本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 人物路徑
<a name="persons"></a>

**注意**  
*支援終止通知：*AWS 將於 2025 年 10 月 31 日停止支援 Amazon Rekognition People Pathing。2025 年 10 月 31 日之後，您將無法再使用 Rekognition 人員路徑功能。如需詳細資訊，請造訪此[部落格文章](https://aws.amazon.com/blogs/machine-learning/transitioning-from-amazon-rekognition-people-pathing-exploring-other-alternatives/)。

Amazon Rekognition Video 能建立人物在影片中的路徑資訊，例如：
+ 偵測到路徑時，該人物出現在影片影格中的位置。
+ 偵測到的臉部特徵點，例如左眼的位置。

已儲存影片中的 Amazon Rekognition Video 人物路徑是一種非同步操作。要啟動人們在影片的路徑呼叫 [StartPersonTracking](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartPersonTracking.html). Amazon Rekognition Video 向 Amazon Simple Notification Service 主題發佈影片的完成狀態。如果影片分析成功，您可以呼叫 [GetPersonTracking](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetPersonTracking.html) 以取得影片分析的結果。如需呼叫 Amazon Rekognition Video API 操作的詳細資訊，請參閱 [呼叫 Amazon Rekognition Video 操作](api-video.md)。

下列程序說明如何在儲存於 Amazon S3 儲存貯體的影片中追蹤人物路徑。此範例會展開 [使用 Java 或 Python (SDK) 分析儲存於 Amazon S3 儲存貯體中的影片](video-analyzing-with-sqs.md) 中的程式碼，這會使用 Amazon Simple Queue Service 佇列來取得影片分析要求的完成狀態。

**若要偵測存放在 Amazon S3 儲存貯體 (SDK) 之影片中的人物**

1. 執行 [使用 Java 或 Python (SDK) 分析儲存於 Amazon S3 儲存貯體中的影片](video-analyzing-with-sqs.md)。

1. 將下列程式碼新增至您在步驟 1 中建立的類別 `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);
               
           }
   ```

   在函數 `main` 中，將下行: 

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

   取代為：

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

------
#### [ Java V2 ]

   此程式碼取自 AWS 文件開發套件範例 GitHub 儲存庫。請參閱[此處](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/VideoPersonDetection.java)的完整範例。

   ```
   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
   ```

   在函數 `main` 中，將下行:

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

   取代為：

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

------
#### [ CLI ]

   執行下列 AWS CLI 命令來啟動影片中的人員路徑。

   ```
   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
   ```

   更新下列的值：
   + 將 `amzn-s3-demo-bucket` 與 `video-name` 變更為您在步驟 2 中指定的 Amazon S3 儲存貯體與文檔名稱。
   + 將 `region-name` 變更為您正在使用的 AWS 區域。
   + 將建立 Rekognition 工作階段的行中 `profile-name` 值取代為您開發人員設定檔的名稱。
   + 將 `topic-ARN` 變更為您在 [設定 Amazon Rekognition Video](api-video-roles.md) 步驟 3 建立的 Amazon SNS 主題 ARN。
   + 將 `role-ARN` 變更為您在步驟 7 建立的 [設定 Amazon Rekognition Video](api-video-roles.md) IAM 服務角色的 ARN。

   如果您在 Windows 裝置上存取 CLI，請使用雙引號而非單引號，並以反斜線 (即\$1) 替代內部雙引號，以解決您可能遇到的任何剖析器錯誤。如需範例，請參閱下列內容：

   ```
   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
   ```

   執行正在進行的程式碼範例之後，複製傳回的程式碼 `jobID`，並將其提供給下列 `GetPersonTracking` 命令，以 `job-id-number` 取代您之前收到的 `jobID` 結果：

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

------
**注意**  
如果您已執行 [使用 Java 或 Python (SDK) 分析儲存於 Amazon S3 儲存貯體中的影片](video-analyzing-with-sqs.md) 以外的影片範例，要取代的程式碼可能會不同。

1. 執行程式碼。將顯示追蹤到的人物之專有識別碼，同時以秒為單位顯示人物路徑被追蹤到的時間。

## GetPersonTracking 操作回應
<a name="getresultspersons-operation-response"></a>

`GetPersonTracking` 傳回 `Persons` 物件的 [PersonDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_PersonDetection.html) 陣列，其中包含影片中追蹤到的人物詳細資訊，以及追蹤到其路徑的時間。

您可以使用 `SortBy` 輸入參數對 `Persons` 排序。追蹤到影片中的人物路徑時，指定 `TIMESTAMP` 對元素排序。指定 `INDEX` 來排序影片中追蹤到的人物。在每個人物的結果組合中，依據追蹤路徑精確度的可信度遞減排序元素。在預設情況下，將根據 `Persons` 的排序傳回 `TIMESTAMP`。以下是來自 `GetPersonDetection` 的 JSON 回應範例。結果會依據人物路徑從影片開始後在影片中被追蹤到的時間排序，以毫秒為單位。在回應中，請注意下列事項：
+ **人物資訊**：`PersonDetection` 陣列元素包含有關偵測到的人物之資訊。例如，該人物被偵測到的時間 (`Timestamp`)、該人物被偵測到時所在的影片影格位置 (`BoundingBox`)、以及 Amazon Rekognition Video 對於正確偵測該人物的可信度 (`Confidence`)。

  不會在每次追蹤到人物路徑的時間戳記時傳回臉部特徵。此外在部分情況下，被追蹤到的人物身體可能無法顯示，因此只會傳回他們的臉部位置。
+ **分頁資訊**：範例顯示人物偵測資訊的一頁。您可以在 `GetPersonTracking` 的 `MaxResults` 輸入參數中指定要傳回的人物元素數目。如果結果數目超過 `MaxResults`，`GetPersonTracking` 會傳回用來取得下一頁結果的字符 (`NextToken`)。如需詳細資訊，請參閱[取得 Amazon Rekognition Video 分析結果](api-video.md#api-video-get)。
+ **索引**：在影片中識別人物的專有識別碼。
+ **影片資訊** - 回應包含 `VideoMetadata` 所傳回之每頁資訊中影片格式 (`GetPersonDetection`) 的相關資訊。

```
{
    "JobStatus": "SUCCEEDED",
    "NextToken": "AcDymG0fSSoaI6+BBYpka5wVlqttysSPP8VvWcujMDluj1QpFo/vf+mrMoqBGk8eUEiFlllR6g==",
    "Persons": [
        {
            "Person": {
                "BoundingBox": {
                    "Height": 0.8787037134170532,
                    "Left": 0.00572916679084301,
                    "Top": 0.12129629403352737,
                    "Width": 0.21666666865348816
                },
                "Face": {
                    "BoundingBox": {
                        "Height": 0.20000000298023224,
                        "Left": 0.029999999329447746,
                        "Top": 0.2199999988079071,
                        "Width": 0.11249999701976776
                    },
                    "Confidence": 99.85971069335938,
                    "Landmarks": [
                        {
                            "Type": "eyeLeft",
                            "X": 0.06842322647571564,
                            "Y": 0.3010137975215912
                        },
                        {
                            "Type": "eyeRight",
                            "X": 0.10543643683195114,
                            "Y": 0.29697132110595703
                        },
                        {
                            "Type": "nose",
                            "X": 0.09569807350635529,
                            "Y": 0.33701086044311523
                        },
                        {
                            "Type": "mouthLeft",
                            "X": 0.0732642263174057,
                            "Y": 0.3757539987564087
                        },
                        {
                            "Type": "mouthRight",
                            "X": 0.10589495301246643,
                            "Y": 0.3722417950630188
                        }
                    ],
                    "Pose": {
                        "Pitch": -0.5589138865470886,
                        "Roll": -5.1093974113464355,
                        "Yaw": 18.69594955444336
                    },
                    "Quality": {
                        "Brightness": 43.052337646484375,
                        "Sharpness": 99.68138885498047
                    }
                },
                "Index": 0
            },
            "Timestamp": 0
        },
        {
            "Person": {
                "BoundingBox": {
                    "Height": 0.9074074029922485,
                    "Left": 0.24791666865348816,
                    "Top": 0.09259258955717087,
                    "Width": 0.375
                },
                "Face": {
                    "BoundingBox": {
                        "Height": 0.23000000417232513,
                        "Left": 0.42500001192092896,
                        "Top": 0.16333332657814026,
                        "Width": 0.12937499582767487
                    },
                    "Confidence": 99.97504425048828,
                    "Landmarks": [
                        {
                            "Type": "eyeLeft",
                            "X": 0.46415066719055176,
                            "Y": 0.2572723925113678
                        },
                        {
                            "Type": "eyeRight",
                            "X": 0.5068183541297913,
                            "Y": 0.23705792427062988
                        },
                        {
                            "Type": "nose",
                            "X": 0.49765899777412415,
                            "Y": 0.28383663296699524
                        },
                        {
                            "Type": "mouthLeft",
                            "X": 0.487221896648407,
                            "Y": 0.3452930748462677
                        },
                        {
                            "Type": "mouthRight",
                            "X": 0.5142884850502014,
                            "Y": 0.33167609572410583
                        }
                    ],
                    "Pose": {
                        "Pitch": 15.966927528381348,
                        "Roll": -15.547388076782227,
                        "Yaw": 11.34195613861084
                    },
                    "Quality": {
                        "Brightness": 44.80223083496094,
                        "Sharpness": 99.95819854736328
                    }
                },
                "Index": 1
            },
            "Timestamp": 0
        }.....

    ],
    "VideoMetadata": {
        "Codec": "h264",
        "DurationMillis": 67301,
        "FileExtension": "mp4",
        "Format": "QuickTime / MOV",
        "FrameHeight": 1080,
        "FrameRate": 29.970029830932617,
        "FrameWidth": 1920
    }
}
```