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DatasetResource
Represents the metadata of a dataset.
Properties
Property
Value
Description
dataset_id
string
Id of the dataset
client_id
string
Id of the workspace the dataset belongs to
dataset_name
string
Name of the dataset
pipeline_status
object
Number of objects/frames completed in each pipeline stage
pipeline_stage_status
object
Number of object/frames for each stage in each status
sensor_config
object
Instance of SensorConfigResource
uploaded_files
array
List of uploaded files
fps
integer
Fps (Frames per second) information for video type datasets
frame_sizes
array
List of number of points in each frame for point cloud datasets
create_timestamp_millis
float
Timestamp at which dataset was created
dataset_format
string
Format of uploaded file for Point Cloud datasets
dataset_type
string
Type of dataset (images, videos, 3d)
labelling_mode
string
Labelling mode for the dataset
    frame_by_frame: for Individual datasets
    label_by_label : for Sequence datasets
tags
array
List of TagResource
files
array
Array of file metadata objects. Each object represents metadata about a file being uploaded as part of the dataset.
processing_status
string
Processing status of the dataset.
files_to_be_uploaded: Dataset created but, dataset files are not yet uploaded, or waiting for the upload to finish.
files_uploaded: Dataset files uploaded. Deepen AI will take a few minutes to preprocess the uploaded dataset files.
preprocessing_error: Preprocess failed. Please check the processing_error_message field for a more detailed description of the error.
files_getting_labelled: Preprocess succeeded, and the files are being labelled by the labellers.
processing_status_message
string
Description of the error in case there is a preprocessing error while preprocessing uploaded dataset files.
status
object
Current labelling status. This has the following fields:
    1.
    finished: Number of files finished labelling and QA and labels ready to download.
    2.
    labelling_in_progress: Number of files getting labelled now.
    3.
    qa_in_progress: Number of files, finished labelling, but waiting for QA.
    4.
    Total: total number of files in the dataset
user_scopes
array
Array of DatasetUserScopeResource
user_group_scopes
array
Array of DatasetUserGroupScopeResource
Example
{ "dataset_id": "dataset_id", "client_id": "client_id", "create_timestamp_millis": 1591397296799, "dataset_format": "json", "dataset_name": "dataset_name", "dataset_type": "3d", "files": [{ "file_size": 18611529.0, "file_type": "application/zip", "resumable_upload_url": "https://www.googleapis.com/upload/storage/v1/b/url_for_file" }], "labelling_mode": "frame_by_frame", "processing_status": "files_getting_labelled", "tags": [], "uploaded_files": ["json_data.zip"], "processing_status_message": "Processing Json Files", "sensor_config": { "primary_sensor_id": "lidar", "sensors": { "lidar": { "content": "path_to_lidar", "sensor_type": "lidar", "calibration": {}, "square_dimensions": [], "sensor_fusion": { "camera_0": { "view_matrix": [ [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0] ] } } }, "camera_0": { "content": "path_to_camera_0_img", "sensor_type": "camera", "extension": "jpg", "calibration": {}, "square_dimensions": [], "sensor_fusion": {} } } }, "folder_path": "clent_id/defaultproject/dataset_id", "frame_sizes": [51162, 51156, 51102, 51046, 51068, 51064, 51041, 50953, 51064, 51065, 51065], "pipeline_stage_status": { "Labelling": { "done": 0, "failed": 0, "in_progress": 0, "ready": 0, "waiting": 0 }, "QA": { "done": 0, "failed": 0, "in_progress": 0, "ready": 0, "waiting": 0 } }, "pipeline_status": { "Labelling": 0, "QA": 0, "__ALL_STAGES_DONE": 0, "__TOTAL": 0 } }

LabelResource

Represents the metadata of a Label.
Properties
file_id
string
File Id of the label resource
label_type
string
Label type of label resource
label_category_id
string
Label category Id of the label resource
label_id
string
Label Id of the label resource
box
array
Array of four float numbers representing the x, y coordinates, x-length, y-length of the box respectively.
polygons
array
Array of polygons. Each polygon is an array of points.
attributes
object
Object containing key value pairs of attribute keys and values.
Example
{ "file_id": "000019.png", "label_category_id": "bus", "label_id": "bus:1", "label_type": "box", "attributes": { "attribute2": "attribute2_value1", "attribute1": "attribute_value1" }, "box": [366, 87, 174, 116], "polygons": [ [[338, 330], [460, 253], [567, 179], [773, 91], [862, 59]] ] }

FileResource

Represents the metadata of a file.
Properties
Property
Value
Description
file_id
string
File Id of the file resource
Example
{ "file_id": "000009.png", }

NotificationResource

Represents a notification from Deepen AI. Currently, we support notification from Depeen AI after labelling of a dataset is complete. The url to notify should be configured at the dataset level.
Properties
Property
Value
Description
dataset_id
string
Dataset Id of the notification
file_id
string
File Id of the notification
notification_type
string
Represents the type of notification. Currently, we support two notification types:
DatasetLabellingComplete: Sent after the dataset is completely labelled
FileLabellingComplete: Send after an individual file is labelled.
Example
{ "dataset_id": "testdataset", "notification_type": "DatasetLabellingComplete" }

IssueResource

Represents the metadata of an Issue.
Properties
issue_id
string
Issue Id of the issue resource
status
string
Status of the issue resource
issue_description
string
Description of the issue resource
file_id
string
File Id of the issue resource
author_email
string
Email of author for issue resource
label_category
string
Label category of the issue resource
label_id
string
Label Id of the issue resource
issue_creation_timestamp
Object (date)
Created time for issue resource in millis
region
Extruded polygon defining the region for the issue.
Example
{ "issues": [ { "author_email": "[email protected]", "issue_title": "test Issue", "label_id": "test", "issue_creation_timestamp": { "$date": 1551194067161 }, "issue_description": "Issue Description", "issue_id": "6005288", "status": "to do", "label_category": "test", "region": { "extruded_polygon": { "center_z": 1.0, "xy_polygon": [ [ 1.0, 2.0 ], [ 3.0, 4.0 ], [ 5.0, 6.0 ] ], "height": 1.0 } } } ] }

TaskResource

Represents the metadata of a task.
Properties
task_id
string
Id of the task resource
status
string
Status of the task resource
client_id
string
Client_id of client
file_id
string
File Id of the task resource
email
string
Assigned user email for task resource
label_category_id
string
Label category of the task resource
sensor_id
string
Sensor Id of the task resource
stage_id
string
Stage Id of the task resource
label_id
string
Label Id of the task resource
label_type
string
Label type of task resource
task_type
string
Task type of task resource. File for a single frame
Label for sequence
dataset_id
string
dataset_id of dataset
previous_task_ids
List
List of task ids to be completed before starting a task
next_task_ids
List
List of task ids to be started after completion of a task
Example
{ "file_tasks": [ { "task_id": "12345", "client_id": "test client", "dataset_id": "test dataset",
"file_id": "test file", "status": "ready", "stage_id": "labelling", "email": "[email protected]",
"next_task_ids": [12345],
"previous_task_ids": [12345678],
} ] }
{ "label_tasks": [ { "task_id": "12345", "client_id": "test client", "dataset_id": "test dataset",
"label_type": "box",
"label_category_id": "car",
"label_id": "car:1",
"sensor_id": "lidar", "status": "ready", "stage_id": "labelling", "email": "[email protected]",
"next_task_ids": [12345],
"previous_task_ids": [12345678],
} ] }

EmbeddedViewerDataResource

Represents the details of an embedded viewer.
Properties
client_id
string
clientId of the viewer
create_timestamp_millis
long
Created timestamp millis of viewer
dataset_id
string
datasetId of the viewer
jwt_token
string
Access token
viewer_id
string
viewerId of viewer
launching_frame_index
int
Frame id of viewer
title
string
Name of viewer
enable_camera_view
boolean
Flag to show camera
expiry_timestamp_millis
long
End timestamp millis of viewer
deletion_status
string
Deletion status of viewer
created_by
string
User created viewer
Example
{ "client_id": "testclient",
"dataset_id": "testdataset",
"jwt_token": "xtlz1235",
"viewer_id": "1234567",
"create_timestamp_millis": 100000000000,
"title": "testviewer",
"launching_frame_index": 1,
"enable_camera_view": true,
"expiry_timestamp_millis": 100000000000,
"created_by": "[email protected]"
}

PolygonResource

Represents the metadata of an extruded polygon for IssueResource.
Properties
center_z
float
Z value of center for the extruded polygon
xy_polygon
list(list(float))
List of xy points for the polygon. First point is the x value and second is the y value.
height
float
Height of the polygon
Example
{ "extruded_polygon": { "center_z": 1.0, "xy_polygon":[ [1.0,2.0], [3.0,4.0], [5.0,6.0] ], "height":1.0 } }

PipelineStageResource

Represents the details of a pipeline stage.
Properties
stage_id
string
Id of the pipeline stage
can_edit
boolean
Flag representing the editability of labels present in this stage.
Example
{ "stage_id": "Labelling", "can_edit": "true" }

LabellingProfileResource

Properties
dataset_id
string
Id of the dataset
client_id
string
Id of the client
created_email
string
Email of author for labelling profile resource
created_timestamp_millis
long
Timestamp during creation
features
Object
Object with keys as label types (‘box’, ‘3d_bbox’, ‘polygon’, ‘lane’ etc) and value of type FeaturesResource
instructions
Object
notification_url
string
pipeline_spec
Object
Pipeline stages
profile_id
string
Id of profile
profile_type
string
Type of profile, e.g : dataset
updated_email
string
Email of the user who modifies it
updated_timestamp_millis
long
Timestamp during modification
Example
{ "dataset_id" : "datset_id", "client_id" : "client_id", "created_email" : "[email protected]", "created_timestamp_millis" : 1590037884298, "features" : { "box" : { "attributes" : {}, "label_category_ids" : [] }, "lane" : { "attributes" : {}, "label_category_ids" : [] }, "polygon" : { "attributes" : {}, "label_category_ids" : [] }, "point" : { "attributes" : {}, "label_category_ids" : [] }, "3d_bbox" : { "attributes" : {}, "label_category_ids" : [ "car" ], "label_category_default_dimensions" : { "car" : { "width" : 2.25, "height" : 2.25, "length" : 2.25 } } }, "3d_polyline" : { "attributes" : {}, "label_category_ids" : [] }, "3d_polygon" : { "attributes" : {}, "label_category_ids" : [] }, "3d_point" : { "attributes" : {}, "label_category_ids" : [] }, "3d_instance_point" : { "attributes" : {}, "label_category_ids" : [] } }, "instructions" : { "text" : "" }, "notification_url" : "", "pipeline_spec" : [ { "stage_id" : "Labelling", "can_edit" : true }, { "stage_id" : "QA", "can_edit" : false } ], "profile_id" : "profile_id", "profile_type" : "dataset", "updated_email" : "[email protected]", "updated_timestamp_millis" : 1590038192070 }

TagResource

Represents the tag details for a dataset. A tag is basically any string that can be used to identify or categories the datasets. For backward compatibility with projects, we define a special tag starting with “project:”. This will enable you to group datasets by project and you can use these tags to filter the datasets. For api calls, a sample tags field will look like below.
tags : ["project: testDeepenProject", "testtag"]

DatasetUserScopeResource

Properties
email
string
Email of the user
scopes
array
Array of user scopes
Example
{ "email" : "[email protected]", "scopes" : ["scope1", "scope2"] }

DatasetUserGroupScopeResource

Properties
user_group_id
string
Id of the user group
scopes
array
Array of user scopes
Example
{ "user_group_id" : "asdfrtfdv", "scopes" : ["scope1", "scope2"] }

SensorConfigResource

Properties
primary_sensor_id
string
Id of the primary sensor
sensors
object
Object containing different sensor ids as keys with value as SensorConfigSensorDetailsResource
Example
{ "primary_sensor_id": "lidar", "sensors": { "lidar": { "content": "path_to_lidar", "sensor_type": "lidar", "sensor_fusion": { "camera_0": { "view_matrix": [ [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0] ] } } }, "camera_0": { "content": "path_to_camera_0_img", "sensor_type": "camera", "extension": "jpg" } } }

SensorConfigSensorDetailsResource

Properties
content
string
Relative path to the directory containing sensor data
sensor_type
string
Type of sensor.
    lidar
    camera
sensor_fusion
object
Object containing each other sensor id as key and the view matrix as value. View matrix is the matrix used to transform the points from the current sensor coordinates to the other sensor coordinates.
Example
{ "content": "path_to_lidar", "sensor_type": "lidar", "sensor_fusion": { "camera_0": { "view_matrix": [ [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0] ] } } }

UserGroupResource

Properties
user_group_id
string
Id of the user group
client_id
string
Id of the client user group belongs to
create_email
string
Email of the user who created the user group
create_timestamp_millis
int
Timestamp in milliseconds for the time at which the user group was created
emails
array
Array of user emails belonging to the group
update_timestamp_millis
int
Timestamp in milliseconds for the time at which the user group was updated
user_group_name
string
Name of the User Group
Example
{ "user_group_id" : "user_group_id", "client_id" : "client_id", "create_email" : "[email protected]", "create_timestamp_millis" : 1595484956292, "emails" : [ "[email protected]", "[email protected]" ], "update_timestamp_millis" : 1595484956292, "user_group_name" : "TestUserGroup" }

FeaturesResource

Properties
label_category_ids
List
List of label category ids
attributes
Object
Object with key as attribute name and value of type AttributesResource
label_category_attributes_config
Object
Object with key as label category name and value of type AttributesConfigResource
label_category_colors
Object
Object with key as attribute name and value as list of integer RGBA values
label_category_default_dimensions
Object
Object with key as label category name and value as DefaultDimensionResource for 3d_bbox label type.
Example
{ "box" : { "attributes" : {}, "label_category_ids" : [ "Person", "VehicleOther", "Tank", "Gator", "PickupTruck" ] }, "lane" : { "attributes" : {}, "label_category_ids" : [] }, "polygon" : { "attributes" : {}, "label_category_ids" : [ "Car", "Road", "Person", "Truck", "Bicycle" ] }, "point" : { "attributes" : {}, "label_category_ids" : [] }, "3d_bbox" : { "attributes" : { "direction" : { "attribute_values_array" : [ "same", "opposite" ] }, "occluded" : { "attribute_values_array" : [ "yes", "no" ], } }, "label_category_ids" : [ "Trailer", "LargeVehicle_Bus", "LargeVehicle_Truck", "Human", "Car", ], "label_category_default_dimensions" : { "Trailer" : { "width" : 2.25, "height" : 2.25, "length" : 2.25 }, "LargeVehicle_Bus" : { "width" : 2.25, "height" : 2.25, "length" : 2.25 }, "Car" : { "width" : 2, "height" : 1, "length" : 4 } }, "label_category_attributes_config" : { "Car" : { "color" : { "attribute_values_array" : [ "red", "black", "blue", "white", "grey", "other" ] } } } }, "3d_polyline" : { "attributes" : {}, "label_category_ids" : [] }, "3d_polygon" : { "attributes" : {}, "label_category_ids" : [] }, "3d_point" : { "attributes" : {}, "label_category_ids" : [ "Building", "Road", "Sidewalk", "Vegetation", "Pedestrian" ] }, "3d_instance_point" : { "attributes" : {}, "label_category_ids" : [] } }

AttributesResource

Properties
attribute_values_array
List
List of attribute values
Example
{ "attribute_values_array" : [ "yes", "no" ] }

AttributesConfigResource

Properties
[attribute name]
Object
Object with attribute name as key and AttrbutesResource as value
Example
{ "Car" : { "color" : { "attribute_values_array" : [ "red", "black", "blue", "white", "grey", "other" ] } } } }

DefaultDimensionResource

Properties
width
float
Float value depicting default width of the bounding box
height
float
Float value depicting default height of the bounding box
length
float
Float value depicting default length of the bounding box
Example
{ "width" : 2.25, "height" : 2.25, "length" : 2.25 }
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