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。

# 分析從本機檔案系統載入的映像
<a name="images-bytes"></a>

Amazon Rekognition Image 操作可以分析做為映像位元組提供的映像，或存放在 Amazon S3 儲存貯體中的映像。

這些主題提供範例，其示範透過使用從本機檔案系統載入的檔案，將映像位元組提供給 Amazon Rekognition Image API 操作。您可以使用[映像](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_Image.html)輸入參數，將映像位元組傳遞至 Amazon Rekognition API 操作。在 `Image` 內，指定 `Bytes` 屬性，以傳遞 base64 編碼的映像位元組。

透過使用 `Bytes` 輸入參數傳遞至 Amazon Rekognition API 操作的映像，必須經過 Base64 編碼。這些範例使用的 AWS SDK，會自動以 base64 編碼映像。在呼叫 Amazon Rekognition API 操作前，您不需要再編碼映像位元組。如需詳細資訊，請參閱 [映像規格](images-information.md)。

在這個範例中，JSON 請求 `DetectLabels`，來源映像位元組以 `Bytes` 輸入參數傳遞。

```
{
    "Image": {
        "Bytes": "/9j/4AAQSk....."
    },
    "MaxLabels": 10,
    "MinConfidence": 77
}
```

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](labels-detect-labels-image.md#detectlabels-response)。

對於使用者端 JavaScript 範例，請參閱 [使用於 JavaScript](image-bytes-javascript.md)。

**偵測本機映像中的標籤**

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. 使用下列範例來呼叫 `DetectLabels` 操作。

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

   下列 Java 範例示範如何從本機檔案系統載入映像，並運用 [detectLabels](https://sdk.amazonaws.com/java/api/latest/software/amazon/awssdk/services/rekognition/model/DetectLabelsRequest.html) AWS SDK 操作來偵測標籤。將 `photo` 的值變更為某個映像檔案的路徑和檔案名稱 (.jpg 或 .png 格式)。

   ```
   //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 java.io.File;
   import java.io.FileInputStream;
   import java.io.InputStream;
   import java.nio.ByteBuffer;
   import java.util.List;
   import com.amazonaws.services.rekognition.AmazonRekognition;
   import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder;
   import com.amazonaws.AmazonClientException;
   import com.amazonaws.services.rekognition.model.AmazonRekognitionException;
   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.Label;
   import com.amazonaws.util.IOUtils;
   
   public class DetectLabelsLocalFile {
       public static void main(String[] args) throws Exception {
       	String photo="input.jpg";
   
   
           ByteBuffer imageBytes;
           try (InputStream inputStream = new FileInputStream(new File(photo))) {
               imageBytes = ByteBuffer.wrap(IOUtils.toByteArray(inputStream));
           }
   
   
           AmazonRekognition rekognitionClient = AmazonRekognitionClientBuilder.defaultClient();
   
           DetectLabelsRequest request = new DetectLabelsRequest()
                   .withImage(new Image()
                           .withBytes(imageBytes))
                   .withMaxLabels(10)
                   .withMinConfidence(77F);
   
           try {
   
               DetectLabelsResult result = rekognitionClient.detectLabels(request);
               List <Label> labels = result.getLabels();
   
               System.out.println("Detected labels for " + photo);
               for (Label label: labels) {
                  System.out.println(label.getName() + ": " + label.getConfidence().toString());
               }
   
           } catch (AmazonRekognitionException e) {
               e.printStackTrace();
           }
   
       }
   }
   ```

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

   下列[適用於 Python 的 AWS SDK](https://aws.amazon.com/sdk-for-python/) 範例示範如何從本機檔案系統載入映像，並呼叫 [detect\_labels](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/rekognition.html#Rekognition.Client.detect_labels) 操作。將 `photo` 的值變更為某個映像檔案的路徑和檔案名稱 (.jpg 或 .png 格式)。

   ```
   #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_local_file(photo):
   
   
       client=boto3.client('rekognition')
      
       with open(photo, 'rb') as image:
           response = client.detect_labels(Image={'Bytes': image.read()})
           
       print('Detected labels in ' + photo)    
       for label in response['Labels']:
           print (label['Name'] + ' : ' + str(label['Confidence']))
   
       return len(response['Labels'])
   
   def main():
       photo='photo'
   
       label_count=detect_labels_local_file(photo)
       print("Labels detected: " + str(label_count))
   
   
   if __name__ == "__main__":
       main()
   ```

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

   下列 Java 範例示範如何從本機檔案系統載入映像，並運用 `DetectLabels` 操作來偵測標籤。將 `photo` 的值變更為某個映像檔案的路徑和檔案名稱 (.jpg 或 .png 格式)。

   ```
   //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 System.IO;
   using Amazon.Rekognition;
   using Amazon.Rekognition.Model;
   
   public class DetectLabelsLocalfile
   {
       public static void Example()
       {
           String photo = "input.jpg";
   
           Amazon.Rekognition.Model.Image image = new Amazon.Rekognition.Model.Image();
           try
           {
               using (FileStream fs = new FileStream(photo, FileMode.Open, FileAccess.Read))
               {
                   byte[] data = null;
                   data = new byte[fs.Length];
                   fs.Read(data, 0, (int)fs.Length);
                   image.Bytes = new MemoryStream(data);
               }
           }
           catch (Exception)
           {
               Console.WriteLine("Failed to load file " + photo);
               return;
           }
   
           AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
   
           DetectLabelsRequest detectlabelsRequest = new DetectLabelsRequest()
           {
               Image = image,
               MaxLabels = 10,
               MinConfidence = 77F
           };
   
           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);
           }
       }
   }
   ```

------
#### [ PHP ]

   下列[適用於 PHP 的 AWS SDK](https://docs.aws.amazon.com/sdk-for-php/v3/developer-guide/welcome.html#getting-started) 範例示範如何從本機檔案系統載入映像，並呼叫 [DetectFaces](https://docs.aws.amazon.com/aws-sdk-php/v3/api/api-rekognition-2016-06-27.html#detectfaces) API 操作。將 `photo` 的值變更為某個映像檔案的路徑和檔案名稱 (.jpg 或 .png 格式)。

   ```
   
   <?php
   //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.)
   
       require 'vendor/autoload.php';
   
       use Aws\Rekognition\RekognitionClient;
   
       $options = [
          'region'            => 'us-west-2',
           'version'           => 'latest'
       ];
   
       $rekognition = new RekognitionClient($options);
   	
       // Get local image
       $photo = 'input.jpg';
       $fp_image = fopen($photo, 'r');
       $image = fread($fp_image, filesize($photo));
       fclose($fp_image);
   
   
       // Call DetectFaces
       $result = $rekognition->DetectFaces(array(
          'Image' => array(
             'Bytes' => $image,
          ),
          'Attributes' => array('ALL')
          )
       );
   
       // Display info for each detected person
       print 'People: Image position and estimated age' . PHP_EOL;
       for ($n=0;$n<sizeof($result['FaceDetails']); $n++){
   
         print 'Position: ' . $result['FaceDetails'][$n]['BoundingBox']['Left'] . " "
         . $result['FaceDetails'][$n]['BoundingBox']['Top']
         . PHP_EOL
         . 'Age (low): '.$result['FaceDetails'][$n]['AgeRange']['Low']
         .  PHP_EOL
         . 'Age (high): ' . $result['FaceDetails'][$n]['AgeRange']['High']
         .  PHP_EOL . PHP_EOL;
       }
   ?>
   ```

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

   此範例顯示一份在輸入映像中偵測到的標籤清單。將 `photo` 的值變更為某個映像檔案的路徑和檔案名稱 (.jpg 或 .png 格式)。

   ```
   #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.)
   
       # gem 'aws-sdk-rekognition'
       require 'aws-sdk-rekognition'
       credentials = Aws::Credentials.new(
          ENV['AWS_ACCESS_KEY_ID'],
          ENV['AWS_SECRET_ACCESS_KEY']
       )
       client   = Aws::Rekognition::Client.new credentials: credentials
       photo = 'photo.jpg'
       path = File.expand_path(photo) # expand path relative to the current directory
       file = File.read(path)
       attrs = {
         image: {
           bytes: file
         },
         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
   ```

------
#### [ 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。

   ```
   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.*;
   
   import java.io.FileInputStream;
   import java.io.FileNotFoundException;
   import java.io.InputStream;
   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 = """
               Usage: <bucketName> <sourceImage>
   
               Where:
                   bucketName - The name of the Amazon S3 bucket where the image is stored
                   sourceImage - The name of the image file (for example, pic1.png).\s
               """;
   
           if (args.length != 2) {
               System.out.println(usage);
               System.exit(1);
           }
   
           String bucketName = args[0] ;
           String sourceImage = args[1] ;
           Region region = Region.US_WEST_2;
           RekognitionClient rekClient = RekognitionClient.builder()
                   .region(region)
                   .build();
   
           detectImageLabels(rekClient, bucketName, sourceImage);
           rekClient.close();
       }
   
       /**
        * Detects the labels in an image stored in an Amazon S3 bucket using the Amazon Rekognition service.
        *
        * @param rekClient     the Amazon Rekognition client used to make the detection request
        * @param bucketName    the name of the Amazon S3 bucket where the image is stored
        * @param sourceImage   the name of the image file to be analyzed
        */
       public static void detectImageLabels(RekognitionClient rekClient, String bucketName, String sourceImage) {
           try {
               S3Object s3ObjectTarget = S3Object.builder()
                       .bucket(bucketName)
                       .name(sourceImage)
                       .build();
   
               Image souImage = Image.builder()
                       .s3Object(s3ObjectTarget)
                       .build();
   
               DetectLabelsRequest detectLabelsRequest = DetectLabelsRequest.builder()
                       .image(souImage)
                       .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);
           }
       }
   }
   ```

------