Analisar um vídeo armazenado em um bucket do Amazon S3 com Java ou Python (SDK) - Amazon Rekognition

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Analisar um vídeo armazenado em um bucket do Amazon S3 com Java ou Python (SDK)

Este procedimento mostra como detectar rótulos em um vídeo usando as operações de detecção de rótulo do Amazon Rekognition Video, um vídeo armazenado em um bucket do Amazon S3 e um tópico do Amazon SNS. O procedimento também mostra como usar uma fila do Amazon SQS para obter o status de conclusão do tópico do Amazon SNS. Para obter mais informações, consulte Chamando as operações de Amazon Rekognition Video. Você não está restrito a usar uma fila do Amazon SQS. Por exemplo, você pode usar uma função do AWS Lambda para obter o status de conclusão. Para obter mais informações, consulte Chamar funções do Lambda usando notificações do Amazon SNS.

O código de exemplo no procedimento mostra como fazer o seguinte:

  1. Crie o tópico do Amazon SNS.

  2. Crie a fila do Amazon SQS.

  3. Conceder permissão ao Amazon Rekognition Video para publicar o status de conclusão de uma operação de análise de vídeo no tópico do Amazon SNS.

  4. Assine a fila do Amazon SQS no tópico do Amazon SNS.

  5. Iniciar uma solicitação de análise de vídeo chamando StartLabelDetection.

  6. Obter o status de conclusão a partir da fila do Amazon SQS. O exemplo rastreia o identificador do trabalho (JobId) que é retornado em StartLabelDetection e somente obtém os resultados para identificadores de trabalho correspondentes que são lidos a partir do status de conclusão. Essa é uma consideração importante se outros aplicativos estiverem usando a mesma fila e tópico. Para simplificar, o exemplo exclui trabalhos não correspondentes. Considere adicioná-los a uma fila dead-letter do Amazon SQS para mais investigações.

  7. Obter e exibir os resultados da análise de vídeo chamando GetLabelDetection.

Pré-requisitos

O código de exemplo para este procedimento é fornecido em Java e Python. É necessário ter o AWS SDK apropriado instalado. Para obter mais informações, consulte Conceitos básicos do Amazon Rekognition. A conta da AWS que você usa deve ter permissões de acesso à API do Amazon Rekognition. Para obter mais informações, consulteAções definidas pelo Amazon Rekognition.

Para detectar rótulos em um vídeo

  1. Configure o acesso do usuário ao Amazon Rekognition Video e configure o acesso ao Amazon Rekognition Video ao Amazon SNS. Para obter mais informações, consulte Configurar o Amazon Rekognition Video. Não é necessário executar as etapas 3, 4, 5 e 6 porque o código de exemplo cria e configura o tópico do Amazon SNS e a fila do Amazon SQS.

  2. Faça upload de um arquivo de vídeo no formato MOV ou MPEG-4 em um bucket do Amazon S3. Para fins de teste, faça upload de um vídeo com até 30 segundos de duração.

    Para obter instruções, consulteUpload de objetos no Amazon S3noGuia do usuário do Amazon Simple Storage Service.

  3. Use os exemplos de código a seguir para detectar rótulos em um vídeo.

    Java

    Na função main:

    • SubstituirroleArnCom o ARN da função de serviço do IAM criada na etapa 7 dePara configurar o Amazon Rekognition Video.

    • Substitua os valores de bucket e video pelo nome do bucket e do arquivo de vídeo especificados por você na etapa 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

    Na função main:

    • SubstituirroleArnCom o ARN da função de serviço do IAM criada na etapa 7 dePara configurar o Amazon Rekognition Video.

    • Substitua os valores de bucket e video pelo nome do bucket e do arquivo de vídeo especificados por você na etapa 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.) import boto3 import json import sys import time class VideoDetect: jobId = '' rek = boto3.client('rekognition') sqs = boto3.client('sqs') sns = boto3.client('sns') roleArn = '' bucket = '' video = '' startJobId = '' sqsQueueUrl = '' snsTopicArn = '' processType = '' def __init__(self, role, bucket, video): self.roleArn = role self.bucket = bucket self.video = video 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}) 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') 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 () 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 = '' bucket = '' video = '' analyzer=VideoDetect(roleArn, bucket,video) analyzer.CreateTopicandQueue() analyzer.StartLabelDetection() if analyzer.GetSQSMessageSuccess()==True: analyzer.GetLabelDetectionResults() analyzer.DeleteTopicandQueue() if __name__ == "__main__": main()
    Node.Js

    No seguinte código de exemplo:

    • Substitua o valor deREGIONcom o nome da região operacional da sua conta.

    • Substitua o valor debucketCom o nome do Amazon S3 bucket que contém o arquivo de vídeo.

    • Substitua o valor devideoNameCom o nome do arquivo de vídeo em seu bucket do Amazon S3.

    • SubstituirroleArnCom o ARN da função de serviço do IAM criada na etapa 7 dePara configurar o Amazon Rekognition Video.

    // snippet-start:[sqs.JavaScript.queues.createQueueV3] // Import required AWS SDK clients and commands for Node.js import { CreateQueueCommand, GetQueueAttributesCommand, GetQueueUrlCommand, SetQueueAttributesCommand, DeleteQueueCommand, ReceiveMessageCommand, DeleteMessageCommand } from "@aws-sdk/client-sqs"; import {CreateTopicCommand, SubscribeCommand, DeleteTopicCommand } from "@aws-sdk/client-sns"; import { SQSClient } from "@aws-sdk/client-sqs"; import { SNSClient } from "@aws-sdk/client-sns"; import { RekognitionClient, StartLabelDetectionCommand, GetLabelDetectionCommand } from "@aws-sdk/client-rekognition"; import { stdout } from "process"; // Set the AWS Region. const REGION = "region-name"; //e.g. "us-east-1" // Create SNS service object. const sqsClient = new SQSClient({ region: REGION }); const snsClient = new SNSClient({ region: REGION }); const rekClient = new RekognitionClient({ region: REGION }); // 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

    Este código é retirado daAWSExemplos de SDK do repositório GitHub. Veja o exemplo completoaqui.

    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=null; 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(); } catch (Exception 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: " + Long.toString(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); } }
  4. Compile e execute o código. A operação pode demorar um pouco para ser concluída. Depois de ser concluída, uma lista de rótulos detectados no vídeo é exibida. Para obter mais informações, consulte Detecção de rótulos em um vídeo.