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Mencari video yang disimpan untuk wajah
Anda dapat mencari koleksi wajah yang cocok dengan wajah orang yang terdeteksi dalam video yang tersimpan atau video streaming. Bagian ini mencakup pencarian wajah dalam video yang tersimpan. Untuk informasi tentang pencarian wajah dalam video streaming, lihat Bekerja dengan acara video streaming.
Wajah yang Anda cari harus terlebih dahulu diindeks ke dalam koleksi dengan menggunakan. IndexFaces Untuk informasi selengkapnya, lihat Menambahkan wajah ke koleksi.
Pencarian wajah Amazon Rekognition Video mengikuti alur kerja tidak sinkron yang sama seperti operasi Amazon Rekognition Video lainnya yang menganalisis video yang disimpan dalam bucket Amazon S3. Untuk mulai mencari wajah dalam video yang disimpan, panggil StartFaceSearchdan berikan ID koleksi yang ingin Anda cari. Amazon Rekognition Video menerbitkan status penyelesaian analisis video ke topik Amazon Simple Notification Service (Amazon SNS). Jika analisis video berhasil, hubungi GetFaceSearchuntuk mendapatkan hasil pencarian. Untuk informasi selengkapnya tentang memulai analisis video dan mendapatkan hasilnya, lihat Memanggil operasi Amazon Rekognition Video.
Prosedur berikut menunjukkan cara mencari koleksi wajah yang sesuai dengan wajah orang yang terdeteksi dalam video. Prosedur ini juga menunjukkan cara mendapatkan data pelacakan untuk orang yang dicocokkan dalam video. Prosedur tersebut melebar ke kode di Menganalisis video yang disimpan di bucket Amazon S3 dengan Java atau Python (SDK), yang menggunakan antrean Amazon Simple Queue Service (Amazon SQS) untuk mendapatkan status penyelesaian permintaan analisis video.
Untuk mencari video untuk mencocokkan wajah (SDK)
-
Buat koleksi.
-
Indeks wajah ke dalam koleksi.
-
Lakukan Menganalisis video yang disimpan di bucket Amazon S3 dengan Java atau Python (SDK).
-
Tambahkan kode berikut ke kelas VideoDetect yang Anda buat di langkah 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);
}
Di fungsi main, ganti baris:
StartLabelDetection(bucket, video);
if (GetSQSMessageSuccess()==true)
GetLabelDetectionResults();
dengan:
String collection="collection";
StartFaceSearchCollection(bucket, video, collection);
if (GetSQSMessageSuccess()==true)
GetFaceSearchCollectionResults();
- Java V2
-
Kode ini diambil dari GitHub repositori contoh SDK AWS Dokumentasi. Lihat contoh lengkapnya di sini.
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.*;
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 VideoDetectFaces {
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();
startFaceDetection(rekClient, channel, bucket, video);
getFaceResults(rekClient);
System.out.println("This example is done!");
rekClient.close();
}
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 smile = face.face().smile().toString();
System.out.println("The detected face is estimated to be"
+ age + " years old.");
System.out.println("There is a smile : " + smile);
}
} 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()
Dalam fungsi main, ganti baris:
analyzer.StartLabelDetection()
if analyzer.GetSQSMessageSuccess()==True:
analyzer.GetLabelDetectionResults()
dengan:
collection='tests'
analyzer.StartFaceSearchCollection(collection)
if analyzer.GetSQSMessageSuccess()==True:
analyzer.GetFaceSearchCollectionResults()
Jika Anda sudah menjalankan contoh video selain Menganalisis video yang disimpan di bucket Amazon S3 dengan Java atau Python (SDK), kode yang akan diganti mungkin berbeda.
-
Ubah nilai collection pada nama koleksi yang Anda buat pada langkah 1.
-
Jalankan kode tersebut. Daftar orang dalam video yang wajahnya cocok dengan yang ada di koleksi input akan ditampilkan. Data pelacakan untuk setiap orang yang cocok juga ditampilkan.
GetFaceSearch respon operasi
Berikut ini adalah respons JSON contoh dari GetFaceSearch.
Respons tersebut meliputi array orang (Persons) yang terdeteksi dalam video yang wajahnya cocok dengan wajah dalam koleksi input. Elemen array, PersonMatch, ada untuk setiap kali orang tersebut dicocokkan dalam video. Masing-masing PersonMatch mencakup serangkaian kecocokan wajah dari koleksi input FaceMatch,, informasi tentang orang yang cocok PersonDetail, dan waktu orang tersebut dicocokkan dalam 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
}
}
