There are more AWS SDK examples available in the [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub repo.

# Use `DetectLabels` with an AWS SDK or CLI
<a name="rekognition_example_rekognition_DetectLabels_section"></a>

The following code examples show how to use `DetectLabels`.

For more information, see [Detecting labels in an image](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html).

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

**SDK for .NET**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples). 

```
    using System;
    using System.Threading.Tasks;
    using Amazon.Rekognition;
    using Amazon.Rekognition.Model;

    /// <summary>
    /// Uses the Amazon Rekognition Service to detect labels within an image
    /// stored in an Amazon Simple Storage Service (Amazon S3) bucket.
    /// </summary>
    public class DetectLabels
    {
        public static async Task Main()
        {
            string photo = "del_river_02092020_01.jpg"; // "input.jpg";
            string bucket = "amzn-s3-demo-bucket"; // "bucket";

            var rekognitionClient = new AmazonRekognitionClient();

            var detectlabelsRequest = new DetectLabelsRequest
            {
                Image = new Image()
                {
                    S3Object = new S3Object()
                    {
                        Name = photo,
                        Bucket = bucket,
                    },
                },
                MaxLabels = 10,
                MinConfidence = 75F,
            };

            try
            {
                DetectLabelsResponse detectLabelsResponse = await rekognitionClient.DetectLabelsAsync(detectlabelsRequest);
                Console.WriteLine("Detected labels for " + photo);
                foreach (Label label in detectLabelsResponse.Labels)
                {
                    Console.WriteLine($"Name: {label.Name} Confidence: {label.Confidence}");
                }
            }
            catch (Exception ex)
            {
                Console.WriteLine(ex.Message);
            }
        }
    }
```
Detect labels in an image file stored on your computer.  

```
    using System;
    using System.IO;
    using System.Threading.Tasks;
    using Amazon.Rekognition;
    using Amazon.Rekognition.Model;

    /// <summary>
    /// Uses the Amazon Rekognition Service to detect labels within an image
    /// stored locally.
    /// </summary>
    public class DetectLabelsLocalFile
    {
        public static async Task Main()
        {
            string photo = "input.jpg";

            var image = new Amazon.Rekognition.Model.Image();
            try
            {
                using var 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;
            }

            var rekognitionClient = new AmazonRekognitionClient();

            var detectlabelsRequest = new DetectLabelsRequest
            {
                Image = image,
                MaxLabels = 10,
                MinConfidence = 77F,
            };

            try
            {
                DetectLabelsResponse detectLabelsResponse = await rekognitionClient.DetectLabelsAsync(detectlabelsRequest);
                Console.WriteLine($"Detected labels for {photo}");
                foreach (Label label in detectLabelsResponse.Labels)
                {
                    Console.WriteLine($"{label.Name}: {label.Confidence}");
                }
            }
            catch (Exception ex)
            {
                Console.WriteLine(ex.Message);
            }
        }
    }
```
+  For API details, see [DetectLabels](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DetectLabels) in *AWS SDK for .NET API Reference*. 

------
#### [ C\$1\$1 ]

**SDK for C\$1\$1**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/rekognition#code-examples). 

```
//! Detect instances of real-world entities within an image by using Amazon Rekognition
/*!
  \param imageBucket: The Amazon Simple Storage Service (Amazon S3) bucket containing an image.
  \param imageKey: The Amazon S3 key of an image object.
  \param clientConfiguration: AWS client configuration.
  \return bool: Function succeeded.
 */
bool AwsDoc::Rekognition::detectLabels(const Aws::String &imageBucket,
                                       const Aws::String &imageKey,
                                       const Aws::Client::ClientConfiguration &clientConfiguration) {
    Aws::Rekognition::RekognitionClient rekognitionClient(clientConfiguration);

    Aws::Rekognition::Model::DetectLabelsRequest request;
    Aws::Rekognition::Model::S3Object s3Object;
    s3Object.SetBucket(imageBucket);
    s3Object.SetName(imageKey);

    Aws::Rekognition::Model::Image image;
    image.SetS3Object(s3Object);

    request.SetImage(image);

    const Aws::Rekognition::Model::DetectLabelsOutcome outcome = rekognitionClient.DetectLabels(request);

    if (outcome.IsSuccess()) {
        const Aws::Vector<Aws::Rekognition::Model::Label> &labels = outcome.GetResult().GetLabels();
        if (labels.empty()) {
            std::cout << "No labels detected" << std::endl;
        } else {
            for (const Aws::Rekognition::Model::Label &label: labels) {
                std::cout << label.GetName() << ": " << label.GetConfidence() << std::endl;
            }
        }
    } else {
        std::cerr << "Error while detecting labels: '"
                  << outcome.GetError().GetMessage()
                  << "'" << std::endl;
    }

    return outcome.IsSuccess();
}
```
+  For API details, see [DetectLabels](https://docs.aws.amazon.com/goto/SdkForCpp/rekognition-2016-06-27/DetectLabels) in *AWS SDK for C\$1\$1 API Reference*. 

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

**AWS CLI**  
**To detect a label in an image**  
The following `detect-labels` example detects scenes and objects in an image stored in an Amazon S3 bucket.  

```
aws rekognition detect-labels \
    --image '{"S3Object":{"Bucket":"bucket","Name":"image"}}'
```
Output:  

```
{
    "Labels": [
        {
            "Instances": [],
            "Confidence": 99.15271759033203,
            "Parents": [
                {
                    "Name": "Vehicle"
                },
                {
                    "Name": "Transportation"
                }
            ],
            "Name": "Automobile"
        },
        {
            "Instances": [],
            "Confidence": 99.15271759033203,
            "Parents": [
                {
                    "Name": "Transportation"
                }
            ],
            "Name": "Vehicle"
        },
        {
            "Instances": [],
            "Confidence": 99.15271759033203,
            "Parents": [],
            "Name": "Transportation"
        },
        {
            "Instances": [
                {
                    "BoundingBox": {
                        "Width": 0.10616336017847061,
                        "Top": 0.5039216876029968,
                        "Left": 0.0037978808395564556,
                        "Height": 0.18528179824352264
                    },
                    "Confidence": 99.15271759033203
                },
                {
                    "BoundingBox": {
                        "Width": 0.2429988533258438,
                        "Top": 0.5251884460449219,
                        "Left": 0.7309805154800415,
                        "Height": 0.21577216684818268
                    },
                    "Confidence": 99.1286392211914
                },
                {
                    "BoundingBox": {
                        "Width": 0.14233611524105072,
                        "Top": 0.5333095788955688,
                        "Left": 0.6494812965393066,
                        "Height": 0.15528248250484467
                    },
                    "Confidence": 98.48368072509766
                },
                {
                    "BoundingBox": {
                        "Width": 0.11086395382881165,
                        "Top": 0.5354844927787781,
                        "Left": 0.10355594009160995,
                        "Height": 0.10271988064050674
                    },
                    "Confidence": 96.45606231689453
                },
                {
                    "BoundingBox": {
                        "Width": 0.06254628300666809,
                        "Top": 0.5573825240135193,
                        "Left": 0.46083059906959534,
                        "Height": 0.053911514580249786
                    },
                    "Confidence": 93.65448760986328
                },
                {
                    "BoundingBox": {
                        "Width": 0.10105438530445099,
                        "Top": 0.534368634223938,
                        "Left": 0.5743985772132874,
                        "Height": 0.12226245552301407
                    },
                    "Confidence": 93.06217193603516
                },
                {
                    "BoundingBox": {
                        "Width": 0.056389667093753815,
                        "Top": 0.5235804319381714,
                        "Left": 0.9427769780158997,
                        "Height": 0.17163699865341187
                    },
                    "Confidence": 92.6864013671875
                },
                {
                    "BoundingBox": {
                        "Width": 0.06003860384225845,
                        "Top": 0.5441341400146484,
                        "Left": 0.22409997880458832,
                        "Height": 0.06737709045410156
                    },
                    "Confidence": 90.4227066040039
                },
                {
                    "BoundingBox": {
                        "Width": 0.02848697081208229,
                        "Top": 0.5107086896896362,
                        "Left": 0,
                        "Height": 0.19150497019290924
                    },
                    "Confidence": 86.65286254882812
                },
                {
                    "BoundingBox": {
                        "Width": 0.04067881405353546,
                        "Top": 0.5566273927688599,
                        "Left": 0.316415935754776,
                        "Height": 0.03428703173995018
                    },
                    "Confidence": 85.36471557617188
                },
                {
                    "BoundingBox": {
                        "Width": 0.043411049991846085,
                        "Top": 0.5394920110702515,
                        "Left": 0.18293385207653046,
                        "Height": 0.0893595889210701
                    },
                    "Confidence": 82.21705627441406
                },
                {
                    "BoundingBox": {
                        "Width": 0.031183116137981415,
                        "Top": 0.5579366683959961,
                        "Left": 0.2853088080883026,
                        "Height": 0.03989990055561066
                    },
                    "Confidence": 81.0157470703125
                },
                {
                    "BoundingBox": {
                        "Width": 0.031113790348172188,
                        "Top": 0.5504819750785828,
                        "Left": 0.2580395042896271,
                        "Height": 0.056484755128622055
                    },
                    "Confidence": 56.13441467285156
                },
                {
                    "BoundingBox": {
                        "Width": 0.08586374670267105,
                        "Top": 0.5438792705535889,
                        "Left": 0.5128012895584106,
                        "Height": 0.08550430089235306
                    },
                    "Confidence": 52.37760925292969
                }
            ],
            "Confidence": 99.15271759033203,
            "Parents": [
                {
                    "Name": "Vehicle"
                },
                {
                    "Name": "Transportation"
                }
            ],
            "Name": "Car"
        },
        {
            "Instances": [],
            "Confidence": 98.9914321899414,
            "Parents": [],
            "Name": "Human"
        },
        {
            "Instances": [
                {
                    "BoundingBox": {
                        "Width": 0.19360728561878204,
                        "Top": 0.35072067379951477,
                        "Left": 0.43734854459762573,
                        "Height": 0.2742200493812561
                    },
                    "Confidence": 98.9914321899414
                },
                {
                    "BoundingBox": {
                        "Width": 0.03801717236638069,
                        "Top": 0.5010883808135986,
                        "Left": 0.9155802130699158,
                        "Height": 0.06597328186035156
                    },
                    "Confidence": 85.02790832519531
                }
            ],
            "Confidence": 98.9914321899414,
            "Parents": [],
            "Name": "Person"
        },
        {
            "Instances": [],
            "Confidence": 93.24951934814453,
            "Parents": [],
            "Name": "Machine"
        },
        {
            "Instances": [
                {
                    "BoundingBox": {
                        "Width": 0.03561960905790329,
                        "Top": 0.6468243598937988,
                        "Left": 0.7850857377052307,
                        "Height": 0.08878646790981293
                    },
                    "Confidence": 93.24951934814453
                },
                {
                    "BoundingBox": {
                        "Width": 0.02217046171426773,
                        "Top": 0.6149078607559204,
                        "Left": 0.04757237061858177,
                        "Height": 0.07136218994855881
                    },
                    "Confidence": 91.5025863647461
                },
                {
                    "BoundingBox": {
                        "Width": 0.016197510063648224,
                        "Top": 0.6274210214614868,
                        "Left": 0.6472989320755005,
                        "Height": 0.04955997318029404
                    },
                    "Confidence": 85.14686584472656
                },
                {
                    "BoundingBox": {
                        "Width": 0.020207518711686134,
                        "Top": 0.6348286867141724,
                        "Left": 0.7295016646385193,
                        "Height": 0.07059963047504425
                    },
                    "Confidence": 83.34547424316406
                },
                {
                    "BoundingBox": {
                        "Width": 0.020280985161662102,
                        "Top": 0.6171894669532776,
                        "Left": 0.08744934946298599,
                        "Height": 0.05297485366463661
                    },
                    "Confidence": 79.9981460571289
                },
                {
                    "BoundingBox": {
                        "Width": 0.018318990245461464,
                        "Top": 0.623889148235321,
                        "Left": 0.6836880445480347,
                        "Height": 0.06730121374130249
                    },
                    "Confidence": 78.87144470214844
                },
                {
                    "BoundingBox": {
                        "Width": 0.021310249343514442,
                        "Top": 0.6167286038398743,
                        "Left": 0.004064912907779217,
                        "Height": 0.08317798376083374
                    },
                    "Confidence": 75.89361572265625
                },
                {
                    "BoundingBox": {
                        "Width": 0.03604431077837944,
                        "Top": 0.7030032277107239,
                        "Left": 0.9254803657531738,
                        "Height": 0.04569442570209503
                    },
                    "Confidence": 64.402587890625
                },
                {
                    "BoundingBox": {
                        "Width": 0.009834849275648594,
                        "Top": 0.5821820497512817,
                        "Left": 0.28094568848609924,
                        "Height": 0.01964157074689865
                    },
                    "Confidence": 62.79907989501953
                },
                {
                    "BoundingBox": {
                        "Width": 0.01475677452981472,
                        "Top": 0.6137543320655823,
                        "Left": 0.5950819253921509,
                        "Height": 0.039063986390829086
                    },
                    "Confidence": 59.40483474731445
                }
            ],
            "Confidence": 93.24951934814453,
            "Parents": [
                {
                    "Name": "Machine"
                }
            ],
            "Name": "Wheel"
        },
        {
            "Instances": [],
            "Confidence": 92.61514282226562,
            "Parents": [],
            "Name": "Road"
        },
        {
            "Instances": [],
            "Confidence": 92.37877655029297,
            "Parents": [
                {
                    "Name": "Person"
                }
            ],
            "Name": "Sport"
        },
        {
            "Instances": [],
            "Confidence": 92.37877655029297,
            "Parents": [
                {
                    "Name": "Person"
                }
            ],
            "Name": "Sports"
        },
        {
            "Instances": [
                {
                    "BoundingBox": {
                        "Width": 0.12326609343290329,
                        "Top": 0.6332163214683533,
                        "Left": 0.44815489649772644,
                        "Height": 0.058117982000112534
                    },
                    "Confidence": 92.37877655029297
                }
            ],
            "Confidence": 92.37877655029297,
            "Parents": [
                {
                    "Name": "Person"
                },
                {
                    "Name": "Sport"
                }
            ],
            "Name": "Skateboard"
        },
        {
            "Instances": [],
            "Confidence": 90.62931060791016,
            "Parents": [
                {
                    "Name": "Person"
                }
            ],
            "Name": "Pedestrian"
        },
        {
            "Instances": [],
            "Confidence": 88.81334686279297,
            "Parents": [],
            "Name": "Asphalt"
        },
        {
            "Instances": [],
            "Confidence": 88.81334686279297,
            "Parents": [],
            "Name": "Tarmac"
        },
        {
            "Instances": [],
            "Confidence": 88.23201751708984,
            "Parents": [],
            "Name": "Path"
        },
        {
            "Instances": [],
            "Confidence": 80.26520538330078,
            "Parents": [],
            "Name": "Urban"
        },
        {
            "Instances": [],
            "Confidence": 80.26520538330078,
            "Parents": [
                {
                    "Name": "Building"
                },
                {
                    "Name": "Urban"
                }
            ],
            "Name": "Town"
        },
        {
            "Instances": [],
            "Confidence": 80.26520538330078,
            "Parents": [],
            "Name": "Building"
        },
        {
            "Instances": [],
            "Confidence": 80.26520538330078,
            "Parents": [
                {
                    "Name": "Building"
                },
                {
                    "Name": "Urban"
                }
            ],
            "Name": "City"
        },
        {
            "Instances": [],
            "Confidence": 78.37934875488281,
            "Parents": [
                {
                    "Name": "Car"
                },
                {
                    "Name": "Vehicle"
                },
                {
                    "Name": "Transportation"
                }
            ],
            "Name": "Parking Lot"
        },
        {
            "Instances": [],
            "Confidence": 78.37934875488281,
            "Parents": [
                {
                    "Name": "Car"
                },
                {
                    "Name": "Vehicle"
                },
                {
                    "Name": "Transportation"
                }
            ],
            "Name": "Parking"
        },
        {
            "Instances": [],
            "Confidence": 74.37590026855469,
            "Parents": [
                {
                    "Name": "Building"
                },
                {
                    "Name": "Urban"
                },
                {
                    "Name": "City"
                }
            ],
            "Name": "Downtown"
        },
        {
            "Instances": [],
            "Confidence": 69.84622955322266,
            "Parents": [
                {
                    "Name": "Road"
                }
            ],
            "Name": "Intersection"
        },
        {
            "Instances": [],
            "Confidence": 57.68518829345703,
            "Parents": [
                {
                    "Name": "Sports Car"
                },
                {
                    "Name": "Car"
                },
                {
                    "Name": "Vehicle"
                },
                {
                    "Name": "Transportation"
                }
            ],
            "Name": "Coupe"
        },
        {
            "Instances": [],
            "Confidence": 57.68518829345703,
            "Parents": [
                {
                    "Name": "Car"
                },
                {
                    "Name": "Vehicle"
                },
                {
                    "Name": "Transportation"
                }
            ],
            "Name": "Sports Car"
        },
        {
            "Instances": [],
            "Confidence": 56.59492111206055,
            "Parents": [
                {
                    "Name": "Path"
                }
            ],
            "Name": "Sidewalk"
        },
        {
            "Instances": [],
            "Confidence": 56.59492111206055,
            "Parents": [
                {
                    "Name": "Path"
                }
            ],
            "Name": "Pavement"
        },
        {
            "Instances": [],
            "Confidence": 55.58770751953125,
            "Parents": [
                {
                    "Name": "Building"
                },
                {
                    "Name": "Urban"
                }
            ],
            "Name": "Neighborhood"
        }
    ],
    "LabelModelVersion": "2.0"
}
```
For more information, see [Detecting Labels in an Image](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html) in the *Amazon Rekognition Developer Guide*.  
+  For API details, see [DetectLabels](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/detect-labels.html) in *AWS CLI Command Reference*. 

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

**SDK for Java 2.x**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples). 

```
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);
        }
    }
}
```
+  For API details, see [DetectLabels](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DetectLabels) in *AWS SDK for Java 2.x API Reference*. 

------
#### [ Kotlin ]

**SDK for Kotlin**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples). 

```
suspend fun detectImageLabels(sourceImage: String) {
    val souImage =
        Image {
            bytes = (File(sourceImage).readBytes())
        }
    val request =
        DetectLabelsRequest {
            image = souImage
            maxLabels = 10
        }

    RekognitionClient.fromEnvironment { region = "us-east-1" }.use { rekClient ->
        val response = rekClient.detectLabels(request)
        response.labels?.forEach { label ->
            println("${label.name} : ${label.confidence}")
        }
    }
}
```
+  For API details, see [DetectLabels](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*. 

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

**SDK for Python (Boto3)**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples). 

```
class RekognitionImage:
    """
    Encapsulates an Amazon Rekognition image. This class is a thin wrapper
    around parts of the Boto3 Amazon Rekognition API.
    """

    def __init__(self, image, image_name, rekognition_client):
        """
        Initializes the image object.

        :param image: Data that defines the image, either the image bytes or
                      an Amazon S3 bucket and object key.
        :param image_name: The name of the image.
        :param rekognition_client: A Boto3 Rekognition client.
        """
        self.image = image
        self.image_name = image_name
        self.rekognition_client = rekognition_client


    def detect_labels(self, max_labels):
        """
        Detects labels in the image. Labels are objects and people.

        :param max_labels: The maximum number of labels to return.
        :return: The list of labels detected in the image.
        """
        try:
            response = self.rekognition_client.detect_labels(
                Image=self.image, MaxLabels=max_labels
            )
            labels = [RekognitionLabel(label) for label in response["Labels"]]
            logger.info("Found %s labels in %s.", len(labels), self.image_name)
        except ClientError:
            logger.info("Couldn't detect labels in %s.", self.image_name)
            raise
        else:
            return labels
```
+  For API details, see [DetectLabels](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DetectLabels) in *AWS SDK for Python (Boto3) API Reference*. 

------
#### [ SAP ABAP ]

**SDK for SAP ABAP**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples). 

```
    TRY.
        " Create S3 object reference for the image
        DATA(lo_s3object) = NEW /aws1/cl_reks3object(
          iv_bucket = iv_s3_bucket
          iv_name = iv_s3_key ).

        " Create image object
        DATA(lo_image) = NEW /aws1/cl_rekimage(
          io_s3object = lo_s3object ).

        " Detect labels in the image
        oo_result = lo_rek->detectlabels(
          io_image = lo_image
          iv_maxlabels = iv_max_labels ).

        DATA(lt_labels) = oo_result->get_labels( ).
        DATA(lv_label_count) = lines( lt_labels ).
        DATA(lv_msg9) = |{ lv_label_count } label(s) detected in image.|.
        MESSAGE lv_msg9 TYPE 'I'.
      CATCH /aws1/cx_rekinvalids3objectex.
        MESSAGE 'Invalid S3 object.' TYPE 'E'.
      CATCH /aws1/cx_rekinvalidparameterex.
        MESSAGE 'Invalid parameter value.' TYPE 'E'.
    ENDTRY.
```
+  For API details, see [DetectLabels](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. 

------