人物的轨迹

检测存储在 Amazon S3 存储桶内的视频中的人员 (SDK)

1. 执行使用 Java 或 Python 分析存储在 Amazon S3 存储桶中的视频 (SDK)。

2. 将以下代码添加到您在步骤 1 中创建的类 VideoDetect。

   Java

          //Copyright 2018 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.)
   
           //Persons========================================================================
           private static void StartPersonDetection(String bucket, String video) throws Exception{
               
               
               NotificationChannel channel= new NotificationChannel()
                       .withSNSTopicArn(snsTopicArn)
                       .withRoleArn(roleArn);
               
            StartPersonTrackingRequest req = new StartPersonTrackingRequest()
                    .withVideo(new Video()
                            .withS3Object(new S3Object()
                                .withBucket(bucket)
                                .withName(video)))
                    .withNotificationChannel(channel);
                                   
                
               
            StartPersonTrackingResult startPersonDetectionResult = rek.startPersonTracking(req);
            startJobId=startPersonDetectionResult.getJobId();
               
           } 
           
           private static void GetPersonDetectionResults() throws Exception{
               int maxResults=10;
               String paginationToken=null;
               GetPersonTrackingResult personTrackingResult=null;
               
               do{
                   if (personTrackingResult !=null){
                       paginationToken = personTrackingResult.getNextToken();
                   }
                   
                   personTrackingResult = rek.getPersonTracking(new GetPersonTrackingRequest()
                        .withJobId(startJobId)
                        .withNextToken(paginationToken)
                        .withSortBy(PersonTrackingSortBy.TIMESTAMP)
                        .withMaxResults(maxResults));
             
                   VideoMetadata videoMetaData=personTrackingResult.getVideoMetadata();
                       
                   System.out.println("Format: " + videoMetaData.getFormat());
                   System.out.println("Codec: " + videoMetaData.getCodec());
                   System.out.println("Duration: " + videoMetaData.getDurationMillis());
                   System.out.println("FrameRate: " + videoMetaData.getFrameRate());
                       
                       
                   //Show persons, confidence and detection times
                   List<PersonDetection> detectedPersons= personTrackingResult.getPersons();
                
                   for (PersonDetection detectedPerson: detectedPersons) { 
                       
                      long seconds=detectedPerson.getTimestamp()/1000;
                      System.out.print("Sec: " + Long.toString(seconds) + " ");
                      System.out.println("Person Identifier: "  + detectedPerson.getPerson().getIndex());
                         System.out.println();             
                   }
               }  while (personTrackingResult !=null && personTrackingResult.getNextToken() != null);
               
           }
   

   在函数 main 中，将以下行:

           StartLabelDetection(amzn-s3-demo-bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetLabelDetectionResults();

   替换为:

           StartPersonDetection(amzn-s3-demo-bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetPersonDetectionResults();

   Java V2

   此代码取自 AWS 文档 SDK 示例 GitHub 存储库。请在此处查看完整示例。

   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);
           }
       }
   }
   

   Python

   #Copyright 2018 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.)
   
       # ============== People pathing ===============  
       def StartPersonPathing(self):
           response=self.rek.start_person_tracking(Video={'S3Object': {'Bucket': self.bucket, 'Name': self.video}},
               NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})
   
           self.startJobId=response['JobId']
           print('Start Job Id: ' + self.startJobId)
       
       def GetPersonPathingResults(self):
           maxResults = 10
           paginationToken = ''
           finished = False
   
           while finished == False:
               response = self.rek.get_person_tracking(JobId=self.startJobId,
                                               MaxResults=maxResults,
                                               NextToken=paginationToken)
   
               print('Codec: ' + response['VideoMetadata']['Codec'])
               print('Duration: ' + str(response['VideoMetadata']['DurationMillis']))
               print('Format: ' + response['VideoMetadata']['Format'])
               print('Frame rate: ' + str(response['VideoMetadata']['FrameRate']))
               print()
   
               for personDetection in response['Persons']:
                   print('Index: ' + str(personDetection['Person']['Index']))
                   print('Timestamp: ' + str(personDetection['Timestamp']))
                   print()
   
               if 'NextToken' in response:
                   paginationToken = response['NextToken']
               else:
                   finished = True       
   

   在函数 main 中，将以下行:

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

   替换为:

       analyzer.StartPersonPathing()
       if analyzer.GetSQSMessageSuccess()==True:
           analyzer.GetPersonPathingResults()

   CLI

   运行以下 AWS CLI 命令开始在视频中跟踪人物轨迹。

   aws rekognition start-person-tracking --video "{"S3Object":{"Bucket":"amzn-s3-demo-bucket","Name":"video-name"}}" \ 
   --notification-channel "{"SNSTopicArn":"topic-ARN","RoleArn":"role-ARN"}" \
   --region region-name --profile profile-name

   更新以下值：

   • 将amzn-s3-demo-bucket和video-name更改为您在步骤 2 中指定的 Amazon S3 存储桶名称和文件名。

   • 将 region-name 更改为您使用的 AWS 区域。

   • 将创建 Rekognition 会话的行中的profile-name值替换为您的开发人员资料的名称。

   • 将 topic-ARN 更改为您在 配置 Amazon Rekognition Video 的步骤 3 中创建的 Amazon SNS 主题的 ARN。

   • 将 role-ARN 更改为您在 配置 Amazon Rekognition Video 的步骤 7 中创建的 IAM 服务角色的 ARN。

   如果您在 Windows 设备上访问 CLI，请使用双引号代替单引号，并用反斜杠（即 \）对内部双引号进行转义，以解决可能遇到的任何解析器错误。有关示例，请参阅以下内容：

   aws rekognition start-person-tracking --video "{\"S3Object\":{\"Bucket\":\"amzn-s3-demo-bucket\",\"Name\":\"video-name\"}}" 
   --notification-channel "{\"SNSTopicArn\":\"topic-ARN\",\"RoleArn\":\"role-ARN\"}" \
   --region region-name --profile profile-name
                   

   运行后续代码示例后，复制返回的 jobID 并将其提供给以下 GetPersonTracking 命令以获取结果，将 job-id-number 替换为您之前收到的 jobID：

   
   aws rekognition get-person-tracking --job-id job-id-number                                
                               

   注意

   如果您已经运行了除 使用 Java 或 Python 分析存储在 Amazon S3 存储桶中的视频 (SDK) 之外的视频示例，则要替换的代码可能会有所不同。

3. 运行该代码。将显示被跟踪人员的唯一标识符以及跟踪人物轨迹的时间（秒）。