Amazon Rekognition および AWS SDK を使用してビデオ内の情報を検索する

次のコード例は、以下を実行する方法を示しています。

• Amazon Rekognition ジョブを開始して、動画内の人物、オブジェクト、テキストなどの要素を検出します。

• ジョブが終了するまでジョブのステータスを確認します。

• 各ジョブで検出された要素のリストを出力します。

Java

SDK for Java 2.x

注記

他にもありますGitHub。用例一覧を検索し、AWS コード例リポジトリでの設定と実行の方法を確認してください。

Amazon S3 バケット内のビデオから有名人の結果を取得します。

    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);
        }
    }

ラベル検出オペレーションによって、ビデオ内のラベルを検出します。

    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);
        }
    }

Amazon S3 バケットに保存されたビデオ内の顔を検出する

    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);
        }
    }

Amazon S3 バケットに保存されたビデオ内の不適切なコンテンツや攻撃的なコンテンツを検出します。

    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);
        }
    }

Amazon S3 バケットに保存されているビデオ内のテクニカルキューセグメントおよびショット検出セグメントを検出します。

    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);
        }
    }

Amazon S3 バケットに保存されたビデオに保存されたビデオ内のテキストを検出します。

    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);
        }
    }

Amazon S3 バケットに保存されたビデオに保存されたビデオ内の人物を検出します。

    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);
        }
    }

• API の詳細については、「AWS SDK for Java 2.x API リファレンス」の以下のトピックを参照してください。

  • GetCelebrityRecognition

  • GetContentModeration

  • GetLabelDetection

  • GetPersonTracking

  • GetSegmentDetection

  • GetTextDetection

  • StartCelebrityRecognition

  • StartContentModeration

  • StartLabelDetection

  • StartPersonTracking

  • StartSegmentDetection

  • StartTextDetection

Kotlin

SDK for Kotlin

注記

これはプレビューリリースの機能に関するプレリリースドキュメントです。このドキュメントは変更される可能性があります。

注記

他にもありますGitHub。用例一覧を検索し、AWS コード例リポジトリでの設定と実行の方法を確認してください。

Amazon S3 バケットに保存されたビデオ内の顔を検出する

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}")
        }
    }
}

Amazon S3 バケットに保存されたビデオ内の不適切なコンテンツや攻撃的なコンテンツを検出します。

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)
        }
    }
}

• API の詳細については、「AWS SDK for Kotlin API リファレンス」の以下のトピックを参照してください。

  • GetCelebrityRecognition

  • GetContentModeration

  • GetLabelDetection

  • GetPersonTracking

  • GetSegmentDetection

  • GetTextDetection

  • StartCelebrityRecognition

  • StartContentModeration

  • StartLabelDetection

  • StartPersonTracking

  • StartSegmentDetection

  • StartTextDetection