Get job results

JOB RESULT

GET https://api.playment.io/v1/projects/:project_id/jobs/:job_id

This endpoint allows you to get the annotations created on a job.

Path Parameters

Name

Type

Description

job_id

string

ID of the job. You would have received this ID in the response when you created the job.

project_id

string

ID of the project where the job was created

Headers

Name

Type

Description

x-api-key

string

API key for authentication

{
    "data": {
        "project_id": "905ea3ee-6f62-4bb7-8c31-471e486d9202",
        "reference_id": "001",
        "job_id": "3f3e8675-ca69-46d7-aa34-96f90fcbb732",
        "batch_id": "468b6ae3-e98d-4d08-9427-b6e7a016054c",
        "status": "completed",
        "work_flow_id": "51bb440c-f0ed-4286-8984-ea399cbc1f9w",
        "priority_weight": 5,
        "tag": "2aae1234-acac-1234-eeff-12a22a237bbc", //tag is the workflow_id for the job  
        "result": "https://playment-data-uploads.s3.ap-south-1.amazonaws.com/sample-result.json"
    },
    "success": true
}

ATTACHMENT

GET https://api.playment.io/v1/attachments?url=:result_url

The result_url (data.result) returned by the GET Job result API is a private URL (Well, it's public till 15th July 2021, after which we'll enforce it to be private). You need to use this Attachment access API to securely fetch the results. Please refer to the Attachment Access API document for more detail.

Path Parameters

Name

Type

Description

result_url

string

Result URL returned by the GET job result API

Headers

Name

Type

Description

x-api-key

string

API key for authentication

The API will redirect to a signed URL of the attachment

Result Object

{
  "annotation_data": {
    "annotations": [
      {
        "_id": "14e4bd90-6bdc-4571-87e1-adf41908b0ed",
        "_isStationary": false,
        "attributes": {},
        "class_id": "24aaea97-002d-41f1-9632-ce36b829445a",
        "color": "rgb(71, 153, 193)",
        "frame_id": "0",
        "grouping_config": null,
        "label": "Vehicle",
        "label_key": 1,
        "sensor_id": "",
        "text": "Vehicle",
        "track_id": "bce3dd47-0938-4fd7-963b-47e459fda54e",
        "type": "segmentation3d"
      }
    ],
    "frames": [
      {
        "_id": "0"
      }
    ],
    "groups": null,
    "masks": [
      {
        "_id": "8feec956-0d5c-4d3c-b8be-b03d1c8f23a9",
        "frame_id": "0",
        "label_url": "https://xyz.s3.com/123.txt",
        "sensor_id": "18158562",
        "type": "segmentation3d",
        "url": ""
      }
    ],
    "sensors": [
      {
        "sensor_id": "18158562",
        "sensor_type": "CAMERA"
      }
    ],
    "tracks": [
      {
        "_id": "bce3dd47-0938-4fd7-963b-47e459fda54e",
        "attributes": null,
        "color": "rgb(71, 153, 193)",
        "grouping_config": null,
        "label": "",
        "label_id": null,
        "state": "",
        "type": ""
      }
    ]
  },
  "sensor_data": {
    "frames": [
      {
        "ego_pose": {
          "heading": {
            "w": 0.979,
            "x": 0,
            "y": -0.02,
            "z": 0.202
          },
          "position": {
            "x": -40,
            "y": -8,
            "z": -1
          }
        },
        "frame_id": "0",
        "sensors": [
          {
            "data_url": "https://xyz.s3.com/123",
            "original_url": "https://xyz.s3.com/123.jpg",
            "sensor_id": "front_center_rgb",
            "sensor_pose": {
              "heading": {
                "w": 0.98,
                "x": 0,
                "y": -0.05,
                "z": 0.189
              },
              "position": {
                "x": -40,
                "y": -8,
                "z": -1
              }
            }
          }
        ]
      }
    ],
    "sensor_meta": [
      {
        "camera_model": "brown_conrady",
        "id": "18158562",
        "intrinsics": {
          "cx": 950,
          "cy": 500,
          "fx": 1000,
          "fy": 1050,
          "k1": 0.31,
          "k2": -0.024,
          "k3": 0.0017,
          "k4": 0,
          "p1": -0.000466,
          "p2": 0.0002909,
          "scale_factor": 0,
          "skew": 0
        },
        "modality": "camera",
        "name": "front_center_rgb",
        "primary_view": false,
        "state": "editable"
      },
      {
        "id": "18158562",
        "modality": "lidar",
        "name": "front_lidar",
        "primary_view": true,
        "state": "editable"
      }
    ]
  }
}

To access the PCD mask (url in the result object), follow these steps :

From the "Frames" object, get the "frame_id".
Go to "Masks" object, and locate the "frame_id" property with the value needed (from step 1).
The "frame_id" property in "Masks" object will have a "label_url" attribute.
Use the GET Attachment API with the url obtained from "label_url" attribute.
This will generate a Mask file, similar to the sample below.

[16, 58, 58, 58, 58, 1, 58, 1, 58, 1, 58, 1, 58, 1, 58, 1, 58, 1, 1, 58, 1, 1,
58, 1, 1, 58, 1, 1, 58, 1, 1, 58, 1, 1, 58, 1, 1, 1, 58, 1, 1, 1, 11, 1, 1, 1,
11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1,
11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1,
11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 1, 11, 1, 1, 11,
1, 1, 11, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 1, 4, 4, 1, 4, 1, 4, 4, 1, 4, 4, 1, 4, 1, 4,
1, 4, 4, 4, 1, 4, 4, 4, 1, 4, 1, 4, 1, 4, 1, 1, 4, 1, 1, 1, 1, 4, 1, 1, 1, 1, 4,
1, 1, 1, 1, 4, 1, 4, 1, 4, 1, 4, 1, 1, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1, 4, 4,
1, 4, 1, 4, 4, 1]

In the annotation list, each class is associated with a distinct label key, which corresponds to a mask within the array. This label key is aligned with the points in the Point Cloud Data, ensuring that the label key is ordered at the same index as the corresponding point in the Point Cloud Data.

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