MR and Azure 304:顔認識

投稿日 2019年3月30日
著者 azure-recipe-user
カテゴリー Microsoft HoloLens

こんにちは、ナレコム菅井です。

今回はMR and Azure 304のアプリを作っていきます。

開発環境は以下の通りです。
・Windows
・Unity 2017.4.11f1
・Visual Studio 2017
・HoloLens

目標はAzure Cognitive ServiceとBluetooth対応キーボードを使って顔認識アプリを作ることです。それではさっそく始めていきましょう！

0.準備

はじめに、以下の順に準備を進めていきます。

ステップ1. Azure PortalでFace APIを追加する。
ステップ2. Person MakerでFace APIに顔を学習させる。

ステップ1
1. Azure Portalにログインします。

2. [リソースの作成]からFace APIと検索します。下のようなアイコンが出てきたら開きます。

3. Face APIの説明画面にスライドしますので、[作成]をクリックします。

4. Face APIに関する情報を入力する画面になりますので、入力していきます(※記入例です)。ただし、[場所]は米国西部とします。入力し終わったら、[作成]をクリックします。

5. お知らせタブを開きます。デプロイが終了したら、[リソースへ移動]します。

6. [Quick start]->[1 キーを取得する]->[key]をクリックし、キーを確認します。これは後ほど使いますのでメモしておきます。

ステップ2
1. Person Makerをダウンロードします。

2. ダウンロードしたPerson Makerフォルダー内のPerson Makerをダブルクリックします。Visual Studioが開きます。

3. [ソリューションの構成]をDebug、[ソリューションプラットフォーム]をx86、[ターゲットプラットフォーム]をローカルコンピュータに変更します。

4. (おそらく)画面右側にある[ソリューションエクスプローラー]から[ソリューション’Person Maker’]を右クリックし[NuGetパッケージの復元]をクリックします。

5. ローカルコンピューターの左側の再生▶︎をおします。

6. PERSON MAKERが開きますので必要な項目を入力していきます。
a. 1で先ほどのFace API の鍵を入れます。
b. 2で Person Group IDとPerson Group Nameを決めます。入力し終わったら、[Create a Person Name]をクリックします。※Person Group IDは後ほど使います。

c. 3で認識したい人物の名前を入力します。入力し終わったら、[Create a Person ]をクリックします。※仮名で大丈夫です。
d. 5で[Create and Open Folder]をクリックして、認識したい人物の画像を10枚以上(多いとなお良い)を選択し、6の[Submit To Azure]をクリックします。無事送信された旨が表示されるたらオッケーです。
e. 7で[Train]をクリックします。学習された旨が表示されるはずです。

以上で準備完了です。

1.Unityの設定

続いてUnityの設定を行っていきます。以下の手順で進めていきましょう。

1.Unityを開き、[New]から新しいプロジェクトを作成します。
名前をFaceRecognitionとして、[Create project]をクリックします。

2. [Build Settings..]からさまざまな項目を編集します。
[File]->[Build Settings..]を開きます。

a.プラットフォームの変更
[PC, Mac & Linux Standalone]を[Universal Windows Platform]に変更し、[Switch Platform]をクリックします

b.[Player Settings..]を編集します。
[Player Settings..]をクリックします。

そのあと[Other Settings]、[Publishing Settings]->[Capabilities]、[XR Settings]を以下のように設定します。

続いて、[Unity C#]にチェックを入れます。

[Build Settings..]の変更は以上です。

3. 続いてシーンを保存します。[Add Open Scenes]をクリックして、[新しいフォルダー]を選択しScenesと名前をつけた後、そのフォルダー内に名前をSceneとして[保存]します。

2.シーンの編集

デフォルトで備わっているMain Cameraの代わりにHoloToolkitを使います。使うものをこちらから以下のToolkitをダウンロードしてください。
HoloToolkit-Unity-2017.4.2.0.unitypackage
HoloToolkit-Unity-Example-2017.4.2.0.unitypackage

a. [Assets]->[Import Package]->[Custom Package..]からダウンロードしたpackageを二つともインポートします。

b. [Hierarchy]->[Main camera]をdeleteします。

c. [Project]から以下の２つを検索し、[Hierarchy]パネルへD&Dします。
・MixedRealityCameraParent
・InputManger

d. [Hierarchy]->[MixedRealityCameraParent]->[MixedRealityCamera]の[Inspector]を編集していきます。
[Camera]
Clear Flags : Solid Color

[Mixed Reality Camera Manager]
Clear Flags : color
Near clip : 0.2
Clear Flags : color

3.スクリプトの作成

ここでは以下の2つのスクリプトを作ります。
・FaceAnalysis
・ImageCaptureFace

スクリプトの作り方はまず、すべてのスクリプトをまとめておくフォルダーを作成します。[Project]->[Create]をクリックし、[Folder]を選択して新しいフォルダーを作ります。名前をScriptsとします。続いて[Project]->[Create]->[C# Script]をクリックし、クラス名をつけていきます。

・FaceAnalysis
このスクリプトはAzure Face Recognition Serviceとやりとりしたり、データをシリアライズ、デシリアライズしたりするためのクラスやメソッドが記述されています。ここで0.準備で取得したFace APIの鍵と入力したPerson Group IDを使います。–Insert your key–、–Insert your ID–へそれぞれ挿入します。コードは以下の通りです。

using Newtonsoft.Json;
using System.Collections;
using System.Collections.Generic;
using System.IO;
using System.Text;
using UnityEngine;
using UnityEngine.Networking;

public class FaceAnalysis : MonoBehaviour
{

    /// <summary>
    /// Allows this class to behave like a singleton
    /// </summary>
    public static FaceAnalysis Instance;

    /// <summary>
    /// The analysis result text
    /// </summary>
    private TextMesh labelText;

    /// <summary>
    /// Bytes of the image captured with camera
    /// </summary>
    internal byte[] imageBytes;

    /// <summary>
    /// Path of the image captured with camera
    /// </summary>
    internal string imagePath;

    /// <summary>
    /// Base endpoint of Face Recognition Service
    /// </summary>
    const string baseEndpoint = "https://westus.api.cognitive.microsoft.com/face/v1.0/";

    /// <summary>
    /// Auth key of Face Recognition Service
    /// </summary>
    private const string key = "--Insert your key--";

    /// <summary>
    /// Id (name) of the created person group 
    /// </summary>
    private const string personGroupId = "--Insert your ID--";

    /// <summary>
    /// Initialises this class
    /// </summary>
    private void Awake()
    {
        // Allows this instance to behave like a singleton
        Instance = this;

        // Add the ImageCapture Class to this Game Object
        gameObject.AddComponent<ImageCaptureFace>();

        // Create the text label in the scene
        CreateLabel();
        Debug.Log("createLabel");
    }

    /// <summary>
    /// Spawns cursor for the Main Camera
    /// </summary>
    private void CreateLabel()
    {
        // Create a sphere as new cursor
        GameObject newLabel = new GameObject();

        // Attach the label to the Main Camera
        newLabel.transform.parent = gameObject.transform;

        // Resize and position the new cursor
        newLabel.transform.localScale = new Vector3(0.4f, 0.4f, 0.4f);
        newLabel.transform.position = new Vector3(0f, 3f, 60f);

        // Creating the text of the Label
        labelText = newLabel.AddComponent<TextMesh>();
        labelText.anchor = TextAnchor.MiddleCenter;
        labelText.alignment = TextAlignment.Center;
        labelText.tabSize = 4;
        labelText.fontSize = 50;
        labelText.text = ".";
    }

    /// <summary>
    /// Detect faces from a submitted image
    /// </summary>
    internal IEnumerator DetectFacesFromImage()
    {
        WWWForm webForm = new WWWForm();
        string detectFacesEndpoint = $"{baseEndpoint}detect";

        // Change the image into a bytes array
        imageBytes = GetImageAsByteArray(imagePath);

        using (UnityWebRequest www =
            UnityWebRequest.Post(detectFacesEndpoint, webForm))
        {
            www.SetRequestHeader("Ocp-Apim-Subscription-Key", key);
            www.SetRequestHeader("Content-Type", "application/octet-stream");
            www.uploadHandler.contentType = "application/octet-stream";
            www.uploadHandler = new UploadHandlerRaw(imageBytes);
            www.downloadHandler = new DownloadHandlerBuffer();

            yield return www.SendWebRequest();
            string jsonResponse = www.downloadHandler.text;
            Face_RootObject[] face_RootObject =
                JsonConvert.DeserializeObject<Face_RootObject[]>(jsonResponse);

            List<string> facesIdList = new List<string>();
            // Create a list with the face Ids of faces detected in image
            foreach (Face_RootObject faceRO in face_RootObject)
            {
                facesIdList.Add(faceRO.faceId);
                Debug.Log($"Detected face - Id: {faceRO.faceId}");
            }

            StartCoroutine(IdentifyFaces(facesIdList));
        }
    }

    /// <summary>
    /// Returns the contents of the specified file as a byte array.
    /// </summary>
    static byte[] GetImageAsByteArray(string imageFilePath)
    {
        FileStream fileStream = new FileStream(imageFilePath, FileMode.Open, FileAccess.Read);
        BinaryReader binaryReader = new BinaryReader(fileStream);
        return binaryReader.ReadBytes((int)fileStream.Length);
    }

    /// <summary>
    /// Identify the faces found in the image within the person group
    /// </summary>
    internal IEnumerator IdentifyFaces(List<string> listOfFacesIdToIdentify)
    {
        // Create the object hosting the faces to identify
        FacesToIdentify_RootObject facesToIdentify = new FacesToIdentify_RootObject();
        facesToIdentify.faceIds = new List<string>();
        facesToIdentify.personGroupId = personGroupId;
        foreach (string facesId in listOfFacesIdToIdentify)
        {
            facesToIdentify.faceIds.Add(facesId);
        }
        facesToIdentify.maxNumOfCandidatesReturned = 1;
        facesToIdentify.confidenceThreshold = 0.5;

        // Serialise to Json format
        string facesToIdentifyJson = JsonConvert.SerializeObject(facesToIdentify);
        // Change the object into a bytes array
        byte[] facesData = Encoding.UTF8.GetBytes(facesToIdentifyJson);

        WWWForm webForm = new WWWForm();
        string detectFacesEndpoint = $"{baseEndpoint}identify";

        using (UnityWebRequest www = UnityWebRequest.Post(detectFacesEndpoint, webForm))
        {
            www.SetRequestHeader("Ocp-Apim-Subscription-Key", key);
            www.SetRequestHeader("Content-Type", "application/json");
            www.uploadHandler.contentType = "application/json";
            www.uploadHandler = new UploadHandlerRaw(facesData);
            www.downloadHandler = new DownloadHandlerBuffer();

            yield return www.SendWebRequest();
            string jsonResponse = www.downloadHandler.text;
            Debug.Log($"Get Person - jsonResponse: {jsonResponse}");
            Candidate_RootObject[] candidate_RootObject = JsonConvert.DeserializeObject<Candidate_RootObject[]>(jsonResponse);

            // For each face to identify that ahs been submitted, display its candidate
            foreach (Candidate_RootObject candidateRO in candidate_RootObject)
            {
                StartCoroutine(GetPerson(candidateRO.candidates[0].personId));

                // Delay the next "GetPerson" call, so all faces candidate are displayed properly
                yield return new WaitForSeconds(3);
            }
        }
    }

    /// <summary>
    /// Provided a personId, retrieve the person name associated with it
    /// </summary>
    internal IEnumerator GetPerson(string personId)
    {
        string getGroupEndpoint = $"{baseEndpoint}persongroups/{personGroupId}/persons/{personId}?";
        WWWForm webForm = new WWWForm();

        using (UnityWebRequest www = UnityWebRequest.Get(getGroupEndpoint))
        {
            www.SetRequestHeader("Ocp-Apim-Subscription-Key", key);
            www.downloadHandler = new DownloadHandlerBuffer();
            yield return www.SendWebRequest();
            string jsonResponse = www.downloadHandler.text;

            Debug.Log($"Get Person - jsonResponse: {jsonResponse}");
            IdentifiedPerson_RootObject identifiedPerson_RootObject = JsonConvert.DeserializeObject<IdentifiedPerson_RootObject>(jsonResponse);

            // Display the name of the person in the UI
            labelText.text = identifiedPerson_RootObject.name;
        }
    }

    /// <summary>
    /// The Person Group object
    /// </summary>
    public class Group_RootObject
    {
        public string personGroupId { get; set; }
        public string name { get; set; }
        public object userData { get; set; }
    }

    /// <summary>
    /// The Person Face object
    /// </summary>
    public class Face_RootObject
    {
        public string faceId { get; set; }
    }

    /// <summary>
    /// Collection of faces that needs to be identified
    /// </summary>
    public class FacesToIdentify_RootObject
    {
        public string personGroupId { get; set; }
        public List<string> faceIds { get; set; }
        public int maxNumOfCandidatesReturned { get; set; }
        public double confidenceThreshold { get; set; }
    }

    /// <summary>
    /// Collection of Candidates for the face
    /// </summary>
    public class Candidate_RootObject
    {
        public string faceId { get; set; }
        public List<Candidate> candidates { get; set; }
    }

    public class Candidate
    {
        public string personId { get; set; }
        public double confidence { get; set; }
    }

    /// <summary>
    /// Name and Id of the identified Person
    /// </summary>
    public class IdentifiedPerson_RootObject
    {
        public string personId { get; set; }
        public string name { get; set; }
    }
}

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using Newtonsoft.Json;

using System.Collections;

using System.Collections.Generic;

using System.IO;

using System.Text;

using UnityEngine;

using UnityEngine.Networking;

public class FaceAnalysis : MonoBehaviour

{

/// <summary>

/// Allows this class to behave like a singleton

/// </summary>

public static FaceAnalysis Instance;

/// <summary>

/// The analysis result text

/// </summary>

private TextMesh labelText;

/// <summary>

/// Bytes of the image captured with camera

/// </summary>

internal byte[] imageBytes;

/// <summary>

/// Path of the image captured with camera

/// </summary>

internal string imagePath;

/// <summary>

/// Base endpoint of Face Recognition Service

/// </summary>

const string baseEndpoint = "https://westus.api.cognitive.microsoft.com/face/v1.0/";

/// <summary>

/// Auth key of Face Recognition Service

/// </summary>

private const string key = "--Insert your key--";

/// <summary>

/// Id (name) of the created person group

/// </summary>

private const string personGroupId = "--Insert your ID--";

/// <summary>

/// Initialises this class

/// </summary>

private void Awake()

{

// Allows this instance to behave like a singleton

Instance = this;

// Add the ImageCapture Class to this Game Object

gameObject.AddComponent<ImageCaptureFace>();

// Create the text label in the scene

CreateLabel();

Debug.Log("createLabel");

}

/// <summary>

/// Spawns cursor for the Main Camera

/// </summary>

private void CreateLabel()

{

// Create a sphere as new cursor

GameObject newLabel = new GameObject();

// Attach the label to the Main Camera

newLabel.transform.parent = gameObject.transform;

// Resize and position the new cursor

newLabel.transform.localScale = new Vector3(0.4f, 0.4f, 0.4f);

newLabel.transform.position = new Vector3(0f, 3f, 60f);

// Creating the text of the Label

labelText = newLabel.AddComponent<TextMesh>();

labelText.anchor = TextAnchor.MiddleCenter;

labelText.alignment = TextAlignment.Center;

labelText.tabSize = 4;

labelText.fontSize = 50;

labelText.text = ".";

}

/// <summary>

/// Detect faces from a submitted image

/// </summary>

internal IEnumerator DetectFacesFromImage()

{

WWWForm webForm = new WWWForm();

string detectFacesEndpoint = $"{baseEndpoint}detect";

// Change the image into a bytes array

imageBytes = GetImageAsByteArray(imagePath);

using (UnityWebRequest www =

UnityWebRequest.Post(detectFacesEndpoint, webForm))

{

www.SetRequestHeader("Ocp-Apim-Subscription-Key", key);

www.SetRequestHeader("Content-Type", "application/octet-stream");

www.uploadHandler.contentType = "application/octet-stream";

www.uploadHandler = new UploadHandlerRaw(imageBytes);

www.downloadHandler = new DownloadHandlerBuffer();

yield return www.SendWebRequest();

string jsonResponse = www.downloadHandler.text;

Face_RootObject[] face_RootObject =

JsonConvert.DeserializeObject<Face_RootObject[]>(jsonResponse);

List<string> facesIdList = new List<string>();

// Create a list with the face Ids of faces detected in image

foreach (Face_RootObject faceRO in face_RootObject)

{

facesIdList.Add(faceRO.faceId);

Debug.Log($"Detected face - Id: {faceRO.faceId}");

}

StartCoroutine(IdentifyFaces(facesIdList));

}

/// <summary>

/// Returns the contents of the specified file as a byte array.

/// </summary>

static byte[] GetImageAsByteArray(string imageFilePath)

{

FileStream fileStream = new FileStream(imageFilePath, FileMode.Open, FileAccess.Read);

BinaryReader binaryReader = new BinaryReader(fileStream);

return binaryReader.ReadBytes((int)fileStream.Length);

}

/// <summary>

/// Identify the faces found in the image within the person group

/// </summary>

internal IEnumerator IdentifyFaces(List<string> listOfFacesIdToIdentify)

{

// Create the object hosting the faces to identify

FacesToIdentify_RootObject facesToIdentify = new FacesToIdentify_RootObject();

facesToIdentify.faceIds = new List<string>();

facesToIdentify.personGroupId = personGroupId;

foreach (string facesId in listOfFacesIdToIdentify)

{

facesToIdentify.faceIds.Add(facesId);

}

facesToIdentify.maxNumOfCandidatesReturned = 1;

facesToIdentify.confidenceThreshold = 0.5;

// Serialise to Json format

string facesToIdentifyJson = JsonConvert.SerializeObject(facesToIdentify);

// Change the object into a bytes array

byte[] facesData = Encoding.UTF8.GetBytes(facesToIdentifyJson);

WWWForm webForm = new WWWForm();

string detectFacesEndpoint = $"{baseEndpoint}identify";

using (UnityWebRequest www = UnityWebRequest.Post(detectFacesEndpoint, webForm))

{

www.SetRequestHeader("Ocp-Apim-Subscription-Key", key);

www.SetRequestHeader("Content-Type", "application/json");

www.uploadHandler.contentType = "application/json";

www.uploadHandler = new UploadHandlerRaw(facesData);

www.downloadHandler = new DownloadHandlerBuffer();

yield return www.SendWebRequest();

string jsonResponse = www.downloadHandler.text;

Debug.Log($"Get Person - jsonResponse: {jsonResponse}");

Candidate_RootObject[] candidate_RootObject = JsonConvert.DeserializeObject<Candidate_RootObject[]>(jsonResponse);

// For each face to identify that ahs been submitted, display its candidate

foreach (Candidate_RootObject candidateRO in candidate_RootObject)

{

StartCoroutine(GetPerson(candidateRO.candidates[0].personId));

// Delay the next "GetPerson" call, so all faces candidate are displayed properly

yield return new WaitForSeconds(3);

}

/// <summary>

/// Provided a personId, retrieve the person name associated with it

/// </summary>

internal IEnumerator GetPerson(string personId)

{

string getGroupEndpoint = $"{baseEndpoint}persongroups/{personGroupId}/persons/{personId}?";

WWWForm webForm = new WWWForm();

using (UnityWebRequest www = UnityWebRequest.Get(getGroupEndpoint))

{

www.SetRequestHeader("Ocp-Apim-Subscription-Key", key);

www.downloadHandler = new DownloadHandlerBuffer();

yield return www.SendWebRequest();

string jsonResponse = www.downloadHandler.text;

Debug.Log($"Get Person - jsonResponse: {jsonResponse}");

IdentifiedPerson_RootObject identifiedPerson_RootObject = JsonConvert.DeserializeObject<IdentifiedPerson_RootObject>(jsonResponse);

// Display the name of the person in the UI

labelText.text = identifiedPerson_RootObject.name;

}

/// <summary>

/// The Person Group object

/// </summary>

public class Group_RootObject

{

public string personGroupId { get; set; }

public string name { get; set; }

public object userData { get; set; }

}

/// <summary>

/// The Person Face object

/// </summary>

public class Face_RootObject

{

public string faceId { get; set; }

}

/// <summary>

/// Collection of faces that needs to be identified

/// </summary>

public class FacesToIdentify_RootObject

{

public string personGroupId { get; set; }

public List<string> faceIds { get; set; }

public int maxNumOfCandidatesReturned { get; set; }

public double confidenceThreshold { get; set; }

}

/// <summary>

/// Collection of Candidates for the face

/// </summary>

public class Candidate_RootObject

{

public string faceId { get; set; }

public List<Candidate> candidates { get; set; }

}

public class Candidate

{

public string personId { get; set; }

public double confidence { get; set; }

}

/// <summary>

/// Name and Id of the identified Person

/// </summary>

public class IdentifiedPerson_RootObject

{

public string personId { get; set; }

public string name { get; set; }

}

記述し終わったら、スクリプトを[Hierarchy]->[MixedRealityCameraParent]->[MixedRealityCamera]へD&Dします。

・ImageCaptureFace
このクラスにはtapジェスチャーやキーボード上の操作を利用して認識を始めるためのメソッドや画像の情報などを利用するためのメソッドなどが記述されています。コードは以下の通りです。

using System.IO;
using System.Linq;
using UnityEngine;
using System.Collections;
using System.Collections.Generic;
using UnityEngine.XR.WSA.Input;
using UnityEngine.XR.WSA.WebCam;
using HoloToolkit.Unity.InputModule;

public class ImageCaptureFace : MonoBehaviour//, IInputClickHandler
{

    /// <summary>
    /// Allows this class to behave like a singleton
    /// </summary>
    public static ImageCaptureFace instance;

    /// <summary>
    /// Keeps track of tapCounts to name the captured images 
    /// </summary>
    private int tapsCount;

    /// <summary>
    /// PhotoCapture object used to capture images on HoloLens 
    /// </summary>
    private PhotoCapture photoCaptureObject = null;

    /// <summary>
    /// HoloLens class to capture user gestures
    /// </summary>
    //private GestureRecognizer recognizer;

    /// <summary>
    /// Initialises this class
    /// </summary>
    private void Awake()
    {
        instance = this;
        Debug.Log("awake imagecapture");
    }

    /// <summary>
    /// Called right after Awake
    /// </summary>
    void Start()
    {
        // Initialises user gestures capture 
        //recognizer = new GestureRecognizer();
        //recognizer.SetRecognizableGestures(GestureSettings.Tap);
        //recognizer.Tapped += TapHandler;
        //recognizer.StartCapturingGestures();
        InputManager.Instance.PushFallbackInputHandler(gameObject);
        Debug.Log("start");
    }

    /// <summary>
    /// Respond to Tap Input.
    /// </summary>
    //private void TapHandler(TappedEventArgs obj)
    //{
    //    tapsCount++;
    //    ExecuteImageCaptureAndAnalysis();
    //}

    /*public void OnInputClicked(InputClickedEventData eventData)
    {
        tapsCount++;
        Debug.Log("call analisis");
        ExecuteImageCaptureAndAnalysis();
    }*/

    void Update()
    {
        if (Input.GetKey(KeyCode.Return))
        {

            tapsCount++;
            Debug.Log("call analisis");
            ExecuteImageCaptureAndAnalysis();
        }
    }
    /// <summary>
    /// Begin process of Image Capturing and send To Azure Computer Vision service.
    /// </summary>
    private void ExecuteImageCaptureAndAnalysis()
    {
        Resolution cameraResolution = PhotoCapture.SupportedResolutions.OrderByDescending
            ((res) => res.width * res.height).First();
        Texture2D targetTexture = new Texture2D(cameraResolution.width, cameraResolution.height);

        PhotoCapture.CreateAsync(false, delegate (PhotoCapture captureObject)
        {
            photoCaptureObject = captureObject;

            CameraParameters c = new CameraParameters();
            c.hologramOpacity = 0.0f;
            c.cameraResolutionWidth = targetTexture.width;
            c.cameraResolutionHeight = targetTexture.height;
            c.pixelFormat = CapturePixelFormat.BGRA32;

            captureObject.StartPhotoModeAsync(c, delegate (PhotoCapture.PhotoCaptureResult result)
            {
                string filename = string.Format(@"CapturedImage{0}.jpg", tapsCount);
                string filePath = Path.Combine(Application.persistentDataPath, filename);

                // Set the image path on the FaceAnalysis class
                FaceAnalysis.Instance.imagePath = filePath;

                photoCaptureObject.TakePhotoAsync
                (filePath, PhotoCaptureFileOutputFormat.JPG, OnCapturedPhotoToDisk);
            });
        });
    }

    /// <summary>
    /// Called right after the photo capture process has concluded
    /// </summary>
    void OnCapturedPhotoToDisk(PhotoCapture.PhotoCaptureResult result)
    {
        photoCaptureObject.StopPhotoModeAsync(OnStoppedPhotoMode);
    }

    /// <summary>
    /// Register the full execution of the Photo Capture. If successfull, it will begin the Image Analysis process.
    /// </summary>
    void OnStoppedPhotoMode(PhotoCapture.PhotoCaptureResult result)
    {
        photoCaptureObject.Dispose();
        photoCaptureObject = null;

        // Request image caputer analysis
        StartCoroutine(FaceAnalysis.Instance.DetectFacesFromImage());
    }

}

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using System.IO;

using System.Linq;

using UnityEngine;

using System.Collections;

using System.Collections.Generic;

using UnityEngine.XR.WSA.Input;

using UnityEngine.XR.WSA.WebCam;

using HoloToolkit.Unity.InputModule;

public class ImageCaptureFace : MonoBehaviour//, IInputClickHandler

{

/// <summary>

/// Allows this class to behave like a singleton

/// </summary>

public static ImageCaptureFace instance;

/// <summary>

/// Keeps track of tapCounts to name the captured images

/// </summary>

private int tapsCount;

/// <summary>

/// PhotoCapture object used to capture images on HoloLens

/// </summary>

private PhotoCapture photoCaptureObject = null;

/// <summary>

/// HoloLens class to capture user gestures

/// </summary>

//private GestureRecognizer recognizer;

/// <summary>

/// Initialises this class

/// </summary>

private void Awake()

{

instance = this;

Debug.Log("awake imagecapture");

}

/// <summary>

/// Called right after Awake

/// </summary>

void Start()

{

// Initialises user gestures capture

//recognizer = new GestureRecognizer();

//recognizer.SetRecognizableGestures(GestureSettings.Tap);

//recognizer.Tapped += TapHandler;

//recognizer.StartCapturingGestures();

InputManager.Instance.PushFallbackInputHandler(gameObject);

Debug.Log("start");

}

/// <summary>

/// Respond to Tap Input.

/// </summary>

//private void TapHandler(TappedEventArgs obj)

//{

// tapsCount++;

// ExecuteImageCaptureAndAnalysis();

//}

/*public void OnInputClicked(InputClickedEventData eventData)

{

tapsCount++;

Debug.Log("call analisis");

ExecuteImageCaptureAndAnalysis();

}*/

void Update()

{

if (Input.GetKey(KeyCode.Return))

{

tapsCount++;

Debug.Log("call analisis");

ExecuteImageCaptureAndAnalysis();

}

/// <summary>

/// Begin process of Image Capturing and send To Azure Computer Vision service.

/// </summary>

private void ExecuteImageCaptureAndAnalysis()

{

Resolution cameraResolution = PhotoCapture.SupportedResolutions.OrderByDescending

((res) => res.width * res.height).First();

Texture2D targetTexture = new Texture2D(cameraResolution.width, cameraResolution.height);

PhotoCapture.CreateAsync(false, delegate (PhotoCapture captureObject)

{

photoCaptureObject = captureObject;

CameraParameters c = new CameraParameters();

c.hologramOpacity = 0.0f;

c.cameraResolutionWidth = targetTexture.width;

c.cameraResolutionHeight = targetTexture.height;

c.pixelFormat = CapturePixelFormat.BGRA32;

captureObject.StartPhotoModeAsync(c, delegate (PhotoCapture.PhotoCaptureResult result)

{

string filename = string.Format(@"CapturedImage{0}.jpg", tapsCount);

string filePath = Path.Combine(Application.persistentDataPath, filename);

// Set the image path on the FaceAnalysis class

FaceAnalysis.Instance.imagePath = filePath;

photoCaptureObject.TakePhotoAsync

(filePath, PhotoCaptureFileOutputFormat.JPG, OnCapturedPhotoToDisk);

});

}

/// <summary>

/// Called right after the photo capture process has concluded

/// </summary>

void OnCapturedPhotoToDisk(PhotoCapture.PhotoCaptureResult result)

{

photoCaptureObject.StopPhotoModeAsync(OnStoppedPhotoMode);

}

/// <summary>

/// Register the full execution of the Photo Capture. If successfull, it will begin the Image Analysis process.

/// </summary>

void OnStoppedPhotoMode(PhotoCapture.PhotoCaptureResult result)

{

photoCaptureObject.Dispose();

photoCaptureObject = null;

// Request image caputer analysis

StartCoroutine(FaceAnalysis.Instance.DetectFacesFromImage());

}

4.ビルド

[File]->[Build Settings..]->[Build]の順に選択します。新しいフォルダー(App)を作ります。このフォルダーを選択し、保存します。

この後の手順についてはこちらを参照してください。

Unity上で実行した時の様子です。

続いてHoloLens上で実行していきたいと思います。Bluetoothキーボードの接続方法はこちらを参考にしました(同じキーボードを使いました)。

HoloLensで実行した時の様子です。エンターキーで画像のキャプチャを始めます。対象の人物にカーソルを合わせてエンターキーを押しましょう。

以上となります。お疲れ様でした。

この記事を書いた人

azure-recipe-user

記事一覧

MR and Azure 304:顔認識

0.準備

1.Unityの設定

2.シーンの編集

3.スクリプトの作成

4.ビルド

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azure-recipe-user

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