本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 在映像中偵測標籤
<a name="labels-detect-labels-image"></a>

您可以使用 [DetectLabel](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectLabels.html) 操作來偵測映像中的標籤 (物件和概念)，並擷取有關映像屬性的資訊。映像屬性包括前景和背景顏色等屬性，以及映像的銳利度、亮度和對比度。您可以只檢索映像中的標籤，只檢索映像的屬性，或兩者兼而有之。如需範例，請參閱 [分析存放在 Amazon S3 儲存貯體中的映像](images-s3.md)。

下列範例使用 AWS SDKs和 AWS CLI 來呼叫 `DetectLabels`。如需有關 `DetectLabels` 操作回應的資訊，請參閱 [DetectLabels 回應](#detectlabels-response)。

**偵測映像中的標籤**

1. 如果您尚未執行：

   1. 建立或更新具有 `AmazonRekognitionFullAccess` 和 `AmazonS3ReadOnlyAccess` 許可的使用者。如需詳細資訊，請參閱[步驟 1：設定 AWS 帳戶並建立使用者](setting-up.md#setting-up-iam)。

   1. 安裝和設定 AWS CLI 和 AWS SDKs。如需詳細資訊，請參閱[步驟 2：設定 AWS CLI 和 AWS SDKs](setup-awscli-sdk.md)。

1. 將包含一個或多個物件的映像 (例如樹、房子和船) 上傳至您的 S3 儲存貯體。映像的格式必須是 *.jpg* 或 *.png* 格式。

   如需指示說明，請參閱《Amazon Simple Storage Service 使用者指南》**中的[上傳物件至 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/upload-objects.html)。

1. 使用下列範例來呼叫 `DetectLabels` 操作。

------
#### [ Java ]

   此範例顯示一份在輸入映像中偵測到的標籤清單。將 `bucket` 與 `photo` 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。

   ```
   package com.amazonaws.samples;
   import java.util.List;
   
   import com.amazonaws.services.rekognition.model.BoundingBox;
   import com.amazonaws.services.rekognition.model.DetectLabelsRequest;
   import com.amazonaws.services.rekognition.model.DetectLabelsResult;
   import com.amazonaws.services.rekognition.model.Image;
   import com.amazonaws.services.rekognition.model.Instance;
   import com.amazonaws.services.rekognition.model.Label;
   import com.amazonaws.services.rekognition.model.Parent;
   import com.amazonaws.services.rekognition.model.S3Object;
   import com.amazonaws.services.rekognition.AmazonRekognition;
   import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder;
   import com.amazonaws.services.rekognition.model.AmazonRekognitionException;
   
   public class DetectLabels {
   
       public static void main(String[] args) throws Exception {
   
           String photo = "photo";
           String bucket = "bucket";
   
           AmazonRekognition rekognitionClient = AmazonRekognitionClientBuilder.defaultClient();
   
           DetectLabelsRequest request = new DetectLabelsRequest()
                   .withImage(new Image().withS3Object(new S3Object().withName(photo).withBucket(bucket)))
                   .withMaxLabels(10).withMinConfidence(75F);
   
           try {
               DetectLabelsResult result = rekognitionClient.detectLabels(request);
               List<Label> labels = result.getLabels();
   
               System.out.println("Detected labels for " + photo + "\n");
               for (Label label : labels) {
                   System.out.println("Label: " + label.getName());
                   System.out.println("Confidence: " + label.getConfidence().toString() + "\n");
   
                   List<Instance> instances = label.getInstances();
                   System.out.println("Instances of " + label.getName());
                   if (instances.isEmpty()) {
                       System.out.println("  " + "None");
                   } else {
                       for (Instance instance : instances) {
                           System.out.println("  Confidence: " + instance.getConfidence().toString());
                           System.out.println("  Bounding box: " + instance.getBoundingBox().toString());
                       }
                   }
                   System.out.println("Parent labels for " + label.getName() + ":");
                   List<Parent> parents = label.getParents();
                   if (parents.isEmpty()) {
                       System.out.println("  None");
                   } else {
                       for (Parent parent : parents) {
                           System.out.println("  " + parent.getName());
                       }
                   }
                   System.out.println("--------------------");
                   System.out.println();
                  
               }
           } catch (AmazonRekognitionException e) {
               e.printStackTrace();
           }
       }
   }
   ```

------
#### [ AWS CLI ]

   此範例顯示 `detect-labels` CLI 操作的 JSON 輸出。將 `bucket` 與 `photo` 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。使用您開發人員設定檔的名稱取代 `profile-name` 的值。

   ```
   aws rekognition detect-labels --image '{ "S3Object": { "Bucket": "bucket-name", "Name": "file-name" } }' \
   --features GENERAL_LABELS IMAGE_PROPERTIES \
   --settings '{"ImageProperties": {"MaxDominantColors":1}, {"GeneralLabels":{"LabelInclusionFilters":["Cat"]}}}' \
   --profile profile-name \
   --region us-east-1
   ```

   如果您在 Windows 裝置上存取 CLI，請使用雙引號而非單引號，並以反斜線 (即\\) 替代內部雙引號，以解決您可能遇到的任何剖析器錯誤。例如，請參閱下列內容：

   ```
   aws rekognition detect-labels --image "{\"S3Object\":{\"Bucket\":\"bucket-name\",\"Name\":\"file-name\"}}" --features GENERAL_LABELS IMAGE_PROPERTIES \
   --settings "{\"GeneralLabels\":{\"LabelInclusionFilters\":[\"Car\"]}}" --profile profile-name --region us-east-1
   ```

------
#### [ Python ]

   此範例顯示在輸入映像中偵測到的標籤。在函數 `main` 中，將 `bucket` 與 `photo` 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。將建立 Rekognition 工作階段的行中 `profile_name` 值取代為您開發人員設定檔的名稱。

   ```
   #Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
   #PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
   
   import boto3
   
   def detect_labels(photo, bucket):
   
        session = boto3.Session(profile_name='profile-name')
        client = session.client('rekognition')
   
        response = client.detect_labels(Image={'S3Object':{'Bucket':bucket,'Name':photo}},
        MaxLabels=10,
        # Uncomment to use image properties and filtration settings
        #Features=["GENERAL_LABELS", "IMAGE_PROPERTIES"],
        #Settings={"GeneralLabels": {"LabelInclusionFilters":["Cat"]},
        # "ImageProperties": {"MaxDominantColors":10}}
        )
   
        print('Detected labels for ' + photo)
        print()
        for label in response['Labels']:
            print("Label: " + label['Name'])
            print("Confidence: " + str(label['Confidence']))
            print("Instances:")
   
            for instance in label['Instances']:
                print(" Bounding box")
                print(" Top: " + str(instance['BoundingBox']['Top']))
                print(" Left: " + str(instance['BoundingBox']['Left']))
                print(" Width: " + str(instance['BoundingBox']['Width']))
                print(" Height: " + str(instance['BoundingBox']['Height']))
                print(" Confidence: " + str(instance['Confidence']))
                print()
   
            print("Parents:")
            for parent in label['Parents']:
               print(" " + parent['Name'])
   
            print("Aliases:")
            for alias in label['Aliases']:
                print(" " + alias['Name'])
   
                print("Categories:")
            for category in label['Categories']:
                print(" " + category['Name'])
                print("----------")
                print()
   
        if "ImageProperties" in str(response):
            print("Background:")
            print(response["ImageProperties"]["Background"])
            print()
            print("Foreground:")
            print(response["ImageProperties"]["Foreground"])
            print()
            print("Quality:")
            print(response["ImageProperties"]["Quality"])
            print()
   
        return len(response['Labels'])
   
   def main():
       photo = 'photo-name'
       bucket = 'amzn-s3-demo-bucket'
       label_count = detect_labels(photo, bucket)
       print("Labels detected: " + str(label_count))
   
   if __name__ == "__main__":
       main()
   ```

------
#### [ .NET ]

   此範例顯示一份在輸入映像中偵測到的標籤清單。將 `bucket` 與 `photo` 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。

   ```
   //Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
   //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
   
   using System;
   using Amazon.Rekognition;
   using Amazon.Rekognition.Model;
   
   public class DetectLabels
   {
       public static void Example()
       {
           String photo = "input.jpg";
           String bucket = "amzn-s3-demo-bucket";
   
           AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
   
           DetectLabelsRequest detectlabelsRequest = new DetectLabelsRequest()
           {
               Image = new Image()
               {
                   S3Object = new S3Object()
                   {
                       Name = photo,
                       Bucket = bucket
                   },
               },
               MaxLabels = 10,
               MinConfidence = 75F
           };
   
           try
           {
               DetectLabelsResponse detectLabelsResponse = rekognitionClient.DetectLabels(detectlabelsRequest);
               Console.WriteLine("Detected labels for " + photo);
               foreach (Label label in detectLabelsResponse.Labels)
                   Console.WriteLine("{0}: {1}", label.Name, label.Confidence);
           }
           catch (Exception e)
           {
               Console.WriteLine(e.Message);
           }
       }
   }
   ```

------
#### [ Ruby ]

   此範例顯示一份在輸入映像中偵測到的標籤清單。將 `bucket` 與 `photo` 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。

   ```
      # Add to your Gemfile
      # gem 'aws-sdk-rekognition'
      require 'aws-sdk-rekognition'
      credentials = Aws::Credentials.new(
         ENV['AWS_ACCESS_KEY_ID'],
         ENV['AWS_SECRET_ACCESS_KEY']
      )
      bucket = 'bucket' # the bucket name without s3://
      photo  = 'photo' # the name of file
      client   = Aws::Rekognition::Client.new credentials: credentials
      attrs = {
        image: {
          s3_object: {
            bucket: bucket,
            name: photo
          },
        },
        max_labels: 10
      }
     response = client.detect_labels attrs
     puts "Detected labels for: #{photo}"
     response.labels.each do |label|
       puts "Label:      #{label.name}"
       puts "Confidence: #{label.confidence}"
       puts "Instances:"
       label['instances'].each do |instance|
         box = instance['bounding_box']
         puts "  Bounding box:"
         puts "    Top:        #{box.top}"
         puts "    Left:       #{box.left}"
         puts "    Width:      #{box.width}"
         puts "    Height:     #{box.height}"
         puts "  Confidence: #{instance.confidence}"
       end
       puts "Parents:"
       label.parents.each do |parent|
         puts "  #{parent.name}"
       end
       puts "------------"
       puts ""
     end
   ```

------
#### [ Node.js ]

   此範例顯示一份在輸入映像中偵測到的標籤清單。將 `bucket` 與 `photo` 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。將建立 Rekognition 工作階段的行中 `profile_name` 值取代為您開發人員設定檔的名稱。

   如果您使用 TypeScript 定義，可能需要使用 `import AWS from 'aws-sdk'` 而不是 `const AWS = require('aws-sdk')`，以便使用 Node.js 執行該程序。您可以查閱[適用於 Javascript 的AWS SDK](https://docs.aws.amazon.com/AWSJavaScriptSDK/latest/)，以獲取更多詳細資訊。視您設定組態的方式而定，您可能還需要使用 `AWS.config.update({region:{{region}}});` 來指定您所在的區域。

   ```
                                   
   // Load the SDK
   var AWS = require('aws-sdk');
   const bucket = 'bucket-name' // the bucketname without s3://
   const photo  = 'image-name' // the name of file
   
   var credentials = new AWS.SharedIniFileCredentials({profile: 'profile-name'});
   AWS.config.credentials = credentials;
   AWS.config.update({region:'region-name'});
   
   const client = new AWS.Rekognition();
   const params = {
     Image: {
       S3Object: {
         Bucket: bucket,
         Name: photo
       },
     },
     MaxLabels: 10
   }
   client.detectLabels(params, function(err, response) {
     if (err) {
       console.log(err, err.stack); // if an error occurred
     } else {
       console.log(`Detected labels for: ${photo}`)
       response.Labels.forEach(label => {
         console.log(`Label:      ${label.Name}`)
         console.log(`Confidence: ${label.Confidence}`)
         console.log("Instances:")
         label.Instances.forEach(instance => {
           let box = instance.BoundingBox
           console.log("  Bounding box:")
           console.log(`    Top:        ${box.Top}`)
           console.log(`    Left:       ${box.Left}`)
           console.log(`    Width:      ${box.Width}`)
           console.log(`    Height:     ${box.Height}`)
           console.log(`  Confidence: ${instance.Confidence}`)
         })
         console.log("Parents:")
         label.Parents.forEach(parent => {
           console.log(`  ${parent.Name}`)
         })
         console.log("------------")
         console.log("")
       }) // for response.labels
     } // if
   });
   ```

------
#### [ Java V2 ]

   此程式碼取自 AWS 文件開發套件範例 GitHub 儲存庫。請參閱[此處](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/DetectLabels.java)的完整範例。

   ```
   //snippet-start:[rekognition.java2.detect_labels.import]
   import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
   import software.amazon.awssdk.regions.Region;
   import software.amazon.awssdk.services.rekognition.RekognitionClient;
   import software.amazon.awssdk.services.rekognition.model.Image;
   import software.amazon.awssdk.services.rekognition.model.DetectLabelsRequest;
   import software.amazon.awssdk.services.rekognition.model.DetectLabelsResponse;
   import software.amazon.awssdk.services.rekognition.model.Label;
   import software.amazon.awssdk.services.rekognition.model.RekognitionException;
   import software.amazon.awssdk.services.rekognition.model.S3Object;
   import java.util.List;
   
   /**
   * Before running this Java V2 code example, set up your development environment, including your credentials.
   *
   * For more information, see the following documentation topic:
   *
   * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
   */
   public class DetectLabels {
   
       public static void main(String[] args) {
   
           final String usage = "\n" +
               "Usage: " +
               "   <bucket> <image>\n\n" +
               "Where:\n" +
               "   bucket - The name of the Amazon S3 bucket that contains the image (for example, ,ImageBucket)." +
               "   image - The name of the image located in the Amazon S3 bucket (for example, Lake.png). \n\n";
   
           if (args.length != 2) {
               System.out.println(usage);
               System.exit(1);
           }
   
           String bucket = args[0];
           String image = args[1];
           Region region = Region.US_WEST_2;
           RekognitionClient rekClient = RekognitionClient.builder()
               .region(region)
               .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
               .build();
   
           getLabelsfromImage(rekClient, bucket, image);
           rekClient.close();
       }
   
       // snippet-start:[rekognition.java2.detect_labels_s3.main]
       public static void getLabelsfromImage(RekognitionClient rekClient, String bucket, String image) {
   
           try {
               S3Object s3Object = S3Object.builder()
                   .bucket(bucket)
                   .name(image)
                   .build() ;
   
               Image myImage = Image.builder()
                   .s3Object(s3Object)
                   .build();
   
               DetectLabelsRequest detectLabelsRequest = DetectLabelsRequest.builder()
                   .image(myImage)
                   .maxLabels(10)
                   .build();
   
               DetectLabelsResponse labelsResponse = rekClient.detectLabels(detectLabelsRequest);
               List<Label> labels = labelsResponse.labels();
               System.out.println("Detected labels for the given photo");
               for (Label label: labels) {
                   System.out.println(label.name() + ": " + label.confidence().toString());
               }
   
           } catch (RekognitionException e) {
               System.out.println(e.getMessage());
               System.exit(1);
           }
       }
    // snippet-end:[rekognition.java2.detect_labels.main]
   }
   ```

------

   

## DetectLabels 操作要求
<a name="detectlabels-request"></a>

`DetectLabel` 的輸入是映像。在此範例 JSON 輸入中，來源映像是從 Amazon S3 儲存貯體載入的。`MaxLabels` 是回應中傳回的最大標籤數量。`MinConfidence` 是 Amazon Rekognition Image 在偵測到的標籤的準確性中必須具有的最低可信度，以便在回應中傳回。

此功能可讓您指定要傳回的映像的一或多個特徵，以便選取 `GENERAL_LABELS` 和 `IMAGE_PROPERTIES`。其包括的 `GENERAL_LABELS` 將傳回輸入映像中偵測到的標籤，`IMAGE_PROPERTIES` 將允許您存取映像的顏色和質量。

設定可讓您篩選 `GENERAL_LABELS` 和 `IMAGE_PROPERTIES` 特徵的傳回專案。對於標籤，您可以使用包容性和獨家篩選器。您還可以按特定標籤，單個標籤或按標籤類別進行篩選：
+ LabelInclusionFilter：可讓您指定要包含在回應中的標籤。
+ LabelExclusionFilter：可讓您指定要從回應中排除哪些標籤。
+ LabelCategoryInclusionFilter：可讓您指定要包含在回應中的標籤類別。
+ LabelCategoryExclusionFilter：可讓您指定要從回應中排除的標籤類別。

 您還可以根據需要組合包含性和排斥性篩選，但不包括某些標籤或類別以及包括其他標籤或類別。

`IMAGE_PROPERTIES` 指映像的主要色彩和品質屬性，例如銳利度、亮度和對比度。偵測 `IMAGE_PROPERTIES` 時，您可以使用 `MaxDominantColors` 參數指定要傳回的最大主色數 (預設值為 10)。

```
{
    "Image": {
        "S3Object": {
            "Bucket": "bucket",
            "Name": "input.jpg"
        }
    },
    "MaxLabels": 10,
    "MinConfidence": 75,
    "Features": [ "GENERAL_LABELS", "IMAGE_PROPERTIES" ],
    "Settings": {
        "GeneralLabels": {
            "LabelInclusionFilters": [<Label(s)>],
            "LabelExclusionFilters": [<Label(s)>],
            "LabelCategoryInclusionFilters": [<Category Name(s)>],
            "LabelCategoryExclusionFilters": [<Category Name(s)>] 
        },
        "ImageProperties": {
            "MaxDominantColors":10
        }
    }
}
```

## DetectLabels 回應
<a name="detectlabels-response"></a>

`DetectLabels` 的回應是映像中偵測到的一系列標籤，以及偵測所依據的可信度層級。

以下是 `DetectLabels` 的回應範例。以下範例回應包含針對 GENERAL\_LABELS 傳回的各種屬性，包括：
+ 名稱：偵測到的標籤的名稱。在此範例中，操作偵測到帶有標籤移動電話的物件。
+ 可信度：每個標籤都有一個相關的可信度等級。在此範例中，標籤的可信度為 99.36%。
+ 父項：偵測到之標籤的祖系標籤。在此範例中，標籤行動電話有一個名為電話的父標籤。
+ 別名：標籤可能有別名的相關資訊。在此範例中，行動電話標籤有可能的行動電話別名。
+ 類別：偵測到的標籤所屬的標籤類別。在這個例子中，其是技術和計算。

常見物件標籤的回應包含輸入映像上標籤位置的週框方塊資訊。例如，人員標籤具有一個實例陣列，其中包含兩個週框方塊。這些是在映像中偵測到的兩個人員位置。

回應還包括有關 IMAGE\_PROPERTIES 的屬性。IMAGE\_PROPERTIES 特徵所呈現的屬性如下：
+ 質量：有關輸入映像的銳利度，亮度和對比度的資訊，得分在 0 到 100 之間。質量會針對整個映像以及映像的背景和前景報告品質 (如果有的話)。不過，只會報告整個映像的對比度，而銳利度和亮度也會用於報告背景和前景。
+  主色：映像中主色的陣列。每種主要顏色都使用簡化的顏色名稱、CSS 調色盤、RGB 值和十六進位程式碼來描述。
+  前景：有關輸入映像前景的主要顏色、銳利度和亮度的資訊。
+  背景：有關輸入映像背景的主要顏色、銳利度和亮度的資訊。

當 GENERAL\_LABELS 和 IMAGE\_PROPERTIES 一起使用做為輸入參數時，Amazon Rekognition Image 也會傳回具有邊界方塊之物件的主要顏色。

欄位 `LabelModelVersion` 包含 `DetectLabels` 所使用之偵測模型的版本編號。

```
{
   
   "Labels": [
        {
            "Name": "Mobile Phone",
            "Parents": [
              { 
                "Name": "Phone" 
              }
            ],
            "Aliases": [
              {
                "Name": "Cell Phone" 
              }
            ], 
            "Categories": [
              {
                "Name": "Technology and Computing"
              }
            ],
            "Confidence": 99.9364013671875,
            "Instances": [
                {
                    "BoundingBox": {
                        "Width": 0.26779675483703613,
                        "Height": 0.8562285900115967,
                        "Left": 0.3604024350643158,
                        "Top": 0.09245597571134567,
                    }
                    "Confidence": 99.9364013671875,
                    "DominantColors": [
                    {
                "Red": 120,
                "Green": 137,
                "Blue": 132,
                "HexCode": "3A7432",
                "SimplifiedColor": "red", 
                "CssColor": "fuscia",    
                "PixelPercentage": 40.10 
                    }       
                        ],
                }
            ]
        }
    ],
    "ImageProperties": {
        "Quality": {
            "Brightness": 40,
            "Sharpness": 40,
            "Contrast": 24,
        },
        "DominantColors": [
            {
                "Red": 120,
                "Green": 137,
                "Blue": 132,
                "HexCode": "3A7432",
                "SimplifiedColor": "red", 
                "CssColor": "fuscia",    
                "PixelPercentage": 40.10 
            }       
        ],
        "Foreground": {
            "Quality": {
                "Brightness": 40,
                "Sharpness": 40,
            },
            "DominantColors": [                
                {                    
                    "Red": 200,
                    "Green": 137,
                    "Blue": 132,
                    "HexCode": "3A7432",
                    "CSSColor": "",
                    "SimplifiedColor": "red", 
                    "PixelPercentage": 30.70             
                }          
            ],   
        }
        "Background": {
            "Quality": {
                "Brightness": 40,
                "Sharpness": 40,
            },
            "DominantColors": [                
                {                    
                    "Red": 200,
                    "Green": 137,
                    "Blue": 132,
                    "HexCode": "3A7432",
                    "CSSColor": "",
                    "SimplifiedColor": "Red", 
                    "PixelPercentage": 10.20              
                }          
            ],   
        }, 
    },
    "LabelModelVersion": "3.0"
}
```

## 轉換 DetectLabels 回應
<a name="detectlabels-transform-response"></a>

使用 DetectLabels API 時，您可能需要回應結構來模擬較舊的 API 回應結構，其中主要標籤和別名都包含在同一個清單中。

以下是目前來自 [DetectLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectLabels.html) 的 API 回應範例：

```
"Labels": [
        {
            "Name": "Mobile Phone",
            "Confidence": 99.99717712402344,
            "Instances": [],
            "Parents": [
                { 
                "Name": "Phone" 
                }
             ],
            "Aliases": [
                {
                "Name": "Cell Phone" 
                }
             ]
        }
 ]
```

下列範例顯示先前來自 [DetectLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectLabels.html) 的 API 回應：

```
"Labels": [
        {
            "Name": "Mobile Phone",
            "Confidence": 99.99717712402344,
            "Instances": [],
            "Parents": [
                {
                "Name": "Phone" 
                }
             ]
         },
         {
            "Name": "Cell Phone",
            "Confidence": 99.99717712402344,
            "Instances": [],
            "Parents": [
                { 
                "Name": "Phone" 
                }
             ]
         },
]
```

如果需要，您可以將當前回應轉換為遵循舊回應的格式。您可以使用下列範例程式碼，將最新的 API 回應轉換為先前的 API 回應結構：

------
#### [ Python ]

下列程式碼範例示範如何從 DetectLabels API 轉換為目前的回應。在下面的程式碼範例中，您可以將 {{EXAMPLE\_INFERENCE\_OUTPUT}} 的值取代為您已執行的 DetectLabels 作業的輸出。

```
from copy import deepcopy

LABEL_KEY = "Labels"
ALIASES_KEY = "Aliases"
INSTANCE_KEY = "Instances"
NAME_KEY = "Name"

#Latest API response sample
EXAMPLE_INFERENCE_OUTPUT = {
    "Labels": [
        {
            "Name": "Mobile Phone",
            "Confidence": 97.530106,
            "Categories": [
                {
                    "Name": "Technology and Computing"
                }
            ],
            "Aliases": [
                {
                    "Name": "Cell Phone"
                }
            ],
            "Instances":[
                {
                    "BoundingBox":{
                        "Height":0.1549897,
                        "Width":0.07747964,
                        "Top":0.50858885,
                        "Left":0.00018205095
                    },
                    "Confidence":98.401276
                }
            ]
        },
        {
            "Name": "Urban",
            "Confidence": 99.99982,
            "Categories": [
                "Colors and Visual Composition"
            ]
        }
    ]
}

def expand_aliases(inferenceOutputsWithAliases):

    if LABEL_KEY in inferenceOutputsWithAliases:
        expandInferenceOutputs = []
        for primaryLabelDict in inferenceOutputsWithAliases[LABEL_KEY]:
            if ALIASES_KEY in primaryLabelDict:
                for alias in primaryLabelDict[ALIASES_KEY]:
                    aliasLabelDict = deepcopy(primaryLabelDict)
                    aliasLabelDict[NAME_KEY] = alias[NAME_KEY]
                    del aliasLabelDict[ALIASES_KEY]
                    if INSTANCE_KEY in aliasLabelDict:
                        del aliasLabelDict[INSTANCE_KEY]
                    expandInferenceOutputs.append(aliasLabelDict)

        inferenceOutputsWithAliases[LABEL_KEY].extend(expandInferenceOutputs)

    return inferenceOutputsWithAliases


if __name__ == "__main__":

    outputWithExpandAliases = expand_aliases(EXAMPLE_INFERENCE_OUTPUT)
    print(outputWithExpandAliases)
```

以下是轉換回應的範例：

```
#Output example after the transformation
{
    "Labels": [
        {
            "Name": "Mobile Phone",
            "Confidence": 97.530106,
            "Categories": [
                {
                    "Name": "Technology and Computing"
                }
            ],
            "Aliases": [
                {
                    "Name": "Cell Phone"
                }
            ],
            "Instances":[
                {
                    "BoundingBox":{
                        "Height":0.1549897,
                        "Width":0.07747964,
                        "Top":0.50858885,
                        "Left":0.00018205095
                    },
                    "Confidence":98.401276
                }
            ]
        },
        {
            "Name": "Cell Phone",
            "Confidence": 97.530106,
            "Categories": [
                {
                    "Name": "Technology and Computing"
                }
            ],
            "Instances":[]
        },
        {
            "Name": "Urban",
            "Confidence": 99.99982,
            "Categories": [
                "Colors and Visual Composition"
            ]
        }
    ]
}
```

------