DetectFaces permintaan operasi DetectFaces respon operasi

Mendeteksi wajah dalam citra

Amazon Rekognition Image menyediakan operasi DetectFaces yang mencari fitur utama wajah seperti mata, hidung, dan mulut untuk mendeteksi wajah dalam citra input. Amazon Rekognition Image mendeteksi 100 wajah terbesar dalam sebuah citra.

Anda dapat menyediakan citra input sebagai array bit citra (bit citra yang dikodekan base64), atau menentukan objek Amazon S3. Dalam prosedur ini, Anda mengunggah gambar (JPEGatauPNG) ke bucket S3 Anda dan menentukan nama kunci objek.

Untuk mendeteksi wajah dalam citra

Jika belum:
1. Buat atau perbarui pengguna dengan AmazonRekognitionFullAccess dan AmazonS3ReadOnlyAccess izin. Untuk informasi selengkapnya, lihat Langkah 1: Siapkan AWS akun dan buat Pengguna.
2. Instal dan konfigurasikan AWS CLI dan AWS SDKs. Untuk informasi selengkapnya, lihat Langkah 2: Siapkan AWS CLI and AWS SDKs.
Unggah citra (yang berisi satu atau beberapa wajah) ke bucket S3 Anda.

Untuk petunjuk, lihat Mengunggah Objek ke Amazon S3 di Panduan Pengguna Layanan Penyimpanan Sederhana Amazon.

Gunakan contoh berikut untuk memanggil DetectFaces.

Java

Contoh ini menampilkan perkiraan rentang usia untuk wajah yang terdeteksi, dan mencantumkan JSON untuk semua atribut wajah yang terdeteksi. Ubah nilai photo ke nama file citra. Ubah nilai bucket ke bucket Amazon S3 tempat citra disimpan.


//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

Kode ini diambil dari GitHub repositori SDK contoh AWS Dokumentasi. Lihat contoh lengkapnya di sini.


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: " +
            "   <bucket> <image>\n\n" +
            "Where:\n" +
            "   bucket - The name of the Amazon S3 bucket that contains the image (for example, ,ImageBucket)." +
            "   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<FaceDetail> 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

Contoh ini menampilkan JSON output dari detect-faces AWS CLI operasi. Ganti file dengan nama file citra. Ganti bucket dengan nama bucket Amazon S3 yang berisi file citra.


aws rekognition detect-faces --image "{"S3Object":{"Bucket":"bucket-name","Name":"image-name"}}"\
                             --attributes "ALL" --profile profile-name --region region-name

Jika Anda mengakses perangkat Windows, gunakan tanda kutip ganda alih-alih tanda kutip tunggal dan hindari tanda kutip ganda bagian dalam dengan garis miring terbalik (yaitu\) untuk mengatasi kesalahan parser yang mungkin Anda temui. CLI Sebagai contoh, lihat berikut ini:



aws rekognition detect-faces --image "{\"S3Object\":{\"Bucket\":\"bucket-name\",\"Name\":\"image-name\"}}" --attributes "ALL" 
--profile profile-name --region region-name

Python

Contoh ini menampilkan perkiraan rentang usia dan atribut lain untuk wajah yang terdeteksi, dan mencantumkan JSON untuk semua atribut wajah yang terdeteksi. Ubah nilai photo ke nama file citra. Ubah nilai bucket ke bucket Amazon S3 tempat citra disimpan. Ganti nilai profile_name di baris yang membuat sesi Rekognition dengan nama profil pengembang Anda.


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='bucket'
    region='region'
    face_count=detect_faces(photo, bucket, region)
    print("Faces detected: " + str(face_count))

if __name__ == "__main__":
    main()

.NET


//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 = "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<String>() { "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

Contoh ini menampilkan perkiraan rentang usia untuk wajah yang terdeteksi, serta daftar berbagai atribut wajah. Ubah nilai photo ke nama file citra. Ubah nilai bucket ke bucket Amazon S3 tempat citra disimpan.




   # 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

Ganti nilai profile_name di baris yang membuat sesi Rekognition dengan nama profil pengembang Anda.

Jika Anda menggunakan TypeScript definisi, Anda mungkin perlu menggunakan import AWS from 'aws-sdk' alih-alihconst AWS = require('aws-sdk'), untuk menjalankan program dengan Node.js. Anda dapat berkonsultasi dengan Javascript AWS SDK untuk lebih jelasnya. Tergantung bagaimana konfigurasi diatur, Anda juga mungkin perlu menentukan wilayah Anda dengan AWS.config.update({region:region});.




// 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 permintaan operasi

Input ke DetectFaces adalah citra. Pada contoh ini, citra dimuat dari bucket Amazon S3. Parameter Attributes menentukan bahwa semua atribut wajah harus dikembalikan. Untuk informasi selengkapnya, lihat Bekerja dengan citra.


{
    "Image": {
        "S3Object": {
            "Bucket": "bucket",
            "Name": "input.jpg"
        }
    },
    "Attributes": [
        "ALL"
    ]
}

DetectFaces respon operasi

DetectFaces mengembalikan informasi berikut untuk setiap wajah yang terdeteksi:

Kotak batas – Koordinat kotak batas yang mengelilingi wajah.
Kepercayaan – Tingkat kepercayaan yang dimiliki kotak batas berisi wajah.
Tengara wajah – Array penanda wajah. Untuk setiap penanda (seperti mata kiri, mata kanan, dan mulut), respons memberikan koordinat x dan y.
Atribut wajah — Satu set atribut wajah, seperti apakah wajah tersumbat, dikembalikan sebagai FaceDetail objek. Set meliputi: AgeRange, Jenggot, Emosi,, Kacamata EyeDirection,,, Jenis Kelamin EyesOpen, FaceOccluded, Kumis MouthOpen, Senyum, dan Kacamata Hitam. Untuk setiap atribut tersebut, respons memberikan nilai. Nilainya bisa dari berbagai jenis, seperti tipe Boolean (apakah seseorang mengenakan kacamata hitam), tali (apakah orang tersebut pria atau wanita), atau nilai derajat sudut (untuk nada/menguap arah pandangan mata). Selain itu, untuk sebagian besar atribut, respons juga memberikan kepercayaan pada nilai yang terdeteksi untuk atribut. Perhatikan bahwa sementara FaceOccluded dan EyeDirection atribut didukung saat menggunakanDetectFaces, atribut tersebut tidak didukung saat menganalisis video dengan StartFaceDetection danGetFaceDetection.
Kualitas – Menggambarkan tingkat kecerahan dan ketajaman wajah. Untuk informasi tentang memastikan deteksi wajah sebaik mungkin, lihat Rekomendasi untuk citra input perbandingan wajah.
Pose – Menggambarkan rotasi wajah di dalam citra.

Permintaan dapat menggambarkan array atribut wajah yang ingin Anda kembalikan. DEFAULTSubset atribut wajah -BoundingBox,,Confidence, PoseQuality, dan Landmarks - akan selalu dikembalikan. Anda dapat meminta pengembalian atribut wajah tertentu (selain daftar default) - dengan menggunakan ["DEFAULT", "FACE_OCCLUDED", "EYE_DIRECTION"] atau hanya satu atribut, seperti["FACE_OCCLUDED"]. Anda dapat meminta semua atribut wajah dengan menggunakan["ALL"]. Meminta lebih banyak atribut dapat meningkatkan waktu respons.

Berikut ini adalah contoh respons DetectFaces API panggilan:


{
  "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",
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          "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
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        {
          "Type": "mouthDown",
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          "Y": 0.7497162818908691
        },
        {
          "Type": "leftPupil",
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          "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
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        {
          "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
  }
}

Perhatikan hal berikut:

Data Pose menggambarkan rotasi wajah yang terdeteksi. Anda dapat menggunakan kombinasi data BoundingBox dan Pose untuk menggambar kotak batas di sekitar wajah yang ditampilkan aplikasi Anda.
Parameter Quality menggambarkan tingkat kecerahan dan ketajaman wajah. Mungkin ini berguna untuk membandingkan wajah di seluruh citra dan menemukan wajah terbaik.
Respons sebelumnya menunjukkan semua landmarks wajah yang dapat dideteksi layanan, semua atribut wajah dan emosi. Untuk mendapatkan semua ini dalam respons, Anda harus menentukan parameter attributes dengan nilai ALL. Secara default, DetectFaces API mengembalikan hanya lima atribut wajah berikut:BoundingBox,Confidence,Pose, Quality danlandmarks. Penanda default yang dikembalikan adalah: eyeLeft, eyeRight, nose, mouthLeft, dan mouthRight.

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Konvensi Dokumen

Pedoman atribut wajah

Membandingkan wajah dalam citra