Detect information in videos using Amazon Rekognition and the AWS SDK - AWS SDK Code Examples

There are more AWS SDK examples available in the AWS Doc SDK Examples GitHub repo.

Detect information in videos using Amazon Rekognition and the AWS SDK

The following code examples show how to:

  • Start Amazon Rekognition jobs to detect elements like people, objects, and text in videos.

  • Check job status until jobs finish.

  • Output the list of elements detected by each job.

Java
SDK for Java 2.x
Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository.

Get celebrity results from a video located in an Amazon S3 bucket.

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: " + seconds + " "); CelebrityDetail details=celeb.celebrity(); System.out.println("Name: " + details.name()); System.out.println("Id: " + details.id()); System.out.println(); } } while (recognitionResponse.nextToken() != null); } catch(RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } }

Detect labels in a video by a label detection operation.

public static void startLabels(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(); StartLabelDetectionRequest labelDetectionRequest = StartLabelDetectionRequest.builder() .jobTag("DetectingLabels") .notificationChannel(channel) .video(vidOb) .minConfidence(50F) .build(); StartLabelDetectionResponse labelDetectionResponse = rekClient.startLabelDetection(labelDetectionRequest); startJobId = labelDetectionResponse.jobId(); boolean ans = true; String status = ""; int yy = 0; while (ans) { GetLabelDetectionRequest detectionRequest = GetLabelDetectionRequest.builder() .jobId(startJobId) .maxResults(10) .build(); GetLabelDetectionResponse result = rekClient.getLabelDetection(detectionRequest); status = result.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) ans = false; else System.out.println(yy +" status is: "+status); Thread.sleep(1000); yy++; } System.out.println(startJobId +" status is: "+status); } catch(RekognitionException | InterruptedException e) { e.getMessage(); System.exit(1); } } public static void getLabelJob(RekognitionClient rekClient, SqsClient sqs, String queueUrl) { List<Message> messages; ReceiveMessageRequest messageRequest = ReceiveMessageRequest.builder() .queueUrl(queueUrl) .build(); try { messages = sqs.receiveMessage(messageRequest).messages(); if (!messages.isEmpty()) { for (Message message: messages) { String notification = message.body(); // Get the status and job id from the notification ObjectMapper mapper = new ObjectMapper(); JsonNode jsonMessageTree = mapper.readTree(notification); JsonNode messageBodyText = jsonMessageTree.get("Message"); ObjectMapper operationResultMapper = new ObjectMapper(); JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue()); JsonNode operationJobId = jsonResultTree.get("JobId"); JsonNode operationStatus = jsonResultTree.get("Status"); System.out.println("Job found in JSON is " + operationJobId); DeleteMessageRequest deleteMessageRequest = DeleteMessageRequest.builder() .queueUrl(queueUrl) .build(); String jobId = operationJobId.textValue(); if (startJobId.compareTo(jobId)==0) { System.out.println("Job id: " + operationJobId ); System.out.println("Status : " + operationStatus.toString()); if (operationStatus.asText().equals("SUCCEEDED")) GetResultsLabels(rekClient); else System.out.println("Video analysis failed"); sqs.deleteMessage(deleteMessageRequest); } else{ System.out.println("Job received was not job " + startJobId); sqs.deleteMessage(deleteMessageRequest); } } } } catch(RekognitionException e) { e.getMessage(); System.exit(1); } catch (JsonMappingException e) { e.printStackTrace(); } catch (JsonProcessingException e) { e.printStackTrace(); } } // Gets the job results by calling GetLabelDetection private static void GetResultsLabels(RekognitionClient rekClient) { int maxResults=10; String paginationToken=null; GetLabelDetectionResponse labelDetectionResult=null; try { do { if (labelDetectionResult !=null) paginationToken = labelDetectionResult.nextToken(); GetLabelDetectionRequest labelDetectionRequest= GetLabelDetectionRequest.builder() .jobId(startJobId) .sortBy(LabelDetectionSortBy.TIMESTAMP) .maxResults(maxResults) .nextToken(paginationToken) .build(); labelDetectionResult = rekClient.getLabelDetection(labelDetectionRequest); VideoMetadata videoMetaData=labelDetectionResult.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()); List<LabelDetection> detectedLabels= labelDetectionResult.labels(); for (LabelDetection detectedLabel: detectedLabels) { long seconds=detectedLabel.timestamp(); Label label=detectedLabel.label(); System.out.println("Millisecond: " + seconds + " "); System.out.println(" Label:" + label.name()); System.out.println(" Confidence:" + detectedLabel.label().confidence().toString()); List<Instance> instances = label.instances(); System.out.println(" Instances of " + label.name()); if (instances.isEmpty()) { System.out.println(" " + "None"); } else { for (Instance instance : instances) { System.out.println(" Confidence: " + instance.confidence().toString()); System.out.println(" Bounding box: " + instance.boundingBox().toString()); } } System.out.println(" Parent labels for " + label.name() + ":"); List<Parent> parents = label.parents(); if (parents.isEmpty()) { System.out.println(" None"); } else { for (Parent parent : parents) { System.out.println(" " + parent.name()); } } System.out.println(); } } while (labelDetectionResult !=null && labelDetectionResult.nextToken() != null); } catch(RekognitionException e) { e.getMessage(); System.exit(1); } }

Detect faces in a video stored in an Amazon S3 bucket.

public static void startLabels(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(); StartLabelDetectionRequest labelDetectionRequest = StartLabelDetectionRequest.builder() .jobTag("DetectingLabels") .notificationChannel(channel) .video(vidOb) .minConfidence(50F) .build(); StartLabelDetectionResponse labelDetectionResponse = rekClient.startLabelDetection(labelDetectionRequest); startJobId = labelDetectionResponse.jobId(); boolean ans = true; String status = ""; int yy = 0; while (ans) { GetLabelDetectionRequest detectionRequest = GetLabelDetectionRequest.builder() .jobId(startJobId) .maxResults(10) .build(); GetLabelDetectionResponse result = rekClient.getLabelDetection(detectionRequest); status = result.jobStatusAsString(); if (status.compareTo("SUCCEEDED") == 0) ans = false; else System.out.println(yy +" status is: "+status); Thread.sleep(1000); yy++; } System.out.println(startJobId +" status is: "+status); } catch(RekognitionException | InterruptedException e) { e.getMessage(); System.exit(1); } } public static void getLabelJob(RekognitionClient rekClient, SqsClient sqs, String queueUrl) { List<Message> messages; ReceiveMessageRequest messageRequest = ReceiveMessageRequest.builder() .queueUrl(queueUrl) .build(); try { messages = sqs.receiveMessage(messageRequest).messages(); if (!messages.isEmpty()) { for (Message message: messages) { String notification = message.body(); // Get the status and job id from the notification ObjectMapper mapper = new ObjectMapper(); JsonNode jsonMessageTree = mapper.readTree(notification); JsonNode messageBodyText = jsonMessageTree.get("Message"); ObjectMapper operationResultMapper = new ObjectMapper(); JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue()); JsonNode operationJobId = jsonResultTree.get("JobId"); JsonNode operationStatus = jsonResultTree.get("Status"); System.out.println("Job found in JSON is " + operationJobId); DeleteMessageRequest deleteMessageRequest = DeleteMessageRequest.builder() .queueUrl(queueUrl) .build(); String jobId = operationJobId.textValue(); if (startJobId.compareTo(jobId)==0) { System.out.println("Job id: " + operationJobId ); System.out.println("Status : " + operationStatus.toString()); if (operationStatus.asText().equals("SUCCEEDED")) GetResultsLabels(rekClient); else System.out.println("Video analysis failed"); sqs.deleteMessage(deleteMessageRequest); } else{ System.out.println("Job received was not job " + startJobId); sqs.deleteMessage(deleteMessageRequest); } } } } catch(RekognitionException e) { e.getMessage(); System.exit(1); } catch (JsonMappingException e) { e.printStackTrace(); } catch (JsonProcessingException e) { e.printStackTrace(); } } // Gets the job results by calling GetLabelDetection private static void GetResultsLabels(RekognitionClient rekClient) { int maxResults=10; String paginationToken=null; GetLabelDetectionResponse labelDetectionResult=null; try { do { if (labelDetectionResult !=null) paginationToken = labelDetectionResult.nextToken(); GetLabelDetectionRequest labelDetectionRequest= GetLabelDetectionRequest.builder() .jobId(startJobId) .sortBy(LabelDetectionSortBy.TIMESTAMP) .maxResults(maxResults) .nextToken(paginationToken) .build(); labelDetectionResult = rekClient.getLabelDetection(labelDetectionRequest); VideoMetadata videoMetaData=labelDetectionResult.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()); List<LabelDetection> detectedLabels= labelDetectionResult.labels(); for (LabelDetection detectedLabel: detectedLabels) { long seconds=detectedLabel.timestamp(); Label label=detectedLabel.label(); System.out.println("Millisecond: " + seconds + " "); System.out.println(" Label:" + label.name()); System.out.println(" Confidence:" + detectedLabel.label().confidence().toString()); List<Instance> instances = label.instances(); System.out.println(" Instances of " + label.name()); if (instances.isEmpty()) { System.out.println(" " + "None"); } else { for (Instance instance : instances) { System.out.println(" Confidence: " + instance.confidence().toString()); System.out.println(" Bounding box: " + instance.boundingBox().toString()); } } System.out.println(" Parent labels for " + label.name() + ":"); List<Parent> parents = label.parents(); if (parents.isEmpty()) { System.out.println(" None"); } else { for (Parent parent : parents) { System.out.println(" " + parent.name()); } } System.out.println(); } } while (labelDetectionResult !=null && labelDetectionResult.nextToken() != null); } catch(RekognitionException e) { e.getMessage(); System.exit(1); } }

Detect inappropriate or offensive content in a video stored in an Amazon S3 bucket.

public static void startModerationDetection(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(); StartContentModerationRequest modDetectionRequest = StartContentModerationRequest.builder() .jobTag("Moderation") .notificationChannel(channel) .video(vidOb) .build(); StartContentModerationResponse startModDetectionResult = rekClient.startContentModeration(modDetectionRequest); startJobId=startModDetectionResult.jobId(); } catch(RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } public static void GetModResults(RekognitionClient rekClient) { try { String paginationToken=null; GetContentModerationResponse modDetectionResponse=null; boolean finished = false; String status; int yy=0 ; do{ if (modDetectionResponse !=null) paginationToken = modDetectionResponse.nextToken(); GetContentModerationRequest modRequest = GetContentModerationRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .maxResults(10) .build(); // Wait until the job succeeds while (!finished) { modDetectionResponse = rekClient.getContentModeration(modRequest); status = modDetectionResponse.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=modDetectionResponse.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<ContentModerationDetection> mods = modDetectionResponse.moderationLabels(); for (ContentModerationDetection mod: mods) { long seconds=mod.timestamp()/1000; System.out.print("Mod label: " + seconds + " "); System.out.println(mod.moderationLabel().toString()); System.out.println(); } } while (modDetectionResponse !=null && modDetectionResponse.nextToken() != null); } catch(RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } }

Detect technical cue segments and shot detection segments in a video stored in an Amazon S3 bucket.

public static void StartSegmentDetection (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(); StartShotDetectionFilter cueDetectionFilter = StartShotDetectionFilter.builder() .minSegmentConfidence(60F) .build(); StartTechnicalCueDetectionFilter technicalCueDetectionFilter = StartTechnicalCueDetectionFilter.builder() .minSegmentConfidence(60F) .build(); StartSegmentDetectionFilters filters = StartSegmentDetectionFilters.builder() .shotFilter(cueDetectionFilter) .technicalCueFilter(technicalCueDetectionFilter) .build(); StartSegmentDetectionRequest segDetectionRequest = StartSegmentDetectionRequest.builder() .jobTag("DetectingLabels") .notificationChannel(channel) .segmentTypes(SegmentType.TECHNICAL_CUE , SegmentType.SHOT) .video(vidOb) .filters(filters) .build(); StartSegmentDetectionResponse segDetectionResponse = rekClient.startSegmentDetection(segDetectionRequest); startJobId = segDetectionResponse.jobId(); } catch(RekognitionException e) { e.getMessage(); System.exit(1); } } public static void getSegmentResults(RekognitionClient rekClient) { try { String paginationToken = null; GetSegmentDetectionResponse segDetectionResponse = null; boolean finished = false; String status; int yy = 0; do { if (segDetectionResponse != null) paginationToken = segDetectionResponse.nextToken(); GetSegmentDetectionRequest recognitionRequest = GetSegmentDetectionRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .maxResults(10) .build(); // Wait until the job succeeds. while (!finished) { segDetectionResponse = rekClient.getSegmentDetection(recognitionRequest); status = segDetectionResponse.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. List<VideoMetadata> videoMetaData = segDetectionResponse.videoMetadata(); for (VideoMetadata metaData : videoMetaData) { System.out.println("Format: " + metaData.format()); System.out.println("Codec: " + metaData.codec()); System.out.println("Duration: " + metaData.durationMillis()); System.out.println("FrameRate: " + metaData.frameRate()); System.out.println("Job"); } List<SegmentDetection> detectedSegments = segDetectionResponse.segments(); for (SegmentDetection detectedSegment : detectedSegments) { String type = detectedSegment.type().toString(); if (type.contains(SegmentType.TECHNICAL_CUE.toString())) { System.out.println("Technical Cue"); TechnicalCueSegment segmentCue = detectedSegment.technicalCueSegment(); System.out.println("\tType: " + segmentCue.type()); System.out.println("\tConfidence: " + segmentCue.confidence().toString()); } if (type.contains(SegmentType.SHOT.toString())) { System.out.println("Shot"); ShotSegment segmentShot = detectedSegment.shotSegment(); System.out.println("\tIndex " + segmentShot.index()); System.out.println("\tConfidence: " + segmentShot.confidence().toString()); } long seconds = detectedSegment.durationMillis(); System.out.println("\tDuration : " + seconds + " milliseconds"); System.out.println("\tStart time code: " + detectedSegment.startTimecodeSMPTE()); System.out.println("\tEnd time code: " + detectedSegment.endTimecodeSMPTE()); System.out.println("\tDuration time code: " + detectedSegment.durationSMPTE()); System.out.println(); } } while (segDetectionResponse !=null && segDetectionResponse.nextToken() != null); } catch(RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } }

Detect text in a video stored in a video stored in an Amazon S3 bucket.

public static void startTextLabels(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(); StartTextDetectionRequest labelDetectionRequest = StartTextDetectionRequest.builder() .jobTag("DetectingLabels") .notificationChannel(channel) .video(vidOb) .build(); StartTextDetectionResponse labelDetectionResponse = rekClient.startTextDetection(labelDetectionRequest); startJobId = labelDetectionResponse.jobId(); } catch (RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } public static void GetTextResults(RekognitionClient rekClient) { try { String paginationToken=null; GetTextDetectionResponse textDetectionResponse=null; boolean finished = false; String status; int yy=0 ; do{ if (textDetectionResponse !=null) paginationToken = textDetectionResponse.nextToken(); GetTextDetectionRequest recognitionRequest = GetTextDetectionRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .maxResults(10) .build(); // Wait until the job succeeds. while (!finished) { textDetectionResponse = rekClient.getTextDetection(recognitionRequest); status = textDetectionResponse.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=textDetectionResponse.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<TextDetectionResult> labels= textDetectionResponse.textDetections(); for (TextDetectionResult detectedText: labels) { System.out.println("Confidence: " + detectedText.textDetection().confidence().toString()); System.out.println("Id : " + detectedText.textDetection().id()); System.out.println("Parent Id: " + detectedText.textDetection().parentId()); System.out.println("Type: " + detectedText.textDetection().type()); System.out.println("Text: " + detectedText.textDetection().detectedText()); System.out.println(); } } while (textDetectionResponse !=null && textDetectionResponse.nextToken() != null); } catch(RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } }

Detect people in a video stored in a video stored in an Amazon S3 bucket.

public static void startPersonLabels(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(); StartPersonTrackingRequest personTrackingRequest = StartPersonTrackingRequest.builder() .jobTag("DetectingLabels") .video(vidOb) .notificationChannel(channel) .build(); StartPersonTrackingResponse labelDetectionResponse = rekClient.startPersonTracking(personTrackingRequest); startJobId = labelDetectionResponse.jobId(); } catch(RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } public static void GetPersonDetectionResults(RekognitionClient rekClient) { try { String paginationToken=null; GetPersonTrackingResponse personTrackingResult=null; boolean finished = false; String status; int yy=0 ; do{ if (personTrackingResult !=null) paginationToken = personTrackingResult.nextToken(); GetPersonTrackingRequest recognitionRequest = GetPersonTrackingRequest.builder() .jobId(startJobId) .nextToken(paginationToken) .maxResults(10) .build(); // Wait until the job succeeds while (!finished) { personTrackingResult = rekClient.getPersonTracking(recognitionRequest); status = personTrackingResult.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 = personTrackingResult.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<PersonDetection> detectedPersons= personTrackingResult.persons(); for (PersonDetection detectedPerson: detectedPersons) { long seconds=detectedPerson.timestamp()/1000; System.out.print("Sec: " + seconds + " "); System.out.println("Person Identifier: " + detectedPerson.person().index()); System.out.println(); } } while (personTrackingResult !=null && personTrackingResult.nextToken() != null); } catch(RekognitionException | InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } }
Kotlin
SDK for Kotlin
Note

This is prerelease documentation for a feature in preview release. It is subject to change.

Note

There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository.

Detect faces in a video stored in an Amazon S3 bucket.

suspend fun startFaceDetection(channelVal: NotificationChannel?, bucketVal: String, videoVal: String) { val s3Obj = S3Object { bucket = bucketVal name = videoVal } val vidOb = Video { s3Object = s3Obj } val request = StartFaceDetectionRequest { jobTag = "Faces" faceAttributes = FaceAttributes.All notificationChannel = channelVal video = vidOb } RekognitionClient { region = "us-east-1" }.use { rekClient -> val startLabelDetectionResult = rekClient.startFaceDetection(request) startJobId = startLabelDetectionResult.jobId.toString() } } suspend fun getFaceResults() { var finished = false var status: String var yy = 0 RekognitionClient { region = "us-east-1" }.use { rekClient -> var response: GetFaceDetectionResponse? = null val recognitionRequest = GetFaceDetectionRequest { jobId = startJobId maxResults = 10 } // Wait until the job succeeds. while (!finished) { response = rekClient.getFaceDetection(recognitionRequest) status = response.jobStatus.toString() if (status.compareTo("SUCCEEDED") == 0) finished = true else { println("$yy status is: $status") delay(1000) } yy++ } // Proceed when the job is done - otherwise VideoMetadata is null. val videoMetaData = response?.videoMetadata println("Format: ${videoMetaData?.format}") println("Codec: ${videoMetaData?.codec}") println("Duration: ${videoMetaData?.durationMillis}") println("FrameRate: ${videoMetaData?.frameRate}") // Show face information. response?.faces?.forEach { face -> println("Age: ${face.face?.ageRange}") println("Face: ${face.face?.beard}") println("Eye glasses: ${face?.face?.eyeglasses}") println("Mustache: ${face.face?.mustache}") println("Smile: ${face.face?.smile}") } } }

Detect inappropriate or offensive content in a video stored in an Amazon S3 bucket.

suspend fun startModerationDetection(channel: NotificationChannel?, bucketVal: String?, videoVal: String?) { val s3Obj = S3Object { bucket = bucketVal name = videoVal } val vidOb = Video { s3Object = s3Obj } val request = StartContentModerationRequest { jobTag = "Moderation" notificationChannel = channel video = vidOb } RekognitionClient { region = "us-east-1" }.use { rekClient -> val startModDetectionResult = rekClient.startContentModeration(request) startJobId = startModDetectionResult.jobId.toString() } } suspend fun getModResults() { var finished = false var status: String var yy = 0 RekognitionClient { region = "us-east-1" }.use { rekClient -> var modDetectionResponse: GetContentModerationResponse? = null val modRequest = GetContentModerationRequest { jobId = startJobId maxResults = 10 } // Wait until the job succeeds. while (!finished) { modDetectionResponse = rekClient.getContentModeration(modRequest) status = modDetectionResponse.jobStatus.toString() if (status.compareTo("SUCCEEDED") == 0) finished = true else { println("$yy status is: $status") delay(1000) } yy++ } // Proceed when the job is done - otherwise VideoMetadata is null. val videoMetaData = modDetectionResponse?.videoMetadata println("Format: ${videoMetaData?.format}") println("Codec: ${videoMetaData?.codec}") println("Duration: ${videoMetaData?.durationMillis}") println("FrameRate: ${videoMetaData?.frameRate}") modDetectionResponse?.moderationLabels?.forEach { mod -> val seconds: Long = mod.timestamp / 1000 print("Mod label: $seconds ") println(mod.moderationLabel) } } }