边缘提取算法.doc

public class EdgeDetect : ImageInfo /* * * Roberts, Sobel, Prewitt, Kirsch, GaussLaplacian * 水平检测、垂直检测、边缘增强、边缘均衡化 * */ / / 对两幅图像进行梯度运算 / / 位图 1 / 位图 2 / private Bitmap Gradient(Bitmap b1, Bitmap b2) int width = b1.Width; int height = b1.Height; BitmapData data1 = b1.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.ReadWrite, PixelFormat.Format32bppArgb); BitmapData data2 = b2.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.ReadOnly, PixelFormat.Format32bppArgb); unsafe byte* p1 = (byte*)data1.Scan0; byte* p2 = (byte*)data2.Scan0; int offset = data1.Stride - width * BPP; for (int y = 0; y height; y+) for (int x = 0; x width; x+) for (int i = 0; i 255 ? 255 : power); / i p1 += BPP; p2 += BPP; / x p1 += offset; p2 += offset; / y b1.UnlockBits(data1); b2.UnlockBits(data2); Bitmap dstImage = (Bitmap)b1.Clone(); b1.Dispose(); b2.Dispose(); return dstImage; / end of Gradient / / 按 Roberts 算子进行边缘检测 / / 位图流 / public Bitmap Roberts(Bitmap b) int width = b.Width; int height = b.Height; Bitmap dstImage = new Bitmap(width, height); BitmapData srcData = b.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.ReadOnly, PixelFormat.Format32bppArgb); BitmapData dstData = dstImage.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.WriteOnly, PixelFormat.Format32bppArgb); int stride = srcData.Stride; int offset = stride - width * BPP; unsafe byte* src = (byte*)srcData.Scan0; byte* dst = (byte*)dstData.Scan0; int A, B; / A(x-1, y-1) B(x, y-1) int C, D; / C(x-1, y) D(x, y) / 指向第一行 src += stride; dst += stride; / 不处理最上边和最左边 for (int y = 1; y height; y+) / 指向每行第一列 src += BPP; dst += BPP; for (int x = 1; x width; x+) for (int i = 0; i 3; i+) A = srci - stride - BPP; B = srci - stride; C = srci - BPP; D = srci; dsti = (byte)(Math.Sqrt(A - D) * (A - D) + (B - C) * (B - C); / i dst3 = src3; src += BPP; dst += BPP; / x src += offset; dst += offset; / y b.UnlockBits(srcData); dstImage.UnlockBits(dstData); b.Dispose(); return dstImage; / end of Roberts / / 按 Sobel 算子进行边缘检测 / / 位图流 / public Bitmap Sobel(Bitmap b) Matrix3x3 m = new Matrix3x3(); / -1 -2 -1 / 0 0 0 / 1 2 1 m.Init(0); m.TopLeft = m.TopRight = -1; m.BottomLeft = m.BottomRight = 1; m.TopMid = -2; m.BottomMid = 2; Bitmap b1 = m.Convolute(Bitmap)b.Clone(); / -1 0 1 / -2 0 2 / -1 0 1 m.Init(0); m.TopLeft = m.BottomLeft = -1; m.TopRight = m.BottomRight = 1; m.MidLeft = -2; m.MidRight = 2; Bitmap b2 = m.Convolute(Bitmap)b.Clone(); / 0 1 2 / -1 0 1 / -2 -1 0 m.Init(0); m.TopMid = m.MidRight = 1; m.MidLeft = m.BottomMid = -1; m.TopRight = 2; m.BottomLeft = -2; Bitmap b3 = m.Convolute(Bitmap)b.Clone(); / -2 -1 0 / -1 0 1 / 0 1 2 m.Init(0); m.TopMid = m.MidLeft = -1; m.MidRight = m.BottomMid = 1; m.TopLeft = -2; m.BottomRight = 2; Bitmap b4 = m.Convolute(Bitmap)b.Clone(); / 梯度运算 b = Gradient(Gradient(b1, b2), Gradient(b3, b4); b1.Dispose(); b2.Dispose(); b3.Dispose(); b4.Dispose(); return b; / end of Sobel / / 按 Prewitt 算子进行边缘检测 / / 位图流 / public Bitmap Prewitt(Bitmap b) Matrix3x3 m = new Matrix3x3(); / -1 -1 -1 / 0 0 0 / 1 1 1 m.Init(0); m.TopLeft = m.TopMid = m.TopRight = -1; m.BottomLeft = m.BottomMid = m.BottomRight = 1; Bitmap b1 = m.Convolute(Bitmap)b.Clone(); / -1 0 1 / -1 0 1 / -1 0 1 m.Init(0); m.TopLeft = m.MidLeft = m.BottomLeft = -1; m.TopRight = m.MidRight = m.BottomRight = 1; Bitmap b2 = m.Convolute(Bitmap)b.Clone(); / -1 -1 0 / -1 0 1 / 0 1 1 m.Init(0); m.TopLeft = m.MidLeft = m.TopMid = -1; m.BottomMid = m.BottomRight = m.MidRight = 1; Bitmap b3 = m.Convolute(Bitmap)b.Clone(); / 0 1 1 / -1 0 1 / -1 -1 0 m.Init(0); m.TopMid = m.TopRight = m.MidRight = 1; m.MidLeft = m.BottomLeft = m.BottomMid = -1; Bitmap b4 = m.Convolute(Bitmap)b.Clone(); / 梯度运算 b = Gradient(Gradient(b1, b2), Gradient(b3, b4); b1.Dispose(); b2.Dispose(); b3.Dispose(); b4.Dispose(); return b; / end of Prewitt / / 按 Kirsch 算子进行边缘检测 / / 位图流 / public Bitmap Kirsch(Bitmap b) Matrix3x3 m = new Matrix3x3(); / 5 5 5 / -3 0 -3 / -3 -3 -3 m.Init(-3); m.Center = 0; m.TopLeft = m.TopMid = m.TopRight = 5; Bitmap b1 = m.Convolute(Bitmap)b.Clone(); / -3 5 5 / -3 0 5 / -3 -3 -3 m.Init(-3); m.Center = 0; m.TopMid = m.TopRight = m.MidRight = 5; Bitmap b2 = m.Convolute(Bitmap)b.Clone(); / -3 -3 5 / -3 0 5 / -3 -3 5 m.Init(-3); m.Center = 0; m.TopRight = m.MidRight = m.BottomRight = 5; Bitmap b3 = m.Convolute(Bitmap)b.Clone(); / -3 -3 -3 / -3 0 5 / -3 5 5 m.Init(-3); m.Center = 0; m.MidRight = m.BottomRight = m.BottomMid = 5; Bitmap b4 = m.Convolute(Bitmap)b.Clone(); / -3 -3 -3 / -3 0 -3 / 5 5 5 m.Init(-3); m.Center = 0; m.BottomLeft = m.BottomMid = m.BottomRight = 5; Bitmap b5 = m.Convolute(Bitmap)b.Clone(); / -3 -3 -3 / 5 0 -3 / 5 5 -3 m.Init(-3); m.Center = 0; m.MidLeft = m.BottomLeft = m.BottomMid = 5; Bitmap b6 = m.Convolute(Bitmap)b.Clone(); / 5 -3 -3 / 5 0 -3 / 5 -3 -3 m.Init(-3); m.Center = 0; m.TopLeft = m.MidLeft = m.BottomLeft = 5; Bitmap b7 = m.Convolute(Bitmap)b.Clone(); / 5 5 -3 / 5 0 -3 / -3 -3 -3 m.Init(-3); m.Center = 0; m.TopLeft = m.MidLeft = m.TopMid = 5; Bitmap b8 = m.Convolute(Bitmap)b.Clone(); / 梯度运算 Bitmap b9 = Gradient(Gradient(b1, b2), Gradient(b3, b4); Bitmap b10 = Gradient(Gradient(b5, b6), Gradient(b7, b8); b = Gradient(b9, b10); b1.Dispose(); b2.Dispose(); b3.Dispose(); b4.Dispose(); b5.Dispose(); b6.Dispose(); b7.Dispose(); b8.Dispose(); b9.Dispose(); b10.Dispose(); return b; / end of Kirsch / / 按 GaussLaplacian 算子进行边缘检测 / / / public Bitmap GaussLaplacian(Bitmap b) int, kernel = -2, -4, -4, -4, -2, -4, 0, 8, 0, -4, -4, 8, 24, 8, -4, -4, 0, 8, 0, -4, -2, -4, -4, -4, -2 ; MatrixNxN m = new MatrixNxN(); m.Kernel = kernel; return m.Convolute(b); / end of GaussLaplacian / / 按水平边缘检测算子进行边缘检测 / / / public Bitmap EdgeDetectHorizontal(Bitmap b) int, kernel = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ; MatrixNxN m = new MatrixNxN(); m.Kernel = kernel; return m.Convolute(b); / end of EdgeDetectHorizontal / / 按垂直边缘检测算子进行边缘检测 / / / public Bitmap EdgeDetectVertical(Bitmap b) int, kernel = 0, 0, -1, 0, 1, 0, 0, 0, 0, -1, 0, 1, 0, 0, 0, 0, -1, 0, 1, 0, 0, 0, 0, -1, 0, 1, 0, 0, 0, 0, -1, 0, 1, 0, 0, 0, 0, -1, 0, 1, 0, 0, 0, 0, -1, 0, 1, 0, 0, ; MatrixNxN m = new MatrixNxN(); m.Kernel = kernel; return m.Convolute(b); / end of EdgeDetectVertical / / 边缘增强 / / 位图流 / 阈值 / public Bitmap EdgeEnhance(Bitmap b, int threshold) int width = b.Width; int height = b.Height; Bitmap dstImage = (Bitmap)b.Clone(); BitmapData srcData = b.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.ReadOnly, PixelFormat.Format32bppArgb); BitmapData dstData = dstImage.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.WriteOnly, PixelFormat.Format32bppArgb); / 图像实际处理区域 / 不考虑最左 1 列和最右 1 列 / 不考虑最上 1 行和最下 1 行 int rectTop = 1; int rectBottom = height - 1; int rectLeft = 1; int rectRight = width - 1; unsafe byte* src = (byte*)srcData.Scan0; byte* dst = (byte*)dstData.Scan0; int stride = srcData.Stride; int offset = stride - width * BPP; int pixel = 0; int maxPixel = 0; / 指向第 1 行 src += stride; dst += stride; for (int y = rectTop; y rectBottom; y+) / 指向每行