Detecting faces in a stored video - Amazon Rekognition

Detecting faces in a stored video

Amazon Rekognition Video can detect faces in videos that are stored in an Amazon S3 bucket and provide information such as:

  • The time or times faces are detected in a video.

  • The location of faces in the video frame at the time they were detected.

  • Facial landmarks such as the position of the left eye.

Amazon Rekognition Video face detection in stored videos is an asynchronous operation. To start the detection of faces in videos, call StartFaceDetection. Amazon Rekognition Video publishes the completion status of the video analysis to an Amazon Simple Notification Service (Amazon SNS) topic. If the video analysis is successful, you can call GetFaceDetection to get the results of the video analysis. 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 Simple Queue Service (Amazon SQS) queue to get the completion status of a video analysis request.

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.

GetFaceDetection operation response

GetFaceDetection returns an array (Faces) that contains information about the faces detected in the video. An array element, FaceDetection, exists for each time a face is detected in the video. The array elements returned are sorted by time, in milliseconds since the start of the video.

The following example is a partial JSON response from GetFaceDetection. In the response, note the following:

  • Face information – The FaceDetection array element contains information about the detected face (FaceDetail) and the time that the face was detected in the video (Timestamp).

  • Paging information – The example shows one page of face detection information. You can specify how many person elements to return in the MaxResults input parameter for GetFaceDetection. If more results than MaxResults exist, GetFaceDetection returns a token (NextToken) that's used to get the next page of results. For more information, see Getting Amazon Rekognition Video analysis results.

  • Video information – The response includes information about the video format (VideoMetadata) in each page of information that's returned by GetFaceDetection.

{ "Faces": [ { "Face": { "BoundingBox": { "Height": 0.23000000417232513, "Left": 0.42500001192092896, "Top": 0.16333332657814026, "Width": 0.12937499582767487 }, "Confidence": 99.97504425048828, "Landmarks": [ { "Type": "eyeLeft", "X": 0.46415066719055176, "Y": 0.2572723925113678 }, { "Type": "eyeRight", "X": 0.5068183541297913, "Y": 0.23705792427062988 }, { "Type": "nose", "X": 0.49765899777412415, "Y": 0.28383663296699524 }, { "Type": "mouthLeft", "X": 0.487221896648407, "Y": 0.3452930748462677 }, { "Type": "mouthRight", "X": 0.5142884850502014, "Y": 0.33167609572410583 } ], "Pose": { "Pitch": 15.966927528381348, "Roll": -15.547388076782227, "Yaw": 11.34195613861084 }, "Quality": { "Brightness": 44.80223083496094, "Sharpness": 99.95819854736328 } }, "Timestamp": 0 }, { "Face": { "BoundingBox": { "Height": 0.20000000298023224, "Left": 0.029999999329447746, "Top": 0.2199999988079071, "Width": 0.11249999701976776 }, "Confidence": 99.85971069335938, "Landmarks": [ { "Type": "eyeLeft", "X": 0.06842322647571564, "Y": 0.3010137975215912 }, { "Type": "eyeRight", "X": 0.10543643683195114, "Y": 0.29697132110595703 }, { "Type": "nose", "X": 0.09569807350635529, "Y": 0.33701086044311523 }, { "Type": "mouthLeft", "X": 0.0732642263174057, "Y": 0.3757539987564087 }, { "Type": "mouthRight", "X": 0.10589495301246643, "Y": 0.3722417950630188 } ], "Pose": { "Pitch": -0.5589138865470886, "Roll": -5.1093974113464355, "Yaw": 18.69594955444336 }, "Quality": { "Brightness": 43.052337646484375, "Sharpness": 99.68138885498047 } }, "Timestamp": 0 }, { "Face": { "BoundingBox": { "Height": 0.2177777737379074, "Left": 0.7593749761581421, "Top": 0.13333334028720856, "Width": 0.12250000238418579 }, "Confidence": 99.63436889648438, "Landmarks": [ { "Type": "eyeLeft", "X": 0.8005779385566711, "Y": 0.20915353298187256 }, { "Type": "eyeRight", "X": 0.8391435146331787, "Y": 0.21049551665782928 }, { "Type": "nose", "X": 0.8191410899162292, "Y": 0.2523227035999298 }, { "Type": "mouthLeft", "X": 0.8093273043632507, "Y": 0.29053622484207153 }, { "Type": "mouthRight", "X": 0.8366993069648743, "Y": 0.29101791977882385 } ], "Pose": { "Pitch": 3.165884017944336, "Roll": 1.4182015657424927, "Yaw": -11.151537895202637 }, "Quality": { "Brightness": 28.910892486572266, "Sharpness": 97.61507415771484 } }, "Timestamp": 0 }....... ], "JobStatus": "SUCCEEDED", "NextToken": "i7fj5XPV/fwviXqz0eag9Ow332Jd5G8ZGWf7hooirD/6V1qFmjKFOQZ6QPWUiqv29HbyuhMNqQ==", "VideoMetadata": { "Codec": "h264", "DurationMillis": 67301, "FileExtension": "mp4", "Format": "QuickTime / MOV", "FrameHeight": 1080, "FrameRate": 29.970029830932617, "FrameWidth": 1920 } }