windows phone 兑现mjpeg流视频的人脸识别

windows phone 实现mjpeg流视频的人脸识别

先上图片，看看效果

首先是wp有自带的mjpeg解码功能
能输出WriteableBitmap格式，方便接下来进行图像分析
速度还行，最大也只是100多MS的延迟
接收图像方法
public void recVideo()
        {
            string sourceURL = "图片流地址";
            while (true)
            {
                HttpWebRequest req = (HttpWebRequest)WebRequest.Create(new Uri(sourceURL));
                req.BeginGetResponse(new AsyncCallback(resResult), req);
                //Thread.Sleep(10);
            }
        }
异步调用方法
      private void resResult(IAsyncResult ir)
        {
            try
            {
                HttpWebRequest req = ir.AsyncState as HttpWebRequest;
                WebResponse resp = req.EndGetResponse(ir);
                Stream stream = resp.GetResponseStream();
                Dispatcher.BeginInvoke(() =>
                {
                    WriteableBitmap myBitmap = PictureDecoder.DecodeJpeg(stream);    这里就是自带的解码方法，一句话就行了，如果只要实现视频输出，下面图像识别就可以删掉了
                    image1.Source = myBitmap;
                    #region 图像识别
                    var w = myBitmap.PixelWidth;
                    var h = myBitmap.PixelHeight;
                    if (_pixelDataGray == null || _pixelDataGray.Length != h * w)
                    {
//定义了几个数组
                        _pixelDataGray = new byte[w / _downsampleFactor * h / _downsampleFactor];
                        _pixelDataDownsampled =
                            new byte[w / _downsampleFactor * h / _downsampleFactor];
                        _pixelDataGrayInt = new int[w / _downsampleFactor * h / _downsampleFactor];
                        _wb = new WriteableBitmap(w / _downsampleFactor, w / _downsampleFactor);
                    }
//另外写了几个类。Utils 和 FaceDetection，其中FaceDetection的检测算法是跟opencv学的
                    Utils.DownSample(myBitmap.Pixels, w, h, ref _pixelDataGrayInt, _downsampleFactor);
                    Utils.ARGBToGreyScale(_pixelDataGrayInt, ref _pixelDataGray);
                    //_pixelDataGray = e.FrameBuffer;
                    Utils.HistogramEqualization(ref _pixelDataGray);
                    Utils.GrayToARGB(_pixelDataGray, ref _pixelDataGrayInt);
                    List<FaceDetection.Rectangle> faces =
                        _detector.getFaces(
                        _pixelDataGrayInt,
                        w / _downsampleFactor,
                        h / _downsampleFactor,
                        2f, 1.25f, 0.1f, 1, false, true);
                    _pixelDataGrayInt.CopyTo(_wb.Pixels, 0);
                    _wb.Invalidate();
//image控件上覆盖了个 canvas控件，用来画圆圈
                    cnvsFaceRegions.Children.Clear();
                    foreach (var r in faces)
                    {
                        Ellipse el = new Ellipse();
                        TranslateTransform loc = new TranslateTransform();
                        loc.X = r.X * _downsampleFactor / (double)w * cnvsFaceRegions.Width;
                        loc.Y = r.Y * _downsampleFactor / (double)w * cnvsFaceRegions.Height;
                        el.RenderTransform = loc;
                        el.Width = r.Width * _downsampleFactor *2;
                        el.Height = r.Height * _downsampleFactor *2;
                        el.Stroke = new SolidColorBrush(Colors.Red);
                        cnvsFaceRegions.Children.Add(el);
                    }
                    #endregion
                });
            }
            catch (Exception ex)
            {
                Dispatcher.BeginInvoke(() => sysInfo.Text = ex.Message);
            }
        }

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http://download.csdn.net/detail/cooska/4895104