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
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，該人物出現在視訊影格中的位置。
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。要啟動人們在視頻通話StartPersonTracking的路徑。Amazon Rekognition Video 向其發佈影片分析操作的物件偵測結果和完成狀態的 Amazon Simple Notification Service 主題。如果視頻分析成功，請致電GetPersonTracking以獲取視頻分析的結果。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。
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 中的程式碼，這會使用 Amazon Simple Queue Service 佇列來取得視訊分析要求的完成狀態。
若要偵測存放在 Amazon S3 儲存貯體 (SDK) 之視訊中的人物
執行 使用 Java 或 Python (SDK) 分析儲存於 Amazon S3 儲存貯體中的影片。
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(bucket, video);
if (GetSQSMessageSuccess()==true)
GetLabelDetectionResults();
取代為：
StartPersonDetection(bucket, video);
if (GetSQSMessageSuccess()==true)
GetPersonDetectionResults();
- Java V2
-
此代碼取自AWS文檔 SDK 示例 GitHub 存儲庫。請參閱此處的完整範例。
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，在視訊中啟動人員路徑分析。
aws rekognition start-person-tracking --video "{"S3Object":{"Bucket":"bucket-name","Name":"video-name"}}" \
--notification-channel "{"SNSTopicArn":"topic-ARN","RoleArn":"role-ARN"}" \
--region region-name --profile profile-name
更新下列的值：
-
將 bucket-name 與 video-name 變更為您在步驟 2 中指定的 Amazon S3 儲存貯體與視訊檔名稱。
-
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 的值取代為您的開發人員設定檔的名稱。
-
將 topic-ARN 變更為您在步驟 3 建立的 設定 Amazon Rekognition Video Amazon SNS 主題的 ARN。
-
將 role-ARN 變更為您在步驟 7 建立的 設定 Amazon Rekognition Video 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\) 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\":\"bucket-name\",\"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
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 作業回應
GetPersonTracking 傳回 Persons 物件的 PersonDetection 陣列，其中包含影片中追蹤到的人物詳細資訊，以及追蹤到他們路徑的時間。
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視訊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視訊影格位置 (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。
-
索引：在影片中識別人物的專有識別碼。
-
視訊資訊：回應包含 GetPersonDetection 所傳回之每頁資訊中視訊格式 (VideoMetadata) 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
}
}
