///////////////////////////////////////////////////////////////////////////////
//
// Tracker.cs
//
// By Philip R. Braica (HoshiKata@aol.com, VeryMadSci@gmail.com)
//
// Distributed under the The Code Project Open License (CPOL)
// http://www.codeproject.com/info/cpol10.aspx
///////////////////////////////////////////////////////////////////////////////
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Drawing;
using System.Data;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using OpenCvSharp;
namespace OpenCVDemo
{
/// <summary>
/// Class for tracking.
/// </summary>
public class Tracker
{
/// <summary>
/// Target list.
/// </summary>
protected List<Target> m_targets = new List<Target>();
/// <summary>
/// Marker list.
/// </summary>
protected ImageMarkerList m_markerList = new ImageMarkerList();
/// <summary>
/// Reset learning.
/// </summary>
public void Reset()
{
for (int i = 0; i < m_targets.Count; i++) m_targets[i].Dispose();
m_targets.Clear();
}
/// <summary>
/// The font.
/// </summary>
public Font Font { get; set; }
/// <summary>
/// Set targets.
/// </summary>
/// <param name="targets"></param>
public void SetTargets(List<Target> targets)
{
m_targets.Clear();
m_targets.AddRange(targets);
}
/// <summary>
/// Track an object in frames based on target array.
/// </summary>
/// <param name="img"></param>
/// <param name="imgOut"></param>
/// <param name="r"></param>
public void Track(IplImage img, Bitmap imgOut, Rectangle r)
{
// Not the initial case.
// Search around last detection.
int best = 0;
float bestScore = 0;
CvRect searchRect = makeSearchRectangle(img.Width, img.Height, 50);
for (int i = 0; i < m_targets.Count; i++)
{
float score = m_targets[i].Correlate(img, searchRect);
if (score > AutoCorrelationThreshold)
{
// We found it, no need to do better.
markLocation(imgOut, m_targets[i].LastRect);
return;
}
if (score > bestScore)
{
best = i; bestScore = score;
}
}
// If needed expand the search.
if (bestScore < CrossCorrelationThreshold)
{
searchRect = new CvRect(0, 0, img.Width, img.Height);
for (int i = 0; i < m_targets.Count; i++)
{
float score = m_targets[i].Correlate(img, searchRect);
if (score > AutoCorrelationThreshold)
{
// We found it, no need to do better.
markLocation(imgOut, m_targets[i].LastRect);
return;
}
if (score > bestScore)
{
best = i; bestScore = score;
}
}
}
if (bestScore > CrossCorrelationThreshold)
{
markLocation(imgOut, m_targets[best].LastRect);
}
}
/// <summary>
/// Save feature.
/// </summary>
/// <param name="fileName"></param>
public void SaveFeature(string fileName, string featureName)
{
int cnt = m_targets.Count;
//serialize and persist it to it's file
try
{
Feature TC = new Feature(
featureName,
m_targets,
AutoCorrelationThreshold,
LearningCorrelationThreshold,
CrossCorrelationThreshold);
TC.Save(fileName);
}
catch (Exception)
{
// Fail silently for now.
}
}
/// <summary>
/// Learn, make a target array.
/// </summary>
/// <param name="img"></param>
/// <param name="imgOut"></param>
/// <param name="r"></param>
public void Learn(IplImage img, Bitmap imgOut, Rectangle r, bool doStats)
{
m_markerList.Clear();
// Handle the initial case if m_targets.Count == 0.
if (m_targets.Count == 0)
{
markLocation(imgOut, r);
m_targets.Add(new Target(img, r));
return;
}
int best = 0;
float bestScore = 0;
if (doStats)
{
CvRect searchRect = new CvRect(0, 0, img.Width, img.Height);
for (int i = 0; i < m_targets.Count; i++)
{
float score = m_targets[i].Correlate(img, searchRect);
if (m_targets[i].Enabled)
{
if (score > bestScore)
{
best = i; bestScore = score;
}
}
if (score >= CrossCorrelationThreshold)
{
m_markerList.Add((score * 100).ToString("F1"), m_targets[i].LastRect);
}
}
}
else
{
// Not the initial case.
// Search around last detection.
CvRect searchRect = makeSearchRectangle(img.Width, img.Height, 50);
for (int i = 0; i < m_targets.Count; i++)
{
if (m_targets[i].Enabled == false)
{
m_targets[i].Scores.Add(0);
continue;
}
float score = m_targets[i].Correlate(img, searchRect);
if (score > AutoCorrelationThreshold)
{
// We found it, no need to do better.
markLocation(imgOut, m_targets[i].LastRect);
for (int j = i; j < m_targets.Count; j++)
{
m_targets[i].Scores.Add(0);
}
m_markerList.Add((score * 100).ToString("F1"), m_targets[i].LastRect);
return;
}
if (score > bestScore)
{
best = i; bestScore = score;
}
if (score >= CrossCorrelationThreshold)
{
m_markerList.Add((score * 100).ToString("F1"), m_targets[i].LastRect);
}
}
// If needed expand the search.
if (bestScore < CrossCorrelationThreshold)
{
searchRect = new CvRect(0, 0, img.Width, img.Height);
for (int i = 0; i < m_targets.Count; i++)
{
if (m_targets[i].Enabled == false)
{
m_targets[i].Scores.Add(0);
continue;
}
float score = m_targets[i].Correlate(img, searchRect);
if (score > AutoCorrelationThreshold)
{
// We found it, no need to do better.
markLocation(imgOut, m_targets[i].LastRect);
for (int j = i; j < m_targets.Count; j++)
{
m_targets[i].Scores.Add(0);
}
m_markerList.Add((score * 100).ToString("F1"), m_targets[i].LastRect);
return;
}
if (score > bestScore)
{
best = i; bestScore = score;
}
if (score >= CrossCorrelationThreshold)
{
m_markerList.Add((score * 100).ToString("F1"), m_targets[i].LastRect);
}
}
}
}
if (bestScore > CrossCorrelationThreshold)
{
markLocation(imgOut, m_targets[best].LastRect);
if (bestScore <= AutoCorrelationThreshold)
{
m_targets.Add(new Target(img, m_targets[best].LastRect));
// Omitting first and just added, if > Replace, remove it.
if ((best != 0) && (bestScore > LearningCorrelationThreshold))
{
m_targets[best].Enabled = false;
}
}
}
if (doStats)
{
for (int i = 0; i < m_targets.Count; i++)
{
m_targets[i].CrossCorrelate(CurrentLocation);
}
}
m_markerList.Render(imgOut, Font);
}
/// <summary>
/// Make a log report.
/// </summary>
/// <returns></returns>
public string MakeLogReport()
{
StringBuilder sb = new StringBuilder();
int max = 0;
for (int i = 0; i < m_targets.Count; i++)
{
max = m_targets[i].Scores.Count > max ? m_targets[i].Scores.Count : max;
}
if (max > 0)
{
sb.Append("Correlations\r\n\r\n");
for (int row = 0; row < max; row++)
{
for (int i = 0; i < m_targets.Count; i++)
{
int delta = row + m_targets[i].Scores.Count - max;
if (delta >= 0)
{
sb.Append(m_targets[i].Scores[delta]);
}
if (i != m_targets.Count - 1) sb.Append(", ");
}
sb.Append("\r\n");
}
sb.Append("\r\nCross correlations\r\n\r\n");
for (int row = 0; row < max; row++)
{
for (int i = 0; i < m_targets.Count; i++)
{
int delta = row + m_targets[i].Scores.Count - max;
if (delta >= 0)
{
sb.Append(m_targets[i].CrossScores[delta]);
}
if (i != m_targets.Count - 1) sb.Append(", ");
}
sb.Append("\r\n");
}
}
return sb.ToString();
}
/// <summary>
/// Threshold to call off match attempt we have an ideal.
/// </summary>
public float AutoCorrelationThreshold = 0.95f;
/// <summary>
/// Good enough to declare a match threshold.
/// </summary>
public float CrossCorrelationThreshold = 0.75f;
/// <summary>
/// Good enough to replace threshold.
/// </summary>
public float LearningCorrelationThreshold = 0.86f;
public Rectangle CurrentLocation = new Rectangle();
/// <summary>
/// Mark the location.
/// </summary>
/// <param name="b"></param>
/// <param name="r"></param>
protected void markLocation(Bitmap b, Rectangle r)
{
CurrentLocation = r;
using (Graphics g = Graphics.FromImage(b))
{
using (Pen p = new Pen(Color.FromArgb(128, Color.Orange), 4f))
{
g.DrawRectangle(p, r);
}
}
}
/// <summary>
/// Make a search rectangle
/// </summary>
/// <param name="maxWidth"></param>
/// <param name="maxHeight"></param>
/// <param name="expansion"></param>
/// <returns></returns>
protected CvRect makeSearchRectangle(int maxWidth, int maxHeight, int expansion)
{
int x = CurrentLocation.X - expansion;
int y = CurrentLocation.Y - expansion;
int w = CurrentLocation.Width + expansion + expansion;
int h = CurrentLocation.Height + expansion + expansion;
x = x < 0 ? 0 : x;
y = y < 0 ? 0 : y;
w = x + w < maxWidth ? w : maxWidth - x - 1;
h = y + h < maxHeight ? h : maxHeight - y - 1;
return new CvRect(x, y, w, h);
}
}
}
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