Weitere AWS SDK-Beispiele sind im GitHub Repo [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) verfügbar.

Die vorliegende Übersetzung wurde maschinell erstellt. Im Falle eines Konflikts oder eines Widerspruchs zwischen dieser übersetzten Fassung und der englischen Fassung (einschließlich infolge von Verzögerungen bei der Übersetzung) ist die englische Fassung maßgeblich.

# Verwendung `DetectLabels` mit einem AWS SDK oder CLI
<a name="rekognition_example_rekognition_DetectLabels_section"></a>

Die folgenden Code-Beispiele zeigen, wie `DetectLabels` verwendet wird.

Weitere Informationen finden Sie unter [Erkennen von Labels in einem Bild](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html).

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

**SDK für .NET**  
 Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Rekognition/#code-examples) einrichten und ausführen. 

```
    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);
            }
        }
    }
```
Erkennen Sie Labels in einer Bilddatei, die auf Ihrem Computer gespeichert ist.  

```
    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);
            }
        }
    }
```
+  Einzelheiten zur API finden Sie [DetectLabels](https://docs.aws.amazon.com/goto/DotNetSDKV3/rekognition-2016-06-27/DetectLabels)in der *AWS SDK für .NET API-Referenz*. 

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

**SDK für C\$1\$1**  
 Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp/example_code/rekognition#code-examples) einrichten und ausführen. 

```
//! 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();
}
```
+  Einzelheiten zur API finden Sie [DetectLabels](https://docs.aws.amazon.com/goto/SdkForCpp/rekognition-2016-06-27/DetectLabels)in der *AWS SDK für C\$1\$1 API-Referenz*. 

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

**AWS CLI**  
**So erkennen Sie ein Label in einem Bild**  
Im folgenden `detect-labels`-Beispiel werden Szenen und Objekte in einem Bild, das in einem Amazon-S3-Bucket gespeichert ist, erkannt.  

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

```
{
    "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"
}
```
Weitere Informationen finden Sie unter [Erkennen von Labels in einem Bild](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html) im *Entwicklerhandbuch für Amazon Rekognition*.  
+  Einzelheiten zur API finden Sie [DetectLabels](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rekognition/detect-labels.html)in der *AWS CLI Befehlsreferenz*. 

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

**SDK für Java 2.x**  
 Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/rekognition/#code-examples) einrichten und ausführen. 

```
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);
        }
    }
}
```
+  Einzelheiten zur API finden Sie [DetectLabels](https://docs.aws.amazon.com/goto/SdkForJavaV2/rekognition-2016-06-27/DetectLabels)in der *AWS SDK for Java 2.x API-Referenz*. 

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

**SDK für Kotlin**  
 Es gibt noch mehr dazu GitHub. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/services/rekognition#code-examples) einrichten und ausführen. 

```
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}")
        }
    }
}
```
+  Einzelheiten zur API finden Sie [DetectLabels](https://sdk.amazonaws.com/kotlin/api/latest/index.html)in der *API-Referenz zum AWS SDK für Kotlin*. 

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

**SDK für Python (Boto3)**  
 Es gibt noch mehr dazu. GitHub Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples) einrichten und ausführen. 

```
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
```
+  Einzelheiten zur API finden Sie [DetectLabels](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DetectLabels)in *AWS SDK for Python (Boto3) API* Reference. 

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

**SDK für SAP ABAP**  
 Es gibt noch mehr dazu. GitHub Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das [AWS -Code-Beispiel-](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/rek#code-examples) einrichten und ausführen. 

```
    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.
```
+  Einzelheiten zur API finden Sie [DetectLabels](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)in der *API-Referenz zum AWS SDK für SAP ABAP*. 

------