Detecting faces in a stored video

To detect faces 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.)
   
   
   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);
         
           
   }

   In the function main, replace the lines:

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

   with:

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

   Java V2

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

       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 beard = face.face().beard().toString();
                       String eyeglasses = face.face().eyeglasses().toString();
                       String eyesOpen = face.face().eyesOpen().toString();
                       String mustache = face.face().mustache().toString();
                       String smile = face.face().smile().toString();
                   }
   
               } while (faceDetectionResponse !=null && faceDetectionResponse.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.)
   
       # ============== 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
   

   In the function main, replace the lines:

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

   with:

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

   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 function name to replace is different.

3. Run the code. Information about the faces that were detected in the video is shown.