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Labelproject
This namespace represents each lebeling projects in LABELING AI.
__init__(info, user)
- info (str) : Information about the current labeling project corresponding to the object.
- user (str) : User for creating, modifying, deleting the label project.
- id : Project id.
- workapp : Project workapp.
- url : DS2 API url information.
- user : User information.
- user_token : User token information.
- labelclasses : Label classes in the labeling projects.
Returns the Labeling AI's project ID as a printable format.
__repr__()
- str
Deletes the project in Labeling AI corresponding to the user object.
delete()
- None
Get each individual labels as a list form with their IDs.
get_labels()
- list
Returns self.labelclasses.
get_labelclasses()
- list
Get each individual image files for labeling as a list form.
get_labelfiles(sorting="created_at", tab="all", count=10, desc=False, searching="", workAssignee=None)
- sorting (str) : Sorting order, such as
created_at
- tab (str) : "all" to get all the files.
- count (int) : Number of labelfiles to get.
- desc (Boolean) : Whether to add description or not.
- searching (str) : Specific status of the project.
- workAssignee (str or None) : Set workAssignee as the assignee's email if you want the specific labeling work of the corresponding assignee.
- list
Creates a new label class and returns the new Labelclass object.
create_labelclass(name, color="#000000")
- name (str) : Label class's name.
- color (str) : Hex color code of the label.
- Labelclass
Uploads a new image file for labelling and add it to the current labeling project.
create_labelfile(data_file)
- data_file (str) : Name of the new file.
- requests(dict)
Creates a custom AI using the variables such as custom ai type and training column information.
create_custom_ai(custom_ai_type="box", use_class_info={},
valueForPredictColumnId=None, trainingColumnInfo={})
- custom_ai_type (str) : Labeling type,
polygon
orbox
. - use_class_info (dict) : Label class to use for Autolabeling.
- valueForPredictColumnId (int) : Id of the column if the data is in csv format. Otherwise, None.
- trainingColumnInfo (dict) : Info of the training column as a dict.
- requests(dict)
Starts auto-labeling with user's preferences as parameters.
autolabeling(amount, ai_type="general", autolabeling_type="box", general_ai_type="person",
model_id=None, custom_ai_stage=0, preprocessing_ai_type={}, labeling_class=None )
- amount (int) : Desired amount of auto-labeling.
- ai_type (str) : Auto-labeling AI type (
custom
orgeneral
orinference
). - autolabeling_type (str) : Auto-labeling type (
box
orpolygon
). - general_ai_type (str) : None(when ai_type is "custom" or "inference") or general AI type(possible_values: 'person', 'animal', 'autonomous_driving', 'face_point detection','people_keypoints').
- model_id (int) : Model's id, as shown at the end of url.
- custom_ai_stage (int)
- preprocessing_ai_type (dict)
- labeling_class (str)
- requests(dict)
Exports the current labeling project.
export(is_get_image=False)
- is_get_image (Boolean) : Whether to get image or not when creating a new Asynctask object.
- Asynctask object
Last modified 1yr ago