Ricerca di volti in video archiviati

Per cercare un video per la corrispondenza di volti (SDK)

1. Creare una raccolta.

2. Indicizzare un volto nella raccolta.

3. Eseguire Analisi di un video archiviato in un bucket Amazon S3 con Java o Python (SDK).

4. Aggiungere il seguente codice alla classe VideoDetect creata nella fase 3.

   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.)
   
          //Face collection search in video ==================================================================
          private static void StartFaceSearchCollection(String bucket, String video, String collection) throws Exception{
   
           NotificationChannel channel= new NotificationChannel()
                   .withSNSTopicArn(snsTopicArn)
                   .withRoleArn(roleArn);
   
           StartFaceSearchRequest req = new StartFaceSearchRequest()
                   .withCollectionId(collection)
                   .withVideo(new Video()
                           .withS3Object(new S3Object()
                                   .withBucket(bucket)
                                   .withName(video)))
                   .withNotificationChannel(channel);
   
   
   
           StartFaceSearchResult startPersonCollectionSearchResult = rek.startFaceSearch(req);
           startJobId=startPersonCollectionSearchResult.getJobId();
   
       } 
   
       //Face collection search in video ==================================================================
       private static void GetFaceSearchCollectionResults() throws Exception{
   
          GetFaceSearchResult faceSearchResult=null;
          int maxResults=10;
          String paginationToken=null;
   
          do {
   
              if (faceSearchResult !=null){
                  paginationToken = faceSearchResult.getNextToken();
              }
   
   
              faceSearchResult  = rek.getFaceSearch(
                      new GetFaceSearchRequest()
                      .withJobId(startJobId)
                      .withMaxResults(maxResults)
                      .withNextToken(paginationToken)
                      .withSortBy(FaceSearchSortBy.TIMESTAMP)
                      );
   
   
              VideoMetadata videoMetaData=faceSearchResult.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();      
   
   
              //Show search results
              List<PersonMatch> matches= 
                      faceSearchResult.getPersons();
   
              for (PersonMatch match: matches) { 
                  long milliSeconds=match.getTimestamp();
                  System.out.print("Timestamp: " + Long.toString(milliSeconds));
                  System.out.println(" Person number: " + match.getPerson().getIndex());
                  List <FaceMatch> faceMatches = match.getFaceMatches();
                  if (faceMatches != null) {
                      System.out.println("Matches in collection...");
                      for (FaceMatch faceMatch: faceMatches){
                          Face face=faceMatch.getFace();
                          System.out.println("Face Id: "+ face.getFaceId());
                          System.out.println("Similarity: " + faceMatch.getSimilarity().toString());
                          System.out.println();
                      }
                  }
                  System.out.println();           
              } 
   
              System.out.println(); 
   
          } while (faceSearchResult !=null && faceSearchResult.getNextToken() != null);
   
      }
   

   Nella funzione main, sostituisci le righe:

           StartLabelDetection(bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetLabelDetectionResults();

   con:

           String collection="collection";
           StartFaceSearchCollection(bucket, video, collection);
   
           if (GetSQSMessageSuccess()==true)
           	GetFaceSearchCollectionResults();

   Java V2

   Questo codice è tratto dal GitHub repository degli esempi di AWS Documentation SDK. Guarda l'esempio completo qui.

   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 VideoDetectFaces {
       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();
   
           startFaceDetection(rekClient, channel, bucket, video);
           getFaceResults(rekClient);
           System.out.println("This example is done!");
           rekClient.close();
       }
   
       public static void startFaceDetection(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();
   
               StartFaceDetectionRequest faceDetectionRequest = StartFaceDetectionRequest.builder()
                       .jobTag("Faces")
                       .faceAttributes(FaceAttributes.ALL)
                       .notificationChannel(channel)
                       .video(vidOb)
                       .build();
   
               StartFaceDetectionResponse startLabelDetectionResult = rekClient.startFaceDetection(faceDetectionRequest);
               startJobId = startLabelDetectionResult.jobId();
   
           } catch (RekognitionException e) {
               System.out.println(e.getMessage());
               System.exit(1);
           }
       }
   
       public static void getFaceResults(RekognitionClient rekClient) {
           try {
               String paginationToken = null;
               GetFaceDetectionResponse faceDetectionResponse = null;
               boolean finished = false;
               String status;
               int yy = 0;
   
               do {
                   if (faceDetectionResponse != null)
                       paginationToken = faceDetectionResponse.nextToken();
   
                   GetFaceDetectionRequest recognitionRequest = GetFaceDetectionRequest.builder()
                           .jobId(startJobId)
                           .nextToken(paginationToken)
                           .maxResults(10)
                           .build();
   
                   // Wait until the job succeeds.
                   while (!finished) {
   
                       faceDetectionResponse = rekClient.getFaceDetection(recognitionRequest);
                       status = faceDetectionResponse.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 = faceDetectionResponse.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");
   
                   // Show face information.
                   List<FaceDetection> faces = faceDetectionResponse.faces();
                   for (FaceDetection face : faces) {
                       String age = face.face().ageRange().toString();
                       String smile = face.face().smile().toString();
                       System.out.println("The detected face is estimated to be"
                               + age + " years old.");
                       System.out.println("There is a smile : " + smile);
                   }
   
               } while (faceDetectionResponse != null && faceDetectionResponse.nextToken() != null);
   
           } catch (RekognitionException | InterruptedException e) {
               System.out.println(e.getMessage());
               System.exit(1);
           }
       }
   }
   

   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.)
   
       # ============== Face Search ===============
       def StartFaceSearchCollection(self,collection):
           response = self.rek.start_face_search(Video={'S3Object':{'Bucket':self.bucket,'Name':self.video}},
               CollectionId=collection,
               NotificationChannel={'RoleArn':self.roleArn, 'SNSTopicArn':self.snsTopicArn})
           
           self.startJobId=response['JobId']
           
           print('Start Job Id: ' + self.startJobId)
   
   
       def GetFaceSearchCollectionResults(self):
           maxResults = 10
           paginationToken = ''
   
           finished = False
   
           while finished == False:
               response = self.rek.get_face_search(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 personMatch in response['Persons']:
                   print('Person Index: ' + str(personMatch['Person']['Index']))
                   print('Timestamp: ' + str(personMatch['Timestamp']))
   
                   if ('FaceMatches' in personMatch):
                       for faceMatch in personMatch['FaceMatches']:
                           print('Face ID: ' + faceMatch['Face']['FaceId'])
                           print('Similarity: ' + str(faceMatch['Similarity']))
                   print()
               if 'NextToken' in response:
                   paginationToken = response['NextToken']
               else:
                   finished = True
               print()

   Nella funzione main, sostituisci le righe:

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

   con:

       collection='tests'
       analyzer.StartFaceSearchCollection(collection)
       
       if analyzer.GetSQSMessageSuccess()==True:
           analyzer.GetFaceSearchCollectionResults()

   Se hai già eseguito un video di esempio diverso da Analisi di un video archiviato in un bucket Amazon S3 con Java o Python (SDK), il codice da sostituire potrebbe essere diverso.

5. Modificare il valore di collection nel nome della raccolta creata nella fase 1.

6. Eseguire il codice. Viene visualizzato un elenco di persone nel video i cui volti corrispondono a quelli della raccolta di input. Vengono inoltre visualizzati i dati di tracciamento per ogni persona con corrispondenza.