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.

user_role

@property
def user_role() -> Optional[ProjectUserRole]

Get the current user’s role in the project.

This may return None if the user is an organisational admin and has accessed the project e.g. using include_org_access=True of :meth:encord.user_client.UserClient.list_projects.

source_projects

@property
def source_projects() -> Optional[List[str]]

Get the source projects for a Training project. Returns None for non-Training projects.

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

@deprecated(version="0.1.154",
            alternative="EncordUserClient.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],
                       branch_name: Optional[str] = None) -> 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
branch_name Optional[str] - Optionally specify a branch name. Defaults to the main branch.

export_coco_labels

def export_coco_labels(
        label_hashes: Optional[List[str]] = None,
        include_object_feature_hashes: Optional[Set[str]] = None,
        include_classification_feature_hashes: Optional[Set[str]] = None,
        branch_name: Optional[str] = None) -> Dict[str, Any]

Export labels from the project to the COCO format. This method requires the ‘coco’ extra to be installed. Install it using: pip install encord[coco].

Arguments:

label_hashes - List of label hashes to include. If not provided, all label rows will be included.
include_object_feature_hashes - If None, all objects will be included. Otherwise, only objects with the specified feature hashes will be included.
include_classification_feature_hashes - If None, all classifications will be included. Otherwise, only classifications with the specified feature hashes will be included.
branch_name - Optionally specify a branch name. Defaults to the main branch.

Returns:

Dict[str, Any]: A dictionary in the COCO format containing the exported labels, including annotations and metadata conforming to COCO standards. The dictionary also includes additional fields specific to Encord, providing supplementary information not defined in the COCO standard.

Raises:

ImportError - If the ‘coco’ extra dependencies are not installed.

list_collections

def list_collections(
        collection_uuids: Optional[List[Union[str, UUID]]] = None,
        page_size: Optional[int] = None) -> Iterator[ProjectCollection]

List all collections associated to the project.

Arguments:

collection_uuids - The unique identifiers (UUIDs) of the collections to retrieve.
page_size int - Number of items to return per page. Default if not specified is 100. Maximum value is 1000.

Returns:

The list of collections which match the given criteria.

Raises:

ValueError - If any of the collection uuids is a badly formed UUID. :class:encord.exceptions.AuthorizationError : If the user does not have access to it.

delete_collection

def delete_collection(collection_uuid: Union[str, UUID]) -> None

Delete a project collection by its UUID if it exists.

Arguments:

collection_uuid - The unique identifier (UUID) of the collection to delete.

Returns:

None

Raises:

ValueError - If collection_uuid is a badly formed UUID. :class:encord.exceptions.AuthorizationError : If the user does not have access to it.

create_collection

def create_collection(
    name: str,
    description: str = "",
    collection_type: ProjectCollectionType = ProjectCollectionType.FRAME
) -> ProjectCollection

Create a project collection.

Arguments:

name - The name of the collection.
description - The description of the collection.
collection_type - The type of the collection, could be either frame or label.

Returns:

ProjectCollection - Newly created collection.

Raises:

:class:encord.exceptions.AuthorizationError : If the user does not have access to the folder.

active_sync

def active_sync() -> None

Sync the associated Active project

active_import

def active_import(project_mode: ActiveProjectMode,
                  *,
                  video_sampling_rate: Optional[float] = None) -> None

Import the associated Active project. Progress in the app

Arguments:

project_mode - Active projects can be imported up to a certain stage. Use the ActiveProjectModeEnum to select the stage
video_sampling_rate - Optional[float]: For videos, what’s the sampling rate of frames for analysis

Returns:

None

list_filter_presets

def list_filter_presets(
        filter_preset_uuids: Optional[List[Union[str, UUID]]] = None,
        page_size: Optional[int] = None) -> Iterator[ProjectFilterPreset]

List all filter presets associated to the project.

Arguments:

filter_preset_uuids - The unique identifiers (UUIDs) of the filter presets to retrieve.
page_size int - Number of items to return per page. Default if not specified is 100. Maximum value is 1000.

Returns:

The list of filter presets which match the given criteria.

Raises:

ValueError - If any of the filter preset uuids is a badly formed UUID. :class:encord.exceptions.AuthorizationError : If the user does not have access to it.

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Dataset Workflow

On this page

Project Objects
project_hash
title
description
created_at
last_edited_at
ontology
ontology_hash
ontology_structure
user_role
source_projects
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
export_coco_labels
list_collections
delete_collection
create_collection
active_sync
active_import
list_filter_presets

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Active API & SDK

​Project Objects

​project_hash

​title

​description

​created_at

​last_edited_at

​ontology

​ontology_hash

​ontology_structure

​user_role

​source_projects

​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

​export_coco_labels

​list_collections

​delete_collection

​create_collection

​active_sync

​active_import

​list_filter_presets

Project Objects

project_hash

title

description

created_at

last_edited_at

ontology

ontology_hash

ontology_structure

user_role

source_projects

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

export_coco_labels

list_collections

delete_collection

create_collection

active_sync

active_import

list_filter_presets