[函数名称]
Harris角点检测函数 HarrisDetect(WriteableBitmap src, int CRF)
[算法说明]
目前的角点检测算法可归纳为3类：基于灰度图像的角点检测、基于二值图像的角点检测、基于轮廓曲线的角点检测。基于灰度图像的角点检测又可分为基于梯度、基于模板和基于模板梯度组合3类方法，其中基于模板的方法主要考虑像素领域点的灰度变化，即图像亮度的变化，将与邻点亮度对比足够大的点定义为角点。本文将介绍一种改进的Harris角点检测算法，该算法是一种基于模板与梯度组合的方法。
[函数代码] ////// Harris counter-detect. /// /// The source image. /// The threshould to control counters number. ///public static int[,] HarrisDetect(WriteableBitmap src, int CRF) { int x = src.PixelWidth; int y = src.PixelHeight; double[,] Ix = new double[x, y]; double[,] Iy = new double[x, y]; double[,] Ixy = new double[x, y]; double[,] cim = new double[x, y]; int[,] re = new int[x, y]; double[,] srcBytes = GetImageBytes(src); GetIV(srcBytes, Ix, Iy, Ixy,x,y); GaussFilter(Ix, Iy, Ixy,x,y); cim = GetCim(Ix, Iy, Ixy,x,y); for (int j = 1; j < y - 1; j++) { for (int i = 1; i < x - 1; i++) { if ((cim[i, j] == GetMax(cim[i - 1, j - 1], cim[i, j - 1], cim[i + 1, j - 1], cim[i - 1, j], cim[i, j], cim[i + 1, j], cim[i - 1, j + 1], cim[i, j + 1], cim[i + 1, j + 1])) && (cim[i, j] > CRF)) { re[i, j] = 1; } } } return re; } //获得角点图像的 原始 信息 public static double[,] GetImageBytes(WriteableBitmap src) { if (src != null) { int w = src.PixelWidth; int h = src.PixelHeight; double[,] imageBytes = new double[w, h]; int b = 0, g = 0, r = 0; byte[] temp = src.PixelBuffer.ToArray(); for (int y = 0; y < h; y++) { for (int x = 0; x < w * 4; x += 4) { b = temp[x + y * w * 4]; g = temp[x + 1 + y * w * 4]; r = temp[x + 2 + y * w * 4]; imageBytes[x, y] = (b * 0.114 + g * 0.587 + r * 0.299); } } return imageBytes; } else { return null; } } //梯度求取函数 private static void GetIV(double[,] src, double[,] Ix, double[,] Iy, double[,] Ixy,int x,int y) { for (int j = 1; j < y - 1; j++) { for (int i = 1; i < x - 1; i++) { Ix[i, j] = Math.Abs(src[i + 1, j - 1] + src[i + 1, j] + src[i + 1, j + 1] - src[i - 1, j - 1] - src[i - 1, j] - src[i - 1, j + 1]); Iy[i, j] = Math.Abs(src[i - 1, j + 1] + src[i, j + 1] + src[i + 1, j + 1] - src[i - 1, j - 1] - src[i, j - 1] - src[i + 1, j - 1]); Ixy[i, j] = Math.Abs(Ix[i, j] * Iy[i, j]); } } } //高斯滤波函数(对梯度图像进行高斯滤波，这里采用的是3*3的高斯滤波模板) private static void GaussFilter(double[,] Ix, double[,] Iy, double[,] Ixy,int x,int y) { for (int j = 1; j < y - 1; j++) { for (int i = 1; i < x - 1; i++) { Ix[i, j] = (Ix[i - 1, j - 1] + Ix[i, j - 1] * 2 + Ix[i + 1, j - 1] + 2 * Ix[i - 1, j] + 4 * Ix[i, j] + 2 * Ix[i + 1, j] + Ix[i - 1, j + 1] + 2 * Ix[i, j + 1] + Ix[i + 1, j + 1]) / 16; Iy[i, j] = (Iy[i - 1, j - 1] + Iy[i, j - 1] * 2 + Iy[i + 1, j - 1] + 2 * Iy[i - 1, j] + 4 * Iy[i, j] + 2 * Iy[i + 1, j] + Iy[i - 1, j + 1] + 2 * Iy[i, j + 1] + Ix[i + 1, j + 1]) / 16; Ixy[i, j] = (Ixy[i - 1, j - 1] + Ixy[i, j - 1] * 2 + Ixy[i + 1, j - 1] + 2 * Ixy[i - 1, j] + 4 * Ixy[i, j] + 2 * Ixy[i + 1, j] + Ixy[i - 1, j + 1] + 2 * Ixy[i, j + 1] + Ix[i + 1, j + 1]) / 16; } } } //图像角点求取函数 private static double[,] GetCim(double[,] Ix, double[,] Iy, double[,] Ixy,int x,int y) { double cim = 0; double[,] results = new double[x, y]; for (int j = 1; j < y - 1; j++) { for (int i = 1; i < x - 1; i++) { if (Ix[i, j] != 0 || Iy[i, j] != 0) { cim = Math.Abs(Ix[i, j] * Iy[i, j] - Ixy[i, j] * Ixy[i, j]) / (Ix[i, j] * Ix[i, j] + Iy[i, j] * Iy[i, j]); results[i, j] = cim; } } } return results; } //最大值获取函数 private static double GetMax(params double[] src) { double tMax = 0; for (int i = 0; i < src.Length; i++) { if (tMax < src[i]) { tMax = src[i]; } } return tMax; }[图像效果]
