public static Bitmap Step2(Bitmap bmp)
{
int[][] mat = ImageConvert.Bitmap2Mat(bmp);
ConvKernel kernel = ConvKernel.GetKMeanKernal(3);
Convolution conv = new Convolution();
conv.Calculate(mat, kernel, out mat);
Bitmap result = ImageConvert.Mat2Bitmap(mat);
return(result);
}
/// <summary>
/// foamliu, 2009/02/01, 用两个核进行卷积.
/// 主要用于边缘算子.
///
/// </summary>
/// <param name="input"></param>
/// <param name="kernel"></param>
/// <param name="output"></param>
public void CalculateEdge(int[][] input, ConvKernel kx, ConvKernel ky, out int[][] output, ConvNorm norm)
{
int width = input.Length;
int height = input[0].Length;
output = Util.BuildMatInt(width, height);
int m = kx.M;
int n = kx.N;
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
double Gx = 0;
double Gy = 0;
for (int j = -n; j <= n; j++)
{
for (int i = -m; i <= m; i++)
{
Gx += Util.GetPixel(input, x - i, y - j) * kx.Filter(i, j);
Gy += Util.GetPixel(input, x - i, y - j) * ky.Filter(i, j);
}
}
if (norm == ConvNorm.Norm_1)
{
// 1-范数 (1-norm)
output[x][y] = System.Convert.ToInt32(Math.Abs(Gx) + Math.Abs(Gy));
// foamliu, 2009/02/03, 用 2-norm 更准确, 但是我看不出差别,
// 而且1-范数较快.
}
else if (norm == ConvNorm.Norm_2)
{
// 2-范数 (2-norm)
output[x][y] = (int)(Math.Sqrt(Gx * Gx + Gy * Gy));
}
// foamliu, 2009/02/03, 这两个梯度可以用于求方向 theta:
// theta = arctan(Gy/Gx).
// 当 theta = 0 时是一条垂直边界, 左侧更暗.
// 梯度大的地方更亮.
}
}
}
/// <summary>
/// foamliu, 2009/01/29, k × k (k = 2n + 1) 均值核.
///
/// </summary>
/// <param name="k"></param>
/// <returns></returns>
public static ConvKernel GetKMeanKernal(int k)
{
int n = (k - 1) / 2;
double[][] filter = Util.BuildMat(k, k);
for (int i = 0; i <= 2 * n; i++)
{
for (int j = 0; j <= 2 * n; j++)
{
filter[i][j] = 1.0 / (k * k);
}
}
ConvKernel conv = new ConvKernel(n, n, filter);
return(conv);
}
public void Calculate(int[][] input, ConvKernel kernel, out int[][] output)
{
int width = input.Length;
int height = input[0].Length;
output = new int[width][];
for (int i = 0; i < width; i++)
{
output[i] = new int[height];
}
if (kernel == null)
{
return;
}
int m = kernel.M;
int n = kernel.N;
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
double sum = 0;
for (int j = -n; j <= n; j++)
{
for (int i = -m; i <= m; i++)
{
sum += Util.GetPixel(input, x - i, y - j) * kernel.Filter(i, j);
}
}
//output[x][y] = (int)Math.Abs(sum);
// foamliu, 2009/02/03, 这里不应取绝对值, 这么做使得在做图像锐化时犯了
// <<数字图像处理 "Digital Image Processing Using MATLAB">> 图3.16 (b) 一样的错误.
// 正确的做法是保持负值, 用正规化处理.
output[x][y] = System.Convert.ToInt32(sum);
}
}
}
