DatasetApi¶

class DatasetApi(api)[source]¶

Bases: UpdateableModule, RemoveableModuleApi

API for working with datasets.

Parameters:

api¶: Api object to use for API connection.

Usage Example:

import supervisely as sly
api = sly.Api.from_env()
ds = api.dataset.get_list(project_id=1951)

Methods

`convert_info_to_json`	Convert information about an entity to a dictionary.
`copy`	Copies given Dataset in destination Project by ID.
`copy_batch`	Copy given Datasets to the destination Project by IDs.
`create`	Create Dataset with given name in the given Project.
`exists`	Checks if the dataset with the given name exists in the project.
`get_free_name`	Generates a free name for an entity with the given parent_id and name.
`get_info_by_id`	Get Datasets information by ID.
`get_info_by_name`	Return Dataset information by name or None if Dataset does not exist.
`get_list`	Returns list of dataset in the given project, or list of nested datasets in the dataset with specified parent_id.
`get_list_all`	List all available datasets from all available teams for the user that match the specified filtering criteria.
`get_list_all_pages`	Get list of all or limited quantity entities from the Supervisely server.
`get_list_all_pages_generator`	This generator function retrieves a list of all or a limited quantity of entities from the Supervisely server, yielding batches of entities as they are retrieved
`get_list_idx_page_async`	Get the list of items for a given page number.
`get_list_page_generator_async`	Yields list of images in dataset asynchronously page by page.
`get_nested`	Returns a list of all nested datasets in the specified dataset.
`get_or_create`	Checks if Dataset with given name already exists in the Project, if not creates Dataset with the given name.
`get_tree`	Returns a tree of all datasets in the project as a dictionary, where the keys are the DatasetInfo objects and the values are dictionaries containing the children of the dataset.
`info_sequence`	NamedTuple DatasetInfo information about Dataset.
`info_tuple_name`	NamedTuple name - DatasetInfo.
`move`	Moves given Dataset in destination Project by ID.
`move_batch`	Moves given Datasets to the destination Project by IDs.
`move_to_dataset`	Moves dataset with specified ID to the dataset with specified destination ID.
`quick_import`	Quick import of images and annotations to the dataset.
`remove`	Remove an entity with the specified ID from the Supervisely server.
`remove_batch`	Remove entities with given IDs from the Supervisely server.
`remove_permanently`	!!! WARNING !!! Be careful, this method deletes data from the database, recovery is not possible.
`tree`	Yields tuples of (path, dataset) for all datasets in the project.
`update`	Update Dataset information by given ID.
`update_custom_data`	Update custom data for Dataset by given ID.

Attributes

`MAX_WAIT_ATTEMPTS`	Maximum number of attempts that will be made to wait for a certain condition to be met.
`WAIT_ATTEMPT_TIMEOUT_SEC`	Number of seconds for intervals between attempts.

InfoType¶: alias of DatasetInfo

classmethod convert_info_to_json(info)¶

Convert information about an entity to a dictionary.

Parameters:

info : NamedTuple¶: Information about the entity.

Returns:

Dictionary with information about the entity.

Return type:

dict

static info_sequence()[source]¶

NamedTuple DatasetInfo information about Dataset.

Usage Example:

DatasetInfo(
    id=452984,
    name="ds0",
    description="",
    size="3997776",
    project_id=118909,
    images_count=11,
    items_count=11,
    created_at="2021-03-03T15:54:08.802Z",
    updated_at="2021-03-16T09:31:37.063Z",
    reference_image_url="https://app.supervisely.com/h5un6l2bnaz1vj8a9qgms4-public/images/original/K/q/jf/...png",
    team_id=1,
    workspace_id=2,
)

static info_tuple_name()[source]¶: NamedTuple name - DatasetInfo.

copy(dst_project_id, id, new_name=None, change_name_if_conflict=False, with_annotations=False)[source]¶

Copies given Dataset in destination Project by ID.

Parameters:

dst_project_id : int¶: Destination Project ID in Supervisely.
id : int¶: ID of copied Dataset.
new_name : str, optional¶: New Dataset name.
change_name_if_conflict : bool, optional¶: Checks if given name already exists and adds suffix to the end of the name.
with_annotations : bool, optional¶: If True copies Dataset with annotations, otherwise copies just items from Dataset without annotation.

Returns:

DatasetInfo object with information about the Dataset.

Return type:

DatasetInfo

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

dst_proj_id = 1982
ds_infos = api.dataset.get_list(dst_proj_id)
print(len(ds_infos)) # 0

new_ds = api.dataset.copy(dst_proj_id, id=2540, new_name="banana", with_annotations=True)
ds_infos = api.dataset.get_list(dst_proj_id)
print(len(ds_infos)) # 1

copy_batch(dst_project_id, ids, new_names=None, change_name_if_conflict=False, with_annotations=False)[source]¶

Copy given Datasets to the destination Project by IDs.

Parameters:

dst_project_id : int¶: Destination Project ID in Supervisely.
ids : List[int]¶: IDs of copied Datasets.
new_names : List[str], optional¶: New Datasets names.
change_name_if_conflict : bool, optional¶: Checks if given name already exists and adds suffix to the end of the name.
with_annotations : bool, optional¶: If True copies Datasets with annotations, otherwise copies just items from Datasets without annotations.

Raises:

RuntimeError – if can not match “ids” and “new_names” lists, len(ids) != len(new_names)

Returns:

List of DatasetInfo objects.

Return type:

List[DatasetInfo]

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

dst_proj_id = 1980
ds = api.dataset.get_list(dst_proj_id)
print(len(ds)) # 0

ds_ids = [2532, 2557]
ds_names = ["lemon_test", "kiwi_test"]

copied_datasets = api.dataset.copy_batch(dst_proj_id, ids=ds_ids, new_names=ds_names, with_annotations=True)
ds = api.dataset.get_list(dst_proj_id)
print(len(ds)) # 2

create(project_id, name, description='', change_name_if_conflict=False, parent_id=None, custom_data=None)[source]¶

Create Dataset with given name in the given Project.

Parameters:

project_id : int¶: Project ID in Supervisely where Dataset will be created.
name : str¶: Dataset Name.
description : str, optional¶: Dataset description.
change_name_if_conflict : bool, optional¶: Checks if given name already exists and adds suffix to the end of the name.
parent_id : Union[int, None]¶: Parent Dataset ID. If set to None, then the Dataset will be created at the top level of the Project, otherwise the Dataset will be created in a specified Dataset.
custom_data : Dict[Any, Any], optional¶: Custom data to store in the Dataset.

Returns:

DatasetInfo object with information about the Dataset.

Return type:

DatasetInfo

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

project_id = 116482
ds_infos = api.dataset.get_list(project_id)
print(len(ds_infos)) # 1

new_ds = api.dataset.create(project_id, 'new_ds')
new_ds_infos = api.dataset.get_list(project_id)
print(len(new_ds_info)) # 2

exists(project_id, name, parent_id=None)[source]¶

Checks if the dataset with the given name exists in the project. If parent_id is not None, the search will be performed in the specified Dataset.

Parameters:

project_id : int¶: Project ID in which the Dataset is located.
name : str¶: Dataset name.
parent_id : Union[int, None]¶: Parent Dataset ID. If the Dataset is not nested, then the value is None.

Returns:

True if the dataset exists, False otherwise.

Return type:

bool

get_free_name(parent_id, name)¶

Generates a free name for an entity with the given parent_id and name. Adds an increasing suffix to original name until a unique name is found.

Parameters:

parent_id : int¶: ID of the parent entity.
name : str¶: Name of the entity.

Returns:

Returns free name.

Return type:

str

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

name = "IMG_0315.jpeg"
dataset_id = 55832
free_name = api.image.get_free_name(dataset_id, name)
print(free_name) # IMG_0315_001.jpeg

get_info_by_id(id, raise_error=False)[source]¶

Get Datasets information by ID.

Parameters:

id : int¶: Dataset ID in Supervisely.

Returns:

DatasetInfo object with information about the Dataset.

Return type:

DatasetInfo

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

dataset_id = 384126
ds_info = api.dataset.get_info_by_id(dataset_id)

get_info_by_name(project_id, name, fields=None, parent_id=None)[source]¶

Return Dataset information by name or None if Dataset does not exist. If parent_id is not None, the search will be performed in the specified Dataset. Otherwise the search will be performed at the top level of the Project.

Parameters:

project_id : int¶: Project ID in which the Dataset is located.
name : str¶: Dataset name.
fields : List[str], optional¶: List of fields to return. If None, then all fields are returned.
parent_id : Union[int, None]¶: Parent Dataset ID. If the Dataset is not nested, then the value is None.

Returns:

DatasetInfo object with information about the Dataset.

Return type:

Union[DatasetInfo, None]

get_list(project_id, filters=None, recursive=False, parent_id=None, include_custom_data=False)[source]¶

Returns list of dataset in the given project, or list of nested datasets in the dataset with specified parent_id. To get list of all datasets including nested, recursive parameter should be set to True. Otherwise, the method will return only datasets in the top level.

Parameters:

project_id : int¶: Project ID in which the Datasets are located.
filters : List[dict], optional¶: List of params to sort output Datasets.
recursive : bool, optional¶: If True, returns all Datasets from the given Project including nested Datasets.
parent_id : Union[int, None], optional¶: Parent Dataset ID. If set to None, the search will be performed at the top level of the Project, otherwise the search will be performed in the specified Dataset.
include_custom_data : bool, optional¶: If True, the response will include the custom_data field for each Dataset.

Returns:

List of all Datasets with information for the given Project.

Return type:

List[DatasetInfo]

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

project_id = 1951
ds = api.dataset.get_list(project_id)

print(ds)
# Output: [
#     DatasetInfo(id=2532,
#                 name="lemons",
#                 description="",
#                 size="861069",
#                 project_id=1951,
#                 images_count=6,
#                 items_count=6,
#                 created_at="2021-03-02T10:04:33.973Z",
#                 updated_at="2021-03-10T09:31:50.341Z",
#                 reference_image_url="http://app.supervisely.com/z6ut6j8bnaz1vj8aebbgs4-public/images/original/...jpg"),
#                 DatasetInfo(id=2557,
#                 name="kiwi",
#                 description="",
#                 size="861069",
#                 project_id=1951,
#                 images_count=6,
#                 items_count=6,
#                 created_at="2021-03-10T09:31:33.701Z",
#                 updated_at="2021-03-10T09:31:44.196Z",
#                 reference_image_url="http://app.supervisely.com/h5un6l2bnaz1vj8a9qgms4-public/images/original/...jpg")
# ]

get_list_all(filters=None, sort=None, sort_order=None, per_page=None, page='all', include_custom_data=False)[source]¶

List all available datasets from all available teams for the user that match the specified filtering criteria.

Parameters:

filters : List[Dict[str, str]], optional¶: List of parameters for filtering the available Datasets. Every Dict must consist of keys: - ‘field’: Takes values ‘id’, ‘projectId’, ‘workspaceId’, ‘groupId’, ‘createdAt’, ‘updatedAt’ - ‘operator’: Takes values ‘=’, ‘eq’, ‘!=’, ‘not’, ‘in’, ‘!in’, ‘>’, ‘gt’, ‘>=’, ‘gte’, ‘<’, ‘lt’, ‘<=’, ‘lte’ - ‘value’: Takes on values according to the meaning of ‘field’ or null
sort : str, optional¶: Specifies by which parameter to sort the project list. Takes values ‘id’, ‘name’, ‘size’, ‘createdAt’, ‘updatedAt’
sort_order : str, optional¶: Determines which value to list from.
per_page : int, optional¶: Number of first items found to be returned. ‘None’ will return the first page with a default size of 20000 datasets.
page : Union[int, Literal["all"]], optional¶: Page number, used to retrieve the following items if the number of them found is more than per_page. The default value is ‘all’, which retrieves all available datasets. ‘None’ will return the first page with datasets, the amount of which is set in param ‘per_page’.
include_custom_data : bool, optional¶: If True, the response will include the custom_data field for each Dataset.

Returns:

Search response information and ‘DatasetInfo’ of all datasets that are searched by a given criterion.

Return type:

dict

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

filter_1 = {
    "field": "updatedAt",
    "operator": "<",
    "value": "2023-12-03T14:53:00.952Z"
}
filter_2 = {
    "field": "updatedAt",
    "operator": ">",
    "value": "2023-04-03T14:53:00.952Z"
}
filters = [filter_1, filter_2]
datasets = api.dataset.get_list_all(filters)
print(datasets)
# Output:
# {
#     "total": 2,
#     "perPage": 20000,
#     "pagesCount": 1,
#     "entities": [ DatasetInfo(id = 16,
#                       name = 'ds1',
#                       description = None,
#                       size = '861069',
#                       project_id = 22,
#                       images_count = None,
#                       items_count = None,
#                       created_at = '2020-04-03T13:43:24.000Z',
#                       updated_at = '2020-04-03T14:53:00.952Z',
#                       reference_image_url = None,
#                       team_id = 2,
#                       workspace_id = 2),
#                   DatasetInfo(id = 17,
#                       name = 'ds1',
#                       description = None,
#                       size = '1177212',
#                       project_id = 23,
#                       images_count = None,
#                       items_count = None,
#                       created_at = '2020-04-03T13:43:24.000Z',
#                       updated_at = '2020-04-03T14:53:00.952Z',
#                       reference_image_url = None,
#                       team_id = 2,
#                       workspace_id = 2
#                       )
#                 ]
# }

get_list_all_pages(method, data, progress_cb=None, convert_json_info_cb=None, limit=None, return_first_response=False)¶

Get list of all or limited quantity entities from the Supervisely server.

Parameters:

method : str¶: Request method name
data : dict¶: Dictionary with request body info
progress_cb : Progress, optional¶: Function for tracking download progress.
convert_json_info_cb : Callable, optional¶: Function for convert json info
limit : int, optional¶: Number of entity to retrieve
return_first_response : bool, optional¶: Specify if return first response

Returns:

List of entities.

Return type:

List[dict]

get_list_all_pages_generator(method, data, progress_cb=None, convert_json_info_cb=None, limit=None, return_first_response=False)¶

This generator function retrieves a list of all or a limited quantity of entities from the Supervisely server, yielding batches of entities as they are retrieved

Parameters:

method : str¶: Request method name
data : dict¶: Dictionary with request body info
progress_cb : Progress, optional¶: Function for tracking download progress.
convert_json_info_cb : Callable, optional¶: Function for convert json info
limit : int, optional¶: Number of entity to retrieve
return_first_response : bool, optional¶: Specify if return first response

async get_list_idx_page_async(method, data)¶

Get the list of items for a given page number. Page number is specified in the data dictionary.

Parameters:

method : str¶: Method to call for listing items.
data : dict¶: Data to pass to the API method.

Returns:

List of items.

Return type:

Tuple[int, List[NamedTuple]]

async get_list_page_generator_async(method, data, pages_count=None, semaphore=None)¶

Yields list of images in dataset asynchronously page by page.

Parameters:

method : str¶: Method to call for listing items.
data : dict¶: Data to pass to the API method.
pages_count : int, optional¶: Preferred number of pages to retrieve if used with a per_page limit. Will be automatically adjusted if the pagesCount differs from the requested number.
semaphore=None¶: Semaphore for limiting the number of simultaneous requests.

Returns:

List of images in dataset.

Return type:

AsyncGenerator[List[ImageInfo]]

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

method = 'images.list'
data = {'datasetId': 123456}

loop = sly.utils.get_or_create_event_loop()
images = loop.run_until_complete(api.image.get_list_generator_async(method, data))

get_nested(project_id, dataset_id)[source]¶

Returns a list of all nested datasets in the specified dataset.

Parameters:

project_id : int¶: Project ID in which the Dataset is located.
dataset_id : int¶: Dataset ID for which the nested datasets are returned.

Returns:

List of nested datasets.

Return type:

List[DatasetInfo]

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

project_id = 123
dataset_id = 456
datasets = api.dataset.get_nested(project_id, dataset_id)
for dataset in datasets:
    print(dataset.name, dataset.id) # Output: ds1 123

get_or_create(project_id, name, description='', parent_id=None)[source]¶

Checks if Dataset with given name already exists in the Project, if not creates Dataset with the given name. If parent id is specified then the search will be performed in the specified Dataset, otherwise the search will be performed at the top level of the Project.

Parameters:

project_id : int¶: Project ID in Supervisely.
name : str¶: Dataset name.
description : str, optional¶: Dataset description.
parent_id : Union[int, None]¶: Parent Dataset ID. If set to None, then the Dataset will be created at the top level of the Project, otherwise the Dataset will be created in a specified Dataset.

Returns:

DatasetInfo object with information about the Dataset.

Return type:

DatasetInfo

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

project_id = 116482
ds_infos = api.dataset.get_list(project_id)
print(len(ds_infos)) # 1

api.dataset.get_or_create(project_id, 'ds1')
ds_infos = api.dataset.get_list(project_id)
print(len(ds_infos)) # 1

api.dataset.get_or_create(project_id, 'new_ds')
ds_infos = api.dataset.get_list(project_id)
print(len(ds_infos)) # 2

get_tree(project_id)[source]¶

Returns a tree of all datasets in the project as a dictionary, where the keys are the DatasetInfo objects and the values are dictionaries containing the children of the dataset. Recommended to use with the dataset_tree method to iterate over the tree.

Parameters:

project_id : int¶: Project ID for which the tree is built.

Returns:

Dictionary of datasets and their children.

Return type:

Dict[DatasetInfo, Dict]

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

project_id = 123
dataset_tree = api.dataset.get_tree(project_id)
print(dataset_tree)
# Output:
# {
#     DatasetInfo(id=2532, name="lemons", description="", ...: {
#         DatasetInfo(id=2557, name="kiwi", description="", ...: {}
#     }
# }

move(dst_project_id, id, new_name=None, change_name_if_conflict=False, with_annotations=False)[source]¶

Moves given Dataset in destination Project by ID.

Parameters:

dst_project_id : int¶: Destination Project ID in Supervisely.
id : int¶: ID of moved Dataset.
new_name : str, optional¶: New Dataset name.
change_name_if_conflict : bool, optional¶: Checks if given name already exists and adds suffix to the end of the name.
with_annotations : bool, optional¶: If True moves Dataset with annotations, otherwise moves just items from Dataset without annotation.

Returns:

DatasetInfo object with information about the Dataset.

Return type:

DatasetInfo

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

dst_proj_id = 1985
ds = api.dataset.get_list(dst_proj_id)
print(len(ds)) # 0

new_ds = api.dataset.move(dst_proj_id, id=2550, new_name="cucumber", with_annotations=True)
ds = api.dataset.get_list(dst_proj_id)
print(len(ds)) # 1

move_batch(dst_project_id, ids, new_names=None, change_name_if_conflict=False, with_annotations=False)[source]¶

Moves given Datasets to the destination Project by IDs.

Parameters:

dst_project_id : int¶: Destination Project ID in Supervisely.
ids : List[int]¶: IDs of moved Datasets.
new_names : List[str], optional¶: New Datasets names.
change_name_if_conflict : bool, optional¶: Checks if given name already exists and adds suffix to the end of the name.
with_annotations : bool, optional¶: If True moves Datasets with annotations, otherwise moves just items from Datasets without annotations.

Raises:

RuntimeError – if can not match “ids” and “new_names” lists, len(ids) != len(new_names)

Returns:

List of DatasetInfo objects.

Return type:

List[DatasetInfo]

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

dst_proj_id = 1978
ds_infos = api.dataset.get_list(dst_proj_id)
print(len(ds_infos)) # 0

ds_ids = [2545, 2560]
ds_names = ["banana_test", "mango_test"]

movied_datasets = api.dataset.move_batch(dst_proj_id, ids=ds_ids, new_names=ds_names, with_annotations=True)
ds_infos = api.dataset.get_list(dst_proj_id)
print(len(ds_infos)) # 2

move_to_dataset(dataset_id, destination_dataset_id)[source]¶

Moves dataset with specified ID to the dataset with specified destination ID.

Parameters:

dataset_id : int¶: ID of the dataset to be moved.
destination_dataset_id : int¶: ID of the destination dataset.

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

dataset_id = 123
destination_dataset_id = 456
api.dataset.move_to_dataset(dataset_id, destination_dataset_id)

quick_import(dataset, blob_path, offsets_path, anns, project_meta=None, project_type=None, log_progress=True)[source]¶

Quick import of images and annotations to the dataset. Used only for extended Supervisely format with blobs. Project will be automatically marked as blob project.

IMPORTANT: Number of annotations must be equal to the number of images in offset file.: Image names in the offset file and annotation files must match.

Parameters:

dataset¶: Dataset ID or DatasetInfo object.
blob_path : str¶: Local path to the blob file.
offsets_path : str¶: Local path to the offsets file.
anns : List[str]¶: List of annotation paths.
project_meta=None¶: ProjectMeta object.
project_type=None¶: Project type.
log_progress : bool, optional¶: If True, show progress bar.

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly
from supervisely.project.project_meta import ProjectMeta, ProjectType

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

dataset_id = 123
workspace_id = 456
blob_path = "/path/to/blob"
offsets_path = "/path/to/offsets"
project_meta_path = "/path/to/project_meta.json"
anns = ["/path/to/ann1.json", "/path/to/ann2.json", ...]

# Create a new project, dataset and update its meta
project = api.project.create(
    workspace_id,
    "Quick Import",
    type=sly.ProjectType.IMAGES,
    change_name_if_conflict=True,
)
dataset = api.dataset.create(project.id, "ds1")
project_meta_json = sly.json.load_json_file(project_meta_path)
meta = api.project.update_meta(project.id, meta=project_meta_json)

dataset_info = api.dataset.quick_import(
    dataset=dataset.id,
    blob_path=blob_path,
    offsets_path=offsets_path,
    anns=anns,
    project_meta=ProjectMeta(),
    project_type=ProjectType.IMAGES,
    log_progress=True
)

remove(id)¶

Remove an entity with the specified ID from the Supervisely server.

Parameters:

id : int¶: Entity ID in Supervisely

Returns:

None

Return type:

None

remove_batch(ids, progress_cb=None)¶

Remove entities with given IDs from the Supervisely server.

Parameters:

ids : List[int]¶: IDs of entities in Supervisely.
progress_cb : Callable¶: Function for control remove progress.

Returns:

None

Return type:

None

remove_permanently(ids, batch_size=50, progress_cb=None)[source]¶

!!! WARNING !!! Be careful, this method deletes data from the database, recovery is not possible.

Delete permanently datasets with given IDs from the Supervisely server. All dataset IDs must belong to the same team. Therefore, it is necessary to sort IDs before calling this method.

Parameters:

ids : Union[int, List]¶: IDs of datasets in Supervisely.
batch_size : int, optional¶: The number of entities that will be deleted by a single API call. This value must be in the range 1-50 inclusive, if you set a value out of range it will automatically adjust to the boundary values.
progress_cb : Callable, optional¶: Function for control delete progress.

Returns:

A list of response content in JSON format for each API call.

Return type:

List[dict]

tree(project_id, dataset_id=None)[source]¶

Yields tuples of (path, dataset) for all datasets in the project. Path of the dataset is a list of parents, e.g. [“ds1”, “ds2”, “ds3”]. For root datasets, the path is an empty list.

Parameters:

project_id : int¶: Project ID in which the Dataset is located.
dataset_id : Optional[int]¶: Optional Dataset ID to start the tree from. If provided, only yields the subtree starting from this dataset (including the dataset itself and all its children).

Returns:

Generator of tuples of (path, dataset).

Return type:

Generator[Tuple[List[str], DatasetInfo], None, None]

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

project_id = 123
# Get all datasets in the project
for parents, dataset in api.dataset.tree(project_id):
    parents: List[str]
    dataset: sly.DatasetInfo
    print(parents, dataset.name)

# Get only a specific branch starting from dataset_id = 456
for parents, dataset in api.dataset.tree(project_id, dataset_id=456):
    parents: List[str]
    dataset: sly.DatasetInfo
    print(parents, dataset.name)

# Output:
# [] ds1
# ["ds1"] ds2
# ["ds1", "ds2"] ds3

update(id, name=None, description=None, custom_data=None)[source]¶

Update Dataset information by given ID.

Parameters:

id : int¶: Dataset ID in Supervisely.
name : str, optional¶: New Dataset name.
description : str, optional¶: New Dataset description.
custom_data : Dict[Any, Any], optional¶: New custom data.

Returns:

DatasetInfo object with information about the Dataset.

Return type:

DatasetInfo

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

dataset_id = 384126
new_ds = api.dataset.update(dataset_id, name='new_ds', description='new description')

update_custom_data(id, custom_data)[source]¶

Update custom data for Dataset by given ID. Custom data is a dictionary that can store any additional information about the Dataset.

Parameters:

id : int¶: Dataset ID in Supervisely.
custom_data : Dict[Any, Any]¶: New custom data.

Returns:

DatasetInfo object with information about the Dataset.

Return type:

DatasetInfo

Usage Example:

import os
from dotenv import load_dotenv

import supervisely as sly

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
if sly.is_development():
    load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

dataset_id = 384126
new_ds = api.dataset.update_custom_data(dataset_id, custom_data={'key': 'value'})