Mendeteksi wajah dalam video yang tersimpan

Untuk mendeteksi wajah dalam video yang disimpan di bucket Amazon S3 (SDK)

1. Lakukan Menganalisis video yang disimpan dalam bucket Amazon S3 dengan Java atau Python () SDK.

2. Tambahkan kode berikut ke kelas VideoDetect yang Anda buat di langkah 1.

   AWS CLI

   • Dalam contoh kode berikut, ubah bucket-name dan video-name ke nama bucket Amazon S3 dan nama file yang Anda tentukan di langkah 2.

   • Ubah region-name ke wilayah AWS yang Anda gunakan. Ganti nilai profile_name dengan nama profil pengembang Anda.

   • Ubah TopicARN ke ARN dari topik Amazon SNS yang Anda buat pada langkah 3 Mengonfigurasi Amazon Rekognition Video.

   • Perubahan RoleARN ke ARN dari peran layanan IAM yang Anda buat di langkah 7 Mengonfigurasi Amazon Rekognition Video.

   
   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
                                

   Jika Anda mengakses CLI pada perangkat Windows, gunakan tanda kutip ganda alih-alih tanda kutip tunggal dan hindari tanda kutip ganda bagian dalam dengan garis miring terbalik (yaitu\) untuk mengatasi kesalahan parser yang mungkin Anda temui. Sebagai contoh, lihat berikut ini:

   
   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
                                

   Setelah menjalankan StartFaceDetection operasi dan mendapatkan nomor ID pekerjaan, jalankan GetFaceDetection operasi berikut dan berikan nomor ID pekerjaan:

   
   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);
         
           
   }

   Dalam fungsi main, ganti baris:

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

   dengan:

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

   Java V2

   Kode ini diambil dari GitHub repositori contoh SDK AWS Dokumentasi. Lihat contoh lengkapnya di sini.

   //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
   

   Dalam fungsi main, ganti baris:

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

   dengan:

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

   catatan

   Jika Anda sudah menjalankan contoh video selain Menganalisis video yang disimpan dalam bucket Amazon S3 dengan Java atau Python () SDK, nama fungsi yang akan diganti berbeda.

3. Jalankan kode tersebut. Informasi tentang wajah yang terdeteksi dalam video ditampilkan.