public Bitplane(Bitplane bitplane)
{
Width = bitplane.Width;
Height = bitplane.Height;
for (int y = 0; y < this.Height; ++y)
{
for (int x = 0; x < this.Width; ++x)
{
SetPixel(x, y, bitplane.GetPixel(x, y));
}
}
}
/// <summary>
/// 应用深度学习模型进行分类
/// </summary>
/// <param name="rasterLayer"></param>
private void RunClassify(GRasterLayer rasterLayer, bool useSLIC, string pbName, string centerName, string labelName)
{
//判断图层结构，选用不同的tensor输入
ShapeEnum shapeEuum;
if (rasterLayer.BandCount == 130)
{
shapeEuum = ShapeEnum.THIRTEEN_TEN;
}
else if (rasterLayer.BandCount == 100)
{
shapeEuum = ShapeEnum.TEN_TEN;
}
else if (rasterLayer.BandCount == 64)
{
shapeEuum = ShapeEnum.EIGHT_EIGHT;
}
else
{
shapeEuum = ShapeEnum.TEN_TEN;
}
//构建结果图层，用于动态绘制
Bitmap bmp = new Bitmap(rasterLayer.XSize, rasterLayer.YSize);
string nodeName = rasterLayer.Name + "结果图层";
TreeNode childrenNode = new TreeNode(nodeName);
_imageDic.Add(nodeName, new Bitmap2(name: nodeName, bmp: bmp));
Invoke(new UpdateTreeNodeHandler(UpdateTreeNode), null, childrenNode);
//获取波段
TensorflowBootstrap model = new TensorflowBootstrap(pbName);
//判断是否基于超像素
if (!useSLIC)
{
for (int i = 0; i < rasterLayer.XSize; i++)
{
for (int j = 0; j < rasterLayer.YSize; j++)
{
float[] input = rasterLayer.GetPixelFloat(i, j).ToArray();
long classified = model.Classify(input, shapeEuum);
Invoke(new PaintPointHandler(PaintPoint), bmp, i, j, Convert.ToByte(classified * 15));
Invoke(new UpdateStatusLabelHandler(UpdateStatusLabel), "应用分类中，总进度：" + i + "列" + j + "行", STATUE_ENUM.WARNING);
}
}
}
else
{
//基于slic的超像素绘制方法
using (StreamReader sr_center = new StreamReader(centerName))
using (StreamReader sr_label = new StreamReader(labelName))
{
Center[] centers = SuperPixelSegment.ReadCenter(sr_center.ReadToEnd());
Bitplane labels = SuperPixelSegment.ReadLabel(sr_label.ReadToEnd());
int[] mask = new int[] { 1, 1, 1, 1, 1, 1, 1, 1, 1 };
for (int i = 0; i < centers.Length; i++)
{
Center center = centers[i];
float[] input = rasterLayer.GetPixelFloatWidthConv((int)center.X, (int)center.Y, mask).ToArray();
long classified = model.Classify(input, shapeEuum);
center.L = classified * 15;
center.A = classified * 15;
center.B = classified * 15;
Invoke(new UpdateStatusLabelHandler(UpdateStatusLabel), "已处理第" + i + "/" + centers.Length + "个中心", STATUE_ENUM.WARNING);
}
//遍历图片进行绘制
for (int i = 0; i < rasterLayer.XSize; i++)
{
for (int j = 0; j < rasterLayer.YSize; j++)
{
Invoke(new PaintPointHandler(PaintPoint), bmp, i, j, Convert.ToByte(centers[(int)Math.Floor(labels.GetPixel(i, j))].L));
}
}
}
}
}
