翻訳は機械翻訳により提供されています。提供された翻訳内容と英語版の間で齟齬、不一致または矛盾がある場合、英語版が優先します。
保存済みビデオ内の有名人の認識
Amazon Rekognition Video による保存済みビデオ内の有名人の認識は非同期オペレーションです。保存されたビデオの有名人を認識するには、StartCelebrityRecognition を使用してビデオ分析を開始します。Amazon Rekognition Video は、ビデオ分析の完了ステータスを Amazon Simple Notification Service トピックに発行します。ビデオ分析が成功したら、GetCelebrityRecognition を呼び出して分析結果を取得します。ビデオ分析の開始と結果の取得の詳細については、「Amazon Rekognition Video オペレーションを呼び出す」を参照してください。
この手順では、Amazon SQS キューを使用してビデオ分析リクエストの完了ステータスを取得する「Java または Python を使用した、Amazon S3 バケットに保存されたビデオの分析 (SDK)」のコードを拡張します。この手順を実行するには、1 つ以上の有名人の顔が含まれているビデオファイルが必要です。
Amazon S3 バケットに保存されたビデオ内の有名人を検出するには (SDK)
「Java または Python を使用した、Amazon S3 バケットに保存されたビデオの分析 (SDK)」を実行します。
ステップ 1 で作成したクラス VideoDetect に次のコードを追加します。
- 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.)
// Celebrities=====================================================================
private static void StartCelebrityDetection(String bucket, String video) throws Exception{
NotificationChannel channel= new NotificationChannel()
.withSNSTopicArn(snsTopicArn)
.withRoleArn(roleArn);
StartCelebrityRecognitionRequest req = new StartCelebrityRecognitionRequest()
.withVideo(new Video()
.withS3Object(new S3Object()
.withBucket(bucket)
.withName(video)))
.withNotificationChannel(channel);
StartCelebrityRecognitionResult startCelebrityRecognitionResult = rek.startCelebrityRecognition(req);
startJobId=startCelebrityRecognitionResult.getJobId();
}
private static void GetCelebrityDetectionResults() throws Exception{
int maxResults=10;
String paginationToken=null;
GetCelebrityRecognitionResult celebrityRecognitionResult=null;
do{
if (celebrityRecognitionResult !=null){
paginationToken = celebrityRecognitionResult.getNextToken();
}
celebrityRecognitionResult = rek.getCelebrityRecognition(new GetCelebrityRecognitionRequest()
.withJobId(startJobId)
.withNextToken(paginationToken)
.withSortBy(CelebrityRecognitionSortBy.TIMESTAMP)
.withMaxResults(maxResults));
System.out.println("File info for page");
VideoMetadata videoMetaData=celebrityRecognitionResult.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());
System.out.println("Job");
System.out.println("Job status: " + celebrityRecognitionResult.getJobStatus());
//Show celebrities
List<CelebrityRecognition> celebs= celebrityRecognitionResult.getCelebrities();
for (CelebrityRecognition celeb: celebs) {
long seconds=celeb.getTimestamp()/1000;
System.out.print("Sec: " + Long.toString(seconds) + " ");
CelebrityDetail details=celeb.getCelebrity();
System.out.println("Name: " + details.getName());
System.out.println("Id: " + details.getId());
System.out.println();
}
} while (celebrityRecognitionResult !=null && celebrityRecognitionResult.getNextToken() != null);
}
関数 main で、次の行を置き換えます。
StartLabelDetection(bucket, video);
if (GetSQSMessageSuccess()==true)
GetLabelDetectionResults();
を:
StartCelebrityDetection(bucket, video);
if (GetSQSMessageSuccess()==true)
GetCelebrityDetectionResults();
- Java V2
-
このコードは、 AWSドキュメント SDK サンプル GitHub リポジトリから取得されます。詳しい事例は [こちら] です。
//snippet-start:[rekognition.java2.recognize_video_celebrity.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.StartCelebrityRecognitionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.CelebrityRecognitionSortBy;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.CelebrityRecognition;
import software.amazon.awssdk.services.rekognition.model.CelebrityDetail;
import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionRequest;
import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionRequest;
import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionResponse;
import java.util.List;
//snippet-end:[rekognition.java2.recognize_video_celebrity.import]
/**
* To run this code example, ensure that you perform the Prerequisites as stated in the Amazon Rekognition Guide:
* https://docs.aws.amazon.com/rekognition/latest/dg/video-analyzing-with-sqs.html
*
* Also, ensure that set up your development environment, including your credentials.
*
* For information, see this documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class RecognizeCelebritiesVideo {
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_EAST_1;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
.build();
NotificationChannel channel = NotificationChannel.builder()
.snsTopicArn(topicArn)
.roleArn(roleArn)
.build();
StartCelebrityDetection(rekClient, channel, bucket, video);
GetCelebrityDetectionResults(rekClient);
System.out.println("This example is done!");
rekClient.close();
}
// snippet-start:[rekognition.java2.recognize_video_celebrity.main]
public static void StartCelebrityDetection(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();
StartCelebrityRecognitionRequest recognitionRequest = StartCelebrityRecognitionRequest.builder()
.jobTag("Celebrities")
.notificationChannel(channel)
.video(vidOb)
.build();
StartCelebrityRecognitionResponse startCelebrityRecognitionResult = rekClient.startCelebrityRecognition(recognitionRequest);
startJobId = startCelebrityRecognitionResult.jobId();
} catch(RekognitionException e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
public static void GetCelebrityDetectionResults(RekognitionClient rekClient) {
try {
String paginationToken=null;
GetCelebrityRecognitionResponse recognitionResponse = null;
boolean finished = false;
String status;
int yy=0 ;
do{
if (recognitionResponse !=null)
paginationToken = recognitionResponse.nextToken();
GetCelebrityRecognitionRequest recognitionRequest = GetCelebrityRecognitionRequest.builder()
.jobId(startJobId)
.nextToken(paginationToken)
.sortBy(CelebrityRecognitionSortBy.TIMESTAMP)
.maxResults(10)
.build();
// Wait until the job succeeds
while (!finished) {
recognitionResponse = rekClient.getCelebrityRecognition(recognitionRequest);
status = recognitionResponse.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=recognitionResponse.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<CelebrityRecognition> celebs= recognitionResponse.celebrities();
for (CelebrityRecognition celeb: celebs) {
long seconds=celeb.timestamp()/1000;
System.out.print("Sec: " + seconds + " ");
CelebrityDetail details=celeb.celebrity();
System.out.println("Name: " + details.name());
System.out.println("Id: " + details.id());
System.out.println();
}
} while (recognitionResponse.nextToken() != null);
} catch(RekognitionException | InterruptedException e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
// snippet-end:[rekognition.java2.recognize_video_celebrity.main]
}
- 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.)
# ============== Celebrities ===============
def StartCelebrityDetection(self):
response=self.rek.start_celebrity_recognition(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 GetCelebrityDetectionResults(self):
maxResults = 10
paginationToken = ''
finished = False
while finished == False:
response = self.rek.get_celebrity_recognition(JobId=self.startJobId,
MaxResults=maxResults,
NextToken=paginationToken)
print(response['VideoMetadata']['Codec'])
print(str(response['VideoMetadata']['DurationMillis']))
print(response['VideoMetadata']['Format'])
print(response['VideoMetadata']['FrameRate'])
for celebrityRecognition in response['Celebrities']:
print('Celebrity: ' +
str(celebrityRecognition['Celebrity']['Name']))
print('Timestamp: ' + str(celebrityRecognition['Timestamp']))
print()
if 'NextToken' in response:
paginationToken = response['NextToken']
else:
finished = True
関数 main で、以下の行を置き換えます。
analyzer.StartLabelDetection()
if analyzer.GetSQSMessageSuccess()==True:
analyzer.GetLabelDetectionResults()
を:
analyzer.StartCelebrityDetection()
if analyzer.GetSQSMessageSuccess()==True:
analyzer.GetCelebrityDetectionResults()
- Node.JS
-
次の Node.Js のコード例では、bucket の値を、ビデオが含まれている S3 バケットの名前に置き換え、videoName の値をそのビデオファイルの名前に置き換えます。また、roleArn の値を、IAM サービスロールに関連付けられた ARN に置き換える必要があります。最後に、region の値を、お使いのアカウントに関連付けられたオペレーティングリージョンの名前に置き換えます。Rekognition セッションを作成する行の profile_name の値を、自分のデベロッパープロファイル名に置き換えます。
//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.)
// Import required AWS SDK clients and commands for Node.js
import { CreateQueueCommand, GetQueueAttributesCommand, GetQueueUrlCommand,
SetQueueAttributesCommand, DeleteQueueCommand, ReceiveMessageCommand, DeleteMessageCommand } from "@aws-sdk/client-sqs";
import {CreateTopicCommand, SubscribeCommand, DeleteTopicCommand } from "@aws-sdk/client-sns";
import { SQSClient } from "@aws-sdk/client-sqs";
import { SNSClient } from "@aws-sdk/client-sns";
import { RekognitionClient, StartLabelDetectionCommand, GetLabelDetectionCommand,
StartCelebrityRecognitionCommand, GetCelebrityRecognitionCommand} from "@aws-sdk/client-rekognition";
import { stdout } from "process";
import {fromIni} from '@aws-sdk/credential-providers';
// Set the AWS Region.
const REGION = "region-name"; //e.g. "us-east-1"
// Set the profile name
const profileName = "profile-name"
// Name the collection
// Create SNS service object.
const sqsClient = new SQSClient({ region: REGION,
credentials: fromIni({profile: profileName,}), });
const snsClient = new SNSClient({ region: REGION,
credentials: fromIni({profile: profileName,}), });
const rekClient = new RekognitionClient({region: REGION,
credentials: fromIni({profile: profileName,}),
});
// Set bucket and video variables
const bucket = "bucket-name";
const videoName = "video-name";
const roleArn = "role-arn"
var startJobId = ""
var ts = Date.now();
const snsTopicName = "AmazonRekognitionExample" + ts;
const snsTopicParams = {Name: snsTopicName}
const sqsQueueName = "AmazonRekognitionQueue-" + ts;
// Set the parameters
const sqsParams = {
QueueName: sqsQueueName, //SQS_QUEUE_URL
Attributes: {
DelaySeconds: "60", // Number of seconds delay.
MessageRetentionPeriod: "86400", // Number of seconds delay.
},
};
const createTopicandQueue = async () => {
try {
// Create SNS topic
const topicResponse = await snsClient.send(new CreateTopicCommand(snsTopicParams));
const topicArn = topicResponse.TopicArn
console.log("Success", topicResponse);
// Create SQS Queue
const sqsResponse = await sqsClient.send(new CreateQueueCommand(sqsParams));
console.log("Success", sqsResponse);
const sqsQueueCommand = await sqsClient.send(new GetQueueUrlCommand({QueueName: sqsQueueName}))
const sqsQueueUrl = sqsQueueCommand.QueueUrl
const attribsResponse = await sqsClient.send(new GetQueueAttributesCommand({QueueUrl: sqsQueueUrl, AttributeNames: ['QueueArn']}))
const attribs = attribsResponse.Attributes
console.log(attribs)
const queueArn = attribs.QueueArn
// subscribe SQS queue to SNS topic
const subscribed = await snsClient.send(new SubscribeCommand({TopicArn: topicArn, Protocol:'sqs', Endpoint: queueArn}))
const policy = {
Version: "2012-10-17",
Statement: [
{
Sid: "MyPolicy",
Effect: "Allow",
Principal: {AWS: "*"},
Action: "SQS:SendMessage",
Resource: queueArn,
Condition: {
ArnEquals: {
'aws:SourceArn': topicArn
}
}
}
]
};
const response = sqsClient.send(new SetQueueAttributesCommand({QueueUrl: sqsQueueUrl, Attributes: {Policy: JSON.stringify(policy)}}))
console.log(response)
console.log(sqsQueueUrl, topicArn)
return [sqsQueueUrl, topicArn]
} catch (err) {
console.log("Error", err);
}
};
const startCelebrityDetection = async(roleArn, snsTopicArn) =>{
try {
//Initiate label detection and update value of startJobId with returned Job ID
const response = await rekClient.send(new StartCelebrityRecognitionCommand({Video:{S3Object:{Bucket:bucket, Name:videoName}},
NotificationChannel:{RoleArn: roleArn, SNSTopicArn: snsTopicArn}}))
startJobId = response.JobId
console.log(`Start Job ID: ${startJobId}`)
return startJobId
} catch (err) {
console.log("Error", err);
}
};
const getCelebrityRecognitionResults = async(startJobId) =>{
try {
//Initiate label detection and update value of startJobId with returned Job ID
var maxResults = 10
var paginationToken = ''
var finished = false
while (finished == false){
var response = await rekClient.send(new GetCelebrityRecognitionCommand({JobId: startJobId, MaxResults: maxResults,
NextToken: paginationToken}))
console.log(response.VideoMetadata.Codec)
console.log(response.VideoMetadata.DurationMillis)
console.log(response.VideoMetadata.Format)
console.log(response.VideoMetadata.FrameRate)
response.Celebrities.forEach(celebrityRecognition => {
console.log(`Celebrity: ${celebrityRecognition.Celebrity.Name}`)
console.log(`Timestamp: ${celebrityRecognition.Timestamp}`)
console.log()
})
// Searh for pagination token, if found, set variable to next token
if (String(response).includes("NextToken")){
paginationToken = response.NextToken
}else{
finished = true
}
}
} catch (err) {
console.log("Error", err);
}
};
// Checks for status of job completion
const getSQSMessageSuccess = async(sqsQueueUrl, startJobId) => {
try {
// Set job found and success status to false initially
var jobFound = false
var succeeded = false
var dotLine = 0
// while not found, continue to poll for response
while (jobFound == false){
var sqsReceivedResponse = await sqsClient.send(new ReceiveMessageCommand({QueueUrl:sqsQueueUrl,
MaxNumberOfMessages:'ALL', MaxNumberOfMessages:10}));
if (sqsReceivedResponse){
var responseString = JSON.stringify(sqsReceivedResponse)
if (!responseString.includes('Body')){
if (dotLine < 40) {
console.log('.')
dotLine = dotLine + 1
}else {
console.log('')
dotLine = 0
};
stdout.write('', () => {
console.log('');
});
await new Promise(resolve => setTimeout(resolve, 5000));
continue
}
}
// Once job found, log Job ID and return true if status is succeeded
for (var message of sqsReceivedResponse.Messages){
console.log("Retrieved messages:")
var notification = JSON.parse(message.Body)
var rekMessage = JSON.parse(notification.Message)
var messageJobId = rekMessage.JobId
if (String(rekMessage.JobId).includes(String(startJobId))){
console.log('Matching job found:')
console.log(rekMessage.JobId)
jobFound = true
console.log(rekMessage.Status)
if (String(rekMessage.Status).includes(String("SUCCEEDED"))){
succeeded = true
console.log("Job processing succeeded.")
var sqsDeleteMessage = await sqsClient.send(new DeleteMessageCommand({QueueUrl:sqsQueueUrl, ReceiptHandle:message.ReceiptHandle}));
}
}else{
console.log("Provided Job ID did not match returned ID.")
var sqsDeleteMessage = await sqsClient.send(new DeleteMessageCommand({QueueUrl:sqsQueueUrl, ReceiptHandle:message.ReceiptHandle}));
}
}
}
return succeeded
} catch(err) {
console.log("Error", err);
}
};
// Start label detection job, sent status notification, check for success status
// Retrieve results if status is "SUCEEDED", delete notification queue and topic
const runCelebRecognitionAndGetResults = async () => {
try {
const sqsAndTopic = await createTopicandQueue();
//const startLabelDetectionRes = await startLabelDetection(roleArn, sqsAndTopic[1]);
//const getSQSMessageStatus = await getSQSMessageSuccess(sqsAndTopic[0], startLabelDetectionRes)
const startCelebrityDetectionRes = await startCelebrityDetection(roleArn, sqsAndTopic[1]);
const getSQSMessageStatus = await getSQSMessageSuccess(sqsAndTopic[0], startCelebrityDetectionRes)
console.log(getSQSMessageSuccess)
if (getSQSMessageSuccess){
console.log("Retrieving results:")
const results = await getCelebrityRecognitionResults(startCelebrityDetectionRes)
}
const deleteQueue = await sqsClient.send(new DeleteQueueCommand({QueueUrl: sqsAndTopic[0]}));
const deleteTopic = await snsClient.send(new DeleteTopicCommand({TopicArn: sqsAndTopic[1]}));
console.log("Successfully deleted.")
} catch (err) {
console.log("Error", err);
}
};
runCelebRecognitionAndGetResults()
- CLI
-
以下の AWS CLI コマンドを実行して、ビデオ内の有名人の検出を開始します。
aws rekognition start-celebrity-recognition --video "{"S3Object":{"Bucket":"bucket-name","Name":"video-name"}}" \
--notification-channel "{"SNSTopicArn":"topic-arn","RoleArn":"role-arn"}" \
--region region-name --profile profile-name
以下の値を更新します。
-
bucket-name と video-name を、ステップ 2 で指定した Amazon S3 バケット名とファイル名に変更します。
-
region-name を、使用している AWS リージョンに変更します。
-
profile-name の値を自分のデベロッパープロファイル名に置き換えます。
-
topic-ARN を、Amazon Rekognition Video の設定 のステップ 3 で作成した Amazon SNS トピックの ARN に変更します。
-
role-ARN を、Amazon Rekognition Video の設定 のステップ 7 で作成した IAM サービスロールの ARN に変更します。
Windows デバイスで CLI にアクセスする場合は、パーサーエラーの発生に対処するため、一重引用符の代わりに二重引用符を使用し、内側の二重引用符をバックスラッシュ (\) でエスケープします。次の例を参照してください。
aws rekognition start-celebrity-recognition --video "{\"S3Object\":{\"Bucket\":\"bucket-name\",\"Name\":\"video-name\"}}" \
--notification-channel "{\"SNSTopicArn\":\"topic-arn\",\"RoleArn\":\"role-arn\"}" \
--region region-name --profile profile-name
上記のコード例を実行した後、返された jobID をコピーして以下の GetCelebrityRecognition コマンドに渡すと、job-id-number が以前に受け取った jobID に置き換わっている結果が得られます。
aws rekognition get-celebrity-recognition --job-id job-id-number --profile profile-name
コードを実行します。ビデオ内で認識された有名人に関する情報が表示されます。
GetCelebrityRecognition オペレーションレスポンス
以下に、JSON レスポンスの例を示します。このレスポンスには、以下が含まれています。
-
認識された有名人 – Celebrities はビデオで認識された有名人と認識された時間の配列です。CelebrityRecognition オブジェクトは、ビデオで有名人が認識されるたびに生成されます。各 CelebrityRecognition には、ビデオ内で認識された有名人 (CelebrityDetail) に関する情報とその有名人が認識された時間 (Timestamp) が含まれています。Timestamp は、ビデオの開始からのミリ秒数で示されます。
-
CelebrityDetail - 認識された有名人に関する情報が含まれます。有名人の名前 (Name)、識別子 (ID)、有名人の既知の性別 (KnownGender)、および関連コンテンツを参照する URL のリスト (Urls) が含まれます。また、認識の精度に対する Amazon Rekognition Video の信頼度、有名人の顔の詳細 (FaceDetail) も含まれます。後で関連コンテンツを取得する必要がある場合は、getCelebrityInfo で ID を使用できます。
-
VideoMetadata - 分析されたビデオに関する情報。
{
"Celebrities": [
{
"Celebrity": {
"Confidence": 0.699999988079071,
"Face": {
"BoundingBox": {
"Height": 0.20555555820465088,
"Left": 0.029374999925494194,
"Top": 0.22333332896232605,
"Width": 0.11562500149011612
},
"Confidence": 99.89837646484375,
"Landmarks": [
{
"Type": "eyeLeft",
"X": 0.06857934594154358,
"Y": 0.30842265486717224
},
{
"Type": "eyeRight",
"X": 0.10396526008844376,
"Y": 0.300625205039978
},
{
"Type": "nose",
"X": 0.0966852456331253,
"Y": 0.34081998467445374
},
{
"Type": "mouthLeft",
"X": 0.075217105448246,
"Y": 0.3811396062374115
},
{
"Type": "mouthRight",
"X": 0.10744428634643555,
"Y": 0.37407416105270386
}
],
"Pose": {
"Pitch": -0.9784082174301147,
"Roll": -8.808176040649414,
"Yaw": 20.28228759765625
},
"Quality": {
"Brightness": 43.312068939208984,
"Sharpness": 99.9305191040039
}
},
"Id": "XXXXXX",
"KnownGender": {
"Type": "Female"
},
"Name": "Celeb A",
"Urls": []
},
"Timestamp": 367
},......
],
"JobStatus": "SUCCEEDED",
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