将人脸添加到集合

您可以使用该IndexFaces操作来检测图像中的人脸并将其添加到集合中。对于检测到的每张人脸，Amazon Rekognition 将提取人脸特征并将特征信息存储到数据库中。此外，该命令将检测到的每个人脸的元数据存储在指定的人脸集合中。Amazon Rekognition 不存储实际的图像字节。

有关提供合适的人脸用于编制索引的信息，请参阅有关面部比较输入图像的建议。

对于每个人脸，IndexFaces 操作保留以下信息：

多维人脸特征 – IndexFaces 使用人脸分析来提取有关人脸特征的多维信息，并将该信息存储在人脸集合中。您无法直接访问此信息。不过，Amazon Rekognition 在人脸集合中搜索匹配的人脸时将使用此信息。
元数据 – 每个人脸的元数据在请求中包括一个边界框、置信度级别（边界框包含人脸）、由 Amazon Rekognition 分配的 ID（人脸 ID 和图像 ID）和一个外部图像 ID (如果已提供)。在响应 IndexFaces API 调用时，将为您返回此信息。有关示例，请参阅以下示例响应中的 face 元素。

该服务将返回此元数据以响应以下 API 调用：
- ListFaces
- 搜索人脸操作-每个匹配人脸的响应SearchFaces并SearchFacesByImage返回对匹配的置信度，以及匹配人脸的元数据。

IndexFaces 编入索引的人脸数量取决于与输入集合关联的人脸检测模型的版本。有关更多信息，请参阅模型版本控制。

有关索引面孔的信息以FaceRecord对象数组的形式返回。

您可能希望将索引的人脸与检测到人脸的图像相关联。例如，您可能想要保留图像的客户端索引和图像中的人脸。要将人脸与图像关联，请在 ExternalImageId 请求参数中指定图像 ID。图像 ID 可以是您创建的文件名或其他 ID。

除了 API 在人脸集合中保留的前面的信息之外，API 还返回集合中未保留的人脸详细信息。(请参阅以下示例响应中的 faceDetail 元素)。

注意

DetectFaces 将返回相同的信息，因此您无需对同一张图像调用 DetectFaces 和 IndexFaces。

筛选人脸

该 IndexFaces 操作使您可以过滤从图像中编制索引的面孔。借助 IndexFaces，您可以指定要编制索引的人脸的最大数目，也可以选择仅为检测到的高质量人脸编制索引。

您可以使用 MaxFaces 输入参数指定由 IndexFaces 编制索引的人脸的最大数目。当您想为图像中的最大人脸编制索引而不想对较小人脸（例如，背景中站立的人的脸）编制索引时，这很有用。

默认情况下， IndexFaces 选择用于筛选出人脸的质量条。您可以使用 QualityFilter 输入参数显式设置质量条。值为：

AUTO – Amazon Rekognition 选择用于筛选出人脸的质量条（默认值）。
LOW – 除了最低质量人脸之外的所有人脸都将进行索引。
MEDIUM
HIGH – 仅对质量最高的人脸进行索引。
NONE -不会根据质量筛选出任何人脸。

IndexFaces 根据以下条件筛选出人脸：

与图像尺寸相比，人脸太小。
人脸太模糊。
图像太暗。
人脸的姿势很极端。
人脸没有足够的细节，不适合人脸搜索。

注意

要使用质量筛选，您需要一个与版本 3 或更高版本的人脸模型关联的集合。要获取与集合关联的人脸模型版本，请调用DescribeCollection。

有关未被索引的人脸的信息将以IndexFacesUnindexedFace对象数组的形式返回。Reasons 数组包含有关未为人脸编制索引的原因的列表。例如，值 EXCEEDS_MAX_FACES 表示未为人脸编制索引，因为检测到的人脸数已达到 MaxFaces 所指定的人脸数。

有关更多信息，请参阅管理集合中的人脸。

将人脸添加到集合 (SDK)

如果您尚未执行以下操作，请：
1. 使用 AmazonRekognitionFullAccess 和 AmazonS3ReadOnlyAccess 权限创建或更新用户。有关更多信息，请参阅步骤 1：设置 AWS 账户并创建用户。
2. 安装和配置 AWS CLI 和 AWS SDK。有关更多信息，请参阅第 2 步：设置 AWS CLI 和 AWS 软件开发工具包。
将图像（包含一个或多个人脸）上传到您的 Amazon S3 存储桶。

有关说明，请参阅《Amazon Simple Storage Service 用户指南》中的将对象上传到 Amazon S3。

使用以下示例调用 IndexFaces 操作。

Java

此示例显示添加到集合的人脸的人脸标识符。

将 collectionId 的值更改为您要向其中添加人脸的集合的名称。将bucket和photo的值替换为您在步骤 2 中使用的 Amazon S3 存储桶和图像的名称。.withMaxFaces(1) 参数将索引的人脸数限制为 1。删除或更改其值以满足您的需求。


//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

此代码取自 AWS 文档 SDK 示例 GitHub 存储库。请在此处查看完整示例。


//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 命令显示 index-faces CLI 操作的 JSON 输出。

将 collection-id 的值替换为您希望在其中存储人脸的集合的名称。将Bucket和Name的值替换为您在步骤 2 中使用的 Amazon S3 存储桶和图像文件。max-faces 参数将索引的人脸数限制为 1。删除或更改其值以满足您的需求。将创建 Rekognition 会话的行中的profile_name值替换为您的开发人员资料的名称。


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 设备上访问 CLI，请使用双引号代替单引号，并用反斜杠（即 \）对内部双引号进行转义，以解决可能遇到的任何解析器错误。例如，请参阅以下内容：


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

此示例显示添加到集合的人脸的人脸标识符。

将 collectionId 的值更改为您要向其中添加人脸的集合的名称。将bucket和photo的值替换为您在步骤 2 中使用的 Amazon S3 存储桶和图像的名称。MaxFaces 输入参数将索引的人脸数限制为 1。删除或更改其值以满足您的需求。将创建 Rekognition 会话的行中的profile_name值替换为您的开发人员资料的名称。


# 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

此示例显示添加到集合的人脸的人脸标识符。

将 collectionId 的值更改为您要向其中添加人脸的集合的名称。将bucket和photo的值替换为您在步骤 2 中使用的 Amazon S3 存储桶和图像的名称。


//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 操作请求

IndexFaces 的输入是要编入索引的图像和要向其中添加人脸的集合。


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

IndexFaces 操作响应

IndexFaces 返回有关在图像中检测到的人脸的信息。例如，以下 JSON 响应包含在输入图像中检测到的人脸的默认检测属性。此示例还显示未为人脸编制索引，因为已超出 MaxFaces 输入参数的值，Reasons 数组包含 EXCEEDS_MAX_FACES。如果因质量原因而未为人脸编制索引，Reasons 将包含 LOW_SHARPNESS 或 LOW_BRIGHTNESS 等值。有关更多信息，请参阅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"
            ]
        }
    ]
}

要获取所有面部信息，对于 DetectionAttributes 请求参数，请指定“ALL”。例如，在以下示例响应中，记住 faceDetail 元素中的其他信息，这些信息不会保留在服务器上：

25 个人脸标记（相较于上一个示例中的仅 5 个人脸标记）
十个人脸属性（眼镜、胡须、遮挡、视线方向等）
情绪（请参阅 emotion 元素）

face 元素提供了服务器上保留的元数据。

FaceModelVersion 是与集合关联的人脸模型的版本。有关更多信息，请参阅模型版本控制。

OrientationCorrection 是估计的图像方向。如果您使用的是高于版本 3 的人脸检测模型版本，则不会返回方向校正信息。有关更多信息，请参阅获取图像方向和边界框坐标。

以下示例响应显示了指定 ["ALL"] 时返回的 JSON：


{
    "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"
            },
            "FaceDetail": {
                "AgeRange": {
                    "High": 25,
                    "Low": 15
                },
                "Beard": {
                    "Confidence": 99.98077392578125,
                    "Value": false
                },
                "BoundingBox": {
                    "Height": 0.06333333253860474,
                    "Left": 0.17185185849666595,
                    "Top": 0.7366666793823242,
                    "Width": 0.11061728745698929
                },
                "Confidence": 99.99999237060547,
                "Emotions": [
                    {
                        "Confidence": 95.40877532958984,
                        "Type": "HAPPY"
                    },
                    {
                        "Confidence": 6.6088080406188965,
                        "Type": "CALM"
                    },
                    {
                        "Confidence": 0.7385611534118652,
                        "Type": "SAD"
                    }
                ],
                 "EyeDirection": {
                    "yaw": 16.299732,
                    "pitch": -6.407457,
                    "confidence": 99.968704
                              }
                "Eyeglasses": {
                    "Confidence": 99.96795654296875,
                    "Value": false
                },
                "EyesOpen": {
                    "Confidence": 64.0671157836914,
                    "Value": true
                },
                "Gender": {
                    "Confidence": 100,
                    "Value": "Female"
                },
                "Landmarks": [
                    {
                        "Type": "eyeLeft",
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}

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