public override void SetAlpha(bool isVisible, OnTaskComplete onTask = null)
{
float to = 0f, from = 0f;
// Desaparecer o aparecer elemento dependiendo de su estado Focus
float transparency = (matBag.GetFloat("Boolean_DAFC24A6") == 1f) ? 1f : focusTransparency;
float currTransparency = matBag.GetVector("Vector2_B5455978").x;
to = (isVisible) ? transparency : 0f;
from = (isVisible) ? 0f : currTransparency;
LeanTween.value(this.gameObject, (f) =>
{
// Cambiar alpha
matBag.SetVector("Vector2_B5455978", new Vector4(f, 0f, 0f, 0f));
}, from, to, TimeTransition).setOnComplete(() =>
{
IsShow = isVisible;
if (onTask != null)
{
onTask();
}
});
}
public override void SetAlpha(bool isVisible, OnTaskComplete onTask = null)
{
float to = 0f, from = 0f;
switch (ModeView)
{
case ModeBagView.Normal:
{
to = (isVisible) ? 0f : 1f;
from = (isVisible) ? 1f : 0f;
LeanTween.value(this.gameObject, (f) =>
{
// Cambiar alpha
matBag.SetFloat("Vector1_FAB99759", f);
}, from, to, TimeTransition).setOnComplete(() =>
{
IsShow = isVisible;
if (onTask != null)
{
onTask();
}
});
break;
}
case ModeBagView.Focus:
{
break;
}
}
}
/// <summary>
/// 初始化一个任务
/// </summary>
/// <param name="cfgFile">任务配置文件路径，如果已经初始化，则返回任务ID</param>
/// <returns>返回任务对象</returns>
public static Task InitTask(string cfgFile)
{
var fullPath = Snail.IO.PathUnity.GetFullPath(cfgFile);
if (string.IsNullOrEmpty(fullPath))
{
return(null);
}
Task task = null;
lock (LockObj)
{
if (!BufferTasks.ContainsKey(fullPath))
{
task = new Task(fullPath);
task.OnStart += (sender, e) =>
{
OnTaskRunning?.Invoke(task, new TaskEventArgs()
{
Task = task
});
};
task.OnStop += (sender, e) =>
{
OnTaskComplete?.Invoke(task, new TaskEventArgs()
{
Task = task
});
};
BufferTasks.Add(fullPath, task);
}
}
return(BufferTasks[fullPath]);
}
/// <summary>
/// 初始化一个任务
/// </summary>
/// <param name="cfgFile">任务配置文件路径，如果已经初始化，则返回任务ID</param>
/// <returns>返回任务对象</returns>
public static Task InitTask(string cfgFile)
{
var fullPath = SnailCore.IO.PathUnity.GetFullPath(cfgFile);
if (string.IsNullOrEmpty(fullPath))
{
throw new Exception("could not find the file with path:" + cfgFile);
}
Task task = null;
lock (LockObj)
{
if (!BufferTasks.ContainsKey(fullPath))
{
task = new Task(fullPath);
foreach (var kv in BufferTasks)
{
if (kv.Value.TaskId == task.TaskId)
{
throw new Exception(string.Format("任务id:{0},重复出现,请检查配置,并重新启动任务.", task.TaskId));
}
}
task.OnStart += (sender, e) =>
{
try
{
OnTaskRunning?.Invoke(sender, e);
}
catch (Exception ex)
{
LoggerProxy.Error(LogSource, "invoke OnTaskRunning failed.", ex);
}
};
task.OnStop += (sender, e) =>
{
try
{
OnTaskComplete?.Invoke(sender, e);
}
catch (Exception ex)
{
LoggerProxy.Error(LogSource, "invoke OnTaskComplete failed.", ex);
}
};
task.OnError += (sender, e) =>
{
try
{
OnTaskError?.Invoke(sender, e);
}
catch (Exception ex)
{
LoggerProxy.Error(LogSource, "invoke OnTaskError failed.", ex);
}
};
BufferTasks.Add(fullPath, task);
}
}
return(BufferTasks[fullPath]);
}
private void OnTaskCompleted()
{
///rasie task begin evnet
if (OnTaskComplete != null)
{
OnTaskComplete.Invoke(this, new QueueTaskEventArgs(this.CurrentJob));
}
}
public JobCNNClassify(GRasterLayer featureRasterLayer, GRasterLayer labelRasterLayer, int epochs, int model, int width, int height, int channel)
{
_t = new Thread(() => {
ImageClassifyEnv env = new ImageClassifyEnv(featureRasterLayer, labelRasterLayer);
CNN cnn = new CNN(new int[] { channel, width, height }, env.ActionNum);
//training
Summary = "模型训练中";
for (int i = 0; i < epochs; i++)
{
int batchSize = cnn.BatchSize;
var(states, labels) = env.RandomEval(batchSize);
double[][] inputX = new double[batchSize][];
for (int j = 0; j < batchSize; j++)
{
inputX[j] = states[j];
}
double loss = cnn.Train(inputX, labels);
Process = (double)i / epochs;
}
//classify
Summary = "分类应用中";
IRasterLayerCursorTool pRasterLayerCursorTool = new GRasterLayerCursorTool();
pRasterLayerCursorTool.Visit(featureRasterLayer);
//GDI graph
Bitmap classificationBitmap = new Bitmap(featureRasterLayer.XSize, featureRasterLayer.YSize);
Graphics g = Graphics.FromImage(classificationBitmap);
//
int seed = 0;
int totalPixels = featureRasterLayer.XSize * featureRasterLayer.YSize;
//应用dqn对图像分类
for (int i = 0; i < featureRasterLayer.XSize; i++)
{
for (int j = 0; j < featureRasterLayer.YSize; j++)
{
//get normalized input raw value
double[] normal = pRasterLayerCursorTool.PickNormalValue(i, j);
//}{debug
double[] action = cnn.Predict(normal);
//convert action to raw byte value
int gray = env.RandomSeedKeys[NP.Argmax(action)];
//后台绘制，报告进度
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));
//report progress
Process = (double)(seed++) / totalPixels;
}
}
//保存结果至tmp
string fullFileName = Directory.GetCurrentDirectory() + @"\tmp\" + DateTime.Now.ToFileTimeUtc() + ".png";
classificationBitmap.Save(fullFileName);
//complete
Summary = "CNN训练分类完成";
Complete = true;
OnTaskComplete?.Invoke(Name, fullFileName);
});
}
public override void Hide(OnTaskComplete onTask = null)
{
if (m_currModeView != ModeView)
{
return;
}
SetAlpha(false, onTask);
}
protected virtual void NotifyCompleted()
{
if (State == TaskState.Completed)
{
if (OnTaskComplete != null)
{
OnTaskComplete.Invoke(this);
}
CompleteWasNotified = true;
}
}
/// <summary>
/// DQN classify task
/// </summary>
/// <param name="featureRasterLayer"></param>
/// <param name="labelRasterLayer"></param>
/// <param name="epochs"></param>
public JobDQNClassify(GRasterLayer featureRasterLayer, GRasterLayer labelRasterLayer, int epochs = 3000)
{
_t = new Thread(() =>
{
ImageClassifyEnv env = new ImageClassifyEnv(featureRasterLayer, labelRasterLayer);
_dqn = new DQN(env);
_dqn.SetParameters(epochs: epochs, gamma: _gamma);
_dqn.OnLearningLossEventHandler += _dqn_OnLearningLossEventHandler;
//training
Summary = "模型训练中";
_dqn.Learn();
//classification
Summary = "分类应用中";
IRasterLayerCursorTool pRasterLayerCursorTool = new GRasterLayerCursorTool();
pRasterLayerCursorTool.Visit(featureRasterLayer);
Bitmap classificationBitmap = new Bitmap(featureRasterLayer.XSize, featureRasterLayer.YSize);
Graphics g = Graphics.FromImage(classificationBitmap);
int seed = 0;
int totalPixels = featureRasterLayer.XSize * featureRasterLayer.YSize;
for (int i = 0; i < featureRasterLayer.XSize; i++)
{
for (int j = 0; j < featureRasterLayer.YSize; j++)
{
//get normalized input raw value
double[] normal = pRasterLayerCursorTool.PickNormalValue(i, j);
var(action, q) = _dqn.ChooseAction(normal);
//convert action to raw byte value
int gray = _dqn.ActionToRawValue(NP.Argmax(action));
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));
//report progress
Process = (double)(seed++) / totalPixels;
}
}
//save result
string fullFileName = Directory.GetCurrentDirectory() + @"\tmp\" + DateTime.Now.ToFileTimeUtc() + ".png";
classificationBitmap.Save(fullFileName);
//complete
Summary = "DQN训练分类完成";
Complete = true;
OnTaskComplete?.Invoke(Name, fullFileName);
});
}
public IJobRPCRectify(double[] a, double[] b, double[] c, double[] d, Dictionary <string, double> paramaters, List <string> rawBinRasterFullFilenames)
{
_t = new Thread(() =>
{
using (IRasterRPCTool pRasterRPCTool = new GRasterRPCTool(a, b, c, d, paramaters))
{
for (int i = 0; i < rawBinRasterFullFilenames.Count; i++)
{
string rasterFilename = rawBinRasterFullFilenames[i];
GRasterLayer rasterLayer = new GRasterLayer(rasterFilename);
Summary = string.Format("total:{1}/{2}, RPC rectify for {0} is in progress.... ", rasterLayer.Name, i + 1, rawBinRasterFullFilenames.Count);
pRasterRPCTool.Visit(rasterLayer);
pRasterRPCTool.DoRPCRectify();
Process = i / (double)rawBinRasterFullFilenames.Count;
}
OnTaskComplete?.Invoke(Name);
}
});
}
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);
});
}
/// <summary>
///
/// </summary>
/// <param name="fullFilename"></param>
public JobReadRaster(string fullFilename)
{
_t = new Thread(() =>
{
Dictionary <string, Bitmap2> dict = new Dictionary <string, Bitmap2>();
string name = Path.GetFileNameWithoutExtension(fullFilename);
GRasterLayer rasterLayer = new GRasterLayer(fullFilename);
//reading
Summary = "数据读取中";
for (int i = 0; i < rasterLayer.BandCount; i++)
{
GRasterBand band = rasterLayer.BandCollection[i];
band.BandName = name + "_band_" + i;
Bitmap2 bmp2 = new Bitmap2(bmp: band.GrayscaleImage, name: band.BandName, gdalBand: band, gdalLayer: rasterLayer);
dict[band.BandName] = bmp2;
Process = (double)(i + 1) / rasterLayer.BandCount;
}
//read complete
Summary = "读取完毕";
Complete = true;
OnTaskComplete?.Invoke(Name, name, dict, rasterLayer);
});
}
/// <summary>
///
/// </summary>
/// <param name="treeCount"></param>
/// <param name="fullFilename"></param>
/// <param name="rasterLayer"></param>
public JobRFClassify(int treeCount, string fullFilename, GRasterLayer rasterLayer)
{
_t = new Thread(() =>
{
RF rf = new RF(treeCount);
//training
Summary = "随机森林训练中";
using (StreamReader sr = new StreamReader(fullFilename))
{
List <List <double> > inputList = new List <List <double> >();
List <int> outputList = new List <int>();
string text = sr.ReadLine().Replace("\t", ",");
do
{
string[] rawdatas = text.Split(',');
outputList.Add(Convert.ToInt32(rawdatas.Last()));
List <double> inputItem = new List <double>();
for (int i = 0; i < rawdatas.Length - 1; i++)
{
inputItem.Add(Convert.ToDouble(rawdatas[i]));
}
inputList.Add(inputItem);
text = sr.ReadLine();
} while (text != null);
double[][] inputs = new double[inputList.Count][];
int[] outputs = outputList.ToArray();
for (int i = 0; i < inputList.Count; i++)
{
inputs[i] = inputList[i].ToArray();
}
rf.Train(inputs, outputs);
}
//image classify
Summary = "分类应用中";
IRasterLayerCursorTool pRasterLayerCursorTool = new GRasterLayerCursorTool();
pRasterLayerCursorTool.Visit(rasterLayer);
//GDI graph
Bitmap classificationBitmap = new Bitmap(rasterLayer.XSize, rasterLayer.YSize);
Graphics g = Graphics.FromImage(classificationBitmap);
//
int seed = 0;
int totalPixels = rasterLayer.XSize * rasterLayer.YSize;
Process = 0.0;
//应用dqn对图像分类
for (int i = 0; i < rasterLayer.XSize; i++)
{
for (int j = 0; j < rasterLayer.YSize; j++)
{
//get normalized input raw value
double[] raw = pRasterLayerCursorTool.PickRawValue(i, j);
double[][] inputs = new double[1][];
inputs[0] = raw;
//}{debug
int[] ouputs = rf.Predict(inputs);
int gray = ouputs[0];
//convert action to raw byte value
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));
//report progress
Process = (double)seed++ / totalPixels;
}
}
//保存结果至tmp
string fullFileName = Directory.GetCurrentDirectory() + @"\tmp\" + DateTime.Now.ToFileTimeUtc() + ".png";
classificationBitmap.Save(fullFileName);
//rf complete
Summary = "RF训练分类完成";
Complete = true;
OnTaskComplete?.Invoke(Name, fullFileName);
});
}
public override void Hide(OnTaskComplete onTask = null)
{
SetAlpha(false);
}
public virtual void Hide(OnTaskComplete onTask = null)
{
}
public virtual void SetAlpha(bool isVisible, OnTaskComplete onTask = null)
{
}
