Labelproject

class Labelproject(object)
This namespace represents each lebeling projects in LABELING AI.
init()
__init__(info, user)
Parmeters
info (str) : 
Information about the current labeling project corresponding to the object.
user (str) : 
User for creating, modifying, deleting the label project.
Fields
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.
repr()
Returns the Labeling AI's project ID as a printable format.
__repr__()
Return type
str
delete()
Deletes the project in Labeling AI corresponding to the user object.
delete()
Return type
None

get_labels()
Get each individual labels as a list form with their IDs.
get_labels()
Return type
list
get_labelclasses()
Returns self.labelclasses.
get_labelclasses()
Return type
list
get_labelfiles()
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)
Parmeters
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.
Return type
list
create_labelclass()
Creates a new label class and returns the new Labelclass object.
create_labelclass(name, color="#000000")
Parameters
name (str) : 
Label class's name.
color (str) : 
Hex color code of the label.
Return type
Labelclass
create_labelfile()
Uploads a new image file for labelling and add it to the current labeling project.
create_labelfile(data_file)
Parameters
data_file (str) : 
Name of the new file.
Return type
requests(dict)
create_custom_ai()
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={})
Parameters
custom_ai_type (str) : 
Labeling type, polygon or box.
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.
Return type
requests(dict)
autolabeling()
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 )
Parameters
amount (int) : 
Desired amount of auto-labeling.
ai_type (str) : 
Auto-labeling AI type (custom or general or inference).
autolabeling_type (str) : 
Auto-labeling type (box or polygon).
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)
Return type
requests(dict)
export()
Exports the current labeling project.
export(is_get_image=False)
Parameters
is_get_image (Boolean) : 
Whether to get image or not when creating a new Asynctask object.
Return type
Asynctask object

Last modified 10mo ago