本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。 # 检测图像中的人脸 Amazon Rekognition Image [DetectFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectFaces.html)提供的操作可查找关键面部特征，例如眼睛、鼻子和嘴巴，以检测输入图像中的人脸。Amazon Rekognition Image 可检测图像中的 100 张最大人脸。 您可以提供输入图像作为图像字节数组（base64 编码的图像字节）或指定 Amazon S3 对象。在此过程中，您将图像（JPEG 或 PNG）上传到您的 S3 存储桶并指定对象键名称。 **检测图像中的人脸** 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. 安装和配置 AWS CLI 和 AWS SDK。有关更多信息，请参阅 [第 2 步：设置 AWS CLI and AWS 软件开发工具包](setup-awscli-sdk.md)。 1. 将包含一张或多张人脸的图像上传到您的 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 `DetectFaces`。 ------ #### [ Java ] 此示例显示检测到的人脸的估计年龄范围，并列出所有检测到的人脸属性的 JSON。将 `photo` 的值更改为图像文件名。将`amzn-s3-demo-bucket`的值更改为存储图像的 Amazon S3 存储桶。 ``` //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.AmazonRekognitionException; import com.amazonaws.services.rekognition.model.Image; import com.amazonaws.services.rekognition.model.S3Object; import com.amazonaws.services.rekognition.model.AgeRange; import com.amazonaws.services.rekognition.model.Attribute; import com.amazonaws.services.rekognition.model.DetectFacesRequest; import com.amazonaws.services.rekognition.model.DetectFacesResult; import com.amazonaws.services.rekognition.model.FaceDetail; import com.fasterxml.jackson.databind.ObjectMapper; import java.util.List; public class DetectFaces { public static void main(String[] args) throws Exception { String photo = "input.jpg"; String bucket = "bucket"; AmazonRekognition rekognitionClient = AmazonRekognitionClientBuilder.defaultClient(); DetectFacesRequest request = new DetectFacesRequest() .withImage(new Image() .withS3Object(new S3Object() .withName(photo) .withBucket(bucket))) .withAttributes(Attribute.ALL); // Replace Attribute.ALL with Attribute.DEFAULT to get default values. try { DetectFacesResult result = rekognitionClient.detectFaces(request); List < FaceDetail > faceDetails = result.getFaceDetails(); for (FaceDetail face: faceDetails) { if (request.getAttributes().contains("ALL")) { AgeRange ageRange = face.getAgeRange(); System.out.println("The detected face is estimated to be between " + ageRange.getLow().toString() + " and " + ageRange.getHigh().toString() + " years old."); System.out.println("Here's the complete set of attributes:"); } else { // non-default attributes have null values. System.out.println("Here's the default set of attributes:"); } ObjectMapper objectMapper = new ObjectMapper(); System.out.println(objectMapper.writerWithDefaultPrettyPrinter().writeValueAsString(face)); } } catch (AmazonRekognitionException e) { e.printStackTrace(); } } } ``` ------ #### [ 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/DetectFaces.java)查看完整示例。 ``` import java.util.List; //snippet-start:[rekognition.java2.detect_labels.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.RekognitionException; import software.amazon.awssdk.services.rekognition.model.S3Object; import software.amazon.awssdk.services.rekognition.model.DetectFacesRequest; import software.amazon.awssdk.services.rekognition.model.DetectFacesResponse; import software.amazon.awssdk.services.rekognition.model.Image; import software.amazon.awssdk.services.rekognition.model.Attribute; import software.amazon.awssdk.services.rekognition.model.FaceDetail; import software.amazon.awssdk.services.rekognition.model.AgeRange; //snippet-end:[rekognition.java2.detect_labels.import] public class DetectFaces { public static void main(String[] args) { final String usage = "\n" + "Usage: " + " \n\n" + "Where:\n" + " bucket - The name of the Amazon S3 bucket that contains the image (for example, ,amzn-s3-demo-bucket)." + " image - The name of the image located in the Amazon S3 bucket (for example, Lake.png). \n\n"; if (args.length != 2) { System.out.println(usage); System.exit(1); } String bucket = args[0]; String image = args[1]; Region region = Region.US_WEST_2; RekognitionClient rekClient = RekognitionClient.builder() .region(region) .credentialsProvider(ProfileCredentialsProvider.create("profile-name")) .build(); getLabelsfromImage(rekClient, bucket, image); rekClient.close(); } // snippet-start:[rekognition.java2.detect_labels_s3.main] public static void getLabelsfromImage(RekognitionClient rekClient, String bucket, String image) { try { S3Object s3Object = S3Object.builder() .bucket(bucket) .name(image) .build() ; Image myImage = Image.builder() .s3Object(s3Object) .build(); DetectFacesRequest facesRequest = DetectFacesRequest.builder() .attributes(Attribute.ALL) .image(myImage) .build(); DetectFacesResponse facesResponse = rekClient.detectFaces(facesRequest); List faceDetails = facesResponse.faceDetails(); for (FaceDetail face : faceDetails) { AgeRange ageRange = face.ageRange(); System.out.println("The detected face is estimated to be between " + ageRange.low().toString() + " and " + ageRange.high().toString() + " years old."); System.out.println("There is a smile : "+face.smile().value().toString()); } } catch (RekognitionException e) { System.out.println(e.getMessage()); System.exit(1); } } // snippet-end:[rekognition.java2.detect_labels.main] } ``` ------ #### [ AWS CLI ] 此示例显示`detect-faces` AWS CLI 操作的 JSON 输出。将 `file` 替换为图像文件的名称。将`amzn-s3-demo-bucket`替换为包含图像文件的 Amazon S3 存储桶的名称。 ``` aws rekognition detect-faces --image "{"S3Object":{"Bucket":"amzn-s3-demo-bucket,"Name":"image-name"}}"\ --attributes "ALL" --profile profile-name --region region-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 detect-faces --image "{\"S3Object\":{\"Bucket\":\"amzn-s3-demo-bucket\",\"Name\":\"image-name\"}}" --attributes "ALL" --profile profile-name --region region-name ``` ------ #### [ Python ] 此示例显示检测到的人脸的估计年龄范围和其他属性，并列出所有检测到的人脸属性的 JSON。将 `photo` 的值更改为图像文件名。将`amzn-s3-demo-bucket`的值更改为存储图像的 Amazon S3 存储桶。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。 ``` import boto3 import json def detect_faces(photo, bucket, region): session = boto3.Session(profile_name='profile-name', region_name=region) client = session.client('rekognition', region_name=region) response = client.detect_faces(Image={'S3Object':{'Bucket':bucket,'Name':photo}}, Attributes=['ALL']) print('Detected faces for ' + photo) for faceDetail in response['FaceDetails']: print('The detected face is between ' + str(faceDetail['AgeRange']['Low']) + ' and ' + str(faceDetail['AgeRange']['High']) + ' years old') print('Here are the other attributes:') print(json.dumps(faceDetail, indent=4, sort_keys=True)) # Access predictions for individual face details and print them print("Gender: " + str(faceDetail['Gender'])) print("Smile: " + str(faceDetail['Smile'])) print("Eyeglasses: " + str(faceDetail['Eyeglasses'])) print("Face Occluded: " + str(faceDetail['FaceOccluded'])) print("Emotions: " + str(faceDetail['Emotions'][0])) return len(response['FaceDetails']) def main(): photo='photo' bucket='amzn-s3-demo-bucket' region='region' face_count=detect_faces(photo, bucket, region) print("Faces detected: " + str(face_count)) if __name__ == "__main__": main() ``` ------ #### [ .NET ] 此示例显示检测到的人脸的估计年龄范围，并列出所有检测到的人脸属性的 JSON。将 `photo` 的值更改为图像文件名。将`amzn-s3-demo-bucket`的值更改为存储图像的 Amazon S3 存储桶。 ``` //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 DetectFaces { public static void Example() { String photo = "input.jpg"; String bucket = "amzn-s3-demo-bucket"; AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient(); DetectFacesRequest detectFacesRequest = new DetectFacesRequest() { Image = new Image() { S3Object = new S3Object() { Name = photo, Bucket = bucket }, }, // Attributes can be "ALL" or "DEFAULT". // "DEFAULT": BoundingBox, Confidence, Landmarks, Pose, and Quality. // "ALL": See https://docs.aws.amazon.com/sdkfornet/v3/apidocs/items/Rekognition/TFaceDetail.html Attributes = new List() { "ALL" } }; try { DetectFacesResponse detectFacesResponse = rekognitionClient.DetectFaces(detectFacesRequest); bool hasAll = detectFacesRequest.Attributes.Contains("ALL"); foreach(FaceDetail face in detectFacesResponse.FaceDetails) { Console.WriteLine("BoundingBox: top={0} left={1} width={2} height={3}", face.BoundingBox.Left, face.BoundingBox.Top, face.BoundingBox.Width, face.BoundingBox.Height); Console.WriteLine("Confidence: {0}\nLandmarks: {1}\nPose: pitch={2} roll={3} yaw={4}\nQuality: {5}", face.Confidence, face.Landmarks.Count, face.Pose.Pitch, face.Pose.Roll, face.Pose.Yaw, face.Quality); if (hasAll) Console.WriteLine("The detected face is estimated to be between " + face.AgeRange.Low + " and " + face.AgeRange.High + " years old."); } } catch (Exception e) { Console.WriteLine(e.Message); } } } ``` ------ #### [ Ruby ] 此示例显示检测到的人脸的估计年龄范围，并列出各种人脸属性。将 `photo` 的值更改为图像文件名。将`amzn-s3-demo-bucket`的值更改为存储图像的 Amazon S3 存储桶。 ``` # Add to your Gemfile # gem 'aws-sdk-rekognition' require 'aws-sdk-rekognition' credentials = Aws::Credentials.new( ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY'] ) bucket = 'bucket' # the bucketname without s3:// photo = 'input.jpg'# the name of file client = Aws::Rekognition::Client.new credentials: credentials attrs = { image: { s3_object: { bucket: bucket, name: photo }, }, attributes: ['ALL'] } response = client.detect_faces attrs puts "Detected faces for: #{photo}" response.face_details.each do |face_detail| low = face_detail.age_range.low high = face_detail.age_range.high puts "The detected face is between: #{low} and #{high} years old" puts "All other attributes:" puts " bounding_box.width: #{face_detail.bounding_box.width}" puts " bounding_box.height: #{face_detail.bounding_box.height}" puts " bounding_box.left: #{face_detail.bounding_box.left}" puts " bounding_box.top: #{face_detail.bounding_box.top}" puts " age.range.low: #{face_detail.age_range.low}" puts " age.range.high: #{face_detail.age_range.high}" puts " smile.value: #{face_detail.smile.value}" puts " smile.confidence: #{face_detail.smile.confidence}" puts " eyeglasses.value: #{face_detail.eyeglasses.value}" puts " eyeglasses.confidence: #{face_detail.eyeglasses.confidence}" puts " sunglasses.value: #{face_detail.sunglasses.value}" puts " sunglasses.confidence: #{face_detail.sunglasses.confidence}" puts " gender.value: #{face_detail.gender.value}" puts " gender.confidence: #{face_detail.gender.confidence}" puts " beard.value: #{face_detail.beard.value}" puts " beard.confidence: #{face_detail.beard.confidence}" puts " mustache.value: #{face_detail.mustache.value}" puts " mustache.confidence: #{face_detail.mustache.confidence}" puts " eyes_open.value: #{face_detail.eyes_open.value}" puts " eyes_open.confidence: #{face_detail.eyes_open.confidence}" puts " mout_open.value: #{face_detail.mouth_open.value}" puts " mout_open.confidence: #{face_detail.mouth_open.confidence}" puts " emotions[0].type: #{face_detail.emotions[0].type}" puts " emotions[0].confidence: #{face_detail.emotions[0].confidence}" puts " landmarks[0].type: #{face_detail.landmarks[0].type}" puts " landmarks[0].x: #{face_detail.landmarks[0].x}" puts " landmarks[0].y: #{face_detail.landmarks[0].y}" puts " pose.roll: #{face_detail.pose.roll}" puts " pose.yaw: #{face_detail.pose.yaw}" puts " pose.pitch: #{face_detail.pose.pitch}" puts " quality.brightness: #{face_detail.quality.brightness}" puts " quality.sharpness: #{face_detail.quality.sharpness}" puts " confidence: #{face_detail.confidence}" puts "------------" puts "" end ``` ------ #### [ Node.js ] 此示例显示检测到的人脸的估计年龄范围，并列出各种人脸属性。将 `photo` 的值更改为图像文件名。将`amzn-s3-demo-bucket`的值更改为存储图像的 Amazon S3 存储桶。 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。 如果您使用的是 TypeScript 定义，则可能需要使用`import AWS from 'aws-sdk'`而不是`const AWS = require('aws-sdk')`，才能使用来运行程序 Node.js。您可以查阅[适用于 JavaScript 的AWS SDK](https://docs.aws.amazon.com/AWSJavaScriptSDK/latest/)，了解更多详情。根据您的配置设置方式，您可能还需要使用 `AWS.config.update({region:{{region}}});` 来指定您的区域。 ``` // Load the SDK var AWS = require('aws-sdk'); const bucket = 'bucket-name' // the bucketname without s3:// const photo = 'photo-name' // the name of file var credentials = new AWS.SharedIniFileCredentials({profile: 'profile-name'}); AWS.config.credentials = credentials; AWS.config.update({region:'region-name'}); const client = new AWS.Rekognition(); const params = { Image: { S3Object: { Bucket: bucket, Name: photo }, }, Attributes: ['ALL'] } client.detectFaces(params, function(err, response) { if (err) { console.log(err, err.stack); // an error occurred } else { console.log(`Detected faces for: ${photo}`) response.FaceDetails.forEach(data => { let low = data.AgeRange.Low let high = data.AgeRange.High console.log(`The detected face is between: ${low} and ${high} years old`) console.log("All other attributes:") console.log(` BoundingBox.Width: ${data.BoundingBox.Width}`) console.log(` BoundingBox.Height: ${data.BoundingBox.Height}`) console.log(` BoundingBox.Left: ${data.BoundingBox.Left}`) console.log(` BoundingBox.Top: ${data.BoundingBox.Top}`) console.log(` Age.Range.Low: ${data.AgeRange.Low}`) console.log(` Age.Range.High: ${data.AgeRange.High}`) console.log(` Smile.Value: ${data.Smile.Value}`) console.log(` Smile.Confidence: ${data.Smile.Confidence}`) console.log(` Eyeglasses.Value: ${data.Eyeglasses.Value}`) console.log(` Eyeglasses.Confidence: ${data.Eyeglasses.Confidence}`) console.log(` Sunglasses.Value: ${data.Sunglasses.Value}`) console.log(` Sunglasses.Confidence: ${data.Sunglasses.Confidence}`) console.log(` Gender.Value: ${data.Gender.Value}`) console.log(` Gender.Confidence: ${data.Gender.Confidence}`) console.log(` Beard.Value: ${data.Beard.Value}`) console.log(` Beard.Confidence: ${data.Beard.Confidence}`) console.log(` Mustache.Value: ${data.Mustache.Value}`) console.log(` Mustache.Confidence: ${data.Mustache.Confidence}`) console.log(` EyesOpen.Value: ${data.EyesOpen.Value}`) console.log(` EyesOpen.Confidence: ${data.EyesOpen.Confidence}`) console.log(` MouthOpen.Value: ${data.MouthOpen.Value}`) console.log(` MouthOpen.Confidence: ${data.MouthOpen.Confidence}`) console.log(` Emotions[0].Type: ${data.Emotions[0].Type}`) console.log(` Emotions[0].Confidence: ${data.Emotions[0].Confidence}`) console.log(` Landmarks[0].Type: ${data.Landmarks[0].Type}`) console.log(` Landmarks[0].X: ${data.Landmarks[0].X}`) console.log(` Landmarks[0].Y: ${data.Landmarks[0].Y}`) console.log(` Pose.Roll: ${data.Pose.Roll}`) console.log(` Pose.Yaw: ${data.Pose.Yaw}`) console.log(` Pose.Pitch: ${data.Pose.Pitch}`) console.log(` Quality.Brightness: ${data.Quality.Brightness}`) console.log(` Quality.Sharpness: ${data.Quality.Sharpness}`) console.log(` Confidence: ${data.Confidence}`) console.log("------------") console.log("") }) // for response.faceDetails } // if }); ``` ------ ## DetectFaces 操作请求 对 `DetectFaces` 的输入是一个图像。在此示例中，图像从 Amazon S3 存储桶加载。`Attributes` 参数指定应返回所有人脸属性。有关更多信息，请参阅 [使用图像](images.md)。 ``` { "Image": { "S3Object": { "Bucket": "amzn-s3-demo-bucket", "Name": "input.jpg" } }, "Attributes": [ "ALL" ] } ``` ## DetectFaces 操作响应 `DetectFaces` 将返回每个检测到的人脸的以下信息： + **边界框** – 人脸周围的边界框的坐标。 + **置信度** - 边界框包含人脸的置信度级别。 + **人脸标记** - 一组人脸标记。对于每个标记（例如，左眼、右眼和嘴），此响应将提供 x 坐标和 y 坐标。 + **面部属性** - 一组面部属性，例如脸部是否被遮挡，作为 `FaceDetail` 对象返回。该套装包括： AgeRange、Beard、Emotions、 EyeDirection、Eyeglasses、、 EyesOpen、Gend FaceOccluded er、、Mustache MouthOpen、Smile 和太阳镜。对于每个此类属性，此响应将提供一个值。该值可以是不同的类型，例如布尔类型（无论用户是否戴墨镜）、字符串（无论该人是男性还是女性）或角度值（视线方向）。 pitch/yaw 此外，对于大多数属性，此响应还为属性提供检测到的值的置信度。请注意，虽然使用时支持 FaceOccluded 和 EyeDirection 属性`DetectFaces`，但使用`StartFaceDetection`和分析视频时不支持这些属性`GetFaceDetection`。 + **质量** - 描述人脸的亮度和锐度。有关确保尽可能最佳的人脸检测的信息，请参阅[有关面部比较输入图像的建议](recommendations-facial-input-images.md)。 + **姿势** - 描述图像中的人脸的旋转。 该请求可以描绘您想要返回的面部属性数组。将始终返回面部属性的`DEFAULT`子集 - `BoundingBox`、`Confidence`、`Pose`、`Quality` 和 `Landmarks`。您可以使用`["DEFAULT", "FACE_OCCLUDED", "EYE_DIRECTION"]`或只使用一个属性（比如`["FACE_OCCLUDED"]`）来请求返回特定的面部属性（除了默认列表之外）。您可以使用`["ALL"]`请求所有面部属性。请求更多属性可能会增加响应时间。 下面是 `DetectFaces` API 调用的示例响应： ``` { "FaceDetails": [ { "BoundingBox": { "Width": 0.7919622659683228, "Height": 0.7510867118835449, "Left": 0.08881539851427078, "Top": 0.151064932346344 }, "AgeRange": { "Low": 18, "High": 26 }, "Smile": { "Value": false, "Confidence": 89.77348327636719 }, "Eyeglasses": { "Value": true, "Confidence": 99.99996948242188 }, "Sunglasses": { "Value": true, "Confidence": 93.65237426757812 }, "Gender": { "Value": "Female", "Confidence": 99.85968780517578 }, "Beard": { "Value": false, "Confidence": 77.52591705322266 }, "Mustache": { "Value": false, "Confidence": 94.48904418945312 }, "EyesOpen": { "Value": true, "Confidence": 98.57169342041016 }, "MouthOpen": { "Value": false, "Confidence": 74.33953094482422 }, "Emotions": [ { "Type": "SAD", "Confidence": 65.56403350830078 }, { "Type": "CONFUSED", "Confidence": 31.277774810791016 }, { "Type": "DISGUSTED", "Confidence": 15.553778648376465 }, { "Type": "ANGRY", "Confidence": 8.012762069702148 }, { "Type": "SURPRISED", "Confidence": 7.621500015258789 }, { "Type": "FEAR", "Confidence": 7.243380546569824 }, { "Type": "CALM", "Confidence": 5.8196024894714355 }, { "Type": "HAPPY", "Confidence": 2.2830512523651123 } ], "Landmarks": [ { "Type": "eyeLeft", "X": 0.30225440859794617, "Y": 0.41018882393836975 }, { "Type": "eyeRight", "X": 0.6439348459243774, "Y": 0.40341562032699585 }, { "Type": "mouthLeft", "X": 0.343580037355423, "Y": 0.6951127648353577 }, { "Type": "mouthRight", "X": 0.6306480765342712, "Y": 0.6898072361946106 }, { "Type": "nose", "X": 0.47164231538772583, "Y": 0.5763645172119141 }, { "Type": "leftEyeBrowLeft", "X": 0.1732882857322693, "Y": 0.34452149271965027 }, { "Type": "leftEyeBrowRight", "X": 0.3655243515968323, "Y": 0.33231860399246216 }, { "Type": "leftEyeBrowUp", "X": 0.2671719491481781, "Y": 0.31669262051582336 }, { "Type": "rightEyeBrowLeft", "X": 0.5613729953765869, "Y": 0.32813435792922974 }, { "Type": "rightEyeBrowRight", "X": 0.7665090560913086, "Y": 0.3318614959716797 }, { "Type": "rightEyeBrowUp", "X": 0.6612788438796997, "Y": 0.3082450032234192 }, { "Type": "leftEyeLeft", "X": 0.2416982799768448, "Y": 0.4085965156555176 }, { "Type": "leftEyeRight", "X": 0.36943578720092773, "Y": 0.41230902075767517 }, { "Type": "leftEyeUp", "X": 0.29974061250686646, "Y": 0.3971870541572571 }, { "Type": "leftEyeDown", "X": 0.30360740423202515, "Y": 0.42347756028175354 }, { "Type": "rightEyeLeft", "X": 0.5755768418312073, "Y": 0.4081145226955414 }, { "Type": "rightEyeRight", "X": 0.7050536870956421, "Y": 0.39924031496047974 }, { "Type": "rightEyeUp", "X": 0.642906129360199, "Y": 0.39026668667793274 }, { "Type": "rightEyeDown", "X": 0.6423097848892212, "Y": 0.41669243574142456 }, { "Type": "noseLeft", "X": 0.4122826159000397, "Y": 0.5987403392791748 }, { "Type": "noseRight", "X": 0.5394935011863708, "Y": 0.5960900187492371 }, { "Type": "mouthUp", "X": 0.478581964969635, "Y": 0.6660456657409668 }, { "Type": "mouthDown", "X": 0.483366996049881, "Y": 0.7497162818908691 }, { "Type": "leftPupil", "X": 0.30225440859794617, "Y": 0.41018882393836975 }, { "Type": "rightPupil", "X": 0.6439348459243774, "Y": 0.40341562032699585 }, { "Type": "upperJawlineLeft", "X": 0.11031254380941391, "Y": 0.3980775475502014 }, { "Type": "midJawlineLeft", "X": 0.19301874935626984, "Y": 0.7034031748771667 }, { "Type": "chinBottom", "X": 0.4939905107021332, "Y": 0.8877836465835571 }, { "Type": "midJawlineRight", "X": 0.7990140914916992, "Y": 0.6899225115776062 }, { "Type": "upperJawlineRight", "X": 0.8548634648323059, "Y": 0.38160091638565063 } ], "Pose": { "Roll": -5.83309268951416, "Yaw": -2.4244730472564697, "Pitch": 2.6216139793395996 }, "Quality": { "Brightness": 96.16363525390625, "Sharpness": 95.51618957519531 }, "Confidence": 99.99872589111328, "FaceOccluded": { "Value": true, "Confidence": 99.99726104736328 }, "EyeDirection": { "Yaw": 16.299732, "Pitch": -6.407457, "Confidence": 99.968704 } } ], "ResponseMetadata": { "RequestId": "8bf02607-70b7-4f20-be55-473fe1bba9a2", "HTTPStatusCode": 200, "HTTPHeaders": { "x-amzn-requestid": "8bf02607-70b7-4f20-be55-473fe1bba9a2", "content-type": "application/x-amz-json-1.1", "content-length": "3409", "date": "Wed, 26 Apr 2023 20:18:50 GMT" }, "RetryAttempts": 0 } } ``` 注意以下几点： + `Pose` 数据描述了检测到的人脸的旋转。您可以使用 `BoundingBox` 和 `Pose` 数据的组合，在应用程序显示的人脸的四周绘制边界框。 + `Quality` 描述人脸的亮度和锐度。您可能会发现这对比较图像之间的人脸和查找最佳人脸会很有用。 + 前面的响应显示服务可检测的所有人脸 `landmarks`、所有人脸属性和情绪。要在响应中获取所有这些项，您必须指定 `attributes` 参数与值 `ALL`。默认情况下，`DetectFaces` API 仅返回以下 5 个人脸属性：`BoundingBox`、`Confidence`、`Pose`、`Quality` 和 `landmarks`。返回的默认标记为：`eyeLeft`、`eyeRight`、`nose`、`mouthLeft` 和 `mouthRight`。