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# 将人脸添加到集合
您可以使用 [IndexFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_IndexFaces.html) 操作检测图像中的人脸并将人脸添加到集合中。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](recommendations-facial-input-images.md)。
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**-每张人脸的元数据包括边界框、由 Amazon Rekognition IDs 分配的置信度（边界框包含人脸）（人脸 ID 和图片 ID），以及请求中的外部图片 ID（如果您提供了）。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](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html)`
+ 搜索人脸操作 - [SearchFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFaces.html) 和 [SearchFacesByImage](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFacesByImage.html) 的响应返回每个匹配人脸的匹配置信度以及已匹配人脸的元数据。
`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 [了解模型版本控制](face-detection-model.md)。
有关索引的人脸的信息以 [FaceRecord](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_FaceRecord.html) 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](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeCollection.html)。
有关 `IndexFaces` 未编制索引的人脸的信息将以 [UnindexedFace](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UnindexedFace.html) 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](managing-face-collections.md#collections-index-faces)。
**将人脸添加到集合 (SDK)**
1. 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](setting-up.md#setting-up-iam)。
1. 安装并配置 AWS CLI 和 AWS SDKs。有关更多信息，请参阅 [第 2 步：设置 AWS CLI 和 AWS SDKs](setup-awscli-sdk.md)。
1. 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](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UploadingObjectsintoAmazonS3.html)。
1. 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 faceRecords = indexFacesResult.getFaceRecords();
for (FaceRecord faceRecord : faceRecords) {
System.out.println(" Face ID: " + faceRecord.getFace().getFaceId());
System.out.println(" Location:" + faceRecord.getFaceDetail().getBoundingBox().toString());
}
List 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](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/AddFacesToCollection.java)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: " +
" \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 faceRecords = facesResponse.faceRecords();
for (FaceRecord faceRecord : faceRecords) {
System.out.println(" Face ID: " + faceRecord.face().faceId());
System.out.println(" Location:" + faceRecord.faceDetail().boundingBox().toString());
}
List 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 \$1）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 = 'amzn-s3-demo-bucket'
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 = "amzn-s3-demo-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(){ "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\$1MAX\$1FACES*。如果因质量原因而未为人脸编制索引，`Reasons` 将包含 *LOW\$1SHARPNESS* 或 *LOW\$1BRIGHTNESS* 等值。有关更多信息，请参阅 [UnindexedFace](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UnindexedFace.html)。
```
{
"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 [了解模型版本控制](face-detection-model.md)。
`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](images-orientation.md)。
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",
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}
```