[函數名稱]html
圖像運動模糊算法 MotionblurProcess(WriteableBitmap src,int k,int direction)算法
[算法說明]函數
運動模糊是指在攝像機獲取圖像時,因爲景物和相機之間的相對運動而形成的圖像上的模糊。這裏spa
咱們主要介紹勻速直線運動所形成的模糊,因爲非勻速直線運動在某些條件下能夠近似爲勻速直線.net
運動,或者能夠分解爲多個勻速直線運動的合成,所以,在攝像機較短的圖像曝光時間內,形成圖code
像模糊的運動狀況能夠近似爲勻速直線運動。htm
對於勻速直線運動,圖像的運動模糊能夠用如下公式表示:blog
/// <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)////運動模糊處理 { 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; } }