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
<a name="faces-sqs-video"></a>

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](guidance-face-attributes.md) 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。若要開始在影片中偵測人臉，請呼叫 [StartFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceDetection.html)。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。如果影片分析成功，您可以呼叫 [GetFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceDetection.html) 取得影片分析的結果。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](api-video.md)。

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](video-analyzing-with-sqs.md) 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**

1. 執行 [使用 Java 或 Python (SDK) 分析儲存於 Amazon S3 儲存貯體中的影片](video-analyzing-with-sqs.md)。

1. 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 `amzn-s3-demo-bucket` 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](api-video-roles.md) 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](api-video-roles.md) IAM 服務角色的 ARN。

   ```
   aws rekognition start-face-detection --video "{"S3Object":{"Bucket":"amzn-s3-demo-bucket","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\":\"amzn-s3-demo-bucket\",\"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(amzn-s3-demo-bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetLabelDetectionResults();
   ```

   取代為：

   ```
           StartFaceDetection(amzn-s3-demo-bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetFaceDetectionResults();
   ```

------
#### [ Java V2 ]

   此程式碼取自 AWS 文件開發套件範例 GitHub 儲存庫。請參閱[此處](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/VideoDetectFaces.java)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, amzn-s3-demo-bucket). \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()
   ```

------
**注意**  
如果您已執行 [使用 Java 或 Python (SDK) 分析儲存於 Amazon S3 儲存貯體中的影片](video-analyzing-with-sqs.md) 以外的影片範例，要取代的函數名稱會不同。

1. 執行程式碼。影片中偵測到之臉部的資訊便會顯示。

## GetFaceDetection 操作回應
<a name="getfacedetection-operation-response"></a>

`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。每當在影片中偵測到人臉時，就會有一個陣列元素 [FaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_FaceDetection.html)。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。
+ **人臉屬性**：一組面部屬性，其中包括：年齡範圍、絡腮鬍、表情、目視方向、眼鏡、眼睛張開、人臉遮擋、性別、嘴巴張開、八字胡、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，雖然使用 `DetectFaces` 時支援人臉遮擋和目視方向屬性，但使用 `StartFaceDetection` 和 `GetFaceDetection` 分析視訊時不支援這些屬性。
+ **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](api-video.md#api-video-get)。
+ **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
    }
}
```