저장된 비디오에서 얼굴 감지

Amazon S3 버킷에 저장된 비디오에서 얼굴을 감지하려면(SDK)

1. Java 또는 Python (SDK) 을 사용하여 Amazon S3 버킷에 저장된 비디오 분석을 수행합니다.

2. 1단계에서 만든 클래스 VideoDetect에 다음 코드를 추가합니다.

   AWS CLI

   • 다음 코드 샘플에서 bucket-name 및 video-name을 2단계에서 지정한 Amazon S3 버킷 이름과 파일 이름으로 변경합니다.

   • region-name을 사용 중인 AWS 리전으로 변경합니다. profile_name의 값을 개발자 프로필 이름으로 바꿉니다.

   • TopicARN을 Amazon Rekognition Video 구성의 3단계에서 생성한 Amazon SNS 주제의 ARN으로 변경합니다.

   • RoleARN을 Amazon Rekognition Video 구성의 7단계에서 생성한 IAM 서비스 역할의 ARN으로 변경합니다.

   
   aws rekognition start-face-detection --video "{"S3Object":{"Bucket":"Bucket-Name","Name":"Video-Name"}}" --notification-channel \
   "{"SNSTopicArn":"Topic-ARN","RoleArn":"Role-ARN"}" --region region-name --profile profile-name
                                

   Windows 디바이스에서 CLI에 액세스하는 경우 작은따옴표 대신 큰따옴표를 사용하고 내부 큰따옴표는 백슬래시(즉 \)로 이스케이프 처리하여 발생할 수 있는 구문 분석 오류를 해결합니다. 예를 들어 다음을 참조하세요.

   
   aws rekognition start-face-detection --video "{\"S3Object\":{\"Bucket\":\"Bucket-Name\",\"Name\":\"Video-Name\"}}" --notification-channel \
   "{\"SNSTopicArn\":\"Topic-ARN\",\"RoleArn\":\"Role-ARN\"}" --region region-name --profile profile-name
                                

   StartFaceDetection 작업을 실행하고 작업 ID 번호를 가져온 후 다음 GetFaceDetection 작업을 실행하고 작업 ID 번호를 제공합니다.

   
   aws rekognition get-face-detection --job-id job-id-number  --profile profile-name
                                

   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.)
   
   
   private static void StartFaceDetection(String bucket, String video) throws Exception{
            
       NotificationChannel channel= new NotificationChannel()
               .withSNSTopicArn(snsTopicArn)
               .withRoleArn(roleArn);
       
       StartFaceDetectionRequest req = new StartFaceDetectionRequest()
               .withVideo(new Video()
                       .withS3Object(new S3Object()
                           .withBucket(bucket)
                           .withName(video)))
               .withNotificationChannel(channel);
                           
                           
       
       StartFaceDetectionResult startLabelDetectionResult = rek.startFaceDetection(req);
       startJobId=startLabelDetectionResult.getJobId();
       
   } 
   
   private static void GetFaceDetectionResults() throws Exception{
       
       int maxResults=10;
       String paginationToken=null;
       GetFaceDetectionResult faceDetectionResult=null;
       
       do{
           if (faceDetectionResult !=null){
               paginationToken = faceDetectionResult.getNextToken();
           }
       
           faceDetectionResult = rek.getFaceDetection(new GetFaceDetectionRequest()
                .withJobId(startJobId)
                .withNextToken(paginationToken)
                .withMaxResults(maxResults));
       
           VideoMetadata videoMetaData=faceDetectionResult.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 faces, confidence and detection times
           List<FaceDetection> faces= faceDetectionResult.getFaces();
        
           for (FaceDetection face: faces) { 
               long seconds=face.getTimestamp()/1000;
               System.out.print("Sec: " + Long.toString(seconds) + " ");
               System.out.println(face.getFace().toString());
               System.out.println();           
           }
       } while (faceDetectionResult !=null && faceDetectionResult.getNextToken() != null);
         
           
   }

   main 함수에서 다음 줄을 바꿉니다.

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

   다음으로 바꿉니다.

           StartFaceDetection(bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetFaceDetectionResults();

   Java V2

   이 코드는 AWS 문서 SDK 예제 GitHub 저장소에서 가져온 것입니다. 전체 예제는 여기에서 확인하세요.

   //snippet-start:[rekognition.java2.recognize_video_faces.import]
   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.*;
   import java.util.List;
   //snippet-end:[rekognition.java2.recognize_video_faces.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 VideoDetectFaces {
   
    private static String startJobId ="";
    public static void main(String[] args) {
   
        final String usage = "\n" +
            "Usage: " +
            "   <bucket> <video> <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 video (for example, people.mp4). \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 != 4) {
            System.out.println(usage);
            System.exit(1);
        }
   
        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
   
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
            .region(region)
            .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
            .build();
   
        NotificationChannel channel = NotificationChannel.builder()
            .snsTopicArn(topicArn)
            .roleArn(roleArn)
            .build();
   
        StartFaceDetection(rekClient, channel, bucket, video);
        GetFaceResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }
   
    // snippet-start:[rekognition.java2.recognize_video_faces.main]
    public static void StartFaceDetection(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();
   
            StartFaceDetectionRequest  faceDetectionRequest = StartFaceDetectionRequest.builder()
                .jobTag("Faces")
                .faceAttributes(FaceAttributes.ALL)
                .notificationChannel(channel)
                .video(vidOb)
                .build();
   
            StartFaceDetectionResponse startLabelDetectionResult = rekClient.startFaceDetection(faceDetectionRequest);
            startJobId=startLabelDetectionResult.jobId();
   
        } catch(RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
   
    public static void GetFaceResults(RekognitionClient rekClient) {
   
        try {
            String paginationToken=null;
            GetFaceDetectionResponse faceDetectionResponse=null;
            boolean finished = false;
            String status;
            int yy=0 ;
   
            do{
                if (faceDetectionResponse !=null)
                    paginationToken = faceDetectionResponse.nextToken();
   
                GetFaceDetectionRequest recognitionRequest = GetFaceDetectionRequest.builder()
                    .jobId(startJobId)
                    .nextToken(paginationToken)
                    .maxResults(10)
                    .build();
   
                // Wait until the job succeeds
                while (!finished) {
   
                    faceDetectionResponse = rekClient.getFaceDetection(recognitionRequest);
                    status = faceDetectionResponse.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=faceDetectionResponse.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");
   
                // Show face information
                List<FaceDetection> faces= faceDetectionResponse.faces();
   
                for (FaceDetection face: faces) {
                    String age = face.face().ageRange().toString();
                    String smile = face.face().smile().toString();
                    System.out.println("The detected face is estimated to be"
                                + age + " years old.");
                    System.out.println("There is a smile : "+smile);
                }
   
            } while (faceDetectionResponse !=null && faceDetectionResponse.nextToken() != null);
   
        } catch(RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
    // snippet-end:[rekognition.java2.recognize_video_faces.main]
   }

   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.)
   
       # ============== Faces===============
       def StartFaceDetection(self):
           response=self.rek.start_face_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 GetFaceDetectionResults(self):
           maxResults = 10
           paginationToken = ''
           finished = False
   
           while finished == False:
               response = self.rek.get_face_detection(JobId=self.startJobId,
                                               MaxResults=maxResults,
                                               NextToken=paginationToken)
   
               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 faceDetection in response['Faces']:
                   print('Face: ' + str(faceDetection['Face']))
                   print('Confidence: ' + str(faceDetection['Face']['Confidence']))
                   print('Timestamp: ' + str(faceDetection['Timestamp']))
                   print()
   
               if 'NextToken' in response:
                   paginationToken = response['NextToken']
               else:
                   finished = True
   

   main 함수에서 다음 줄을 바꿉니다.

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

   다음으로 바꿉니다.

       analyzer.StartFaceDetection()
       if analyzer.GetSQSMessageSuccess()==True:
           analyzer.GetFaceDetectionResults()

   참고

   Java 또는 Python (SDK) 을 사용하여 Amazon S3 버킷에 저장된 비디오 분석 이외에 비디오 예제를 이미 실행한 경우, 바꿀 함수 이름이 다릅니다.

3. 코드를 실행합니다. 비디오에서 감지된 얼굴에 관한 정보가 표시됩니다.