Erkennen von Prominenten in einem gespeicherten Video

So erkennen Sie Prominente in einem Video, das in einem Amazon-S3-Bucket (SDK) gespeichert ist

1. Führen Sie Analysieren eines in einem Amazon S3-Bucket gespeicherten Videos mit Java oder Python (SDK) aus.

2. Fügen Sie den folgenden Code in der Klasse VideoDetect ein, die Sie in Schritt 1 erstellt haben.

   Java

           //Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
           //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
   
         // Celebrities=====================================================================
         private static void StartCelebrityDetection(String bucket, String video) throws Exception{
       	  
               NotificationChannel channel= new NotificationChannel()
                       .withSNSTopicArn(snsTopicArn)
                       .withRoleArn(roleArn);
     
              StartCelebrityRecognitionRequest req = new StartCelebrityRecognitionRequest()
                    .withVideo(new Video()
                          .withS3Object(new S3Object()
                                .withBucket(bucket)
                                .withName(video)))
                    .withNotificationChannel(channel);
     
     
     
              StartCelebrityRecognitionResult startCelebrityRecognitionResult = rek.startCelebrityRecognition(req);
              startJobId=startCelebrityRecognitionResult.getJobId();
     
           } 
     
           private static void GetCelebrityDetectionResults() throws Exception{
     
              int maxResults=10;
              String paginationToken=null;
              GetCelebrityRecognitionResult celebrityRecognitionResult=null;
     
              do{
                 if (celebrityRecognitionResult !=null){
                    paginationToken = celebrityRecognitionResult.getNextToken();
                 }
                 celebrityRecognitionResult = rek.getCelebrityRecognition(new GetCelebrityRecognitionRequest()
                       .withJobId(startJobId)
                       .withNextToken(paginationToken)
                       .withSortBy(CelebrityRecognitionSortBy.TIMESTAMP)
                       .withMaxResults(maxResults));
     
     
                 System.out.println("File info for page");
                 VideoMetadata videoMetaData=celebrityRecognitionResult.getVideoMetadata();
     
                 System.out.println("Format: " + videoMetaData.getFormat());
                 System.out.println("Codec: " + videoMetaData.getCodec());
                 System.out.println("Duration: " + videoMetaData.getDurationMillis());
                 System.out.println("FrameRate: " + videoMetaData.getFrameRate());
     
                 System.out.println("Job");
     
                 System.out.println("Job status: " + celebrityRecognitionResult.getJobStatus());
     
     
                 //Show celebrities
                 List<CelebrityRecognition> celebs= celebrityRecognitionResult.getCelebrities();
     
                 for (CelebrityRecognition celeb: celebs) { 
                    long seconds=celeb.getTimestamp()/1000;
                    System.out.print("Sec: " + Long.toString(seconds) + " ");
                    CelebrityDetail details=celeb.getCelebrity();
                    System.out.println("Name: " + details.getName());
                    System.out.println("Id: " + details.getId());
                    System.out.println(); 
                 }
              } while (celebrityRecognitionResult !=null && celebrityRecognitionResult.getNextToken() != null);
     
           }
     

   Ersetzen Sie in der Funktion main die Zeile:

           StartLabelDetection(amzn-s3-demo-bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetLabelDetectionResults();

   mit:

           StartCelebrityDetection(amzn-s3-demo-bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetCelebrityDetectionResults();
          

   Java V2

   Dieser Code stammt aus dem GitHub Beispiel-Repository des AWS Documentation SDK. Das vollständige Beispiel finden Sie hier.

   //snippet-start:[rekognition.java2.recognize_video_celebrity.import]
   import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
   import software.amazon.awssdk.regions.Region;
   import software.amazon.awssdk.services.rekognition.RekognitionClient;
   import software.amazon.awssdk.services.rekognition.model.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;
   //snippet-end:[rekognition.java2.recognize_video_celebrity.import]
   
   /**
   *  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 RecognizeCelebritiesVideo {
   
   private static String startJobId ="";
   
   public static void main(String[] args) {
   
      final String usage = "\n" +
          "Usage: " +
          "   <bucket> <video> <topicArn> <roleArn>\n\n" +
          "Where:\n" +
          "   bucket - The name of the bucket in which the video is located (for example, (for example, amzn-s3-demo-bucket). \n\n"+
          "   video - The name of video (for example, people.mp4). \n\n" +
          "   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic. \n\n" +
          "   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use. \n\n" ;
   
      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)
          .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
          .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();
   }
   
   // snippet-start:[rekognition.java2.recognize_video_celebrity.main]
   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);
      }
   }
   // snippet-end:[rekognition.java2.recognize_video_celebrity.main]
   }

   Python

   #Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
   #PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
   
       # ============== Celebrities ===============
       def StartCelebrityDetection(self):
           response=self.rek.start_celebrity_recognition(Video={'S3Object': {'Bucket': self.bucket, 'Name': self.video}},
               NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})
   
           self.startJobId=response['JobId']
           print('Start Job Id: ' + self.startJobId)
   
       def GetCelebrityDetectionResults(self):
           maxResults = 10
           paginationToken = ''
           finished = False
   
           while finished == False:
               response = self.rek.get_celebrity_recognition(JobId=self.startJobId,
                                                       MaxResults=maxResults,
                                                       NextToken=paginationToken)
   
               print(response['VideoMetadata']['Codec'])
               print(str(response['VideoMetadata']['DurationMillis']))
               print(response['VideoMetadata']['Format'])
               print(response['VideoMetadata']['FrameRate'])
   
               for celebrityRecognition in response['Celebrities']:
                   print('Celebrity: ' +
                       str(celebrityRecognition['Celebrity']['Name']))
                   print('Timestamp: ' + str(celebrityRecognition['Timestamp']))
                   print()
   
               if 'NextToken' in response:
                   paginationToken = response['NextToken']
               else:
                   finished = True

   Ersetzen Sie in der Funktion main die folgenden Zeilen:

       analyzer.StartLabelDetection()
       if analyzer.GetSQSMessageSuccess()==True:
           analyzer.GetLabelDetectionResults()

   mit:

       analyzer.StartCelebrityDetection()
       if analyzer.GetSQSMessageSuccess()==True:
           analyzer.GetCelebrityDetectionResults()

   Node.JS

   Ersetzen Sie im folgenden Codebeispiel für Node.Js den Wert von amzn-s3-demo-bucket durch den Namen des S3-Buckets, der Ihr Video enthält, und den Wert von videoName durch den Namen der Videodatei. Außerdem müssen Sie den Wert von roleArn durch den ARN ersetzen, der Ihrer IAM-Servicerolle zugeordnet ist. Ersetzen Sie abschließend den Wert von region durch den Namen der Betriebsregion, die Ihrem Konto zugeordnet ist. Ersetzen Sie den Wert von profile_name in der Zeile, die die Rekognition-Sitzung erstellt, durch den Namen Ihres Entwicklerprofils.

   //Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
   //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
   
   // Import required AWS SDK clients and commands for Node.js
   import { CreateQueueCommand, GetQueueAttributesCommand, GetQueueUrlCommand, 
     SetQueueAttributesCommand, DeleteQueueCommand, ReceiveMessageCommand, DeleteMessageCommand } from  "@aws-sdk/client-sqs";
   import {CreateTopicCommand, SubscribeCommand, DeleteTopicCommand } from "@aws-sdk/client-sns";
   import  { SQSClient } from "@aws-sdk/client-sqs";
   import  { SNSClient } from "@aws-sdk/client-sns";
   import  { RekognitionClient, StartLabelDetectionCommand, GetLabelDetectionCommand, 
     StartCelebrityRecognitionCommand, GetCelebrityRecognitionCommand} from "@aws-sdk/client-rekognition";
   import { stdout } from "process";
   import {fromIni} from '@aws-sdk/credential-providers';
   
   // Set the AWS Region.
   const REGION = "region-name"; //e.g. "us-east-1"
   // Set the profile name
   const profileName = "profile-name"
   // Name the collection
   // Create SNS service object.
   const sqsClient = new SQSClient({ region: REGION, 
     credentials: fromIni({profile: profileName,}), });
   const snsClient = new SNSClient({ region: REGION, 
     credentials: fromIni({profile: profileName,}), });
   const rekClient = new RekognitionClient({region: REGION, 
     credentials: fromIni({profile: profileName,}), 
   });
   
   // Set bucket and video variables
   const bucket = "bucket-name";
   const videoName = "video-name";
   const roleArn = "role-arn"
   var startJobId = ""
   
   var ts = Date.now();
   const snsTopicName = "AmazonRekognitionExample" + ts;
   const snsTopicParams = {Name: snsTopicName}
   const sqsQueueName = "AmazonRekognitionQueue-" + ts;
   
    // Set the parameters
    const sqsParams = {
     QueueName: sqsQueueName, //SQS_QUEUE_URL
     Attributes: {
       DelaySeconds: "60", // Number of seconds delay.
       MessageRetentionPeriod: "86400", // Number of seconds delay.
     },
   };
   
   const createTopicandQueue = async () => {
     try {
       // Create SNS topic
       const topicResponse = await snsClient.send(new CreateTopicCommand(snsTopicParams));
       const topicArn = topicResponse.TopicArn
       console.log("Success", topicResponse);
       // Create SQS Queue
       const sqsResponse = await sqsClient.send(new CreateQueueCommand(sqsParams));
       console.log("Success", sqsResponse);
       const sqsQueueCommand = await sqsClient.send(new GetQueueUrlCommand({QueueName: sqsQueueName}))
       const sqsQueueUrl = sqsQueueCommand.QueueUrl
       const attribsResponse = await sqsClient.send(new GetQueueAttributesCommand({QueueUrl: sqsQueueUrl, AttributeNames: ['QueueArn']}))
       const attribs = attribsResponse.Attributes
       console.log(attribs)
       const queueArn = attribs.QueueArn
       // subscribe SQS queue to SNS topic
       const subscribed = await snsClient.send(new SubscribeCommand({TopicArn: topicArn, Protocol:'sqs', Endpoint: queueArn}))
       const policy = {
         Version: "2012-10-17",
         Statement: [
           {
             Sid: "MyPolicy",
             Effect: "Allow",
             Principal: {AWS: "*"},
             Action: "SQS:SendMessage",
             Resource: queueArn,
             Condition: {
               ArnEquals: {
                 'aws:SourceArn': topicArn
               }
             }
           }
         ]
       };
   
       const response = sqsClient.send(new SetQueueAttributesCommand({QueueUrl: sqsQueueUrl, Attributes: {Policy: JSON.stringify(policy)}}))
       console.log(response)
       console.log(sqsQueueUrl, topicArn)
       return [sqsQueueUrl, topicArn]
   
     } catch (err) {
       console.log("Error", err);
     }
   };
   
   const startCelebrityDetection = async(roleArn, snsTopicArn) =>{
     try {
         //Initiate label detection and update value of startJobId with returned Job ID
         const response = await rekClient.send(new StartCelebrityRecognitionCommand({Video:{S3Object:{Bucket:bucket, Name:videoName}},
             NotificationChannel:{RoleArn: roleArn, SNSTopicArn: snsTopicArn}}))
             startJobId = response.JobId
             console.log(`Start Job ID: ${startJobId}`)
             return startJobId
       } catch (err) {
         console.log("Error", err);
       }
     };
   
   const getCelebrityRecognitionResults = async(startJobId) =>{
     try {
         //Initiate label detection and update value of startJobId with returned Job ID
         var maxResults = 10
         var paginationToken = ''
         var finished = false
   
         while (finished == false){
             var response = await rekClient.send(new GetCelebrityRecognitionCommand({JobId: startJobId, MaxResults: maxResults, 
                 NextToken: paginationToken}))
             console.log(response.VideoMetadata.Codec)
             console.log(response.VideoMetadata.DurationMillis)
             console.log(response.VideoMetadata.Format)
             console.log(response.VideoMetadata.FrameRate)
             response.Celebrities.forEach(celebrityRecognition => {
                 console.log(`Celebrity: ${celebrityRecognition.Celebrity.Name}`)
                 console.log(`Timestamp: ${celebrityRecognition.Timestamp}`)
                 console.log()
             })
             // Searh for pagination token, if found, set variable to next token
             if (String(response).includes("NextToken")){
                 paginationToken = response.NextToken
         
             }else{
                 finished = true
             }
         }
       } catch (err) {
         console.log("Error", err);
       }
     };
   
   // Checks for status of job completion
   const getSQSMessageSuccess = async(sqsQueueUrl, startJobId) => {
     try {
       // Set job found and success status to false initially
       var jobFound = false
       var succeeded = false
       var dotLine = 0
       // while not found, continue to poll for response
       while (jobFound == false){
         var sqsReceivedResponse = await sqsClient.send(new ReceiveMessageCommand({QueueUrl:sqsQueueUrl, 
           MaxNumberOfMessages:'ALL', MaxNumberOfMessages:10}));
         if (sqsReceivedResponse){
           var responseString = JSON.stringify(sqsReceivedResponse)
           if (!responseString.includes('Body')){
             if (dotLine < 40) {
               console.log('.')
               dotLine = dotLine + 1
             }else {
               console.log('')
               dotLine = 0 
             };
             stdout.write('', () => {
               console.log('');
             });
             await new Promise(resolve => setTimeout(resolve, 5000));
             continue
           }
         }
   
         // Once job found, log Job ID and return true if status is succeeded
         for (var message of sqsReceivedResponse.Messages){
           console.log("Retrieved messages:")
           var notification = JSON.parse(message.Body)
           var rekMessage = JSON.parse(notification.Message)
           var messageJobId = rekMessage.JobId
           if (String(rekMessage.JobId).includes(String(startJobId))){
             console.log('Matching job found:')
             console.log(rekMessage.JobId)
             jobFound = true
             console.log(rekMessage.Status)
             if (String(rekMessage.Status).includes(String("SUCCEEDED"))){
               succeeded = true
               console.log("Job processing succeeded.")
               var sqsDeleteMessage = await sqsClient.send(new DeleteMessageCommand({QueueUrl:sqsQueueUrl, ReceiptHandle:message.ReceiptHandle}));
             }
           }else{
             console.log("Provided Job ID did not match returned ID.")
             var sqsDeleteMessage = await sqsClient.send(new DeleteMessageCommand({QueueUrl:sqsQueueUrl, ReceiptHandle:message.ReceiptHandle}));
           }
         }
       }
     return succeeded
     } catch(err) {
       console.log("Error", err);
     }
   };
   
   // Start label detection job, sent status notification, check for success status
   // Retrieve results if status is "SUCEEDED", delete notification queue and topic
   const runCelebRecognitionAndGetResults = async () => {
     try {
       const sqsAndTopic = await createTopicandQueue();
       //const startLabelDetectionRes = await startLabelDetection(roleArn, sqsAndTopic[1]);
       //const getSQSMessageStatus = await getSQSMessageSuccess(sqsAndTopic[0], startLabelDetectionRes)
       const startCelebrityDetectionRes = await startCelebrityDetection(roleArn, sqsAndTopic[1]);
       const getSQSMessageStatus = await getSQSMessageSuccess(sqsAndTopic[0], startCelebrityDetectionRes)
       console.log(getSQSMessageSuccess)
       if (getSQSMessageSuccess){
         console.log("Retrieving results:")
         const results = await getCelebrityRecognitionResults(startCelebrityDetectionRes)
       }
       const deleteQueue = await sqsClient.send(new DeleteQueueCommand({QueueUrl: sqsAndTopic[0]}));
       const deleteTopic = await snsClient.send(new DeleteTopicCommand({TopicArn: sqsAndTopic[1]}));
       console.log("Successfully deleted.")
     } catch (err) {
       console.log("Error", err);
     }
   };
   
   runCelebRecognitionAndGetResults()

   CLI

   Führen Sie den folgenden AWS CLI Befehl aus, um mit der Erkennung von Prominenten in einem Video zu beginnen.

   aws rekognition start-celebrity-recognition --video "{"S3Object":{"Bucket":"amzn-s3-demo-bucket","Name":"video-name"}}" \
   --notification-channel "{"SNSTopicArn":"topic-arn","RoleArn":"role-arn"}" \
   --region region-name --profile profile-name

   Aktualisieren Sie die folgenden Werte:

   • Ändern Sie amzn-s3-demo-bucket und video-name in den Amazon-S3-Bucket-Namen und den Dateinamen, die Sie in Schritt 2 angegeben haben.

   • Ändern Sie region-name in die von Ihnen verwendete AWS-Region.

   • Ersetzen Sie den Wert von profile-name mit dem Namen Ihres Entwicklerprofils.

   • Ändern Sie topic-ARN in den ARN des Amazon-SNS-Themas, das Sie in Schritt 3 von Amazon Rekognition Video konfigurieren erstellt haben.

   • Ändern Sie role-ARN in den ARN der IAM-Servicerolle, die Sie in Schritt 7 von Amazon Rekognition Video konfigurieren erstellt haben.

   Wenn Sie auf einem Windows-Gerät auf die CLI zugreifen, verwenden Sie doppelte Anführungszeichen anstelle von einfachen Anführungszeichen und maskieren Sie die inneren doppelten Anführungszeichen durch einen Backslash (d. h. \), um eventuell auftretende Parserfehler zu beheben. Ein Beispiel finden Sie unten:

   aws rekognition start-celebrity-recognition --video "{\"S3Object\":{\"Bucket\":\"amzn-s3-demo-bucket\",\"Name\":\"video-name\"}}" \
   --notification-channel "{\"SNSTopicArn\":\"topic-arn\",\"RoleArn\":\"role-arn\"}" \
   --region region-name --profile profile-name
                   

   Nachdem Sie das vorangegangene Codebeispiel ausgeführt haben, kopieren Sie die zurückgegebene jobID und geben Sie sie an den folgenden GetCelebrityRecognition-Befehl weiter, um Ihre Ergebnisse zu erhalten, und ersetzen Sie job-id-number durch jobID, die Sie zuvor erhalten haben:

   aws rekognition get-celebrity-recognition --job-id job-id-number --profile profile-name                               
                               

   Anmerkung

   Wenn Sie zusätzlich zu Analysieren eines in einem Amazon S3-Bucket gespeicherten Videos mit Java oder Python (SDK) bereits ein anderes Videobeispiel ausgeführt haben, ist der zu ersetzende Code möglicherweise anders.

3. Führen Sie den Code aus. Die Informationen über die Prominenten im Video werden angezeigt.