如何对齐kinect's深度图像与彩色图像

关键是调用' runtime . nuiccamera . getcolorpixelcoordinatesfromdepthpixel '

这是Runtime类的一个扩展方法。它返回一个WriteableBitmap对象。WriteableBitmap在新帧进入时自动更新。所以它的用法非常简单:

    kinect = new Runtime();
    kinect.Initialize(RuntimeOptions.UseColor | RuntimeOptions.UseSkeletalTracking | RuntimeOptions.UseDepthAndPlayerIndex);
    kinect.DepthStream.Open(ImageStreamType.Depth, 2, ImageResolution.Resolution320x240, ImageType.DepthAndPlayerIndex);
    kinect.VideoStream.Open(ImageStreamType.Video, 2, ImageResolution.Resolution640x480, ImageType.Color);
    myImageControl.Source = kinect.CreateLivePlayerRenderer(); 

，下面是代码本身:

public static class RuntimeExtensions
{
   public static WriteableBitmap CreateLivePlayerRenderer(this Runtime runtime)
   {
      if (runtime.DepthStream.Width == 0)
         throw new InvalidOperationException("Either open the depth stream before calling this method or use the overload which takes in the resolution that the depth stream will later be opened with.");
      return runtime.CreateLivePlayerRenderer(runtime.DepthStream.Width, runtime.DepthStream.Height);
   }
   public static WriteableBitmap CreateLivePlayerRenderer(this Runtime runtime, int depthWidth, int depthHeight)
   {
      PlanarImage depthImage = new PlanarImage();
      WriteableBitmap target = new WriteableBitmap(depthWidth, depthHeight, 96, 96, PixelFormats.Bgra32, null);
      var depthRect = new System.Windows.Int32Rect(0, 0, depthWidth, depthHeight);
      runtime.DepthFrameReady += (s, e) =>
            {
                depthImage = e.ImageFrame.Image;
                Debug.Assert(depthImage.Height == depthHeight && depthImage.Width == depthWidth);
            };
      runtime.VideoFrameReady += (s, e) =>
            {
                // don't do anything if we don't yet have a depth image
                if (depthImage.Bits == null) return;
                byte[] color = e.ImageFrame.Image.Bits;
                byte[] output = new byte[depthWidth * depthHeight * 4];
                // loop over each pixel in the depth image
                int outputIndex = 0;
                for (int depthY = 0, depthIndex = 0; depthY < depthHeight; depthY++)
                {
                    for (int depthX = 0; depthX < depthWidth; depthX++, depthIndex += 2)
                    {
                        // combine the 2 bytes of depth data representing this pixel
                        short depthValue = (short)(depthImage.Bits[depthIndex] | (depthImage.Bits[depthIndex + 1] << 8));
                        // extract the id of a tracked player from the first bit of depth data for this pixel
                        int player = depthImage.Bits[depthIndex] & 7;
                        // find a pixel in the color image which matches this coordinate from the depth image
                        int colorX, colorY;
                        runtime.NuiCamera.GetColorPixelCoordinatesFromDepthPixel(
                            e.ImageFrame.Resolution,
                            e.ImageFrame.ViewArea,
                            depthX, depthY, // depth coordinate
                            depthValue,  // depth value
                            out colorX, out colorY);  // color coordinate
                        // ensure that the calculated color location is within the bounds of the image
                        colorX = Math.Max(0, Math.Min(colorX, e.ImageFrame.Image.Width - 1));
                        colorY = Math.Max(0, Math.Min(colorY, e.ImageFrame.Image.Height - 1));
                        output[outputIndex++] = color[(4 * (colorX + (colorY * e.ImageFrame.Image.Width))) + 0];
                        output[outputIndex++] = color[(4 * (colorX + (colorY * e.ImageFrame.Image.Width))) + 1];
                        output[outputIndex++] = color[(4 * (colorX + (colorY * e.ImageFrame.Image.Width))) + 2];
                        output[outputIndex++] = player > 0 ? (byte)255 : (byte)0;
                    }
                }
                target.WritePixels(depthRect, output, depthWidth * PixelFormats.Bgra32.BitsPerPixel / 8, 0);
            };
            return target;
        }
    }

一种方法是假设颜色和深度图像具有相似的变化，并交叉关联这两个图像(或它们的较小版本)。

• 预白图像以获得潜在的变化。
• 交叉关联预白图像或较小版本的图像。
• 互相关的峰值位置会告诉你x和y
的偏移量