本文為英文版的機器翻譯版本,如內容有任何歧義或不一致之處,概以英文版為準。
使用 Java 或 Python(SDK)分析存儲在 Amazon S3 存儲桶中的視頻
此程序說明如何使用 Amazon Rekognition 影片標籤偵測操作、存放在 Amazon S3 儲存貯體的影片以及 Amazon 主題來偵測影片中的標籤。SNS該過程還顯示瞭如何使用 Amazon SQS 隊列從 Amazon SNS 主題獲取完成狀態。如需詳細資訊,請參閱呼叫 Amazon Rekognition Video 操作。您不僅限於使用 Amazon SQS 隊列。例如,您可以使用 AWS Lambda 函數來取得完成狀態。如需詳細資訊,請參閱使用 Amazon SNS 通知叫用 Lambda 函數。
此程序中的範例程式碼說明如何執行下列動作:
-
創建 Amazon SNS 主題。
-
創建 Amazon SQS 隊列。
-
授予 Amazon Rekognition Video 權限,以便將影片分析操作的完成狀態發佈到 Amazon 主題。SNS
-
訂閱 Amazon SQS 隊列 Amazon SNS 主題。
-
通過呼叫啟動視頻分析請求StartLabelDetection。
-
從 Amazon SQS 隊列獲取完成狀態。此範例追蹤在
StartLabelDetection
傳回的任務識別碼 (JobId
),在自完成狀態中讀取後,只會顯示相符的工作識別碼之結果。如果其他應用程式使用相同的佇列和主題,這便是項重要的考量條件。為了方便起見,此範例刪除不符合的任務。考慮將它們添加到 Amazon SQS 無效信件隊列中以進行進一步調查。 -
通過呼叫獲取並顯示視頻分析結果GetLabelDetection。
必要條件
此程序的範例程式碼以 Java 和 Python 提供。您需要 AWS SDK安裝適當的。如需詳細資訊,請參閱Amazon Rekognition 入門。您使用的AWS帳戶必須具有 Amazon Reko API gnition 的存取權限。如需詳細資訊,請參閱 Amazon Rekognition 定義的動作。
若要偵測影片中的標籤
-
設定使用者對 Amazon Rekognition Video 的存取權限,並設定 Amazon Rekognition Video 存取 Amazon。SNS如需詳細資訊,請參閱設定 Amazon Rekognition Video。您不需要執行步驟 3、4、5 和 6,因為範例程式碼會建立並設定 Amazon SNS 主題和 Amazon SQS 佇列。
-
將MOV或 MPEG -4 格式的視訊檔案上傳到 Amazon S3 儲存貯體。為達測試目的,請上傳長度不超過 30 秒的影片。
如需指示說明,請參閱《Amazon Simple Storage Service 使用者指南》中的上傳物件至 Amazon S3。
-
使用以下程式碼範例來偵測影片中的標籤。
- Java
-
在函數
main
中:-
取代
roleArn
為您在ARN的步驟 7 中建立的IAM服務角色若要設定 Amazon Rekognition Video。 -
以您在步驟 2 中指定的儲存貯體與影片檔名稱來取代
bucket
與video
的值。
//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
-
在函數
main
中:-
取代
roleArn
為您在ARN的步驟 7 中建立的IAM服務角色若要設定 Amazon Rekognition Video。 -
以您在步驟 2 中指定的儲存貯體與影片檔名稱來取代
bucket
與video
的值。 -
將建立 Rekognition 工作階段的行中
profile_name
值取代為您開發人員設定檔的名稱。 -
您還可以在設定參數中包括過濾條件。例如,您可以在所需值的清單之外,邊使用
LabelsInclusionFilter
或LabelsExclusionFilter
。在下面的程式碼中,您可以取消註釋Features
和Settings
部分,並提供自己的值,以將傳回的結果限制為只有您感興趣的標籤。 -
在呼叫
GetLabelDetection
中,您可以提供SortBy
和AggregateBy
引數的值。若要依時間排序,將SortBy
輸入參數值設為TIMESTAMP
。若要依據實體排序,使用SortBy
輸入參數搭配適合您要執行的操作值。若要依時間戳記彙總結果,請將AggregateBy
參數值設定為TIMESTAMPS
。若要依影片片段彙總,請使用SEGMENTS
。
## 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
-
請參閱以下範本程式碼:
-
將
REGION
的值取代為您帳戶營運地區的名稱。 -
以包含影片檔案的 Amazon S3 儲存貯體之名稱取代
bucket
的值。 -
在 Amazon S3 儲存貯體中,以您的 S3 儲存貯體名稱取代
videoName
的值。 -
將建立 Rekognition 工作階段的行中
profile_name
值取代為您開發人員設定檔的名稱。 -
取代
roleArn
為您在ARN的步驟 7 中建立的IAM服務角色若要設定 Amazon Rekognition Video。
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()
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- Java V2
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此代碼取自 AWS 文檔SDK示例 GitHub 存儲庫。請參閱此處
的完整範例。 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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建置並執行程式碼。此操作可能需要一些時間來完成。完成之後,將顯示在影片中偵測到的標籤清單。如需詳細資訊,請參閱偵測影片中的標籤。