Analyzing images stored in an Amazon S3 bucket

To detect labels in an image

1. If you haven't already:

   1. Create or update a user with AmazonRekognitionFullAccess and AmazonS3ReadOnlyAccess permissions. For more information, see Step 1: Set up an AWS account and create a User.

   2. Install and configure the AWS CLI and the AWS SDKs. For more information, see Step 2: Set up the AWS CLI and AWS SDKs. Ensure that you have given the user calling the API operations the proper permissions for programmatic access, see Grant programmatic access for instructions on how to do this.

2. Upload an image that contains one or more objects—such as trees, houses, and boat—to your S3 bucket. The image must be in .jpg or .png format.

   For instructions, see Uploading Objects into Amazon S3 in the Amazon Simple Storage Service User Guide.

3. Use the following examples to call the DetectLabels operation.

   Java

   This example displays a list of labels that were detected in the input image. Replace the values of bucket and photo with the names of the Amazon S3 bucket and image that you used in step 2.

   //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 com.amazonaws.samples;
   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.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.services.rekognition.model.S3Object;
   import java.util.List;
   
   public class DetectLabels {
   
      public static void main(String[] args) throws Exception {
   
         String photo = "input.jpg";
         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);
            for (Label label: labels) {
               System.out.println(label.getName() + ": " + label.getConfidence().toString());
            }
         } catch(AmazonRekognitionException e) {
            e.printStackTrace();
         }
      }
   }

   AWS CLI

   This example displays the JSON output from the detect-labels CLI operation. Replace the values of bucket and photo with the names of the Amazon S3 bucket and image that you used in Step 2. Replace the value of profile_name in the line that creates the Rekognition session with the name of your developer profile.

   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 

   If you are using Windows you may need to escape the qutations marks as seen in the example below.

   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

   Java V2

   This code is taken from the AWS Documentation SDK examples GitHub repository. See the full example here.

   //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]
   }

   Python

   This example displays the labels that were detected in the input image. Replace the values of bucket and photo with the names of the Amazon S3 bucket and image that you used in Step 2. Replace the value of profile_name in the line that creates the Rekognition session with the name of your developer profile.

   #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()
   

   Node.Js

   This example displays information about labels detected in an image.

   Change the value of photo to the path and file name of an image file that contains one or more celebrity faces. Change the value of bucket to the name of the S3 bucket containing the provided image file. Change the value of REGION to the name of the region associated with your account. Replace the value of profile_name in the line that creates the Rekognition session with the name of your developer profile.

   // Import required AWS SDK clients and commands for Node.js
   import { DetectLabelsCommand } from  "@aws-sdk/client-rekognition";
   import  { RekognitionClient } from "@aws-sdk/client-rekognition";
   
   import {fromIni} from '@aws-sdk/credential-providers';
   
   // Set the AWS Region.
   const REGION = "region-name"; //e.g. "us-east-1"
   
   // Create SNS service object.
   const rekogClient = new RekognitionClient({
     region: REGION,
     credentials: fromIni({
          profile: 'profile-name',
     }),
   });
   
   const bucket = 'bucket-name'
   const photo = 'photo-name'
   
   // Set params
   const params = {For example, to grant
       Image: {
         S3Object: {
           Bucket: bucket,
           Name: photo
         },
       },
     }
   
   const detect_labels = async () => {
       try {
           const response = await rekogClient.send(new DetectLabelsCommand(params));
           console.log(response.Labels)
           response.Labels.forEach(label =>{
               console.log(`Confidence: ${label.Confidence}`)
               console.log(`Name: ${label.Name}`)
               console.log('Instances:')
               label.Instances.forEach(instance => {
                   console.log(instance)
               })
               console.log('Parents:')
               label.Parents.forEach(name => {
                   console.log(name)
               })
               console.log("-------")
           })
           return response; // For unit tests.
         } catch (err) {
           console.log("Error", err);
         }
   };
   
   detect_labels();

   .NET

   This example displays a list of labels that were detected in the input image. Replace the values of bucket and photo with the names of the Amazon S3 bucket and image that you used in Step 2.

   //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

   This example displays a list of labels that were detected in the input image. Replace the values of bucket and photo with the names of the Amazon S3 bucket and image that you used in Step 2.

   
   
      # 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

   anchoranchoranchoranchoranchoranchoranchor
   • Java
   • AWS CLI
   • Java V2
   • Python
   • Node.Js
   • .NET
   • Ruby

   This example displays a list of labels that were detected in the input image. Replace the values of bucket and photo with the names of the Amazon S3 bucket and image that you used in step 2.

   //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 com.amazonaws.samples;
   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.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.services.rekognition.model.S3Object;
   import java.util.List;
   
   public class DetectLabels {
   
      public static void main(String[] args) throws Exception {
   
         String photo = "input.jpg";
         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);
            for (Label label: labels) {
               System.out.println(label.getName() + ": " + label.getConfidence().toString());
            }
         } catch(AmazonRekognitionException e) {
            e.printStackTrace();
         }
      }
   }