/// <summary>
/// foamliu, 2009/03/03, 边缘锐化.
/// 从原图像中减去拉普拉斯算子处理后的结果.
/// 我做的效果是图像锐化的同时产生了噪音. 与这个结果类似:
///
/// http://www.dfanning.com/ip_tips/sharpen.html
///
/// </summary>
/// <param name="bmp"></param>
/// <param name="type"></param>
/// <returns></returns>
public static Bitmap SharpenEdges(Bitmap bmp)
{
int width, height;
int[][][] mat, filtered = new int[3][][];
Bitmap newBmp;
ImageConvert.Bitmap2MatColor(bmp, out mat, out width, out height);
Convolution conv = new Convolution();
conv.Calculate(mat[0], ConvKernel.Laplacian_4, out filtered[0]);
conv.Calculate(mat[1], ConvKernel.Laplacian_4, out filtered[1]);
conv.Calculate(mat[2], ConvKernel.Laplacian_4, out filtered[2]);
NcvMatrix.MatSubtract(mat[0], filtered[0]);
NcvMatrix.MatSubtract(mat[1], filtered[1]);
NcvMatrix.MatSubtract(mat[2], filtered[2]);
GrayScaleImageLib.Normalize(mat[0]);
GrayScaleImageLib.Normalize(mat[1]);
GrayScaleImageLib.Normalize(mat[2]);
ImageConvert.Mat2BitmapColor(mat, width, height, out newBmp);
return(newBmp);
}
public static Bitmap Gaussian(Bitmap bmp, double sigma)
{
int width, height;
int[][][] img, newImg;
Bitmap newBmp;
ImageConvert.Bitmap2MatColor(bmp, out img, out width, out height);
newImg = new int[3][][];
int n = 4;
ConvKernel kernel = Util.GetGaussianKernal(sigma, n);
Convolution conv = new Convolution();
// RGB
conv.Calculate(img[0], kernel, out newImg[0]);
conv.Calculate(img[1], kernel, out newImg[1]);
conv.Calculate(img[2], kernel, out newImg[2]);
ImageConvert.Mat2BitmapColor(newImg, width, height, out newBmp);
return(newBmp);
}
/// <summary>
/// foamliu, 2009/02/01, 动态或自适应阈值化, 与局部背景差别大于 gdiff 的点进入ROI.
///
/// </summary>
/// <param name="mat"></param>
/// <param name="gdiff"></param>
/// <param name="k">用于模糊的K-均值的k</param>
public static void ThresholdDynamic(int[][] mat, int gdiff, int k)
{
int width = mat.Length;
int height = mat[0].Length;
int[][] newImg;
ConvKernel kernel = ConvKernel.GetKMeanKernal(k);
Convolution conv = new Convolution();
conv.Calculate(mat, kernel, out newImg);
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
int g = mat[x][y];
int newg = newImg[x][y];
if (Math.Abs(g - newg) >= gdiff)
{
// foamliu, 2009/01/31, 感兴趣的部分1.
mat[x][y] = 1;
}
else
{
// foamliu, 2009/01/31, 不感兴趣的部分0.
mat[x][y] = 0;
}
}
}
}
/// <summary>
/// foamliu, 2009/03/03, 试图整合这些滤波器函数.
///
/// </summary>
/// <param name="bmp"></param>
/// <param name="type"></param>
/// <returns></returns>
public static Bitmap Filter(Bitmap bmp, FilterType type)
{
int width, height;
int[][] mat, filtered;
Bitmap newBmp;
ImageConvert.Bitmap2Mat(bmp, out mat, out width, out height);
Convolution conv = new Convolution();
ConvKernel kernel = null;
switch (type)
{
case FilterType.Sobel_Gx:
kernel = ConvKernel.Sobel_Gx;
break;
case FilterType.Sobel_Gy:
kernel = ConvKernel.Sobel_Gy;
break;
case FilterType.Prewitt_Gx:
kernel = ConvKernel.Prewitt_Gx;
break;
case FilterType.Prewitt_Gy:
kernel = ConvKernel.Prewitt_Gy;
break;
case FilterType.Laplacian_4:
kernel = ConvKernel.Laplacian_4;
break;
case FilterType.Laplacian_8:
kernel = ConvKernel.Laplacian_8;
break;
default:
break;
}
conv.Calculate(mat, kernel, out filtered);
GrayScaleImageLib.Normalize(filtered);
ImageConvert.Mat2Bitmap(filtered, width, height, out newBmp);
return(newBmp);
}
public static int[][] Gaussian(int[][] mat, double sigma)
{
int[][] filtered;
int n = 4;
ConvKernel kernel = Util.GetGaussianKernal(sigma, n);
Convolution conv = new Convolution();
conv.Calculate(mat, kernel, out filtered);
GrayScaleImageLib.Normalize(filtered);
return(filtered);
}
public static Bitmap K_Mean(Bitmap bmp, int k)
{
int width, height;
int[][][] img, newImg;
Bitmap newBmp;
ImageConvert.Bitmap2MatColor(bmp, out img, out width, out height);
newImg = new int[3][][];
ConvKernel kernel = ConvKernel.GetKMeanKernal(k);
Convolution conv = new Convolution();
// RGB
conv.Calculate(img[0], kernel, out newImg[0]);
conv.Calculate(img[1], kernel, out newImg[1]);
conv.Calculate(img[2], kernel, out newImg[2]);
ImageConvert.Mat2BitmapColor(newImg, width, height, out newBmp);
return(newBmp);
}
public static Bitmap K_Mean(Bitmap bmp, int k)
{
int width, height;
int[][] img, filtered;
Bitmap newBmp;
ImageConvert.Bitmap2Mat(bmp, out img, out width, out height);
ConvKernel kernel = ConvKernel.GetKMeanKernal(k);
Convolution conv = new Convolution();
conv.Calculate(img, kernel, out filtered);
ImageConvert.Mat2Bitmap(filtered, width, height, out newBmp);
return(newBmp);
}
public static Bitmap SharpenMore(Bitmap bmp)
{
int width, height;
int[][] mat, filtered;
Bitmap newBmp;
ImageConvert.Bitmap2Mat(bmp, out mat, out width, out height);
Convolution conv = new Convolution();
conv.Calculate(mat, ConvKernel.Laplacian_8, out filtered);
NcvMatrix.MatSubtract(mat, filtered);
GrayScaleImageLib.Normalize(mat);
ImageConvert.Mat2Bitmap(mat, width, height, out newBmp);
return(newBmp);
}
public static Bitmap Gaussian(Bitmap bmp, double sigma)
{
int width, height;
int[][] img, filtered;
Bitmap newBmp;
ImageConvert.Bitmap2Mat(bmp, out img, out width, out height);
//double sigma = 1.0;
int n = 4;
ConvKernel kernel = Util.GetGaussianKernal(sigma, n);
Convolution conv = new Convolution();
conv.Calculate(img, kernel, out filtered);
GrayScaleImageLib.Normalize(filtered);
ImageConvert.Mat2Bitmap(filtered, width, height, out newBmp);
return(newBmp);
}
