更新时间:2022-09-27 14:27:16
[函数名称]
图像运动模糊算法 MotionblurProcess(WriteableBitmap src,int k,int direction)
[算法说明]
运动模糊是指在摄像机获取图像时,由于景物和相机之间的相对运动而造成的图像上的模糊。这里
我们主要介绍匀速直线运动所造成的模糊,由于非匀速直线运动在某些条件下可以近似为匀速直线
运动,或者可以分解为多个匀速直线运动的合成,因此,在摄像机较短的图像曝光时间内,造成图
像模糊的运动情况可以近似为匀速直线运动。
对于匀速直线运动,图像的运动模糊可以用以下公式表示:
/// <summary> /// Motion blur process. /// </summary> /// <param name="src">The source image.</param> /// <param name="k">The offset of motion, from 0 to 200.</param> /// <param name="direction">The direction of motion, x:1, y:2.</param> /// <returns></returns> public static WriteableBitmap MotionblurProcess(WriteableBitmap src,int k,int direction)////运动模糊处理 { if (src != null) { int w = src.PixelWidth; int h = src.PixelHeight; WriteableBitmap srcImage = new WriteableBitmap(w, h); byte[] temp = src.PixelBuffer.ToArray(); byte[] tempMask = (byte[])temp.Clone(); int b, g, r; for (int y = 0; y < h; y++) { for (int x = 0; x < w; x ++) { b = g = r = 0; switch (direction) { case 1: if (x >= k) { for (int i = 0; i <= k; i++) { b += (int)tempMask[(x - i) * 4 + y * w * 4]; g += (int)tempMask[(x - i) * 4 + 1 + y * w * 4]; r += (int)tempMask[(x - i) * 4 + 2 + y * w * 4]; } temp[x * 4 + y * w * 4] = (byte)(b / (k + 1)); temp[x * 4 + 1 + y * w * 4] = (byte)(g / (k + 1)); temp[x * 4 + 2 + y * w * 4] = (byte)(r / (k + 1)); } else { if (x > 0) { for (int i = 0; i < x; i++) { b += (int)tempMask[(x - i) * 4 + y * w * 4]; g += (int)tempMask[(x - i) * 4 + 1 + y * w * 4]; r += (int)tempMask[(x - i) * 4 + 2 + y * w * 4]; } temp[x * 4 + y * w * 4] = (byte)(b/(x+1)); temp[x * 4 + 1 + y * w * 4] = (byte)(g/(x+1)); temp[x * 4 + 2 + y * w * 4] = (byte)(r/(x+1)); } else { temp[x * 4 + y * w * 4] = (byte)(tempMask[x * 4 + y * w * 4] / k); temp[x * 4 + 1 + y * w * 4] = (byte)(tempMask[x * 4 + 1 + y * w * 4] / k); temp[x * 4 + 2 + y * w * 4] = (byte)(tempMask[x * 4 + 2 + y * w * 4] / k); } } break; case 2: if (y >= k) { for (int i = 0; i <= k; i++) { b += (int)tempMask[x * 4 + (y - i) * w * 4]; g += (int)tempMask[x * 4 + 1 + (y - i) * w * 4]; r += (int)tempMask[x * 4 + 2 + (y - i) * w * 4]; } temp[x * 4 + y * w * 4] = (byte)(b / (k + 1)); temp[x * 4 + 1 + y * w * 4] = (byte)(g / (k + 1)); temp[x * 4 + 2 + y * w * 4] = (byte)(r / (k + 1)); } else { if (y > 0) { for (int i = 0; i < y; i++) { b += (int)tempMask[x * 4 + (y - i) * w * 4]; g += (int)tempMask[x * 4 + 1 + (y - i) * w * 4]; r += (int)tempMask[x * 4 + 2 + (y - i) * w * 4]; } temp[x * 4 + y * w * 4] = (byte)(b/(y+1)); temp[x * 4 + 1 + y * w * 4] = (byte)(g/(y+1)); temp[x * 4 + 2 + y * w * 4] = (byte)(r/(y+1)); } else { temp[x * 4 + y * w * 4] = (byte)(tempMask[x * 4 + y * w * 4] / k); temp[x * 4 + 1 + y * w * 4] = (byte)(tempMask[x * 4 + 1 + y * w * 4] / k); temp[x * 4 + 2 + y * w * 4] = (byte)(tempMask[x * 4 + 2 + y * w * 4] / k); } } break; default : break; } } } Stream sTemp = srcImage.PixelBuffer.AsStream(); sTemp.Seek(0, SeekOrigin.Begin); sTemp.Write(temp, 0, w * 4 * h); return srcImage; } else { return null; } }