label row.

Returns:

LabelRow - A label row instance.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
ResourceNotFoundError - If no label exists by the specified label_hash (uid).
UnknownError - If an error occurs while retrieving the label.
OperationNotAllowed - If the read operation is not allowed by the API key.

get_label_rows

@deprecated(version="0.1.123", alternative=".list_label_rows_v2")
def get_label_rows(uids: List[str],
                   get_signed_url: bool = True,
                   *,
                   include_object_feature_hashes: Optional[Set[str]] = None,
                   include_classification_feature_hashes: Optional[
                       Set[str]] = None,
                   include_reviews: bool = False) -> List[LabelRow]

DEPRECATED: Prefer using the list_label_rows_v2 function to interact with label rows.

Retrieve a list of label rows. Duplicates will be dropped. The result will come back in a random order.

Arguments:

uids - A list of label_hash (uid).
get_signed_url - Whether to generate signed urls to the data asset. Generating these should be disabled if the signed urls are not used to speed up the request.
include_object_feature_hashes - If None all the objects will be included. Otherwise, only objects labels will be included of which the feature_hash has been added.
include_classification_feature_hashes - If None all the classifications will be included. Otherwise, only classification labels will be included of which the feature_hash has been added.
include_reviews - Whether to request read only information about the reviews of the label row.

Returns:

List of LabelRow instances.

Raises:

MultiLabelLimitError - If too many labels were requested. Check the error’s maximum_labels_allowed field to read the most up to date error limit.
AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
ResourceNotFoundError - If no label exists by the specified label_hash (uid).
UnknownError - If an error occurs while retrieving the label.
OperationNotAllowed - If the read operation is not allowed by the API key.

save_label_row

@deprecated(version="0.1.123", alternative=".list_label_rows_v2")
def save_label_row(uid, label, validate_before_saving: bool = False)

DEPRECATED: Prefer using the list_label_rows_v2 function to interact with label rows.

Save an existing label row.

If you have a series of frame labels and have not updated answer dictionaries, call the construct_answer_dictionaries utility function to do so prior to saving labels.

Arguments:

uid - A label_hash (uid) string.
label - A label row instance.
validate_before_saving - Enable stricter server-side integrity checks. Boolean, False by default.

Returns:

bool - True if the label row is successfully saved, False otherwise.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
ResourceNotFoundError - If no label exists by the specified label_hash (uid).
UnknownError - If an error occurs while saving the label.
OperationNotAllowed - If the write operation is not allowed by the API key.
AnswerDictionaryError - If an object or classification instance is missing in answer dictionaries.
CorruptedLabelError - If a blurb is corrupted (e.g., if the frame labels have more frames than the video).

create_label_row

@deprecated(version="0.1.123", alternative=".list_label_rows_v2")
def create_label_row(uid: str)

DEPRECATED: Prefer using the list_label_rows_v2 function to interact with label rows.

Create a label row (for data in a project not previously labeled).

Arguments:

uid - The data_hash (uid) of the data unit being labeled. Available in client.get_project().get(‘label_rows’) where label_status is NOT_LABELLED.

Returns:

LabelRow - A label row instance.

Raises:

AuthenticationError - If the project API key is invalid.
AuthorisationError - If access to the specified resource is restricted.
UnknownError - If an error occurs while saving the label.
OperationNotAllowed - If the write operation is not allowed by the API key.
AnswerDictionaryError - If an object or classification instance is missing in answer dictionaries.
CorruptedLabelError - If a blurb is corrupted (e.g., if the frame labels have more frames than the video).
ResourceExistsError - If a label row already exists for this project data. Avoids overriding existing work.

create_bundle

def create_bundle(bundle_size: Optional[int] = None) -> Bundle

Initializes a bundle to reduce the number of network calls performed by the Encord SDK.

See the :class:encord.http.bundle.Bundle documentation for more details.

Arguments:

bundle_size - maximum number of items bundled. If more actions provided to the bundle, they will be automatically split into separate api calls.

Returns:

Bundle - An instance of the Bundle class.

list_collaborator_timers

def list_collaborator_timers(
        after: datetime.datetime,
        before: Optional[datetime.datetime] = None,
        group_by_data_unit: bool = True) -> Iterable[CollaboratorTimer]

Provides information about time spent by each collaborator who has worked on the project within a specified range of dates.

Arguments:

after - The beginning of the period of interest.
before - The end of the period of interest.
group_by_data_unit - If True, time spent by a collaborator for each data unit is provided separately. If False, all time spent in the scope of the project is aggregated together.

Yields:

CollaboratorTimer - Information about the time spent by each collaborator.

list_datasets

def list_datasets() -> Iterable[ProjectDataset]

List all datasets associated with the project.

Returns:

Iterable[ProjectDataset] - An iterable of ProjectDataset instances.

import_coco_labels

def import_coco_labels(
        labels_dict: Dict[str,
                          Any], category_id_to_feature_hash: Dict[CategoryID,
                                                                  str],
        image_id_to_frame_index: Dict[ImageID, FrameIndex]) -> None

Import labels from a COCO format into your Encord project

Arguments:

labels_dict Dict[str, Any] - Raw label dictionary conforming to Encord format
category_id_to_feature_hash Dict[CategoryID, str] - Dictionary mapping category_id as used in the COCO data to the feature hash for the corresponding element in this Ontology
image_id_to_frame_index Dict[ImageID, FrameIndex] - Dictionary mapping int to FrameIndex(data_hash, frame_offset) which is used to identify the corresponding frame in the Encord setting

Was this page helpful?

Dataset Workflow

On this page

Project Objects
project_hash
title
description
created_at
last_edited_at
ontology
ontology_hash
ontology_structure
datasets
project_type
label_rows
refetch_data
refetch_ontology
get_project
workflow
list_label_rows_v2
add_users
list_groups
add_group
remove_group
copy_project
submit_label_row_for_review
add_datasets
remove_datasets
get_project_ontology
add_object
add_classification
list_models
get_training_metadata
create_model_row
model_delete
model_inference
model_train_start
model_train_get_result
object_interpolation
fitted_bounding_boxes
get_data
get_label_logs
get_cloud_integrations
list_label_rows
set_label_status
get_label_row
get_label_rows
save_label_row
create_label_row
create_bundle
list_collaborator_timers
list_datasets
import_coco_labels

Get Started

General

SDK Ref

Index

Ontologies

Projects

Labels

Datasets

DICOM

Active API & SDK

​Project Objects

​project_hash

​title

​description

​created_at

​last_edited_at

​ontology

​ontology_hash

​ontology_structure

​datasets

​project_type

​label_rows

​refetch_data

​refetch_ontology

​get_project

​workflow

​list_label_rows_v2

​add_users

​list_groups

​add_group

​remove_group

​copy_project

​submit_label_row_for_review

​add_datasets

​remove_datasets

​get_project_ontology

​add_object

​add_classification

​list_models

​get_training_metadata

​create_model_row

​model_delete

​model_inference

​model_train_start

​model_train_get_result

​object_interpolation

​fitted_bounding_boxes

​get_data

​get_label_logs

​get_cloud_integrations

​list_label_rows

​set_label_status

​get_label_row

​get_label_rows

​save_label_row

​create_label_row

​create_bundle

​list_collaborator_timers

​list_datasets

​import_coco_labels

Project Objects

project_hash

title

description

created_at

last_edited_at

ontology

ontology_hash

ontology_structure

datasets

project_type

label_rows

refetch_data

refetch_ontology

get_project

workflow

list_label_rows_v2

add_users

list_groups

add_group

remove_group

copy_project

submit_label_row_for_review

add_datasets

remove_datasets

get_project_ontology

add_object

add_classification

list_models

get_training_metadata

create_model_row

model_delete

model_inference

model_train_start

model_train_get_result

object_interpolation

fitted_bounding_boxes

get_data

get_label_logs

get_cloud_integrations

list_label_rows

set_label_status

get_label_row

get_label_rows

save_label_row

create_label_row

create_bundle

list_collaborator_timers

list_datasets

import_coco_labels