public void RasterBandPickValueTool()
{
GRasterLayer rasterLayer = new GRasterLayer(fullFilename);
IRasterBandCursorTool pRasterBandCursorTool = new GRasterBandCursorTool();
pRasterBandCursorTool.Visit(rasterLayer.BandCollection[0]);
double normalValue = pRasterBandCursorTool.PickNormalValue(100, 100);
//the col and row should be odd number
double[] rangeNormalValue = pRasterBandCursorTool.PickRangeNormalValue(100, 100, 3, 3);
double rawValue = pRasterBandCursorTool.PickRawValue(100, 100);
//the col and row should be odd number
double[] rangeRawValue = pRasterBandCursorTool.PickRangeRawValue(100, 100, 3, 3);
//
Assert.AreEqual(normalValue, 0.62204724409448819);
Assert.AreEqual(string.Join(",", rangeNormalValue), "0.645669291338583,0.637795275590551,0.661417322834646,0.614173228346457,0.622047244094488,0.645669291338583,0.614173228346457,0.618110236220472,0.622047244094488");
Assert.AreEqual(rawValue, 159);
Assert.AreEqual(string.Join(",", rangeRawValue), "165,163,169,157,159,165,157,158,159");
}
public JobCOVRaster(GRasterBand target1band, GRasterBand target2band)
{
_t = new Thread(() => {
IRasterBandCursorTool pRasterBandCursorTool1 = new GRasterBandCursorTool();
IRasterBandCursorTool pRasterBandCursorTool2 = new GRasterBandCursorTool();
pRasterBandCursorTool1.Visit(target1band);
pRasterBandCursorTool2.Visit(target2band);
//
int seed = 0;
int totalPixels = target1band.Width * target1band.Height;
Bitmap bitmap = new Bitmap(target1band.Width, target1band.Height);
Graphics g = Graphics.FromImage(bitmap);
//
for (int i = 0; i < target1band.Width; i++)
{
for (int j = 0; j < target1band.Height; j++)
{
double[] raw1 = pRasterBandCursorTool1.PickRangeRawValue(i, j, 3, 3);
double[] raw2 = pRasterBandCursorTool2.PickRangeRawValue(i, j, 3, 3);
double cov = ConvarianceIndex.CalcuteConvarianceIndex(raw1, raw2);
//拉伸-1 - 1
int gray = Convert.ToInt32((cov + 1.0) * 20);
Color c = Color.FromArgb(gray, gray, gray);
Pen p = new Pen(c);
SolidBrush brush = new SolidBrush(c);
g.FillRectangle(brush, new Rectangle(i, j, 1, 1));
Process = (double)(seed++) / totalPixels;
}
}
//save result
string fullFileName = Directory.GetCurrentDirectory() + @"\tmp\" + DateTime.Now.ToFileTimeUtc() + ".png";
bitmap.Save(fullFileName);
//
Summary = "计算完成";
Complete = true;
OnTaskComplete?.Invoke(Name, fullFileName);
});
}
public static (int[, ] matrix, double kappa, int actionsNumber, double oa) Calcute(GRasterLayer truthLayer, GRasterLayer predLayer)
{
//statical label band graph
IRasterBandStatisticTool pBandStaticTool = new GRasterBandStatisticTool();
pBandStaticTool.Visit(truthLayer.BandCollection[0]);
Dictionary <int, List <Point> > memory = pBandStaticTool.StaisticalRawGraph;
//key index
List <int> Keys = memory.Keys.ToList();
int actionsNumber = Keys.Count;
int[,] matrix = new int[actionsNumber, actionsNumber];
IRasterBandCursorTool pBandCursorTool = new GRasterBandCursorTool();
pBandCursorTool.Visit(predLayer.BandCollection[0]);
//
pBandStaticTool.Visit(predLayer.BandCollection[0]);
var m = pBandStaticTool.StaisticalRawGraph;
//
for (int i = 0; i < actionsNumber; i++)
{
int key = Keys[i];
List <Point> points = memory[key];
//计算realKey类分类结果,存入混淆矩阵
points.ForEach(p =>
{
int rawType = (int)pBandCursorTool.PickRawValue(p.X, p.Y);
int indexType = Keys.IndexOf(rawType);
if (indexType != -1)
{
matrix[i, indexType]++;
}
});
}
// Create a new multi-class Confusion Matrix
var cm = new GeneralConfusionMatrix(matrix);
//
int totalNum = cm.NumberOfSamples;
//p0
double p0 = 0;
for (int i = 0; i < actionsNumber; i++)
{
p0 += Convert.ToDouble(matrix[i, i]);
}
//pc
double pc = 0;
for (int i = 0; i < actionsNumber; i++)
{
pc += Convert.ToDouble(cm.ColumnTotals[i]) * Convert.ToDouble(cm.RowTotals[i]);
}
pc = pc / totalNum;
//
double kappa = (p0 - pc) / (totalNum - pc);
double oa = p0 / totalNum;
//
return(matrix, kappa, actionsNumber, oa);
}
