新增人臉到集合

您可以使用此IndexFaces作業偵測影像中的臉孔並將其加入集合中。對於每個偵測到的人臉，Amazon Rekognition 將擷取人臉特徵並將特徵資訊存放於資料庫中。此外，該命令將為每個在指定人臉集合中偵測到的人臉儲存中繼資料。Amazon Rekognition 不會儲存實際的映像位元組。

如需為編制索引提供適合臉部的相關資訊，請參閱人臉比較輸入映像的建議。

IndexFaces 操作將保留每個人臉的下列資訊：

多維度人臉特徵：IndexFaces 使用人臉分析來擷取人臉特徵的多維度資訊，並將資訊儲存於人臉集合中。您無法直接存取此資訊。不過，Amazon Rekognition 會在搜尋人臉集合並比對人臉時使用此資訊。
中繼資料 — 每個臉孔的中繼資料包括邊界方框、信賴度 (邊界方框包含臉孔)、由 Amazon Rekognition (臉部 ID 和影像 ID) IDs 指派的外部影像 ID，以及外部影像 ID (如果您已提供)。此信息將返回給您以響應IndexFacesAPI呼叫。如需範例，請參閱下列範例回應中的 face 元素。

服務會傳回此中繼資料以回應下列API呼叫：
- ListFaces
- 搜尋臉孔操作 — 回應SearchFaces並SearchFacesByImage傳回每個相符臉孔的相符項目的信賴度，以及相符臉孔的這個中繼資料。

由 IndexFaces 索引的臉部數量取決於與輸入集合相關聯的臉部偵測模型的版本。如需詳細資訊，請參閱了解模型版本控。

有關索引面的資訊會傳回FaceRecord物件陣列中。

您可能想要將編入索引的人臉與偵測到這些人臉的映像連結。例如，您可能想要維護映像的使用者端索引以及映像中的人臉。若要將人臉與映像連結，請在 ExternalImageId 請求參數中指定一個映像 ID。映像 ID 可以是檔案名稱或建立的另一個 ID。

除了先前保API留在臉部集合中的資訊之外，API還會傳回集合中未保留的臉孔詳細資料。(請參閱下列範例回應中的 faceDetail 元素)。

注意

DetectFaces 將傳回相同的資訊，因此您不需要同時呼叫 DetectFaces 和 IndexFaces。

篩選人臉

此 IndexFaces 作業可讓您篩選從影像建立索引的臉孔。運用 IndexFaces，您可以指定索引建立時所要使用的最多人臉數目，或者選擇只對高品質偵測人臉建立索引。

使用 MaxFaces 輸入參數，您可以指定 IndexFaces 在建立索引時所用的最多人臉數目。若您在建立索引時想要運用映像中尺寸最大的人臉，而不要使用尺寸最小的人臉時，例如，背景中站立人群的人臉，就能使用這項參數。

在預設情況下，IndexFaces 選擇用於篩選出臉部的品質列。您可以使用 QualityFilter 輸入參數，以明確設定品質列。這些值為：

AUTO：Amazon Rekognition 選擇並不用於篩選出人臉的品質列 (預設值)。
LOW：除了品質最低的人臉之外，所有人臉都會加以索引。
MEDIUM
HIGH：僅對品質最高的人臉行索引。
NONE：不會根據品質來篩選掉任何人臉。

IndexFaces 篩選人臉的原因如下：

相較於映像尺寸，人臉過小。
臉部過於模糊。
映像過暗。
臉部表情過於誇張。
臉部沒有足夠的細節，不適合臉部搜尋。

注意

若要使用品質篩選，您必須使用臉部模型第 3 版或更高版本的相關聯集合。若要取得與集合關聯的臉部模型版本，請呼叫DescribeCollection。

未建立索引之臉孔的相關資訊會傳回UnindexedFace物件陣列中。IndexFacesReasons 陣列包含為何無法為人臉建立索引的原因清單。例如，當值為 EXCEEDS_MAX_FACES 時，表示因為已偵測到透過 MaxFaces 指定的人臉數目，所以無法為人臉建立索引。

如需詳細資訊，請參閱管理集合中的人臉。

將面加入至集合的步驟 (SDK)

如果您尚未執行：
1. 建立或更新具有 AmazonRekognitionFullAccess 和 AmazonS3ReadOnlyAccess 許可的使用者。如需詳細資訊，請參閱步驟 1：設定AWS帳戶並建立使用者。
2. 安裝並設定 AWS CLI 和 AWS SDKs. 如需詳細資訊，請參閱步驟 2：設定 AWS CLI 以及 AWS SDKs。
將映像 (含有一個或多個人臉) 上傳至您的 Amazon S3 儲存貯體。

如需指示說明，請參閱《Amazon Simple Storage Service 使用者指南》中的上傳物件至 Amazon S3。

使用下列範例來呼叫 IndexFaces 操作。

Java

此範例顯示新增到集合的人臉的人臉識別符。

將 collectionId 的值變更為要新增到人臉的集合的名稱。將 bucket 與 photo 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。.withMaxFaces(1) 參數會將用於建立索引的人臉數目限制為 1。移除或變更其值以符合您的需求。


//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.)

package aws.example.rekognition.image;

import com.amazonaws.services.rekognition.AmazonRekognition;
import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder;
import com.amazonaws.services.rekognition.model.FaceRecord;
import com.amazonaws.services.rekognition.model.Image;
import com.amazonaws.services.rekognition.model.IndexFacesRequest;
import com.amazonaws.services.rekognition.model.IndexFacesResult;
import com.amazonaws.services.rekognition.model.QualityFilter;
import com.amazonaws.services.rekognition.model.S3Object;
import com.amazonaws.services.rekognition.model.UnindexedFace;
import java.util.List;

public class AddFacesToCollection {
    public static final String collectionId = "MyCollection";
    public static final String bucket = "bucket";
    public static final String photo = "input.jpg";

    public static void main(String[] args) throws Exception {

        AmazonRekognition rekognitionClient = AmazonRekognitionClientBuilder.defaultClient();

        Image image = new Image()
                .withS3Object(new S3Object()
                .withBucket(bucket)
                .withName(photo));
        
        IndexFacesRequest indexFacesRequest = new IndexFacesRequest()
                .withImage(image)
                .withQualityFilter(QualityFilter.AUTO)
                .withMaxFaces(1)
                .withCollectionId(collectionId)
                .withExternalImageId(photo)
                .withDetectionAttributes("DEFAULT");

        IndexFacesResult indexFacesResult = rekognitionClient.indexFaces(indexFacesRequest);
        
        System.out.println("Results for " + photo);
        System.out.println("Faces indexed:");
        List<FaceRecord> faceRecords = indexFacesResult.getFaceRecords();
        for (FaceRecord faceRecord : faceRecords) {
            System.out.println("  Face ID: " + faceRecord.getFace().getFaceId());
            System.out.println("  Location:" + faceRecord.getFaceDetail().getBoundingBox().toString());
        }
        
        List<UnindexedFace> unindexedFaces = indexFacesResult.getUnindexedFaces();
        System.out.println("Faces not indexed:");
        for (UnindexedFace unindexedFace : unindexedFaces) {
            System.out.println("  Location:" + unindexedFace.getFaceDetail().getBoundingBox().toString());
            System.out.println("  Reasons:");
            for (String reason : unindexedFace.getReasons()) {
                System.out.println("   " + reason);
            }
        }
    }
}

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.add_faces_collection.import]
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.IndexFacesResponse;
import software.amazon.awssdk.services.rekognition.model.IndexFacesRequest;
import software.amazon.awssdk.services.rekognition.model.Image;
import software.amazon.awssdk.services.rekognition.model.QualityFilter;
import software.amazon.awssdk.services.rekognition.model.Attribute;
import software.amazon.awssdk.services.rekognition.model.FaceRecord;
import software.amazon.awssdk.services.rekognition.model.UnindexedFace;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.Reason;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.InputStream;
import java.util.List;
//snippet-end:[rekognition.java2.add_faces_collection.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 AddFacesToCollection {

 public static void main(String[] args) {

     final String usage = "\n" +
         "Usage: " +
         "    <collectionId> <sourceImage>\n\n" +
         "Where:\n" +
         "    collectionName - The name of the collection.\n" +
         "    sourceImage - The path to the image (for example, C:\\AWS\\pic1.png). \n\n";

     if (args.length != 2) {
         System.out.println(usage);
         System.exit(1);
     }

     String collectionId = args[0];
     String sourceImage = args[1];
     Region region = Region.US_EAST_1;
     RekognitionClient rekClient = RekognitionClient.builder()
         .region(region)
         .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
         .build();

     addToCollection(rekClient, collectionId, sourceImage);
     rekClient.close();
 }

 // snippet-start:[rekognition.java2.add_faces_collection.main]
 public static void addToCollection(RekognitionClient rekClient, String collectionId, String sourceImage) {

     try {
         InputStream sourceStream = new FileInputStream(sourceImage);
         SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream);
         Image souImage = Image.builder()
             .bytes(sourceBytes)
             .build();

         IndexFacesRequest facesRequest = IndexFacesRequest.builder()
             .collectionId(collectionId)
             .image(souImage)
             .maxFaces(1)
             .qualityFilter(QualityFilter.AUTO)
             .detectionAttributes(Attribute.DEFAULT)
             .build();

         IndexFacesResponse facesResponse = rekClient.indexFaces(facesRequest);
         System.out.println("Results for the image");
         System.out.println("\n Faces indexed:");
         List<FaceRecord> faceRecords = facesResponse.faceRecords();
         for (FaceRecord faceRecord : faceRecords) {
             System.out.println("  Face ID: " + faceRecord.face().faceId());
             System.out.println("  Location:" + faceRecord.faceDetail().boundingBox().toString());
         }

         List<UnindexedFace> unindexedFaces = facesResponse.unindexedFaces();
         System.out.println("Faces not indexed:");
         for (UnindexedFace unindexedFace : unindexedFaces) {
             System.out.println("  Location:" + unindexedFace.faceDetail().boundingBox().toString());
             System.out.println("  Reasons:");
             for (Reason reason : unindexedFace.reasons()) {
                 System.out.println("Reason:  " + reason);
             }
         }

     } catch (RekognitionException | FileNotFoundException e) {
         System.out.println(e.getMessage());
         System.exit(1);
     }
 }
 // snippet-end:[rekognition.java2.add_faces_collection.main]
}

AWS CLI

此 AWS CLI 指令會顯示index-facesCLI作業的JSON輸出。

以您要儲存臉部的集合之名稱取代 collection-id 的值。將 Bucket 與 Name 的值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像檔案名稱。max-faces 參數會將用於建立索引的人臉數目限制為 1。移除或變更其值以符合您的需求。將建立 Rekognition 工作階段的行中 profile_name 值取代為您開發人員設定檔的名稱。


aws rekognition index-faces --image '{"S3Object":{"Bucket":"bucket-name","Name":"file-name"}}' --collection-id "collection-id" \
                                --max-faces 1 --quality-filter "AUTO" --detection-attributes "ALL" \ 
                                --external-image-id "example-image.jpg" --profile profile-name

如果您在 Windows 裝置CLI上存取，請使用雙引號而非單引號，並以反斜線 (即\) 逸出內部雙引號，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 index-faces --image "{\"S3Object\":{\"Bucket\":\"bucket-name\",\"Name\":\"image-name\"}}" \
--collection-id "collection-id" --max-faces 1 --quality-filter "AUTO" --detection-attributes "ALL" \ 
--external-image-id "example-image.jpg" --profile profile-name

Python

此範例顯示新增到集合的人臉的人臉識別符。

將 collectionId 的值變更為要新增到人臉的集合的名稱。將 bucket 與 photo 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。MaxFaces 輸入參數會將用於建立索引的人臉數目限制為 1。移除或變更其值以符合您的需求。將建立 Rekognition 工作階段的行中 profile_name 值取代為您開發人員設定檔的名稱。


# 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.)

import boto3

def add_faces_to_collection(bucket, photo, collection_id):

    session = boto3.Session(profile_name='profile-name')
    client = session.client('rekognition')

    response = client.index_faces(CollectionId=collection_id,
                                  Image={'S3Object': {'Bucket': bucket, 'Name': photo}},
                                  ExternalImageId=photo,
                                  MaxFaces=1,
                                  QualityFilter="AUTO",
                                  DetectionAttributes=['ALL'])

    print('Results for ' + photo)
    print('Faces indexed:')
    for faceRecord in response['FaceRecords']:
        print('  Face ID: ' + faceRecord['Face']['FaceId'])
        print('  Location: {}'.format(faceRecord['Face']['BoundingBox']))

    print('Faces not indexed:')
    for unindexedFace in response['UnindexedFaces']:
        print(' Location: {}'.format(unindexedFace['FaceDetail']['BoundingBox']))
        print(' Reasons:')
        for reason in unindexedFace['Reasons']:
            print('   ' + reason)
    return len(response['FaceRecords'])

def main():
    bucket = 'bucket-name'
    collection_id = 'collection-id'
    photo = 'photo-name'

    indexed_faces_count = add_faces_to_collection(bucket, photo, collection_id)
    print("Faces indexed count: " + str(indexed_faces_count))

if __name__ == "__main__":
    main()

.NET

此範例顯示新增到集合的人臉的人臉識別符。

將 collectionId 的值變更為要新增到人臉的集合的名稱。將 bucket 與 photo 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。


//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.)

using System;
using System.Collections.Generic;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;

public class AddFaces
{
    public static void Example()
    {
        String collectionId = "MyCollection";
        String bucket = "bucket";
        String photo = "input.jpg";

        AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();

        Image image = new Image()
        {
            S3Object = new S3Object()
            {
                Bucket = bucket,
                Name = photo
            }
        };

        IndexFacesRequest indexFacesRequest = new IndexFacesRequest()
        {
            Image = image,
            CollectionId = collectionId,
            ExternalImageId = photo,
            DetectionAttributes = new List<String>(){ "ALL" }
        };

        IndexFacesResponse indexFacesResponse = rekognitionClient.IndexFaces(indexFacesRequest);

        Console.WriteLine(photo + " added");
        foreach (FaceRecord faceRecord in indexFacesResponse.FaceRecords)
            Console.WriteLine("Face detected: Faceid is " +
               faceRecord.Face.FaceId);
    }
}

IndexFaces 操作請求

IndexFaces 的輸入是要索引的映像和新增人臉或人臉的集合。


{
    "CollectionId": "MyCollection",
    "Image": {
        "S3Object": {
            "Bucket": "bucket",
            "Name": "input.jpg"
        }
    },
    "ExternalImageId": "input.jpg",
    "DetectionAttributes": [
        "DEFAULT"
    ],
    "MaxFaces": 1,
    "QualityFilter": "AUTO"
}

IndexFaces 作業回應

IndexFaces 傳回有關在映像中偵測到的人臉的資訊。例如，下列JSON回應包括輸入影像中偵測到的臉孔的預設偵測屬性。此範例也會顯示因為超過MaxFaces輸入參數值而未建立索引的面孔 — Reasons 陣列包含 EXCEEDS_ MAX _ FACES。如果面因品質原因而未建立索引，則會Reasons包含諸如 LOW_ SHARPNESS 或 LOW_ 之類的值BRIGHTNESS。如需詳細資訊，請參閱UnindexedFace。


{
    "FaceModelVersion": "3.0",
    "FaceRecords": [
        {
            "Face": {
                "BoundingBox": {
                    "Height": 0.3247932195663452,
                    "Left": 0.5055555701255798,
                    "Top": 0.2743072211742401,
                    "Width": 0.21444444358348846
                },
                "Confidence": 99.99998474121094,
                "ExternalImageId": "input.jpg",
                "FaceId": "b86e2392-9da1-459b-af68-49118dc16f87",
                "ImageId": "09f43d92-02b6-5cea-8fbd-9f187db2050d"
            },
            "FaceDetail": {
                "BoundingBox": {
                    "Height": 0.3247932195663452,
                    "Left": 0.5055555701255798,
                    "Top": 0.2743072211742401,
                    "Width": 0.21444444358348846
                },
                "Confidence": 99.99998474121094,
                "Landmarks": [
                    {
                        "Type": "eyeLeft",
                        "X": 0.5751981735229492,
                        "Y": 0.4010535478591919
                    },
                    {
                        "Type": "eyeRight",
                        "X": 0.6511467099189758,
                        "Y": 0.4017036259174347
                    },
                    {
                        "Type": "nose",
                        "X": 0.6314528584480286,
                        "Y": 0.4710812568664551
                    },
                    {
                        "Type": "mouthLeft",
                        "X": 0.5879443287849426,
                        "Y": 0.5171778798103333
                    },
                    {
                        "Type": "mouthRight",
                        "X": 0.6444502472877502,
                        "Y": 0.5164633989334106
                    }
                ],
                "Pose": {
                    "Pitch": -10.313642501831055,
                    "Roll": -1.0316886901855469,
                    "Yaw": 18.079818725585938
                },
                "Quality": {
                    "Brightness": 71.2919921875,
                    "Sharpness": 78.74752044677734
                }
            }
        }
    ],
    "OrientationCorrection": "",
    "UnindexedFaces": [
        {
            "FaceDetail": {
                "BoundingBox": {
                    "Height": 0.1329464465379715,
                    "Left": 0.5611110925674438,
                    "Top": 0.6832437515258789,
                    "Width": 0.08777777850627899
                },
                "Confidence": 92.37225341796875,
                "Landmarks": [
                    {
                        "Type": "eyeLeft",
                        "X": 0.5796897411346436,
                        "Y": 0.7452847957611084
                    },
                    {
                        "Type": "eyeRight",
                        "X": 0.6078574657440186,
                        "Y": 0.742687463760376
                    },
                    {
                        "Type": "nose",
                        "X": 0.597953200340271,
                        "Y": 0.7620673179626465
                    },
                    {
                        "Type": "mouthLeft",
                        "X": 0.5884202122688293,
                        "Y": 0.7920381426811218
                    },
                    {
                        "Type": "mouthRight",
                        "X": 0.60627681016922,
                        "Y": 0.7919750809669495
                    }
                ],
                "Pose": {
                    "Pitch": 15.658954620361328,
                    "Roll": -4.583454608917236,
                    "Yaw": 10.558992385864258
                },
                "Quality": {
                    "Brightness": 42.54612350463867,
                    "Sharpness": 86.93206024169922
                }
            },
            "Reasons": [
                "EXCEEDS_MAX_FACES"
            ]
        }
    ]
}

要獲取所有面部信息，請為DetectionAttributes請求參數指定 ALL ''。例如，在以下範例回應中可找到 faceDetail 元素中的其它資訊，而這些資訊不會儲存在伺服器上：

25 個臉部特徵點 (相較於上述範例中只有 5 個特徵點)
十個面部屬性 (眼鏡、鬍鬚、人臉遮挡、眼睛凝視方向等)
表情 (請參閱 emotion 元素)

face 元素提供保留在伺服器上的中繼資料。

FaceModelVersion 是與集合相關聯的臉部模型版本。如需詳細資訊，請參閱了解模型版本控。

OrientationCorrection 是映像的估計方向。如果您使用的臉部偵測模型版本大於第 3 版，則不會傳回方向更正資訊。如需詳細資訊，請參閱取得映像方向與週框方塊座標。

下列範例回應顯示指定 [」ALL「] JSON 時傳回的回應：


{
    "FaceModelVersion": "3.0",
    "FaceRecords": [
        {
            "Face": {
                "BoundingBox": {
                    "Height": 0.06333333253860474,
                    "Left": 0.17185185849666595,
                    "Top": 0.7366666793823242,
                    "Width": 0.11061728745698929
                },
                "Confidence": 99.99999237060547,
                "ExternalImageId": "input.jpg",
                "FaceId": "578e2e1b-d0b0-493c-aa39-ba476a421a34",
                "ImageId": "9ba38e68-35b6-5509-9d2e-fcffa75d1653"
            },
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}

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