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人物的轨迹
Amazon Rekognition Video 可以创建视频中的人物轨迹并提供如下信息：
在跟踪人物的轨迹时人物在视频帧中的位置。
人脸标记，如左眼的位置（如果检测到）。
存储视频中的 Amazon Rekognition Video 人物轨迹跟踪是一个异步操作。要开始在视频中寻找人物路径，请致电StartPersonTracking。Amazon Rekognition Video 会将视频分析的完成状态发布到 Amazon Simple Notification Service 主题。如果视频分析成功，请致电GetPersonTracking以获取视频分析的结果。有关调用 Amazon Rekognition Video API 操作的更多信息，请参阅 调用 Amazon Rekognition Video 操作。
以下过程说明如何通过存储在 Amazon S3 存储桶内的视频跟踪人物轨迹。此示例扩展了 使用 Java 或 Python 分析存储在 Amazon S3 存储桶中的视频 (SDK)（使用 Amazon Simple Queue Service 队列获取视频分析请求的完成状态）中的代码。
检测存储在 Amazon S3 存储桶内的视频中的人员 (SDK)
执行使用 Java 或 Python 分析存储在 Amazon S3 存储桶中的视频 (SDK)。
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(bucket, video);
if (GetSQSMessageSuccess()==true)
GetLabelDetectionResults();
替换为:
StartPersonDetection(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":"bucket-name","Name":"video-name"}}" \
--notification-channel "{"SNSTopicArn":"topic-ARN","RoleArn":"role-ARN"}" \
--region region-name --profile profile-name
更新以下值：
-
将bucket-name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\":\"bucket-name\",\"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
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）。
GetPersonTracking 操作响应
GetPersonTracking 将返回一个数组 Persons，其中包括 PersonDetection 对象，这些对象包含有关所检测人物的详细信息以及跟踪人物轨迹的时间。
您可通过使用 SortBy 输入参数来为 Persons 排序。指定 TIMESTAMP 以按在视频中检测人物轨迹的时间为元素排序。指定 INDEX 以按在视频中跟踪的人员排序。在每组人物的结果中，元素是按人物轨迹跟踪的准确性的置信度的降序顺序排序的。默认情况下，Persons 将按 TIMESTAMP 返回/排序。以下示例是 GetPersonDetection 的 JSON 响应。结果按在视频中跟踪人物轨迹的时间（播放视频的毫秒数）排序。在响应中，请注意以下内容：
-
人员信息 – PersonDetection 数组元素包含有关检测到的人员的信息。例如，检测到人员的时间 (Timestamp)、检测到时人员在视频帧中的位置 (BoundingBox) 以及 Amazon Rekognition Video 对正确检测到人员的置信度 (Confidence)。
跟踪人物轨迹的每个时间戳不会返回面部特征。而且，在被跟踪人员的身体可能不可见的某些情况下，仅返回其脸部位置。
-
分页信息 – 此示例显示一页的人员检测信息。您可以在 GetPersonTracking 的 MaxResults 输入参数中指定要返回的人员元素数量。如果存在的结果的数量超过了 MaxResults，则 GetPersonTracking 会返回一个令牌 (NextToken)，用于获取下一页的结果。有关更多信息，请参见 获取 Amazon Rekognition Video 分析结果。
-
索引 – 用于在整个视频中识别人物的唯一标识符。
-
视频信息 – 此响应包含有关由 GetPersonDetection 返回的每页信息中的视频格式 (VideoMetadata) 的信息。
{
"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
}
}
