예제: 저장된 비디오에서 세그먼트 감지

다음 절차에서는 Amazon S3 버킷에 저장된 비디오에서 기술적 큐 세그먼트 및 샷 감지 세그먼트를 감지하는 방법을 보여 줍니다. 이 절차에서는 Amazon Rekognition Video가 감지 정확성에 대해 갖는 신뢰도를 기반으로 감지된 세그먼트를 필터링하는 방법도 보여 줍니다.

이 예제는 Amazon Simple Queue Service 대기열을 사용하여 비디오 분석 요청의 완료 상태를 가져오는 Java 또는 Python (SDK) 을 사용하여 Amazon S3 버킷에 저장된 비디오 분석의 코드를 확장합니다.

Amazon S3 버킷에 저장된 비디오에서 세그먼트를 감지하려면(SDK)

Java 또는 Python (SDK) 을 사용하여 Amazon S3 버킷에 저장된 비디오 분석을 수행합니다.

1단계에서 사용한 코드에 다음을 추가합니다.

Java

다음과 같은 가져오기를 추가합니다.


import com.amazonaws.services.rekognition.model.GetSegmentDetectionRequest;
import com.amazonaws.services.rekognition.model.GetSegmentDetectionResult;
import com.amazonaws.services.rekognition.model.SegmentDetection;
import com.amazonaws.services.rekognition.model.SegmentType;
import com.amazonaws.services.rekognition.model.SegmentTypeInfo;
import com.amazonaws.services.rekognition.model.ShotSegment;
import com.amazonaws.services.rekognition.model.StartSegmentDetectionFilters;
import com.amazonaws.services.rekognition.model.StartSegmentDetectionRequest;
import com.amazonaws.services.rekognition.model.StartSegmentDetectionResult;
import com.amazonaws.services.rekognition.model.StartShotDetectionFilter;
import com.amazonaws.services.rekognition.model.StartTechnicalCueDetectionFilter;
import com.amazonaws.services.rekognition.model.TechnicalCueSegment;
import com.amazonaws.services.rekognition.model.AudioMetadata;

다음 코드를 VideoDetect 클래스에 추가합니다.


    //Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
    //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)


    private static void StartSegmentDetection(String bucket, String video) throws Exception{
            
        NotificationChannel channel= new NotificationChannel()
                .withSNSTopicArn(snsTopicArn)
                .withRoleArn(roleArn);

        float minTechnicalCueConfidence = 80F; 
        float minShotConfidence = 80F; 
                
        StartSegmentDetectionRequest req = new StartSegmentDetectionRequest()
                .withVideo(new Video()
                        .withS3Object(new S3Object()
                                .withBucket(bucket)
                                .withName(video)))
                .withSegmentTypes("TECHNICAL_CUE" , "SHOT")
                .withFilters(new StartSegmentDetectionFilters()
                        .withTechnicalCueFilter(new StartTechnicalCueDetectionFilter()
                                .withMinSegmentConfidence(minTechnicalCueConfidence))
                        .withShotFilter(new StartShotDetectionFilter()
                                .withMinSegmentConfidence(minShotConfidence)))
                .withJobTag("DetectingVideoSegments")
                .withNotificationChannel(channel);

        StartSegmentDetectionResult startLabelDetectionResult = rek.startSegmentDetection(req);
        startJobId=startLabelDetectionResult.getJobId();
        
    }

    private static void GetSegmentDetectionResults() throws Exception{

        int maxResults=10;
        String paginationToken=null;
        GetSegmentDetectionResult segmentDetectionResult=null;
        Boolean firstTime=true;
        

        do {
            if (segmentDetectionResult !=null){
                paginationToken = segmentDetectionResult.getNextToken();
            }

            GetSegmentDetectionRequest segmentDetectionRequest= new GetSegmentDetectionRequest()
                    .withJobId(startJobId)
                    .withMaxResults(maxResults)
                    .withNextToken(paginationToken);

            segmentDetectionResult = rek.getSegmentDetection(segmentDetectionRequest);
            
            if(firstTime) {
                System.out.println("\nStatus\n------");
                System.out.println(segmentDetectionResult.getJobStatus());
                System.out.println("\nRequested features\n------------------");
                 for (SegmentTypeInfo requestedFeatures : segmentDetectionResult.getSelectedSegmentTypes()) {
                    System.out.println(requestedFeatures.getType());
                }
                 int count=1;
                 List<VideoMetadata> videoMetaDataList = segmentDetectionResult.getVideoMetadata();
                 System.out.println("\nVideo Streams\n-------------");
                 for (VideoMetadata videoMetaData: videoMetaDataList) {
                     System.out.println("Stream: " + count++);
                     System.out.println("\tFormat: " + videoMetaData.getFormat());
                     System.out.println("\tCodec: " + videoMetaData.getCodec());
                     System.out.println("\tDuration: " + videoMetaData.getDurationMillis());
                     System.out.println("\tFrameRate: " + videoMetaData.getFrameRate());
                 } 

                 
                 List<AudioMetadata> audioMetaDataList = segmentDetectionResult.getAudioMetadata();
                 System.out.println("\nAudio streams\n-------------");

                 count=1;
                 for (AudioMetadata audioMetaData: audioMetaDataList) {
                     System.out.println("Stream: " + count++);
                     System.out.println("\tSample Rate: " + audioMetaData.getSampleRate());
                     System.out.println("\tCodec: " + audioMetaData.getCodec());
                     System.out.println("\tDuration: " + audioMetaData.getDurationMillis());
                     System.out.println("\tNumber of Channels: " + audioMetaData.getNumberOfChannels());
                 }
                 System.out.println("\nSegments\n--------");

                firstTime=false;
            }


            //Show segment information

            List<SegmentDetection> detectedSegments= segmentDetectionResult.getSegments();
            
            for (SegmentDetection detectedSegment: detectedSegments) { 
                
               if (detectedSegment.getType().contains(SegmentType.TECHNICAL_CUE.toString())) {
                    System.out.println("Technical Cue");
                    TechnicalCueSegment segmentCue=detectedSegment.getTechnicalCueSegment();
                    System.out.println("\tType: " + segmentCue.getType()); 
                    System.out.println("\tConfidence: " + segmentCue.getConfidence().toString());
                }
               if (detectedSegment.getType().contains(SegmentType.SHOT.toString())) { 
                    System.out.println("Shot");
                    ShotSegment segmentShot=detectedSegment.getShotSegment();
                    System.out.println("\tIndex " + segmentShot.getIndex()); 
                    System.out.println("\tConfidence: " + segmentShot.getConfidence().toString());
                }
                long seconds=detectedSegment.getDurationMillis();
                System.out.println("\tDuration : " + Long.toString(seconds) + " milliseconds");
                System.out.println("\tStart time code: " + detectedSegment.getStartTimecodeSMPTE());
                System.out.println("\tEnd time code: " + detectedSegment.getEndTimecodeSMPTE());
                System.out.println("\tDuration time code: " + detectedSegment.getDurationSMPTE());
                System.out.println();
                                
             } 
                   
        } while (segmentDetectionResult !=null && segmentDetectionResult.getNextToken() != null);

    }

main 함수에서 다음 줄을 바꿉니다.


        StartLabelDetection(bucket, video);

        if (GetSQSMessageSuccess()==true)
        	GetLabelDetectionResults();

다음으로 바꿉니다.


        StartSegmentDetection(bucket, video);

        if (GetSQSMessageSuccess()==true)
        	GetSegmentDetectionResults();

Java V2


//snippet-start:[rekognition.java2.recognize_video_text.import]
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
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;
//snippet-end:[rekognition.java2.recognize_video_text.import]

/**
* 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 DetectVideoSegments {

 private static String startJobId ="";
 public static void main(String[] args) {

     final String usage = "\n" +
         "Usage: " +
         "   <bucket> <video> <topicArn> <roleArn>\n\n" +
         "Where:\n" +
         "   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket). \n\n"+
         "   video - The name of video (for example, people.mp4). \n\n" +
         "   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic. \n\n" +
         "   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use. \n\n" ;

     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_WEST_2;
     RekognitionClient rekClient = RekognitionClient.builder()
         .region(region)
         .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
         .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();
 }

 // snippet-start:[rekognition.java2.recognize_video_text.main]
 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);
     }
 }
 // snippet-end:[rekognition.java2.recognize_video_text.main]
}

Python

1단계에서 만든 클래스 VideoDetect에 다음 코드를 추가합니다.


# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)

    def StartSegmentDetection(self):

        min_Technical_Cue_Confidence = 80.0
        min_Shot_Confidence = 80.0
        max_pixel_threshold = 0.1
        min_coverage_percentage = 60

        response = self.rek.start_segment_detection(
            Video={"S3Object": {"Bucket": self.bucket, "Name": self.video}},
            NotificationChannel={
                "RoleArn": self.roleArn,
                "SNSTopicArn": self.snsTopicArn,
            },
            SegmentTypes=["TECHNICAL_CUE", "SHOT"],
            Filters={
                "TechnicalCueFilter": {
                    "BlackFrame": {
                        "MaxPixelThreshold": max_pixel_threshold,
                        "MinCoveragePercentage": min_coverage_percentage,
                    },
                    "MinSegmentConfidence": min_Technical_Cue_Confidence,
                },
                "ShotFilter": {"MinSegmentConfidence": min_Shot_Confidence},
            }
        )

        self.startJobId = response["JobId"]
        print(f"Start Job Id: {self.startJobId}")

    def GetSegmentDetectionResults(self):
        maxResults = 10
        paginationToken = ""
        finished = False
        firstTime = True

        while finished == False:
            response = self.rek.get_segment_detection(
                JobId=self.startJobId, MaxResults=maxResults, NextToken=paginationToken
            )

            if firstTime == True:
                print(f"Status\n------\n{response['JobStatus']}")
                print("\nRequested Types\n---------------")
                for selectedSegmentType in response['SelectedSegmentTypes']:
                    print(f"\tType: {selectedSegmentType['Type']}")
                    print(f"\t\tModel Version: {selectedSegmentType['ModelVersion']}")

                print()
                print("\nAudio metadata\n--------------")
                for audioMetadata in response['AudioMetadata']:
                    print(f"\tCodec: {audioMetadata['Codec']}")
                    print(f"\tDuration: {audioMetadata['DurationMillis']}")
                    print(f"\tNumber of Channels: {audioMetadata['NumberOfChannels']}")
                    print(f"\tSample rate: {audioMetadata['SampleRate']}")
                print()
                print("\nVideo metadata\n--------------")
                for videoMetadata in response["VideoMetadata"]:
                    print(f"\tCodec: {videoMetadata['Codec']}")
                    print(f"\tColor Range: {videoMetadata['ColorRange']}")
                    print(f"\tDuration: {videoMetadata['DurationMillis']}")
                    print(f"\tFormat: {videoMetadata['Format']}")
                    print(f"\tFrame rate: {videoMetadata['FrameRate']}")
                    print("\nSegments\n--------")

                firstTime = False

            for segment in response['Segments']:

                if segment["Type"] == "TECHNICAL_CUE":
                    print("Technical Cue")
                    print(f"\tConfidence: {segment['TechnicalCueSegment']['Confidence']}")
                    print(f"\tType: {segment['TechnicalCueSegment']['Type']}")

                if segment["Type"] == "SHOT":
                    print("Shot")
                    print(f"\tConfidence: {segment['ShotSegment']['Confidence']}")
                    print(f"\tIndex: " + str(segment["ShotSegment"]["Index"]))

                print(f"\tDuration (milliseconds): {segment['DurationMillis']}")
                print(f"\tStart Timestamp (milliseconds): {segment['StartTimestampMillis']}")
                print(f"\tEnd Timestamp (milliseconds): {segment['EndTimestampMillis']}")
                
                print(f"\tStart timecode: {segment['StartTimecodeSMPTE']}")
                print(f"\tEnd timecode: {segment['EndTimecodeSMPTE']}")
                print(f"\tDuration timecode: {segment['DurationSMPTE']}")

                print(f"\tStart frame number {segment['StartFrameNumber']}")
                print(f"\tEnd frame number: {segment['EndFrameNumber']}")
                print(f"\tDuration frames: {segment['DurationFrames']}")

                print()

            if "NextToken" in response:
                paginationToken = response["NextToken"]
            else:
                finished = True

main 함수에서 다음 줄을 바꿉니다.


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

다음으로 바꿉니다.


    analyzer.StartSegmentDetection()
    if analyzer.GetSQSMessageSuccess()==True:
        analyzer.GetSegmentDetectionResults()

참고

Java 또는 Python (SDK) 을 사용하여 Amazon S3 버킷에 저장된 비디오 분석 이외에 비디오 예제를 이미 실행한 경우, 바꿀 코드가 다를 수 있습니다.

코드를 실행합니다. 입력 비디오에서 감지된 세그먼트에 대한 정보가 표시됩니다.

javascript가 브라우저에서 비활성화되거나 사용이 불가합니다.

AWS 설명서를 사용하려면 Javascript가 활성화되어야 합니다. 지침을 보려면 브라우저의 도움말 페이지를 참조하십시오.

문서 규칙

Amazon Rekognition Segment API 사용

얼굴 생체 확인 감지