快速入門：使用臉部服務

文章
06/13/2024

重要

如果您使用 Microsoft 產品或服務來處理生物特徵辨識資料，您必須負責：(i) 向資料主體提供通知，包括與保留期間和毀損有關的通知；(ii) 向資料主體取得同意；以及 (iii) 刪除生物特徵辨識資料，應根據適用資料保護要求酌情採取上述措施。「生物特徵辨識資料」具有 GDPR 第 4 條中所述的意義，以及其他資料保護要求中的對等字詞 (如適用)。如需相關資訊，請參閱臉部的資料和隱私權。

警告

臉部辨識服務存取受限於資格和使用準則，以支援我們的「負責任的 AI 原則」。臉部辨識服務僅供 Microsoft 受管理的客戶和合作夥伴使用。請使用臉部辨識受理表單以申請存取。如需詳細資訊，請參閱臉部的有限存取權頁面。

開始使用適用於 .NET 的臉部用戶端程式庫進行臉部辨識。 Azure AI 臉部服務可讓您存取進階演算法，以偵測和辨識影像中的人臉。請遵循下列步驟來安裝套件，並試用使用遠端影像進行基本臉部識別的程式碼範例。

參考文件 | 程式庫來源程式碼 | 套件 (NuGet) | 範例

必要條件

Azure 訂用帳戶 - 建立免費帳戶
Visual Studio IDE 或目前版本的 .NET Core。
Azure 帳戶必須已獲指派 Cognitive Services Contributor 角色，您才能同意負責任的 AI 條款並建立資源。若要將此角色指派給您的帳戶，請遵循指派角色文件中的步驟，或連絡系統管理員。
擁有 Azure 訂用帳戶之後，在 Azure 入口網站中建立臉部資源，以取得您的金鑰和端點。在其部署後，選取 [前往資源]。
- 您需要來自所建立資源的金鑰和端點，以將應用程式連線至 Face API。
- 您可以使用免費定價層 (F0) 來試用服務，之後可升級至付費層以用於實際執行環境。

建立環境變數

在此範例中，在執行應用程式的本機電腦上將認證寫入環境變數。

前往 Azure 入口網站。如果已成功部署您在 [必要條件] 區段中建立的資源，請選取 [後續步驟] 下的 [前往資源] 按鈕。您可以在 [金鑰和端點] 頁面中 [資源管理] 底下找到金鑰和端點。您的資源金鑰與您的 Azure 訂用帳戶識別碼不同。

提示

請勿在程式碼中直接包含索引碼，且切勿公開張貼索引碼。如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。

若要設定金鑰和端點的環境變數，請開啟主控台視窗，然後遵循作業系統和開發環境的指示進行。

若要設定 VISION_KEY 環境變數，請以您其中一個資源索引碼取代 your-key。
若要設定 VISION_ENDPOINT 環境變數，請將 your-endpoint 取代為您資源的端點。

Windows
Linux

setx VISION_KEY your-key

setx VISION_ENDPOINT your-endpoint

新增環境變數之後，您可能需要重新啟動任何將讀取環境變數的執行中程式，包括主控台視窗。

export VISION_KEY=your-key

export VISION_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bashrc，讓變更生效。

識別並驗證臉部

建立新的 C# 應用程式
- Visual Studio IDE
- CLI
使用 Visual Studio，建立新的 .NET Core 應用程式。

安裝用戶端程式庫

建立新專案後，以滑鼠右鍵按一下 [方案總管] 中的專案解決方案，然後選取 [管理 NuGet 套件]，以安裝用戶端程式庫。在開啟的套件管理員中，選取 [瀏覽]、核取 [包含發行前版本]，然後搜尋 Microsoft.Azure.CognitiveServices.Vision.Face。選取最新版本，然後選取 [安裝] 。
在主控台視窗中 (例如 cmd、PowerShell 或 Bash)，使用 dotnet new 命令建立名為 face-quickstart 的新主控台應用程式。此命令會建立簡單的 "Hello World" C# 專案，內含單一原始程式檔：program.cs。
```
dotnet new console -n face-quickstart
```
將目錄變更為新建立的應用程式資料夾。您可以使用下列命令來建置應用程式：
```
dotnet build
```
建置輸出應該不會有警告或錯誤。
```
...
Build succeeded.
 0 Warning(s)
 0 Error(s)
...
```
安裝用戶端程式庫

在應用程式目錄中，使用下列命令安裝適用於 .NET 的臉部用戶端程式庫：
```
dotnet add package Microsoft.Azure.CognitiveServices.Vision.Face --prerelease
```

將下列程式碼新增至 Program.cs 檔案。

注意

如果您尚未使用登記表單接收臉部服務的存取權限，其中部分功能將無法運作。

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;

using Microsoft.Azure.CognitiveServices.Vision.Face;
using Microsoft.Azure.CognitiveServices.Vision.Face.Models;

namespace FaceQuickstart
{
    class Program
    {
        static string personGroupId = Guid.NewGuid().ToString();

        // URL path for the images.
        const string IMAGE_BASE_URL = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/";

        // From your Face subscription in the Azure portal, get your subscription key and endpoint.
        const string SUBSCRIPTION_KEY = Environment.GetEnvironmentVariable("VISION_KEY");
        const string ENDPOINT = Environment.GetEnvironmentVariable("VISION_ENDPOINT");


         static void Main(string[] args)
        {
            // Recognition model 4 was released in 2021 February.
            // It is recommended since its accuracy is improved
            // on faces wearing masks compared with model 3,
            // and its overall accuracy is improved compared
            // with models 1 and 2.
            const string RECOGNITION_MODEL4 = RecognitionModel.Recognition04;

            // Authenticate.
            IFaceClient client = Authenticate(ENDPOINT, SUBSCRIPTION_KEY);

            // Identify - recognize a face(s) in a person group (a person group is created in this example).
            IdentifyInPersonGroup(client, IMAGE_BASE_URL, RECOGNITION_MODEL4).Wait();

            Console.WriteLine("End of quickstart.");
        }

        /*
         *	AUTHENTICATE
         *	Uses subscription key and region to create a client.
         */
        public static IFaceClient Authenticate(string endpoint, string key)
        {
            return new FaceClient(new ApiKeyServiceClientCredentials(key)) { Endpoint = endpoint };
        }

        // Detect faces from image url for recognition purposes. This is a helper method for other functions in this quickstart.
        // Parameter `returnFaceId` of `DetectWithUrlAsync` must be set to `true` (by default) for recognition purposes.
        // Parameter `FaceAttributes` is set to include the QualityForRecognition attribute. 
        // Recognition model must be set to recognition_03 or recognition_04 as a result.
        // Result faces with insufficient quality for recognition are filtered out. 
        // The field `faceId` in returned `DetectedFace`s will be used in Face - Face - Verify and Face - Identify.
        // It will expire 24 hours after the detection call.
        private static async Task<List<DetectedFace>> DetectFaceRecognize(IFaceClient faceClient, string url, string recognition_model)
        {
            // Detect faces from image URL. Since only recognizing, use the recognition model 1.
            // We use detection model 3 because we are not retrieving attributes.
            IList<DetectedFace> detectedFaces = await faceClient.Face.DetectWithUrlAsync(url, recognitionModel: recognition_model, detectionModel: DetectionModel.Detection03, returnFaceAttributes: new List<FaceAttributeType> { FaceAttributeType.QualityForRecognition });
            List<DetectedFace> sufficientQualityFaces = new List<DetectedFace>();
            foreach (DetectedFace detectedFace in detectedFaces){
                var faceQualityForRecognition = detectedFace.FaceAttributes.QualityForRecognition;
                if (faceQualityForRecognition.HasValue && (faceQualityForRecognition.Value >= QualityForRecognition.Medium)){
                    sufficientQualityFaces.Add(detectedFace);
                }
            }
            Console.WriteLine($"{detectedFaces.Count} face(s) with {sufficientQualityFaces.Count} having sufficient quality for recognition detected from image `{Path.GetFileName(url)}`");

            return sufficientQualityFaces.ToList();
        }

        /*
         * IDENTIFY FACES
         * To identify faces, you need to create and define a person group.
         * The Identify operation takes one or several face IDs from DetectedFace or PersistedFace and a PersonGroup and returns 
         * a list of Person objects that each face might belong to. Returned Person objects are wrapped as Candidate objects, 
         * which have a prediction confidence value.
         */
        public static async Task IdentifyInPersonGroup(IFaceClient client, string url, string recognitionModel)
        {
            Console.WriteLine("========IDENTIFY FACES========");
            Console.WriteLine();

            // Create a dictionary for all your images, grouping similar ones under the same key.
            Dictionary<string, string[]> personDictionary =
                new Dictionary<string, string[]>
                    { { "Family1-Dad", new[] { "Family1-Dad1.jpg", "Family1-Dad2.jpg" } },
                      { "Family1-Mom", new[] { "Family1-Mom1.jpg", "Family1-Mom2.jpg" } },
                      { "Family1-Son", new[] { "Family1-Son1.jpg", "Family1-Son2.jpg" } },
                      { "Family1-Daughter", new[] { "Family1-Daughter1.jpg", "Family1-Daughter2.jpg" } },
                      { "Family2-Lady", new[] { "Family2-Lady1.jpg", "Family2-Lady2.jpg" } },
                      { "Family2-Man", new[] { "Family2-Man1.jpg", "Family2-Man2.jpg" } }
                    };
            // A group photo that includes some of the persons you seek to identify from your dictionary.
            string sourceImageFileName = "identification1.jpg";

            // Create a person group. 
            Console.WriteLine($"Create a person group ({personGroupId}).");
            await client.PersonGroup.CreateAsync(personGroupId, personGroupId, recognitionModel: recognitionModel);
            // The similar faces will be grouped into a single person group person.
            foreach (var groupedFace in personDictionary.Keys)
            {
                // Limit TPS
                await Task.Delay(250);
                Person person = await client.PersonGroupPerson.CreateAsync(personGroupId: personGroupId, name: groupedFace);
                Console.WriteLine($"Create a person group person '{groupedFace}'.");

                // Add face to the person group person.
                foreach (var similarImage in personDictionary[groupedFace])
                {
                    Console.WriteLine($"Check whether image is of sufficient quality for recognition");
                    IList<DetectedFace> detectedFaces1 = await client.Face.DetectWithUrlAsync($"{url}{similarImage}", 
                        recognitionModel: recognitionModel, 
                        detectionModel: DetectionModel.Detection03,
                        returnFaceAttributes: new List<FaceAttributeType> { FaceAttributeType.QualityForRecognition });
                    bool sufficientQuality = true;
                    foreach (var face1 in detectedFaces1)
                    {
                        var faceQualityForRecognition = face1.FaceAttributes.QualityForRecognition;
                        //  Only "high" quality images are recommended for person enrollment
                        if (faceQualityForRecognition.HasValue && (faceQualityForRecognition.Value != QualityForRecognition.High)){
                            sufficientQuality = false;
                            break;
                        }
                    }

                    if (!sufficientQuality){
                        continue;
                    }

                    // add face to the person group
                    Console.WriteLine($"Add face to the person group person({groupedFace}) from image `{similarImage}`");
                    PersistedFace face = await client.PersonGroupPerson.AddFaceFromUrlAsync(personGroupId, person.PersonId,
                        $"{url}{similarImage}", similarImage);
                }
            }

            // Start to train the person group.
            Console.WriteLine();
            Console.WriteLine($"Train person group {personGroupId}.");
            await client.PersonGroup.TrainAsync(personGroupId);

            // Wait until the training is completed.
            while (true)
            {
                await Task.Delay(1000);
                var trainingStatus = await client.PersonGroup.GetTrainingStatusAsync(personGroupId);
                Console.WriteLine($"Training status: {trainingStatus.Status}.");
                if (trainingStatus.Status == TrainingStatusType.Succeeded) { break; }
            }
            Console.WriteLine();

            List<Guid> sourceFaceIds = new List<Guid>();
            // Detect faces from source image url.
            List<DetectedFace> detectedFaces = await DetectFaceRecognize(client, $"{url}{sourceImageFileName}", recognitionModel);

            // Add detected faceId to sourceFaceIds.
            foreach (var detectedFace in detectedFaces) { sourceFaceIds.Add(detectedFace.FaceId.Value); }
            
            // Identify the faces in a person group. 
            var identifyResults = await client.Face.IdentifyAsync(sourceFaceIds, personGroupId);

            foreach (var identifyResult in identifyResults)
            {
                if (identifyResult.Candidates.Count==0) {
                    Console.WriteLine($"No person is identified for the face in: {sourceImageFileName} - {identifyResult.FaceId},");
                    continue;
                }
                Person person = await client.PersonGroupPerson.GetAsync(personGroupId, identifyResult.Candidates[0].PersonId);
                Console.WriteLine($"Person '{person.Name}' is identified for the face in: {sourceImageFileName} - {identifyResult.FaceId}," +
                    $" confidence: {identifyResult.Candidates[0].Confidence}.");

                VerifyResult verifyResult = await client.Face.VerifyFaceToPersonAsync(identifyResult.FaceId, person.PersonId, personGroupId);
                Console.WriteLine($"Verification result: is a match? {verifyResult.IsIdentical}. confidence: {verifyResult.Confidence}");
            }
            Console.WriteLine();
        }
    }
}

執行應用程式
- Visual Studio IDE
- CLI
按一下 IDE 視窗頂端的 [偵錯] 按鈕，以執行應用程式。
使用 dotnet run 命令從您的應用程式目錄執行應用程式。
```
dotnet run
```

輸出

========IDENTIFY FACES========

Create a person group (3972c063-71b3-4328-8579-6d190ee76f99).
Create a person group person 'Family1-Dad'.
Add face to the person group person(Family1-Dad) from image `Family1-Dad1.jpg`
Add face to the person group person(Family1-Dad) from image `Family1-Dad2.jpg`
Create a person group person 'Family1-Mom'.
Add face to the person group person(Family1-Mom) from image `Family1-Mom1.jpg`
Add face to the person group person(Family1-Mom) from image `Family1-Mom2.jpg`
Create a person group person 'Family1-Son'.
Add face to the person group person(Family1-Son) from image `Family1-Son1.jpg`
Add face to the person group person(Family1-Son) from image `Family1-Son2.jpg`
Create a person group person 'Family1-Daughter'.
Create a person group person 'Family2-Lady'.
Add face to the person group person(Family2-Lady) from image `Family2-Lady1.jpg`
Add face to the person group person(Family2-Lady) from image `Family2-Lady2.jpg`
Create a person group person 'Family2-Man'.
Add face to the person group person(Family2-Man) from image `Family2-Man1.jpg`
Add face to the person group person(Family2-Man) from image `Family2-Man2.jpg`

Train person group 3972c063-71b3-4328-8579-6d190ee76f99.
Training status: Succeeded.

4 face(s) with 4 having sufficient quality for recognition detected from image `identification1.jpg`
Person 'Family1-Dad' is identified for face in: identification1.jpg - 994bfd7a-0d8f-4fae-a5a6-c524664cbee7, confidence: 0.96725.
Person 'Family1-Mom' is identified for face in: identification1.jpg - 0c9da7b9-a628-429d-97ff-cebe7c638fb5, confidence: 0.96921.
No person is identified for face in: identification1.jpg - a881259c-e811-4f7e-a35e-a453e95ca18f,
Person 'Family1-Son' is identified for face in: identification1.jpg - 53772235-8193-46eb-bdfc-1ebc25ea062e, confidence: 0.92886.

End of quickstart.

提示

臉部 API 會在一組本質為靜態的預建模型上執行 (模型的效能不會在服務執行時衰退或改善)。如果 Microsoft 更新模型的後端，而未遷移到全新的模型版本，則模型產生的結果可能會變更。若要利用較新版本的模型，您可以使用相同的註冊映像來重新訓練 PersonGroup，進而將較新的模型指定為參數。

清除資源

如果您想要清除和移除 Azure AI 服務訂用帳戶，則可以刪除資源或資源群組。刪除資源群組也會刪除與其相關聯的任何其他資源。

若要刪除您在本快速入門中建立的 PersonGroup，請在程式中執行下列程式碼：

// At end, delete person groups in both regions (since testing only)
Console.WriteLine("========DELETE PERSON GROUP========");
Console.WriteLine();
DeletePersonGroup(client, personGroupId).Wait();

以下列程式碼定義刪除方法：

/*
 * DELETE PERSON GROUP
 * After this entire example is executed, delete the person group in your Azure account,
 * otherwise you cannot recreate one with the same name (if running example repeatedly).
 */
public static async Task DeletePersonGroup(IFaceClient client, String personGroupId)
{
    await client.PersonGroup.DeleteAsync(personGroupId);
    Console.WriteLine($"Deleted the person group {personGroupId}.");
}

下一步

在本快速入門中，您已了解如何使用適用於 .NET 的臉部用戶端程式庫來執行基本臉部識別。接下來，了解不同的臉部偵測模型，以及如何為您的使用案例指定正確的模型。

指定臉部偵測模型版本

什麼是臉部辨識服務？
您可以在 GitHub 上找到更廣泛的範例程式碼。

開始使用適用於 Python 的臉部用戶端程式庫進行臉部辨識。請遵循下列步驟來安裝套件，並試用基本工作的程式碼範例。臉部服務可讓您存取先進的演算法，以偵測和辨識影像中的人臉。請遵循下列步驟來安裝套件，並試用使用遠端影像進行基本臉部識別的程式碼範例。

參考文件 | 程式庫原始程式碼 | 套件 (PiPy) | 範例

必要條件

Azure 訂用帳戶 - 建立免費帳戶
Python 3.x
- 您安裝的 Python 應包含 pip。您可以在命令列上執行 pip --version 來檢查是否已安裝 pip。安裝最新版本的 Python 以取得 pip。
Azure 帳戶必須已獲指派 Cognitive Services Contributor 角色，您才能同意負責任的 AI 條款並建立資源。若要將此角色指派給您的帳戶，請遵循指派角色文件中的步驟，或連絡系統管理員。
擁有 Azure 訂用帳戶之後，在 Azure 入口網站中建立臉部資源，以取得您的金鑰和端點。在其部署後，選取 [前往資源]。
- 您需要來自所建立資源的金鑰和端點，以將應用程式連線至 Face API。
- 您可以使用免費定價層 (F0) 來試用服務，之後可升級至付費層以用於實際執行環境。

建立環境變數

在此範例中，在執行應用程式的本機電腦上將認證寫入環境變數。

提示

請勿在程式碼中直接包含索引碼，且切勿公開張貼索引碼。如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。

若要設定金鑰和端點的環境變數，請開啟主控台視窗，然後遵循作業系統和開發環境的指示進行。

若要設定 VISION_KEY 環境變數，請以您其中一個資源索引碼取代 your-key。
若要設定 VISION_ENDPOINT 環境變數，請將 your-endpoint 取代為您資源的端點。

Windows
Linux

setx VISION_KEY your-key

setx VISION_ENDPOINT your-endpoint

新增環境變數之後，您可能需要重新啟動任何將讀取環境變數的執行中程式，包括主控台視窗。

export VISION_KEY=your-key

export VISION_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bashrc，讓變更生效。

識別並驗證臉部

安裝用戶端程式庫

安裝 Python 之後，您可以透過以下項目安裝用戶端程式庫：
```
pip install --upgrade azure-cognitiveservices-vision-face
```

建立新的 Python 應用程式

建立新的 Python 指令碼—例如，quickstart-file.py。然後在您慣用的編輯器或 IDE 中開啟該檔案，並貼上下列程式碼。

注意

如果您尚未使用登記表單接收臉部服務的存取權限，其中部分功能將無法運作。

import asyncio
import io
import os
import sys
import time
import uuid
import requests
from urllib.parse import urlparse
from io import BytesIO
# To install this module, run:
# python -m pip install Pillow
from PIL import Image, ImageDraw
from azure.cognitiveservices.vision.face import FaceClient
from msrest.authentication import CognitiveServicesCredentials
from azure.cognitiveservices.vision.face.models import TrainingStatusType, Person, QualityForRecognition


# This key will serve all examples in this document.
KEY = os.environ["VISION_KEY"]

# This endpoint will be used in all examples in this quickstart.
ENDPOINT = os.environ["VISION_ENDPOINT"]

# Base url for the Verify and Facelist/Large Facelist operations
IMAGE_BASE_URL = 'https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/'

# Used in the Person Group Operations and Delete Person Group examples.
# You can call list_person_groups to print a list of preexisting PersonGroups.
# SOURCE_PERSON_GROUP_ID should be all lowercase and alphanumeric. For example, 'mygroupname' (dashes are OK).
PERSON_GROUP_ID = str(uuid.uuid4()) # assign a random ID (or name it anything)

# Used for the Delete Person Group example.
TARGET_PERSON_GROUP_ID = str(uuid.uuid4()) # assign a random ID (or name it anything)

# Create an authenticated FaceClient.
face_client = FaceClient(ENDPOINT, CognitiveServicesCredentials(KEY))

'''
Create the PersonGroup
'''
# Create empty Person Group. Person Group ID must be lower case, alphanumeric, and/or with '-', '_'.
print('Person group:', PERSON_GROUP_ID)
face_client.person_group.create(person_group_id=PERSON_GROUP_ID, name=PERSON_GROUP_ID, recognition_model='recognition_04')

# Define woman friend
woman = face_client.person_group_person.create(PERSON_GROUP_ID, name="Woman")
# Define man friend
man = face_client.person_group_person.create(PERSON_GROUP_ID, name="Man")
# Define child friend
child = face_client.person_group_person.create(PERSON_GROUP_ID, name="Child")

'''
Detect faces and register them to each person
'''
# Find all jpeg images of friends in working directory (TBD pull from web instead)
woman_images = ["https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/Family1-Mom1.jpg", "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/Family1-Mom2.jpg"]
man_images = ["https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/Family1-Dad1.jpg", "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/Family1-Dad2.jpg"]
child_images = ["https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/Family1-Son1.jpg", "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/Family1-Son2.jpg"]

# Add to woman person
for image in woman_images:
    # Check if the image is of sufficent quality for recognition.
    sufficientQuality = True
    detected_faces = face_client.face.detect_with_url(url=image, detection_model='detection_03', recognition_model='recognition_04', return_face_attributes=['qualityForRecognition'])
    for face in detected_faces:
        if face.face_attributes.quality_for_recognition != QualityForRecognition.high:
            sufficientQuality = False
            break
        face_client.person_group_person.add_face_from_url(PERSON_GROUP_ID, woman.person_id, image)
        print("face {} added to person {}".format(face.face_id, woman.person_id))

    if not sufficientQuality: continue

# Add to man person
for image in man_images:
    # Check if the image is of sufficent quality for recognition.
    sufficientQuality = True
    detected_faces = face_client.face.detect_with_url(url=image, detection_model='detection_03', recognition_model='recognition_04', return_face_attributes=['qualityForRecognition'])
    for face in detected_faces:
        if face.face_attributes.quality_for_recognition != QualityForRecognition.high:
            sufficientQuality = False
            break
        face_client.person_group_person.add_face_from_url(PERSON_GROUP_ID, man.person_id, image)
        print("face {} added to person {}".format(face.face_id, man.person_id))

    if not sufficientQuality: continue

# Add to child person
for image in child_images:
    # Check if the image is of sufficent quality for recognition.
    sufficientQuality = True
    detected_faces = face_client.face.detect_with_url(url=image, detection_model='detection_03', recognition_model='recognition_04', return_face_attributes=['qualityForRecognition'])
    for face in detected_faces:
        if face.face_attributes.quality_for_recognition != QualityForRecognition.high:
            sufficientQuality = False
            print("{} has insufficient quality".format(face))
            break
        face_client.person_group_person.add_face_from_url(PERSON_GROUP_ID, child.person_id, image)
        print("face {} added to person {}".format(face.face_id, child.person_id))
    if not sufficientQuality: continue


'''
Train PersonGroup
'''
# Train the person group
print("pg resource is {}".format(PERSON_GROUP_ID))
rawresponse = face_client.person_group.train(PERSON_GROUP_ID, raw= True)
print(rawresponse)

while (True):
    training_status = face_client.person_group.get_training_status(PERSON_GROUP_ID)
    print("Training status: {}.".format(training_status.status))
    print()
    if (training_status.status is TrainingStatusType.succeeded):
        break
    elif (training_status.status is TrainingStatusType.failed):
        face_client.person_group.delete(person_group_id=PERSON_GROUP_ID)
        sys.exit('Training the person group has failed.')
    time.sleep(5)

'''
Identify a face against a defined PersonGroup
'''
# Group image for testing against
test_image = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/identification1.jpg"

print('Pausing for 10 seconds to avoid triggering rate limit on free account...')
time.sleep (10)

# Detect faces
face_ids = []
# We use detection model 3 to get better performance, recognition model 4 to support quality for recognition attribute.
faces = face_client.face.detect_with_url(test_image, detection_model='detection_03', recognition_model='recognition_04', return_face_attributes=['qualityForRecognition'])
for face in faces:
    # Only take the face if it is of sufficient quality.
    if face.face_attributes.quality_for_recognition == QualityForRecognition.high or face.face_attributes.quality_for_recognition == QualityForRecognition.medium:
        face_ids.append(face.face_id)

# Identify faces
results = face_client.face.identify(face_ids, PERSON_GROUP_ID)
print('Identifying faces in image')
if not results:
    print('No person identified in the person group')
for identifiedFace in results:
    if len(identifiedFace.candidates) > 0:
        print('Person is identified for face ID {} in image, with a confidence of {}.'.format(identifiedFace.face_id, identifiedFace.candidates[0].confidence)) # Get topmost confidence score

        # Verify faces
        verify_result = face_client.face.verify_face_to_person(identifiedFace.face_id, identifiedFace.candidates[0].person_id, PERSON_GROUP_ID)
        print('verification result: {}. confidence: {}'.format(verify_result.is_identical, verify_result.confidence))
    else:
        print('No person identified for face ID {} in image.'.format(identifiedFace.face_id))
 

print()
print('End of quickstart.')

使用 python 命令，從應用程式目錄執行臉部辨識應用程式。
```
python quickstart-file.py
```
提示

臉部 API 會在一組本質為靜態的預建模型上執行 (模型的效能不會在服務執行時衰退或改善)。如果 Microsoft 更新模型的後端，而未遷移到全新的模型版本，則模型產生的結果可能會變更。若要利用較新版本的模型，您可以使用相同的註冊映像來重新訓練 PersonGroup，進而將較新的模型指定為參數。

輸出

Person group: c8e679eb-0b71-43b4-aa91-ab8200cae7df
face 861d769b-d014-40e8-8b4a-7fd3bc9b425b added to person f80c1cfa-b8cb-46f8-9f7f-e72fbe402bc3
face e3c356a4-1ac3-4c97-9219-14648997f195 added to person f80c1cfa-b8cb-46f8-9f7f-e72fbe402bc3
face f9119820-c374-4c4d-b795-96ae2fec5069 added to person be4084a7-0c7b-4cf9-9463-3756d2e28e17
face 67d626df-3f75-4801-9364-601b63c8296a added to person be4084a7-0c7b-4cf9-9463-3756d2e28e17
face 19e2e8cc-5029-4087-bca0-9f94588fb850 added to person 3ff07c65-6193-4d3e-bf18-d7c106393cd5
face dcc61e80-16b1-4241-ae3f-9721597bae4c added to person 3ff07c65-6193-4d3e-bf18-d7c106393cd5
pg resource is c8e679eb-0b71-43b4-aa91-ab8200cae7df
<msrest.pipeline.ClientRawResponse object at 0x00000240DAD47310>
Training status: running.

Training status: succeeded.

Pausing for 10 seconds to avoid triggering rate limit on free account...
Identifying faces in image
Person for face ID 40582995-d3a8-41c4-a9d1-d17ae6b46c5c is identified in image, with a confidence of 0.96725.
Person for face ID 7a0368a2-332c-4e7a-81c4-2db3d74c78c5 is identified in image, with a confidence of 0.96921.
No person identified for face ID c4a3dd28-ef2d-457e-81d1-a447344242c4 in image.
Person for face ID 360edf1a-1e8f-402d-aa96-1734d0c21c1c is identified in image, with a confidence of 0.92886.

清除資源

如果您想要清除和移除 Azure AI 服務訂用帳戶，則可以刪除資源或資源群組。刪除資源群組也會刪除與其相關聯的任何其他資源。

若要刪除您在本快速入門中建立的 PersonGroup，請在指令碼中執行下列程式碼：

# Delete the main person group.
face_client.person_group.delete(person_group_id=PERSON_GROUP_ID)
print("Deleted the person group {} from the source location.".format(PERSON_GROUP_ID))
print()

下一步

在本快速入門中，您已了解如何使用適用於 Python 的臉部用戶端程式庫來執行基本臉部識別。接下來，了解不同的臉部偵測模型，以及如何為您的使用案例指定正確的模型。

指定臉部偵測模型版本

什麼是臉部辨識服務？
您可以在 GitHub 上找到更廣泛的範例程式碼。

開始使用適用於 JavaScript 的臉部用戶端程式庫進行臉部辨識。請遵循下列步驟來安裝套件，並試用基本工作的程式碼範例。臉部服務可讓您存取先進的演算法，以偵測和辨識影像中的人臉。請遵循下列步驟來安裝套件，並試用使用遠端影像進行基本臉部識別的程式碼範例。

參考文件 | 程式庫來源程式碼 | 套件 (npm) | 範例

必要條件

Azure 訂用帳戶 - 建立免費帳戶
最新版的 Node.js
Azure 帳戶必須已獲指派 Cognitive Services Contributor 角色，您才能同意負責任的 AI 條款並建立資源。若要將此角色指派給您的帳戶，請遵循指派角色文件中的步驟，或連絡系統管理員。
擁有 Azure 訂用帳戶之後，在 Azure 入口網站中建立臉部資源，以取得您的金鑰和端點。在其部署後，選取 [前往資源]。
- 您需要來自所建立資源的金鑰和端點，以將應用程式連線至 Face API。
- 您可以使用免費定價層 (F0) 來試用服務，之後可升級至付費層以用於實際執行環境。

建立環境變數

在此範例中，在執行應用程式的本機電腦上將認證寫入環境變數。

提示

請勿在程式碼中直接包含索引碼，且切勿公開張貼索引碼。如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。

若要設定金鑰和端點的環境變數，請開啟主控台視窗，然後遵循作業系統和開發環境的指示進行。

若要設定 VISION_KEY 環境變數，請以您其中一個資源索引碼取代 your-key。
若要設定 VISION_ENDPOINT 環境變數，請將 your-endpoint 取代為您資源的端點。

Windows
Linux

setx VISION_KEY your-key

setx VISION_ENDPOINT your-endpoint

新增環境變數之後，您可能需要重新啟動任何將讀取環境變數的執行中程式，包括主控台視窗。

export VISION_KEY=your-key

export VISION_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bashrc，讓變更生效。

識別並驗證臉部

建立新的 Node.js 應用程式

在主控台視窗 (例如 cmd、PowerShell 或 Bash) 中，為您的應用程式建立新的目錄，並瀏覽至該目錄。
```
mkdir myapp && cd myapp
```
執行命令 npm init，以使用 package.json 檔案建立節點應用程式。
```
npm init
```
@azure-rest/ai-vision-face安裝 npm 套件：
```
npm install @azure-rest/ai-vision-face
```
您應用程式的 package.json 檔案會隨著相依性而更新。

建立名為 index.js 的檔案，在文字編輯器中開啟，並貼上下列程式碼：

注意

如果您尚未使用登記表單接收臉部服務的存取權限，其中部分功能將無法運作。

const { randomUUID } = require("crypto");

const { AzureKeyCredential } = require("@azure/core-auth");

const createFaceClient = require("@azure-rest/ai-vision-face").default,
  { FaceAttributeTypeRecognition04, getLongRunningPoller } = require("@azure-rest/ai-vision-face");

/**
 * NOTE This sample might not work with the free tier of the Face service because it might exceed the rate limits.
 * If that happens, try inserting calls to sleep() between calls to the Face service.
 */
const sleep = (ms) => new Promise((resolve) => setTimeout(resolve, ms));

const main = async () => {
  const endpoint = process.env["FACE_ENDPOINT"] ?? "<endpoint>";
  const apikey = process.env["FACE_APIKEY"] ?? "<apikey>";
  const credential = new AzureKeyCredential(apikey);
  const client = createFaceClient(endpoint, credential);

  const imageBaseUrl =
    "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/";
  const personGroupId = randomUUID();

  console.log("========IDENTIFY FACES========");
  console.log();

  // Create a dictionary for all your images, grouping similar ones under the same key.
  const personDictionary = {
    "Family1-Dad": ["Family1-Dad1.jpg", "Family1-Dad2.jpg"],
    "Family1-Mom": ["Family1-Mom1.jpg", "Family1-Mom2.jpg"],
    "Family1-Son": ["Family1-Son1.jpg", "Family1-Son2.jpg"],
    "Family1-Daughter": ["Family1-Daughter1.jpg", "Family1-Daughter2.jpg"],
    "Family2-Lady": ["Family2-Lady1.jpg", "Family2-Lady2.jpg"],
    "Family2-Man": ["Family2-Man1.jpg", "Family2-Man2.jpg"],
  };

  // A group photo that includes some of the persons you seek to identify from your dictionary.
  const sourceImageFileName = "identification1.jpg";

  // Create a person group.
  console.log(`Creating a person group with ID: ${personGroupId}`);
  await client.path("/persongroups/{personGroupId}", personGroupId).put({
    body: {
      name: personGroupId,
      recognitionModel: "recognition_04",
    },
  });

  // The similar faces will be grouped into a single person group person.
  console.log("Adding faces to person group...");
  await Promise.all(
    Object.keys(personDictionary).map(async (name) => {
      console.log(`Create a persongroup person: ${name}`);
      const createPersonGroupPersonResponse = await client
        .path("/persongroups/{personGroupId}/persons", personGroupId)
        .post({
          body: { name },
        });

      const { personId } = createPersonGroupPersonResponse.body;

      await Promise.all(
        personDictionary[name].map(async (similarImage) => {
          // Check if the image is of sufficent quality for recognition.
          const detectResponse = await client.path("/detect").post({
            contentType: "application/json",
            queryParameters: {
              detectionModel: "detection_03",
              recognitionModel: "recognition_04",
              returnFaceId: false,
              returnFaceAttributes: [FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION],
            },
            body: { url: `${imageBaseUrl}${similarImage}` },
          });

          const sufficientQuality = detectResponse.body.every(
            (face) => face.faceAttributes?.qualityForRecognition === "high",
          );
          if (!sufficientQuality) {
            return;
          }

          // Quality is sufficent, add to group.
          console.log(
            `Add face to the person group person: (${name}) from image: (${similarImage})`,
          );
          await client
            .path(
              "/persongroups/{personGroupId}/persons/{personId}/persistedfaces",
              personGroupId,
              personId,
            )
            .post({
              queryParameters: { detectionModel: "detection_03" },
              body: { url: `${imageBaseUrl}${similarImage}` },
            });
        }),
      );
    }),
  );
  console.log("Done adding faces to person group.");

  // Start to train the person group.
  console.log();
  console.log(`Training person group: ${personGroupId}`);
  const trainResponse = await client
    .path("/persongroups/{personGroupId}/train", personGroupId)
    .post();
  const poller = await getLongRunningPoller(client, trainResponse);
  await poller.pollUntilDone();
  console.log(`Training status: ${poller.getOperationState().status}`);
  if (poller.getOperationState().status !== "succeeded") {
    return;
  }

  // Detect faces from source image url and only take those with sufficient quality for recognition.
  const detectResponse = await client.path("/detect").post({
    contentType: "application/json",
    queryParameters: {
      detectionModel: "detection_03",
      recognitionModel: "recognition_04",
      returnFaceId: true,
    },
    body: { url: `${imageBaseUrl}${sourceImageFileName}` },
  });
  const faceIds = detectResponse.body.map((face) => face.faceId);

  // Identify the faces in a person group.
  const identifyResponse = await client.path("/identify").post({
    body: { faceIds, personGroupId },
  });
  await Promise.all(
    identifyResponse.body.map(async (result) => {
      try {
        const getPersonGroupPersonResponse = await client
          .path(
            "/persongroups/{personGroupId}/persons/{personId}",
            personGroupId,
            result.candidates[0].personId,
          )
          .get();
        const person = getPersonGroupPersonResponse.body;
        console.log(
          `Person: ${person.name} is identified for face in: ${sourceImageFileName} with ID: ${result.faceId}. Confidence: ${result.candidates[0].confidence}`,
        );

        // Verification:
        const verifyResponse = await client.path("/verify").post({
          body: {
            faceId: result.faceId,
            personGroupId,
            personId: person.personId,
          },
        });
        console.log(
          `Verification result between face ${result.faceId} and person ${person.personId}: ${verifyResponse.body.isIdentical} with confidence: ${verifyResponse.body.confidence}`,
        );
      } catch (error) {
        console.log(`No persons identified for face with ID ${result.faceId}`);
      }
    }),
  );
  console.log();

  // Delete person group.
  console.log(`Deleting person group: ${personGroupId}`);
  await client.path("/persongroups/{personGroupId}", personGroupId).delete();
  console.log();

  console.log("Done.");
};

main().catch(console.error);

使用快速入門檔案上使用 node 命令執行應用程式。
```
node index.js
```

輸出

========IDENTIFY FACES========

Creating a person group with ID: c08484e0-044b-4610-8b7e-c957584e5d2d
Adding faces to person group...
Create a persongroup person: Family1-Dad.
Create a persongroup person: Family1-Mom.
Create a persongroup person: Family2-Lady.
Create a persongroup person: Family1-Son.
Create a persongroup person: Family1-Daughter.
Create a persongroup person: Family2-Man.
Add face to the person group person: (Family1-Son) from image: Family1-Son2.jpg.
Add face to the person group person: (Family1-Dad) from image: Family1-Dad2.jpg.
Add face to the person group person: (Family1-Mom) from image: Family1-Mom1.jpg.
Add face to the person group person: (Family2-Man) from image: Family2-Man1.jpg.
Add face to the person group person: (Family1-Son) from image: Family1-Son1.jpg.
Add face to the person group person: (Family2-Lady) from image: Family2-Lady2.jpg.
Add face to the person group person: (Family1-Mom) from image: Family1-Mom2.jpg.
Add face to the person group person: (Family1-Dad) from image: Family1-Dad1.jpg.
Add face to the person group person: (Family2-Man) from image: Family2-Man2.jpg.
Add face to the person group person: (Family2-Lady) from image: Family2-Lady1.jpg.
Done adding faces to person group.

Training person group: c08484e0-044b-4610-8b7e-c957584e5d2d.
Training status: succeeded.
No persons identified for face with ID 259dd648-be70-499c-9942-3512594e21eb
Person: Family1-Mom is identified for face in: identification1.jpg with ID: b7f7f542-c338-4a40-ad52-e61772bc6e14. Confidence: 0.96921.
Person: Family1-Son is identified for face in: identification1.jpg with ID: 600dc1b4-b2c4-4516-87de-edbbdd8d7632. Confidence: 0.92886.
Person: Family1-Dad is identified for face in: identification1.jpg with ID: e83b494f-9ad2-473f-9d86-3de79c01e345. Confidence: 0.96725.
Verification result between face bb7f7f542-c338-4a40-ad52-e61772bc6e14 and person de1d7dea-a393-4f69-9062-10cb66d4cf17: true with confidence: 0.96921
Verification result between face 600dc1b4-b2c4-4516-87de-edbbdd8d7632 and person 05fd84e4-41b0-4716-b767-4376e33fa207: true with confidence: 0.92886
Verification result between face e83b494f-9ad2-473f-9d86-3de79c01e345 and person c5124fe2-39dd-47ba-9163-1ed2998fdeb2: true with confidence: 0.96725

Deleting person group: c08484e0-044b-4610-8b7e-c957584e5d2d

Done.

清除資源

如果您想要清除和移除 Azure AI 服務訂用帳戶，則可以刪除資源或資源群組。刪除資源群組也會刪除與其相關聯的任何其他資源。

下一步

在本快速入門中，您已了解如何使用適用於 JavaScript 的臉部用戶端程式庫來執行基本臉部識別。接下來，了解不同的臉部偵測模型，以及如何為您的使用案例指定正確的模型。

指定臉部偵測模型版本

什麼是臉部辨識服務？
您可以在 GitHub 上找到更廣泛的範例程式碼。

開始使用臉部 REST API 進行臉部辨識。臉部服務可讓您存取先進的演算法，以偵測和辨識影像中的人臉。

注意

本快速入門會使用 cURL 命令來呼叫 REST API。您也可以使用程式設計語言來呼叫 REST API。使用語言 SDK 實作臉部識別等複雜案例會比較容易。如需 C#、Python、Java、JavaScript 和 Go 的範例，請參閱 GitHub 範例。

必要條件

Azure 訂用帳戶 - 建立免費帳戶
Azure 帳戶必須已獲指派 Cognitive Services Contributor 角色，您才能同意負責任的 AI 條款並建立資源。若要將此角色指派給您的帳戶，請遵循指派角色文件中的步驟，或連絡系統管理員。
擁有 Azure 訂用帳戶之後，在 Azure 入口網站中建立臉部資源，以取得您的金鑰和端點。在其部署後，選取 [前往資源]。
- 您需要來自所建立資源的金鑰和端點，以將應用程式連線至 Face API。您稍後會在快速入門中將金鑰和端點貼到下列程式碼中。
- 您可以使用免費定價層 (F0) 來試用服務，之後可升級至付費層以用於實際執行環境。
PowerShell 6.0 版以上，或類似的命令列應用程式。
已安裝 cURL。

識別並驗證臉部

注意

如果您尚未使用登記表單接收臉部服務的存取權限，其中部分功能將無法運作。

首先，在來源臉部上呼叫偵測 API。這是我們嘗試從較大群組中識別的臉部。將下列命令複製到文本編輯器、插入您自己的金鑰和端點，然後將它複製到殼層視窗並加以執行。

Windows
Linux

curl.exe -v -X POST "https://{resource endpoint}/face/v1.0/detect?returnFaceId=true&returnFaceLandmarks=false&recognitionModel=recognition_04&returnRecognitionModel=false&detectionModel=detection_03&faceIdTimeToLive=86400" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data-ascii "{""url"":""https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/identification1.jpg""}"

curl -v -X POST "https://{resource endpoint}/face/v1.0/detect?returnFaceId=true&returnFaceLandmarks=false&recognitionModel=recognition_04&returnRecognitionModel=false&detectionModel=detection_03&faceIdTimeToLive=86400" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data-ascii "{\"url\":\"https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/identification1.jpg\"}"

將傳回的臉部識別碼字串儲存至暫存位置。您會在結尾再次使用。

接下來，您必須建立 LargePersonGroup ，併為其提供符合 regex 模式 ^[a-z0-9-_]+$的任意標識符。此物件會儲存數個人員的彙總臉部資料。執行下列命令，插入您自己的金鑰。選擇性地變更要求本文中的群組名稱和中繼資料。

Windows
Linux

curl.exe -v -X PUT "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data-ascii "{
    ""name"": ""large-person-group-name"",
    ""userData"": ""User-provided data attached to the large person group."",
    ""recognitionModel"": ""recognition_04""
}"

curl -v -X PUT "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data-ascii "{
    \"name\": \"large-person-group-name\",
    \"userData\": \"User-provided data attached to the large person group.\",
    \"recognitionModel\": \"recognition_04\"
}"

將所建立群組的指定標識符儲存到暫存位置。

接下來，您將建立屬於群組的 Person 物件。執行下列命令，插入您自己的金鑰和上一個步驟中的 LargePersonGroup 識別碼。此命令會建立名為「Family1-Dad」的 Person。

Windows
Linux

curl.exe -v -X POST "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}/persons" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data-ascii "{
    ""name"": ""Family1-Dad"",
    ""userData"": ""User-provided data attached to the person.""
}"

curl -v -X POST "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}/persons" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data-ascii "{
    \"name\": \"Family1-Dad\",
    \"userData\": \"User-provided data attached to the person.\"
}"

執行此命令之後，請使用不同的輸入資料再次執行命令，以建立更多 Person 物件：「Family1-Mom」、「Family1-Son」、「Family1-Daughter」、「Family2-Lady」和「Family2-Man」。

儲存所建立每個 Person 的識別碼；請務必追蹤哪個識別碼屬於哪個人員。

接下來，您必須偵測新的臉部，並將其與現有的 Person 物件產生關聯。下列命令會從影像 Family1-Dad1.jpg 偵測臉部，並將它新增至對應的人員。您必須指定 personId 作為建立「Family1-Dad」Person 物件時傳回的識別碼。影像名稱對應至所建立 Person 的名稱。此外，請在適當的欄位中輸入 LargePersonGroup 識別碼和您的金鑰。
- Windows
- Linux
```
curl.exe -v -X POST "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}/persons/{personId}/persistedfaces?detectionModel=detection_03" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data-ascii "{""url"":""https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/Family1-Dad1.jpg""}"
```
```
curl -v -X POST "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}/persons/{personId}/persistedfaces?detectionModel=detection_03" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data-ascii "{\"url\":\"https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/Face/images/Family1-Dad1.jpg\"}"
```
然後，以不同的來源影像和目標 Person 再次執行上述命令。可用影像包括：Family1-Dad1.jpg、Family1-Dad2.jpg、Family1-Mom1.jpg、Family1-Mom2.jpg、Family1-Son1.jpg、Family1-Son2.jpg、Family1-Daughter1.jpg、Family1-Daughter2.jpg、Family2-Lady1.jpg、Family2-Lady2.jpg、Family2-Man1.jpg 和 Family2-Man2.jpg。請確定您在 API 呼叫中所指定 Person 的識別碼，與要求本文中影像檔案的名稱相符。

在此步驟結束時，您應該有多個 Person 物件，每個物件都有一或多個對應的臉部，直接從提供的影像偵測。

接下來，使用目前的臉部資料來定型 LargePersonGroup。定型作業會教導模型如何將臉部特徵 (有時是從多個來源影像彙總) 關聯至每一個人。在執行命令之前，請先插入 LargePersonGroup 識別碼和金鑰。

Windows
Linux

curl.exe -v -X POST "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}/train" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data ""

curl -v -X POST "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}/train" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data ""

檢查定型狀態是否成功。如果沒有，請等候一段時間，然後再查詢一次。

Windows
Linux

curl.exe -v "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}/training" -H "Ocp-Apim-Subscription-Key: {subscription key}"

curl -v "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}/training" -H "Ocp-Apim-Subscription-Key: {subscription key}"

現在您已準備好使用第一個步驟的來源臉部識別碼和 LargePersonGroup 識別碼來呼叫識別 API。將這些值插入要求本文中適當的欄位，然後插入您的金鑰。

Windows
Linux

curl.exe -v -X POST "https://{resource endpoint}/face/v1.0/identify" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data-ascii "{
    ""largePersonGroupId"": ""INSERT_PERSONGROUP_ID"",
    ""faceIds"": [
        ""INSERT_SOURCE_FACE_ID""
    ],
    ""maxNumOfCandidatesReturned"": 1,
    ""confidenceThreshold"": 0.5
}"

curl -v -X POST "https://{resource endpoint}/face/v1.0/identify" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {subscription key}" --data-ascii "{
    \"largePersonGroupId\": \"INSERT_PERSONGROUP_ID\",
    \"faceIds\": [
        \"INSERT_SOURCE_FACE_ID\"
    ],
    \"maxNumOfCandidatesReturned\": 1,
    \"confidenceThreshold\": 0.5
}"

回應應該會為您提供 Person 識別碼，指出以來源臉部識別的人員。應該是對應到「Family1-Dad」人員的識別碼，因為來源臉部是該人員。

若要進行臉部驗證，您將使用上一個步驟中傳回的人員識別碼、LargePersonGroup 識別碼，以及來源臉部識別碼。將這些值插入要求本文中的欄位，然後插入金鑰。

Windows
Linux

curl.exe -v -X POST "https://{resource endpoint}/face/v1.0/verify" `
-H "Content-Type: application/json" `
-H "Ocp-Apim-Subscription-Key: {subscription key}" `
--data-ascii "{
    ""faceId"": ""INSERT_SOURCE_FACE_ID"",
    ""personId"": ""INSERT_PERSON_ID"",
    ""largePersonGroupId"": ""INSERT_PERSONGROUP_ID""
}"

curl -v -X POST "https://{resource endpoint}/face/v1.0/verify" \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: {subscription key}" \
--data-ascii "{
    \"faceId\": \"INSERT_SOURCE_FACE_ID\",
    \"personId\": \"INSERT_PERSON_ID\",
    \"largePersonGroupId\": \"INSERT_PERSONGROUP_ID\"
}"

回應應該會提供布林驗證結果以及信賴度值。

清除資源

若要刪除您在此練習中建立的 LargePersonGroup ，請執行 LargePersonGroup - Delete 呼叫。

Windows
Linux

curl.exe -v -X DELETE "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}" -H "Ocp-Apim-Subscription-Key: {subscription key}"

curl -v -X DELETE "https://{resource endpoint}/face/v1.0/largepersongroups/{largePersonGroupId}" -H "Ocp-Apim-Subscription-Key: {subscription key}"

如果您想要清除和移除 Azure AI 服務訂用帳戶，則可以刪除資源或資源群組。刪除資源群組也會刪除與其相關聯的任何其他資源。

下一步

在本快速入門中，您已了解如何使用臉部 REST API 執行基本臉部辨識工作。接下來，了解不同的臉部偵測模型，以及如何為您的使用案例指定正確的模型。

指定臉部偵測模型版本

什麼是臉部辨識服務？

分享方式：

快速入門：使用臉部服務

必要條件

建立環境變數

識別並驗證臉部

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安裝用戶端程式庫

輸出

清除資源

下一步

必要條件

建立環境變數

識別並驗證臉部

輸出

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下一步

必要條件

建立環境變數

識別並驗證臉部

輸出

清除資源

下一步

必要條件

識別並驗證臉部

清除資源

下一步

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