Beispiele für Amazon Rekognition unter Verwendung von SDK für Java 2.x

Die folgenden Codebeispiele zeigen, wie Sie Aktionen durchführen und gängige Szenarien implementieren, indem Sie AWS SDK for Java 2.x mit Amazon Rekognition nutzen.

Aktionen sind Codeauszüge aus größeren Programmen und müssen im Kontext ausgeführt werden. Während Aktionen Ihnen zeigen, wie Sie einzelne Servicefunktionen aufrufen, können Sie Aktionen im Kontext der zugehörigen Szenarien anzeigen.

Szenarien sind Codebeispiele, die Ihnen zeigen, wie Sie bestimmte Aufgaben ausführen, indem Sie mehrere Funktionen innerhalb eines Services aufrufen oder mit anderen AWS-Services kombinieren.

Jedes Beispiel enthält einen Link zum vollständigen Quellcode, wo Sie Anweisungen zum Einrichten und Ausführen des Codes im Kodex finden.

Aktionen

CompareFaces

Die folgenden Codebeispiele zeigen, wie CompareFaces verwendet wird.

Weitere Informationen finden Sie unter Vergleich von Gesichtern in Bildern.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.*;
import software.amazon.awssdk.core.SdkBytes;

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.
 * <p>
 * For more information, see the following documentation topic:
 * <p>
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class CompareFaces {
    public static void main(String[] args) {
        final String usage = """
            Usage: <bucketName> <sourceKey> <targetKey>
           
            Where:
                bucketName - The name of the S3 bucket where the images are stored.
                sourceKey  - The S3 key (file name) for the source image.
                targetKey  - The S3 key (file name) for the target image.
           """;

        if (args.length != 3) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucketName = args[0];
        String sourceKey = args[1];
        String targetKey = args[2];

        Region region = Region.US_WEST_2;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();
        compareTwoFaces(rekClient, bucketName, sourceKey, targetKey);
     }

    /**
     * Compares two faces from images stored in an Amazon S3 bucket using AWS Rekognition.
     *
     * <p>This method takes two image keys from an S3 bucket and compares the faces within them.
     * It prints out the confidence level of matched faces and reports the number of unmatched faces.</p>
     *
     * @param rekClient   The {@link RekognitionClient} used to call AWS Rekognition.
     * @param bucketName  The name of the S3 bucket containing the images.
     * @param sourceKey   The object key (file path) for the source image in the S3 bucket.
     * @param targetKey   The object key (file path) for the target image in the S3 bucket.
     * @throws RuntimeException If the Rekognition service returns an error.
     */
    public static void compareTwoFaces(RekognitionClient rekClient, String bucketName, String sourceKey, String targetKey) {
        try {
            Float similarityThreshold = 70F;
            S3Object s3ObjectSource = S3Object.builder()
                    .bucket(bucketName)
                    .name(sourceKey)
                    .build();

            Image sourceImage = Image.builder()
                    .s3Object(s3ObjectSource)
                    .build();

            S3Object s3ObjectTarget = S3Object.builder()
                    .bucket(bucketName)
                    .name(targetKey)
                    .build();

            Image targetImage = Image.builder()
                    .s3Object(s3ObjectTarget)
                    .build();

            CompareFacesRequest facesRequest = CompareFacesRequest.builder()
                    .sourceImage(sourceImage)
                    .targetImage(targetImage)
                    .similarityThreshold(similarityThreshold)
                    .build();

            // Compare the two images.
            CompareFacesResponse compareFacesResult = rekClient.compareFaces(facesRequest);
            List<CompareFacesMatch> faceDetails = compareFacesResult.faceMatches();

            for (CompareFacesMatch match : faceDetails) {
                ComparedFace face = match.face();
                BoundingBox position = face.boundingBox();
                System.out.println("Face at " + position.left().toString()
                        + " " + position.top()
                        + " matches with " + face.confidence().toString()
                        + "% confidence.");
            }

            List<ComparedFace> unmatchedFaces = compareFacesResult.unmatchedFaces();
            System.out.println("There were " + unmatchedFaces.size() + " face(s) that did not match.");

        } catch (RekognitionException e) {
            System.err.println("Error comparing faces: " + e.awsErrorDetails().errorMessage());
            throw new RuntimeException(e);
        }
    }
}

• Weitere API-Informationen finden Sie unter CompareFaces in der AWS SDK for Java 2.x-API-Referenz.

CreateCollection

Die folgenden Codebeispiele zeigen, wie CreateCollection verwendet wird.

Weitere Informationen finden Sie unter Erstellen einer Sammlung.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.CreateCollectionResponse;
import software.amazon.awssdk.services.rekognition.model.CreateCollectionRequest;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;

/**
 * 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 CreateCollection {
    public static void main(String[] args) {
        final String usage = """

            Usage: <collectionName>\s

            Where:
                collectionName - The name of the collection.\s
            """;

        if (args.length != 1) {
            System.out.println(usage);
            System.exit(1);
        }

        String collectionId = args[0];
        Region region = Region.US_WEST_2;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        System.out.println("Creating collection: " + collectionId);
        createMyCollection(rekClient, collectionId);
        rekClient.close();
    }

    /**
     * Creates a new Amazon Rekognition collection.
     *
     * @param rekClient    the Amazon Rekognition client used to interact with the Rekognition service
     * @param collectionId the unique identifier for the collection to be created
     */
    public static void createMyCollection(RekognitionClient rekClient, String collectionId) {
        try {
            CreateCollectionRequest collectionRequest = CreateCollectionRequest.builder()
                    .collectionId(collectionId)
                    .build();

            CreateCollectionResponse collectionResponse = rekClient.createCollection(collectionRequest);
            System.out.println("CollectionArn: " + collectionResponse.collectionArn());
            System.out.println("Status code: " + collectionResponse.statusCode().toString());

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter CreateCollection in der AWS SDK for Java 2.x-API-Referenz.

DeleteCollection

Die folgenden Codebeispiele zeigen, wie DeleteCollection verwendet wird.

Weitere Informationen finden Sie unter Löschen einer Sammlung.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.DeleteCollectionRequest;
import software.amazon.awssdk.services.rekognition.model.DeleteCollectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;

/**
 * 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 DeleteCollection {
    public static void main(String[] args) {
        final String usage = """
            Usage: <collectionId>\s

            Where:
                collectionId - The id of the collection to delete.\s
            """;

        if (args.length != 1) {
            System.out.println(usage);
            System.exit(1);
        }

        String collectionId = args[0];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        System.out.println("Deleting collection: " + collectionId);
        deleteMyCollection(rekClient, collectionId);
        rekClient.close();
    }

    /**
     * Deletes an Amazon Rekognition collection.
     *
     * @param rekClient      An instance of the {@link RekognitionClient} class, which is used to interact with the Amazon Rekognition service.
     * @param collectionId   The ID of the collection to be deleted.
     */
    public static void deleteMyCollection(RekognitionClient rekClient, String collectionId) {
        try {
            DeleteCollectionRequest deleteCollectionRequest = DeleteCollectionRequest.builder()
                    .collectionId(collectionId)
                    .build();

            DeleteCollectionResponse deleteCollectionResponse = rekClient.deleteCollection(deleteCollectionRequest);
            System.out.println(collectionId + ": " + deleteCollectionResponse.statusCode().toString());

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter DeleteCollection in der AWS SDK for Java 2.x-API-Referenz.

DeleteFaces

Die folgenden Codebeispiele zeigen, wie DeleteFaces verwendet wird.

Weitere Informationen finden Sie unter Löschen von Gesichtern aus einer Sammlung.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.DeleteFacesRequest;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;

/**
 * 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 DeleteFacesFromCollection {
    public static void main(String[] args) {
        final String usage = """
            Usage: <collectionId> <faceId>\s

            Where:
                collectionId - The id of the collection from which faces are deleted.\s
                faceId - The id of the face to delete.\s
           """;

        if (args.length != 2) {
            System.out.println(usage);
            System.exit(1);
        }

        String collectionId = args[0];
        String faceId = args[1];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        System.out.println("Deleting collection: " + collectionId);
        deleteFacesCollection(rekClient, collectionId, faceId);
        rekClient.close();
    }

    /**
     * Deletes a face from the specified Amazon Rekognition collection.
     *
     * @param rekClient     an instance of the Amazon Rekognition client
     * @param collectionId  the ID of the collection from which the face should be deleted
     * @param faceId        the ID of the face to be deleted
     * @throws RekognitionException if an error occurs while deleting the face
     */
    public static void deleteFacesCollection(RekognitionClient rekClient,
            String collectionId,
            String faceId) {

        try {
            DeleteFacesRequest deleteFacesRequest = DeleteFacesRequest.builder()
                    .collectionId(collectionId)
                    .faceIds(faceId)
                    .build();

            rekClient.deleteFaces(deleteFacesRequest);
            System.out.println("The face was deleted from the collection.");

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter DeleteFaces in der AWS SDK for Java 2.x-API-Referenz.

DescribeCollection

Die folgenden Codebeispiele zeigen, wie DescribeCollection verwendet wird.

Weitere Informationen finden Sie unter Beschreiben einer Sammlung.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.DescribeCollectionRequest;
import software.amazon.awssdk.services.rekognition.model.DescribeCollectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;

/**
 * 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 DescribeCollection {
    public static void main(String[] args) {
        final String usage = """
            Usage:    <collectionName>

            Where:
                collectionName - The name of the Amazon Rekognition collection.\s
            """;

        if (args.length != 1) {
            System.out.println(usage);
            System.exit(1);
        }

        String collectionName = args[0];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        describeColl(rekClient, collectionName);
        rekClient.close();
    }

    /**
     * Describes an Amazon Rekognition collection.
     *
     * @param rekClient         The Amazon Rekognition client used to make the request.
     * @param collectionName    The name of the collection to describe.
     *
     * @throws RekognitionException If an error occurs while describing the collection.
     */
    public static void describeColl(RekognitionClient rekClient, String collectionName) {
        try {
            DescribeCollectionRequest describeCollectionRequest = DescribeCollectionRequest.builder()
                    .collectionId(collectionName)
                    .build();

            DescribeCollectionResponse describeCollectionResponse = rekClient
                    .describeCollection(describeCollectionRequest);
            System.out.println("Collection Arn : " + describeCollectionResponse.collectionARN());
            System.out.println("Created : " + describeCollectionResponse.creationTimestamp().toString());

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter DescribeCollection in der AWS SDK for Java 2.x-API-Referenz.

DetectFaces

Die folgenden Codebeispiele zeigen, wie DetectFaces verwendet wird.

Weitere Informationen finden Sie unter Erkennen von Gesichtern in einem Bild.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.*;

import java.util.List;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 * <p>
 * For more information, see the following documentation topic:
 * <p>
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class DetectFaces {
    public static void main(String[] args) {
        final String usage = """
                
            Usage:   <bucketName> <sourceImage>
                
            Where:
                bucketName = The name of the Amazon S3 bucket where the source image is stored.
                sourceImage - The name of the source image file in the Amazon S3 bucket. (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();

        detectFacesinImage(rekClient, bucketName, sourceImage);
        rekClient.close();
    }

    /**
     * Detects faces in an image stored in an Amazon S3 bucket using the Amazon Rekognition service.
     *
     * @param rekClient    The Amazon Rekognition client used to interact with the Rekognition service.
     * @param bucketName   The name of the Amazon S3 bucket where the source image is stored.
     * @param sourceImage  The name of the source image file in the Amazon S3 bucket.
     */
    public static void detectFacesinImage(RekognitionClient rekClient, String bucketName, String sourceImage) {
        try {
            S3Object s3ObjectTarget = S3Object.builder()
                .bucket(bucketName)
                .name(sourceImage)
                .build();

            Image targetImage = Image.builder()
                .s3Object(s3ObjectTarget)
                .build();

            DetectFacesRequest facesRequest = DetectFacesRequest.builder()
                .attributes(Attribute.ALL)
                .image(targetImage)
                .build();

            DetectFacesResponse facesResponse = rekClient.detectFaces(facesRequest);
            List<FaceDetail> faceDetails = facesResponse.faceDetails();
            for (FaceDetail face : faceDetails) {
                AgeRange ageRange = face.ageRange();
                System.out.println("The detected face is estimated to be between "
                        + ageRange.low().toString() + " and " + ageRange.high().toString()
                        + " years old.");

                System.out.println("There is a smile : " + face.smile().value().toString());
            }

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter DetectFaces in der AWS SDK for Java 2.x-API-Referenz.

DetectLabels

Die folgenden Codebeispiele zeigen, wie DetectLabels verwendet wird.

Weitere Informationen finden Sie unter Erkennen von Labels in einem Bild.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository 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);
        }
    }
}

• Weitere API-Informationen finden Sie unter DetectLabels in der AWS SDK for Java 2.x-API-Referenz.

DetectModerationLabels

Die folgenden Codebeispiele zeigen, wie DetectModerationLabels verwendet wird.

Weitere Informationen finden Sie unter Erkennen von unangemessenen Bildern.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

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 DetectModerationLabels {

    public static void main(String[] args) {
        final String usage = """
            Usage:  <bucketName>  <sourceImage>

            Where:
                bucketName - The name of the S3 bucket where the images are stored.
                sourceImage - The name of the image (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();

        detectModLabels(rekClient, bucketName, sourceImage);
        rekClient.close();
    }

    /**
     * Detects moderation labels in an image stored in an Amazon S3 bucket.
     *
     * @param rekClient      the Amazon Rekognition client to use for the detection
     * @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
     *
     * @throws RekognitionException if there is an error during the image detection process
     */
    public static void detectModLabels(RekognitionClient rekClient, String bucketName, String sourceImage) {
        try {
            S3Object s3ObjectTarget = S3Object.builder()
                    .bucket(bucketName)
                    .name(sourceImage)
                    .build();

            Image targetImage = Image.builder()
                    .s3Object(s3ObjectTarget)
                    .build();

            DetectModerationLabelsRequest moderationLabelsRequest = DetectModerationLabelsRequest.builder()
                    .image(targetImage)
                    .minConfidence(60F)
                    .build();

            DetectModerationLabelsResponse moderationLabelsResponse = rekClient
                    .detectModerationLabels(moderationLabelsRequest);
            List<ModerationLabel> labels = moderationLabelsResponse.moderationLabels();
            System.out.println("Detected labels for image");
            for (ModerationLabel label : labels) {
                System.out.println("Label: " + label.name()
                        + "\n Confidence: " + label.confidence().toString() + "%"
                        + "\n Parent:" + label.parentName());
            }

        } catch (RekognitionException e) {
            e.printStackTrace();
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter DetectModerationLabels in der AWS SDK for Java 2.x-API-Referenz.

DetectText

Die folgenden Codebeispiele zeigen, wie DetectText verwendet wird.

Weitere Informationen finden Sie unter Erkennen von Text in einem Bild.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository 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 DetectText {
    public static void main(String[] args) {
        final String usage = "\n" +
            "Usage:   <bucketName> <sourceImage>\n" +
            "\n" +
            "Where:\n" +
            "   bucketName - The name of the S3 bucket where the image is stored\n" +
            "   sourceImage - The path to the image that contains text (for example, pic1.png). \n";

        if (args.length != 2) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucketName = args[0];
        String sourceImage = args[1];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        detectTextLabels(rekClient, bucketName, sourceImage);
        rekClient.close();
    }

    /**
     * Detects text labels in an image stored in an S3 bucket using Amazon Rekognition.
     *
     * @param rekClient    an instance of the Amazon Rekognition client
     * @param bucketName   the name of the S3 bucket where the image is stored
     * @param sourceImage  the name of the image file in the S3 bucket
     * @throws RekognitionException if an error occurs while calling the Amazon Rekognition API
     */
    public static void detectTextLabels(RekognitionClient rekClient, String bucketName, String sourceImage) {
        try {
            S3Object s3ObjectTarget = S3Object.builder()
                    .bucket(bucketName)
                    .name(sourceImage)
                    .build();

            Image souImage = Image.builder()
                    .s3Object(s3ObjectTarget)
                    .build();

            DetectTextRequest textRequest = DetectTextRequest.builder()
                    .image(souImage)
                    .build();

            DetectTextResponse textResponse = rekClient.detectText(textRequest);
            List<TextDetection> textCollection = textResponse.textDetections();
            System.out.println("Detected lines and words");
            for (TextDetection text : textCollection) {
                System.out.println("Detected: " + text.detectedText());
                System.out.println("Confidence: " + text.confidence().toString());
                System.out.println("Id : " + text.id());
                System.out.println("Parent Id: " + text.parentId());
                System.out.println("Type: " + text.type());
                System.out.println();
            }

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter DetectText in der AWS SDK for Java 2.x-API-Referenz.

IndexFaces

Die folgenden Codebeispiele zeigen, wie IndexFaces verwendet wird.

Weitere Informationen finden Sie unter Hinzufügen von Gesichtern zu einer Sammlung.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.*;
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 AddFacesToCollection {
    public static void main(String[] args) {
        final String usage = """
            Usage: <collectionId> <sourceImage> <bucketName>

            Where:
                collectionName - The name of the collection.
                sourceImage - The name of the image (for example, pic1.png).
                bucketName - The name of the S3 bucket.
            """;

        if (args.length != 3) {
            System.out.println(usage);
            System.exit(1);
        }

        String collectionId = args[0];
        String sourceImage = args[1];
        String bucketName = args[2];;
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        addToCollection(rekClient, collectionId, bucketName, sourceImage);
        rekClient.close();
    }

    /**
     * Adds a face from an image to an Amazon Rekognition collection.
     *
     * @param rekClient     the Amazon Rekognition client
     * @param collectionId  the ID of the collection to add the face to
     * @param bucketName    the name of the Amazon S3 bucket containing the image
     * @param sourceImage   the name of the image file to add to the collection
     * @throws RekognitionException if there is an error while interacting with the Amazon Rekognition service
     */
    public static void addToCollection(RekognitionClient rekClient, String collectionId, String bucketName, String sourceImage) {
        try {
            S3Object s3ObjectTarget = S3Object.builder()
                    .bucket(bucketName)
                    .name(sourceImage)
                    .build();

            Image targetImage = Image.builder()
                    .s3Object(s3ObjectTarget)
                    .build();

            IndexFacesRequest facesRequest = IndexFacesRequest.builder()
                    .collectionId(collectionId)
                    .image(targetImage)
                    .maxFaces(1)
                    .qualityFilter(QualityFilter.AUTO)
                    .detectionAttributes(Attribute.DEFAULT)
                    .build();

            IndexFacesResponse facesResponse = rekClient.indexFaces(facesRequest);
            System.out.println("Results for the image");
            System.out.println("\n Faces indexed:");
            List<FaceRecord> faceRecords = facesResponse.faceRecords();
            for (FaceRecord faceRecord : faceRecords) {
                System.out.println("  Face ID: " + faceRecord.face().faceId());
                System.out.println("  Location:" + faceRecord.faceDetail().boundingBox().toString());
            }

            List<UnindexedFace> unindexedFaces = facesResponse.unindexedFaces();
            System.out.println("Faces not indexed:");
            for (UnindexedFace unindexedFace : unindexedFaces) {
                System.out.println("  Location:" + unindexedFace.faceDetail().boundingBox().toString());
                System.out.println("  Reasons:");
                for (Reason reason : unindexedFace.reasons()) {
                    System.out.println("Reason:  " + reason);
                }
            }

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter IndexFaces in der AWS SDK for Java 2.x-API-Referenz.

ListCollections

Die folgenden Codebeispiele zeigen, wie ListCollections verwendet wird.

Weitere Informationen finden Sie unter Sammlungen auflisten.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.ListCollectionsRequest;
import software.amazon.awssdk.services.rekognition.model.ListCollectionsResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
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 ListCollections {
    public static void main(String[] args) {
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        System.out.println("Listing collections");
        listAllCollections(rekClient);
        rekClient.close();
    }

    public static void listAllCollections(RekognitionClient rekClient) {
        try {
            ListCollectionsRequest listCollectionsRequest = ListCollectionsRequest.builder()
                    .maxResults(10)
                    .build();

            ListCollectionsResponse response = rekClient.listCollections(listCollectionsRequest);
            List<String> collectionIds = response.collectionIds();
            for (String resultId : collectionIds) {
                System.out.println(resultId);
            }

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter ListCollections in der AWS SDK for Java 2.x-API-Referenz.

ListFaces

Die folgenden Codebeispiele zeigen, wie ListFaces verwendet wird.

Weitere Informationen finden Sie unter Gesichter in einer Sammlung auflisten.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.Face;
import software.amazon.awssdk.services.rekognition.model.ListFacesRequest;
import software.amazon.awssdk.services.rekognition.model.ListFacesResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
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 ListFacesInCollection {
    public static void main(String[] args) {
        final String usage = """

                Usage:    <collectionId>

                Where:
                   collectionId - The name of the collection.\s
                """;

        if (args.length < 1) {
            System.out.println(usage);
            System.exit(1);
        }

        String collectionId = args[0];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        System.out.println("Faces in collection " + collectionId);
        listFacesCollection(rekClient, collectionId);
        rekClient.close();
    }

    public static void listFacesCollection(RekognitionClient rekClient, String collectionId) {
        try {
            ListFacesRequest facesRequest = ListFacesRequest.builder()
                    .collectionId(collectionId)
                    .maxResults(10)
                    .build();

            ListFacesResponse facesResponse = rekClient.listFaces(facesRequest);
            List<Face> faces = facesResponse.faces();
            for (Face face : faces) {
                System.out.println("Confidence level there is a face: " + face.confidence());
                System.out.println("The face Id value is " + face.faceId());
            }

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter ListFaces in der AWS SDK for Java 2.x-API-Referenz.

RecognizeCelebrities

Die folgenden Codebeispiele zeigen, wie RecognizeCelebrities verwendet wird.

Weitere Informationen finden Sie unter Erkennen von Prominenten in einem Bild.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.core.SdkBytes;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.InputStream;
import java.util.List;

import software.amazon.awssdk.services.rekognition.model.*;

/**
 * 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 RecognizeCelebrities {
    public static void main(String[] args) {
        final String usage = """
                Usage:   <bucketName> <sourceImage>

                Where:
                   bucketName - The name of the S3 bucket where the images are stored.
                   sourceImage - The path to the image (for example, C:\\AWS\\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();

        System.out.println("Locating celebrities in " + sourceImage);
        recognizeAllCelebrities(rekClient, bucketName, sourceImage);
        rekClient.close();
    }

    /**
     * Recognizes all celebrities in an image stored in an Amazon S3 bucket.
     *
     * @param rekClient    the Amazon Rekognition client used to perform the celebrity recognition operation
     * @param bucketName   the name of the Amazon S3 bucket where the source image is stored
     * @param sourceImage  the name of the source image file stored in the Amazon S3 bucket
     */
    public static void recognizeAllCelebrities(RekognitionClient rekClient, String bucketName, String sourceImage) {
        try {
            S3Object s3ObjectTarget = S3Object.builder()
                .bucket(bucketName)
                .name(sourceImage)
                .build();

            Image souImage = Image.builder()
                    .s3Object(s3ObjectTarget)
                    .build();

            RecognizeCelebritiesRequest request = RecognizeCelebritiesRequest.builder()
                    .image(souImage)
                    .build();

            RecognizeCelebritiesResponse result = rekClient.recognizeCelebrities(request);
            List<Celebrity> celebs = result.celebrityFaces();
            System.out.println(celebs.size() + " celebrity(s) were recognized.\n");
            for (Celebrity celebrity : celebs) {
                System.out.println("Celebrity recognized: " + celebrity.name());
                System.out.println("Celebrity ID: " + celebrity.id());

                System.out.println("Further information (if available):");
                for (String url : celebrity.urls()) {
                    System.out.println(url);
                }
                System.out.println();
            }
            System.out.println(result.unrecognizedFaces().size() + " face(s) were unrecognized.");

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter RecognizeCelebrities in der AWS SDK for Java 2.x-API-Referenz.

SearchFaces

Die folgenden Codebeispiele zeigen, wie SearchFaces verwendet wird.

Weitere Informationen finden Sie unter Nach einem Gesicht suchen (Gesichts-ID).

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository 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.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.SearchFacesByImageRequest;
import software.amazon.awssdk.services.rekognition.model.Image;
import software.amazon.awssdk.services.rekognition.model.SearchFacesByImageResponse;
import software.amazon.awssdk.services.rekognition.model.FaceMatch;
import java.io.File;
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 SearchFaceMatchingImageCollection {
    public static void main(String[] args) {
        final String usage = """

                Usage:    <collectionId> <sourceImage>

                Where:
                   collectionId - The id of the collection. \s
                   sourceImage - The path to the image (for example, C:\\AWS\\pic1.png).\s

                """;

        if (args.length != 2) {
            System.out.println(usage);
            System.exit(1);
        }

        String collectionId = args[0];
        String sourceImage = args[1];
        Region region = Region.US_WEST_2;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        System.out.println("Searching for a face in a collections");
        searchFaceInCollection(rekClient, collectionId, sourceImage);
        rekClient.close();
    }

    public static void searchFaceInCollection(RekognitionClient rekClient, String collectionId, String sourceImage) {
        try {
            InputStream sourceStream = new FileInputStream(new File(sourceImage));
            SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream);
            Image souImage = Image.builder()
                    .bytes(sourceBytes)
                    .build();

            SearchFacesByImageRequest facesByImageRequest = SearchFacesByImageRequest.builder()
                    .image(souImage)
                    .maxFaces(10)
                    .faceMatchThreshold(70F)
                    .collectionId(collectionId)
                    .build();

            SearchFacesByImageResponse imageResponse = rekClient.searchFacesByImage(facesByImageRequest);
            System.out.println("Faces matching in the collection");
            List<FaceMatch> faceImageMatches = imageResponse.faceMatches();
            for (FaceMatch face : faceImageMatches) {
                System.out.println("The similarity level is  " + face.similarity());
                System.out.println();
            }

        } catch (RekognitionException | FileNotFoundException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter SearchFaces in der AWS SDK for Java 2.x-API-Referenz.

SearchFacesByImage

Die folgenden Codebeispiele zeigen, wie SearchFacesByImage verwendet wird.

Weitere Informationen finden Sie unter Nach einem Gesicht suchen (Bild).

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.SearchFacesRequest;
import software.amazon.awssdk.services.rekognition.model.SearchFacesResponse;
import software.amazon.awssdk.services.rekognition.model.FaceMatch;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
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 SearchFaceMatchingIdCollection {
    public static void main(String[] args) {
        final String usage = """

                Usage:    <collectionId> <sourceImage>

                Where:
                   collectionId - The id of the collection. \s
                   sourceImage - The path to the image (for example, C:\\AWS\\pic1.png).\s
                """;

        if (args.length != 2) {
            System.out.println(usage);
            System.exit(1);
        }

        String collectionId = args[0];
        String faceId = args[1];
        Region region = Region.US_WEST_2;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        System.out.println("Searching for a face in a collections");
        searchFacebyId(rekClient, collectionId, faceId);
        rekClient.close();
    }

    public static void searchFacebyId(RekognitionClient rekClient, String collectionId, String faceId) {
        try {
            SearchFacesRequest searchFacesRequest = SearchFacesRequest.builder()
                    .collectionId(collectionId)
                    .faceId(faceId)
                    .faceMatchThreshold(70F)
                    .maxFaces(2)
                    .build();

            SearchFacesResponse imageResponse = rekClient.searchFaces(searchFacesRequest);
            System.out.println("Faces matching in the collection");
            List<FaceMatch> faceImageMatches = imageResponse.faceMatches();
            for (FaceMatch face : faceImageMatches) {
                System.out.println("The similarity level is  " + face.similarity());
                System.out.println();
            }

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie unter SearchFacesByImage in der AWS SDK for Java 2.x-API-Referenz.

Szenarien

Erstellen einer Serverless-Anwendung zur Verwaltung von Fotos

Das folgende Codebeispiel zeigt, wie eine Serverless-Anwendung erstellt wird, mit der Benutzer Fotos mithilfe von Labels erstellen können.

SDK für Java 2.x

Zeigt, wie eine Anwendung zur Verwaltung von Fotobeständen entwickelt wird, die mithilfe von Amazon Rekognition Labels in Bildern erkennt und sie für einen späteren Abruf speichert.

Vollständiger Quellcode und Anweisungen zum Einrichten und Ausführen finden Sie im vollständigen Beispiel unter GitHub.

Einen tiefen Einblick in den Ursprung dieses Beispiels finden Sie im Beitrag in der AWS-Community.

In diesem Beispiel verwendete Services

• API Gateway

• DynamoDB

• Lambda

• Amazon Rekognition

• Amazon S3

• Amazon SNS

Erkennen von PSA in Bildern

Das folgende Codebeispiel zeigt, wie eine App erstellt wird, die Amazon Rekognition verwendet, um persönliche Schutzausrüstung (PSA) auf Bildern zu erkennen.

SDK für Java 2.x

Zeigt, wie eine AWS Lambda-Funktion erstellt wird, die Bilder mit persönlicher Schutzausrüstung erkennt.

Den kompletten Quellcode und Anweisungen zum Einrichten und Ausführen finden Sie im vollständigen Beispiel unter GitHub.

In diesem Beispiel verwendete Services

• DynamoDB

• Amazon Rekognition

• Amazon S3

• Amazon SES

Informationen in Videos erkennen

Wie das aussehen kann, sehen Sie am nachfolgenden Beispielcode:

• Starten Sie Amazon-Rekognition-Aufträge, um Elemente wie Personen, Objekte und Text in Videos zu erkennen.

• Überprüfen Sie den Auftragsstatus, bis die Aufträge abgeschlossen sind.

• Gibt die Liste der von jedem Auftrag erkannten Elemente aus.

SDK für Java 2.x

Anmerkung

Auf GitHub finden Sie noch mehr. Hier finden Sie das vollständige Beispiel und erfahren, wie Sie das AWS-Codebeispiel-Repository einrichten und ausführen.

Abrufen von Informationen aus einem Video, das sich in einem Amazon-S3-Bucket befindet.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.CelebrityRecognitionSortBy;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.CelebrityRecognition;
import software.amazon.awssdk.services.rekognition.model.CelebrityDetail;
import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionRequest;
import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionRequest;
import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionResponse;
import java.util.List;

/**
 * To run this code example, ensure that you perform the Prerequisites as stated
 * in the Amazon Rekognition Guide:
 * https://docs.aws.amazon.com/rekognition/latest/dg/video-analyzing-with-sqs.html
 *
 * Also, ensure that set up your development environment, including your
 * credentials.
 *
 * For information, see this documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */

public class VideoCelebrityDetection {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startCelebrityDetection(rekClient, channel, bucket, video);
        getCelebrityDetectionResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startCelebrityDetection(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartCelebrityRecognitionRequest recognitionRequest = StartCelebrityRecognitionRequest.builder()
                    .jobTag("Celebrities")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .build();

            StartCelebrityRecognitionResponse startCelebrityRecognitionResult = rekClient
                    .startCelebrityRecognition(recognitionRequest);
            startJobId = startCelebrityRecognitionResult.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getCelebrityDetectionResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetCelebrityRecognitionResponse recognitionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (recognitionResponse != null)
                    paginationToken = recognitionResponse.nextToken();

                GetCelebrityRecognitionRequest recognitionRequest = GetCelebrityRecognitionRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .sortBy(CelebrityRecognitionSortBy.TIMESTAMP)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds
                while (!finished) {
                    recognitionResponse = rekClient.getCelebrityRecognition(recognitionRequest);
                    status = recognitionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = recognitionResponse.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<CelebrityRecognition> celebs = recognitionResponse.celebrities();
                for (CelebrityRecognition celeb : celebs) {
                    long seconds = celeb.timestamp() / 1000;
                    System.out.print("Sec: " + seconds + " ");
                    CelebrityDetail details = celeb.celebrity();
                    System.out.println("Name: " + details.name());
                    System.out.println("Id: " + details.id());
                    System.out.println();
                }

            } while (recognitionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

Erkennen Sie Labels in einem Video mithilfe einer Labelerkennung.

import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.JsonMappingException;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.LabelDetectionSortBy;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.LabelDetection;
import software.amazon.awssdk.services.rekognition.model.Label;
import software.amazon.awssdk.services.rekognition.model.Instance;
import software.amazon.awssdk.services.rekognition.model.Parent;
import software.amazon.awssdk.services.sqs.SqsClient;
import software.amazon.awssdk.services.sqs.model.Message;
import software.amazon.awssdk.services.sqs.model.ReceiveMessageRequest;
import software.amazon.awssdk.services.sqs.model.DeleteMessageRequest;
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 VideoDetect {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <queueUrl> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of the video (for example, people.mp4).\s
                   queueUrl- The URL of a SQS queue.\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 5) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String queueUrl = args[2];
        String topicArn = args[3];
        String roleArn = args[4];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        SqsClient sqs = SqsClient.builder()
                .region(Region.US_EAST_1)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startLabels(rekClient, channel, bucket, video);
        getLabelJob(rekClient, sqs, queueUrl);
        System.out.println("This example is done!");
        sqs.close();
        rekClient.close();
    }

    public static void startLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartLabelDetectionRequest labelDetectionRequest = StartLabelDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .minConfidence(50F)
                    .build();

            StartLabelDetectionResponse labelDetectionResponse = rekClient.startLabelDetection(labelDetectionRequest);
            startJobId = labelDetectionResponse.jobId();

            boolean ans = true;
            String status = "";
            int yy = 0;
            while (ans) {

                GetLabelDetectionRequest detectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .maxResults(10)
                        .build();

                GetLabelDetectionResponse result = rekClient.getLabelDetection(detectionRequest);
                status = result.jobStatusAsString();

                if (status.compareTo("SUCCEEDED") == 0)
                    ans = false;
                else
                    System.out.println(yy + " status is: " + status);

                Thread.sleep(1000);
                yy++;
            }

            System.out.println(startJobId + " status is: " + status);

        } catch (RekognitionException | InterruptedException e) {
            e.getMessage();
            System.exit(1);
        }
    }

    public static void getLabelJob(RekognitionClient rekClient, SqsClient sqs, String queueUrl) {
        List<Message> messages;
        ReceiveMessageRequest messageRequest = ReceiveMessageRequest.builder()
                .queueUrl(queueUrl)
                .build();

        try {
            messages = sqs.receiveMessage(messageRequest).messages();

            if (!messages.isEmpty()) {
                for (Message message : messages) {
                    String notification = message.body();

                    // Get the status and job id from the notification
                    ObjectMapper mapper = new ObjectMapper();
                    JsonNode jsonMessageTree = mapper.readTree(notification);
                    JsonNode messageBodyText = jsonMessageTree.get("Message");
                    ObjectMapper operationResultMapper = new ObjectMapper();
                    JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue());
                    JsonNode operationJobId = jsonResultTree.get("JobId");
                    JsonNode operationStatus = jsonResultTree.get("Status");
                    System.out.println("Job found in JSON is " + operationJobId);

                    DeleteMessageRequest deleteMessageRequest = DeleteMessageRequest.builder()
                            .queueUrl(queueUrl)
                            .build();

                    String jobId = operationJobId.textValue();
                    if (startJobId.compareTo(jobId) == 0) {
                        System.out.println("Job id: " + operationJobId);
                        System.out.println("Status : " + operationStatus.toString());

                        if (operationStatus.asText().equals("SUCCEEDED"))
                            getResultsLabels(rekClient);
                        else
                            System.out.println("Video analysis failed");

                        sqs.deleteMessage(deleteMessageRequest);
                    } else {
                        System.out.println("Job received was not job " + startJobId);
                        sqs.deleteMessage(deleteMessageRequest);
                    }
                }
            }

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        } catch (JsonMappingException e) {
            e.printStackTrace();
        } catch (JsonProcessingException e) {
            e.printStackTrace();
        }
    }

    // Gets the job results by calling GetLabelDetection
    private static void getResultsLabels(RekognitionClient rekClient) {

        int maxResults = 10;
        String paginationToken = null;
        GetLabelDetectionResponse labelDetectionResult = null;

        try {
            do {
                if (labelDetectionResult != null)
                    paginationToken = labelDetectionResult.nextToken();

                GetLabelDetectionRequest labelDetectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .sortBy(LabelDetectionSortBy.TIMESTAMP)
                        .maxResults(maxResults)
                        .nextToken(paginationToken)
                        .build();

                labelDetectionResult = rekClient.getLabelDetection(labelDetectionRequest);
                VideoMetadata videoMetaData = labelDetectionResult.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());

                List<LabelDetection> detectedLabels = labelDetectionResult.labels();
                for (LabelDetection detectedLabel : detectedLabels) {
                    long seconds = detectedLabel.timestamp();
                    Label label = detectedLabel.label();
                    System.out.println("Millisecond: " + seconds + " ");

                    System.out.println("   Label:" + label.name());
                    System.out.println("   Confidence:" + detectedLabel.label().confidence().toString());

                    List<Instance> instances = label.instances();
                    System.out.println("   Instances of " + label.name());

                    if (instances.isEmpty()) {
                        System.out.println("        " + "None");
                    } else {
                        for (Instance instance : instances) {
                            System.out.println("        Confidence: " + instance.confidence().toString());
                            System.out.println("        Bounding box: " + instance.boundingBox().toString());
                        }
                    }
                    System.out.println("   Parent labels for " + label.name() + ":");
                    List<Parent> parents = label.parents();

                    if (parents.isEmpty()) {
                        System.out.println("        None");
                    } else {
                        for (Parent parent : parents) {
                            System.out.println("   " + parent.name());
                        }
                    }
                    System.out.println();
                }
            } while (labelDetectionResult != null && labelDetectionResult.nextToken() != null);

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        }
    }
}

Erkennen von Gesichtern in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.JsonMappingException;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.LabelDetectionSortBy;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.LabelDetection;
import software.amazon.awssdk.services.rekognition.model.Label;
import software.amazon.awssdk.services.rekognition.model.Instance;
import software.amazon.awssdk.services.rekognition.model.Parent;
import software.amazon.awssdk.services.sqs.SqsClient;
import software.amazon.awssdk.services.sqs.model.Message;
import software.amazon.awssdk.services.sqs.model.ReceiveMessageRequest;
import software.amazon.awssdk.services.sqs.model.DeleteMessageRequest;
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 VideoDetect {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <queueUrl> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of the video (for example, people.mp4).\s
                   queueUrl- The URL of a SQS queue.\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 5) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String queueUrl = args[2];
        String topicArn = args[3];
        String roleArn = args[4];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        SqsClient sqs = SqsClient.builder()
                .region(Region.US_EAST_1)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startLabels(rekClient, channel, bucket, video);
        getLabelJob(rekClient, sqs, queueUrl);
        System.out.println("This example is done!");
        sqs.close();
        rekClient.close();
    }

    public static void startLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartLabelDetectionRequest labelDetectionRequest = StartLabelDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .minConfidence(50F)
                    .build();

            StartLabelDetectionResponse labelDetectionResponse = rekClient.startLabelDetection(labelDetectionRequest);
            startJobId = labelDetectionResponse.jobId();

            boolean ans = true;
            String status = "";
            int yy = 0;
            while (ans) {

                GetLabelDetectionRequest detectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .maxResults(10)
                        .build();

                GetLabelDetectionResponse result = rekClient.getLabelDetection(detectionRequest);
                status = result.jobStatusAsString();

                if (status.compareTo("SUCCEEDED") == 0)
                    ans = false;
                else
                    System.out.println(yy + " status is: " + status);

                Thread.sleep(1000);
                yy++;
            }

            System.out.println(startJobId + " status is: " + status);

        } catch (RekognitionException | InterruptedException e) {
            e.getMessage();
            System.exit(1);
        }
    }

    public static void getLabelJob(RekognitionClient rekClient, SqsClient sqs, String queueUrl) {
        List<Message> messages;
        ReceiveMessageRequest messageRequest = ReceiveMessageRequest.builder()
                .queueUrl(queueUrl)
                .build();

        try {
            messages = sqs.receiveMessage(messageRequest).messages();

            if (!messages.isEmpty()) {
                for (Message message : messages) {
                    String notification = message.body();

                    // Get the status and job id from the notification
                    ObjectMapper mapper = new ObjectMapper();
                    JsonNode jsonMessageTree = mapper.readTree(notification);
                    JsonNode messageBodyText = jsonMessageTree.get("Message");
                    ObjectMapper operationResultMapper = new ObjectMapper();
                    JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue());
                    JsonNode operationJobId = jsonResultTree.get("JobId");
                    JsonNode operationStatus = jsonResultTree.get("Status");
                    System.out.println("Job found in JSON is " + operationJobId);

                    DeleteMessageRequest deleteMessageRequest = DeleteMessageRequest.builder()
                            .queueUrl(queueUrl)
                            .build();

                    String jobId = operationJobId.textValue();
                    if (startJobId.compareTo(jobId) == 0) {
                        System.out.println("Job id: " + operationJobId);
                        System.out.println("Status : " + operationStatus.toString());

                        if (operationStatus.asText().equals("SUCCEEDED"))
                            getResultsLabels(rekClient);
                        else
                            System.out.println("Video analysis failed");

                        sqs.deleteMessage(deleteMessageRequest);
                    } else {
                        System.out.println("Job received was not job " + startJobId);
                        sqs.deleteMessage(deleteMessageRequest);
                    }
                }
            }

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        } catch (JsonMappingException e) {
            e.printStackTrace();
        } catch (JsonProcessingException e) {
            e.printStackTrace();
        }
    }

    // Gets the job results by calling GetLabelDetection
    private static void getResultsLabels(RekognitionClient rekClient) {

        int maxResults = 10;
        String paginationToken = null;
        GetLabelDetectionResponse labelDetectionResult = null;

        try {
            do {
                if (labelDetectionResult != null)
                    paginationToken = labelDetectionResult.nextToken();

                GetLabelDetectionRequest labelDetectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .sortBy(LabelDetectionSortBy.TIMESTAMP)
                        .maxResults(maxResults)
                        .nextToken(paginationToken)
                        .build();

                labelDetectionResult = rekClient.getLabelDetection(labelDetectionRequest);
                VideoMetadata videoMetaData = labelDetectionResult.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());

                List<LabelDetection> detectedLabels = labelDetectionResult.labels();
                for (LabelDetection detectedLabel : detectedLabels) {
                    long seconds = detectedLabel.timestamp();
                    Label label = detectedLabel.label();
                    System.out.println("Millisecond: " + seconds + " ");

                    System.out.println("   Label:" + label.name());
                    System.out.println("   Confidence:" + detectedLabel.label().confidence().toString());

                    List<Instance> instances = label.instances();
                    System.out.println("   Instances of " + label.name());

                    if (instances.isEmpty()) {
                        System.out.println("        " + "None");
                    } else {
                        for (Instance instance : instances) {
                            System.out.println("        Confidence: " + instance.confidence().toString());
                            System.out.println("        Bounding box: " + instance.boundingBox().toString());
                        }
                    }
                    System.out.println("   Parent labels for " + label.name() + ":");
                    List<Parent> parents = label.parents();

                    if (parents.isEmpty()) {
                        System.out.println("        None");
                    } else {
                        for (Parent parent : parents) {
                            System.out.println("   " + parent.name());
                        }
                    }
                    System.out.println();
                }
            } while (labelDetectionResult != null && labelDetectionResult.nextToken() != null);

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        }
    }
}

Erkennen von unangemessenen oder anstößigen Inhalten in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartContentModerationRequest;
import software.amazon.awssdk.services.rekognition.model.StartContentModerationResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetContentModerationResponse;
import software.amazon.awssdk.services.rekognition.model.GetContentModerationRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.ContentModerationDetection;
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 VideoDetectInappropriate {
    private static String startJobId = "";

    public static void main(String[] args) {

        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startModerationDetection(rekClient, channel, bucket, video);
        getModResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startModerationDetection(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {

        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartContentModerationRequest modDetectionRequest = StartContentModerationRequest.builder()
                    .jobTag("Moderation")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .build();

            StartContentModerationResponse startModDetectionResult = rekClient
                    .startContentModeration(modDetectionRequest);
            startJobId = startModDetectionResult.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getModResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetContentModerationResponse modDetectionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (modDetectionResponse != null)
                    paginationToken = modDetectionResponse.nextToken();

                GetContentModerationRequest modRequest = GetContentModerationRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds.
                while (!finished) {
                    modDetectionResponse = rekClient.getContentModeration(modRequest);
                    status = modDetectionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = modDetectionResponse.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<ContentModerationDetection> mods = modDetectionResponse.moderationLabels();
                for (ContentModerationDetection mod : mods) {
                    long seconds = mod.timestamp() / 1000;
                    System.out.print("Mod label: " + seconds + " ");
                    System.out.println(mod.moderationLabel().toString());
                    System.out.println();
                }

            } while (modDetectionResponse != null && modDetectionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

Erkennen Sie technische Signal-Segmente und Einstellungserkennungssegmente in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartShotDetectionFilter;
import software.amazon.awssdk.services.rekognition.model.StartTechnicalCueDetectionFilter;
import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionFilters;
import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetSegmentDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.GetSegmentDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.SegmentDetection;
import software.amazon.awssdk.services.rekognition.model.TechnicalCueSegment;
import software.amazon.awssdk.services.rekognition.model.ShotSegment;
import software.amazon.awssdk.services.rekognition.model.SegmentType;
import software.amazon.awssdk.services.sqs.SqsClient;
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 VideoDetectSegment {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];

        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        SqsClient sqs = SqsClient.builder()
                .region(Region.US_EAST_1)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startSegmentDetection(rekClient, channel, bucket, video);
        getSegmentResults(rekClient);
        System.out.println("This example is done!");
        sqs.close();
        rekClient.close();
    }

    public static void startSegmentDetection(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartShotDetectionFilter cueDetectionFilter = StartShotDetectionFilter.builder()
                    .minSegmentConfidence(60F)
                    .build();

            StartTechnicalCueDetectionFilter technicalCueDetectionFilter = StartTechnicalCueDetectionFilter.builder()
                    .minSegmentConfidence(60F)
                    .build();

            StartSegmentDetectionFilters filters = StartSegmentDetectionFilters.builder()
                    .shotFilter(cueDetectionFilter)
                    .technicalCueFilter(technicalCueDetectionFilter)
                    .build();

            StartSegmentDetectionRequest segDetectionRequest = StartSegmentDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .segmentTypes(SegmentType.TECHNICAL_CUE, SegmentType.SHOT)
                    .video(vidOb)
                    .filters(filters)
                    .build();

            StartSegmentDetectionResponse segDetectionResponse = rekClient.startSegmentDetection(segDetectionRequest);
            startJobId = segDetectionResponse.jobId();

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        }
    }

    public static void getSegmentResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetSegmentDetectionResponse segDetectionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (segDetectionResponse != null)
                    paginationToken = segDetectionResponse.nextToken();

                GetSegmentDetectionRequest recognitionRequest = GetSegmentDetectionRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds.
                while (!finished) {
                    segDetectionResponse = rekClient.getSegmentDetection(recognitionRequest);
                    status = segDetectionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }
                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                List<VideoMetadata> videoMetaData = segDetectionResponse.videoMetadata();
                for (VideoMetadata metaData : videoMetaData) {
                    System.out.println("Format: " + metaData.format());
                    System.out.println("Codec: " + metaData.codec());
                    System.out.println("Duration: " + metaData.durationMillis());
                    System.out.println("FrameRate: " + metaData.frameRate());
                    System.out.println("Job");
                }

                List<SegmentDetection> detectedSegments = segDetectionResponse.segments();
                for (SegmentDetection detectedSegment : detectedSegments) {
                    String type = detectedSegment.type().toString();
                    if (type.contains(SegmentType.TECHNICAL_CUE.toString())) {
                        System.out.println("Technical Cue");
                        TechnicalCueSegment segmentCue = detectedSegment.technicalCueSegment();
                        System.out.println("\tType: " + segmentCue.type());
                        System.out.println("\tConfidence: " + segmentCue.confidence().toString());
                    }

                    if (type.contains(SegmentType.SHOT.toString())) {
                        System.out.println("Shot");
                        ShotSegment segmentShot = detectedSegment.shotSegment();
                        System.out.println("\tIndex " + segmentShot.index());
                        System.out.println("\tConfidence: " + segmentShot.confidence().toString());
                    }

                    long seconds = detectedSegment.durationMillis();
                    System.out.println("\tDuration : " + seconds + " milliseconds");
                    System.out.println("\tStart time code: " + detectedSegment.startTimecodeSMPTE());
                    System.out.println("\tEnd time code: " + detectedSegment.endTimecodeSMPTE());
                    System.out.println("\tDuration time code: " + detectedSegment.durationSMPTE());
                    System.out.println();
                }

            } while (segDetectionResponse != null && segDetectionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

Erkennen Sie Text in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartTextDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.StartTextDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetTextDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.GetTextDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.TextDetectionResult;
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 VideoDetectText {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];

        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startTextLabels(rekClient, channel, bucket, video);
        getTextResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startTextLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartTextDetectionRequest labelDetectionRequest = StartTextDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .build();

            StartTextDetectionResponse labelDetectionResponse = rekClient.startTextDetection(labelDetectionRequest);
            startJobId = labelDetectionResponse.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getTextResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetTextDetectionResponse textDetectionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (textDetectionResponse != null)
                    paginationToken = textDetectionResponse.nextToken();

                GetTextDetectionRequest recognitionRequest = GetTextDetectionRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds.
                while (!finished) {
                    textDetectionResponse = rekClient.getTextDetection(recognitionRequest);
                    status = textDetectionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = textDetectionResponse.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<TextDetectionResult> labels = textDetectionResponse.textDetections();
                for (TextDetectionResult detectedText : labels) {
                    System.out.println("Confidence: " + detectedText.textDetection().confidence().toString());
                    System.out.println("Id : " + detectedText.textDetection().id());
                    System.out.println("Parent Id: " + detectedText.textDetection().parentId());
                    System.out.println("Type: " + detectedText.textDetection().type());
                    System.out.println("Text: " + detectedText.textDetection().detectedText());
                    System.out.println();
                }

            } while (textDetectionResponse != null && textDetectionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

Erkennen Sie Personen in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingRequest;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingResponse;
import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.PersonDetection;
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 VideoPersonDetection {
    private static String startJobId = "";

    public static void main(String[] args) {

        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startPersonLabels(rekClient, channel, bucket, video);
        getPersonDetectionResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startPersonLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartPersonTrackingRequest personTrackingRequest = StartPersonTrackingRequest.builder()
                    .jobTag("DetectingLabels")
                    .video(vidOb)
                    .notificationChannel(channel)
                    .build();

            StartPersonTrackingResponse labelDetectionResponse = rekClient.startPersonTracking(personTrackingRequest);
            startJobId = labelDetectionResponse.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getPersonDetectionResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetPersonTrackingResponse personTrackingResult = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (personTrackingResult != null)
                    paginationToken = personTrackingResult.nextToken();

                GetPersonTrackingRequest recognitionRequest = GetPersonTrackingRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds
                while (!finished) {

                    personTrackingResult = rekClient.getPersonTracking(recognitionRequest);
                    status = personTrackingResult.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = personTrackingResult.videoMetadata();

                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<PersonDetection> detectedPersons = personTrackingResult.persons();
                for (PersonDetection detectedPerson : detectedPersons) {
                    long seconds = detectedPerson.timestamp() / 1000;
                    System.out.print("Sec: " + seconds + " ");
                    System.out.println("Person Identifier: " + detectedPerson.person().index());
                    System.out.println();
                }

            } while (personTrackingResult != null && personTrackingResult.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• Weitere API-Informationen finden Sie in den folgenden Themen der AWS SDK for Java 2.x-API-Referenz.

  • GetCelebrityRecognition

  • GetContentModeration

  • GetLabelDetection

  • GetPersonTracking

  • GetSegmentDetection

  • GetTextDetection

  • StartCelebrityRecognition

  • StartContentModeration

  • StartLabelDetection

  • StartPersonTracking

  • StartSegmentDetection

  • StartTextDetection

Erkennen von Objekten in Bildern

Die folgenden Codebeispiele zeigen, wie man eine App erstellt, die Amazon Rekognition verwendet, um Objekte nach Kategorien in Bildern zu erkennen.

SDK für Java 2.x

Zeigt, wie man die Amazon-Rekognition-Java-API verwendet, um eine App zu erstellen, die Amazon Rekognition verwendet, um Objekte nach Kategorien in Bildern zu identifizieren, die sich in einem Amazon Simple Storage Service (Amazon S3)-Bucket befinden. Die App sendet dem Administrator eine E-Mail-Benachrichtigung mit den Ergebnissen über Amazon Simple Email Service (Amazon SES).

Den kompletten Quellcode und Anweisungen zum Einrichten und Ausführen finden Sie im vollständigen Beispiel unter GitHub.

In diesem Beispiel verwendete Services

• Amazon Rekognition

• Amazon S3

• Amazon SES

Erkennen Sie Personen und Objekte in einem Video

Das folgende Codebeispiel zeigt, wie Sie Personen und Objekte in einem Video mit Amazon Rekognition erkennen.

SDK für Java 2.x

Zeigt, wie man die Amazon-Rekognition-Java-API verwendet, um eine App zu erstellen, die Gesichter und Objekte in Videos erkennt, die sich in einem Amazon Simple Storage Service (Amazon S3)-Bucket befinden. Die App sendet dem Administrator eine E-Mail-Benachrichtigung mit den Ergebnissen über Amazon Simple Email Service (Amazon SES).

Den kompletten Quellcode und Anweisungen zum Einrichten und Ausführen finden Sie im vollständigen Beispiel unter GitHub.

In diesem Beispiel verwendete Services

• Amazon Rekognition

• Amazon S3

• Amazon SES

• Amazon SNS

• Amazon SQS