Win8 Metro(C#)数字图像处理--2.52图像K均值聚类
原文:Win8 Metro(C#)数字图像处理--2.52图像K均值聚类
[函数名称]
图像KMeans聚类 KMeansCluster(WriteableBitmap src,int k)
/// <summary>
/// KMeans Cluster process.
/// </summary>
/// <param name="src">The source image.</param>
/// <param name="k">Cluster threshould, from 2 to 255.</param>
/// <returns></returns>
public static WriteableBitmap KMeansCluster(WriteableBitmap src,int k)////KMeansCluster
{
if (src != null)
{
int w = src.PixelWidth;
int h = src.PixelHeight;
WriteableBitmap dstImage = new WriteableBitmap(w, h);
byte[] temp = src.PixelBuffer.ToArray();
byte[] tempMask = (byte[])temp.Clone();
int b = 0, g = 0, r = 0;
//定义灰度图像信息存储变量
byte[] imageData = new byte[w * h];
//定义聚类均值存储变量(存储每一个聚类的均值)
double[] meanCluster = new double[k];
//定义聚类标记变量(标记当前像素属于哪一类)
int[] markCluster = new int[w * h];
//定义聚类像素和存储变量(存储每一类像素值之和)
double[] sumCluster = new double[k];
//定义聚类像素统计变量(存储每一类像素的数目)
int []countCluster = new int[k];
//定义聚类RGB分量存储变量(存储每一类的RGB三分量大小)
int[] sumR = new int[k];
int[] sumG = new int[k];
int[] sumB = new int[k];
//临时变量
int sum = 0;
//循环控制变量
bool s = true;
double[] mJduge = new double[k];
double tempV = 0;
int cou = 0;
//获取灰度图像信息
for (int j = 0; j < h; j++)
{
for (int i = 0; i < w; i++)
{
b = tempMask[i * 4 + j * w * 4];
g = tempMask[i * 4 + 1 + j * w * 4];
r = tempMask[i * 4 + 2 + j * w * 4];
imageData[i + j * w] = (byte)(b * 0.114 + g * 0.587 + r * 0.299);
}
}
while (s)
{
sum = 0;
//初始化聚类均值
for (int i = 0; i < k; i++)
{
meanCluster[i] = i * 255.0 / (k - 1);
}
//计算聚类归属
for (int i = 0; i < imageData.Length; i++)
{
tempV = Math.Abs((double)imageData[i] - meanCluster[0]);
cou = 0;
for (int j = 1; j < k; j++)
{
double t = Math.Abs((double)imageData[i] - meanCluster[j]);
if (tempV > t)
{
tempV = t;
cou = j;
}
}
countCluster[cou]++;
sumCluster[cou] += (double)imageData[i];
markCluster[i] = cou;
}
//更新聚类均值
for (int i = 0; i < k; i++)
{
meanCluster[i] = sumCluster[i] / (double)countCluster[i];
sum += (int)(meanCluster[i] - mJduge[i]);
mJduge[i] = meanCluster[i];
}
if (sum == 0)
{
s = false;
}
}
//计算聚类RGB分量
for (int j = 0; j < h; j++)
{
for (int i = 0; i < w; i++)
{
sumB[markCluster[i + j * w]] += tempMask[i * 4 + j * w * 4];
sumG[markCluster[i + j * w]] += tempMask[i * 4 + 1 + j * w * 4];
sumR[markCluster[i + j * w]] += tempMask[i * 4 + 2 + j * w * 4];
}
}
for (int j = 0; j < h; j++)
{
for (int i = 0; i < w; i++)
{
temp[i * 4 + j * 4 * w] = (byte)(sumB[markCluster[i + j * w]] / countCluster[markCluster[i + j * w]]);
temp[i * 4 + 1 + j * 4 * w] = (byte)(sumG[markCluster[i + j * w]] / countCluster[markCluster[i + j * w]]);
temp[i * 4 + 2 + j * 4 * w] = (byte)(sumR[markCluster[i + j * w]] / countCluster[markCluster[i + j * w]]);
}
}
Stream sTemp = dstImage.PixelBuffer.AsStream();
sTemp.Seek(0, SeekOrigin.Begin);
sTemp.Write(temp, 0, w * 4 * h);
return dstImage;
}
else
{
return null;
}
}
[函数名称]
图像KMeans聚类 KMeansCluster(WriteableBitmap src,int k)
/// <summary>
/// KMeans Cluster process.
/// </summary>
/// <param name="src">The source image.</param>
/// <param name="k">Cluster threshould, from 2 to 255.</param>
/// <returns></returns>
public static WriteableBitmap KMeansCluster(WriteableBitmap src,int k)////KMeansCluster
{
if (src != null)
{
int w = src.PixelWidth;
int h = src.PixelHeight;
WriteableBitmap dstImage = new WriteableBitmap(w, h);
byte[] temp = src.PixelBuffer.ToArray();
byte[] tempMask = (byte[])temp.Clone();
int b = 0, g = 0, r = 0;
//定义灰度图像信息存储变量
byte[] imageData = new byte[w * h];
//定义聚类均值存储变量(存储每一个聚类的均值)
double[] meanCluster = new double[k];
//定义聚类标记变量(标记当前像素属于哪一类)
int[] markCluster = new int[w * h];
//定义聚类像素和存储变量(存储每一类像素值之和)
double[] sumCluster = new double[k];
//定义聚类像素统计变量(存储每一类像素的数目)
int []countCluster = new int[k];
//定义聚类RGB分量存储变量(存储每一类的RGB三分量大小)
int[] sumR = new int[k];
int[] sumG = new int[k];
int[] sumB = new int[k];
//临时变量
int sum = 0;
//循环控制变量
bool s = true;
double[] mJduge = new double[k];
double tempV = 0;
int cou = 0;
//获取灰度图像信息
for (int j = 0; j < h; j++)
{
for (int i = 0; i < w; i++)
{
b = tempMask[i * 4 + j * w * 4];
g = tempMask[i * 4 + 1 + j * w * 4];
r = tempMask[i * 4 + 2 + j * w * 4];
imageData[i + j * w] = (byte)(b * 0.114 + g * 0.587 + r * 0.299);
}
}
while (s)
{
sum = 0;
//初始化聚类均值
for (int i = 0; i < k; i++)
{
meanCluster[i] = i * 255.0 / (k - 1);
}
//计算聚类归属
for (int i = 0; i < imageData.Length; i++)
{
tempV = Math.Abs((double)imageData[i] - meanCluster[0]);
cou = 0;
for (int j = 1; j < k; j++)
{
double t = Math.Abs((double)imageData[i] - meanCluster[j]);
if (tempV > t)
{
tempV = t;
cou = j;
}
}
countCluster[cou]++;
sumCluster[cou] += (double)imageData[i];
markCluster[i] = cou;
}
//更新聚类均值
for (int i = 0; i < k; i++)
{
meanCluster[i] = sumCluster[i] / (double)countCluster[i];
sum += (int)(meanCluster[i] - mJduge[i]);
mJduge[i] = meanCluster[i];
}
if (sum == 0)
{
s = false;
}
}
//计算聚类RGB分量
for (int j = 0; j < h; j++)
{
for (int i = 0; i < w; i++)
{
sumB[markCluster[i + j * w]] += tempMask[i * 4 + j * w * 4];
sumG[markCluster[i + j * w]] += tempMask[i * 4 + 1 + j * w * 4];
sumR[markCluster[i + j * w]] += tempMask[i * 4 + 2 + j * w * 4];
}
}
for (int j = 0; j < h; j++)
{
for (int i = 0; i < w; i++)
{
temp[i * 4 + j * 4 * w] = (byte)(sumB[markCluster[i + j * w]] / countCluster[markCluster[i + j * w]]);
temp[i * 4 + 1 + j * 4 * w] = (byte)(sumG[markCluster[i + j * w]] / countCluster[markCluster[i + j * w]]);
temp[i * 4 + 2 + j * 4 * w] = (byte)(sumR[markCluster[i + j * w]] / countCluster[markCluster[i + j * w]]);
}
}
Stream sTemp = dstImage.PixelBuffer.AsStream();
sTemp.Seek(0, SeekOrigin.Begin);
sTemp.Write(temp, 0, w * 4 * h);
return dstImage;
}
else
{
return null;
}
}