Recognizing celebrities in a stored video - Amazon Rekognition

Recognizing celebrities in a stored video

Amazon Rekognition Video celebrity recognition in stored videos is an asynchronous operation. To recognize celebrities in a stored video, use StartCelebrityRecognition to start video analysis. Amazon Rekognition Video publishes the completion status of the video analysis to an Amazon Simple Notification Service topic. If the video analysis is succesful, call GetCelebrityRecognition. to get the analysis results. For more information about starting video analysis and getting the results, see Calling Amazon Rekognition Video operations.

This procedure expands on the code in Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK), which uses an Amazon SQS queue to get the completion status of a video analysis request. To run this procedure, you need a video file that contains one or more celebrity faces.

To detect celebrities in a video stored in an Amazon S3 bucket (SDK)

  1. Perform Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK).

  2. Add the following code to the class VideoDetect that you created in step 1.

    Java
    //Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) // Celebrities===================================================================== private static void StartCelebrityDetection(String bucket, String video) throws Exception{ NotificationChannel channel= new NotificationChannel() .withSNSTopicArn(snsTopicArn) .withRoleArn(roleArn); StartCelebrityRecognitionRequest req = new StartCelebrityRecognitionRequest() .withVideo(new Video() .withS3Object(new S3Object() .withBucket(bucket) .withName(video))) .withNotificationChannel(channel); StartCelebrityRecognitionResult startCelebrityRecognitionResult = rek.startCelebrityRecognition(req); startJobId=startCelebrityRecognitionResult.getJobId(); } private static void GetCelebrityDetectionResults() throws Exception{ int maxResults=10; String paginationToken=null; GetCelebrityRecognitionResult celebrityRecognitionResult=null; do{ if (celebrityRecognitionResult !=null){ paginationToken = celebrityRecognitionResult.getNextToken(); } celebrityRecognitionResult = rek.getCelebrityRecognition(new GetCelebrityRecognitionRequest() .withJobId(startJobId) .withNextToken(paginationToken) .withSortBy(CelebrityRecognitionSortBy.TIMESTAMP) .withMaxResults(maxResults)); System.out.println("File info for page"); VideoMetadata videoMetaData=celebrityRecognitionResult.getVideoMetadata(); System.out.println("Format: " + videoMetaData.getFormat()); System.out.println("Codec: " + videoMetaData.getCodec()); System.out.println("Duration: " + videoMetaData.getDurationMillis()); System.out.println("FrameRate: " + videoMetaData.getFrameRate()); System.out.println("Job"); System.out.println("Job status: " + celebrityRecognitionResult.getJobStatus()); //Show celebrities List<CelebrityRecognition> celebs= celebrityRecognitionResult.getCelebrities(); for (CelebrityRecognition celeb: celebs) { long seconds=celeb.getTimestamp()/1000; System.out.print("Sec: " + Long.toString(seconds) + " "); CelebrityDetail details=celeb.getCelebrity(); System.out.println("Name: " + details.getName()); System.out.println("Id: " + details.getId()); System.out.println(); } } while (celebrityRecognitionResult !=null && celebrityRecognitionResult.getNextToken() != null); }

    In the function main, replace the line:

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

    with:

    StartCelebrityDetection(bucket, video); if (GetSQSMessageSuccess()==true) GetCelebrityDetectionResults();
    Java V2

    This code is taken from the AWS Documentation SDK examples GitHub repository. See the full example here.

    public static void StartCelebrityDetection(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(); StartCelebrityRecognitionRequest recognitionRequest = StartCelebrityRecognitionRequest.builder() .jobTag("Celebrities") .notificationChannel(channel) .video(vidOb) .build(); StartCelebrityRecognitionResponse startCelebrityRecognitionResult = rekClient.startCelebrityRecognition(recognitionRequest); startJobId = startCelebrityRecognitionResult.jobId(); } catch(RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } public static void GetCelebrityDetectionResults(RekognitionClient rekClient) { try { String paginationToken=null; GetCelebrityRecognitionResponse recognitionResponse = null; Boolean finished = false; String status=""; int yy=0 ; do{ if (recognitionResponse !=null) paginationToken = recognitionResponse.nextToken(); GetCelebrityRecognitionRequest recognitionRequest = GetCelebrityRecognitionRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .sortBy(CelebrityRecognitionSortBy.TIMESTAMP) .maxResults(10) .build(); // Wait until the job succeeds while (!finished) { recognitionResponse = rekClient.getCelebrityRecognition(recognitionRequest); status = recognitionResponse.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) finished = true; else { System.out.println(yy + " status is: " + status); Thread.sleep(1000); } yy++; } finished = false; // Proceed when the job is done - otherwise VideoMetadata is null VideoMetadata videoMetaData=recognitionResponse.videoMetadata(); System.out.println("Format: " + videoMetaData.format()); System.out.println("Codec: " + videoMetaData.codec()); System.out.println("Duration: " + videoMetaData.durationMillis()); System.out.println("FrameRate: " + videoMetaData.frameRate()); System.out.println("Job"); List<CelebrityRecognition> celebs= recognitionResponse.celebrities(); for (CelebrityRecognition celeb: celebs) { long seconds=celeb.timestamp()/1000; System.out.print("Sec: " + Long.toString(seconds) + " "); CelebrityDetail details=celeb.celebrity(); System.out.println("Name: " + details.name()); System.out.println("Id: " + details.id()); System.out.println(); } } while (recognitionResponse !=null && recognitionResponse.nextToken() != null); } catch(RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } }
    Python
    #Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. #PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) # ============== Celebrities =============== def StartCelebrityDetection(self): response=self.rek.start_celebrity_recognition(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 GetCelebrityDetectionResults(self): maxResults = 10 paginationToken = '' finished = False while finished == False: response = self.rek.get_celebrity_recognition(JobId=self.startJobId, MaxResults=maxResults, NextToken=paginationToken) print(response['VideoMetadata']['Codec']) print(str(response['VideoMetadata']['DurationMillis'])) print(response['VideoMetadata']['Format']) print(response['VideoMetadata']['FrameRate']) for celebrityRecognition in response['Celebrities']: print('Celebrity: ' + str(celebrityRecognition['Celebrity']['Name'])) print('Timestamp: ' + str(celebrityRecognition['Timestamp'])) print() if 'NextToken' in response: paginationToken = response['NextToken'] else: finished = True

    In the function main, replace the lines:

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

    with:

    analyzer.StartCelebrityDetection() if analyzer.GetSQSMessageSuccess()==True: analyzer.GetCelebrityDetectionResults()
    Node.JS

    In the following Node.Js code example, replace the value of bucket with the name of the S3 bucket containing your video and the value of videoName with the name of the video file. You'll also need to replace the value of roleArn with the Arn associated with your IAM service role. Finally, replace the value of region with the name of the operating region associated with your account.

    // 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, StartCelebrityRecognitionCommand, GetCelebrityRecognitionCommand} 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 = "RoleArn" 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 startCelebrityDetection = async(roleArn, snsTopicArn) =>{ try { //Initiate label detection and update value of startJobId with returned Job ID const response = await rekClient.send(new StartCelebrityRecognitionCommand({Video:{S3Object:{Bucket:bucket, Name:videoName}}, NotificationChannel:{RoleArn: roleArn, SNSTopicArn: snsTopicArn}})) startJobId = response.JobId console.log(`Start Job ID: ${startJobId}`) return startJobId } catch (err) { console.log("Error", err); } }; const getCelebrityRecognitionResults = async(startJobId) =>{ try { //Initiate label detection and update value of startJobId with returned Job ID var maxResults = 10 var paginationToken = '' var finished = false while (finished == false){ var response = await rekClient.send(new GetCelebrityRecognitionCommand({JobId: startJobId, MaxResults: maxResults, NextToken: paginationToken})) console.log(response.VideoMetadata.Codec) console.log(response.VideoMetadata.DurationMillis) console.log(response.VideoMetadata.Format) console.log(response.VideoMetadata.FrameRate) response.Celebrities.forEach(celebrityRecognition => { console.log(`Celebrity: ${celebrityRecognition.Celebrity.Name}`) console.log(`Timestamp: ${celebrityRecognition.Timestamp}`) console.log() }) // Searh for pagination token, if found, set variable to next token if (String(response).includes("NextToken")){ paginationToken = response.NextToken }else{ finished = true } } } catch (err) { console.log("Error", err); } }; // 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 runCelebRecognitionAndGetResults = async () => { try { const sqsAndTopic = await createTopicandQueue(); //const startLabelDetectionRes = await startLabelDetection(roleArn, sqsAndTopic[1]); //const getSQSMessageStatus = await getSQSMessageSuccess(sqsAndTopic[0], startLabelDetectionRes) const startCelebrityDetectionRes = await startCelebrityDetection(roleArn, sqsAndTopic[1]); const getSQSMessageStatus = await getSQSMessageSuccess(sqsAndTopic[0], startCelebrityDetectionRes) console.log(getSQSMessageSuccess) if (getSQSMessageSuccess){ console.log("Retrieving results:") const results = await getCelebrityRecognitionResults(startCelebrityDetectionRes) } 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); } }; runCelebRecognitionAndGetResults()
    Note

    If you've already run a video example other than Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK), the code to replace might be different.

  3. Run the code. Information about the celebrities recognized in the video is shown.

GetCelebrityRecognition operation response

The following is an example JSON response. The response includes the following:

  • Recognized celebritiesCelebrities is an array of celebrities and the times that they are recognized in a video. A CelebrityRecognition object exists for each time the celebrity is recognized in the video. Each CelebrityRecognition contains information about a recognized celebrity (CelebrityDetail) and the time (Timestamp) the celebrity was recognized in the video. Timestamp is measured in milliseconds from the start of the video.

  • CelebrityDetail – Contains information about a recognized celebrity. It includes the celebrity name (Name), identifier (ID), the celebrity's known gender(KnownGender), and a list of URLs pointing to related content (Urls). It also includes the confidence level that Amazon Rekognition Video has in the accuracy of the recognition, and details about the celebrity's face, FaceDetail. If you need to get the related content later, you can use ID with getCelebrityInfo.

  • VideoMetadata – Information about the video that was analyzed.

{ "Celebrities": [ { "Celebrity": { "Confidence": 0.699999988079071, "Face": { "BoundingBox": { "Height": 0.20555555820465088, "Left": 0.029374999925494194, "Top": 0.22333332896232605, "Width": 0.11562500149011612 }, "Confidence": 99.89837646484375, "Landmarks": [ { "Type": "eyeLeft", "X": 0.06857934594154358, "Y": 0.30842265486717224 }, { "Type": "eyeRight", "X": 0.10396526008844376, "Y": 0.300625205039978 }, { "Type": "nose", "X": 0.0966852456331253, "Y": 0.34081998467445374 }, { "Type": "mouthLeft", "X": 0.075217105448246, "Y": 0.3811396062374115 }, { "Type": "mouthRight", "X": 0.10744428634643555, "Y": 0.37407416105270386 } ], "Pose": { "Pitch": -0.9784082174301147, "Roll": -8.808176040649414, "Yaw": 20.28228759765625 }, "Quality": { "Brightness": 43.312068939208984, "Sharpness": 99.9305191040039 } }, "Id": "XXXXXX", "KnownGender": { "Type": "Female" }, "Name": "Celeb A", "Urls": [] }, "Timestamp": 367 },...... ], "JobStatus": "SUCCEEDED", "NextToken": "XfXnZKiyMOGDhzBzYUhS5puM+g1IgezqFeYpv/H/+5noP/LmM57FitUAwSQ5D6G4AB/PNwolrw==", "VideoMetadata": { "Codec": "h264", "DurationMillis": 67301, "FileExtension": "mp4", "Format": "QuickTime / MOV", "FrameHeight": 1080, "FrameRate": 29.970029830932617, "FrameWidth": 1920 } }