private void DoMeasurements(object state)
{
try
{
m_SigmaDict = new Dictionary <int, double>();
Rectangle testRect = new Rectangle(
(m_ImageProvider.Width / 2) - 16,
(m_ImageProvider.Height / 2) - 16,
32, 32);
int framesToMeasure = 65;
int firstFrame = Math.Max(0, StartFrameId);
int lastFrame = Math.Min(m_ImageProvider.LastFrame, StartFrameId + framesToMeasure);
RaiseOnBeginProgress(firstFrame, lastFrame - 1);
int[,] pixels1 = null;
int[,] pixels2 = null;
for (int i = firstFrame; i < lastFrame - 1; i++)
{
m_SigmaDict.Add(i, double.NaN);
}
PotentialIntegrationFit foundIntegration = null;
for (int k = 0; k < 4; k++)
{
if (!m_IsRunning)
{
break;
}
for (int i = firstFrame; i < lastFrame - 1; i++)
{
if (!m_IsRunning)
{
break;
}
RaiseOnProgress(i);
List <AverageCalculator> calcList = new List <AverageCalculator>();
for (int x = 0; x < 32; x++)
{
for (int y = 0; y < 32; y++)
{
calcList.Add(new AverageCalculator());
}
}
if (pixels1 == null)
{
pixels1 = m_ImageProvider.GetPixelArray(i, testRect);
RaiseOnFramePixels(i, pixels1);
}
else
{
for (int x = 0; x < 32; x++)
{
for (int y = 0; y < 32; y++)
{
pixels1[x, y] = pixels2[x, y];
}
}
}
pixels2 = m_ImageProvider.GetPixelArray(i + 1, testRect);
RaiseOnFramePixels(i + 1, pixels1);
double sigmaSum = 0;
for (int x = 0; x < 32; x++)
{
for (int y = 0; y < 32; y++)
{
int value = pixels1[x, y];
int value2 = pixels2[x, y];
calcList[32 * y + x].AddDataPoint(value);
calcList[32 * y + x].AddDataPoint(value2);
sigmaSum += Math.Abs(value - value2) / 2.0;
}
}
calcList.ForEach(c => c.Compute());
m_SigmaDict[i] = sigmaSum / 1024;
RaiseOnFrameData(calcList, m_SigmaDict);
m_AverageCalculatorsPerFrame.Add(calcList);
}
foundIntegration = ComputeIntegration();
if (foundIntegration != null)
{
if (k == 0)
{
if (foundIntegration.Interval < 32 && foundIntegration.Certainty > 1)
{
break;
}
}
else if (k == 1)
{
if (foundIntegration.Interval < 64 && foundIntegration.Certainty > 1)
{
break;
}
}
else if (k == 2)
{
if (foundIntegration.Interval < 128 && foundIntegration.Certainty > 1)
{
break;
}
}
}
if (k < 3)
{
if (k < 2)
{
framesToMeasure += 65;
}
else
{
framesToMeasure += 129;
}
firstFrame = lastFrame - 1;
lastFrame = Math.Min(m_ImageProvider.LastFrame, StartFrameId + framesToMeasure);
RaiseOnProgress(lastFrame - 1);
int currSize = m_SigmaDict.Count;
for (int i = currSize + StartFrameId; i < lastFrame - 1; i++)
{
m_SigmaDict.Add(i, double.NaN);
}
}
}
if (m_IsRunning)
{
RaiseOnPotentialIntegration(foundIntegration);
}
}
catch (Exception ex)
{
Trace.WriteLine(ex.GetFullStackTrace());
}
finally
{
m_IsRunning = false;
}
}
private void DoMeasurements()
{
m_SigmaDict = new Dictionary <int, double>();
Rectangle testRect = new Rectangle(
(m_ImageProvider.Width / 2) - 16,
(m_ImageProvider.Height / 2) - 16,
32, 32);
int framesToMeasure = 65;
int firstFrame = m_StartFrameId;
int lastFrame = Math.Min(m_ImageProvider.LastFrame, m_StartFrameId + framesToMeasure);
progressBar1.Minimum = firstFrame;
progressBar1.Maximum = lastFrame - 1;
progressBar1.Style = ProgressBarStyle.Marquee;
int[,] pixels1 = null;
int[,] pixels2 = null;
for (int i = firstFrame; i < lastFrame - 1; i++)
{
m_SigmaDict.Add(i, double.NaN);
}
PotentialIntegrationFit foundIntegration = null;
for (int k = 0; k < 4; k++)
{
for (int i = firstFrame; i < lastFrame - 1; i++)
{
progressBar1.Value = i;
progressBar1.Refresh();
List <AverageCalculator> calcList = new List <AverageCalculator>();
for (int x = 0; x < 32; x++)
{
for (int y = 0; y < 32; y++)
{
calcList.Add(new AverageCalculator());
}
}
if (pixels1 == null)
{
pixels1 = m_ImageProvider.GetPixelArray(i, testRect);
}
else
{
for (int x = 0; x < 32; x++)
{
for (int y = 0; y < 32; y++)
{
pixels1[x, y] = pixels2[x, y];
}
}
}
pixels2 = m_ImageProvider.GetPixelArray(i + 1, testRect);
double sigmaSum = 0;
for (int x = 0; x < 32; x++)
{
for (int y = 0; y < 32; y++)
{
int value = pixels1[x, y];
int value2 = pixels2[x, y];
calcList[32 * y + x].AddDataPoint(value);
calcList[32 * y + x].AddDataPoint(value2);
sigmaSum += Math.Abs(value - value2) / 2.0;
}
}
calcList.ForEach(c => c.Compute());
m_SigmaDict[i] = sigmaSum / 1024;
Plot(calcList);
PlotSigmas(m_SigmaDict);
Refresh();
m_AverageCalculatorsPerFrame.Add(calcList);
}
foundIntegration = ComputeIntegration();
if (foundIntegration != null)
{
if (k == 0)
{
if (foundIntegration.Interval < 32 && foundIntegration.Certainty > 1)
{
break;
}
}
else if (k == 1)
{
if (foundIntegration.Interval < 64 && foundIntegration.Certainty > 1)
{
break;
}
}
else if (k == 2)
{
if (foundIntegration.Interval < 128 && foundIntegration.Certainty > 1)
{
break;
}
}
}
if (k < 3)
{
if (k < 2)
{
framesToMeasure += 65;
}
else
{
framesToMeasure += 129;
}
firstFrame = lastFrame - 1;
lastFrame = Math.Min(m_ImageProvider.LastFrame, m_StartFrameId + framesToMeasure);
progressBar1.Maximum = lastFrame - 1;
int currSize = m_SigmaDict.Count;
for (int i = currSize + m_StartFrameId; i < lastFrame - 1; i++)
{
m_SigmaDict.Add(i, double.NaN);
}
}
}
progressBar1.Value = progressBar1.Maximum;
progressBar1.Style = ProgressBarStyle.Continuous;
progressBar1.Refresh();
pnlResult.Visible = true;
pnlResult.BringToFront();
DisplayFoundIntegration(foundIntegration);
}