Dataset

Dataset Objects

class Dataset()

Access dataset-related data and manipulate the dataset.

dataset_hash

@property
def dataset_hash() -> str

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

str - The dataset hash.

title

@property
def title() -> str

Get the title of the dataset. Returns:

str - The title of the dataset.

description

@property
def description() -> str

Get the description of the dataset. Returns:

str - The description of the dataset.

storage_location

@property
def storage_location() -> StorageLocation

Get the storage location of the dataset. Returns:

StorageLocation - The storage location of the dataset.

backing_folder_uuid

@property
def backing_folder_uuid() -> Optional[UUID]

Get the UUID of the backing folder. Returns:

Optional[UUID] - The UUID of the backing folder, if any.

data_rows

@property
def data_rows() -> List[DataRow]

Get the data rows of the dataset. Part of the response of this function can be configured by the set_access_settings() method.

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

Returns:

List[DataRow] - A list of DataRow objects.

list_data_rows

def list_data_rows(title_eq: Optional[str] = None,
                   title_like: Optional[str] = None,
                   created_before: Optional[Union[str, datetime]] = None,
                   created_after: Optional[Union[str, datetime]] = None,
                   data_types: Optional[List[DataType]] = None,
                   data_hashes: Optional[List[str]] = None) -> List[DataRow]

Retrieve dataset rows (pointers to data, labels). Arguments:

title_eq - Optional exact title row filter.
title_like - Optional fuzzy title row filter; SQL syntax.
created_before - Optional datetime row filter.
created_after - Optional datetime row filter.
data_types - Optional data types row filter.
data_hashes - Optional list of individual data unit hashes to include.

Returns:

List[DataRow] - A list of DataRow objects that match the filter.

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.

refetch_data

def refetch_data() -> None

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 Use this function if you suspect the state of those properties to be outdated.

get_dataset

def get_dataset() -> OrmDataset

Get the dataset instance. This function is exposed for convenience. You are encouraged to use the property accessors instead. Returns:

OrmDataset - The dataset instance.

set_access_settings

def set_access_settings(dataset_access_settings: DatasetAccessSettings,
                        *,
                        refetch_data: bool = True) -> None

Set access settings for the dataset. Arguments:

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

add_users

def add_users(user_emails: List[str],
              user_role: DatasetUserRole) -> List[DatasetUser]

Add users to the dataset. If the user already exists in the Dataset, this operation succeeds but the user_role remains unchanged. The existing user_role is reflected in the DatasetUser instance returned. Arguments:

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

Returns:

List[DatasetUser] - A list of DatasetUser instances reflecting the added users.

list_groups

def list_groups() -> Iterable[DatasetGroup]

List all groups that have access to the dataset. Returns:

Iterable[DatasetGroup] - An iterable of DatasetGroup instances.

add_group

def add_group(group_hash: Union[List[UUID], UUID],
              user_role: DatasetUserRole) -> None

Add a group to the dataset. Arguments:

group_hash - List of group hashes to be added.
user_role - The user role that the group will be given.

Returns: None

remove_group

def remove_group(group_hash: Union[List[UUID], UUID]) -> None

Remove a group from the dataset. Arguments:

group_hash - List of group hashes to be removed.

Returns: None

upload_video

def upload_video(
        file_path: Union[str, Path],
        cloud_upload_settings: CloudUploadSettings = CloudUploadSettings(),
        title: Optional[str] = None,
        folder: Optional[Union[UUID, StorageFolder]] = None) -> Video

Upload a video to Encord storage. Arguments:

file_path - Path to the video, e.g., ‘/home/user/data/video.mp4’.
cloud_upload_settings - Settings for uploading data into the cloud. Change this object to overwrite the default values.
title - The video title. If unspecified, this will be the file name. This title should include an extension. For example: “encord_video.mp4”.
folder - When uploading to a non-mirror dataset, you have to specify the folder to store the file in. This can be either a StorageFolder instance or the UUID of the folder.

Returns:

Video - An object describing the created video, see Video.

Raises:

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

create_image_group

def create_image_group(
        file_paths: Iterable[Union[str, Path]],
        max_workers: Optional[int] = None,
        cloud_upload_settings: CloudUploadSettings = CloudUploadSettings(),
        title: Optional[str] = None,
        *,
        create_video: bool = True,
        folder: Optional[Union[UUID,
                               StorageFolder]] = None) -> List[ImageGroup]

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

file_paths - A list of paths to images, e.g., [‘/home/user/data/img1.png’, ‘/home/user/data/img2.png’].
max_workers - DEPRECATED: This argument will be ignored.
cloud_upload_settings - Settings for uploading data into the cloud. Change this object to overwrite the default values.
title - 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 - 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.
folder - When uploading to a non-mirror dataset, you have to specify the folder to store the file in. This can be either a StorageFolder instance or the UUID of the folder.

Returns:

List[ImageGroup] - A list containing the object(s) describing the created data unit(s). See ImageGroup.

Raises:

UploadOperationNotSupportedError - If trying to upload to external datasets (e.g., S3/GPC/Azure).
InvalidArgumentError - If the folder is specified, but the dataset is a mirror dataset.

create_dicom_series

def create_dicom_series(
        file_paths: Union[Collection[str], Collection[Path],
                          Collection[Union[Path, str]]],
        cloud_upload_settings: CloudUploadSettings = CloudUploadSettings(),
        title: Optional[str] = None,
        folder: Optional[Union[UUID, StorageFolder]] = None) -> Dict

Upload a DICOM series to Encord storage. Arguments:

file_paths - A collection of paths to DICOM files. Example: [‘/home/user/data/DICOM_1.dcm’, ‘/home/user/data/DICOM_2.dcm’]
cloud_upload_settings - Settings for uploading data into the cloud. Change this object to overwrite the default values.
title - The title of the DICOM series. If unspecified, a random title will be generated. This title should NOT include an extension. Example: “encord_image_group”
folder - When uploading to a non-mirror dataset, specify the folder to store the file in. This can be either a StorageFolder instance or the UUID of the folder.

Returns: A dictionary describing the created series. Raises:

UploadOperationNotSupportedError - If trying to upload to external datasets (e.g., S3/GPC/Azure).
InvalidArgumentError - If the folder is specified, but the dataset is a mirror dataset.

upload_image

def upload_image(
        file_path: Union[Path, str],
        title: Optional[str] = None,
        cloud_upload_settings: CloudUploadSettings = CloudUploadSettings(),
        folder: Optional[Union[UUID, StorageFolder]] = None) -> Image

Upload a single image to Encord storage. For sequential images, consider creating an image group using the create_image_group() function. Arguments:

file_path - The file path to the image.
title - The image title. If unspecified, the file name will be used. This title should include an extension. Example: “encord_image.png”
cloud_upload_settings - Settings for uploading data into the cloud. Change this object to overwrite the default values.
folder - When uploading to a non-mirror dataset, specify the folder to store the file in. This can be either a StorageFolder instance or the UUID of the folder.

Returns: Uploaded Image object.

link_items

def link_items(
    item_uuids: List[UUID],
    duplicates_behavior: DataLinkDuplicatesBehavior = DataLinkDuplicatesBehavior
    .SKIP
) -> List[DataRow]

Link storage items to the dataset, creating new data rows. Arguments:

item_uuids - List of item UUIDs to link to the dataset.
duplicates_behavior - The behavior to follow when encountering duplicates. Defaults to SKIP. See also DataLinkDuplicatesBehavior.

Returns: List of DataRow objects representing linked items.

delete_data

def delete_data(data_hashes: Union[List[str], str])

Delete a video/image group from a dataset. Arguments:

data_hashes - List of hashes of the videos/image_groups you’d like to delete. All should belong to the same dataset.

add_private_data_to_dataset

def add_private_data_to_dataset(
        integration_id: str,
        private_files: Union[str, Dict, Path, TextIO],
        ignore_errors: bool = False) -> AddPrivateDataResponse

Append data hosted on a private cloud to an existing dataset. For a more complete example of safe uploads, please follow the guide found in our docs under :ref:https://python.docs.encord.com/tutorials/datasets.html#adding-data-from-a-private-cloud Arguments:

integration_id - The EntityId of the cloud integration you wish to use.
private_files - A path to a JSON file, JSON string, Python dictionary, or a Path object containing the files you wish to add.
ignore_errors - When set to True, prevent individual errors from stopping the upload process.

Returns: List of DatasetDataInfo objects containing data_hash and title.

add_private_data_to_dataset_start

def add_private_data_to_dataset_start(
        integration_id: str,
        private_files: Union[str, Dict, Path, TextIO],
        ignore_errors: bool = False,
        *,
        folder: Optional[Union[StorageFolder, UUID]] = None) -> str

Append data hosted on a private cloud to an existing dataset. This method initializes the upload in Encord’s backend. Once the upload ID has been returned, you can exit the terminal while the job continues uninterrupted. You can check upload job status at any point using the add_private_data_to_dataset_get_result() method. This can be done in a separate Python session to the one where the upload was initialized. Arguments:

integration_id - The EntityId of the cloud integration you wish to use. private_files:A path to a JSON file, JSON string, Python dictionary, or a Path object containing the files you wish to add.
ignore_errors - When set to True, prevent individual errors from stopping the upload process.
folder - When uploading to a non-mirror dataset, specify the folder to store the file in. This can be either a StorageFolder instance or the UUID of the folder.

Returns: UUID Identifier of the upload job. This ID enables the user to track the job progress via SDK or web app.

add_private_data_to_dataset_get_result

def add_private_data_to_dataset_get_result(
        upload_job_id: str,
        timeout_seconds: int = 7 * 24 * 60 * 60) -> DatasetDataLongPolling

Fetch data upload status, perform long polling process for timeout_seconds. Arguments:

upload_job_id - UUID Identifier of the upload job. This ID enables the user to track the job progress via SDK or web app.
timeout_seconds - Number of seconds the method will wait while waiting for a response. If timeout_seconds == 0, only a single checking request is performed. Response will be immediately returned.

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

update_data_item

@deprecated(version="0.1.192", alternative="encord.storage.StorageItem.update")
def update_data_item(data_hash: str, new_title: str) -> bool

DEPRECATED: Use update() to update the :attr:encord.storage.StorageItem.name of the underlying StorageItem. Update a data item. Arguments:

data_hash - Data hash of the item being updated.
new_title - New title of the data item being updated.

Returns: Boolean indicating whether the update was successful.

re_encode_data

def re_encode_data(data_hashes: List[str])

Launch an async task that can re-encode a list of videos. Arguments:

data_hashes - List of hashes of the videos you’d like to re-encode. All should belong to the same dataset.

Returns: Entity ID of the async task launched.

re_encode_data_status

def re_encode_data_status(job_id: int)

Returns the status of an existing async task aimed at re-encoding videos. Arguments:

job_id - 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.

run_ocr

def run_ocr(image_group_id: str) -> List[ImageGroupOCR]

Returns an optical character recognition result for a given image group. Arguments:

image_group_id - The ID of the image group in this dataset to run OCR on.

Returns: List of ImageGroupOCR objects representing the text and corresponding coordinates found in each frame of the image group.

get_cloud_integrations

@deprecated(version="0.1.154",
            alternative="EncordUserClient.get_cloud_integrations")
def get_cloud_integrations() -> List[CloudIntegration]

Retrieve a list of cloud integrations configured in Encord. Returns: List of CloudIntegration objects representing configured cloud integrations.

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General

Data

Ontologies

Projects

Labels

DICOM

Dataset Objects

dataset_hash

title

description

storage_location

backing_folder_uuid

data_rows

list_data_rows

refetch_data

get_dataset

set_access_settings

add_users

list_groups

add_group

remove_group

upload_video

create_image_group

create_dicom_series

upload_image

link_items

delete_data

add_private_data_to_dataset

add_private_data_to_dataset_start

add_private_data_to_dataset_get_result

update_data_item

re_encode_data

re_encode_data_status

run_ocr

get_cloud_integrations

​Dataset Objects

​dataset_hash

​title

​description

​storage_location

​backing_folder_uuid

​data_rows

​list_data_rows

​refetch_data

​get_dataset

​set_access_settings

​add_users

​list_groups

​add_group

​remove_group

​upload_video

​create_image_group

​create_dicom_series

​upload_image

​link_items

​delete_data

​add_private_data_to_dataset

​add_private_data_to_dataset_start

​add_private_data_to_dataset_get_result

​update_data_item

​re_encode_data

​re_encode_data_status

​run_ocr

​get_cloud_integrations

Dataset Objects

dataset_hash

title

description

storage_location

backing_folder_uuid

data_rows

list_data_rows

refetch_data

get_dataset

set_access_settings

add_users

list_groups

add_group

remove_group

upload_video

create_image_group

create_dicom_series

upload_image

link_items

delete_data

add_private_data_to_dataset

add_private_data_to_dataset_start

add_private_data_to_dataset_get_result

update_data_item

re_encode_data

re_encode_data_status

run_ocr

get_cloud_integrations