如何在 Ground Truth 中將自訂 UI 範本與 AWS 提供的 Lambda 函式結合使用？

解決方法

為標記作業建立自訂 UI 範本，如以下範例所示：

1. 對於語義分割作業，請將 name 變數設定為 crowd-semantic-segmentation，如以下範例所示。對於邊界框作業，請將 name 變數設定為 boundingBox。如需自訂範本增強型 HTML 元素的完整清單，請參閱 Crowd HTML 元素參考。

   <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. 建立標籤的 JSON 檔案。範例：

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

3. 為影像建立輸入資訊清單檔案。範例：

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

4. 將 HTML、資訊清單和 JSON 檔案上傳至 Amazon Simple Storage Service (Amazon S3)。範例：

   import boto3import os
   
   bucket = 'awsdoc-example-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')

5. 擷取預先處理和註釋整合 Lambda 函式的 Amazon Resource Name (ARN)。例如，以下是語義分割 ARN：
   arn:aws:lambda:eu-west-1:111122223333:function:PRE-SemanticSegmentation
   arn:aws:lambda:eu-west-1:111122223333:function:ACS-SemanticSegmentation

6. 若要建立標記作業，請使用 AWS SDK，例如 boto3：

   import boto3
   
   client = boto3.client("sagemaker")
   client.create_labeling_job(
       LabelingJobName="SemanticSeg-CustomUI",
       LabelAttributeName="output-ref",
       InputConfig={
           "DataSource": {"S3DataSource": {"ManifestS3Uri": "INPUT_MANIFEST_IN_S3"}},
           "DataAttributes": {
               "ContentClassifiers": [
                   "FreeOfPersonallyIdentifiableInformation",
               ]
           },
       },
       OutputConfig={"S3OutputPath": "S3_OUTPUT_PATH"},
       RoleArn="IAM_ROLE_ARN",
       LabelCategoryConfigS3Uri="LABELS_JSON_FILE_IN_S3",
       StoppingConditions={"MaxPercentageOfInputDatasetLabeled": 100},
       HumanTaskConfig={
           "WorkteamArn": "WORKTEAM_ARN",
           "UiConfig": {"UiTemplateS3Uri": "HTML_TEMPLATE_IN_S3"},
           "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"}],
   )

在前述範例中，完成以下步驟：

• 將 S3_OUTPUT_PATH 替換為 S3 輸出路徑
• 將 IAM_ROLE_ARN 替換為角色 ARN
• 將 WORKTEAM_ARN 替換為工作團隊 ARN
• 將 INPUT_MANIFEST_IN_S3 替換為輸入資訊清單 URI
• 將 LABELS_JSON_IN_S3 替換為標籤 JSON URI
• 將 HTML_TEMPLATE_IN_S3 替換為 HTML 範本 URI

相關資訊

語義分割演算法