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Analyse d'une vidéo stockée dans un compartiment Amazon S3 avec Java ou Python (SDK)
Cette procédure explique comment détecter des étiquettes dans une vidéo à l'aide des opérations de détection d'étiquettes Amazon Rekognition Video, d'une vidéo stockée dans un compartiment Amazon S3 et d'une rubrique Amazon. SNS La procédure explique également comment utiliser une SQS file d'attente Amazon pour obtenir le statut d'achèvement à partir de la SNS rubrique Amazon. Pour de plus amples informations, veuillez consulter Appeler les opérations de Vidéo Amazon Rekognition. Vous n'êtes pas limité à l'utilisation d'une SQS file d'attente Amazon. Par exemple, vous pouvez utiliser une AWS Lambda fonction pour obtenir le statut d'achèvement. Pour plus d'informations, consultez Invoquer des fonctions Lambda à l'aide des notifications Amazon SNS.
L’exemple de code de cette procédure montre comment effectuer les opérations suivantes :
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Créez le SNS sujet Amazon.
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Créez la SQS file d'attente Amazon.
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Autorisez Amazon Rekognition Video à publier l'état d'achèvement d'une opération d'analyse vidéo dans la rubrique Amazon. SNS
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Abonnez la SQS file d'attente Amazon à la SNS rubrique Amazon.
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Lancez la demande d'analyse vidéo en appelant StartLabelDetection.
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Obtenez le statut d'achèvement dans la SQS file d'attente Amazon. L’exemple suit l’identifiant de tâche (
JobId) renvoyé dansStartLabelDetectionet 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 SQS lettres mortes Amazon pour une enquête plus approfondie. -
Obtenez et affichez les résultats de l'analyse vidéo en appelant GetLabelDetection.
Prérequis
L’exemple de code pour cette procédure est fourni en Java et Python. Vous devez avoir AWS SDK installé le système approprié. Pour de plus amples informations, veuillez consulter Premiers pas avec Amazon Rekognition. Le AWS compte que vous utilisez doit disposer des autorisations d'accès à Amazon Rekognition. API Pour plus d’informations, consultez Actions définies par Amazon Rekognition.
Pour détecter des étiquettes dans une vidéo
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Configurez l'accès des utilisateurs à Amazon Rekognition Video et configurez l'accès d'Amazon Rekognition Video à Amazon. SNS Pour de plus amples informations, veuillez consulter Configuration de Vidéo Amazon Rekognition. Vous n'avez pas besoin de suivre les étapes 3, 4, 5 et 6 car l'exemple de code crée et configure la SNS rubrique Amazon et la SQS file d'attente Amazon.
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Chargez un fichier vidéo au format MPEG -4 MOV ou au format -4 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 dans le Guide de l’utilisateur Amazon Simple Storage Service.
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Utilisez les exemples de code suivants pour détecter des étiquettes dans une vidéo.
- Java
-
Dans la fonction
main:-
roleArnRemplacez-le par le rôle ARN de IAM service que vous avez créé à l'étape 7 dePour configurer Vidéo Amazon Rekognition. -
Remplacez les valeurs de
bucketetvideopar 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:-
roleArnRemplacez-le par le rôle ARN de IAM service que vous avez créé à l'étape 7 dePour configurer Vidéo Amazon Rekognition. -
Remplacez les valeurs de
bucketetvideopar le nom du compartiment et le nom du fichier vidéo que vous avez spécifiés à l’étape 2. -
Remplacez la valeur de
profile_namedans 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
LabelsInclusionFilterou unLabelsExclusionFilterà côté d’une liste de valeurs souhaitées. Dans le code ci-dessous, vous pouvez supprimer les commentaires de la sectionFeaturesetSettingset 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 argumentsSortByetAggregateBy. Pour trier en fonction de l’heure, définissez la valeur du paramètre d’entréeSortBysurTIMESTAMP. Pour trier en fonction de l’entité, utilisez le paramètre d’entréeSortByavec la valeur appropriée pour l’opération que vous exécutez. Pour agréger les résultats par horodatage, définissez la valeur du paramètreAggregateBysurTIMESTAMPS. Pour agréger par segment vidéo, utilisezSEGMENTS.
## 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 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
REGIONpar le nom de la région d’exploitation de votre compte. -
Remplacez la valeur de
bucketpar le nom du compartiment Amazon S3 qui contient votre fichier vidéo. -
Remplacez la valeur de
videoNamepar le nom du fichier vidéo dans votre compartiment Amazon S3. -
Remplacez la valeur de
profile_namedans la ligne qui crée la session de Rekognition par le nom de votre profil de développeur. -
roleArnRemplacez-le par le rôle ARN de IAM service que vous avez créé à l'étape 7 dePour configurer Vidéo Amazon Rekognition.
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", 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
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Ce code est extrait du GitHub référentiel d'SDKexemples de AWS documentation. Voir l’exemple complet ici
. 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, myBucket). \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] }
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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.
Configuration de Vidéo Amazon Rekognition
Analyse d’une vidéo (AWS CLI)