Win8 Metro(C#)數字圖像處理--2.38Hough變換直線檢測

原文: Win8 Metro(C#)數字圖像處理--2.38Hough變換直線檢測



[函數名稱]

Hough 變換直線檢測         HoughLineDetect(WriteableBitmap src, int threshould)

[算法說明]

  Hough變換是數字圖像處理中一種經常使用的幾何形狀識別方法，它能夠識別直線，圓，橢圓，弧線等

等幾何形狀，其基本原理是利用圖像二維空間和Hough參數空間的點-線對偶性，把圖像空間中的形

狀檢測問題轉換到Hough的參數空間中去，最終以尋找參數空間中的峯值問題，獲得形狀檢測的最優

結果。

/// <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 變換直線檢測
        {
            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;">[圖像效果]</span></strong>

注意：圖中沒有標紅的線，是由於threshold=80，若是這個值改變，會影響檢測結果，這個值足夠小，另外兩條直線也將被標紅。