/// <summary>
/// 背光不规则区域基准值保存
/// </summary>
/// <param name="index"></param>
/// <param name="cellName"></param>
/// <param name="htResult"></param>
/// <returns></returns>
public StateComprehensive_enum CalibStdValue(int index, string cellName, out Hashtable htResult)
{
htResult = null;
try
{
StateComprehensive_enum stateComprehensive_e = g_BaseDealComprehensive.DealComprehensivePosNoDisplay(
g_UCDisplayCamera, g_HtUCDisplay, 1, out htResult);
ResultBlob resultBlob = (ResultBlob)htResult[cellName];
if (!DealTypeResult(resultBlob))
{
ShowState(string.Format("精定位自动标定第{0}点拍照失败", index + 1));
return(StateComprehensive_enum.False);
}
//保存当前匹配结果
pt2Calib[index] = new Point2D(resultBlob.X, resultBlob.Y);
if (index == 1)
{
LogicRobot.L_I.WriteRobotCMD(ModelParams.cmd_PreciseCalib2);
//第二次的话保存基准值到算子中
if (!(RecordStdValue(cellName, pt2Calib[0], pt2Calib[1])))
{
return(StateComprehensive_enum.False);
}
}
else
{
ShowState(string.Format("相机{0}基准值标定第1点计算成功", g_NoCamera));
LogicRobot.L_I.WriteRobotCMD(ModelParams.cmd_PreciseCalib1);
}
return(StateComprehensive_enum.True);
}
catch (Exception ex)
{
Log.L_I.WriteError(NameClass, ex);
return(StateComprehensive_enum.False);
}
}
public void Run()
{
/* Read from the corpus */
var corpusBlob = m_context.CorpusBlobRepository
.GetByCorpusID(m_corpusID);
/* Iterate over all corpus content */
m_progress = 0.0f;
var dictionary = new SortedDictionary <string, RankFrequencyPair>();
for (int i = 0; i < corpusBlob.Content.Count(); ++i)
{
var cont = corpusBlob.Content[i];
/* Note that we only have the content hash at this point. We
* must retrieve the actual content from the
* ContentBlobRepository. */
var content =
m_context.ContentBlobRepository.GetByHash(cont.Hash);
var text = content.Contents;
/* TODO: We split the text into words and compute the frequency
* of each */
/* From: http://stackoverflow.com/a/16734675 */
var punctuation = text.Where(Char.IsPunctuation).Distinct().ToArray();
var words = text.Split().Select(x => x.Trim(punctuation));
foreach (var word in words)
{
if (!dictionary.ContainsKey(word))
{
dictionary.Add(word, new RankFrequencyPair(
word, /* name */
-1, /* rank */
0 /* frequency */
));
}
dictionary[word].Frequency += 1;
}
m_progress = 0.9f * ((float)i / (float)corpusBlob.Content.Count());
}
/* We sort the words by frequency to determine the rank of each
* word */
var rankedWords = dictionary.Values.OrderBy(word => word.Frequency);
m_progress = 0.93f;
int j = 0;
foreach (var word in rankedWords)
{
word.Rank = rankedWords.Count() - j;
j += 1;
}
m_progress = 0.96f;
/* TODO: Implement thread safety here */
m_words = new List <RankFrequencyPair>(rankedWords);
m_progress = 1.0f;
/* Store the result in the Merkle tree */
var resultBlob = new ResultBlob(this.Result);
resultBlob.Children = new List <MerkleNode> {
corpusBlob
};
resultBlob.Pinned = true; /* Not subject to garbage collection */
m_context.ResultBlobRepository.Add(resultBlob);
this.Status = "done";
}
public bool BlobAngleCalc(string cellName, out Hashtable htResult)
{
htResult = null;
double[] dblResult = new double[2];
bool blResult = true;
try
{
#region 空跑
if (ParStateSoft.StateMachine_e == StateMachine_enum.NullRun)
{
ShowState(string.Format("相机{0}空跑，默认发送OK", g_NoCamera));
LogicRobot.L_I.WriteRobotCMD(ModelParams.PrecisePos, ModelParams.cmd_PreciseAngle);
return(true);
}
#endregion
StateComprehensive_enum stateComprehensive_e = g_BaseDealComprehensive.DealComprehensivePosNoDisplay(
g_UCDisplayCamera, g_HtUCDisplay, Pos_enum.Pos1, out htResult);
ResultBlob resultBlob = (ResultBlob)htResult[cellName];
//算子基准值记录到基准值当中
if (ModelParams.PreciseStdValue.DblValue1 == 0)
{
ModelParams.PreciseStdValue = new Point2D(resultBlob.StdX, resultBlob.StdY);
}
//面积判断
#region 面积检测
if (!BlobAreaDetect(resultBlob.Area, out dblResult))
{
ShowState("精定位第一次拍照失败");
blResult = false;
return(false);
}
#endregion
//一次拍照计算偏差，目前用于和二次拍照进行对比
BackLightLocation.Verify(new Point2D(resultBlob.X, resultBlob.Y),
new Point2D(resultBlob.StdX, resultBlob.StdY), //不规则区域基准值，即旋转中心
resultBlob.R_J, //不规则区域角度
ParCalibWorld.V_I[g_NoCamera], //相机系数
ModelParams.BLDisplayType, //背光在相机显示中的方向
ModelParams.PreciseRobotAngle, //精定位机器人u轴角度
ModelParams.BotWastageAngle, //机器人残材平台放片角度
ModelParams.BotPlaceAngle, //机器人放置角度
ModelParams.DisplayAngle, //相机显示角度
ModelParams.BLPlaceAngle); //背光放置方向
blResult = BackLightLocation.BlobAngleCalc(
resultBlob.R_J, //不规则区域角度
ModelParams.PrecisePos, //机器人精定位位置
ModelParams.cmd_PreciseAngle, //精定位角度调整指令
ModelParams.cmd_PreciseFailed, //精定位失败指令
ModelParams.PreciseThreadR, //精定位角度偏差阈值
ModelParams.BLDisplayType, //背光在相机显示中的方向
RegeditMain.R_I.ID, //UniqueID
ModelParams.IfRecordData, //是否进行数据记录
out phi); //输出实际给机器人补偿的角度
return(blResult);
}
catch (Exception ex)
{
Log.L_I.WriteError(NameClass, ex);
return(false);
}
finally
{
string strResult = "OK";
if (!blResult)
{
strResult = "NG";
}
g_UCDisplayCamera.ShowResult("面积比例：" + dblResult[1].ToString(ReservDigits) +
"\n角度补正：" + phi.ToString(ReservDigits) +
"\n角度阈值：" + ModelParams.PreciseThreadR.ToString(ReservDigits) +
"\n" + strResult, blResult);
}
}
/// <summary>
/// 不规则区域偏差计算
/// </summary>
/// <param name="cellName"></param>
/// <param name="htResult"></param>
/// <returns></returns>
public bool BlobDeviation(string cellName, out Hashtable htResult)
{
htResult = null;
Point2D pDelta = new Point2D();
bool blResult = true;
double[] dblResult = new double[2];
try
{
#region 空跑
if (ParStateSoft.StateMachine_e == StateMachine_enum.NullRun)
{
ShowState("空跑默认发送平台1坐标");
LogicRobot.L_I.WriteRobotCMD(ModelParams.PosWastagePlat1, ModelParams.cmd_PreciseResult);
return(true);
}
#endregion
StateComprehensive_enum stateComprehensive_e = g_BaseDealComprehensive.DealComprehensivePosNoDisplay(
g_UCDisplayCamera, g_HtUCDisplay, Pos_enum.Pos1, out htResult);
ResultBlob resultBlob = (ResultBlob)htResult[cellName];
#region 面积检测
if (!BlobAreaDetect(resultBlob.Area, out dblResult))
{
blResult = false;
return(false);
}
#endregion
//获取像素偏差
//Point2D delta = new Point2D((ModelParams.isMirrorX ? 1 : -1) * resultBlob.DeltaX * AMP,
// (ModelParams.isMirrorY ? 1 : -1) * resultBlob.DeltaY * AMP);
double deltaX = resultBlob.X - ModelParams.PreciseStdValue.DblValue1;
double deltaY = resultBlob.Y - ModelParams.PreciseStdValue.DblValue2;
Point2D delta = new Point2D((ModelParams.isMirrorX ? 1 : -1) * deltaX * AMP,
(ModelParams.isMirrorY ? 1 : -1) * deltaY * AMP);
//将像素偏差代入计算
blResult = BolbDevationCalc(phi, delta, out pDelta);
return(blResult);
}
catch (Exception ex)
{
blResult = false;
Log.L_I.WriteError(NameClass, ex);
return(false);
}
finally
{
string strResult = "OK";
if (!blResult)
{
strResult = "NG";
}
g_UCDisplayCamera.ShowResult(
"面积比例：" + dblResult[1].ToString("f3") +
" 阈值：" + ModelParams.AreaMin.ToString(ReservDigits) + "-" + ModelParams.AreaMax.ToString(ReservDigits) +
"\n补正值X：" + pDelta.DblValue1.ToString("f3") +
" 阈值：" + ModelParams.PreciseThreadX.ToString(ReservDigits) +
"\n补正值Y：" + pDelta.DblValue2.ToString("f3") +
" 阈值：" + ModelParams.PreciseThreadY.ToString(ReservDigits) +
"\n" + strResult, blResult);
}
}