How can I use a custom UI template with AWS provided Lambda functions in Ground Truth?

Resolution

Create a custom UI template for the labeling job, as shown in the following example:

1. For semantic segmentation jobs, set the name variable to crowd-semantic-segmentation, as shown in the following example. For bounding box jobs, set the name variable to boundingBox. For a full list of enhanced HTML elements for custom templates, see Crowd HTML elements reference.

   <script src="https://assets.crowd.aws/crowd-html-elements.js"></script>
   <crowd-form>
       <crowd-semantic-segmentation name="crowd-semantic-segmentation" src="{{ task.input.taskObject | grant_read_access }}" header= "{{ task.input.header }}" labels="{{ task.input.labels | to_json | escape }}">
   
           <full-instructions header= "Segmentation Instructions">
               <ol>
                   <li>Read the task carefully and inspect the image.</li>
                   <li>Read the options and review the examples provided to understand more about the labels.</li>
                   <li>Choose the appropriate label that best suits the image.</li>
               </ol>
           </full-instructions>
   
           <short-instructions>
               <p>Use the tools to label the requested items in the image</p>
           </short-instructions>
       </crowd-semantic-segmentation>
   </crowd-form>

2. Create a JSON file for the labels. Example:

   {
     "labels": [
       {
         "label": "Chair"
       },
     ...
       {
         "label": "Oven"
         }
      ]
   }

3. Create an input manifest file for the images. Example:

   {"source-ref":"s3://amzn-s3-demo-bucket/input_manifest/apartment-chair.jpg"}
   {"source-ref":"s3://amzn-s3-demo-bucket/input_manifest/apartment-carpet.jpg"}

   Note: Replace amzn-s3-demo-bucket with your S3 bucket.

4. Upload the HTML, manifest, and JSON files to Amazon Simple Storage Service (Amazon S3). Example:

   import boto3
   import os
   
   bucket = 'amzn-s3-demo-bucket'
   prefix = 'GroundTruthCustomUI'
   
   boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'customUI.html')).upload_file('customUI.html')
   boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'input.manifest')).upload_file('input.manifest')
   boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'testLabels.json')).upload_file('testLabels.json')

   Note: Replace amzn-s3-demo-bucket with your S3 bucket.

5. Retrieve the Amazon Resource Names (ARNs) for the pre-processing and annotation consolidation Lambda functions. For example, here are the semantic segmentation ARNs:
   arn:aws:lambda:eu-west-1:111122223333:function:PRE-SemanticSegmentation
   arn:aws:lambda:eu-west-1:111122223333:function:ACS-SemanticSegmentation

6. To create the labeling job, use an AWS SDK, such as boto3:

   import boto3
   
   client = boto3.client("sagemaker")
   client.create_labeling_job(
       LabelingJobName="SemanticSeg-CustomUI",
       LabelAttributeName="output-ref",
       InputConfig={
           "DataSource": {"S3DataSource": {"ManifestS3Uri": "s3://amzn-s3-demo-bucket/GroundTruthCustomUI/input.manifest"}},
           "DataAttributes": {
               "ContentClassifiers": [
                   "FreeOfPersonallyIdentifiableInformation",
               ]
           },
       },
       OutputConfig={"S3OutputPath": "s3://amzn-s3-demo-bucket/GroundTruthCustomUI/output/"},
       RoleArn="arn:aws:iam::111122223333:role/SageMakerExecutionRole",
       LabelCategoryConfigS3Uri="s3://amzn-s3-demo-bucket/GroundTruthCustomUI/testLabels.json",
       StoppingConditions={"MaxPercentageOfInputDatasetLabeled": 100},
       HumanTaskConfig={
           "WorkteamArn": "arn:aws:sagemaker:eu-west-1:111122223333:workteam/private-crowd/ExampleWorkteam",
           "UiConfig": {"UiTemplateS3Uri": "s3://amzn-s3-demo-bucket/GroundTruthCustomUI/customUI.html"},
           "PreHumanTaskLambdaArn": "arn:aws:lambda:eu-west-1:111122223333:function:PRE-SemanticSegmentation",
           "TaskKeywords": [
               "SemanticSegmentation",
           ],
           "TaskTitle": "Semantic Segmentation",
           "TaskDescription": "Draw around the specified labels using the tools",
           "NumberOfHumanWorkersPerDataObject": 1,
           "TaskTimeLimitInSeconds": 3600,
           "TaskAvailabilityLifetimeInSeconds": 1800,
           "MaxConcurrentTaskCount": 1,
           "AnnotationConsolidationConfig": {
               "AnnotationConsolidationLambdaArn": "arn:aws:lambda:eu-west-1:111122223333:function:ACS-SemanticSegmentation"
           },
       },
       Tags=[{"Key": "reason", "Value": "CustomUI"}],
   )

Note: Replace the following values with your own:

• Replace amzn-s3-demo-bucket/GroundTruthCustomUI/input.manifest with your input manifest S3 URI
• Replace amzn-s3-demo-bucket/GroundTruthCustomUI/output/ with your S3 output path
• Replace 111122223333 with your AWS account ID
• Replace SageMakerExecutionRole with your IAM role name
• Replace ExampleWorkteam with your workteam name
• Replace amzn-s3-demo-bucket/GroundTruthCustomUI/testLabels.json with your labels JSON S3 URI
• Replace amzn-s3-demo-bucket/GroundTruthCustomUI/customUI.html with your HTML template S3 URI

Related information

Semantic segmentation algorithm