Detecte información en vídeos con Amazon Rekognition y el AWS SDK - AWS SDKEjemplos de código

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Detecte información en vídeos con Amazon Rekognition y el AWS SDK

En el siguiente ejemplo de código, se muestra cómo:

  • Inicie Amazon Rekognition Jobs para detectar elementos como personas, objetos y texto en los vídeos.

  • Compruebe el estado de los trabajos hasta que se terminan.

  • Obtener la lista de elementos detectados por cada trabajo.

Java
SDKpara Java 2.x
nota

Hay más información. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el Repositorio de ejemplos de código de AWS.

Obtenga información sobre famosos a partir de un vídeo ubicado en un bucket de Amazon S3.

import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.CelebrityRecognitionSortBy; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.CelebrityRecognition; import software.amazon.awssdk.services.rekognition.model.CelebrityDetail; import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionRequest; import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionRequest; import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionResponse; import java.util.List; /** * To run this code example, ensure that you perform the Prerequisites as stated * in the Amazon Rekognition Guide: * https://docs.aws.amazon.com/rekognition/latest/dg/video-analyzing-with-sqs.html * * Also, ensure that set up your development environment, including your * credentials. * * For information, see this documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html */ public class VideoCelebrityDetection { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of video (for example, people.mp4).\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; 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) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startCelebrityDetection(rekClient, channel, bucket, video); getCelebrityDetectionResults(rekClient); System.out.println("This example is done!"); rekClient.close(); } 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); } } }

Detecte etiquetas en un vídeo mediante una operación de detección de etiquetas.

import com.fasterxml.jackson.core.JsonProcessingException; import com.fasterxml.jackson.databind.JsonMappingException; import com.fasterxml.jackson.databind.JsonNode; import com.fasterxml.jackson.databind.ObjectMapper; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionResponse; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionRequest; import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionRequest; import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.LabelDetectionSortBy; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.LabelDetection; import software.amazon.awssdk.services.rekognition.model.Label; import software.amazon.awssdk.services.rekognition.model.Instance; import software.amazon.awssdk.services.rekognition.model.Parent; import software.amazon.awssdk.services.sqs.SqsClient; import software.amazon.awssdk.services.sqs.model.Message; import software.amazon.awssdk.services.sqs.model.ReceiveMessageRequest; import software.amazon.awssdk.services.sqs.model.DeleteMessageRequest; import java.util.List; /** * 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 VideoDetect { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <queueUrl> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of the video (for example, people.mp4).\s queueUrl- The URL of a SQS queue.\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; if (args.length != 5) { System.out.println(usage); System.exit(1); } String bucket = args[0]; String video = args[1]; String queueUrl = args[2]; String topicArn = args[3]; String roleArn = args[4]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); SqsClient sqs = SqsClient.builder() .region(Region.US_EAST_1) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startLabels(rekClient, channel, bucket, video); getLabelJob(rekClient, sqs, queueUrl); System.out.println("This example is done!"); sqs.close(); rekClient.close(); } 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); } } }

Detecte rostros en un vídeo almacenado en un bucket de Amazon S3.

import com.fasterxml.jackson.core.JsonProcessingException; import com.fasterxml.jackson.databind.JsonMappingException; import com.fasterxml.jackson.databind.JsonNode; import com.fasterxml.jackson.databind.ObjectMapper; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionResponse; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionRequest; import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionRequest; import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.LabelDetectionSortBy; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.LabelDetection; import software.amazon.awssdk.services.rekognition.model.Label; import software.amazon.awssdk.services.rekognition.model.Instance; import software.amazon.awssdk.services.rekognition.model.Parent; import software.amazon.awssdk.services.sqs.SqsClient; import software.amazon.awssdk.services.sqs.model.Message; import software.amazon.awssdk.services.sqs.model.ReceiveMessageRequest; import software.amazon.awssdk.services.sqs.model.DeleteMessageRequest; import java.util.List; /** * 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 VideoDetect { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <queueUrl> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of the video (for example, people.mp4).\s queueUrl- The URL of a SQS queue.\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; if (args.length != 5) { System.out.println(usage); System.exit(1); } String bucket = args[0]; String video = args[1]; String queueUrl = args[2]; String topicArn = args[3]; String roleArn = args[4]; Region region = Region.US_EAST_1; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .build(); SqsClient sqs = SqsClient.builder() .region(Region.US_EAST_1) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startLabels(rekClient, channel, bucket, video); getLabelJob(rekClient, sqs, queueUrl); System.out.println("This example is done!"); sqs.close(); rekClient.close(); } 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); } } }

Detecte contenido inapropiado u ofensivo en un vídeo almacenado en un bucket de Amazon S3.

import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartContentModerationRequest; import software.amazon.awssdk.services.rekognition.model.StartContentModerationResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.GetContentModerationResponse; import software.amazon.awssdk.services.rekognition.model.GetContentModerationRequest; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.ContentModerationDetection; import java.util.List; /** * 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 VideoDetectInappropriate { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of video (for example, people.mp4).\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; 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) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startModerationDetection(rekClient, channel, bucket, video); getModResults(rekClient); System.out.println("This example is done!"); rekClient.close(); } 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); } } }

Detecte segmentos de señales técnicas y segmentos de detección de tomas en un vídeo almacenado en un bucket de Amazon S3.

import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartShotDetectionFilter; import software.amazon.awssdk.services.rekognition.model.StartTechnicalCueDetectionFilter; import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionFilters; import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionRequest; import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.GetSegmentDetectionResponse; import software.amazon.awssdk.services.rekognition.model.GetSegmentDetectionRequest; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.SegmentDetection; import software.amazon.awssdk.services.rekognition.model.TechnicalCueSegment; import software.amazon.awssdk.services.rekognition.model.ShotSegment; import software.amazon.awssdk.services.rekognition.model.SegmentType; import software.amazon.awssdk.services.sqs.SqsClient; import java.util.List; /** * 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 VideoDetectSegment { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of video (for example, people.mp4).\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; 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) .build(); SqsClient sqs = SqsClient.builder() .region(Region.US_EAST_1) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startSegmentDetection(rekClient, channel, bucket, video); getSegmentResults(rekClient); System.out.println("This example is done!"); sqs.close(); rekClient.close(); } 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); } } }

Detecte texto en un vídeo almacenado en un bucket de Amazon S3.

import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartTextDetectionRequest; import software.amazon.awssdk.services.rekognition.model.StartTextDetectionResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.GetTextDetectionResponse; import software.amazon.awssdk.services.rekognition.model.GetTextDetectionRequest; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.TextDetectionResult; import java.util.List; /** * 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 VideoDetectText { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of video (for example, people.mp4).\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; 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) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startTextLabels(rekClient, channel, bucket, video); getTextResults(rekClient); System.out.println("This example is done!"); rekClient.close(); } 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); } } }

Detecte personas en un vídeo almacenado en un bucket de Amazon S3.

import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.rekognition.RekognitionClient; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.NotificationChannel; import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingRequest; import software.amazon.awssdk.services.rekognition.model.Video; import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingResponse; import software.amazon.awssdk.services.rekognition.model.RekognitionException; import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingResponse; import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingRequest; import software.amazon.awssdk.services.rekognition.model.VideoMetadata; import software.amazon.awssdk.services.rekognition.model.PersonDetection; import java.util.List; /** * 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 VideoPersonDetection { private static String startJobId = ""; public static void main(String[] args) { final String usage = """ Usage: <bucket> <video> <topicArn> <roleArn> Where: bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s video - The name of video (for example, people.mp4).\s topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s """; 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) .build(); NotificationChannel channel = NotificationChannel.builder() .snsTopicArn(topicArn) .roleArn(roleArn) .build(); startPersonLabels(rekClient, channel, bucket, video); getPersonDetectionResults(rekClient); System.out.println("This example is done!"); rekClient.close(); } 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
SDKpara Kotlin
nota

Hay más información. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el Repositorio de ejemplos de código de AWS.

Detecte rostros en un vídeo almacenado en un bucket de Amazon S3.

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}") } } }

Detecte contenido inapropiado u ofensivo en un vídeo almacenado en un bucket de Amazon S3.

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) } } }