Encord Python SDK API Reference — Encord Python SDK documentation

Datasets / Data / Adding data / Adding data from a private cloud

Datasets / Managing a dataset

get_project(project_hash)[source]#

Get the Project class to access project fields and manipulate a project.

You will only have access to this project if you are one of the following

Project admin

Project team manager

Organisation admin of the project

Parameters: project_hash (str) – The Project ID
Return type: Project

Expand for references to encord.user_client.EncordUserClient.get_project

Authentication / User authentication / Authenticate with Encord - user authentication

Projects / Ontology / Adding components to a project ontology / Adding a classification to an ontology

Projects / Ontology / Adding components to a project ontology / Adding an object to an ontology

Projects / Ontology / Fetching a project’s ontology

Projects / Managing a project

get_ontology(ontology_hash)[source]#

Return type: Ontology

create_private_dataset(dataset_title, dataset_type, dataset_description=None)[source]#

DEPRECATED - please use create_dataset instead.

Return type: CreateDatasetResponse

create_dataset(dataset_title, dataset_type, dataset_description=None)[source]#

Parameters

dataset_title (str) – Title of dataset.
dataset_type (StorageLocation) – StorageLocation type where data will be stored.
dataset_description (Optional[str]) – Optional description of the dataset.

Return type

CreateDatasetResponse

Returns

CreateDatasetResponse

Expand for references to encord.user_client.EncordUserClient.create_dataset

Datasets / API keys / Creating a dataset API key with specific rights

create_dataset_api_key(dataset_hash, api_key_title, dataset_scopes)[source]#

Return type: DatasetAPIKey

Expand for references to encord.user_client.EncordUserClient.create_dataset_api_key

get_dataset_api_keys(dataset_hash)[source]#

Return type: List[DatasetAPIKey]

Expand for references to encord.user_client.EncordUserClient.get_dataset_api_keys

Datasets / API keys / Creating a master API key with full rights

get_or_create_dataset_api_key(dataset_hash)[source]#

Return type: DatasetAPIKey

Expand for references to encord.user_client.EncordUserClient.get_or_create_dataset_api_key

get_datasets(title_eq=None, title_like=None, desc_eq=None, desc_like=None, created_before=None, created_after=None, edited_before=None, edited_after=None)[source]#

List either all (if called with no arguments) or matching datasets the user has access to.

Parameters

title_eq – optional exact title filter
title_like – optional fuzzy title filter; SQL syntax
desc_eq – optional exact description filter
desc_like – optional fuzzy description filter; SQL syntax
created_before – optional creation date filter, ‘less’
created_after – optional creation date filter, ‘greater’
edited_before – optional last modification date filter, ‘less’
edited_after – optional last modification date filter, ‘greater’

Returns

list of (role, dataset) pairs for datasets matching filter conditions.

Expand for references to encord.user_client.EncordUserClient.get_datasets

Datasets / Listing existing datasets

static create_with_ssh_private_key(ssh_private_key=None, password=None, requests_settings=RequestsSettings(max_retries=0, backoff_factor=0.1), **kwargs)[source]#

Return type: EncordUserClient

Expand for references to encord.user_client.EncordUserClient.create_with_ssh_private_key

Authentication / User authentication / Authenticate with Encord - user authentication

Datasets / Data / Adding data / Adding data from a private cloud

Datasets / API keys / Creating a dataset API key with specific rights

Datasets / API keys / Creating a master API key with full rights

Datasets / Listing existing datasets

Datasets / Managing a dataset

Projects / Ontology / Adding components to a project ontology / Adding a classification to an ontology

Projects / Ontology / Adding components to a project ontology / Adding an object to an ontology

Projects / API keys / Creating a project API key / Creating a master API key with full rights

Projects / Creating a Project

Projects / API keys / Creating a project API key / Creating a project API key with specific rights

Projects / Ontology / Fetching a project’s ontology

Projects / Other Resources / CVAT Integration / Importing with the Encord SDK

Projects / Listing existing projects

Projects / Managing a project

get_projects(title_eq=None, title_like=None, desc_eq=None, desc_like=None, created_before=None, created_after=None, edited_before=None, edited_after=None)[source]#

List either all (if called with no arguments) or matching projects the user has access to.

Parameters

title_eq – optional exact title filter
title_like – optional fuzzy title filter; SQL syntax
desc_eq – optional exact description filter
desc_like – optional fuzzy description filter; SQL syntax
created_before – optional creation date filter, ‘less’
created_after – optional creation date filter, ‘greater’
edited_before – optional last modification date filter, ‘less’
edited_after – optional last modification date filter, ‘greater’

Returns

list of (role, projects) pairs for project matching filter conditions.

Expand for references to encord.user_client.EncordUserClient.get_projects

Projects / Listing existing projects

create_project(project_title, dataset_hashes, project_description='', ontology_hash='', workflow_settings=<encord.orm.project.ManualReviewWorkflowSettings object>)[source]#

Creates a new project and returns its uid (‘project_hash’)

Parameters

project_title (str) – the title of the project
dataset_hashes (List[str]) – a list of the dataset uids that the project will use
project_description (str) – the optional description of the project
ontology_hash (str) – the uid of an ontology to be used. If omitted, a new empty ontology will be created
workflow_settings (Union[ManualReviewWorkflowSettings, BenchmarkQaWorkflowSettings]) – selects and configures the type of the quality control workflow to use, See encord.orm.project.ProjectWorkflowSettings for details. If omitted, ManualReviewWorkflowSettings is used.

Return type

str

Returns

the uid of the project.

Expand for references to encord.user_client.EncordUserClient.create_project

Projects / Creating a Project

create_project_api_key(project_hash, api_key_title, scopes)[source]#

Return type: str
Returns: The created project API key.

Expand for references to encord.user_client.EncordUserClient.create_project_api_key

Projects / API keys / Creating a project API key / Creating a project API key with specific rights

get_project_api_keys(project_hash)[source]#

Return type: List[ProjectAPIKey]

Expand for references to encord.user_client.EncordUserClient.get_project_api_keys

Projects / API keys / Creating a project API key / Creating a master API key with full rights

get_or_create_project_api_key(project_hash)[source]#

Return type: str

Expand for references to encord.user_client.EncordUserClient.get_or_create_project_api_key

get_dataset_client(dataset_hash, dataset_access_settings=DatasetAccessSettings(fetch_client_metadata=False), **kwargs)[source]#

DEPRECATED - prefer using get_dataset() instead.

Return type: EncordClientDataset

Expand for references to encord.user_client.EncordUserClient.get_dataset_client

Projects / Other Resources / CVAT Integration / Importing with the Encord SDK

get_project_client(project_hash, **kwargs)[source]#

DEPRECATED - prefer using get_project() instead.

Return type: Union[EncordClientProject, EncordClientDataset]

create_project_from_cvat(import_method, dataset_name, review_mode=ReviewMode.LABELLED, max_workers=None, *, transform_bounding_boxes_to_polygons=False)[source]#

Export your CVAT project with the “CVAT for images 1.1” option and use this function to import: your images and annotations into encord. Ensure that during you have the “Save images” checkbox enabled when exporting from CVAT.

Parameters

import_method (LocalImport) – The chosen import method. See the ImportMethod class for details.
dataset_name (str) – The name of the dataset that will be created.
review_mode (ReviewMode) –

Set how much interaction is needed from the labeler and from the reviewer for the CVAT labels.
See the ReviewMode documentation for more details.
max_workers (Optional[int]) – DEPRECATED: This argument will be ignored
transform_bounding_boxes_to_polygons – All instances of CVAT bounding boxes will be converted to polygons in the final Encord project.

Returns

If the project was successfully imported. CvatImporterError: If the project could not be imported.

Return type

CvatImporterSuccess

Raises

ValueError – If the CVAT directory has an invalid format.

Expand for references to encord.user_client.EncordUserClient.create_project_from_cvat

get_cloud_integrations()[source]#

Return type: List[CloudIntegration]

Expand for references to encord.user_client.EncordUserClient.get_cloud_integrations

Datasets / Data / Adding data / Adding data from a private cloud

get_ontologies(title_eq=None, title_like=None, desc_eq=None, desc_like=None, created_before=None, created_after=None, edited_before=None, edited_after=None)[source]#

List either all (if called with no arguments) or matching ontologies the user has access to.

Parameters

title_eq – optional exact title filter
title_like – optional fuzzy title filter; SQL syntax
desc_eq – optional exact description filter
desc_like – optional fuzzy description filter; SQL syntax
created_before – optional creation date filter, ‘less’
created_after – optional creation date filter, ‘greater’
edited_before – optional last modification date filter, ‘less’
edited_after – optional last modification date filter, ‘greater’

Returns

list of (role, projects) pairs for ontologies matching filter conditions.

create_ontology(title, description='', structure=None)[source]#

Return type: Ontology

deidentify_dicom_files(dicom_urls, integration_hash)[source]#

Deidentify DICOM files in external storage. Given links to DICOM files pointing to AWS, GCP, AZURE or OTC, for example: [ “https://s3.region-code.amazonaws.com/bucket-name/dicom-file-input.dcm” ] Function executes deidentification on those files, it removes all DICOM tags (https://dicom.nema.org/medical/Dicom/2017e/output/chtml/part06/chapter_6.html) from metadata except for:

x00080018 SOPInstanceUID
x00100010 PatientName
x00180050 SliceThickness
x00180088 SpacingBetweenSlices
x0020000d StudyInstanceUID
x0020000e SeriesInstanceUID
x00200032 ImagePositionPatient
x00200037 ImageOrientationPatient
x00280008 NumberOfFrames
x00281050 WindowCenter
x00281051 WindowWidth
x00520014 ALinePixelSpacing

Parameters

self – Encord client object.
dicom_urls (List[str]) – a list of urls to DICOM files, e.g. [ “https://s3.region-code.amazonaws.com/bucket-name/dicom-file-input.dcm” ]
integration_hash (str) – integration_hash parameter of Encord platform external storage integration

Return type

List[str]

Returns

Function returns list of links pointing to deidentified DICOM files, those will be saved to the same bucket and the same directory as original files with prefix ( deid_{timestamp}_ ). Example output: [ “https://s3.region-code.amazonaws.com/bucket-name/deid_167294769118005312_dicom-file-input.dcm” ]

class encord.user_client.ListingFilter(value)[source]#

Available properties_filter keys for get_projects() and get_datasets().

The values for _before and _after should be datetime objects.

TITLE_EQ = 'title_eq'#

TITLE_LIKE = 'title_like'#

DESC_EQ = 'desc_eq'#

DESC_LIKE = 'desc_like'#

CREATED_BEFORE = 'created_before'#

CREATED_AFTER = 'created_after'#

EDITED_BEFORE = 'edited_before'#

EDITED_AFTER = 'edited_after'#

encord.user_client.CordUserClient#: alias of encord.user_client.EncordUserClient

Project#

class encord.project.Project(client)[source]#

Access project related data and manipulate the project.

Expand for references to encord.project.Project

Authentication / User authentication / Authenticate with Encord - user authentication

Projects / Ontology / Adding components to a project ontology / Adding a classification to an ontology

Projects / Ontology / Adding components to a project ontology / Adding an object to an ontology

Projects / Ontology / Fetching a project’s ontology

Projects / Managing a project

property project_hash: str#

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

Return type: str

property title: str#

Get the title of the project.

Return type: str

property description: str#

Get the description of the project.

Return type: str

property created_at: datetime.datetime#

Get the time the project was created at.

Return type: datetime

property last_edited_at: datetime.datetime#

Get the time the project was last edited at.

Return type: datetime

property ontology: dict#

Get the ontology of the project.

BETA: Prefer using the encord.objects.ontology_structure.OntologyStructure class to work with the data.

from encord.objects.ontology_structure import OntologyStructure

project = user_client.get_project("<project_hash>")

ontology = OntologyStructure.from_dict(project.ontology)

Return type: dict

property datasets: list#

Get the associated datasets.

Prefer using the encord.objects.project.ProjectDataset() class to work with the data.

from encord.objects.project import ProjectDataset

project = user_client.get_project("<project_hash>")

project_datasets = ProjectDataset.from_list(project.datasets)

Return type: list

property label_rows: dict#

Get the label rows.

Prefer using the encord.orm.label_row.LabelRowMetadata() class to work with the data.

from encord.orm.label_row import LabelRowMetadata

project = user_client.get_project("<project_hash>")

label_rows = LabelRowMetadata.from_list(project.label_rows)

Return type: dict

Expand for references to encord.project.Project.label_rows

refetch_data()[source]#

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

Return type: None

get_project()[source]#

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

Return type: Project

list_label_rows(edited_before=None, edited_after=None, label_statuses=None, shadow_data_state=None)[source]#

Parameters

self – Encord client object.
edited_before (Union[str, datetime, None]) – Optionally filter to only rows last edited before the specified time
edited_after (Union[str, datetime, None]) – Optionally filter to only rows last edited after the specified time
label_statuses (Optional[List[AnnotationTaskStatus]]) – Optionally filter to only those label rows that have one of the specified :class:`~encord.orm.label_row.AnnotationTaskStatus`es
shadow_data_state (Optional[ShadowDataState]) – On Optionally filter by data type in Benchmark QA projects. See ShadowDataState

Return type

List[LabelRowMetadata]

Returns

A list of LabelRowMetadata instances for all the matching label rows

Raises

AuthorisationError – If not a member of the project.
UnknownError – If an error occurs while retrieving the data.

set_label_status(label_hash, label_status)[source]#

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

Parameters

self – Encord client object.
label_hash (str) – unique identifier of the label row whose status is to be updated.
label_status (LabelStatus) – the new status that needs to be set.

Return type

bool

Returns

Bool.

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.

add_users(user_emails, user_role)[source]#

Add users to project

Parameters

user_emails (List[str]) – list of user emails to be added
user_role (ProjectUserRole) – the user role to assign to all users

Return type

List[ProjectUser]

Returns

ProjectUser

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

Expand for references to encord.project.Project.add_users

copy_project(copy_datasets=False, copy_collaborators=False, copy_models=False)[source]#

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

Parameters

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 – currently if True, all models with their training information will be copied into the new project

Return type

str

Returns

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.

Expand for references to encord.project.Project.copy_project

get_label_row(uid, get_signed_url=True, *, include_object_feature_hashes=None, include_classification_feature_hashes=None, include_reviews=False)[source]#

Retrieve label row. If you need to retrieved multiple label rows, prefer using encord.project.Project.get_label_rows() instead.

A code example using the include_object_feature_hashes and include_classification_feature_hashes filters can be found in encord.project.Project.get_label_row().

Parameters

uid (str) – A label_hash (uid) string.
get_signed_url (bool) – 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 (Optional[Set[str]]) – 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 (Optional[Set[str]]) – 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 (bool) – Whether to request read only information about the reviews of the label row.

Returns

A label row instance.

Return type

LabelRow

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.

Expand for references to encord.project.Project.get_label_row

get_label_rows(uids, get_signed_url=True, *, include_object_feature_hashes=None, include_classification_feature_hashes=None, include_reviews=False)[source]#

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

This return is undefined behaviour if any of the uids are invalid (i.e. it may randomly fail or randomly succeed and should not be relied upon).

# Code example of using the object filters.
from encord.objects.common import Shape
from encord.objects.ontology_structure import OntologyStructure

project = ... # assuming you already have instantiated this Project object

# Get all feature hashes of the objects which are of type `Shape.BOUNDING_BOX`
ontology = OntologyStructure.from_dict(project.ontology)
only_bounding_box_feature_hashes = set()
for object_ in ontology.objects:
    if object_.shape == Shape.BOUNDING_BOX:
        only_bounding_box_feature_hashes.add(object_.feature_node_hash)

no_classification_feature_hashes = set()  # deliberately left empty

# Get all labels of tasks that have already been initiated.
# Include only labels of bounding boxes and exclude all
# classifications
label_hashes = []
for label_row in project.label_rows:
    # Trying to run `get_label_row` on a label_row without a `label_hash` would fail.
    if label_row["label_hash"] is not None:
        label_hashes.append(label_row["label_hash"])

all_labels = project.get_label_rows(
    label_hashes,
    include_object_feature_hashes=only_bounding_box_feature_hashes,
    include_classification_feature_hashes=no_classification_feature_hashes,
)

Parameters

uids (List[str]) – A list of label_hash (uid).
get_signed_url (bool) –

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.

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.

Return type

List[LabelRow]

Expand for references to encord.project.Project.get_label_rows

save_label_row(uid, label)[source]#

Save existing label row.

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

Parameters

uid – A label_hash (uid) string.
label – A label row instance.

Returns

Bool.

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(uid)[source]#

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

Parameters

uid (str) – 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

A label row instance.

Return type

LabelRow

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.

submit_label_row_for_review(uid)[source]#

Submit a label row for review.

Parameters

uid (str) – A label_hash (uid) string.

Returns

Bool.

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(dataset_hashes)[source]#

Add a dataset to a project

Parameters

dataset_hashes (List[str]) – List of dataset hashes of the datasets to be added

Return type

bool

Returns

Bool.

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.

Expand for references to encord.project.Project.add_datasets

remove_datasets(dataset_hashes)[source]#

Remove datasets from project

Parameters

dataset_hashes (List[str]) – List of dataset hashes of the datasets to be removed

Return type

bool

Returns

Bool.

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.

Expand for references to encord.project.Project.remove_datasets

Projects / Ontology / Fetching a project’s ontology

get_project_ontology()[source]#

DEPRECATED - prefer using the ontology property accessor instead.

Return type: Ontology

Expand for references to encord.project.Project.get_project_ontology

add_object(name, shape)[source]#

Add object to an ontology.

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

Parameters

name (str) – the name of the object
shape (ObjectShape) – the shape of the object. (BOUNDING_BOX, POLYGON, POLYLINE or KEY_POINT)

Return type

bool

Returns

True if the object was added successfully and 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 add te 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

Expand for references to encord.project.Project.add_object

Projects / Ontology / Adding components to a project ontology / Adding an object to an ontology

add_classification(name, classification_type, required, options=None)[source]#

Add classification to an ontology.

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

Parameters

name (str) – the name of the classification
classification_type (ClassificationType) – the classification type (RADIO, TEXT or CHECKLIST)
required (whether this classification is required by the annotator) –
options (Optional[Iterable[str]]) – 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 add te 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

Expand for references to encord.project.Project.add_classification

Projects / Ontology / Adding components to a project ontology / Adding a classification to an ontology

list_models()[source]#

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

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,
)

Return type: List[ModelConfiguration]

get_training_metadata(model_iteration_uids, get_created_at=False, get_training_final_loss=False, get_model_training_labels=False)[source]#

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

Parameters

model_iteration_uids (Iterable[str]) – The model iteration uids
get_created_at (bool) – Whether the created_at field should be retrieved.
get_training_final_loss (bool) – Whether the training_final_loss field should be retrieved.
get_model_training_labels (bool) – Whether the model_training_labels field should be retrieved.

Return type

List[TrainingMetadata]

create_model_row(title, description, features, model)[source]#

Create a model row.

Parameters

title (str) – Model title.
description (str) – Model description.
features (List[str]) – List of <feature_node_hashes> which is id’s of ontology objects or classifications to be included in the model.
model (Union[AutomationModels, str]) – the model type to be used. For backwards compatibility purposes, we continuously allow strings corresponding to the values of the AutomationModels Enum.

Return type

str

Returns

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 than what is supported by the model (e.g. if creating a classification model using a bounding box).

model_delete(uid)[source]#

Delete a model created on the platform.

Parameters

uid (str) – A model_hash (uid) string.

Return type

bool

Returns

bool

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 training.

model_inference(uid, file_paths=None, base64_strings=None, conf_thresh=0.6, iou_thresh=0.3, device=Device.CUDA, detection_frame_range=None, allocation_enabled=False, data_hashes=None, rdp_thresh=0.005)[source]#

Run inference with 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.

Parameters

uid (str) – A model_iteration_hash (uid) string.
file_paths (Optional[List[str]]) – List of local file paths to image(s) or video(s) - if running inference on files.
base64_strings (Optional[List[bytes]]) – List of base 64 strings of image(s) or video(s) - if running inference on base64 strings.
conf_thresh (float) – Confidence threshold (default 0.6).
iou_thresh (float) – Intersection over union threshold (default 0.3).
device (Device) – Device (CPU or CUDA, default is CUDA).
detection_frame_range (Optional[List[int]]) – Detection frame range (for videos).
allocation_enabled (bool) – Object UID allocation (tracking) enabled (disabled by default).
data_hashes (Optional[List[str]]) – list of hash of the videos/image_groups you’d like to run inference on.
rdp_thresh (float) – parameter specifying the polygon coarseness to be used while running inference. The higher the value, the less points in the segmented image

Returns

A dict of inference results.

Return type

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(uid, label_rows=None, epochs=None, batch_size=24, weights=None, device=Device.CUDA)[source]#

Train a model created on the platform.

Parameters

uid (str) – A model_hash (uid) string.
label_rows (Optional[List[str]]) – List of label row uid’s (hashes) for training.
epochs (Optional[int]) – Number of passes through training dataset.
batch_size (int) – Number of training examples utilized in one iteration.
weights (Optional[ModelTrainingWeights]) – Model weights.
device (Device) – Device (CPU or CUDA, default is CUDA).

Returns

A model iteration object.

Raises

AuthenticationError – If the project API key is invalid.
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).
UnknownError – If an error occurs during training.

object_interpolation(key_frames, objects_to_interpolate)[source]#

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

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

Parameters

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 uid’s (hashes) of objects to interpolate.

Returns

Full set of filled frames including interpolated objects.

Return type

Interpolation results

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(frames, video)[source]#

Parameters

frames (dict) –

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 (dict) –
Metadata of the video for which bounding box fitting needs to be run:
```
{
    "width" : w,
    "height" : h,
}
```

Returns

Full set of filled frames including fitted objects.

Return type

Fitting results

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(data_hash, get_signed_url=False)[source]#

Retrieve information about a video or image group.

Parameters

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

Return type

Tuple[Optional[Video], Optional[List[Image]]]

Returns

A 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_websocket_url()[source]#

Return type: str

get_label_logs(user_hash=None, data_hash=None, from_unix_seconds=None, to_unix_seconds=None)[source]#

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.

Parameters

user_hash (Optional[str]) – Filter the label logs by the user.
data_hash (Optional[str]) – Filter the label logs by the data_hash.
from_unix_seconds (Optional[int]) – Filter the label logs to only include labels after this timestamp.
from_unix_seconds – Filter the label logs to only include labels before this timestamp.

Return type

List[LabelLog]

get_cloud_integrations()[source]#

Return type: List[CloudIntegration]

Dataset#

class encord.dataset.Dataset(client)[source]#

Access dataset related data and manipulate the dataset.

Expand for references to encord.dataset.Dataset

Authentication / User authentication / Authenticate with Encord - user authentication

Datasets / Data / Adding data / Adding data from a private cloud

Datasets / Managing a dataset

property dataset_hash: str#

Get the dataset hash (i.e. the Dataset ID).

Return type: str

property title: str#

Return type: str

property description: str#

Return type: str

property storage_location: encord.orm.dataset.StorageLocation#

Return type: StorageLocation

property data_rows: List[encord.orm.dataset.DataRow]#

Part of the response of this function can be configured by the encord.dataset.Dataset.set_access_settings() method.

dataset.set_access_settings(DatasetAccessSettings(fetch_client_metadata=True))
print(dataset.data_rows)

Return type: List[DataRow]

refetch_data()[source]#

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

Return type: None

get_dataset()[source]#

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

Return type: Dataset

set_access_settings(dataset_access_settings, *, refetch_data=True)[source]#

Parameters

dataset_access_settings (DatasetAccessSettings) – The access settings to use going forward
refetch_data (bool) – Whether a refetch_data() call should follow the update of the dataset access settings.

Return type

None

add_users(user_emails, user_role)[source]#

Add users to dataset. If the user was already added, this operation will succeed but the user_role will be unchanged. The existing user_role will be reflected in the DatasetUser instance.

Parameters

user_emails (List[str]) – list of user emails to be added
user_role (DatasetUserRole) – the user role to assign to all users

Return type

List[DatasetUser]

upload_video(file_path, cloud_upload_settings=CloudUploadSettings(max_retries=None, backoff_factor=None, allow_failures=False), title=None)[source]#

Upload video to Encord storage.

Parameters

file_path (str) – path to video e.g. ‘/home/user/data/video.mp4’
cloud_upload_settings (CloudUploadSettings) – Settings for uploading data into the cloud. Change this object to overwrite the default values.
title (Optional[str]) – The video title. If unspecified, this will be the file name. This title should include an extension. For example “encord_video.mp4”.

Returns

Bool.

Raises

UploadOperationNotSupportedError – If trying to upload to external datasets (e.g. S3/GPC/Azure)

create_image_group(file_paths, max_workers=None, cloud_upload_settings=CloudUploadSettings(max_retries=None, backoff_factor=None, allow_failures=False), title=None, *, create_video=True)[source]#

Create an image group in Encord storage. Choose this type of image upload for sequential images. Else, you can choose the Dataset.upload_image() function.

Parameters

file_paths (Iterable[str]) – a list of paths to images, e.g. [‘/home/user/data/img1.png’, ‘/home/user/data/img2.png’]
max_workers (Optional[int]) – DEPRECATED: This argument will be ignored
cloud_upload_settings (CloudUploadSettings) – Settings for uploading data into the cloud. Change this object to overwrite the default values.
title (Optional[str]) – The title of the image group. If unspecified this will be randomly generated for you. This title should NOT include an extension. For example “encord_image_group”.
create_video (bool) – A flag specifying how image groups are stored. If True, a compressed video will be created from the image groups. True was the previous default support. If False, the images are saved as a sequence of images.

Returns

Bool.

Raises

UploadOperationNotSupportedError – If trying to upload to external datasets (e.g. S3/GPC/Azure)

create_dicom_series(file_paths, cloud_upload_settings=CloudUploadSettings(max_retries=None, backoff_factor=None, allow_failures=False), title=None)[source]#

Upload a DICOM series to Encord storage

Parameters

file_paths (List[str]) – a list of paths to DICOM files, e.g. [‘/home/user/data/DICOM_1.dcm’, ‘/home/user/data/DICOM_2.dcm’]
cloud_upload_settings (CloudUploadSettings) – Settings for uploading data into the cloud. Change this object to overwrite the default values.
title (Optional[str]) – The title of the DICOM series. If unspecified this will be randomly generated for you. This title should NOT include an extension. For example “encord_image_group”.

Returns

Bool.

Raises

UploadOperationNotSupportedError – If trying to upload to external datasets (e.g. S3/GPC/Azure)

upload_image(file_path, title=None, cloud_upload_settings=CloudUploadSettings(max_retries=None, backoff_factor=None, allow_failures=False))[source]#

Upload a single image to Encord storage. If your images are sequential we recommend creating an image group via the Dataset.create_image_group() function. For more information please compare https://docs.encord.com/docs/annotate/editor/images and https://docs.encord.com/docs/annotate/editor/videos

Parameters

file_path (Union[Path, str]) – The file path to the image
title (Optional[str]) – The image title. If unspecified, this will be the file name. This title should include an extension. For example “encord_image.png”.
cloud_upload_settings (CloudUploadSettings) – Settings for uploading data into the cloud. Change this object to overwrite the default values.

Return type

Image

delete_image_group(data_hash)[source]#

Delete an image group in Encord storage.

Parameters: data_hash (str) – the hash of the image group to delete

delete_data(data_hashes)[source]#

Delete a video/image group from a dataset.

Parameters: data_hashes (List[str]) – list of hash of the videos/image_groups you’d like to delete, all should belong to the same dataset

add_private_data_to_dataset(integration_id, private_files, ignore_errors=False)[source]#

Append data hosted on private clouds to existing dataset.

For a more complete example of safe uploads, please follow the guide found in our docs under https://python.docs.encord.com/tutorials/datasets.html#adding-data-from-a-private-cloud

Parameters

integration_id (str) – str EntityId of the cloud integration to be used when accessing those files
private_files (Union[str, Dict, Path, TextIO]) – A str path or Path object to a json file, json str or python dictionary of the files you wish to add
ignore_errors (bool) – bool, optional Ignore individual errors when trying to access the specified files

Return type

AddPrivateDataResponse

Returns

add_private_data_response List of DatasetDataInfo objects containing data_hash and title

Expand for references to encord.dataset.Dataset.add_private_data_to_dataset

Datasets / Data / Adding data / Adding data from a private cloud

update_data_item(data_hash, new_title)[source]#

Update a data item

Parameters

data_hash (str) – str Data hash of the item being updated
new_title (str) – String containing the new title of the data item being updated

Return type

bool

Returns

Returns a boolean for whether the update was successful

re_encode_data(data_hashes)[source]#

Launches an async task that can re-encode a list of videos.

Parameters: data_hashes (List[str]) – list of hash of the videos you’d like to re_encode, all should belong to the same dataset
Returns: EntityId(integer) of the async task launched.

re_encode_data_status(job_id)[source]#

Returns the status of an existing async task which is aimed at re-encoding videos.

Parameters

job_id (int) – id of the async task that was launched to re-encode the videos

Returns

Object containing the status of the task, along with info about the new encoded videos: in case the task has been completed

Return type

ReEncodeVideoTask

run_ocr(image_group_id)[source]#

Returns an optical character recognition result for a given image group :type image_group_id: str :param image_group_id: the id of the image group in this dataset to run OCR on

Return type: List[ImageGroupOCR]
Returns: Returns a list of ImageGroupOCR objects representing the text and corresponding coordinates found in each frame of the image group

get_cloud_integrations()[source]#

Return type: List[CloudIntegration]

Expand for references to encord.dataset.Dataset.get_cloud_integrations

Datasets / Data / Adding data / Adding data from a private cloud

Ontology#

class encord.ontology.Ontology(querier, config, instance=None)[source]#

Access ontology related data and manipulate the ontology.

property ontology_hash: str#

Get the ontology hash (i.e. the Ontology ID).

Return type: str

property title: str#

Get the title of the ontology.

Return type: str

property description: str#

Get the description of the ontology.

Return type: str

property created_at: datetime.datetime#

Get the time the ontology was created at.

Return type: datetime

property last_edited_at: datetime.datetime#

Get the time the ontology was last edited at.

Return type: datetime

property structure: encord.objects.ontology_structure.OntologyStructure#

Get the structure of the ontology.

Return type: OntologyStructure

refetch_data()[source]#

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

Return type: None

save()[source]#

Sync local state to the server, if updates are made to structure, title or description fields

Return type: None

Client#

encord.client provides a simple Python client that allows you to query project resources through the Encord API.

Here is a simple example for instantiating the client for a project and obtaining project info:

Expand for references to encord.client

Authentication / Resource authentication / Authenticate with Encord - resource authentication

Authentication / Resource authentication / Authenticate with Encord - resource authentication

class encord.client.EncordClient(querier, config)[source]#

Encord client. Allows you to query db items associated with a project (e.g. label rows, datasets).

Expand for references to encord.client.EncordClient

static initialise(resource_id=None, api_key=None, domain='https://api.encord.com', requests_settings=RequestsSettings(max_retries=0, backoff_factor=0.1))[source]#

Create and initialize a Encord client from a resource EntityId and API key.

Parameters

resource_id (Optional[str]) –
either of the following
- A <project_hash>. If None, uses the ENCORD_PROJECT_ID environment variable. The CORD_PROJECT_ID environment variable is supported for backwards compatibility.
- A <dataset_hash>. If None, uses the ENCORD_DATASET_ID environment variable. The CORD_DATASET_ID environment variable is supported for backwards compatibility.
api_key (Optional[str]) – An API key. If None, uses the ENCORD_API_KEY environment variable. The CORD_API_KEY environment variable is supported for backwards compatibility.
domain (str) – The encord api-server domain. If None, the encord.configs.ENCORD_DOMAIN is used
requests_settings (RequestsSettings) – The RequestsSettings from this config

Returns

A Encord client instance.

Return type

EncordClient

Expand for references to encord.client.EncordClient.initialise

Authentication / Resource authentication / Authenticate with Encord - resource authentication

static initialise_with_config(config)[source]#

Create and initialize a Encord client from a Encord config instance.

Parameters: config (ApiKeyConfig) – A Encord config instance.
Returns: A Encord client instance.
Return type: EncordClient

get_cloud_integrations()[source]#

Return type: List[CloudIntegration]

encord.client.CordClient#: alias of encord.client.EncordClient

class encord.client.EncordClientDataset(querier, config, dataset_access_settings=DatasetAccessSettings(fetch_client_metadata=False))[source]#

DEPRECATED - prefer using the encord.dataset.Dataset instead

Expand for references to encord.client.EncordClientDataset

Authentication / Resource authentication / Authenticate with Encord - resource authentication

Authentication / Resource authentication / Authenticate with Encord - resource authentication

static initialise(resource_id=None, api_key=None, domain='https://api.encord.com', requests_settings=RequestsSettings(max_retries=0, backoff_factor=0.1), dataset_access_settings=DatasetAccessSettings(fetch_client_metadata=False))[source]#

Create and initialize a Encord client from a resource EntityId and API key.

Parameters

resource_id (Optional[str]) –
either of the following
- A <project_hash>. If None, uses the ENCORD_PROJECT_ID environment variable. The CORD_PROJECT_ID environment variable is supported for backwards compatibility.
- A <dataset_hash>. If None, uses the ENCORD_DATASET_ID environment variable. The CORD_DATASET_ID environment variable is supported for backwards compatibility.
api_key (Optional[str]) – An API key. If None, uses the ENCORD_API_KEY environment variable. The CORD_API_KEY environment variable is supported for backwards compatibility.
domain (str) – The encord api-server domain. If None, the encord.configs.ENCORD_DOMAIN is used
requests_settings (RequestsSettings) – The RequestsSettings from this config
dataset_access_settings (DatasetAccessSettings) – Change the default encord.orm.dataset.DatasetAccessSettings.

Returns

A Encord client dataset instance.

Return type

EncordClientDataset

static initialise_with_config(config, dataset_access_settings=DatasetAccessSettings(fetch_client_metadata=False))[source]#

Create and initialize a Encord client from a Encord config instance.

Parameters

config (ApiKeyConfig) – A Encord config instance.
dataset_access_settings (DatasetAccessSettings) – Set the dataset_access_settings if you would like to change the defaults.

Returns

An Encord client dataset instance.

Return type

EncordClientDataset

get_dataset()[source]#

Retrieve dataset info (pointers to data, labels).

Returns

A dataset record instance.

Return type

OrmDataset

Raises

AuthorisationError – If the dataset API key is invalid.
ResourceNotFoundError – If no dataset exists by the specified dataset EntityId.
UnknownError – If an error occurs while retrieving the dataset.

set_access_settings(dataset_access_settings=<class 'encord.orm.dataset.DatasetAccessSettings'>)[source]#

Return type: None

add_users(user_emails, user_role)[source]#

This function is documented in encord.project.Dataset.add_users().

Return type: List[DatasetUser]

upload_video(file_path, cloud_upload_settings=CloudUploadSettings(max_retries=None, backoff_factor=None, allow_failures=False), title=None)[source]#: This function is documented in encord.dataset.Dataset.upload_video().

create_image_group(file_paths, max_workers=None, cloud_upload_settings=CloudUploadSettings(max_retries=None, backoff_factor=None, allow_failures=False), title=None, *, create_video=True)[source]#: This function is documented in encord.dataset.Dataset.create_image_group().

create_dicom_series(file_paths, title=None, cloud_upload_settings=CloudUploadSettings(max_retries=None, backoff_factor=None, allow_failures=False))[source]#: This function is documented in encord.dataset.Dataset.create_dicom_series().

upload_image(file_path, title=None, cloud_upload_settings=CloudUploadSettings(max_retries=None, backoff_factor=None, allow_failures=False))[source]#

This function is documented in encord.dataset.Dataset.upload_image().

Return type: Image

delete_image_group(data_hash)[source]#: This function is documented in encord.dataset.Dataset.delete_image_group().

delete_data(data_hashes)[source]#: This function is documented in encord.dataset.Dataset.delete_data().

add_private_data_to_dataset(integration_id, private_files, ignore_errors=False)[source]#

This function is documented in encord.dataset.Dataset.AddPrivateDataResponse().

Return type: AddPrivateDataResponse

update_data_item(data_hash, new_title)[source]#

This function is documented in encord.dataset.Dataset.update_data_item().

Return type: bool

re_encode_data(data_hashes)[source]#: This function is documented in encord.dataset.Dataset.re_encode_data().

re_encode_data_status(job_id)[source]#: This function is documented in encord.dataset.Dataset.re_encode_data_status().

run_ocr(image_group_id)[source]#

This function is documented in encord.dataset.Dataset.run_ocr().

Return type: List[ImageGroupOCR]

encord.client.CordClientDataset#: alias of encord.client.EncordClientDataset

class encord.client.EncordClientProject(querier, config)[source]#

DEPRECATED - prefer using the encord.project.Project instead

Expand for references to encord.client.EncordClientProject

get_project()[source]#

Retrieve project info (pointers to data, labels).

Returns

A project record instance.

Return type

OrmProject

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 retrieving the project.

list_label_rows(edited_before=None, edited_after=None, label_statuses=None, shadow_data_state=None)[source]#

Parameters

edited_before (Union[str, datetime, None]) – Optionally filter to only rows last edited before the specified time
edited_after (Union[str, datetime, None]) – Optionally filter to only rows last edited after the specified time
label_statuses (Optional[List[AnnotationTaskStatus]]) – Optionally filter to only those label rows that have one of the specified :class:`~encord.orm.label_row.AnnotationTaskStatus`es
shadow_data_state (Optional[ShadowDataState]) – On Optionally filter by data type in Benchmark QA projects. See ShadowDataState

Return type

List[LabelRowMetadata]

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(label_hash, label_status)[source]#

This function is documented in encord.project.Project.set_label_status().

Return type: bool

add_users(user_emails, user_role)[source]#

This function is documented in encord.project.Project.add_users().

Return type: List[ProjectUser]

copy_project(copy_datasets=False, copy_collaborators=False, copy_models=False)[source]#

This function is documented in encord.project.Project.copy_project().

Return type: str

get_label_row(uid, get_signed_url=True, *, include_object_feature_hashes=None, include_classification_feature_hashes=None, include_reviews=False)[source]#

This function is documented in encord.project.Project.get_label_row().

Return type: LabelRow

get_label_rows(uids, get_signed_url=True, *, include_object_feature_hashes=None, include_classification_feature_hashes=None, include_reviews=False)[source]#

This function is documented in encord.project.Project.get_label_rows().

Return type: List[LabelRow]

save_label_row(uid, label)[source]#: This function is documented in encord.project.Project.save_label_row().

create_label_row(uid)[source]#: This function is documented in encord.project.Project.create_label_row().

submit_label_row_for_review(uid)[source]#: This function is documented in encord.project.Project.submit_label_row_for_review().

add_datasets(dataset_hashes)[source]#

This function is documented in encord.project.Project.add_datasets().

Return type: bool

remove_datasets(dataset_hashes)[source]#

This function is documented in encord.project.Project.remove_datasets().

Return type: bool

get_project_ontology()[source]#

Return type: Ontology

add_object(name, shape)[source]#

This function is documented in encord.project.Project.add_object().

Return type: bool

add_classification(name, classification_type, required, options=None)[source]#: This function is documented in encord.project.Project.add_classification().

list_models()[source]#

This function is documented in encord.project.Project.list_models().

Return type: List[ModelConfiguration]

get_training_metadata(model_iteration_uids, get_created_at=False, get_training_final_loss=False, get_model_training_labels=False)[source]#

This function is documented in encord.project.Project.get_training_metadata().

Return type: List[TrainingMetadata]

create_model_row(title, description, features, model)[source]#

This function is documented in encord.project.Project.create_model_row().

Return type: str

model_delete(uid)[source]#

This function is documented in encord.project.Project.model_delete().

Return type: bool

model_inference(uid, file_paths=None, base64_strings=None, conf_thresh=0.6, iou_thresh=0.3, device=Device.CUDA, detection_frame_range=None, allocation_enabled=False, data_hashes=None, rdp_thresh=0.005)[source]#: This function is documented in encord.project.Project.model_inference().

model_train(uid, label_rows=None, epochs=None, batch_size=24, weights=None, device=Device.CUDA)[source]#: This function is documented in encord.project.Project.model_train().

object_interpolation(key_frames, objects_to_interpolate)[source]#: This function is documented in encord.project.Project.object_interpolation().

fitted_bounding_boxes(frames, video)[source]#: This function is documented in encord.project.Project.fitted_bounding_boxes().

get_data(data_hash, get_signed_url=False)[source]#

This function is documented in encord.project.Project.get_data().

Return type: Tuple[Optional[Video], Optional[List[Image]]]

get_websocket_url()[source]#

Return type: str

get_label_logs(user_hash=None, data_hash=None, from_unix_seconds=None, to_unix_seconds=None)[source]#

This function is documented in encord.project.Project.get_label_logs().

Return type: List[LabelLog]

encord.client.CordClientProject#: alias of encord.client.EncordClientProject

Encord Objects#

class encord.objects.ontology_structure.OntologyStructure(objects=<factory>, classifications=<factory>)[source]#

This class is currently in BETA. Its API might change in future minor version releases.

objects: List[encord.objects.ontology_object.Object]#

classifications: List[encord.objects.classification.Classification]#

classmethod from_dict(d)[source]#

Parameters: d (dict) – a JSON blob of an “ontology structure” (e.g. from Encord web app)
Raises: KeyError – If the dict is missing a required field.
Return type: OntologyStructure

to_dict()[source]#

Return type: dict
Returns: The dict equivalent to the ontology.
Raises: KeyError – If the dict is missing a required field.

add_object(name, shape, uid=None, color=None, feature_node_hash=None)[source]#

Adds an object class definition to the structure.

structure = ontology_structure.OntologyStructure()

eye = structure.add_object(
    name="Eye",
)
nose = structure.add_object(
    name="Nose",
)
nose_detail = nose.add_attribute(
    encord.objects.common.ChecklistAttribute,
)
nose_detail.add_option(feature_node_hash="2bc17c88", label="Is it a cute nose?")
nose_detail.add_option(feature_node_hash="86eaa4f2", label="Is it a wet nose? ")

Parameters

name (str) – the user-visible name of the object
shape (Shape) – the kind of object (bounding box, polygon, etc). See encord.objects.common.Shape enum for possible values
uid (Optional[int]) – integer identifier of the object. Normally auto-generated; omit this unless the aim is to create an exact clone of existing structure
color (Optional[str]) – the color of the object in the label editor. Normally auto-assigned, should be in ‘#1A2B3F’ syntax.
feature_node_hash (Optional[str]) – global identifier of the object. Normally auto-generated; omit this unless the aim is to create an exact clone of existing structure

Return type

Object

Returns

the created object class that can be further customised with attributes.

add_classification(uid=None, feature_node_hash=None)[source]#

Adds an classification definition to the ontology.

structure = ontology_structure.OntologyStructure()

cls = structure.add_classification(feature_node_hash="a39d81c0")
cat_standing = cls.add_attribute(
    encord.objects.common.RadioAttribute,
    feature_node_hash="a6136d14",
    name="Is the cat standing?",
    required=True,
)
cat_standing.add_option(feature_node_hash="a3aeb48d", label="Yes")
cat_standing.add_option(feature_node_hash="d0a4b373", label="No")

Parameters

uid (Optional[int]) – integer identifier of the object. Normally auto-generated; omit this unless the aim is to create an exact clone of existing structure
feature_node_hash (Optional[str]) – global identifier of the object. Normally auto-generated; omit this unless the aim is to create an exact clone of existing structure

Return type

Classification

Returns

the created classification node. Note that classification attribute should be further specified by calling its add_attribute() method.

class encord.objects.ontology_object.Object(uid, name, color, shape, feature_node_hash, attributes=<factory>)[source]#

This class is currently in BETA. Its API might change in future minor version releases.

uid: int#

name: str#

color: str#

shape: encord.objects.common.Shape#

feature_node_hash: str#

attributes: List[Union[encord.objects.common.RadioAttribute, encord.objects.common.ChecklistAttribute, encord.objects.common.TextAttribute]]#

classmethod from_dict(d)[source]#

Return type: Object

to_dict()[source]#

Return type: dict

T#: alias of TypeVar(‘T’, bound=Union[encord.objects.common.RadioAttribute, encord.objects.common.ChecklistAttribute, encord.objects.common.TextAttribute])

add_attribute(cls, name, local_uid=None, feature_node_hash=None, required=False, dynamic=False)[source]#

Adds an attribute to the object.

Parameters

cls (Type[T]) – attribute type, one of RadioAttribute, ChecklistAttribute, TextAttribute
name (str) – the user-visible name of the attribute
local_uid (Optional[int]) – integer identifier of the attribute. Normally auto-generated; omit this unless the aim is to create an exact clone of existing ontology
feature_node_hash (Optional[str]) – global identifier of the attribute. Normally auto-generated; omit this unless the aim is to create an exact clone of existing ontology
required (bool) – whether the label editor would mark this attribute as ‘required’
dynamic (bool) – whether the attribute can have a different answer for the same object across different frames.

Return type

Returns

the created attribute that can be further specified with Options, where appropriate

Raises

ValueError – if specified local_uid or feature_node_hash violate uniqueness constraints

class encord.objects.classification.Classification(uid, feature_node_hash, attributes)[source]#

This class is currently in BETA. Its API might change in future minor version releases.

Represents a whole-image classification as part of Ontology structure. Wraps a single Attribute that describes the image in general rather then individual object.

uid: int#

feature_node_hash: str#

attributes: List[Union[encord.objects.common.RadioAttribute, encord.objects.common.ChecklistAttribute, encord.objects.common.TextAttribute]]#

classmethod from_dict(d)[source]#

Return type: Classification

to_dict()[source]#

Return type: dict

T#: alias of TypeVar(‘T’, bound=Union[encord.objects.common.RadioAttribute, encord.objects.common.ChecklistAttribute, encord.objects.common.TextAttribute])

add_attribute(cls, name, local_uid=None, feature_node_hash=None, required=False)[source]#

Adds an attribute to the classification.

Parameters

cls (Type[T]) – attribute type, one of RadioAttribute, ChecklistAttribute, TextAttribute
name (str) – the user-visible name of the attribute
local_uid (Optional[int]) – integer identifier of the attribute. Normally auto-generated; omit this unless the aim is to create an exact clone of existing ontology
feature_node_hash (Optional[str]) – global identifier of the attribute. Normally auto-generated; omit this unless the aim is to create an exact clone of existing ontology
required (bool) – whether the label editor would mark this attribute as ‘required’

Return type

Returns

the created attribute that can be further specified with Options, where appropriate

Raises

ValueError – if the classification already has an attribute assigned

class encord.objects.common.PropertyType(value)[source]#

An enumeration.

RADIO = 'radio'#

TEXT = 'text'#

CHECKLIST = 'checklist'#

class encord.objects.common.Shape(value)[source]#

An enumeration.

BOUNDING_BOX = 'bounding_box'#

POLYGON = 'polygon'#

POINT = 'point'#

SKELETON = 'skeleton'#

POLYLINE = 'polyline'#

ROTATABLE_BOUNDING_BOX = 'rotatable_bounding_box'#

class encord.objects.common.RadioAttribute(uid, feature_node_hash, name, required, dynamic, options=<factory>)[source]#

This class is currently in BETA. Its API might change in future minor version releases.

options: List[encord.objects.common.NestableOption]#

get_property_type()[source]#

Return type: PropertyType

classmethod has_options_field()[source]#

Return type: bool

add_option(label, value=None, local_uid=None, feature_node_hash=None)[source]#

Parameters

label (str) – user-visible name of the option
value (Optional[str]) – internal unique value; optional; normally mechanically constructed from the label
local_uid (Optional[int]) – integer identifier of the option. Normally auto-generated; omit this unless the aim is to create an exact clone of existing ontology
feature_node_hash (Optional[str]) – global identifier of the option. Normally auto-generated; omit this unless the aim is to create an exact clone of existing ontology

Return type

NestableOption

Returns

a NestableOption instance attached to the attribute. This can be further specified by adding nested attributes.

class encord.objects.common.ChecklistAttribute(uid, feature_node_hash, name, required, dynamic, options=<factory>)[source]#

This class is currently in BETA. Its API might change in future minor version releases.

options: List[encord.objects.common.FlatOption]#

get_property_type()[source]#

Return type: PropertyType

classmethod has_options_field()[source]#

Return type: bool

add_option(label, value=None, local_uid=None, feature_node_hash=None)[source]#

Parameters

label (str) – user-visible name of the option
value (Optional[str]) – internal unique value; optional; normally mechanically constructed from the label
local_uid (Optional[int]) – integer identifier of the option. Normally auto-generated; omit this unless the aim is to create an exact clone of existing ontology
feature_node_hash (Optional[str]) – global identifier of the option. Normally auto-generated; omit this unless the aim is to create an exact clone of existing ontology

Returns

a FlatOption instance attached to the attribute.

class encord.objects.common.TextAttribute(uid, feature_node_hash, name, required, dynamic)[source]#

This class is currently in BETA. Its API might change in future minor version releases.

get_property_type()[source]#

Return type: PropertyType

classmethod has_options_field()[source]#

Return type: bool

uid: List[int]#

feature_node_hash: str#

name: str#

required: bool#

dynamic: bool#: The dynamic member is part of every attribute. However it can only be true for top level (not nested) attributes that are part of an encord.objects.ontology_object.Object.

encord.objects.common.Attribute#

This class is currently in BETA. Its API might change in future minor version releases.

alias of Union[encord.objects.common.RadioAttribute, encord.objects.common.ChecklistAttribute, encord.objects.common.TextAttribute]

encord.objects.common.OptionAttribute#

This class is currently in BETA. Its API might change in future minor version releases.

alias of Union[encord.objects.common.RadioAttribute, encord.objects.common.ChecklistAttribute]

encord.objects.common.attribute_from_dict(d)[source]#

Convenience functions as you cannot call static member on union types.

Return type: Union[RadioAttribute, ChecklistAttribute, TextAttribute]

encord.objects.common.attributes_to_list_dict(attributes)[source]#

Return type: list

class encord.objects.common.OptionType(value)[source]#

An enumeration.

FLAT = 1#

NESTABLE = 2#

class encord.objects.common.FlatOption(uid, feature_node_hash, label, value)[source]#

This class is currently in BETA. Its API might change in future minor version releases.

get_option_type()[source]#

Return type: OptionType

classmethod from_dict(d)[source]#

Return type: FlatOption

uid: List[int]#

feature_node_hash: str#

label: str#

value: str#

class encord.objects.common.NestableOption(uid, feature_node_hash, label, value, nested_options=<factory>)[source]#

This class is currently in BETA. Its API might change in future minor version releases.

nested_options: List[Union[encord.objects.common.RadioAttribute, encord.objects.common.ChecklistAttribute, encord.objects.common.TextAttribute]]#

get_option_type()[source]#

Return type: OptionType

classmethod from_dict(d)[source]#

Return type: NestableOption

add_nested_option(cls, name, local_uid=None, feature_node_hash=None, required=False)[source]#

Adds a nested attribute to a RadioAttribute option.

Parameters

cls (Type[~T]) – attribute type, one of RadioAttribute, ChecklistAttribute, TextAttribute
name (str) – the user-visible name of the attribute
local_uid (Optional[int]) – integer identifier of the attribute. Normally auto-generated; omit this unless the aim is to create an exact clone of existing ontology
feature_node_hash (Optional[str]) – global identifier of the object. Normally auto-generated; omit this unless the aim is to create an exact clone of existing ontology
required (bool) – whether the label editor would mark this attribute as ‘required’

Return type

Returns

the created attribute that can be further specified with Options, where appropriate

Raises

ValueError – if specified local_uid or feature_node_hash violate uniqueness constraints

encord.objects.common.Option#

This class is currently in BETA. Its API might change in future minor version releases.

alias of Union[encord.objects.common.FlatOption, encord.objects.common.NestableOption]

class encord.objects.project.ProjectDataset(dataset_hash, title, description)[source]#

dataset_hash: str#

title: str#

description: str#

static from_dict(d)[source]#

Return type: ProjectDataset

classmethod from_list(l)[source]#

Return type: List[ProjectDataset]

Project Ontology#

class encord.project_ontology.classification_attribute.ClassificationAttribute(id, name, classification_type, required, feature_node_hash, options=None)[source]#

A dataclass which holds classification attributes.

id: str#: A unique (to the ontology) identifier of the attribute.

name: str#: The descriptive name of the attribute.

classification_type: encord.project_ontology.classification_type.ClassificationType#: What type of attribute it is. E.g., Checkbox or radio button.

required: bool#: Whether annotating this attribute is required.

feature_node_hash: str#: An 8-character hex string uniquely defining the attribute.

options: Optional[Iterable[encord.project_ontology.classification_option.ClassificationOption]] = None#: Nested classification options.

class encord.project_ontology.classification_option.ClassificationOption(id, label, value, feature_node_hash)[source]#

A dataclass which holds nested options for the ClassificationAttribute.

id: str#: A unique (to the ontology) identifier of the option.

label: str#: A description of the option.

value: str#: A snake-case concatenated version of the label.

feature_node_hash: str#: An 8-character hex string uniquely defining the option.

Expand for references to encord.project_ontology.classification_type

Projects / Ontology / Adding components to a project ontology / Adding a classification to an ontology

class encord.project_ontology.classification_type.ClassificationType(value)[source]#

Enum used to define classification type in ontologies.

Expand for references to encord.project_ontology.classification_type.ClassificationType

Projects / Ontology / Adding components to a project ontology / Adding a classification to an ontology

RADIO = 'radio'#

Single select option

Expand for references to encord.project_ontology.classification_type.ClassificationType.RADIO

Projects / Ontology / Adding components to a project ontology / Adding a classification to an ontology

TEXT = 'text'#: Text option for free text input.

CHECKLIST = 'checklist'#: Multi select option

Expand for references to encord.project_ontology.object_type

Projects / Ontology / Adding components to a project ontology / Adding an object to an ontology

class encord.project_ontology.object_type.ObjectShape(value)[source]#

An enumeration.

Expand for references to encord.project_ontology.object_type.ObjectShape

Projects / Ontology / Adding components to a project ontology / Adding an object to an ontology

POLYGON = 'polygon'#

POLYLINE = 'polyline'#

BOUNDING_BOX = 'bounding_box'#

KEY_POINT = 'point'#

SKELETON = 'skeleton'#

ROTATABLE_BOUNDING_BOX = 'rotatable_bounding_box'#

Expand for references to encord.project_ontology.ontology

Projects / Ontology / Fetching a project’s ontology

encord.project_ontology.ontology.generate_feature_node_hash()[source]#

Utility function to generate an 8-character hex string.

Return type: str

class encord.project_ontology.ontology.Ontology[source]#

BETA: prefer using encord.objects.ontology_structure.OntologyStructure

Expand for references to encord.project_ontology.ontology.Ontology

Projects / Ontology / Fetching a project’s ontology

COLORS = ('#D33115', '#E27300', '#16406C', '#FE9200', '#FCDC00', '#DBDF00', '#A4DD00', '#68CCCA', '#73D8FF', '#AEA1FF', '#FCC400', '#B0BC00', '#68BC00', '#16A5A5', '#009CE0', '#7B64FF', '#FA28FF', '#B3B3B3', '#9F0500', '#C45100', '#FB9E00', '#808900', '#194D33', '#0C797D', '#0062B1', '#653294', '#AB149E')#

classmethod from_dict(ontology_dict)[source]#

Convert python dictionary too an Ontology object.

Parameters: ontology_dict (Dict) – The dictionary to convert.

add_object(name, shape)[source]#

Add an OntologyObject to the ontology.

Parameters

name (str) – A descriptive name of the object.
shape (ObjectShape) – The shape of the object.

Return type

None

add_classification(name, classification_type, required, options=None)[source]#

Add a classification to the ontology.

Parameters

name (str) – A descriptive name of the classification.
classification_type (ClassificationType) – The type of the classification.
required (bool) – Indicate whether annotating this classification is required.
options (Optional[Iterable[str]]) – Nested classification options.

Return type

None

to_dict()[source]#

Convert the ontology object to a python dictionary.

Return type: dict

Expand for references to encord.project_ontology.ontology.Ontology.to_dict

Projects / Ontology / Fetching a project’s ontology

ontology_object_to_dict(ontology_object)[source]#

Return type: Dict

ontology_classification_to_dict(ontology_classification)[source]#

Return type: Dict

class encord.project_ontology.ontology_classification.OntologyClassification(id, feature_node_hash, attributes)[source]#

A dataclass which holds classifications of the encord.project_ontology.Ontology.

id: str#: A unique (to the ontology) identifier of the classification.

feature_node_hash: str#: An 8-character hex string uniquely defining the option.

attributes: List[encord.project_ontology.classification_attribute.ClassificationAttribute]#: A List of attributes for the classification.

class encord.project_ontology.ontology_object.OntologyObject(id, color, name, shape, feature_node_hash)[source]#

A dataclass which holds an object for of the encord.project_ontology.Ontology.

id: str#: A unique (to the ontology) identifier of the classification.

color: str#: The color which is displayed in the web-app.

name: str#: The name of the object.

shape: encord.project_ontology.object_type.ObjectShape#: The shape of the object. E.g., polygon, polyline, and bounding_box.

feature_node_hash: str#: An 8-character hex string uniquely defining the option.

Utilities - Client#

Expand for references to encord.utilities.client_utilities

Projects / API keys / Creating a project API key / Creating a project API key with specific rights

Projects / Other Resources / CVAT Integration / Importing with the Encord SDK

encord.utilities.client_utilities.pretty_print(data)[source]#

class encord.utilities.client_utilities.APIKeyScopes(value)[source]#

The APIKeyScope is used to provide specific access rights to a project through EncordUserClient.create_project_api_key(). The options are a follows:

LABEL_READ: access to Getting label rows

LABEL_WRITE: access to Saving label rows

MODEL_INFERENCE: access to Inference

MODEL_TRAIN: access to Creating a model row and Training

LABEL_LOGS_READ: access to Reviewing label logs

ALGO_LIBRARY: access to algorithms like Object interpolation

Expand for references to encord.utilities.client_utilities.APIKeyScopes

Projects / API keys / Creating a project API key / Creating a project API key with specific rights

LABEL_READ = 'label.read'#

LABEL_WRITE = 'label.write'#

MODEL_INFERENCE = 'model.inference'#

MODEL_TRAIN = 'model.train'#

LABEL_LOGS_READ = 'label_logs.read'#

ALGO_LIBRARY = 'algo.library'#

class encord.utilities.client_utilities.LocalImport(file_path)[source]#

file_path: Supply the path of the exported folder which contains the images and annotations.xml file. Make sure to select “Save images” when exporting your CVAT Task or Project.

Expand for references to encord.utilities.client_utilities.LocalImport

Projects / Other Resources / CVAT Integration / Importing with the Encord SDK

file_path: str#

encord.utilities.client_utilities.ImportMethod#: Using images/videos in cloud storage as an alternative import method will be supported in the future.

class encord.utilities.client_utilities.Issue(issue_type, instances)[source]#

For each issue_type there may be multiple occurrences which are documented in the instances. The instances list can provide additional information on how the issue was encountered. If there is no additional information available, the instances list will be empty.

issue_type: str#

instances: List[str]#

class encord.utilities.client_utilities.Issues(errors, warnings, infos)[source]#

Any issues that came up during importing a project. These usually come from incompatibilities between data saved on different platforms.

errors: List[encord.utilities.client_utilities.Issue]#

warnings: List[encord.utilities.client_utilities.Issue]#

infos: List[encord.utilities.client_utilities.Issue]#

static from_dict(d)[source]#

Return type: Issues

class encord.utilities.client_utilities.CvatImporterSuccess(project_hash, dataset_hash, issues)[source]#

Expand for references to encord.utilities.client_utilities.CvatImporterSuccess

Projects / Other Resources / CVAT Integration / Importing with the Encord SDK

project_hash: str#

dataset_hash: str#

issues: encord.utilities.client_utilities.Issues#

class encord.utilities.client_utilities.CvatImporterError(dataset_hash, issues)[source]#

dataset_hash: str#

issues: encord.utilities.client_utilities.Issues#

encord.utilities.client_utilities.parse_datetime(key, val)[source]#

encord.utilities.client_utilities.optional_set_to_list(s)[source]#

Return type: Optional[List[~T]]

Utilities - Project User#

Expand for references to encord.utilities.project_user

class encord.utilities.project_user.ProjectUserRole(value)[source]#

An enumeration.

Expand for references to encord.utilities.project_user.ProjectUserRole

Projects / Labels / Saving label rows

ADMIN = 0#

ANNOTATOR = 1#

REVIEWER = 2#

ANNOTATOR_REVIEWER = 3#

TEAM_MANAGER = 4#

class encord.utilities.project_user.ProjectUser(user_email, user_role, project_hash)[source]#

user_email: str#

user_role: encord.utilities.project_user.ProjectUserRole#

project_hash: str#

classmethod from_dict(json_dict)[source]#

Utilities - Label#

Expand for references to encord.utilities.label_utilities

encord.utilities.label_utilities.construct_answer_dictionaries(label_row)[source]#

Adds answer object and classification answer dictionaries from a label row if they do not exist. Integrity checks are conducted upon saving of labels.

Parameters: label_row – A label row.
Returns: A label row instance with updated answer dictionaries
Return type: LabelRow

Expand for references to encord.utilities.label_utilities.construct_answer_dictionaries

Projects / Labels / Saving label rows

encord.utilities.label_utilities.add_answers_to_items(items, classification_answers, object_answers)[source]#: If object_hash (uid) or classification_hash (uid) are not in answer dictionaries, add key entry with empty classification list.

Utilities - ORM classes#

class encord.orm.api_key.ApiKeyMeta(dic)[source]#

ApiKeyMeta contains key information.

ORM:

title, resource_type

DB_FIELDS: collections.OrderedDict = {'resource_type': <class 'str'>, 'title': <class 'str'>}#

NON_UPDATABLE_FIELDS: set = {'resource_type'}#

class encord.orm.base_orm.BaseORM(dic)[source]#

Base ORM for all database objects.

DB_FIELDS: collections.OrderedDict = {}#

NON_UPDATABLE_FIELDS: set = {}#

static from_db_row(row, db_field)[source]#: Static method for conveniently converting db row to client object. :param row: :param db_field: :return:

to_dic(time_str=True)[source]#

Conveniently set client object as dict. Only considers the dict items, no other object attr will be counted

Parameters: time_str (bool) – if set to True, will convert datetime field to str with format %Y-%m-%d %H:%M:%S. If False, will keep the original datetime type. Default will be True.

updatable_fields()[source]#

class encord.orm.base_orm.BaseListORM(iter_)[source]#

A wrapper for a list of objects of a specific ORM.

BASE_ORM_TYPE#: alias of encord.orm.base_orm.BaseORM

Expand for references to encord.orm.cloud_integration

Datasets / Data / Adding data / Adding data from a private cloud

class encord.orm.cloud_integration.CloudIntegration(id, title)[source]#

Expand for references to encord.orm.cloud_integration.CloudIntegration

Datasets / Data / Adding data / Adding data from a private cloud

id: str#

title: str#

Expand for references to encord.orm.dataset

Datasets / Data / Adding data / Adding data from a private cloud

Datasets / Data / Re-encoding videos / Check the status of a re-encoding task

Datasets / API keys / Creating a dataset API key with specific rights

Datasets / API keys / Creating a master API key with full rights

Datasets / Listing existing datasets

class encord.orm.dataset.DatasetUserRole(value)[source]#

An enumeration.

ADMIN = 0#

USER = 1#

class encord.orm.dataset.DatasetUser(user_email, user_role, dataset_hash)[source]#

user_email: str#

user_role: encord.orm.dataset.DatasetUserRole#

dataset_hash: str#

classmethod from_dict(json_dict)[source]#

class encord.orm.dataset.DatasetUsers[source]#

class encord.orm.dataset.DataClientMetadata(payload)[source]#

payload: dict#

class encord.orm.dataset.DataRow(uid, title, data_type, created_at, client_metadata)[source]#

property uid: str#

Return type: str

property title: str#

Return type: str

property data_type: encord.constants.enums.DataType#

Return type: DataType

property created_at: datetime.datetime#

Return type: datetime

property client_metadata: Optional[dict]#

Custom client metadata. This is null if it is disabled via the encord.orm.dataset.DatasetAccessSettings

Return type: Optional[dict]

classmethod from_dict(json_dict)[source]#

Return type: DataRow

classmethod from_dict_list(json_list)[source]#

Return type: List[DataRow]

class encord.orm.dataset.DatasetInfo(dataset_hash, user_hash, title, description, type, created_at, last_edited_at)[source]#

This class represents a dataset in the context of listing

Expand for references to encord.orm.dataset.DatasetInfo

dataset_hash: str#

user_hash: str#

title: str#

description: str#

type: int#

created_at: datetime.datetime#

last_edited_at: datetime.datetime#

class encord.orm.dataset.Dataset(title, storage_location, data_rows, dataset_hash, description=None)[source]#

property dataset_hash: str#

Return type: str

property title: str#

Return type: str

property description: str#

Return type: str

property storage_location: encord.orm.dataset.StorageLocation#

Return type: StorageLocation

property data_rows: List[encord.orm.dataset.DataRow]#

Return type: List[DataRow]

classmethod from_dict(json_dict)[source]#

Return type: Dataset

class encord.orm.dataset.DatasetDataInfo(data_hash, title)[source]#

data_hash: str#

title: str#

classmethod from_dict(json_dict)[source]#

Return type: DatasetDataInfo

class encord.orm.dataset.AddPrivateDataResponse(dataset_data_list)[source]#

Response of add_private_data_to_dataset

Expand for references to encord.orm.dataset.AddPrivateDataResponse

Datasets / Data / Adding data / Adding data from a private cloud

dataset_data_list: List[encord.orm.dataset.DatasetDataInfo]#

classmethod from_dict(json_dict)[source]#

Return type: AddPrivateDataResponse

class encord.orm.dataset.DatasetAPIKey(dataset_hash, api_key, title, key_hash, scopes)[source]#

Expand for references to encord.orm.dataset.DatasetAPIKey

Datasets / API keys / Creating a dataset API key with specific rights

Datasets / API keys / Creating a master API key with full rights

dataset_hash: str#

api_key: str#

title: str#

key_hash: str#

scopes: List[encord.orm.dataset.DatasetScope]#

classmethod from_dict(json_dict)[source]#

Return type: DatasetAPIKey

class encord.orm.dataset.CreateDatasetResponse(title, storage_location, dataset_hash, user_hash)[source]#

Expand for references to encord.orm.dataset.CreateDatasetResponse

property title: str#

Return type: str

property storage_location: encord.orm.dataset.StorageLocation#

Return type: StorageLocation

property dataset_hash: str#

Return type: str

property user_hash: str#

Return type: str

classmethod from_dict(json_dict)[source]#

Return type: CreateDatasetResponse

class encord.orm.dataset.StorageLocation(value)[source]#

An enumeration.

Expand for references to encord.orm.dataset.StorageLocation

Datasets / API keys / Creating a dataset API key with specific rights

CORD_STORAGE = 0#

AWS = 1#

GCP = 2#

AZURE = 3#

OTC = 4#

static from_str(string_location)[source]#

Return type: StorageLocation

get_str()[source]#

Return type: str

encord.orm.dataset.DatasetType#: For backwards compatibility

class encord.orm.dataset.DatasetScope(value)[source]#

An enumeration.

Expand for references to encord.orm.dataset.DatasetScope

READ = 'dataset.read'#

WRITE = 'dataset.write'#

class encord.orm.dataset.DatasetData(dic)[source]#

Video base ORM.

DB_FIELDS: collections.OrderedDict = {'data_hash': <class 'str'>, 'images': <class 'list'>, 'video': <class 'dict'>}#

class encord.orm.dataset.SignedVideoURL(dic)[source]#

A signed URL object with supporting information.

DB_FIELDS: collections.OrderedDict = {'data_hash': <class 'str'>, 'file_link': <class 'str'>, 'signed_url': <class 'str'>, 'title': <class 'str'>}#

class encord.orm.dataset.SignedImageURL(dic)[source]#

A signed URL object with supporting information.

DB_FIELDS: collections.OrderedDict = {'data_hash': <class 'str'>, 'file_link': <class 'str'>, 'signed_url': <class 'str'>, 'title': <class 'str'>}#

class encord.orm.dataset.SignedImagesURL(iter_)[source]#

A signed URL object with supporting information.

BASE_ORM_TYPE#: alias of encord.orm.dataset.SignedImageURL

class encord.orm.dataset.SignedDicomURL(dic)[source]#

A signed URL object with supporting information.

DB_FIELDS: collections.OrderedDict = {'data_hash': <class 'str'>, 'file_link': <class 'str'>, 'signed_url': <class 'str'>, 'title': <class 'str'>}#

class encord.orm.dataset.SignedDicomsURL(iter_)[source]#

A signed URL object with supporting information.

BASE_ORM_TYPE#: alias of encord.orm.dataset.SignedDicomURL

class encord.orm.dataset.Video(dic)[source]#

A video object with supporting information.

DB_FIELDS: collections.OrderedDict = {'data_hash': <class 'str'>, 'file_link': <class 'str'>, 'title': <class 'str'>}#

NON_UPDATABLE_FIELDS: set = {'data_hash'}#

class encord.orm.dataset.ImageGroup(dic)[source]#

An image group object with supporting information.

DB_FIELDS: collections.OrderedDict = {'data_hash': <class 'str'>, 'file_link': <class 'str'>, 'title': <class 'str'>}#

NON_UPDATABLE_FIELDS: set = {'data_hash'}#

class encord.orm.dataset.Image(dic)[source]#

An image object with supporting information.

DB_FIELDS: collections.OrderedDict = {'data_hash': <class 'str'>, 'file_link': <class 'str'>, 'title': <class 'str'>}#

NON_UPDATABLE_FIELDS: set = {'data_hash'}#

class encord.orm.dataset.SingleImage(dic)[source]#

For native single image upload.

success: bool#

class encord.orm.dataset.Images(success)[source]#

Uploading multiple images in a batch mode.

success: bool#

class encord.orm.dataset.DicomSeries(data_hash, title)[source]#

data_hash: str#

title: str#

class encord.orm.dataset.DicomDeidentifyTask(dicom_urls, integration_hash)[source]#

dicom_urls: List[str]#

integration_hash: str#

class encord.orm.dataset.ImageGroupOCR(processed_texts)[source]#

processed_texts: Dict#

class encord.orm.dataset.ReEncodeVideoTaskResult(data_hash, signed_url, bucket_path)[source]#

data_hash: str#

signed_url: Optional[str]#

bucket_path: str#

class encord.orm.dataset.ReEncodeVideoTask(status, result=None)[source]#

A re encode video object with supporting information.

Expand for references to encord.orm.dataset.ReEncodeVideoTask

Datasets / Data / Re-encoding videos / Check the status of a re-encoding task

status: str#

result: List[encord.orm.dataset.ReEncodeVideoTaskResult] = None#

classmethod from_dict(json_dict)[source]#

class encord.orm.dataset.DatasetAccessSettings(fetch_client_metadata)[source]#

Settings for using the dataset object.

fetch_client_metadata: bool#: Whether client metadata should be retrieved for each data_row.

class encord.orm.dataset_with_user_role.DatasetWithUserRole(user_role, dataset)[source]#

This is a helper class denoting the relationship between the current user an a project

user_role: str#

dataset: dict#

class encord.orm.formatter.Formatter[source]#

abstract classmethod from_dict(json_dict)[source]#

class encord.orm.label_log.LabelLog(log_hash, user_hash, user_email, annotation_hash, identifier, data_hash, feature_hash, action, label_name, time_taken, created_at, frame)[source]#

log_hash: str#

user_hash: str#

user_email: str#

annotation_hash: str#

identifier: str#

data_hash: str#

feature_hash: str#

action: Action#

label_name: str#

time_taken: int#

created_at: datetime.datetime#

frame: int#

class encord.orm.label_log.Action(value)[source]#

An enumeration.

ADD = 0#

EDIT = 1#

DELETE = 2#

START = 3#

END = 4#

MARK_AS_NOT_LABELLED = 5#

MARK_AS_IN_PROGRESS = 6#

MARK_AS_LABELLED = 7#

MARK_AS_REVIEW_REQUIRED = 8#

MARK_AS_REVIEWED = 9#

MARK_AS_REVIEWED_TWICE = 10#

SUBMIT_TASK = 11#

APPROVE_LABEL = 12#

REJECT_LABEL = 13#

CLICK_SAVE = 14#

CLICK_UNDO = 15#

CLICK_REDO = 16#

CLICK_BULK = 17#

CLICK_ZOOM = 19#

CLICK_BRIGHTNESS = 20#

CLICK_HOTKEYS = 21#

CLICK_SETTINGS = 22#

ADD_ATTRIBUTE = 23#

EDIT_ATTRIBUTE = 24#

DELETE_ATTRIBUTE = 25#

APPROVE_NESTED_ATTRIBUTE = 26#

REJECT_NESTED_ATTRIBUTE = 27#

SUBMIT_LABEL = 28#

SUBMIT_NESTED_ATTRIBUTE = 29#

class encord.orm.label_row.LabelRow(dic)[source]#

A label row contains a data unit or a collection of data units and associated labels, and is specific to a data asset with type video or img_group:

A label row with a data asset of type video contains a single data unit.
A label row with a data asset of type img_group contains any number of data units.

The label row ORM is as follows:

label_hash (uid) is the unique identifier of the label row
dataset_hash (uid) is the unique identifier of the dataset which contains the particular video or image group
dataset_title is the title of the dataset which contains the particular video or image group
data_title is the title of the video or image group
data_type either video or img_group depending on data type
data_units a dictionary with (key: data hash, value: data unit) pairs.
object_answers is a dictionary with (key: object hash, value: object answer) pairs.
classification_answers is a dictionary with (key: classification hash, value: classification answer) pairs.
object_actions is a dictionary with (key: <object_hash>, value: object action) pairs.
label_status is a string indicating label status. It can take the values enumerated in encord.orm.label_row.LabelStatus. Note that this does not reflect thes status shown in the Projects->Labels section on the web-app.

A data unit, mentioned for the dictionary entry data_units above, has in the form:

label_row = {  # The label row
    # ...
    "data_units": {
        "<data_hash>": {
            "data_hash": "<data_hash>",  # A data_hash (uid) string
            "data_title": "A data title",
            "data_link": "<data_link>",  # Signed URL that expiring after 7 days
            "data_type": "<data_type>",  # (video/mp4, image/jpeg, etc.)
            "data_fps": 24.95,           # For video, the frame rate
            "data_sequence": "0",        # Defines order of data units
            "width": 640,                # The width of the content
            "height": 610,               # The height of the content
            "labels": {
                # ...
            }
        },
        # ...,
    }
}

A data unit can have any number of vector labels (e.g. bounding box, polygon, keypoint) and classifications.

Objects and classifications

A data unit can have any number of vector labels (e.g., bounding boxes, polygons, polylines, keypoints) and classifications. Each frame-level object and classification has unique identifiers ‘objectHash’ and ‘classificationHash’. Each frame-level entity has a unique feature identifier ‘featureHash’, defined in the editor ontology.

The object and classification answers are contained separately from the individual data units to preserve space for video, sequential images, DICOM, etc.

The objects and classifications answer dictionaries contain classification ‘answers’ (i.e. attributes that describe the object or classification). This is to avoid storing the information at every frame in the blurb, of particular importance for videos.

A labels dictionary for video is in the form:

label_row["data_units"]["<data_hash>"]["labels"] = {
    "<frame_number>": {
        "objects": [
            # { object 1 },
            # { object 2 },
            # ...
        ],
        "classifications": [
            # { classification 1 },
            # { classification 2 },
            # ...
        ],
    }
}

A labels dictionary for an img_group data unit is in the form:

label_row["data_units"]["<data_hash>"]["labels"] = {
    "objects": [
        # { object 1 },
        # { object 2 },
        # ...
    ],
    "classifications": [
        # { classification 1 },
        # { classification 2 },
        # ...
    ],
}

The object answers dictionary is in the form:

label_row["object_answers"] = {
    "<object_hash>": {
        "objectHash": "<object_hash>",
        "classifications": [
            # {answer 1},
            # {answer 2},
            # ...
        ]
    },
    # ...
}

The classification answers dictionary is in the form:

label_row["classification_answers"] = {
    "<classification_hash>": {
        "classificationHash": "<classification_hash>",
        "classifications": [
            # {answer 1},
            # {answer 2},
            # ...
        ],
    },
    # ...
}

The object actions dictionary is in the form:

label_row["object_actions"] = {
    "<object_hash>": {
        "objectHash": "<object_hash>",
        "actions": [
            # {answer 1},
            # {answer 2},
            # ...
        ],
    },
    # ...
}

DB_FIELDS: collections.OrderedDict = {'classification_answers': <class 'dict'>, 'data_title': <class 'str'>, 'data_type': <class 'str'>, 'data_units': <class 'dict'>, 'dataset_hash': <class 'str'>, 'dataset_title': <class 'str'>, 'label_hash': <class 'str'>, 'label_status': <class 'str'>, 'object_actions': <class 'dict'>, 'object_answers': <class 'dict'>}#

NON_UPDATABLE_FIELDS: set = {'label_hash'}#

class encord.orm.label_row.Review[source]#

class encord.orm.label_row.AnnotationTaskStatus(value)[source]#

An enumeration.

QUEUED = 'QUEUED'#

ASSIGNED = 'ASSIGNED'#

IN_REVIEW = 'IN_REVIEW'#

RETURNED = 'RETURNED'#

COMPLETED = 'COMPLETED'#

class encord.orm.label_row.ShadowDataState(value)[source]#

Specifies the kind of data to fetch when working with a BenchmarkQa project

ALL_DATA = 'ALL_DATA'#: Fetch all the label rows

SHADOW_DATA = 'SHADOW_DATA'#

the annotator’s view of the benchmark

Type: Only fetch the label rows that were submitted against “shadow data”

NOT_SHADOW_DATA = 'NOT_SHADOW_DATA'#: Only fetch the label rows for “production” data

class encord.orm.label_row.LabelStatus(value)[source]#

An enumeration.

NOT_LABELLED = 'NOT_LABELLED'#

LABEL_IN_PROGRESS = 'LABEL_IN_PROGRESS'#

LABELLED = 'LABELLED'#

REVIEW_IN_PROGRESS = 'REVIEW_IN_PROGRESS'#

REVIEWED = 'REVIEWED'#

REVIEWED_TWICE = 'REVIEWED_TWICE'#

class encord.orm.label_row.LabelRowMetadata(label_hash, data_hash, dataset_hash, data_title, data_type, label_status, annotation_task_status, is_shadow_data)[source]#

Contains helpful information about a LabelRow.

label_hash: str#

data_hash: str#

dataset_hash: str#

data_title: str#

data_type: str#

label_status: encord.orm.label_row.LabelStatus#

annotation_task_status: encord.orm.label_row.AnnotationTaskStatus#

is_shadow_data: bool#

classmethod from_dict(json_dict)[source]#

Return type: LabelRowMetadata

classmethod from_list(json_list)[source]#

Return type: List[LabelRowMetadata]

to_dict()[source]#

Return type: dict
Returns: The dict equivalent of LabelRowMetadata.

class encord.orm.labeling_algorithm.LabelingAlgorithm(dic)[source]#

Labeling algorithm base ORM.

ORM:

algorithm_name, algorithm_params

DB_FIELDS: collections.OrderedDict = {'algorithm_name': <class 'str'>, 'algorithm_parameters': <class 'dict'>}#

class encord.orm.labeling_algorithm.ObjectInterpolationParams(dic)[source]#

Labeling algorithm parameters for interpolation algorithm

ORM:

key_frames, objects_to_interpolate

DB_FIELDS: collections.OrderedDict = {'key_frames': <class 'dict'>, 'objects_to_interpolate': <class 'list'>}#

class encord.orm.labeling_algorithm.BoundingBoxFittingParams(dic)[source]#

Labeling algorithm parameters for bounding box fitting algorithm

ORM:

labels, video

DB_FIELDS: collections.OrderedDict = {'labels': <class 'dict'>, 'video': <class 'dict'>}#

class encord.orm.model.ModelOperations(value)[source]#

An enumeration.

INFERENCE = 0#

TRAIN = 1#

CREATE = 2#

class encord.orm.model.Model(dic)[source]#

Model base ORM.

ORM:

model_operation, model_parameters,

DB_FIELDS: collections.OrderedDict = {'model_operation': <class 'int'>, 'model_parameters': <class 'dict'>}#

class encord.orm.model.ModelConfiguration(model_uid, title, description, feature_node_hashes, model, model_iteration_uids)[source]#

model_uid: str#

title: str#

description: str#

feature_node_hashes: List[str]#: The corresponding feature node hashes of the ontology object

model: encord.constants.model.AutomationModels#

model_iteration_uids: List[str]#: All the UIDs of individual model training instances

classmethod from_dict(json_dict)[source]#

class encord.orm.model.ModelTrainingLabelMetadata(label_uid, data_uid, data_link)[source]#

label_uid: str#

data_uid: str#

data_link: str#

static from_dict(json_dict)[source]#

Return type: ModelTrainingLabelMetadata

classmethod from_list(json_list)[source]#

Return type: List[ModelTrainingLabelMetadata]

class encord.orm.model.ModelTrainingLabel(label_metadata_list, feature_uids)[source]#

label_metadata_list: List[encord.orm.model.ModelTrainingLabelMetadata]#

feature_uids: List[str]#

static from_dict(json_dict)[source]#

Return type: ModelTrainingLabel

class encord.orm.model.TrainingMetadata(model_iteration_uid, created_at=None, training_final_loss=None, model_training_labels=None)[source]#

model_iteration_uid: str#

created_at: Optional[datetime.datetime] = None#

training_final_loss: Optional[float] = None#

model_training_labels: Optional[List[encord.orm.model.ModelTrainingLabel]] = None#

classmethod from_dict(json_dict)[source]#

static get_created_at(json_dict)[source]#

Return type: Optional[datetime]

static get_model_training_labels(json_dict)[source]#

Return type: Optional[List[ModelTrainingLabel]]

class encord.orm.model.ModelRow(dic)[source]#

A model row contains a set of features and a model (resnet18, resnet34, resnet50, resnet101, resnet152, vgg16, vgg19, yolov5, faster_rcnn, mask_rcnn).

ORM:

model_hash (uid), title, description, features, model,

DB_FIELDS: collections.OrderedDict = {'description': <class 'str'>, 'features': <class 'list'>, 'model': <class 'str'>, 'model_hash': <class 'str'>, 'title': <class 'str'>}#

class encord.orm.model.ModelInferenceParams(dic)[source]#

Model inference parameters for running models trained via the platform.

ORM:

local_file_path, conf_thresh, iou_thresh, device detection_frame_range (optional)

DB_FIELDS: collections.OrderedDict = {'allocation_enabled': <class 'bool'>, 'conf_thresh': <class 'float'>, 'data_hashes': <class 'list'>, 'detection_frame_range': <class 'list'>, 'device': <class 'str'>, 'files': <class 'list'>, 'iou_thresh': <class 'float'>, 'rdp_thresh': <class 'float'>}#

class encord.orm.model.ModelTrainingWeights(dic)[source]#

Model training weights.

ORM:

training_config_link, training_weights_link,

DB_FIELDS: collections.OrderedDict = {'model': <class 'str'>, 'training_config_link': <class 'str'>, 'training_weights_link': <class 'str'>}#

class encord.orm.model.ModelTrainingParams(dic)[source]#

Model training parameters.

ORM:

model_hash, label_rows, epochs, batch_size, weights, device

DB_FIELDS: collections.OrderedDict = {'batch_size': <class 'int'>, 'device': <class 'str'>, 'epochs': <class 'int'>, 'label_rows': <class 'list'>, 'model_hash': <class 'str'>, 'weights': <class 'encord.orm.model.ModelTrainingWeights'>}#

class encord.orm.project.Project(dic)[source]#

DEPRECATED - prefer using the encord.project.Project class instead.

A project defines a label ontology and is a collection of datasets and label rows.

ORM:

title,
description,
editor_ontology,
ontology_hash,

datasets:

[
   {
        dataset_hash (uid),
        title,
        description,
        dataset_type (internal vs. AWS/GCP/Azure),
   },
   ...
],

label_rows:

[
    {
        label_hash (uid),
        data_hash (uid),
        dataset_hash (uid),
        dataset_title,
        data_title,
        data_type,
        label_status
    },
    ...
]

annotation_task_status

DB_FIELDS: collections.OrderedDict = {'created_at': <class 'datetime.datetime'>, 'datasets': (<class 'list'>, <class 'str'>), 'description': <class 'str'>, 'editor_ontology': (<class 'dict'>, <class 'str'>), 'label_rows': (<class 'list'>, <class 'str'>), 'last_edited_at': <class 'datetime.datetime'>, 'ontology_hash': <class 'str'>, 'project_hash': <class 'str'>, 'source_projects': <class 'list'>, 'title': <class 'str'>}#

NON_UPDATABLE_FIELDS: set = {'datasets', 'editor_ontology', 'label_rows'}#

get_labels_list()[source]#

Returns a list of all optional label row IDs (label_hash uid) in a project. If no label_hash is found, a None value is appended. This can be useful for working with fetching additional label row data via encord.project.Project.get_label_rows() for example.

project = client_instance.get_project(<project_hash>)
project_orm = project.get_project()

labels_list = project_orm.get_labels_list()
created_labels_list = []
for label in labels_list:
    if label is not None:  # None values will fail the operation
        created_labels_list.append(label)

label_rows = project.get_label_rows(created_labels_list, get_signed_url=False)

Return type: List[Optional[str]]

property project_hash#

property title#

property description#

property editor_ontology#

property datasets#

property label_rows#

property ontology_hash#

property source_projects#

class encord.orm.project.ProjectCopy[source]#

class encord.orm.project.ProjectUsers[source]#

class encord.orm.project.ProjectDataset[source]#

class encord.orm.project.ProjectCopyOptions(value)[source]#

An enumeration.

COLLABORATORS = 'collaborators'#

DATASETS = 'datasets'#

MODELS = 'models'#

class encord.orm.project.ProjectWorkflowType(value)[source]#

An enumeration.

MANUAL_QA = 'manual'#

BENCHMARK_QA = 'benchmark'#

class encord.orm.project.ManualReviewWorkflowSettings[source]#: Sets the project QA workflow to “manual reviews”. There are currently no available settings for this workflow

class encord.orm.project.BenchmarkQaWorkflowSettings(source_projects=<factory>)[source]#

Sets the project QA workflow to “Automatic”, with benchmark data being presented to all the annotators

source_projects: List[str]#: For Benchmark QA projects, a list of project ids (project_hash-es) that contain the benchmark source data

encord.orm.project.ProjectWorkflowSettings#

A variant type that allows you to select the type of quality assurance workflow for the project, and configure it.

Currently one of:: ManualReviewWorkflowSettings: a workflow with optional manual reviews BenchmarkQaWorkflowSettings: annotators are presented with “benchmark” or “honeypot” data

alias of Union[encord.orm.project.ManualReviewWorkflowSettings, encord.orm.project.BenchmarkQaWorkflowSettings]

class encord.orm.project.StringEnum(value)[source]#

Use this enum class if you need the helper that creates the enum instance from a string.

classmethod from_string(string)[source]#

Return type: Optional[StringEnum]

class encord.orm.project.ReviewMode(value)[source]#

UNLABELLED:

The labels are added to the images. However, the one person must still go over: all of the labels before submitting them for review.

LABELLED:

The labels are added to the images and are marked as labelled. A reviewer will: still need to approve those.

REVIEWED:

The labels are added to the images and considered reviewed. No more action is: required from the labeler or reviewer.

UNLABELLED = 'unlabelled'#

LABELLED = 'labelled'#

REVIEWED = 'reviewed'#

class encord.orm.project.ProjectImporter(dic)[source]#

DB_FIELDS: collections.OrderedDict = {'errors': <class 'list'>, 'project_hash': typing.Union[str, NoneType]}#

class encord.orm.project.CvatExportType(value)[source]#

An enumeration.

PROJECT = 'project'#

TASK = 'task'#

class encord.orm.project.ProjectImporterCvatInfo(dic)[source]#

DB_FIELDS: collections.OrderedDict = {'export_type': <enum 'CvatExportType'>}#

Expand for references to encord.orm.project_api_key

class encord.orm.project_api_key.ProjectAPIKey(api_key, title, scopes)[source]#

Expand for references to encord.orm.project_api_key.ProjectAPIKey

api_key: str#

title: str#

scopes: List[encord.utilities.client_utilities.APIKeyScopes]#

classmethod from_dict(json_dict)[source]#

class encord.orm.project_with_user_role.ProjectWithUserRole(user_role, project)[source]#

This is a helper class denoting the relationship between the current user an a project

user_role: int#

project: dict#

Utilities - Other#

class encord.http.utils.CloudUploadSettings(max_retries=None, backoff_factor=None, allow_failures=False)[source]#

The settings for uploading data into the GCP cloud storage. These apply for each individual upload. These settings will overwrite the encord.http.constants.RequestsSettings() which is set during encord.EncordUserClient creation.

max_retries: Optional[int] = None#: Number of allowed retries when uploading

backoff_factor: Optional[float] = None#: With each retry, there will be a sleep of backoff_factor * (2 ** (retry_number - 1) )

allow_failures: bool = False#: If failures are allowed, the upload will continue even if some items were not successfully uploaded even after retries. For example, upon creation of a large image group, you might want to create the image group even if a few images were not successfully uploaded. The unsuccessfully uploaded images will then be logged.

Expand for references to encord.http.constants

class encord.http.constants.RequestsSettings(max_retries=0, backoff_factor=0.1)[source]#

The settings for all outgoing network requests. These apply for each individual request.

Expand for references to encord.http.constants.RequestsSettings