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。

# 偵測不適當的映像
<a name="procedure-moderate-images"></a>

您可以使用 [DetectModerationLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectModerationLabels.html) 操作來判斷映像是否包含不適當或令人反感的內容。如需 Amazon Rekognition 中的管制標籤清單，請參閱[使用映像和影片管制 API](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api)。



## 偵測映像中的不適當內容
<a name="moderate-images-sdk"></a>

映像的格式必須是 .jpg 或 .png。您可以用映像位元組陣列 (Base64 編碼映像位元組) 的方式提供輸入映像，或指定 Amazon S3 物件。在這些程序中，您會將映像 (.jpg 或 .png) 上傳至 S3 儲存貯體。

若要執行這些程序，您需要安裝 AWS CLI 或適當的 AWS SDK。如需詳細資訊，請參閱[Amazon Rekognition 入門](getting-started.md)。您使用的 AWS 帳戶必須有 Amazon Rekognition API 的存取權限。如需詳細資訊，請參閱 [Amazon Rekognition 定義的動作](https://docs.aws.amazon.com/IAM/latest/UserGuide/list_amazonrekognition.html#amazonrekognition-actions-as-permissions)。

**偵測映像中的管制標籤 (SDK)**

1. 如果您尚未執行：

   1. 建立或更新具有 `AmazonRekognitionFullAccess` 和 `AmazonS3ReadOnlyAccess` 權限的​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. 將映像上傳至您的 S3 儲存貯體。

   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/UploadingObjectsintoAmazonS3.html)。

1. 使用下列範例來呼叫 `DetectModerationLabels` 操作。

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

   此範例輸出偵測到的不適當內容標籤名稱、信心度以及偵測到的管制標籤的父標籤。

   將 `amzn-s3-demo-bucket` 與 `photo` 的值取代為您在步驟 2 中所使用的 S3 儲存貯體名稱與映像檔案名稱。

   ```
   //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.)
   
   package aws.example.rekognition.image;
   import com.amazonaws.services.rekognition.AmazonRekognition;
   import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder;
   import com.amazonaws.services.rekognition.model.AmazonRekognitionException;
   import com.amazonaws.services.rekognition.model.DetectModerationLabelsRequest;
   import com.amazonaws.services.rekognition.model.DetectModerationLabelsResult;
   import com.amazonaws.services.rekognition.model.Image;
   import com.amazonaws.services.rekognition.model.ModerationLabel;
   import com.amazonaws.services.rekognition.model.S3Object;
   
   import java.util.List;
   
   public class DetectModerationLabels
   {
      public static void main(String[] args) throws Exception
      {
         String photo = "input.jpg";
         String bucket = "bucket";
         
         AmazonRekognition rekognitionClient = AmazonRekognitionClientBuilder.defaultClient();
         
         DetectModerationLabelsRequest request = new DetectModerationLabelsRequest()
           .withImage(new Image().withS3Object(new S3Object().withName(photo).withBucket(bucket)))
           .withMinConfidence(60F);
         try
         {
              DetectModerationLabelsResult result = rekognitionClient.detectModerationLabels(request);
              List<ModerationLabel> labels = result.getModerationLabels();
              System.out.println("Detected labels for " + photo);
              for (ModerationLabel label : labels)
              {
                 System.out.println("Label: " + label.getName()
                  + "\n Confidence: " + label.getConfidence().toString() + "%"
                  + "\n Parent:" + label.getParentName());
             }
          }
          catch (AmazonRekognitionException e)
          {
            e.printStackTrace();
          }
       }
   }
   ```

------
#### [ 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/DetectModerationLabels.java)的完整範例。

   ```
   //snippet-start:[rekognition.java2.recognize_video_text.import]
   //snippet-start:[rekognition.java2.detect_mod_labels.import]
   import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
   import software.amazon.awssdk.core.SdkBytes;
   import software.amazon.awssdk.regions.Region;
   import software.amazon.awssdk.services.rekognition.RekognitionClient;
   import software.amazon.awssdk.services.rekognition.model.RekognitionException;
   import software.amazon.awssdk.services.rekognition.model.Image;
   import software.amazon.awssdk.services.rekognition.model.DetectModerationLabelsRequest;
   import software.amazon.awssdk.services.rekognition.model.DetectModerationLabelsResponse;
   import software.amazon.awssdk.services.rekognition.model.ModerationLabel;
   import java.io.FileInputStream;
   import java.io.FileNotFoundException;
   import java.io.InputStream;
   import java.util.List;
   //snippet-end:[rekognition.java2.detect_mod_labels.import]
   
   /**
   * 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 ModerateLabels {
   
    public static void main(String[] args) {
   
        final String usage = "\n" +
            "Usage: " +
            "   <sourceImage>\n\n" +
            "Where:\n" +
            "   sourceImage - The path to the image (for example, C:\\AWS\\pic1.png). \n\n";
   
        if (args.length < 1) {
            System.out.println(usage);
            System.exit(1);
        }
   
        String sourceImage = args[0];
        Region region = Region.US_WEST_2;
        RekognitionClient rekClient = RekognitionClient.builder()
            .region(region)
            .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
            .build();
   
        detectModLabels(rekClient, sourceImage);
        rekClient.close();
    }
   
    // snippet-start:[rekognition.java2.detect_mod_labels.main]
    public static void detectModLabels(RekognitionClient rekClient, String sourceImage) {
   
        try {
            InputStream sourceStream = new FileInputStream(sourceImage);
            SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream);
            Image souImage = Image.builder()
                .bytes(sourceBytes)
                .build();
   
            DetectModerationLabelsRequest moderationLabelsRequest = DetectModerationLabelsRequest.builder()
                .image(souImage)
                .minConfidence(60F)
                .build();
   
            DetectModerationLabelsResponse moderationLabelsResponse = rekClient.detectModerationLabels(moderationLabelsRequest);
            List<ModerationLabel> labels = moderationLabelsResponse.moderationLabels();
            System.out.println("Detected labels for image");
   
            for (ModerationLabel label : labels) {
                System.out.println("Label: " + label.name()
                    + "\n Confidence: " + label.confidence().toString() + "%"
                    + "\n Parent:" + label.parentName());
            }
   
        } catch (RekognitionException | FileNotFoundException e) {
            e.printStackTrace();
            System.exit(1);
        }
    }
    // snippet-end:[rekognition.java2.detect_mod_labels.main]
   ```

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

   此 AWS CLI 命令會顯示 CLI 操作的 JSON `detect-moderation-labels` 輸出。

   將 `amzn-s3-demo-bucket` 與 `input.jpg` 取代為您在步驟 2 中所使用的 S3 儲存貯體名稱與映像檔案名稱。使用您開發人員設定檔的名稱取代 `profile_name` 的值。若要使用轉接器，請將專案版本的 ARN 提供給 `project-version` 參數。

   ```
   aws rekognition detect-moderation-labels --image "{S3Object:{Bucket:<amzn-s3-demo-bucket>,Name:<image-name>}}" \ 
   --profile profile-name \
   --project-version "ARN"
   ```

   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\$1) 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-moderation-labels --image "{\"S3Object\":{\"Bucket\":\"amzn-s3-demo-bucket\",\"Name\":\"image-name\"}}" \
   --profile profile-name
   ```

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

   此範例輸出偵測到的不適當或令人反感的內容標籤名稱、信心度以及偵測到的不安全內容標籤的父標籤。

   在函數 `main` 中，將 `amzn-s3-demo-bucket` 與 `photo` 的值取代為您在步驟 2 中所使用的 S3 儲存貯體名稱與映像檔案名稱。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 moderate_image(photo, bucket):
       
       session = boto3.Session(profile_name='profile-name')
       client = session.client('rekognition')
   
       response = client.detect_moderation_labels(Image={'S3Object':{'Bucket':bucket,'Name':photo}})
   
       print('Detected labels for ' + photo)
       for label in response['ModerationLabels']:
           print (label['Name'] + ' : ' + str(label['Confidence']))
           print (label['ParentName'])
       return len(response['ModerationLabels'])
   
   def main():
   
       photo='image-name'
       bucket='amzn-s3-demo-bucket'
       label_count=moderate_image(photo, bucket)
       print("Labels detected: " + str(label_count))
   
   if __name__ == "__main__":
       main()
   ```

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

   此範例輸出偵測到的令人反感的內容標籤名稱、信心度以及偵測到的管制標籤的父標籤。

   將 `amzn-s3-demo-bucket` 與 `photo` 的值取代為您在步驟 2 中所使用的 S3 儲存貯體名稱與映像檔案名稱。

   ```
   //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 DetectModerationLabels
   {
       public static void Example()
       {
           String photo = "input.jpg";
           String bucket = "amzn-s3-demo-bucket";
   
           AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
   
           DetectModerationLabelsRequest detectModerationLabelsRequest = new DetectModerationLabelsRequest()
           {
               Image = new Image()
               {
                   S3Object = new S3Object()
                   {
                       Name = photo,
                       Bucket = bucket
                   },
               },
               MinConfidence = 60F
           };
   
           try
           {
               DetectModerationLabelsResponse detectModerationLabelsResponse = rekognitionClient.DetectModerationLabels(detectModerationLabelsRequest);
               Console.WriteLine("Detected labels for " + photo);
               foreach (ModerationLabel label in detectModerationLabelsResponse.ModerationLabels)
                   Console.WriteLine("Label: {0}\n Confidence: {1}\n Parent: {2}", 
                       label.Name, label.Confidence, label.ParentName);
           }
           catch (Exception e)
           {
               Console.WriteLine(e.Message);
           }
       }
   }
   ```

------

## DetectModerationLabels 操作要求
<a name="detectmoderation-labels-operation-request"></a>

`DetectModerationLabels` 的輸入是映像。在此範例 JSON 輸入中，來源映像從 Amazon S3 儲存貯體載入。`MinConfidence` 是在偵測到的標籤的準確性中 Amazon Rekognition Image 必須具有的最低可信度，以便在回應中傳回。

```
{
    "Image": {
        "S3Object": {
            "Bucket": "amzn-s3-demo-bucket",
            "Name": "input.jpg"
        }
    },
    "MinConfidence": 60
}
```

## DetectModerationLabel 操作回應
<a name="detectmoderationlabels-operation-response"></a>

 `DetectModerationLabels` 可從 S3 儲存貯體擷取輸入映像，您也可以用映像位元組的方式提供輸入映像。下列範例是來自 `DetectModerationLabels` 呼叫的回應。

在下列範例 JSON 回應中，請注意以下事項：
+ **不適當的映像偵測資訊**：此範例會顯示映像中發現不當或冒犯性內容的標籤清單。此清單包含最上層標籤與映像中偵測到的每個第二層標籤。

  **標籤**：每個標籤都具有名稱、Amazon Rekognition 對標籤正確性估計的可信度，以及其父標籤的名稱。最上層標籤的父名稱為 `""`。

  **標籤可信度**：每個標籤都有一個介於 0 到 100 之間的可信度值，該值表示 Amazon Rekognition 对標籤正確性估计的可信度百分比。您可以為要在 API 操作要求回應中傳回的標籤指定要求的可信度。

```
{
    "ModerationLabels": [
        {
            "Confidence": 99.44782257080078,
            "Name": "Smoking",
            "ParentName": "Drugs & Tobacco Paraphernalia & Use",
            "TaxonomyLevel": 3
        },
        {
            "Confidence": 99.44782257080078,
            "Name": "Drugs & Tobacco Paraphernalia & Use",
            "ParentName": "Drugs & Tobacco",
            "TaxonomyLevel": 2
        },
        {
            "Confidence": 99.44782257080078,
            "Name": "Drugs & Tobacco",
            "ParentName": "",
            "TaxonomyLevel": 1
        }
    ],
    "ModerationModelVersion": "7.0",
    "ContentTypes": [
        {
            "Confidence": 99.9999008178711,
            "Name": "Illustrated"
        }
    ]
}
```