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# Analyse d’une vidéo stockée dans un compartiment Amazon S3 avec Java or Python (SDK)
<a name="video-analyzing-with-sqs"></a>

Cette procédure vous montre comment détecter des étiquettes dans une vidéo en utilisant les opérations de détection d’étiquette Vidéo Amazon Rekognition, une vidéo stockée dans un compartiment Amazon S3 et une rubrique Amazon SNS. La procédure montre également comment utiliser une file d’attente Amazon SQS pour obtenir le statut d’achèvement à partir de la rubrique Amazon SNS. Pour de plus amples informations, veuillez consulter [Appeler les opérations de Vidéo Amazon Rekognition](api-video.md). Vous n’êtes pas limité à l’utilisation d’une file d’attente Amazon SQS. Par exemple, vous pouvez utiliser une AWS Lambda fonction pour obtenir le statut d'achèvement. Pour en savoir plus, consultez [Invocation des fonctions Lambda en utilisant des notifications Amazon SNS](https://docs.aws.amazon.com/sns/latest/dg/sns-lambda.html).

L’exemple de code de cette procédure montre comment effectuer les opérations suivantes :

1. Créer la rubrique Amazon SNS.

1. Créer la file d’attente Amazon SQS.

1. Accorder à Vidéo Amazon Rekognition l’autorisation de publier le statut d’achèvement d’une opération d’analyse vidéo dans la rubrique Amazon SNS.

1. Abonner la file d’attente Amazon SNS à la rubrique Amazon SNS.

1. Lancer la demande d'analyse vidéo en appelant [StartLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartLabelDetection.html). 

1. Obtenir le statut d’achèvement à partir de la file d’attente Amazon SQS. L’exemple suit l’identifiant de tâche (`JobId`) renvoyé dans `StartLabelDetection` et obtient uniquement les résultats pour les identifiants de tâche correspondants qui sont lus à partir du statut d’achèvement. Ceci doit être pris en considération si d’autres applications utilisent la même file d’attente et la même rubrique. Par souci de simplicité, l’exemple supprime les tâches qui ne correspondent pas. Envisagez de les ajouter à une file d’attente de lettres mortes Amazon SQS pour un examen plus approfondi.

1. Obtenir et afficher les résultats de l'analyse vidéo en appelant [GetLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetLabelDetection.html).

## Conditions préalables
<a name="video-prerequisites"></a>

L’exemple de code pour cette procédure est fourni en Java et Python. Le AWS SDK approprié doit être installé. Pour de plus amples informations, veuillez consulter [Premiers pas avec Amazon Rekognition](getting-started.md). Le compte AWS que vous utilisez doit avoir les autorisations d’accès à l’API Amazon Rekognition. Pour plus d’informations, consultez [Actions définies par Amazon Rekognition](https://docs.aws.amazon.com/IAM/latest/UserGuide/list_amazonrekognition.html#amazonrekognition-actions-as-permissions). 

**Pour détecter des étiquettes dans une vidéo**

1. Configurez l’accès des utilisateurs à Vidéo Amazon Rekognition et configurez l’accès de Vidéo Amazon Rekognition à Amazon SNS. Pour de plus amples informations, veuillez consulter [Configuration de Vidéo Amazon Rekognition](api-video-roles.md). Vous n’avez pas besoin d’effectuer les étapes 3, 4, 5 et 6, car l’exemple de code crée et configure la rubrique Amazon SNS et la file d’attente Amazon SQS. 

1. Chargez un fichier MOV ou un fichier vidéo MPEG-4 formaté dans un compartiment Amazon S3. À des fins de test, téléchargez une vidéo de 30 secondes au maximum.

   Pour en savoir plus, consultez [Chargement d’objets dans Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UploadingObjectsintoAmazonS3.html) dans le *Guide de l’utilisateur Amazon Simple Storage Service*.

   

1. Utilisez les exemples de code suivants pour détecter des étiquettes dans une vidéo. 

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

   Dans la fonction `main` :
   + Remplacez `roleArn` par l’ARN de la fonction du service IAM que vous avez créé au cours de l’étape 7 de [Pour configurer Vidéo Amazon Rekognition](api-video-roles.md#configure-rekvid-procedure).
   + Remplacez les valeurs de `amzn-s3-demo-bucket` et `video` par le nom du compartiment et le nom du fichier vidéo que vous avez spécifiés à l’étape 2. 

   ```
   //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.)
   
   package com.amazonaws.samples;
   import com.amazonaws.auth.policy.Policy;
   import com.amazonaws.auth.policy.Condition;
   import com.amazonaws.auth.policy.Principal;
   import com.amazonaws.auth.policy.Resource;
   import com.amazonaws.auth.policy.Statement;
   import com.amazonaws.auth.policy.Statement.Effect;
   import com.amazonaws.auth.policy.actions.SQSActions;
   import com.amazonaws.services.rekognition.AmazonRekognition;
   import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder;
   import com.amazonaws.services.rekognition.model.CelebrityDetail;
   import com.amazonaws.services.rekognition.model.CelebrityRecognition;
   import com.amazonaws.services.rekognition.model.CelebrityRecognitionSortBy;
   import com.amazonaws.services.rekognition.model.ContentModerationDetection;
   import com.amazonaws.services.rekognition.model.ContentModerationSortBy;
   import com.amazonaws.services.rekognition.model.Face;
   import com.amazonaws.services.rekognition.model.FaceDetection;
   import com.amazonaws.services.rekognition.model.FaceMatch;
   import com.amazonaws.services.rekognition.model.FaceSearchSortBy;
   import com.amazonaws.services.rekognition.model.GetCelebrityRecognitionRequest;
   import com.amazonaws.services.rekognition.model.GetCelebrityRecognitionResult;
   import com.amazonaws.services.rekognition.model.GetContentModerationRequest;
   import com.amazonaws.services.rekognition.model.GetContentModerationResult;
   import com.amazonaws.services.rekognition.model.GetFaceDetectionRequest;
   import com.amazonaws.services.rekognition.model.GetFaceDetectionResult;
   import com.amazonaws.services.rekognition.model.GetFaceSearchRequest;
   import com.amazonaws.services.rekognition.model.GetFaceSearchResult;
   import com.amazonaws.services.rekognition.model.GetLabelDetectionRequest;
   import com.amazonaws.services.rekognition.model.GetLabelDetectionResult;
   import com.amazonaws.services.rekognition.model.GetPersonTrackingRequest;
   import com.amazonaws.services.rekognition.model.GetPersonTrackingResult;
   import com.amazonaws.services.rekognition.model.Instance;
   import com.amazonaws.services.rekognition.model.Label;
   import com.amazonaws.services.rekognition.model.LabelDetection;
   import com.amazonaws.services.rekognition.model.LabelDetectionSortBy;
   import com.amazonaws.services.rekognition.model.NotificationChannel;
   import com.amazonaws.services.rekognition.model.Parent;
   import com.amazonaws.services.rekognition.model.PersonDetection;
   import com.amazonaws.services.rekognition.model.PersonMatch;
   import com.amazonaws.services.rekognition.model.PersonTrackingSortBy;
   import com.amazonaws.services.rekognition.model.S3Object;
   import com.amazonaws.services.rekognition.model.StartCelebrityRecognitionRequest;
   import com.amazonaws.services.rekognition.model.StartCelebrityRecognitionResult;
   import com.amazonaws.services.rekognition.model.StartContentModerationRequest;
   import com.amazonaws.services.rekognition.model.StartContentModerationResult;
   import com.amazonaws.services.rekognition.model.StartFaceDetectionRequest;
   import com.amazonaws.services.rekognition.model.StartFaceDetectionResult;
   import com.amazonaws.services.rekognition.model.StartFaceSearchRequest;
   import com.amazonaws.services.rekognition.model.StartFaceSearchResult;
   import com.amazonaws.services.rekognition.model.StartLabelDetectionRequest;
   import com.amazonaws.services.rekognition.model.StartLabelDetectionResult;
   import com.amazonaws.services.rekognition.model.StartPersonTrackingRequest;
   import com.amazonaws.services.rekognition.model.StartPersonTrackingResult;
   import com.amazonaws.services.rekognition.model.Video;
   import com.amazonaws.services.rekognition.model.VideoMetadata;
   import com.amazonaws.services.sns.AmazonSNS;
   import com.amazonaws.services.sns.AmazonSNSClientBuilder;
   import com.amazonaws.services.sns.model.CreateTopicRequest;
   import com.amazonaws.services.sns.model.CreateTopicResult;
   import com.amazonaws.services.sqs.AmazonSQS;
   import com.amazonaws.services.sqs.AmazonSQSClientBuilder;
   import com.amazonaws.services.sqs.model.CreateQueueRequest;
   import com.amazonaws.services.sqs.model.Message;
   import com.amazonaws.services.sqs.model.QueueAttributeName;
   import com.amazonaws.services.sqs.model.SetQueueAttributesRequest;
   import com.fasterxml.jackson.databind.JsonNode;
   import com.fasterxml.jackson.databind.ObjectMapper;
   import java.util.*;
   
   public class VideoDetect {
    
       
       private static String sqsQueueName=null;
       private static String snsTopicName=null;
       private static String snsTopicArn = null;
       private static String roleArn= null;
       private static String sqsQueueUrl = null;
       private static String sqsQueueArn = null;
       private static String startJobId = null;
       private static String bucket = null;
       private static String video = null; 
       private static AmazonSQS sqs=null;
       private static AmazonSNS sns=null;
       private static AmazonRekognition rek = null;
       
       private static NotificationChannel channel= new NotificationChannel()
               .withSNSTopicArn(snsTopicArn)
               .withRoleArn(roleArn);
   
   
       public static void main(String[] args) throws Exception {
           
           video = "";
           bucket = "";
           roleArn= "";
   
           sns = AmazonSNSClientBuilder.defaultClient();
           sqs= AmazonSQSClientBuilder.defaultClient();
           rek = AmazonRekognitionClientBuilder.defaultClient();
     
           CreateTopicandQueue();
           
           //=================================================
           
           StartLabelDetection(bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetLabelDetectionResults();
           
          //=================================================  
           
   
           DeleteTopicandQueue();
           System.out.println("Done!");
          
       }
   
       
       static boolean GetSQSMessageSuccess() throws Exception
       {
       	boolean success=false;
   
      
           System.out.println("Waiting for job: " + startJobId);
           //Poll queue for messages
           List<Message> messages=null;
           int dotLine=0;
           boolean jobFound=false;
   
           //loop until the job status is published. Ignore other messages in queue.
           do{
               messages = sqs.receiveMessage(sqsQueueUrl).getMessages();
               if (dotLine++<40){
                   System.out.print(".");
               }else{
                   System.out.println();
                   dotLine=0;
               }
   
               if (!messages.isEmpty()) {
                   //Loop through messages received.
                   for (Message message: messages) {
                       String notification = message.getBody();
   
                       // Get status and job id from 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 was " + operationJobId);
                       // Found job. Get the results and display.
                       if(operationJobId.asText().equals(startJobId)){
                           jobFound=true;
                           System.out.println("Job id: " + operationJobId );
                           System.out.println("Status : " + operationStatus.toString());
                           if (operationStatus.asText().equals("SUCCEEDED")){
                           	success=true;
                           }
                           else{
                               System.out.println("Video analysis failed");
                           }
   
                           sqs.deleteMessage(sqsQueueUrl,message.getReceiptHandle());
                       }
   
                       else{
                           System.out.println("Job received was not job " +  startJobId);
                           //Delete unknown message. Consider moving message to dead letter queue
                           sqs.deleteMessage(sqsQueueUrl,message.getReceiptHandle());
                       }
                   }
               }
               else {
                   Thread.sleep(5000);
               }
           } while (!jobFound);
   
           System.out.println("Finished processing video");
           return success;
       }
     
   
       private static void StartLabelDetection(String bucket, String video) throws Exception{
       	
           NotificationChannel channel= new NotificationChannel()
                   .withSNSTopicArn(snsTopicArn)
                   .withRoleArn(roleArn);
   
   
           StartLabelDetectionRequest req = new StartLabelDetectionRequest()
                   .withVideo(new Video()
                           .withS3Object(new S3Object()
                                   .withBucket(bucket)
                                   .withName(video)))
                   .withMinConfidence(50F)
                   .withJobTag("DetectingLabels")
                   .withNotificationChannel(channel);
   
           StartLabelDetectionResult startLabelDetectionResult = rek.startLabelDetection(req);
           startJobId=startLabelDetectionResult.getJobId();
           
       }
     
       private static void GetLabelDetectionResults() throws Exception{
   
           int maxResults=10;
           String paginationToken=null;
           GetLabelDetectionResult labelDetectionResult=null;
   
           do {
               if (labelDetectionResult !=null){
                   paginationToken = labelDetectionResult.getNextToken();
               }
   
               GetLabelDetectionRequest labelDetectionRequest= new GetLabelDetectionRequest()
                       .withJobId(startJobId)
                       .withSortBy(LabelDetectionSortBy.TIMESTAMP)
                       .withMaxResults(maxResults)
                       .withNextToken(paginationToken);
   
   
               labelDetectionResult = rek.getLabelDetection(labelDetectionRequest);
   
               VideoMetadata videoMetaData=labelDetectionResult.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());
   
   
               //Show labels, confidence and detection times
               List<LabelDetection> detectedLabels= labelDetectionResult.getLabels();
   
               for (LabelDetection detectedLabel: detectedLabels) {
                   long seconds=detectedLabel.getTimestamp();
                   Label label=detectedLabel.getLabel();
                   System.out.println("Millisecond: " + Long.toString(seconds) + " ");
                   
                   System.out.println("   Label:" + label.getName()); 
                   System.out.println("   Confidence:" + detectedLabel.getLabel().getConfidence().toString());
         
                   List<Instance> instances = label.getInstances();
                   System.out.println("   Instances of " + label.getName());
                   if (instances.isEmpty()) {
                       System.out.println("        " + "None");
                   } else {
                       for (Instance instance : instances) {
                           System.out.println("        Confidence: " + instance.getConfidence().toString());
                           System.out.println("        Bounding box: " + instance.getBoundingBox().toString());
                       }
                   }
                   System.out.println("   Parent labels for " + label.getName() + ":");
                   List<Parent> parents = label.getParents();
                   if (parents.isEmpty()) {
                       System.out.println("        None");
                   } else {
                       for (Parent parent : parents) {
                           System.out.println("        " + parent.getName());
                       }
                   }
                   System.out.println();
               }
           } while (labelDetectionResult !=null && labelDetectionResult.getNextToken() != null);
   
       } 
   
       // Creates an SNS topic and SQS queue. The queue is subscribed to the topic. 
       static void CreateTopicandQueue()
       {
           //create a new SNS topic
           snsTopicName="AmazonRekognitionTopic" + Long.toString(System.currentTimeMillis());
           CreateTopicRequest createTopicRequest = new CreateTopicRequest(snsTopicName);
           CreateTopicResult createTopicResult = sns.createTopic(createTopicRequest);
           snsTopicArn=createTopicResult.getTopicArn();
           
           //Create a new SQS Queue
           sqsQueueName="AmazonRekognitionQueue" + Long.toString(System.currentTimeMillis());
           final CreateQueueRequest createQueueRequest = new CreateQueueRequest(sqsQueueName);
           sqsQueueUrl = sqs.createQueue(createQueueRequest).getQueueUrl();
           sqsQueueArn = sqs.getQueueAttributes(sqsQueueUrl, Arrays.asList("QueueArn")).getAttributes().get("QueueArn");
           
           //Subscribe SQS queue to SNS topic
           String sqsSubscriptionArn = sns.subscribe(snsTopicArn, "sqs", sqsQueueArn).getSubscriptionArn();
           
           // Authorize queue
             Policy policy = new Policy().withStatements(
                     new Statement(Effect.Allow)
                     .withPrincipals(Principal.AllUsers)
                     .withActions(SQSActions.SendMessage)
                     .withResources(new Resource(sqsQueueArn))
                     .withConditions(new Condition().withType("ArnEquals").withConditionKey("aws:SourceArn").withValues(snsTopicArn))
                     );
                     
   
             Map queueAttributes = new HashMap();
             queueAttributes.put(QueueAttributeName.Policy.toString(), policy.toJson());
             sqs.setQueueAttributes(new SetQueueAttributesRequest(sqsQueueUrl, queueAttributes)); 
           
   
            System.out.println("Topic arn: " + snsTopicArn);
            System.out.println("Queue arn: " + sqsQueueArn);
            System.out.println("Queue url: " + sqsQueueUrl);
            System.out.println("Queue sub arn: " + sqsSubscriptionArn );
        }
       static void DeleteTopicandQueue()
       {
           if (sqs !=null) {
               sqs.deleteQueue(sqsQueueUrl);
               System.out.println("SQS queue deleted");
           }
           
           if (sns!=null) {
               sns.deleteTopic(snsTopicArn);
               System.out.println("SNS topic deleted");
           }
       }
   }
   ```

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

   Dans la fonction `main` :
   + Remplacez `roleArn` par l’ARN de la fonction du service IAM que vous avez créé au cours de l’étape 7 de [Pour configurer Vidéo Amazon Rekognition](api-video-roles.md#configure-rekvid-procedure).
   + Remplacez les valeurs de `amzn-s3-demo-bucket` et `video` par le nom du compartiment et le nom du fichier vidéo que vous avez spécifiés à l’étape 2. 
   + Remplacez la valeur de `profile_name` dans la ligne qui crée la session de Rekognition par le nom de votre profil de développeur.
   + Vous pouvez également inclure des critères de filtrage dans le paramètre des paramètres. Par exemple, vous pouvez utiliser un `LabelsInclusionFilter` ou un `LabelsExclusionFilter` à côté d’une liste de valeurs souhaitées. Dans le code ci-dessous, vous pouvez supprimer les commentaires de la section `Features` et `Settings` et fournir vos propres valeurs afin de limiter les résultats renvoyés aux seules étiquettes qui vous intéressent.
   + Dans l’appel à `GetLabelDetection`, vous pouvez fournir des valeurs pour les arguments `SortBy` et `AggregateBy`. Pour trier en fonction de l’heure, définissez la valeur du paramètre d’entrée `SortBy` sur `TIMESTAMP`. Pour trier en fonction de l’entité, utilisez le paramètre d’entrée `SortBy` avec la valeur appropriée pour l’opération que vous exécutez. Pour agréger les résultats par horodatage, définissez la valeur du paramètre `AggregateBy` sur `TIMESTAMPS`. Pour agréger par segment vidéo, utilisez `SEGMENTS`. 

   Dans l'exemple de code, placez la politique IAM suivante entre guillemets sur la ligne. `policy = """""".format(sqsQueueArn, self.snsTopicArn)`

   ```
   ## 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 boto3
   import json
   import sys
   import time
   
   class VideoDetect:
   
       jobId = ''
   
       roleArn = ''
       bucket = ''
       video = ''
       startJobId = ''
   
       sqsQueueUrl = ''
       snsTopicArn = ''
       processType = ''
   
       def __init__(self, role, bucket, video, client, rek, sqs, sns):
           self.roleArn = role
           self.bucket = bucket
           self.video = video
           self.client = client
           self.rek = rek
           self.sqs = sqs
           self.sns = sns
   
       def GetSQSMessageSuccess(self):
   
           jobFound = False
           succeeded = False
   
           dotLine = 0
           while jobFound == False:
               sqsResponse = self.sqs.receive_message(QueueUrl=self.sqsQueueUrl, MessageAttributeNames=['ALL'],
                                                      MaxNumberOfMessages=10)
   
               if sqsResponse:
   
                   if 'Messages' not in sqsResponse:
                       if dotLine < 40:
                           print('.', end='')
                           dotLine = dotLine + 1
                       else:
                           print()
                           dotLine = 0
                       sys.stdout.flush()
                       time.sleep(5)
                       continue
   
                   for message in sqsResponse['Messages']:
                       notification = json.loads(message['Body'])
                       rekMessage = json.loads(notification['Message'])
                       print(rekMessage['JobId'])
                       print(rekMessage['Status'])
                       if rekMessage['JobId'] == self.startJobId:
                           print('Matching Job Found:' + rekMessage['JobId'])
                           jobFound = True
                           if (rekMessage['Status'] == 'SUCCEEDED'):
                               succeeded = True
   
                           self.sqs.delete_message(QueueUrl=self.sqsQueueUrl,
                                                   ReceiptHandle=message['ReceiptHandle'])
                       else:
                           print("Job didn't match:" +
                                 str(rekMessage['JobId']) + ' : ' + self.startJobId)
                       # Delete the unknown message. Consider sending to dead letter queue
                       self.sqs.delete_message(QueueUrl=self.sqsQueueUrl,
                                               ReceiptHandle=message['ReceiptHandle'])
   
           return succeeded
   
       def StartLabelDetection(self):
           response = self.rek.start_label_detection(Video={'S3Object': {'Bucket': self.bucket, 'Name': self.video}},
                                                     NotificationChannel={'RoleArn': self.roleArn,
                                                                          'SNSTopicArn': self.snsTopicArn},
                                                     MinConfidence=90,
                                                     # Filtration options, uncomment and add desired labels to filter returned labels
                                                     # Features=['GENERAL_LABELS'],
                                                     # Settings={
                                                     # 'GeneralLabels': {
                                                     # 'LabelInclusionFilters': ['Clothing']
                                                     # }}
                                                      )
   
           self.startJobId = response['JobId']
           print('Start Job Id: ' + self.startJobId)
   
       def GetLabelDetectionResults(self):
           maxResults = 10
           paginationToken = ''
           finished = False
   
           while finished == False:
               response = self.rek.get_label_detection(JobId=self.startJobId,
                                                       MaxResults=maxResults,
                                                       NextToken=paginationToken,
                                                       SortBy='TIMESTAMP',
                                                       AggregateBy="TIMESTAMPS")
   
               print('Codec: ' + response['VideoMetadata']['Codec'])
               print('Duration: ' + str(response['VideoMetadata']['DurationMillis']))
               print('Format: ' + response['VideoMetadata']['Format'])
               print('Frame rate: ' + str(response['VideoMetadata']['FrameRate']))
               print()
   
               for labelDetection in response['Labels']:
                   label = labelDetection['Label']
   
                   print("Timestamp: " + str(labelDetection['Timestamp']))
                   print("   Label: " + label['Name'])
                   print("   Confidence: " + str(label['Confidence']))
                   print("   Instances:")
                   for instance in label['Instances']:
                       print("      Confidence: " + str(instance['Confidence']))
                       print("      Bounding box")
                       print("        Top: " + str(instance['BoundingBox']['Top']))
                       print("        Left: " + str(instance['BoundingBox']['Left']))
                       print("        Width: " + str(instance['BoundingBox']['Width']))
                       print("        Height: " + str(instance['BoundingBox']['Height']))
                       print()
                   print()
   
                   print("Parents:")
                   for parent in label['Parents']:
                       print("   " + parent['Name'])
   
                   print("Aliases:")
                   for alias in label['Aliases']:
                       print("   " + alias['Name'])
   
                   print("Categories:")
                   for category in label['Categories']:
                       print("   " + category['Name'])
                   print("----------")
                   print()
   
                   if 'NextToken' in response:
                       paginationToken = response['NextToken']
                   else:
                       finished = True
   
       def CreateTopicandQueue(self):
   
           millis = str(int(round(time.time() * 1000)))
   
           # Create SNS topic
   
           snsTopicName = "AmazonRekognitionExample" + millis
   
           topicResponse = self.sns.create_topic(Name=snsTopicName)
           self.snsTopicArn = topicResponse['TopicArn']
   
           # create SQS queue
           sqsQueueName = "AmazonRekognitionQueue" + millis
           self.sqs.create_queue(QueueName=sqsQueueName)
           self.sqsQueueUrl = self.sqs.get_queue_url(QueueName=sqsQueueName)['QueueUrl']
   
           attribs = self.sqs.get_queue_attributes(QueueUrl=self.sqsQueueUrl,
                                                   AttributeNames=['QueueArn'])['Attributes']
   
           sqsQueueArn = attribs['QueueArn']
   
           # Subscribe SQS queue to SNS topic
           self.sns.subscribe(
               TopicArn=self.snsTopicArn,
               Protocol='sqs',
               Endpoint=sqsQueueArn)
   
           # Authorize SNS to write SQS queue 
           # Insert the policy from the documentation from 
           policy = """
           {{
              "Version":"2012-10-17",              
              "Statement":[
               {{
                   "Sid":"MyPolicy",
                   "Effect":"Allow",
                   "Principal" : {{"AWS" : "*"}},
                   "Action":"SQS:SendMessage",
                   "Resource": "{}",
                   "Condition":{{
                       "ArnEquals":{{
                           "aws:SourceArn": "{}"
                        }}
                   }}
               }}
               ]
           }}""".format(sqsQueueArn, self.snsTopicArn)
   
           response = self.sqs.set_queue_attributes(
               QueueUrl=self.sqsQueueUrl,
               Attributes={
                   'Policy': policy
               })
   
       def DeleteTopicandQueue(self):
           self.sqs.delete_queue(QueueUrl=self.sqsQueueUrl)
           self.sns.delete_topic(TopicArn=self.snsTopicArn)
   
   def main():
       
       roleArn = 'role-arn'
       bucket = 'bucket-name'
       video = 'video-name'
   
       session = boto3.Session(profile_name='profile-name')
       client = session.client('rekognition')
       rek = boto3.client('rekognition')
       sqs = boto3.client('sqs')
       sns = boto3.client('sns')
   
       analyzer = VideoDetect(roleArn, bucket, video, client, rek, sqs, sns)
       analyzer.CreateTopicandQueue()
   
       analyzer.StartLabelDetection()
       if analyzer.GetSQSMessageSuccess() == True:
           analyzer.GetLabelDetectionResults()
   
       analyzer.DeleteTopicandQueue()
   
   if __name__ == "__main__":
       main()
   ```

------
#### [ Node.Js ]

   Dans l’exemple de code suivant :
   + Remplacez la valeur de `REGION` par le nom de la région d’exploitation de votre compte.
   + Remplacez la valeur de `amzn-s3-demo-bucket` par le nom du compartiment Amazon S3 qui contient votre fichier vidéo.
   + Remplacez la valeur de `videoName` par le nom du fichier vidéo dans votre compartiment Amazon S3.
   +  Remplacez la valeur de `profile_name` dans la ligne qui crée la session de Rekognition par le nom de votre profil de développeur. 
   + Remplacez `roleArn` par l’ARN de la fonction du service IAM que vous avez créé au cours de l’étape 7 de [Pour configurer Vidéo Amazon Rekognition](api-video-roles.md#configure-rekvid-procedure).

   ```
   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 } 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"
   const profileName = "profile-name"
   // 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",&TCX5-2025-waiver;                
         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 startLabelDetection = async (roleArn, snsTopicArn) => {
     try {
       //Initiate label detection and update value of startJobId with returned Job ID
      const labelDetectionResponse = await rekClient.send(new StartLabelDetectionCommand({Video:{S3Object:{Bucket:bucket, Name:videoName}}, 
         NotificationChannel:{RoleArn: roleArn, SNSTopicArn: snsTopicArn}}));
         startJobId = labelDetectionResponse.JobId
         console.log(`JobID: ${startJobId}`)
         return startJobId
     } catch (err) {
       console.log("Error", err);
     }
   };
   
   const getLabelDetectionResults = async(startJobId) => {
     console.log("Retrieving Label Detection results")
     // Set max results, paginationToken and finished will be updated depending on response values
     var maxResults = 10
     var paginationToken = ''
     var finished = false
   
     // Begin retrieving label detection results
     while (finished == false){
       var response = await rekClient.send(new GetLabelDetectionCommand({JobId: startJobId, MaxResults: maxResults, 
         NextToken: paginationToken, SortBy:'TIMESTAMP'}))
         // Log metadata
         console.log(`Codec: ${response.VideoMetadata.Codec}`)
         console.log(`Duration: ${response.VideoMetadata.DurationMillis}`)
         console.log(`Format: ${response.VideoMetadata.Format}`)
         console.log(`Frame Rate: ${response.VideoMetadata.FrameRate}`)
         console.log()
         // For every detected label, log label, confidence, bounding box, and timestamp
         response.Labels.forEach(labelDetection => {
           var label = labelDetection.Label
           console.log(`Timestamp: ${labelDetection.Timestamp}`)
           console.log(`Label: ${label.Name}`)
           console.log(`Confidence: ${label.Confidence}`)
           console.log("Instances:")
           label.Instances.forEach(instance =>{
             console.log(`Confidence: ${instance.Confidence}`)
             console.log("Bounding Box:")
             console.log(`Top: ${instance.Confidence}`)
             console.log(`Left: ${instance.Confidence}`)
             console.log(`Width: ${instance.Confidence}`)
             console.log(`Height: ${instance.Confidence}`)
             console.log()
           })
         console.log()
         // Log parent if found
         console.log("   Parents:")
         label.Parents.forEach(parent =>{
           console.log(`    ${parent.Name}`)
         })
         console.log()
         // Searh for pagination token, if found, set variable to next token
         if (String(response).includes("NextToken")){
           paginationToken = response.NextToken
   
         }else{
           finished = true
         }
   
         })
     }
   }
   
   // 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 runLabelDetectionAndGetResults = async () => {
     try {
       const sqsAndTopic = await createTopicandQueue();
       const startLabelDetectionRes = await startLabelDetection(roleArn, sqsAndTopic[1]);
       const getSQSMessageStatus = await getSQSMessageSuccess(sqsAndTopic[0], startLabelDetectionRes)
       console.log(getSQSMessageSuccess)
       if (getSQSMessageSuccess){
         console.log("Retrieving results:")
         const results = await getLabelDetectionResults(startLabelDetectionRes)
       }
       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);
     }
   };
   
   runLabelDetectionAndGetResults()
   ```

------
#### [ Java V2 ]

   Ce code est extrait du GitHub référentiel d'exemples du SDK de AWS documentation. Voir l’exemple complet [ici](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/VideoDetect.java).

   ```
   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.auth.credentials.ProfileCredentialsProvider;
   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;
   //snippet-end:[rekognition.java2.recognize_video_detect.import]
   
   /**
   * 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 = "\n" +
            "Usage: " +
            "   <bucket> <video> <queueUrl> <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 the video (for example, people.mp4). \n\n" +
            "   queueUrl- The URL of a SQS queue. \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 != 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_WEST_2;
        RekognitionClient rekClient = RekognitionClient.builder()
            .region(region)
            .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
            .build();
   
        SqsClient sqs = SqsClient.builder()
            .region(Region.US_WEST_2)
            .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
            .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();
    }
   
    // snippet-start:[rekognition.java2.recognize_video_detect.main]
    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);
        }
    }
    // snippet-end:[rekognition.java2.recognize_video_detect.main]
   }
   ```

------

1. Créez et exécutez le code. L’opération peut prendre un certain temps pour s’exécuter. Lorsqu’elle est terminée, une liste des étiquettes détectées dans la vidéo s’affiche. Pour de plus amples informations, veuillez consulter [Détection des étiquettes dans une vidéo](labels-detecting-labels-video.md).