Reconocimiento de famosos en un vídeo almacenado

Para detectar famosos en un vídeo almacenado en un bucket de Amazon S3 (SDK)

1. Realice Análisis de un vídeo almacenado en un bucket de Amazon S3 con Java o Python (SDK).

2. Añada el código siguiente a la clase VideoDetect que ha creado en el paso 1.

   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);
     
           }
     

   En la función main, reemplace la línea:

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

   por:

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

   Java V2

   Este código se ha tomado del GitHub repositorio de ejemplos del SDK de AWS documentación. Consulte el ejemplo completo aquí.

   //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

   En la función main, reemplace las líneas:

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

   por:

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

   Node.JS

   En el siguiente ejemplo de código de Node.Js, sustituya el valor de amzn-s3-demo-bucket por el nombre del bucket de S3 que contiene el vídeo y el valor de videoName por el nombre del archivo de vídeo. También tendrá que reemplazar el valor de roleArn por el Arn asociado a su rol de servicio de IAM. Por último, sustituya el valor de region por el nombre de la región de operación asociada a su cuenta. Sustituya el valor de profile_name en la línea que crea la sesión de Rekognition por el nombre de su perfil de desarrollador.

   //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

   Ejecute el siguiente AWS CLI comando para empezar a detectar celebridades en un vídeo.

   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

   Actualice los siguientes valores:

   • Cambie amzn-s3-demo-bucket y video-name por el nombre del bucket de Amazon S3 y el nombre de archivo que especificó en el paso 2.

   • Cambie region-name por la región de AWS que está utilizando.

   • Sustituya el valor de profile-name de por el nombre de su perfil de desarrollador.

   • Reemplace topic-ARN por el ARN del tema de Amazon SNS que creó en el paso 3 de Configuración de Amazon Rekognition Video.

   • Cambie role-ARN por el ARN del rol de servicio de IAM que creó en el paso 7 de Configuración de Amazon Rekognition Video.

   Si accede a la CLI en un dispositivo Windows, utilice comillas dobles en lugar de comillas simples y evite las comillas dobles internas con una barra invertida (es decir, \) para corregir cualquier error del analizador que pueda encontrar. Consulte a continuación un ejemplo:

   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
                   

   Tras ejecutar el ejemplo de código de procedimiento, copie el jobID devuelto y envíelo al siguiente comando GetCelebrityRecognition para obtener los resultados, sustituyendo job-id-number por el jobID que recibió anteriormente:

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

   nota

   Si ya ha ejecutado un ejemplo de vídeo distinto de Análisis de un vídeo almacenado en un bucket de Amazon S3 con Java o Python (SDK), el código que se va a reemplazar podría ser diferente.

3. Ejecute el código. Se muestra información sobre los famosos reconocidos en el vídeo.