Resources
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
|
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:
|
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
}
} |
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]]
]
} |
Represents the metadata of a file.
Properties
Property | Value | Description |
file_id | string | File Id of the file resource |
Example
{
"file_id": "000009.png",
} |
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"
} |
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
}
}
}
]
} |
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", "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", "next_task_ids": [12345], "previous_task_ids": [12345678], }
]
} |
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]" } |
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
}
} |
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"
} |
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
} |
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"] |
Properties
email | string | Email of the user |
scopes | array | Array of user scopes |
Example
{
"email" : "[email protected]",
"scopes" : ["scope1", "scope2"]
} |
Properties
user_group_id | string | Id of the user group |
scopes | array | Array of user scopes |
Example
{
"user_group_id" : "asdfrtfdv",
"scopes" : ["scope1", "scope2"]
} |
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"
}
}
} |
Properties
content | string | Relative path to the directory containing sensor data |
sensor_type | string | Type of sensor.
|
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]
]
}
}
} |
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"
} |
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" : []
}
} |
Properties
attribute_values_array | List | List of attribute values |
Example
{
"attribute_values_array" : [ "yes", "no" ]
} |
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"
]
}
}
}
} |
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
} |
Last modified 1yr ago