Searching stored videos for faces
You can search a collection for faces that match faces of people who are detected
in a stored video or a streaming video. This section covers searching for faces in
a
stored video. For information about searching for faces in a streaming video, see
Working with streaming videos.
The faces that you search for must first be indexed into a collection by using
IndexFaces. For more
information, see Adding faces to a collection.
Amazon Rekognition Video face searching follows the same asynchronous workflow as
other
Amazon Rekognition Video operations that analyze videos stored in an Amazon S3 bucket.
To start searching for
faces in a stored video, call StartFaceSearch and provide the ID of the collection that you
want to search. Amazon Rekognition Video publishes the completion status of the video
analysis to an
Amazon Simple Notification Service (Amazon SNS) topic. If the video analysis is successful,
call GetFaceSearch to get the search
results. For more information about starting video analysis and getting the results,
see
Calling Amazon Rekognition Video operations.
The following procedure shows how to search a collection for faces that match
the faces of people who are detected in a video. The procedure also shows how to get
the tracking data for people who are matched in the video. The procedure expands on
the
code in Analyzing a video stored in an Amazon S3
bucket with Java or Python (SDK),
which uses an Amazon Simple Queue Service (Amazon SQS) queue to get the completion
status of a video
analysis request.
To search a video for matching faces (SDK)
-
Create a collection.
-
Index a face into the collection.
-
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 3.
- 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.)
//Face collection search in video ==================================================================
private static void StartFaceSearchCollection(String bucket, String video, String collection) throws Exception{
NotificationChannel channel= new NotificationChannel()
.withSNSTopicArn(snsTopicArn)
.withRoleArn(roleArn);
StartFaceSearchRequest req = new StartFaceSearchRequest()
.withCollectionId(collection)
.withVideo(new Video()
.withS3Object(new S3Object()
.withBucket(bucket)
.withName(video)))
.withNotificationChannel(channel);
StartFaceSearchResult startPersonCollectionSearchResult = rek.startFaceSearch(req);
startJobId=startPersonCollectionSearchResult.getJobId();
}
//Face collection search in video ==================================================================
private static void GetFaceSearchCollectionResults() throws Exception{
GetFaceSearchResult faceSearchResult=null;
int maxResults=10;
String paginationToken=null;
do {
if (faceSearchResult !=null){
paginationToken = faceSearchResult.getNextToken();
}
faceSearchResult = rek.getFaceSearch(
new GetFaceSearchRequest()
.withJobId(startJobId)
.withMaxResults(maxResults)
.withNextToken(paginationToken)
.withSortBy(FaceSearchSortBy.TIMESTAMP)
);
VideoMetadata videoMetaData=faceSearchResult.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();
//Show search results
List<PersonMatch> matches=
faceSearchResult.getPersons();
for (PersonMatch match: matches) {
long milliSeconds=match.getTimestamp();
System.out.print("Timestamp: " + Long.toString(milliSeconds));
System.out.println(" Person number: " + match.getPerson().getIndex());
List <FaceMatch> faceMatches = match.getFaceMatches();
if (faceMatches != null) {
System.out.println("Matches in collection...");
for (FaceMatch faceMatch: faceMatches){
Face face=faceMatch.getFace();
System.out.println("Face Id: "+ face.getFaceId());
System.out.println("Similarity: " + faceMatch.getSimilarity().toString());
System.out.println();
}
}
System.out.println();
}
System.out.println();
} while (faceSearchResult !=null && faceSearchResult.getNextToken() != null);
}
In the function main, replace the lines:
StartLabelDetection(bucket, video);
if (GetSQSMessageSuccess()==true)
GetLabelDetectionResults();
with:
String collection="collection";
StartFaceSearchCollection(bucket, video, collection);
if (GetSQSMessageSuccess()==true)
GetFaceSearchCollectionResults();
- Java V2
-
This code is taken from the AWS Documentation SDK examples GitHub repository. See
the full example
here.
public static void StartFaceDetection(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();
StartFaceDetectionRequest faceDetectionRequest = StartFaceDetectionRequest.builder()
.jobTag("Faces")
.faceAttributes(FaceAttributes.ALL)
.notificationChannel(channel)
.video(vidOb)
.build();
StartFaceDetectionResponse startLabelDetectionResult = rekClient.startFaceDetection(faceDetectionRequest);
startJobId=startLabelDetectionResult.jobId();
} catch(RekognitionException e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
public static void GetFaceResults(RekognitionClient rekClient) {
try {
String paginationToken=null;
GetFaceDetectionResponse faceDetectionResponse=null;
Boolean finished = false;
String status="";
int yy=0 ;
do{
if (faceDetectionResponse !=null)
paginationToken = faceDetectionResponse.nextToken();
GetFaceDetectionRequest recognitionRequest = GetFaceDetectionRequest.builder()
.jobId(startJobId)
.nextToken(paginationToken)
.maxResults(10)
.build();
// Wait until the job succeeds
while (!finished) {
faceDetectionResponse = rekClient.getFaceDetection(recognitionRequest);
status = faceDetectionResponse.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=faceDetectionResponse.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");
// Show face information
List<FaceDetection> faces= faceDetectionResponse.faces();
for (FaceDetection face: faces) {
String age = face.face().ageRange().toString();
String beard = face.face().beard().toString();
String eyeglasses = face.face().eyeglasses().toString();
String eyesOpen = face.face().eyesOpen().toString();
String mustache = face.face().mustache().toString();
String smile = face.face().smile().toString();
}
} while (faceDetectionResponse !=null && faceDetectionResponse.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.)
# ============== Face Search ===============
def StartFaceSearchCollection(self,collection):
response = self.rek.start_face_search(Video={'S3Object':{'Bucket':self.bucket,'Name':self.video}},
CollectionId=collection,
NotificationChannel={'RoleArn':self.roleArn, 'SNSTopicArn':self.snsTopicArn})
self.startJobId=response['JobId']
print('Start Job Id: ' + self.startJobId)
def GetFaceSearchCollectionResults(self):
maxResults = 10
paginationToken = ''
finished = False
while finished == False:
response = self.rek.get_face_search(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 personMatch in response['Persons']:
print('Person Index: ' + str(personMatch['Person']['Index']))
print('Timestamp: ' + str(personMatch['Timestamp']))
if ('FaceMatches' in personMatch):
for faceMatch in personMatch['FaceMatches']:
print('Face ID: ' + faceMatch['Face']['FaceId'])
print('Similarity: ' + str(faceMatch['Similarity']))
print()
if 'NextToken' in response:
paginationToken = response['NextToken']
else:
finished = True
print()
In the function main, replace the lines:
analyzer.StartLabelDetection()
if analyzer.GetSQSMessageSuccess()==True:
analyzer.GetLabelDetectionResults()
with:
collection='tests'
analyzer.StartFaceSearchCollection(collection)
if analyzer.GetSQSMessageSuccess()==True:
analyzer.GetFaceSearchCollectionResults()
If you've already run a video example other than Analyzing a video stored in an Amazon S3
bucket with Java or Python (SDK), the code to replace might be different.
-
Change the value of collection to the name of the collection you created
in step 1.
-
Run the code. A list of people in the video whose faces match those in the input collection
is
displayed. The tracking data for each matched person is also
displayed.
GetFaceSearch operation response
The following is an example JSON response from GetFaceSearch.
The response includes an array of people (Persons)
detected in the video whose faces match a face in the input collection. An array
element, PersonMatch, exists
for each time the person is matched in the video. Each PersonMatch
includes an array of face matches from the input collection, FaceMatch, information about the
matched person, PersonDetail,
and the time the person was matched in the video.
{
"JobStatus": "SUCCEEDED",
"NextToken": "IJdbzkZfvBRqj8GPV82BPiZKkLOGCqDIsNZG/gQsEE5faTVK9JHOz/xxxxxxxxxxxxxxx",
"Persons": [
{
"FaceMatches": [
{
"Face": {
"BoundingBox": {
"Height": 0.527472972869873,
"Left": 0.33530598878860474,
"Top": 0.2161169946193695,
"Width": 0.35503000020980835
},
"Confidence": 99.90239715576172,
"ExternalImageId": "image.PNG",
"FaceId": "a2f2e224-bfaa-456c-b360-7c00241e5e2d",
"ImageId": "eb57ed44-8d8d-5ec5-90b8-6d190daff4c3"
},
"Similarity": 98.40909576416016
}
],
"Person": {
"BoundingBox": {
"Height": 0.8694444298744202,
"Left": 0.2473958283662796,
"Top": 0.10092592239379883,
"Width": 0.49427083134651184
},
"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": 0
},
"Timestamp": 0
},
{
"Person": {
"BoundingBox": {
"Height": 0.2177777737379074,
"Left": 0.7593749761581421,
"Top": 0.13333334028720856,
"Width": 0.12250000238418579
},
"Face": {
"BoundingBox": {
"Height": 0.2177777737379074,
"Left": 0.7593749761581421,
"Top": 0.13333334028720856,
"Width": 0.12250000238418579
},
"Confidence": 99.63436889648438,
"Landmarks": [
{
"Type": "eyeLeft",
"X": 0.8005779385566711,
"Y": 0.20915353298187256
},
{
"Type": "eyeRight",
"X": 0.8391435146331787,
"Y": 0.21049551665782928
},
{
"Type": "nose",
"X": 0.8191410899162292,
"Y": 0.2523227035999298
},
{
"Type": "mouthLeft",
"X": 0.8093273043632507,
"Y": 0.29053622484207153
},
{
"Type": "mouthRight",
"X": 0.8366993069648743,
"Y": 0.29101791977882385
}
],
"Pose": {
"Pitch": 3.165884017944336,
"Roll": 1.4182015657424927,
"Yaw": -11.151537895202637
},
"Quality": {
"Brightness": 28.910892486572266,
"Sharpness": 97.61507415771484
}
},
"Index": 1
},
"Timestamp": 0
},
{
"Person": {
"BoundingBox": {
"Height": 0.8388888835906982,
"Left": 0,
"Top": 0.15833333134651184,
"Width": 0.2369791716337204
},
"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": 2
},
"Timestamp": 0
}......
],
"VideoMetadata": {
"Codec": "h264",
"DurationMillis": 67301,
"Format": "QuickTime / MOV",
"FrameHeight": 1080,
"FrameRate": 29.970029830932617,
"FrameWidth": 1920
}
}
