Create a 3D-2D point cloud object tracking labeling job - Amazon SageMaker AI
Create a labeling job (API)Input data format

Create a 3D-2D point cloud object tracking labeling job

You can create a 3D-2D point cloud labeling job using the SageMaker API operation, CreateLabelingJob. To create a labeling job for this task type you need the following:

  • A work team from a private or vendor workforce. You cannot use Amazon Mechanical Turk for 3D point cloud labeling jobs. To learn how to create workforces and work teams, see Workforces.

  • Add a CORS policy to an S3 bucket that contains input data in the Amazon S3 console. To set the required CORS headers on the S3 bucket that contains your input images in the S3 console, follow the directions detailed in CORS Permission Requirement.

  • Additionally, make sure that you have reviewed and satisfied the Assign IAM Permissions to Use Ground Truth.

To learn how to create a labeling job using the API, see the following sections.

Create a labeling job (API)

This section covers details you need to know when you create a 3D-2D object tracking labeling job using the SageMaker API operation CreateLabelingJob. This API defines this operation for all AWS SDKs. To see a list of language-specific SDKs supported for this operation, review the See Also section of CreateLabelingJob.

Create a Labeling Job (API) provides an overview of the CreateLabelingJob operation. Follow these instructions and do the following while you configure your request:

  • You must enter an ARN for HumanTaskUiArn. Use arn:aws:sagemaker:<region>:394669845002:human-task-ui/PointCloudObjectTracking. Replace <region> with the AWS Region you are creating the labeling job in.

    There should not be an entry for the UiTemplateS3Uri parameter.

  • Your LabelAttributeName must end in -ref. For example, ot-labels-ref.

  • Your input manifest file must be a point cloud frame sequence manifest file. For more information, see Create a Point Cloud Sequence Input Manifest. You also need to provide a label category configuration file as mentioned above.

  • You need to provide pre-defined ARNs for the pre-annotation and post-annotation (ACS) Lambda functions. These ARNs are specific to the AWS Region you use to create your labeling job.

    • To find the pre-annotation Lambda ARN, refer to PreHumanTaskLambdaArn. Use the Region you are creating your labeling job in to find the correct ARN that ends with PRE-3DPointCloudObjectTracking.

    • To find the post-annotation Lambda ARN, refer to AnnotationConsolidationLambdaArn. Use the Region you are creating your labeling job in to find the correct ARN that ends with ACS-3DPointCloudObjectTracking.

  • The number of workers specified in NumberOfHumanWorkersPerDataObject should be 1.

  • Automated data labeling is not supported for 3D point cloud labeling jobs. You should not specify values for parameters in LabelingJobAlgorithmsConfig.

  • 3D-2D object tracking labeling jobs can take multiple hours to complete. You can specify a longer time limit for these labeling jobs in TaskTimeLimitInSeconds (up to 7 days, or 604,800 seconds).

Note

After you have successfully created a 3D-2D object tracking job, it shows up on the console under labeling jobs. The task type for the job is displayed as Point Cloud Object Tracking.

Input data format

You can create a 3D-2D object tracking job using the SageMaker API operation, CreateLabelingJob. To create a labeling job for this task type you need the following:

  • A sequence input manifest file. To learn how to create this type of manifest file, see Create a Point Cloud Sequence Input Manifest. If you are a new user of Ground Truth 3D point cloud labeling modalities, we recommend that you review Accepted Raw 3D Data Formats.

  • You specify your labels, label category and frame attributes, and worker instructions in a label category configuration file. For more information, see Create a Labeling Category Configuration File with Label Category and Frame Attributes to learn how to create this file. The following is an example showing a label category configuration file for creating a 3D-2D object tracking job.

    {
    "document-version": "2020-03-01",
    "categoryGlobalAttributes": [
        {
            "name": "Occlusion",
            "description": "global attribute that applies to all label categories",
            "type": "string",
            "enum":[
                "Partial",
                "Full"
            ]
        }
    ],
    "labels":[
        {
            "label": "Car",
            "attributes": [
                {
                    "name": "Type",
                    "type": "string",
                    "enum": [
                        "SUV",
                        "Sedan"
                    ]
                } 
            ]
        },
        {
            "label": "Bus",
            "attributes": [
                {
                    "name": "Size",
                    "type": "string",
                    "enum": [
                        "Large",
                        "Medium",
                        "Small"
                    ]
                }
            ]
        }
    ],
    "instructions": {
        "shortIntroduction": "Draw a tight cuboid around objects after you select a category.",
        "fullIntroduction": "<p>Use this area to add more detailed worker instructions.</p>"
    },
    "annotationType": [
        {
            "type": "BoundingBox"
        },
        {
            "type": "Cuboid"
        }
    ]
}
    Note

    You need to provide BoundingBox and Cuboid as annotationType in the label category configuration file to create a 3D-2D object tracking job.