/// <summary>
/// If the optimization has stopped, then check whether it was
/// completed successfully or whether it was aborted due to errors or
/// to user interaction.
/// Depending on the failure or success of the run, the GUI is notified
/// for visual update of the results and obtained statistics.
/// </summary>
public override void Stop()
{
if (ImageCount == 0)
{
RecoginzedAndStatisticedDel(MatchModel.ShapeModelAssistant.ERR_NO_TESTIMAGE);
RecoginzedAndStatisticedDel(ShapeModelOpt.RUN_FAILED);
}
else if (!OptSuccess && (_currentScoreMin <= 0.0))
{
RecoginzedAndStatisticedDel(ShapeModelOpt.UPDATE_RECOG_ERR);
RecoginzedAndStatisticedDel(ShapeModelOpt.RUN_FAILED);
}
else if (!OptSuccess)
{
RecoginzedAndStatisticedDel(ShapeModelOpt.UPDATE_TEST_ERR);
RecoginzedAndStatisticedDel(ShapeModelOpt.RUN_FAILED);
}
else
{
OptimizationStatus = "参数优化成功完成!";
RecoginzedAndStatisticedDel(ShapeModelOpt.UPDATE_RECOG_STATISTICS_STATUS);
MatchAssistant.SetMinScore(_optScoreMin / 100.0);
MatchAssistant.SetGreediness(_optGreediness / 100.0);
RecoginzedAndStatisticedDel(ShapeModelOpt.RUN_SUCCESSFUL);
}
}
/// <summary>
/// In each execution step a certain parameter set is applied
/// to the whole set of test images and the performance is then
/// evaluated.
/// </summary>
public override bool ExecuteStep()
{
double cScoreMin, cGreediness, cRecogRate;
string fileKey;
int actualMatchNum, specifiedMatchNum;
bool satisfiedRate;
//----------------------第一步:检测是否有测试图像-------------------------//
if (!Iterator.MoveNext()) //未有测试图像,停止优化
{
return(false);
}
//----------------------第二步:当前检测参数下图像处理---------------------//
cScoreMin = _currentScoreMin / 100.0;
cGreediness = _currentGreediness / 100.0;
OptimizationStatus = "测试图像 " + (CurrentImageIndex + 1).ToString() +
"-最小得分:" + cScoreMin +
"-贪婪度:" + cGreediness;
RecoginzedAndStatisticedDel(ShapeModelOpt.UPDATE_RECOG_STATISTICS_STATUS);
fileKey = (string)Iterator.Current;
MatchAssistant.SetTestImage(fileKey);
MatchAssistant.SetMinScore(cScoreMin);
MatchAssistant.SetGreediness(cGreediness);
if (!MatchAssistant.DetectShapeModel()) //查找匹配模板的实例失败,停止优化
{
return(false);
}
Result = MatchAssistant.Result;
actualMatchNum = Result.MatchCount;
specifiedMatchNum = 0; //每张图像中的指定匹配实例数
switch (Parameter.RecogAccuracyMode)
{
case Communal.ShapeModelParameter.RECOG_MODE_SPECIFIEDNUM:
specifiedMatchNum = Parameter.RecogSpecifiedMatchNum;
break;
case Communal.ShapeModelParameter.RECOG_MODE_ATLEASTONE:
specifiedMatchNum = 1;
if (actualMatchNum > 1)
{
actualMatchNum = 1;
}
break;
case Communal.ShapeModelParameter.RECOG_MODE_MAXIMUMNUM:
specifiedMatchNum = Parameter.NumToMatch;
break;
default: break;
}
_matchNum += actualMatchNum; //实际匹配数
_specifiedMathcNum += specifiedMatchNum; //指定匹配数总数(所有图像中指定匹配实例数之和)
cRecogRate = (_specifiedMathcNum > 0) ? (100.0 * _matchNum / _specifiedMathcNum) : 0.0;
_currentElapseTime = _currentElapseTime * CurrentImageIndex + Result.ElapseTime;
_currentElapseTime /= ++CurrentImageIndex;
//Write data into strings and call for update
RecogTabOpimizationData[0] = "" + System.Math.Round(cScoreMin, 2);
RecogTabOpimizationData[1] = "" + System.Math.Round(cGreediness, 2);
RecogTabOpimizationData[2] = "" + System.Math.Round(cRecogRate, 2) + "%";
if (_currentElapseTime < 1000)
{
RecogTabOpimizationData[3] = "" + System.Math.Round(_currentElapseTime, 2) + " ms";
}
else
{
RecogTabOpimizationData[3] = "" + System.Math.Round(_currentElapseTime / 1000.0, 2) + " s";
}
RecoginzedAndStatisticedDel(ShapeModelOpt.UPDATE_RECOG_UPDATED_VALS); //更新:识别更新后的参数值
if (CurrentImageIndex < ImageCount) //未达到最后图像,返回True(继续按当前参数处理图像:识别并显示)
{
return(true);
}
//----------------------第三步:当前参数条件下，判断识别率-------------------------//
Iterator.Reset();
CurrentImageIndex = 0;
_matchNum = 0;
_specifiedMathcNum = 0;
satisfiedRate = (Parameter.RecogRateOpt == 0) ? //评估搜索的启发式选项:==识别率,>=识别率
(System.Math.Abs((double)cRecogRate - Parameter.RecogRate) < 0.001) : (cRecogRate >= Parameter.RecogRate);
//----------------------第三步A:识别率满足设定,递增贪婪度-------------------------//
if (satisfiedRate) //当前参数条件下,满足识别率条件
{
OptSuccess = true;
if (_currentElapseTime < _optElapseTime)//识别平均时间是否比上次短,短则更新优化后的参数
{
_optScoreMin = _currentScoreMin;
_optGreediness = _currentGreediness;
_optElapseTime = _currentElapseTime;
RecogTabOpimizationData[4] = "" + System.Math.Round(_optScoreMin / 100.0, 2);
RecogTabOpimizationData[5] = "" + System.Math.Round(_optGreediness / 100.0, 2);
RecogTabOpimizationData[6] = "" + System.Math.Round(cRecogRate, 2) + " %";
RecogTabOpimizationData[7] = RecogTabOpimizationData[3];
RecoginzedAndStatisticedDel(ShapeModelOpt.UPDATE_RECOG_OPTIMAL_VALS); //更新:最优化参数值
}
_currentGreediness += _greedinessStep;
return(_currentGreediness <= 100); //贪婪度是否超出限定值:未超出--返回True(继续优化贪婪度),超出--返回False(停止优化)
}
//----------------------第三步B:识别率不满足设定,递减匹配得分-------------------------//
_currentScoreMin += _scoreMinStep; //未满足识别率条件,递减最小匹配得分,准备再优化
if (OptSuccess) //[优化参数成功],若当前最小匹配得分:大于10,则返回True(继续优化);否则返回False(停止优化)
{
return(_currentScoreMin >= 10); //即优化贪婪度到极限前,出现某个贪婪度下的识别率不满足条件,则再递减匹配得分,若可以满足识别率,则因贪婪度到底极限而停止优化;否则只能到最低匹配得分而停止优化
}
//----------------------第四步B:判断匹配得分是否超出底线-------------------------//
return(_currentScoreMin > 0); //[优化参数未成功]若当前最小匹配得分:大于0,则返回True(继续优化);否则返回False(停止优化)
}
