本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
偵測已存放影片中的人臉
Amazon Rekognition Video 可以偵測存放在 Amazon S3 儲存貯體之影片中的人臉，並提供下列資訊：
-
在影片中偵測到臉部的次數。
-
偵測到臉部時臉部在影片影格中的位置。
-
臉部特徵點，例如左眼的位置。
-
其他屬性，如 人臉屬性的準則 頁面上所述。
Amazon Rekognition Video 已儲存影片中的人臉偵測是一項非同步操作。要開始檢測視頻中的臉部，請致電StartFaceDetection。Amazon Rekognition Video 會將影片分析的完成状态发布至 Amazon Simple Notification Service (Amazon SNS) 主题。如果視頻分析成功，則可以調用GetFaceDetection以獲取視頻分析的結果。如需開始影片分析並取得結果的詳細資訊，請參閱 呼叫 Amazon Rekognition Video 操作。
此程序會展開 使用 Java 或 Python (SDK) 分析儲存於 Amazon S3 儲存貯體中的影片 中的程式碼，該操作使用 Amazon Simple Queue Service (Amazon SQS) 佇列來取得影片分析要求的完成狀態。
偵測存放在 Amazon S3 儲存貯體 (SDK) 之影片中的人臉
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執行 使用 Java 或 Python (SDK) 分析儲存於 Amazon S3 儲存貯體中的影片。
-
將下列程式碼新增至您在步驟 1 中建立的類別 VideoDetect。
- AWS CLI
-
-
在以下程式碼範例中，將 bucket-name 與 video-name 變更為您在步驟 2 中指定的 Amazon S3 儲存貯體與文檔名稱。
-
將 region-name 變更為您正在使用的 AWS 區域。使用您開發人員設定檔的名稱取代 profile_name 的值。
-
將 TopicARN 變更為您在 設定 Amazon Rekognition Video 步驟 3 建立的 Amazon SNS 主題 ARN。
-
將 RoleARN 變更為您在步驟 7 建立的 設定 Amazon Rekognition Video IAM 服務角色的 ARN。
aws rekognition start-face-detection --video "{"S3Object":{"Bucket":"Bucket-Name","Name":"Video-Name"}}" --notification-channel \
"{"SNSTopicArn":"Topic-ARN","RoleArn":"Role-ARN"}" --region region-name --profile profile-name
如果您在 Windows 裝置上存取 CLI，請使用雙引號而非單引號，並以反斜線 (即\) 替代內部雙引號，以解決您可能遇到的任何剖析器錯誤。例如，請參閱下列內容：
aws rekognition start-face-detection --video "{\"S3Object\":{\"Bucket\":\"Bucket-Name\",\"Name\":\"Video-Name\"}}" --notification-channel \
"{\"SNSTopicArn\":\"Topic-ARN\",\"RoleArn\":\"Role-ARN\"}" --region region-name --profile profile-name
執行 StartFaceDetection 操作，並取得工作 ID 編號之後，請執行下列 GetFaceDetection 作業並提供工作 ID 號碼：
aws rekognition get-face-detection --job-id job-id-number --profile profile-name
- 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.)
private static void StartFaceDetection(String bucket, String video) throws Exception{
NotificationChannel channel= new NotificationChannel()
.withSNSTopicArn(snsTopicArn)
.withRoleArn(roleArn);
StartFaceDetectionRequest req = new StartFaceDetectionRequest()
.withVideo(new Video()
.withS3Object(new S3Object()
.withBucket(bucket)
.withName(video)))
.withNotificationChannel(channel);
StartFaceDetectionResult startLabelDetectionResult = rek.startFaceDetection(req);
startJobId=startLabelDetectionResult.getJobId();
}
private static void GetFaceDetectionResults() throws Exception{
int maxResults=10;
String paginationToken=null;
GetFaceDetectionResult faceDetectionResult=null;
do{
if (faceDetectionResult !=null){
paginationToken = faceDetectionResult.getNextToken();
}
faceDetectionResult = rek.getFaceDetection(new GetFaceDetectionRequest()
.withJobId(startJobId)
.withNextToken(paginationToken)
.withMaxResults(maxResults));
VideoMetadata videoMetaData=faceDetectionResult.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 faces, confidence and detection times
List<FaceDetection> faces= faceDetectionResult.getFaces();
for (FaceDetection face: faces) {
long seconds=face.getTimestamp()/1000;
System.out.print("Sec: " + Long.toString(seconds) + " ");
System.out.println(face.getFace().toString());
System.out.println();
}
} while (faceDetectionResult !=null && faceDetectionResult.getNextToken() != null);
}
在函數 main 中，將下行:
StartLabelDetection(bucket, video);
if (GetSQSMessageSuccess()==true)
GetLabelDetectionResults();
取代為：
StartFaceDetection(bucket, video);
if (GetSQSMessageSuccess()==true)
GetFaceDetectionResults();
- Java V2
-
此代碼取自 AWS 文檔 SDK 示例 GitHub 存儲庫。請參閱此處的完整範例。
//snippet-start:[rekognition.java2.recognize_video_faces.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.*;
import java.util.List;
//snippet-end:[rekognition.java2.recognize_video_faces.import]
/**
* 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 = "\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();
StartFaceDetection(rekClient, channel, bucket, video);
GetFaceResults(rekClient);
System.out.println("This example is done!");
rekClient.close();
}
// snippet-start:[rekognition.java2.recognize_video_faces.main]
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);
}
}
// snippet-end:[rekognition.java2.recognize_video_faces.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.)
# ============== Faces===============
def StartFaceDetection(self):
response=self.rek.start_face_detection(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 GetFaceDetectionResults(self):
maxResults = 10
paginationToken = ''
finished = False
while finished == False:
response = self.rek.get_face_detection(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 faceDetection in response['Faces']:
print('Face: ' + str(faceDetection['Face']))
print('Confidence: ' + str(faceDetection['Face']['Confidence']))
print('Timestamp: ' + str(faceDetection['Timestamp']))
print()
if 'NextToken' in response:
paginationToken = response['NextToken']
else:
finished = True
在函數 main 中，將下行:
analyzer.StartLabelDetection()
if analyzer.GetSQSMessageSuccess()==True:
analyzer.GetLabelDetectionResults()
取代為：
analyzer.StartFaceDetection()
if analyzer.GetSQSMessageSuccess()==True:
analyzer.GetFaceDetectionResults()
-
執行程式碼。影片中偵測到之臉部的資訊便會顯示。
GetFaceDetection 作業回應
GetFaceDetection 會傳回陣列 (Faces)，其中包含影片中偵測到之臉孔的相關資訊。每次在視訊中偵測到臉孔時，都會存在陣列元素。FaceDetection傳回的陣列元素會依從影片開頭起的時間 (以毫秒為單位) 順序排序。
下列範例是 GetFaceDetection 的部分 JSON 回應。在回應中，請注意下列事項：
-
週框方塊：人臉周圍的週框方塊座標。
-
可信度：週框方塊包含人臉的可信度。
-
人臉特徵點：人臉特徵點的陣列。對於每個特徵點 (例如左眼、右眼與嘴巴)，回應均會提供 x 和 y 座標。
-
臉部屬性 — 一組面部屬性，其中包括：鬍子 AgeRange，情感，眼鏡 EyesOpen，性別，鬍子 MouthOpen，微笑和太陽鏡。該值的類型可能不同，例如布林值類型 (此人是否戴著太陽眼鏡) 或字串 (此人是男性或女性)。此外，對於大部分屬性，該回應也會提供偵測到之屬性值的可信度。請注意，雖 FaceOccluded 然使用時支持和 EyeDirection屬性DetectFaces，但使用StartFaceDetection和分析視頻時不支持它們GetFaceDetection。
-
時間戳記：在影片中偵測到臉孔的時間。
-
分頁資訊：該範例顯示一頁人臉偵測資訊。您可以在 GetFaceDetection 的 MaxResults 輸入參數中指定要傳回的人物元素數目。如果結果數目超過 MaxResults，GetFaceDetection 會傳回用來取得下一頁結果的字符 (NextToken)。如需詳細資訊，請參閱 取得 Amazon Rekognition Video 分析結果。
-
影片資訊：回應包含 GetFaceDetection 所傳回之每頁資訊中影片格式 (VideoMetadata) 的相關資訊。
-
畫質：描述人臉的亮度與銳利度。
-
姿態：描述映像內人臉的旋轉角度。
{
"Faces": [
{
"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
}
},
"Timestamp": 0
},
{
"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
}
},
"Timestamp": 0
},
{
"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
}
},
"Timestamp": 0
}.......
],
"JobStatus": "SUCCEEDED",
"NextToken": "i7fj5XPV/fwviXqz0eag9Ow332Jd5G8ZGWf7hooirD/6V1qFmjKFOQZ6QPWUiqv29HbyuhMNqQ==",
"VideoMetadata": {
"Codec": "h264",
"DurationMillis": 67301,
"FileExtension": "mp4",
"Format": "QuickTime / MOV",
"FrameHeight": 1080,
"FrameRate": 29.970029830932617,
"FrameWidth": 1920
}
}
