[函数名称]

Hough 变换直线检测         HoughLineDetect(WriteableBitmap src, int threshould)

[算法说明]

  Hough变换是数字图像处理中一种常用的几何形状识别方法，它可以识别直线，圆，椭圆，弧线等

等几何形状，其基本原理是利用图像二维空间和Hough参数空间的点-线对偶性，把图像空间中的形

状检测问题转换到Hough的参数空间中去，最终以寻找参数空间中的峰值问题，得到形状检测的最优

结果。

       /// <summary>
        /// Hough transform of line detectting process.
        /// </summary>
        /// <param name="src">The source image.</param>
        /// <param name="threshould">The threshould to adjust the number of lines.</param>
        /// <returns></returns>
        public static WriteableBitmap HoughLineDetect(WriteableBitmap src, int threshould)////2 Hough 变换直线检测
        {
            if (src != null)
            {
                int w = src.PixelWidth;
                int h = src.PixelHeight;
                WriteableBitmap srcImage = new WriteableBitmap(w, h);
                byte[] temp = src.PixelBuffer.ToArray();
                int roMax = (int)Math.Sqrt(w * w + h * h) + 1;
                int[,] mark = new int[roMax, 180];
                double[] theta = new double[180];
                for (int i = 0; i < 180; i++)
                {
                    theta[i] = (double)i * Math.PI / 180.0;
                }
                double roValue = 0.0;
                int transValue=0;
                for (int y = 0; y < h; y++)
                {
                    for (int x = 0; x < w; x++)
                    {
                        if (temp[x * 4 + y * w*4] == 0)
                        {
                            for (int k = 0; k < 180; k++)
                            {
                                roValue = (double)x * Math.Cos(theta[k]) + (double)y * Math.Sin(theta[k]);
                                transValue = (int)Math.Round(roValue / 2 + roMax / 2);
                                mark[transValue, k]++;
                            }
                        }
                    }
                }
                for (int y = 0; y < h; y++)
                {
                    for (int x = 0; x < w; x++)
                    {
                        int T = x * 4 + y * w * 4;
                        if (temp[T] == 0)
                        {
                            for (int k = 0; k < 180; k++)
                            {
                                roValue = (double)x * Math.Cos(theta[k]) + (double)y * Math.Sin(theta[k]);
                                transValue = (int)Math.Round(roValue / 2 + roMax / 2);
                                if (mark[transValue, k] > threshould)
                                {
                                    temp[T + 2] = (byte)255;
                                }
                            }
                        }
                    }
                }
                Stream sTemp = srcImage.PixelBuffer.AsStream();
                sTemp.Seek(0, SeekOrigin.Begin);
                sTemp.Write(temp, 0, w * 4 * h);
                return srcImage;
            }
            else
            {
                return null;
            }
        }
<strong><span style="font-size:14px;">[图像效果]</span></strong>

注意：图中没有标红的线，是因为threshold=80，如果这个值改变，会影响检测结果，这个值足够小，另外两条直线也将被标红。