Recognizing celebrities in a stored video
Amazon Rekognition Video celebrity recognition in stored videos is an asynchronous
operation.
To recognize celebrities in a stored video,
use StartCelebrityRecognition
to start video analysis. Amazon Rekognition Video publishes the
completion status of the video analysis to an Amazon Simple Notification Service topic.
If the video analysis is succesful,
call GetCelebrityRecognition.
to get the analysis results. For more information about starting video analysis and
getting the results,
see Calling Amazon Rekognition Video operations.
This procedure expands on the code in Analyzing a video stored in an Amazon S3
bucket with Java or Python (SDK), which uses an Amazon SQS queue to get the
completion status of a video analysis request. To run this procedure, you need a
video file that contains one or more celebrity faces.
To detect celebrities in a video stored in an Amazon S3 bucket (SDK)
-
Perform Analyzing a video stored in an Amazon S3
bucket with Java or Python (SDK).
-
Add the following code to the class VideoDetect that you created in step 1.
- 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);
}
In the function main, replace the line:
StartLabelDetection(bucket, video);
if (GetSQSMessageSuccess()==true)
GetLabelDetectionResults();
with:
StartCelebrityDetection(bucket, video);
if (GetSQSMessageSuccess()==true)
GetCelebrityDetectionResults();
- Java V2
-
This code is taken from the AWS Documentation SDK examples GitHub repository. See
the full example
here.
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: " + Long.toString(seconds) + " ");
CelebrityDetail details=celeb.celebrity();
System.out.println("Name: " + details.name());
System.out.println("Id: " + details.id());
System.out.println();
}
} while (recognitionResponse !=null && recognitionResponse.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.)
# ============== 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
In the function main, replace the lines:
analyzer.StartLabelDetection()
if analyzer.GetSQSMessageSuccess()==True:
analyzer.GetLabelDetectionResults()
with:
analyzer.StartCelebrityDetection()
if analyzer.GetSQSMessageSuccess()==True:
analyzer.GetCelebrityDetectionResults()
-
Run the code. Information about the celebrities recognized in the video is shown.
GetCelebrityRecognition operation response
The following is an example JSON response. The response includes the following:
-
Recognized celebrities –
Celebrities is an array of celebrities and the times that
they are recognized in a video. A CelebrityRecognition object exists for each time
the celebrity is recognized in the video. Each
CelebrityRecognition contains information about a
recognized celebrity (CelebrityDetail) and the time
(Timestamp) the celebrity was recognized in the video.
Timestamp is measured in milliseconds from the start of the
video.
-
CelebrityDetail – Contains information
about a recognized celebrity. It includes the celebrity name
(Name), identifier (ID), and a list of URLs
pointing to related content (Urls). It also includes the
bounding box for the celebrity's body, the confidence level that Amazon Rekognition
Video
has in the accuracy of the recognition, and details about the celebrity's
face, FaceDetail. If
you need to get the related content later, you can use ID with
GetCelebrityInfo.
-
VideoMetadata – Information about the video that was analyzed.
{
"Celebrities": [
{
"Celebrity": {
"BoundingBox": {
"Height": 0.8842592835426331,
"Left": 0,
"Top": 0.11574073880910873,
"Width": 0.24427083134651184
},
"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",
"Name": "Celeb A",
"Urls": []
},
"Timestamp": 367
},......
],
"JobStatus": "SUCCEEDED",
"NextToken": "XfXnZKiyMOGDhzBzYUhS5puM+g1IgezqFeYpv/H/+5noP/LmM57FitUAwSQ5D6G4AB/PNwolrw==",
"VideoMetadata": {
"Codec": "h264",
"DurationMillis": 67301,
"FileExtension": "mp4",
"Format": "QuickTime / MOV",
"FrameHeight": 1080,
"FrameRate": 29.970029830932617,
"FrameWidth": 1920
}
}
