LabelingJobApi¶
- class LabelingJobApi[source]¶
Bases:
supervisely.api.module_api.RemoveableBulkModuleApi,supervisely.api.module_api.ModuleWithStatusAPI for working with Labeling Jobs.
LabelingJobApiobject is immutable.- Parameters
- api : Api
API connection to the server.
- 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() # Pass values into the API constructor (optional, not recommended) # api = sly.Api(server_address="https://app.supervise.ly", token="4r47N...xaTatb") jobs = api.labeling_job.get_list(9) # api usage example
Methods
Archives Labeling Job with given ID.
Clone Labeling Job with given ID.
Creates Labeling Job and assigns given Users to it.
Checks if an entity with the given parent_id and name exists
get_activity- rtype
DataFrame
Return annotations for given image ids from labeling job with given id.
Generates a free name for an entity with the given parent_id and name.
Get Labeling Job information by ID.
Get information about an entity by its name from the Supervisely server.
Get list of information about Labeling Job in the given Team.
Get list of all or limited quantity entities from the Supervisely server.
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
Returns project meta with classes and tags used in the labeling job with given id.
Get stats of given Labeling Job ID.
Get status of Labeling Job with given ID.
NamedTuple LabelingJobInfo information about Labeling Job.
NamedTuple name - LabelingJobInfo.
raise_for_statusReject annotations for all or unmarked entities in the labeling job with given id.
Remove an entity with the specified ID from the Supervisely server.
Remove entities in batches from the Supervisely server.
Restart Labeling Job with given ID.
Sets review status for entity with given ID.
Sets Labeling Job status.
Makes Labeling Job unavailable for labeler with given User ID.
Wait for a Labeling Job to change to the expected target status.
Attributes
MAX_WAIT_ATTEMPTSMaximum number of attempts that will be made to wait for a certain condition to be met.
WAIT_ATTEMPT_TIMEOUT_SECNumber of seconds for intervals between attempts.
- InfoType¶
alias of
supervisely.api.module_api.LabelingJobInfo
- class Status[source]¶
Bases:
supervisely.collection.str_enum.StrEnumLabeling Job status.
-
COMPLETED =
'completed'¶
-
IN_PROGRESS =
'in_progress'¶
-
ON_REVIEW =
'on_review'¶
-
PENDING =
'pending'¶
-
STOPPED =
'stopped'¶
-
COMPLETED =
- archive(id)[source]¶
Archives Labeling Job with given ID.
- Parameters
- id : int
Labeling Job ID in Supervisely.
- Returns
None
- Return type
NoneType- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() api.labeling_job.archive(23)
-
clone(id, new_title=
None, reviewer_id=None, assignee_ids=None)[source]¶ Clone Labeling Job with given ID.
- Parameters
- Returns
List of information about Labeling Jobs. See
info_sequence- Return type
List[LabelingJobInfo]
-
create(name, dataset_id, user_ids, readme=
None, description=None, classes_to_label=None, objects_limit_per_image=None, tags_to_label=None, tags_limit_per_image=None, include_images_with_tags=None, exclude_images_with_tags=None, images_range=None, reviewer_id=None, images_ids=[], dynamic_classes=False, dynamic_tags=False)[source]¶ Creates Labeling Job and assigns given Users to it.
- Parameters
- name : str
Labeling Job name in Supervisely.
- dataset_id : int
Dataset ID in Supervisely.
- user_ids : List[int]
User IDs in Supervisely to assign Users as labelers to Labeling Job.
- readme : str, optional
Additional information about Labeling Job.
- description : str, optional
Description of Labeling Job.
- classes_to_label : List[str], optional
List of classes to label in Dataset.
- objects_limit_per_image : int, optional
Limit the number of objects that the labeler can create on each image.
- tags_to_label : List[str], optional
List of tags to label in Dataset.
- tags_limit_per_image : int, optional
Limit the number of tags that the labeler can create on each image.
- include_images_with_tags : List[str], optional
Include images with given tags for processing by labeler.
- exclude_images_with_tags : List[str], optional
Exclude images with given tags for processing by labeler.
- images_range : List[int, int], optional
Limit number of images to be labeled for each labeler.
- reviewer_id : int, optional
User ID in Supervisely to assign User as Reviewer to Labeling Job.
- images_ids : List[int], optional
List of images ids to label in dataset
- dynamic_classes : bool, optional
If True, classes created after creating the job will be available for annotators
- dynamic_tags : bool, optional
If True, tags created after creating the job will be available for annotators
- Returns
List of information about new Labeling Job. See
info_sequence- Return type
List[LabelingJobInfo]- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() user_name = 'alex' dataset_id = 602 new_label_jobs = api.labeling_job.create(user_name, dataset_id, user_ids=[111, 222], readme='Readmy text', description='Work for labelers', objects_limit_per_image=5, tags_limit_per_image=3) print(new_label_jobs) # Output: [ # [ # 92, # "alex (#1) (#3)", # "Readmy text", # "Work for labelers", # 13, # 29, # "Labelling Workspace", # 494, # "Test Dataset", # 602, # "ds1", # 8, # "alex", # 111, # "quantigo273", # 8, # "alex", # "2021-03-25T11:04:34.031Z", # null, # null, # "pending", # false, # 3, # 0, # 0, # 0, # 0, # [], # [], # [ # null, # null # ], # 5, # 3, # [], # [], # [], # [ # { # "reviewStatus": "none", # "id": 287244, # "name": "IMG_0813" # }, # { # "reviewStatus": "none", # "id": 287246, # "name": "IMG_0432" # }, # { # "reviewStatus": "none", # "id": 287245, # "name": "IMG_0315" # } # ] # ], # [ # 93, # "alex (#2) (#3)", # "Readmy text", # "Work for labelers", # 13, # 29, # "Labelling Workspace", # 494, # "Test Dataset", # 602, # "ds1", # 8, # "alex", # 222, # "quantigo19", # 8, # "alex", # "2021-03-25T11:04:34.031Z", # null, # null, # "pending", # false, # 3, # 0, # 0, # 0, # 0, # [], # [], # [ # null, # null # ], # 5, # 3, # [], # [], # [], # [ # { # "reviewStatus": "none", # "id": 287248, # "name": "IMG_8454" # }, # { # "reviewStatus": "none", # "id": 287249, # "name": "IMG_6896" # }, # { # "reviewStatus": "none", # "id": 287247, # "name": "IMG_1942" # } # ] # ] # ]
- exists(parent_id, name)¶
Checks if an entity with the given parent_id and name exists
- Parameters
- Returns
Returns True if entity exists, and False if not
- Return type
- Usage example
import supervisely as sly # You can connect to API directly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Or you can use API from environment os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() name = "IMG_0315.jpeg" dataset_id = 55832 exists = api.image.exists(dataset_id, name) print(exists) # True
-
get_annotations(id, image_ids, project_meta=
None)[source]¶ Return annotations for given image ids from labeling job with given id.
- Parameters
- id : int
Labeling Job ID in Supervisely.
- image_ids : int
Image IDs in Supervisely.
- project_meta :
ProjectMeta, optional Project meta of the labeling job with given id. Can be retrieved with
get_project_meta().
- Returns
Annotation for given image id from labeling job with given id.
- Return type
Annotation
- 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
- Returns
Returns free name.
- Return type
- Usage example
import supervisely as sly # You can connect to API directly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Or you can use API from environment os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' 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)[source]¶
Get Labeling Job information by ID.
- Parameters
- id : int
Labeling Job ID in Supervisely.
- Returns
Information about Labeling Job. See
info_sequence- Return type
LabelingJobInfo- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() label_job_info = api.labeling_job.get_info_by_id(2) print(label_job_info) # Output: [ # 2, # "Annotation Job (#1) (#1) (dataset_01)", # "", # "", # 4, # 8, # "First Workspace", # 58, # "tutorial_project", # 54, # "dataset_01", # 4, # "anna", # 4, # "anna", # 4, # "anna", # "2020-04-08T15:10:12.618Z", # "2020-04-08T15:10:19.833Z", # "2020-04-08T15:13:39.788Z", # "completed", # false, # 3, # 0, # 1, # 2, # 2, # [], # [], # [ # 1, # 5 # ], # null, # null, # [], # [], # [], # [ # { # "reviewStatus": "rejected", # "id": 283, # "name": "image_03" # }, # { # "reviewStatus": "accepted", # "id": 282, # "name": "image_02" # }, # { # "reviewStatus": "accepted", # "id": 281, # "name": "image_01" # } # ] # ]
-
get_info_by_name(parent_id, name, fields=
[])¶ Get information about an entity by its name from the Supervisely server.
- Parameters
- Usage example
import supervisely as sly # You can connect to API directly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Or you can use API from environment os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() dataset_id = 55832 name = "IMG_0315.jpeg" info = api.image.get_info_by_name(dataset_id, name) print(info) # Output: ImageInfo(id=19369643, name='IMG_0315.jpeg', ...)
-
get_list(team_id, created_by_id=
None, assigned_to_id=None, project_id=None, dataset_id=None, show_disabled=False, reviewer_id=None)[source]¶ Get list of information about Labeling Job in the given Team.
- Parameters
- team_id : int
Team ID in Supervisely.
- created_by_id : int, optional
ID of the User who created the LabelingJob.
- assigned_to_id : int, optional
ID of the assigned User.
- project_id : int, optional
Project ID in Supervisely.
- dataset_id : int, optional
Dataset ID in Supervisely.
- show_disabled : bool, optional
Show disabled Labeling Jobs.
- reviewer_id : int, optional
ID of the User who reviews the LabelingJob.
- Returns
List of information about Labeling Jobs. See
info_sequence- Return type
List[LabelingJobInfo]- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() label_jobs = api.labeling_job.get_list(4) print(label_jobs) # Output: [ # [ # 2, # "Annotation Job (#1) (#1) (dataset_01)", # "", # "", # 4, # 8, # "First Workspace", # 58, # "tutorial_project", # 54, # "dataset_01", # 4, # "anna", # 4, # "anna", # 4, # "anna", # "2020-04-08T15:10:12.618Z", # "2020-04-08T15:10:19.833Z", # "2020-04-08T15:13:39.788Z", # "completed", # false, # 3, # 0, # 1, # 2, # 2, # [], # [], # [ # 1, # 5 # ], # null, # null, # [], # [], # [], # null # ], # [ # 3, # "Annotation Job (#1) (#2) (dataset_02)", # "", # "", # 4, # 8, # "First Workspace", # 58, # "tutorial_project", # 55, # "dataset_02", # 4, # "anna", # 4, # "anna", # 4, # "anna", # "2020-04-08T15:10:12.618Z", # "2020-04-08T15:15:46.749Z", # "2020-04-08T15:17:33.572Z", # "completed", # false, # 2, # 0, # 0, # 2, # 2, # [], # [], # [ # 1, # 5 # ], # null, # null, # [], # [], # [], # null # ] # ]
-
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
-
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
- get_project_meta(id)[source]¶
Returns project meta with classes and tags used in the labeling job with given id.
- Parameters
- id : int
Labeling Job ID in Supervisely.
- Returns
Project meta of the labeling job with given id.
- Return type
ProjectMeta
- get_stats(id)[source]¶
Get stats of given Labeling Job ID.
- Parameters
- id : int
Labeling Job ID in Supervisely.
- Returns
Dict with information about given Labeling Job
- Return type
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() status = api.labeling_job.get_stats(3) print(status) # Output: { # "job": { # "editingDuration": 0, # "annotationDuration": 720, # "id": 3, # "name": "Annotation Job (#1) (#2) (dataset_02)", # "startedAt": "2020-04-08T15:15:46.749Z", # "finishedAt": "2020-04-08T15:17:33.572Z", # "imagesCount": 2, # "finishedImagesCount": 2, # "tagsStats": [ # { # "id": 24, # "color": "#ED68A1", # "images": 1, # "figures": 1, # "name": "car_color" # }, # { # "id": 19, # "color": "#A0A08C", # "images": 0, # "figures": 1, # "name": "cars_number" # }, # { # "id": 20, # "color": "#D98F7E", # "images": 1, # "figures": 1, # "name": "like" # }, # { # "id": 23, # "color": "#65D37C", # "images": 0, # "figures": 1, # "name": "person_gender" # }, # { # "parentId": 23, # "color": "#65D37C", # "images": 0, # "figures": 1, # "name": "person_gender (male)" # }, # { # "parentId": 23, # "color": "#65D37C", # "images": 0, # "figures": 0, # "name": "person_gender (female)" # }, # { # "id": 21, # "color": "#855D79", # "images": 1, # "figures": 1, # "name": "situated" # }, # { # "parentId": 21, # "color": "#855D79", # "images": 1, # "figures": 1, # "name": "situated (inside)" # }, # { # "parentId": 21, # "color": "#855D79", # "images": 0, # "figures": 0, # "name": "situated (outside)" # }, # { # "id": 22, # "color": "#A2B4FA", # "images": 0, # "figures": 1, # "name": "vehicle_age" # }, # { # "parentId": 22, # "color": "#A2B4FA", # "images": 0, # "figures": 1, # "name": "vehicle_age (modern)" # }, # { # "parentId": 22, # "color": "#A2B4FA", # "images": 0, # "figures": 0, # "name": "vehicle_age (vintage)" # } # ] # }, # "classes": [ # { # "id": 43, # "color": "#F6FF00", # "shape": "rectangle", # "totalDuration": 0, # "imagesCount": 0, # "avgDuration": null, # "name": "bike", # "labelsCount": 0 # }, # { # "id": 42, # "color": "#BE55CE", # "shape": "polygon", # "totalDuration": 0, # "imagesCount": 0, # "avgDuration": null, # "name": "car", # "labelsCount": 0 # }, # { # "id": 41, # "color": "#FD0000", # "shape": "polygon", # "totalDuration": 0, # "imagesCount": 0, # "avgDuration": null, # "name": "dog", # "labelsCount": 0 # }, # { # "id": 40, # "color": "#00FF12", # "shape": "bitmap", # "totalDuration": 0, # "imagesCount": 0, # "avgDuration": null, # "name": "person", # "labelsCount": 0 # } # ], # "images": { # "total": 2, # "images": [ # { # "id": 285, # "reviewStatus": "accepted", # "annotationDuration": 0, # "totalDuration": 0, # "name": "image_01", # "labelsCount": 0 # }, # { # "id": 284, # "reviewStatus": "accepted", # "annotationDuration": 0, # "totalDuration": 0, # "name": "image_02", # "labelsCount": 0 # } # ] # } # }
- get_status(id)[source]¶
Get status of Labeling Job with given ID.
- Parameters
- id : int
Labeling job ID in Supervisely.
- Returns
Labeling Job Status
- Return type
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() job_status = api.labeling_job.get_status(4) print(job_status) # pending
- static info_sequence()[source]¶
NamedTuple LabelingJobInfo information about Labeling Job.
- Example
LabelingJobInfo(id=2, name='Annotation Job (#1) (#1) (dataset_01)', readme='', description='', team_id=4, workspace_id=8, workspace_name='First Workspace', project_id=58, project_name='tutorial_project', dataset_id=54, dataset_name='dataset_01', created_by_id=4, created_by_login='anna', assigned_to_id=4, assigned_to_login='anna', reviewer_id=4, reviewer_login='anna', created_at='2020-04-08T15:10:12.618Z', started_at='2020-04-08T15:10:19.833Z', finished_at='2020-04-08T15:13:39.788Z', status='completed', disabled=False, images_count=3, finished_images_count=0, rejected_images_count=1, accepted_images_count=2, progress_images_count=2, classes_to_label=[], tags_to_label=[], images_range=(1, 5), objects_limit_per_image=None, tags_limit_per_image=None, filter_images_by_tags=[], include_images_with_tags=[], exclude_images_with_tags=[], entities=None)
-
reject_annotations(id, mode=
'all')[source]¶ Reject annotations for all or unmarked entities in the labeling job with given id.
- remove(id)¶
Remove an entity with the specified ID from the Supervisely server.
- Parameters
- id : int
Entity ID in Supervisely.
- Usage example
import supervisely as sly # You can connect to API directly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Or you can use API from environment os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() image_id = 19369643 api.image.remove(image_id)
-
remove_batch(ids, progress_cb=
None, batch_size=50)¶ Remove entities in batches from the Supervisely server. All entity IDs must belong to the same nesting (for example team, or workspace, or project, or dataset). Therefore, it is necessary to sort IDs before calling this method.
- Parameters
- ids : List[int]
IDs of entities in Supervisely.
- progress_cb : Callable
Function for control remove progress.
- Usage example
import supervisely as sly # You can connect to API directly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Or you can use API from environment os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() image_ids = [19369645, 19369646, 19369647] api.image.remove_batch(image_ids)
-
restart(id, assignee_ids=
None, reviewer_id=None, title=None, complete_existing=True, only_rejected_entities=True, ignore_no_rejected_error=False)[source]¶ Restart Labeling Job with given ID.
- Parameters
- id : int
Labeling Job ID in Supervisely.
- assignee_ids : List[int], optional
List of User IDs to assign the job. If not set, the job will be assigned to the same user as the existing job.
- reviewer_id : int, optional
ID of the reviewer
- title : str, optional
New title for the job <= 255 characters
- complete_existing : bool, optional
If False, existing job will not be completed.
- only_rejected_entities : bool, optional
If False, all entities that do not have an “accepted” status will be included in new job, all unmarked entities will be rejected for the existing job.
- ignore_errors : bool, optional
If True, the job will not be restarted if there are errors in request data.
- Returns
List of dicts with information about created Labeling Jobs.
- Return type
List[dict]- Usage example
import supervisely as sly api = sly.Api("https://app.supervisely.com", "your_api_token") job_info_list = api.labeling_job.restart(222) print(job_info_list) # Output: # [ # { # 'id': 940, # 'userId': 342, # 'type': 'annotation', # 'name': 'Annotation Job (#2)' # } # ]
- set_entity_review_status(id, entity_id, status)[source]¶
Sets review status for entity with given ID.
- set_status(id, status)[source]¶
Sets Labeling Job status.
- Parameters
- Returns
None
- Return type
NoneType- Usage example
import supervisely as sly from supervisely.api.labeling_job_api.LabelingJobApi.Status import COMPLETED os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() api.labeling_job.set_status(id=9, status="completed")
- stop(id)[source]¶
Makes Labeling Job unavailable for labeler with given User ID.
- Parameters
- id : int
User ID in Supervisely.
- Returns
None
- Return type
NoneType- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() api.labeling_job.stop(9)
-
wait(id, target_status, wait_attempts=
None, wait_attempt_timeout_sec=None)[source]¶ Wait for a Labeling Job to change to the expected target status.
- Parameters
- Raises
WaitingTimeExceeded, if waiting time exceeded- Returns
None
- Return type
NoneType- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervise.ly' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() api.labeling_job.wait(4, 'completed', wait_attempts=2, wait_attempt_timeout_sec=1) # supervisely.api.module_api.WaitingTimeExceeded: Waiting time exceeded