Kinect 开发 —— 语音识别（下）

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使用定向麦克风进行波束追踪 （Beam Tracking for a Directional Microphone）

可以使用这4个麦克风来模拟定向麦克风产生的效果，这个过程称之为波束追踪(beam tracking)

界面上的细长矩形用来指示某一时刻探测到的说话者的语音方向。矩形有一个旋转变换，在垂直轴上左右摆动，以表示声音的不同来源方向。

<Rectangle Fill="#1BA78B" HorizontalAlignment="Left" Margin="240,41,0,39" Stroke="Black" Width="10" RenderTransformOrigin="0.5,0">
    <Rectangle.RenderTransform>
        <TransformGroup>
            <ScaleTransform/>
            <SkewTransform/>
            <RotateTransform Angle="{Binding BeamAngle}"/>
            <TranslateTransform/>
        </TransformGroup>
    </Rectangle.RenderTransform>
</Rectangle>

 

上图是程序的UI界面。后台逻辑代码和之前的例子大部分都是相同的。首先实例化一个KinectAudioSource对象，然后将主窗体的DataContext赋值给本身。将BeamAngleMode设置为Adaptive，使得能够自动追踪说话者的声音。我们需要编写KinectAudioSource对象的BeamChanged事件对应的处理方法。当用户的说话时，位置发生变化时就会触发该事件。我们需要创建一个名为BeamAngle的属性，使得矩形的RotateTransform可以绑定这个属性。

public partial class MainWindow : Window, INotifyPropertyChanged
{
    public MainWindow()
    {
        InitializeComponent();
        this.DataContext = this;
        this.Loaded += delegate { ListenForBeamChanges(); };
    }

    private KinectAudioSource CreateAudioSource()
    {
        var source = KinectSensor.KinectSensors[0].AudioSource;
        source.NoiseSuppression = true;
        source.AutomaticGainControlEnabled = true;
        source.BeamAngleMode = BeamAngleMode.Adaptive;
        return source;
    }

    private void ListenForBeamChanges()
    {
        KinectSensor.KinectSensors[0].Start();
        var audioSource = CreateAudioSource();
        audioSource.BeamAngleChanged += audioSource_BeamAngleChanged;
        audioSource.Start();
    }

    public event PropertyChangedEventHandler PropertyChanged;

    private void OnPropertyChanged(string propName)
    {
        if (PropertyChanged != null)
            PropertyChanged(this, new PropertyChangedEventArgs(propName));
    }

    private double _beamAngle;
    public double BeamAngle
    {
        get { return _beamAngle; }
        set
        {
            _beamAngle = value;
            OnPropertyChanged("BeamAngle");
        }
    }
}

 

以上代码中，还需要对BeamChanged事件编写对应的处理方法。每次当波束的方向发生改变时，就更改BeamAngle的属性。SDK中使用弧度表示角度。所以在事件处理方法中我们需要将弧度换成度。为了能达到说话者移到左边，矩形条也能够向左边移动的效果，我们需要将角度乘以一个 –1

void audioSource_BeamAngleChanged(object sender, BeamAngleChangedEventArgs e)
{
    BeamAngle = -1 * e.Angle;
}

语音命令识别

结合KinectAudioSource和SpeechRecognitionEngine来演示语音命令识别的强大功能。为了展示语音命令能够和骨骼追踪高效结合，我们会使用语音命令向窗体上绘制图形，并使用命令移动这些图形到光标的位置

CrossHair用户控件简单的以十字光标形式显示当前用户右手的位置。下面的代码显示了这个自定义控件的XAML文件。注意到对象于容器有一定的偏移使得十字光标的中心能够处于Grid的零点。

自定义控件  CrossHairs

CrossHair用户控件简单的以十字光标形式显示当前用户右手的位置。下面的代码显示了这个自定义控件的XAML文件。注意到对象于容器有一定的偏移使得十字光标的中心能够处于Grid的零点。

<Grid Height="50" Width="50" RenderTransformOrigin="0.5,0.5">
    <Grid.RenderTransform>
        <TransformGroup>
            <ScaleTransform/>
            <SkewTransform/>
            <RotateTransform/>
            <TranslateTransform X="-25" Y="-25"/>
        </TransformGroup>
    </Grid.RenderTransform>
    <Rectangle Fill="#FFF4F4F5" Margin="22,0,20,0" Stroke="#FFF4F4F5"/>
    <Rectangle Fill="#FFF4F4F5" Margin="0,22,0,21" Stroke="#FFF4F4F5"/>  
</Grid>

在应用程序的主窗体中，将根节点从 grid 对象改为 canvas对象。Canvas对象使得将十字光标使用动画滑动到手的位置比较容易。在主窗体上添加一个CrossHairs自定义控件。在下面的代码中，我们可以看到将Canvas对象嵌套在了一个Viewbox控件中。这是一个比较老的处理不同屏幕分辨率的技巧。ViewBox控件会自动的将内容进行缩放以适应实际屏幕的大小。设置MainWindows的背景色，并将Canvas的颜色设置为黑色。然后在Canvas的底部添加两个标签。一个标签用来显示SpeechRecognitionEngine将要处理的语音指令，另一个标签显示匹配正确的置信度。CrossHair自定义控件绑定了HandTop和HandLeft属性。两个标签分别绑定了HypothesizedText和Confidence属性。

<Window x:Class="KinectPutThatThere.MainWindow"
        xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
        xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
        xmlns:local="clr-namespace:KinectPutThatThere"
        Title="Put That There"  Background="Black">
    <Viewbox>
        <Canvas x:Name="MainStage" Height="1080" Width="1920" Background="Black" VerticalAlignment="Bottom">
            <local:CrossHairs Canvas.Top="{Binding HandTop}" Canvas.Left="{Binding HandLeft}" />
                <Label  Foreground="White"  Content="{Binding HypothesizedText}" Height="55"   FontSize="32" Width="965"  Canvas.Left="115" Canvas.Top="1025" />
            <Label Foreground="Green" Content="{Binding Confidence}" Height="55"  Width="114" FontSize="32" Canvas.Left="0" Canvas.Top="1025"  />
        </Canvas>
    </Viewbox>
</Window>

在后台逻辑代码中，让MainWindows对象实现INofityPropertyChanged事件并添加OnPropertyChanged帮助方法。我们将创建4个属性用来为前台UI界面进行绑定。

public partial class MainWindow : Window, INotifyPropertyChanged
{
    private double _handLeft;
    public double HandLeft
    {
        get { return _handLeft; }
        set
        {
            _handLeft = value;
            OnPropertyChanged("HandLeft");
        }

    }

    private double _handTop;
    public double HandTop
    {
        get { return _handTop; }
        set
        {
            _handTop = value;
            OnPropertyChanged("HandTop");
        }
    }

    private string _hypothesizedText;
    public string HypothesizedText
    {
        get { return _hypothesizedText; }
        set
        {
            _hypothesizedText = value;
            OnPropertyChanged("HypothesizedText");
        }
    }

    private string _confidence;
    public string Confidence
    {
        get { return _confidence; }
        set
        {
            _confidence = value;
            OnPropertyChanged("Confidence");
        }
    }


    public event PropertyChangedEventHandler PropertyChanged;

    private void OnPropertyChanged(string propertyName)
    {
        if (PropertyChanged != null)
        {
            PropertyChanged(this, new PropertyChangedEventArgs(propertyName));
        }

    }
}

 

添加CreateAudioSource方法，在该方法中，将KinectAudioSource对象的AutoGainControlEnabled的属性设置为false。

private KinectAudioSource CreateAudioSource()
{
    var source = KinectSensor.KinectSensors[0].AudioSource;
    source.AutomaticGainControlEnabled = false;
    source.EchoCancellationMode = EchoCancellationMode.None;
    return source;
}

 

接下来实现骨骼追踪部分逻辑来获取右手的坐标，相信看完骨骼追踪那两篇文章后这部分的代码应该会比较熟悉。首先创建一个私有字段_kinectSensor来保存当前的KienctSensor对象，同时创建SpeechRecognitionEngine对象。在窗体的构造函数中，对这几个变量进行初始化。例外注册骨骼追踪系统的Skeleton事件并将主窗体的DataContext对象赋给自己。

KinectSensor _kinectSensor;
SpeechRecognitionEngine _sre;
KinectAudioSource _source;

public MainWindow()
{
    InitializeComponent();
    this.DataContext = this;
    this.Unloaded += delegate
    {
        _kinectSensor.SkeletonStream.Disable();
        _sre.RecognizeAsyncCancel();
        _sre.RecognizeAsyncStop();
        _sre.Dispose();
    };
    this.Loaded += delegate
    {
        _kinectSensor = KinectSensor.KinectSensors[0];
        _kinectSensor.SkeletonStream.Enable(new TransformSmoothParameters()
        {
            Correction = 0.5f,
            JitterRadius = 0.05f,
            MaxDeviationRadius = 0.04f,
            Smoothing = 0.5f
        });
        _kinectSensor.SkeletonFrameReady += nui_SkeletonFrameReady;
        _kinectSensor.Start();
        StartSpeechRecognition();
    };
}

 

在上面的代码中，我们添加了一些TransformSmoothParameters参数来使得骨骼追踪更加平滑。nui_SkeletonFrameReady方法如下。方式使用骨骼追踪数据来获取我们感兴趣的右手的关节点位置。这部分代码和之前文章中的类似。大致流程是：遍历当前处在追踪状态下的骨骼信息。然后找到右手关节点的矢量信息，然后使用SkeletonToDepthImage来获取相对于屏幕尺寸的X,Y坐标信息。

void nui_SkeletonFrameReady(object sender, SkeletonFrameReadyEventArgs e)
{
    using (SkeletonFrame skeletonFrame = e.OpenSkeletonFrame())
    {
        if (skeletonFrame == null)
            return;

        var skeletons = new Skeleton[skeletonFrame.SkeletonArrayLength];
        skeletonFrame.CopySkeletonDataTo(skeletons);
        foreach (Skeleton skeletonData in skeletons)
        {
            if (skeletonData.TrackingState == SkeletonTrackingState.Tracked)
            {
                Microsoft.Kinect.SkeletonPoint rightHandVec = skeletonData.Joints[JointType.HandRight].Position;
                var depthPoint = _kinectSensor.MapSkeletonPointToDepth(rightHandVec
                    , DepthImageFormat.Resolution640x480Fps30);
                HandTop = depthPoint.Y * this.MainStage.ActualHeight / 480;
                HandLeft = depthPoint.X * this.MainStage.ActualWidth / 640;
            }
        }
    }
}

 

接下来我们需要实现语音识别部分的逻辑。SpeechRecognitionEngine中的StartSpeechRecognition方法必须找到正确的语音识别库来进行语音识别。下面的代码展示了如何设置语音识别库预计如何将KinectAudioSource传递给语音识别引起。我们还添加了SpeechRecognized，SpeechHypothesized以及SpeechRejected事件对应的方法。SetInputToAudioStream中的参数和前篇文章中的含义一样，这里不多解释了。注意到SpeechRecognitionEngine和KinectAudioSource都是Disposable类型，因此在整个应用程序的周期内，我们要保证这两个对象都处于打开状态。

private void StartSpeechRecognition()
{
    _source = CreateAudioSource();

    Func<RecognizerInfo, bool> matchingFunc = r =>
    {
        string value;
        r.AdditionalInfo.TryGetValue("Kinect", out value);
        return "True".Equals(value, StringComparison.InvariantCultureIgnoreCase)
            && "en-US".Equals(r.Culture.Name, StringComparison.InvariantCultureIgnoreCase);
    };
    RecognizerInfo ri = SpeechRecognitionEngine.InstalledRecognizers().Where(matchingFunc).FirstOrDefault();

    _sre = new SpeechRecognitionEngine(ri.Id);
    CreateGrammars(ri);
    _sre.SpeechRecognized += sre_SpeechRecognized;
    _sre.SpeechHypothesized += sre_SpeechHypothesized;
    _sre.SpeechRecognitionRejected += sre_SpeechRecognitionRejected;

    Stream s = _source.Start();
    _sre.SetInputToAudioStream(s,
                                new SpeechAudioFormatInfo(
                                    EncodingFormat.Pcm, 16000, 16, 1,
                                    32000, 2, null));
    _sre.RecognizeAsync(RecognizeMode.Multiple);
}

要完成程序逻辑部分，我们还需要处理语音识别时间以及语音逻辑部分，以使得引擎能够直到如何处理和执行我们的语音命令。SpeechHypothesized以及SpeechRejected事件代码如下，这两个事件的逻辑很简单，就是更新UI界面上的label。SpeechRecognized事件有点复杂，他负责处理传进去的语音指令，并对识别出的指令执行相应的操作。另外，该事件还负责创建一些GUI对象（实际就是命令模式），我们必须使用Dispatcher对象来发挥InterpretCommand到主UI线程中来。

void sre_SpeechRecognitionRejected(object sender, SpeechRecognitionRejectedEventArgs e)
{
    HypothesizedText += " Rejected";
    Confidence = Math.Round(e.Result.Confidence, 2).ToString();
}

void sre_SpeechHypothesized(object sender, SpeechHypothesizedEventArgs e)
{
    HypothesizedText = e.Result.Text;
}

void sre_SpeechRecognized(object sender, SpeechRecognizedEventArgs e)
{
    Dispatcher.BeginInvoke(new Action<SpeechRecognizedEventArgs>(InterpretCommand), e);
}

 

现在到了程序核心的地方。创建语法逻辑并对其进行解析。本例中的程序识别普通的以“put”或者“create”开头的命令。前面是什么我们不关心，紧接着应该是一个颜色，然后是一种形状，最后一个词应该是“there”。下面的代码显示了创建的语法。

private void CreateGrammars(RecognizerInfo ri)
{
    var colors = new Choices();
    colors.Add("cyan");
    colors.Add("yellow");
    colors.Add("magenta");
    colors.Add("blue");
    colors.Add("green");
    colors.Add("red");

    var create = new Choices();
    create.Add("create");
    create.Add("put");

    var shapes = new Choices();
    shapes.Add("circle");
    shapes.Add("triangle");
    shapes.Add("square");
    shapes.Add("diamond");

    var gb = new GrammarBuilder();
    gb.Culture = ri.Culture;
    gb.Append(create);
    gb.AppendWildcard();
    gb.Append(colors);
    gb.Append(shapes);
    gb.Append("there");

    var g = new Grammar(gb);
    _sre.LoadGrammar(g);

    var q = new GrammarBuilder{ Culture = ri.Culture };
    q.Append("quit application");
    var quit = new Grammar(q);

    _sre.LoadGrammar(quit);
}

 

上面的代码中，我们首先创建一个Choices对象，这个对象会在命令解析中用到。在程序中我们需要颜色和形状对象。另外，第一个单词是“put”或者“create”，因此我们也创建Choices对象。然后使用GrammarBuilder类将这些对象组合到一起。首先是”put”或者“create”然后是一个占位符，因为我们不关心内容，然后是一个颜色Choices对象，然后是一个形状Choices对象，最后是一个“there”单词。

    我们将这些语法规则加载进语音识别引擎。同时我们也需要有一个命令来停止语音识别引擎。因此我们创建了第二个语法对象，这个对象只有一个”Quit”命令。然后也将这个语法规则加载到引擎中。

    一旦识别引擎确定了要识别的语法，真正的识别工作就开始了。被识别的句子必须被解译，出别出来想要的指令后，我们必须决定如何进行下一步处理。下面的代码展示了如何处理识别出的命令，以及如何根据特定的指令来讲图形元素绘制到UI界面上去。

private void InterpretCommand(SpeechRecognizedEventArgs e)
{
    var result = e.Result;
    Confidence = Math.Round(result.Confidence, 2).ToString();
    if (result.Confidence < 95 && result.Words[0].Text == "quit" && result.Words[1].Text == "application")
    {
        this.Close();
    }
    if (result.Words[0].Text == "put" || result.Words[0].Text == "create")
    {
        var colorString = result.Words[2].Text;
        Color color;
        switch (colorString)
        {
            case "cyan": color = Colors.Cyan;
                break;
            case "yellow": color = Colors.Yellow;
                break;
            case "magenta": color = Colors.Magenta;
                break;
            case "blue": color = Colors.Blue;
                break;
            case "green": color = Colors.Green;
                break;
            case "red": color = Colors.Red;
                break;
            default:
                return;
        }

        var shapeString = result.Words[3].Text;
        Shape shape;
        switch (shapeString)
        {
            case "circle":
                shape = new Ellipse();
                shape.Width = 150;
                shape.Height = 150;
                break;
            case "square":
                shape = new Rectangle();
                shape.Width = 150;
                shape.Height = 150;
                break;
            case "triangle":
                var poly = new Polygon();
                poly.Points.Add(new Point(0, 0));
                poly.Points.Add(new Point(150, 0));
                poly.Points.Add(new Point(75, -150));
                shape = poly;
                break;
            case "diamond":
                var poly2 = new Polygon();
                poly2.Points.Add(new Point(0, 0));
                poly2.Points.Add(new Point(75, 150));
                poly2.Points.Add(new Point(150, 0));
                poly2.Points.Add(new Point(75, -150));
                shape = poly2;
                break;
            default:
                return;
        }
        shape.SetValue(Canvas.LeftProperty, HandLeft);
        shape.SetValue(Canvas.TopProperty, HandTop);
        shape.Fill = new SolidColorBrush(color);
        MainStage.Children.Add(shape);
    }
}

 

方法中，我们首先检查语句识别出的单词是否是”Quit”如果是的，紧接着判断第二个单词是不是”application”如果两个条件都满足了，就不进行绘制图形，直接返回。如果有一个条件不满足，就继续执行下一步。

     InterpretCommand方法然后判断第一个单词是否是“create”或者“put”，如果不是这两个单词开头就什么也不执行。如果是的，就判断第三个单词，并根据识别出来的颜色创建对象。如果第三个单词没有正确识别，应用程序也停止处理。否则，程序判断第四个单词，根据接收到的命令创建对应的形状。到这一步，基本的逻辑已经完成，最后第五个单词用来确定整个命令是否正确。命令处理完了之后，将当前受的X,Y坐标赋给创建好的对象的位置。

namespace KinectPutThatThere
{
    /// <summary>
    /// Interaction logic for MainWindow.xaml
    /// </summary>
    public partial class MainWindow : Window, INotifyPropertyChanged
    {
        KinectSensor _kinectSensor;
        SpeechRecognitionEngine _sre;
        KinectAudioSource _source;

        public MainWindow()
        {
            InitializeComponent();
            this.DataContext = this;
            this.Unloaded += delegate
            {
                _kinectSensor.SkeletonStream.Disable();
                _sre.RecognizeAsyncCancel();
                _sre.RecognizeAsyncStop();
                //_source.Dispose();
                _sre.Dispose();
            };
            this.Loaded += delegate
            {
                _kinectSensor = KinectSensor.KinectSensors[0];
                _kinectSensor.SkeletonStream.Enable(new TransformSmoothParameters() // 对骨骼数据进行平滑处理
                {
                    // This struct is used to setup the skeleton smoothing values
                    Correction = 0.5f,
                    JitterRadius = 0.05f,
                    MaxDeviationRadius = 0.04f,
                    Smoothing = 0.5f
                });
                _kinectSensor.SkeletonFrameReady += nui_SkeletonFrameReady;
                _kinectSensor.Start();
                StartSpeechRecognition();
            };
        }

#region 骨骼数据处理

        void nui_SkeletonFrameReady(object sender, SkeletonFrameReadyEventArgs e)
        {
            using (SkeletonFrame skeletonFrame = e.OpenSkeletonFrame())
            {
                if (skeletonFrame == null)
                    return;

                var skeletons = new Skeleton[skeletonFrame.SkeletonArrayLength];    // 不定类型 —— Skeleton
                skeletonFrame.CopySkeletonDataTo(skeletons);
                foreach (Skeleton skeletonData in skeletons)
                {
                    if (skeletonData.TrackingState == SkeletonTrackingState.Tracked)
                    {
                        Microsoft.Kinect.SkeletonPoint rightHandVec = skeletonData.Joints[JointType.HandRight].Position;
                        var depthPoint = _kinectSensor.MapSkeletonPointToDepth(rightHandVec
                            , DepthImageFormat.Resolution640x480Fps30);
                        HandTop = depthPoint.Y * this.MainStage.ActualHeight / 480;
                        HandLeft = depthPoint.X * this.MainStage.ActualWidth / 640;
                    }
                }
            }
        }
#endregion

        private KinectAudioSource CreateAudioSource()
        {
            var source = KinectSensor.KinectSensors[0].AudioSource;
            source.AutomaticGainControlEnabled = false;
            source.EchoCancellationMode = EchoCancellationMode.None;
            return source;
        }

        private void StartSpeechRecognition()
        {
            _source = CreateAudioSource();

            Func<RecognizerInfo, bool> matchingFunc = r =>
            {
                string value;
                r.AdditionalInfo.TryGetValue("Kinect", out value);
                return "True".Equals(value, StringComparison.InvariantCultureIgnoreCase)
                    && "en-US".Equals(r.Culture.Name, StringComparison.InvariantCultureIgnoreCase);
            };
                // 识别库
            
            RecognizerInfo ri = SpeechRecognitionEngine.InstalledRecognizers().Where(matchingFunc).FirstOrDefault();

            _sre = new SpeechRecognitionEngine(ri.Id);  // 需要设置识别引擎的ID编号
            CreateGrammars(ri);
            _sre.SpeechRecognized += sre_SpeechRecognized;
            _sre.SpeechHypothesized += sre_SpeechHypothesized;
            _sre.SpeechRecognitionRejected += sre_SpeechRecognitionRejected;

            Stream s = _source.Start();
            _sre.SetInputToAudioStream(s,
                                        new SpeechAudioFormatInfo(
                                            EncodingFormat.Pcm, 16000, 16, 1,
                                            32000, 2, null));
            _sre.RecognizeAsync(RecognizeMode.Multiple);
        }

        private void CreateGrammars(RecognizerInfo ri)
        {
                // 创建语法

            var colors = new Choices(); // 通配符 —— 择类(Choices)是通配符类(Wildcard)的一种，它可以包含多个值。但与通配符不同的是，我们可以指定可接受的值的顺序。
            colors.Add("cyan");
            colors.Add("yellow");
            colors.Add("magenta");
            colors.Add("blue");
            colors.Add("green");
            colors.Add("red");

            var create = new Choices();
            create.Add("create");
            create.Add("put");

            var shapes = new Choices();
            shapes.Add("circle");
            shapes.Add("triangle");
            shapes.Add("square");
            shapes.Add("diamond");

            var gb = new GrammarBuilder();
            gb.Culture = ri.Culture;
            gb.Append(create);
            gb.AppendWildcard();
            gb.Append(colors);
            gb.Append(shapes);
            gb.Append("there");

            var g = new Grammar(gb);
            _sre.LoadGrammar(g);

            var q = new GrammarBuilder { Culture = ri.Culture };
            q.Append("quit application");
            var quit = new Grammar(q);

            _sre.LoadGrammar(quit);
        }

#region 语音事件处理

        void sre_SpeechRecognitionRejected(object sender, SpeechRecognitionRejectedEventArgs e)
        {
            HypothesizedText += " Rejected";
            Confidence = Math.Round(e.Result.Confidence, 2).ToString();
        }

        void sre_SpeechHypothesized(object sender, SpeechHypothesizedEventArgs e)
        {
            HypothesizedText = e.Result.Text;
        }

        void sre_SpeechRecognized(object sender, SpeechRecognizedEventArgs e)
        {
            Dispatcher.BeginInvoke(new Action<SpeechRecognizedEventArgs>(InterpretCommand), e);
        }

#endregion
        private void InterpretCommand(SpeechRecognizedEventArgs e)
        {
            var result = e.Result;
            Confidence = Math.Round(result.Confidence, 2).ToString();
            if (result.Confidence < 95 && result.Words[0].Text == "quit" && result.Words[1].Text == "application")
            {
                this.Close();
            }
            if (result.Words[0].Text == "put" || result.Words[0].Text == "create")
            {
                var colorString = result.Words[2].Text;
                Color color;
                switch (colorString)
                {
                    case "cyan": color = Colors.Cyan;
                        break;
                    case "yellow": color = Colors.Yellow;
                        break;
                    case "magenta": color = Colors.Magenta;
                        break;
                    case "blue": color = Colors.Blue;
                        break;
                    case "green": color = Colors.Green;
                        break;
                    case "red": color = Colors.Red;
                        break;
                    default:
                        return;
                }

                var shapeString = result.Words[3].Text;
                Shape shape;
                switch (shapeString)
                {
                    case "circle":
                        shape = new Ellipse();
                        shape.Width = 150;
                        shape.Height = 150;
                        break;
                    case "square":
                        shape = new Rectangle();
                        shape.Width = 150;
                        shape.Height = 150;
                        break;
                    case "triangle":
                        var poly = new Polygon();
                        poly.Points.Add(new Point(0, 0));
                        poly.Points.Add(new Point(150, 0));
                        poly.Points.Add(new Point(75, -150));
                        shape = poly;
                        break;
                    case "diamond":
                        var poly2 = new Polygon();
                        poly2.Points.Add(new Point(0, 0));
                        poly2.Points.Add(new Point(75, 150));
                        poly2.Points.Add(new Point(150, 0));
                        poly2.Points.Add(new Point(75, -150));
                        shape = poly2;
                        break;
                    default:
                        return;
                }
                shape.SetValue(Canvas.LeftProperty, HandLeft);
                shape.SetValue(Canvas.TopProperty, HandTop);
                shape.Fill = new SolidColorBrush(color);
                MainStage.Children.Add(shape);
            }
        }

#region 前台控件的绑定

        private double _handLeft;
        public double HandLeft
        {
            get { return _handLeft; }
            set
            {
                _handLeft = value;
                OnPropertyChanged("HandLeft");
            }

        }

        private double _handTop;
        public double HandTop
        {
            get { return _handTop; }
            set
            {
                _handTop = value;
                OnPropertyChanged("HandTop");   // 驱动控件
            }
        }

        private string _hypothesizedText;
        public string HypothesizedText
        {
            get { return _hypothesizedText; }
            set
            {
                _hypothesizedText = value;
                OnPropertyChanged("HypothesizedText");
            }
        }

        private string _confidence;
        public string Confidence
        {
            get { return _confidence; }
            set
            {
                _confidence = value;
                OnPropertyChanged("Confidence");
            }
        }


        public event PropertyChangedEventHandler PropertyChanged;

        private void OnPropertyChanged(string propertyName)
        {
            if (PropertyChanged != null)
            {
                PropertyChanged(this, new PropertyChangedEventArgs(propertyName));
            }

        }

#endregion
    }
}

Kinect 开发 —— 语音识别（下）,布布扣,bubuko.com

Kinect 开发 —— 语音识别（下）

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原文地址：http://www.cnblogs.com/sprint1989/p/3854982.html