public NBestList Recognize(ArrayList points) // candidate points
{
// resample to a common number of points
points = Utils.Resample(points, NumResamplePoints);
// rotate so that the centroid-to-1st-point is at zero degrees
double radians = Utils.AngleInRadians(Utils.Centroid(points), (PointR)points[0], false); // indicative angle
points = Utils.RotateByRadians(points, -radians); // undo angle
// scale to a common (square) dimension
points = Utils.ScaleTo(points, ResampleScale);
// translate to a common origin
points = Utils.TranslateCentroidTo(points, ResampleOrigin);
NBestList nbest = new NBestList();
foreach (Gesture p in _gestures.Values)
{
double[] best = GoldenSectionSearch(
points, // to rotate
p.Points, // to match
Utils.Deg2Rad(-45.0), // lbound
Utils.Deg2Rad(+45.0), // ubound
Utils.Deg2Rad(2.0)); // threshold
double score = 1d - best[0] / HalfDiagonal;
nbest.AddResult(p.Name, score, best[0], best[1]); // name, score, distance, angle
}
nbest.AddResult("Try Again", tolerance, 0.0, 0.0);
nbest.SortDescending(); // sort so that nbest[0] is best result
return(nbest);
}
public NBestList Recognize(ArrayList points) // candidate points
{
// resample to a common number of points
points = Utils.Resample(points, NumResamplePoints);
// rotate so that the centroid-to-1st-point is at zero degrees
double radians = Utils.AngleInRadians(Utils.Centroid(points), (PointR) points[0], false); // indicative angle
points = Utils.RotateByRadians(points, -radians); // undo angle
// scale to a common (square) dimension
points = Utils.ScaleTo(points, ResampleScale);
// translate to a common origin
points = Utils.TranslateCentroidTo(points, ResampleOrigin);
NBestList nbest = new NBestList();
foreach (Gesture p in _gestures.Values)
{
double[] best = GoldenSectionSearch(
points, // to rotate
p.Points, // to match
Utils.Deg2Rad(-45.0), // lbound
Utils.Deg2Rad(+45.0), // ubound
Utils.Deg2Rad(2.0)); // threshold
double score = 1d - best[0] / HalfDiagonal;
nbest.AddResult(p.Name, score, best[0], best[1]); // name, score, distance, angle
}
nbest.AddResult("Try Again", tolerance, 0.0, 0.0);
nbest.SortDescending(); // sort so that nbest[0] is best result
return nbest;
}
/// <summary>
/// Tests an entire batch of files. See comments atop MainForm.TestBatch_Click().
/// </summary>
/// <param name="subject">Subject number.</param>
/// <param name="speed">"fast", "medium", or "slow"</param>
/// <param name="categories">A list of gesture categories that each contain lists of
/// prototypes (examples) within that gesture category.</param>
/// <param name="dir">The directory into which to write the output files.</param>
/// <returns>True if successful; false otherwise.</returns>
public bool TestBatch(int subject, string speed, ArrayList categories, string dir)
{
bool success = true;
StreamWriter mainWriter = null;
StreamWriter recWriter = null;
try
{
//
// set up a main results file and detailed recognition results file
//
int start = Environment.TickCount;
string mainFile = String.Format("{0}\\geometric_main_{1}.txt", dir, start);
string recFile = String.Format("{0}\\geometric_data_{1}.txt", dir, start);
mainWriter = new StreamWriter(mainFile, false, Encoding.UTF8);
mainWriter.WriteLine("Subject = {0}, Recognizer = geometric, Speed = {1}, StartTime(ms) = {2}", subject, speed, start);
mainWriter.WriteLine("Subject Recognizer Speed NumTraining GestureType RecognitionRate\n");
recWriter = new StreamWriter(recFile, false, Encoding.UTF8);
recWriter.WriteLine("Subject = {0}, Recognizer = geometric, Speed = {1}, StartTime(ms) = {2}", subject, speed, start);
recWriter.WriteLine("Correct? NumTrain Tested 1stCorrect Pts Ms Angle : (NBestNames) [NBestScores]\n");
//
// determine the number of gesture categories and the number of examples in each one
//
int numCategories = categories.Count;
int numExamples = ((Category)categories[0]).NumExamples;
double totalTests = (numExamples - 1) * NumRandomTests;
//
// outermost loop: trains on N=1..9, tests on 10-N (for e.g., numExamples = 10)
//
for (int n = 1; n <= numExamples - 1; n++)
{
// storage for the final avg results for each category for this N
double[] results = new double[numCategories];
//
// run a number of tests at this particular N number of training examples
//
for (int r = 0; r < NumRandomTests; r++)
{
_gestures.Clear(); // clear any (old) loaded prototypes
// load (train on) N randomly selected gestures in each category
for (int i = 0; i < numCategories; i++)
{
Category c = (Category)categories[i]; // the category to load N examples for
int[] chosen = Utils.Random(0, numExamples - 1, n); // select N unique indices
for (int j = 0; j < chosen.Length; j++)
{
Gesture p = c[chosen[j]]; // get the prototype from this category at chosen[j]
_gestures.Add(p.Name, p); // load the randomly selected test gestures into the recognizer
}
}
//
// testing loop on all unloaded gestures in each category. creates a recognition
// rate (%) by averaging the binary outcomes (correct, incorrect) for each test.
//
for (int i = 0; i < numCategories; i++)
{
// pick a random unloaded gesture in this category for testing
// instead of dumbly picking, first find out what indices aren't
// loaded, and then randomly pick from those.
Category c = (Category)categories[i];
int[] notLoaded = new int[numExamples - n];
for (int j = 0, k = 0; j < numExamples; j++)
{
Gesture g = c[j];
if (!_gestures.ContainsKey(g.Name))
{
notLoaded[k++] = j; // jth gesture in c is not loaded
}
}
int chosen = Utils.Random(0, notLoaded.Length - 1); // index
Gesture p = c[notLoaded[chosen]]; // gesture to test
Debug.Assert(!_gestures.ContainsKey(p.Name));
// do the recognition!
ArrayList testPts = Utils.RotateByDegrees(p.RawPoints, Utils.Random(0, 359));
NBestList result = this.Recognize(testPts);
string category = Category.ParseName(result.Name);
int correct = (c.Name == category) ? 1 : 0;
recWriter.WriteLine("{0} {1} {2} {3} {4} {5} {6:F1}{7} : ({8}) [{9}]",
correct, // Correct?
n, // NumTrain
p.Name, // Tested
FirstCorrect(p.Name, result.Names), // 1stCorrect
p.RawPoints.Count, // Pts
p.Duration, // Ms
Math.Round(result.Angle, 1), (char)176, // Angle tweaking :
result.NamesString, // (NBestNames)
result.ScoresString); // [NBestScores]
results[i] += correct;
}
// provide feedback as to how many tests have been performed thus far.
double testsSoFar = ((n - 1) * NumRandomTests) + r;
ProgressChangedEvent(this, new ProgressEventArgs(testsSoFar / totalTests)); // callback
}
//
// now create the final results for this N and write them to a file
//
for (int i = 0; i < numCategories; i++)
{
results[i] /= (double)NumRandomTests; // normalize by the number of tests at this N
Category c = (Category)categories[i];
// Subject Recognizer Speed NumTraining GestureType RecognitionRate
mainWriter.WriteLine("{0} geometric {1} {2} {3} {4:F3}", subject, speed, n, c.Name, Math.Round(results[i], 3));
}
}
// time-stamp the end of the processing
int end = Environment.TickCount;
mainWriter.WriteLine("\nEndTime(ms) = {0}, Minutes = {1:F2}", end, Math.Round((end - start) / 60000.0, 2));
recWriter.WriteLine("\nEndTime(ms) = {0}, Minutes = {1:F2}", end, Math.Round((end - start) / 60000.0, 2));
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
success = false;
}
finally
{
if (mainWriter != null)
{
mainWriter.Close();
}
if (recWriter != null)
{
recWriter.Close();
}
}
return(success);
}
