範例：偵測已存放影片中的區段

偵測存放在 Amazon S3 儲存貯體 (SDK) 之影片中的區段

1. 執行 使用 Java 或 Python（SDK）分析存儲在 Amazon S3 存儲桶中的視頻。

2. 將下列專案新增至您在步驟 1 中使用的程式碼。

   Java

   1. 新增以下匯入專案。

      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;
      

   2. 將下列程式碼新增至類別 VideoDetect。

          //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);
      
          } 
      

   3. 在函數 main 中，將下行:

              StartLabelDetection(bucket, video);
      
              if (GetSQSMessageSuccess()==true)
              	GetLabelDetectionResults();

      取代為：

              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

   1. 將下列程式碼新增至您在步驟 1 中建立的類別 VideoDetect。

      # 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

   2. 在函數 main 中，將下行:

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

      取代為：

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

   注意

   如果您已執行 使用 Java 或 Python（SDK）分析存儲在 Amazon S3 存儲桶中的視頻 以外的影片範例，要取代的程式碼可能會不同。

3. 執行程式碼。隨即顯示在輸入影片中偵測到的區段相關資訊。