public byte[] Denoise(byte[] data, int width, int height, int padding, out int it)
{
it = 0;
byte[,] arr = Utils.ByteArr1DTo2D(data, width, height);
bool[,] vi = (bool[, ])Array.CreateInstance(typeof(bool), height, width);
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
vi[i, j] = false;
}
}
List <Point> points = new List <Point>();
List <int> areas = new List <int>();
for (int i = padding; i < height - padding; ++i)
{
for (int j = padding; j < width - padding; ++j)
{
if (vi[i, j])
{
continue;
}
if (arr[i, j] == backgroundColor)
{
continue;
}
int sum = 0;
dfsSearch(arr, vi, i, j, width, height, padding, ref sum);
points.Add(new Point(j, i));
areas.Add(sum);
}
}
if (points.Count > 1)
{
Vector2[] vs = areas.Select(i => new Vector2(i, 0)).ToArray();
it = 0;
KMeansCluster result = KMeansClustering.AnnealCluster(vs, 2, out it);
int bigcluster = (result[0].Centroid.ModSqr() > result[1].Centroid.ModSqr()) ? 0 : 1;
for (int i = 0; i < points.Count; ++i)
{
if (result.ClusterIndex[i] != bigcluster)
{
//dfsColor(arr, points[i].Y, points[i].X, width, height, 1);
dfsClear(arr, points[i].Y, points[i].X, width, height, 1);
}
}
}
else
{
dfsColor(arr, points[0].Y, points[0].X, width, height, 1);
}
byte[] bytes = Utils.ByteArr2DTo1D(arr);
return(bytes);
}
public Image <Gray, byte> Process(Image <Gray, byte> src)
{
Connectivity.Connect8 = this.Connect8;
var cl = src.connectLevel(1, 8, 0).Domains.ToList();
var dst = src.Clone();
if (cl.Count > 1)
{
Vector2[] vs = cl.Select(i => new Vector2(i.Area, 0)).ToArray();
KMeansClustering.MaxIterate = this.MaxIteration;
var result = KMeansClustering.AnnealCluster(vs, 2);
int foreCluster = (result[0].Centroid.ModSqr() > result[1].Centroid.ModSqr()) ? 0 : 1;
for (int i = 0; i < cl.Count; ++i)
{
if (result.ClusterIndex[i] != foreCluster)
{
foreach (var p in cl[i].Points)
{
dst[p] = new Gray(0);
}
}
}
}
return(dst);
}
public Image <Gray, Byte> Denoise(Image <Gray, Byte> img, int padding, out int it)
{
it = 0;
var output = img.Clone();
int width = img.Width, height = img.Height;
if (padding > 0)
{
for (int p = 0; p < padding; ++p)
{
for (int i = 0; i < height; ++i)
{
// 左右两边
output[i, 0 + p] = ColorDefination.BackColor;
output[i, width - 1 - p] = ColorDefination.BackColor;
}
for (int j = 0; j < width; ++j)
{
// 上下两边
output[0 + p, j] = ColorDefination.BackColor;
output[height - 1 - p, j] = ColorDefination.BackColor;
}
}
} // 清除宽为padding的边框
bool[,] vi = (bool[, ])Array.CreateInstance(typeof(bool), height, width);
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
vi[i, j] = false;
}
} // 不知道会不会自动初始化成false，如果会，这段可以去掉
List <Point> points = new List <Point>();
List <int> areas = new List <int>();
for (int i = padding; i < height - padding; ++i)
{
for (int j = padding; j < width - padding; ++j)
{
if (vi[i, j])
{
continue;
}
if (img[i, j].Equals(ColorDefination.BackColor))
{
continue;
}
//int sum = 0;
//dfsSearch(img, vi, i, j, padding, ref sum);
Point p = new Point(j, i);
int sum = bfsSearch(img, vi, p, padding);
points.Add(p);
areas.Add(sum);
}
}
if (points.Count > 1)
{
//var vs = areas.Select(d => (double)d).ToArray();
//KMeans<double> kmeans = new KMeans<double>(vs, 2,
// (a, b) => Math.Abs(a - b),
// arr => arr.Average());
//var result = kmeans.AnnealCluster(
// (a, b) => a + b,
// (a, b) => a - b,
// (a, b) => a / b);
Vector2[] vs = areas.Select(i => new Vector2(i, 0)).ToArray();
var result = KMeansClustering.AnnealCluster(vs, 2, out it);
int foreCluster = (result[0].Centroid.ModSqr() > result[1].Centroid.ModSqr()) ? 0 : 1;
//int foreCluster = (result[0].Center > result[1].Center) ? 0 : 1;
for (int i = 0; i < points.Count; ++i)
{
if (result.ClusterIndex[i] != foreCluster)
{
bfsClear(output, points[i], padding);
}
}
}
else
{
//dfsColor(output, points[0].Y, points[0].X, padding);
}
return(output);
}