public void GetSum(double[] x, double[] y, int sakoeChibaBand, double expected)
{
var dtw = new DTW(x, y, sakoeChibaBand);
var result = dtw.GetSum();
Assert.Equal(expected, result);
}
/// <summary>
/// This function is called to get normalised result before passing to DTW algorithm.
/// The input from the Kinect and the text files are normalised and passed into DTW.
/// If the gesture name matches with the input, the name of gesture is received in the parameter finalgesture.
/// </summary>
/// <param name="gesturename">It is used to describe the name of gesture in dataset with which the Body coordinates are going to be compared with</param>
/// <returns>returns the gesture matched from DTW</returns>
private string passToDTW(string gesturename)
{
try
{
Recorder recorder = new Recorder();
ArrayList gesturefromkinect = recorder.readFiles(KINECT_COORDINATES);// getting normalised co ordinates here.....
string storedtextLines = System.IO.File.ReadAllText(gesturename);
List <double[]> normalisedresult = new List <double[]>();
string[] result = storedtextLines.Split('@');
for (int i = 0; i < result.Length; i++)
{
normalisedresult = recorder.explode_array(result, ',');
}
string txtFiles = Path.GetFileName(gesturename);
int dimension = 12;
int threshold = 3;
int firstThreshold = 5;
int maxSlope = 2;
int minimumLength = 10;
_dtw = new DTW(dimension, threshold, firstThreshold, maxSlope, minimumLength);
ArrayList arrlstTemp = ArrayList.Adapter(normalisedresult);
Regex regex = new Regex(@"_(.+?)_");
MatchCollection mc2 = regex.Matches(txtFiles);
_dtw.Add(arrlstTemp, "The gesture is " + mc2[0]);
finalgesture = _dtw.recognize(gesturefromkinect);
KINECT_COORDINATES = new string[] { };
}
catch (Exception ex)
{ }
return(finalgesture);
}
public void GetPath(double[] x, double[] y, int sakoeChibaBand, Tuple <int, int>[] expected)
{
var dtw = new DTW(x, y, sakoeChibaBand);
var result = dtw.GetPath();
Assert.Equal(expected, result);
}
public void Refresh(int i, int j, int minI, int minJ)
{
List <DTW.Pair> path = DTW.OptimalWarpingPath(totalCostMatrix, minI, minJ);
int iOffset = i - writeableBitmap.PixelWidth;
int jOffset = j - writeableBitmap.PixelHeight;
if (iOffset < 0)
{
iOffset = 0;
}
if (jOffset < 0)
{
jOffset = 0;
}
// draw cost matrix
int color;
for (int x = 0; x < writeableBitmap.PixelWidth; x++)
{
for (int y = 0; y < writeableBitmap.PixelHeight; y++)
{
double val = cellCostMatrix[iOffset + x, jOffset + y];
if (totalCostMatrix[iOffset + x, jOffset + y] == double.PositiveInfinity)
{
color = undefColor;
}
else if (val < 0)
{
color = minColor;
}
else if (val >= colorPalette.Length)
{
color = maxColor;
}
else
{
color = colorPalette[(int)val];
}
pixels[writeableBitmap.PixelWidth * y + x] = color;
}
}
// draw path
foreach (DTW.Pair p in path)
{
if (p.i1 <= iOffset || p.i2 <= jOffset)
{
break;
}
pixels[writeableBitmap.PixelWidth * (p.i2 - jOffset - 1) + (p.i1 - iOffset - 1)] = pathColor;
}
// paint
writeableBitmap.WritePixels(new Int32Rect(0, 0, writeableBitmap.PixelWidth, writeableBitmap.PixelHeight),
pixels, writeableBitmap.PixelWidth * 4, 0);
InvalidateVisual();
}
public RecognitionThread(ActionInstance seq, ActionData subseq, mIndex[] stepConstraints)
{
_seq = seq;
_subseq = subseq;
dtw = new DTW(stepConstraints);
_isDone = false;
}
/// <summary>
/// This function is called to get normalised result before passing to DTW algorithm.
/// The input from the Kinect and the text files are normalised and passed into DTW.
/// If the gesture name matches with the input, the name of gesture is received in the parameter finalgesture.
/// </summary>
/// <returns>returns the gesture matched from DTW with least cost</returns>
private string passToDTW()
{
string finalgesture = String.Empty;
try
{ //initialization of Dictionary where key=gesture and value is the cost from DTW
var mySortedDictionary = new SortedDictionary <string, double>();
Recorder rd = new Recorder();
ArrayList gesturefromkinect = rd.readfiles(KINECT_COORDINATES);// getting normalised co ordinates here.....
string sourceDirectory = @"d:\DTW_files\2D\Hristo";
var txtFiles = Directory.EnumerateFiles(sourceDirectory, "*.txt");
foreach (string currentFile in txtFiles)
{
System.Diagnostics.Debug.WriteLine("\nComparing with " + currentFile);
string storedtextLines = System.IO.File.ReadAllText(currentFile);
List <double[]> normalisedresult = new List <double[]>();
string[] result = storedtextLines.Split('@');
for (int i = 0; i < result.Length; i++)
{
normalisedresult = rd.explode_array(result, ',');
}
int dimension = 12;
int threshold = 2;
int firstThreshold = 4;
int maxSlope = 2;
int minimumLength = 10;
_dtw = new DTW(dimension, threshold, firstThreshold, maxSlope, minimumLength);
ArrayList arrlstTemp = ArrayList.Adapter(normalisedresult);
_dtw.Add(arrlstTemp, Path.GetFileNameWithoutExtension(currentFile));
finalgesture = _dtw.recognize(gesturefromkinect);
if ((!finalgesture.Contains("__UNKNOWN")))
{
string[] words = finalgesture.Split('@');
double Num;
bool isNum = double.TryParse(words[1], out Num);
if (isNum)
{
mySortedDictionary.Add(words[0], Num);
}
}
KINECT_COORDINATES = new string[] { };
}
//find the gesture with the least cost from the set of the gestures
if (mySortedDictionary.Count >= 1)
{
double[] indexableValues = mySortedDictionary.Values.ToArray();
double min = indexableValues.Min();
string key = (from d in mySortedDictionary where d.Value == min select d).First().Key;
GestureStore ig = new GestureStore("Recoginised as:" + key);
}
}
catch (Exception ex)
{
}
return(finalgesture);
}
static void Main(string[] args)
{
List <Double> seriesA = (new double[] { 4, 5, 6, 7, 8 }).ToList();
List <Double> seriesB = (new double[] { 4, 5, 6, 7, 8 }).ToList();
var distance = new EuclidianDistance();
var cost = DTW <Double> .getDistance(seriesA, seriesB, distance, 4);
Console.WriteLine("Cost: " + cost);
Console.ReadLine();
}
public void DeterminePerformance(List <Skeleton> skeletons)
{
try
{
this.Performance = DTW <Skeleton> .Distance(this.Skeletons, skeletons, new SkeletonDistance());
}
catch (Exception)
{
this.Performance = 100;
}
}
void OnRecognitionDone(ActionData matchedinst, ActionInstance insttomatch, DTW dtw)
{
ActionInstance warpedinst = new ActionInstance();
foreach (mIndex idx in dtw.WarpPath)
{
warpedinst.poses.Add(insttomatch.poses[idx.x]);
}
print("-- cost " + matchedinst.actionId + " " + dtw.getWarpPathCost());
onRecognitionFinished(matchedinst.actionId, warpedinst);
}
static void Main(string[] args)
{
DTW ucrDtw;
Query query;
DTWResult result;
int dimensions = Int16.Parse(args[2]);
int queryStretchLength = Int16.Parse(args[3]);
float warpingWindow = (float)Double.Parse(args[4]);
ucrDtw = new DTW(dimensions, warpingWindow);
query = ucrDtw.Query();
using (TextReader reader = new StreamReader(args[0]))
{
string line;
while ((line = reader.ReadLine()) != null)
{
string[] strs = line.Split(' ');
double[] a = new double[strs.Length];
for (int itt = 0; itt < strs.Length; itt++)
{
if (String.IsNullOrEmpty(strs[itt]))
{
continue;
}
a[itt] = double.Parse(strs[itt]);
}
ucrDtw.addDataItem(dimensions, a);
}
}
List <double[]> source = new List <double[]>();
List <double[]> stretched;
using (TextReader reader = new StreamReader(args[1]))
{
string line;
while ((line = reader.ReadLine()) != null)
{
if (String.IsNullOrEmpty(line))
{
continue;
}
string[] strs = line.Split(' ');
double[] a = new double[dimensions];
for (int itt = 0; itt < strs.Length; itt++)
{
if (String.IsNullOrEmpty(strs[itt]))
{
continue;
}
a[itt] = double.Parse(strs[itt]);
}
source.Add(a);
}
}
stretched = Utilities.stretchData(source, dimensions, queryStretchLength);
//writeCsvFile("source.csv", source, dimensions);
//writeCsvFile("stretched.csv", stretched, dimensions);
foreach (double[] row in stretched)
{
query.addQueryItem(row);
}
result = ucrDtw.warp(query);
Console.WriteLine("Distance: " + result.Distance);
Console.WriteLine("Location: " + result.Location);
}
private void TimeWarp(TimeWarpType type, AudioTrack t1, TimeSpan t1From, TimeSpan t1To, AudioTrack t2, TimeSpan t2From, TimeSpan t2To, bool calculateSimilarity, bool normalizeSimilarity, bool cueIn, bool cueOut)
{
IAudioStream s1 = t1.CreateAudioStream();
IAudioStream s2 = t2.CreateAudioStream();
s1 = new CropStream(s1, TimeUtil.TimeSpanToBytes(t1From, s1.Properties), TimeUtil.TimeSpanToBytes(t1To, s1.Properties));
s2 = new CropStream(s2, TimeUtil.TimeSpanToBytes(t2From, s2.Properties), TimeUtil.TimeSpanToBytes(t2To, s2.Properties));
List <Tuple <TimeSpan, TimeSpan> > path = null;
DTW dtw = null;
// execute time warping
if (type == TimeWarpType.DTW)
{
dtw = new DTW(TimeWarpSearchWidth, progressMonitor);
}
else if (type == TimeWarpType.OLTW)
{
dtw = new OLTW2(TimeWarpSearchWidth, progressMonitor);
}
if (TimeWarpDisplay)
{
this.Dispatcher.BeginInvoke((Action) delegate {
dtwPathViewer = new DtwPathViewer();
dtwPathViewer.Show();
});
dtw.OltwInit += new DTW.OltwInitDelegate(delegate(int windowSize, IMatrix <double> cellCostMatrix, IMatrix <double> totalCostMatrix) {
dtwPathViewer.Dispatcher.BeginInvoke((Action) delegate {
dtwPathViewer.DtwPath.Init(windowSize, cellCostMatrix, totalCostMatrix);
});
});
bool drawing = false;
dtw.OltwProgress += new DTW.OltwProgressDelegate(delegate(int i, int j, int minI, int minJ, bool force) {
if (!drawing || force)
{
dtwPathViewer.Dispatcher.BeginInvoke((Action) delegate {
drawing = true;
dtwPathViewer.DtwPath.Refresh(i, j, minI, minJ);
drawing = false;
});
}
});
}
path = dtw.Execute(s1, s2);
if (path == null)
{
return;
}
// convert resulting path to matches and filter them
int filterSize = TimeWarpFilterSize; // take every n-th match and drop the rest
bool smoothing = TimeWarpSmoothing;
int smoothingWidth = Math.Max(1, Math.Min(filterSize / 10, filterSize));
bool inOutCue = TimeWarpInOutCue;
TimeSpan inOutCueSpan = TimeWarpSearchWidth;
List <Match> matches = new List <Match>();
float maxSimilarity = 0; // needed for normalization
IProgressReporter progressReporter = progressMonitor.BeginTask("post-process resulting path...", true);
double totalProgress = path.Count;
double progress = 0;
/* Leave out matches in the in/out cue areas...
* The matches in the interval at the beginning and end of the calculated time warping path with a width
* equal to the search width should be left out because they might not be correct - since the time warp
* path needs to start at (0,0) in the matrix and end at (m,n), they would only be correct if the path gets
* calculated between two synchronization points. Paths calculated from the start of a track to the first
* sync point, or from the last sync point to end of the track are probably wrong in this interval since
* the start and end points don't match if there is time drift so it is better to leave them out in those
* areas... in those short a few second long intervals the drict actually will never be that extreme that
* someone would notice it anyway. */
if (inOutCue)
{
int startIndex = 0;
int endIndex = path.Count;
// this needs a temporally ordered mapping path (no matter if ascending or descending)
foreach (Tuple <TimeSpan, TimeSpan> mapping in path)
{
if (cueIn && (mapping.Item1 < inOutCueSpan || mapping.Item2 < inOutCueSpan))
{
startIndex++;
}
if (cueOut && (mapping.Item1 > (t1To - t1From - inOutCueSpan) || mapping.Item2 > (t2To - t2From - inOutCueSpan)))
{
endIndex--;
}
}
path = path.GetRange(startIndex, endIndex - startIndex);
}
for (int i = 0; i < path.Count; i += filterSize)
{
//List<Tuple<TimeSpan, TimeSpan>> section = path.GetRange(i, Math.Min(path.Count - i, filterSize));
List <Tuple <TimeSpan, TimeSpan> > smoothingSection = path.GetRange(
Math.Max(0, i - smoothingWidth / 2), Math.Min(path.Count - i, smoothingWidth));
Tuple <TimeSpan, TimeSpan> match = path[i];
if (smoothingSection.Count == 0)
{
throw new InvalidOperationException("must not happen");
}
else if (smoothingSection.Count == 1 || !smoothing || i == 0)
{
// do nothing, match doesn't need any processing
// the first and last match must not be smoothed since they must sit at the bounds
}
else
{
List <TimeSpan> offsets = new List <TimeSpan>(smoothingSection.Select(t => t.Item2 - t.Item1).OrderBy(t => t));
int middle = offsets.Count / 2;
// calculate median
// http://en.wikiversity.org/wiki/Primary_mathematics/Average,_median,_and_mode#Median
TimeSpan smoothedDriftTime = new TimeSpan((offsets[middle - 1] + offsets[middle]).Ticks / 2);
match = new Tuple <TimeSpan, TimeSpan>(match.Item1, match.Item1 + smoothedDriftTime);
}
float similarity = calculateSimilarity ? (float)Math.Abs(CrossCorrelation.Correlate(
s1, new Interval(match.Item1.Ticks, match.Item1.Ticks + TimeUtil.SECS_TO_TICKS),
s2, new Interval(match.Item2.Ticks, match.Item2.Ticks + TimeUtil.SECS_TO_TICKS))) : 1;
if (similarity > maxSimilarity)
{
maxSimilarity = similarity;
}
matches.Add(new Match()
{
Track1 = t1,
Track1Time = match.Item1 + t1From,
Track2 = t2,
Track2Time = match.Item2 + t2From,
Similarity = similarity,
Source = type.ToString()
});
progressReporter.ReportProgress(progress / totalProgress * 100);
progress += filterSize;
}
// add last match if it hasn't been added
if (path.Count > 0 && path.Count % filterSize != 1)
{
Tuple <TimeSpan, TimeSpan> lastMatch = path[path.Count - 1];
matches.Add(new Match()
{
Track1 = t1,
Track1Time = lastMatch.Item1 + t1From,
Track2 = t2,
Track2Time = lastMatch.Item2 + t2From,
Similarity = 1,
Source = type.ToString()
});
}
progressReporter.Finish();
multiTrackViewer.Dispatcher.BeginInvoke((Action) delegate {
foreach (Match match in matches)
{
if (normalizeSimilarity)
{
match.Similarity /= maxSimilarity; // normalize to 1
}
multiTrackViewer.Matches.Add(match);
}
});
s1.Close();
s2.Close();
}
void Start()
{
Random.seed = System.DateTime.Now.Millisecond;
Pose[] vec1 = new Pose[] {
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random()
};
Pose[] svec = new Pose[] {
vec1[4], vec1[6], vec1[8], vec1[10],
vec1[12], vec1[13], vec1[14], vec1[15],
vec1[16], vec1[18], vec1[20], vec1[22],
vec1[24], vec1[27], vec1[30], vec1[31],
vec1[32], vec1[33], vec1[34], vec1[35],
vec1[37], vec1[38], vec1[40], vec1[42]
};
// Float[] vec1 = new Float[] {
// new Float(0), new Float(0.75f), new Float(1.5f), new Float(0.75f), new Float(0), new Float(-0.75f), new Float(-1.5f), new Float(-0.75f), new Float(0)
// };
// Float[] svec = new Float[] {
// new Float(0), new Float(-0.5f), new Float(-1), new Float(-1.5f)
// };
//
// float[] vec1 = new float[] { 0, 0.75f, 1.5f, 0.75f, 0, -0.75f, -1.5f, -0.75f, 0};
// float[] svec = new float[] { 0, -0.5f, -1, -1.5f};
int start = System.DateTime.Now.Millisecond;
DTW dtw = new DTW(new mIndex[] {new mIndex(1,1), new mIndex(1,2), new mIndex(2,1)});
List<mIndex> wres = dtw.warpSubsequenceDTW(vec1, svec);
Debug.Log("-- Time " + (System.DateTime.Now.Millisecond-start));
mDebug.printMatrix(dtw.CostMatrix);
mDebug.printMatrix(dtw.AccumCostMatrix);
foreach(mIndex step in wres)
{
print(step + " " + dtw.AccumCostMatrix[step.y][step.x]);
}
// Texture2D tmpT = mDebug.drawMatrix(dtw.CostMatrix);
Texture2D tmpT = mDebug.drawMatrix(dtw.AccumCostMatrix);
tmpT = mDebug.drawPoints(tmpT, wres.ToArray(), Color.red);
costMatrixTexture.texture = tmpT;
costMatrixTexture.transform.localScale = new Vector3(0.3f, 0.3f*((float)svec.Length/(float)vec1.Length), 1);
costMatrixTexture.transform.position = new Vector3(0.5f*costMatrixTexture.transform.localScale.x, costMatrixTexture.transform.localScale.y, 0);
}
void Start()
{
Random.seed = System.DateTime.Now.Millisecond;
Pose[] vec1 = new Pose[] {
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(),
Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random(), Pose.random()
};
Pose[] svec = new Pose[] {
vec1[4], vec1[6], vec1[8], vec1[10],
vec1[12], vec1[13], vec1[14], vec1[15],
vec1[16], vec1[18], vec1[20], vec1[22],
vec1[24], vec1[27], vec1[30], vec1[31],
vec1[32], vec1[33], vec1[34], vec1[35],
vec1[37], vec1[38], vec1[40], vec1[42]
};
// Float[] vec1 = new Float[] {
// new Float(0), new Float(0.75f), new Float(1.5f), new Float(0.75f), new Float(0), new Float(-0.75f), new Float(-1.5f), new Float(-0.75f), new Float(0)
// };
// Float[] svec = new Float[] {
// new Float(0), new Float(-0.5f), new Float(-1), new Float(-1.5f)
// };
//
// float[] vec1 = new float[] { 0, 0.75f, 1.5f, 0.75f, 0, -0.75f, -1.5f, -0.75f, 0};
// float[] svec = new float[] { 0, -0.5f, -1, -1.5f};
int start = System.DateTime.Now.Millisecond;
DTW dtw = new DTW(new mIndex[] { new mIndex(1, 1), new mIndex(1, 2), new mIndex(2, 1) });
List <mIndex> wres = dtw.warpSubsequenceDTW(vec1, svec);
Debug.Log("-- Time " + (System.DateTime.Now.Millisecond - start));
mDebug.printMatrix(dtw.CostMatrix);
mDebug.printMatrix(dtw.AccumCostMatrix);
foreach (mIndex step in wres)
{
print(step + " " + dtw.AccumCostMatrix[step.y][step.x]);
}
// Texture2D tmpT = mDebug.drawMatrix(dtw.CostMatrix);
Texture2D tmpT = mDebug.drawMatrix(dtw.AccumCostMatrix);
tmpT = mDebug.drawPoints(tmpT, wres.ToArray(), Color.red);
costMatrixTexture.texture = tmpT;
costMatrixTexture.transform.localScale = new Vector3(0.3f, 0.3f * ((float)svec.Length / (float)vec1.Length), 1);
costMatrixTexture.transform.position = new Vector3(0.5f * costMatrixTexture.transform.localScale.x, costMatrixTexture.transform.localScale.y, 0);
}
public RecognitionThread(ActionInstance seq, ActionData subseq, mIndex[] stepConstraints)
{
_seq = seq;
_subseq = subseq;
dtw = new DTW(stepConstraints);
_isDone = false;
}
void OnRecognitionDone(ActionData matchedinst, ActionInstance insttomatch, DTW dtw)
{
ActionInstance warpedinst = new ActionInstance();
foreach(mIndex idx in dtw.WarpPath)
{
warpedinst.poses.Add(insttomatch.poses[idx.x]);
}
print("-- cost " + matchedinst.actionId + " " + dtw.getWarpPathCost());
onRecognitionFinished(matchedinst.actionId, warpedinst);
}
static void Main()
{
var server = new WebSocketServer("ws://127.0.0.1:8181");
server.Start(socket =>
{
socket.OnOpen = () => Console.WriteLine("Open!");
socket.OnClose = () => Console.WriteLine("Close!");
socket.OnMessage = message =>
{
JObject j = JObject.Parse(message);
int k = j.GetValue("k").ToObject <int>();
JArray searchseries = (JArray)j["searchseries"];
int dimensions = searchseries[0].ToObject <double[]>().Length;
var sw = Stopwatch.StartNew();
DTW ucrDtw = new DTW(dimensions, k);
foreach (JArray datapoint in searchseries)
{
ucrDtw.addDataItem(datapoint.ToObject <double[]>());
}
JArray snippets = (JArray)j["querysnippets"];
DTWResult localresults;
JArray matches = new JArray();
JArray distances = new JArray();
JArray queryids = new JArray();
int quid = 0;
foreach (JArray snippet in snippets)
{
Query query = ucrDtw.Query();
foreach (JArray datapoint in snippet)
{
query.addQueryItem(datapoint.ToObject <double[]>());
}
localresults = ucrDtw.warp(query);
matches.Add(localresults.Locations);
distances.Add(localresults.Distances);
foreach (int thatloc in localresults.Locations)
{
queryids.Add(quid);
}
Console.WriteLine("Query " + quid + " finished.");
quid = quid + 1;
}
sw.Stop();
JObject response = new JObject();
response.Add("querytime", Math.Round(sw.Elapsed.TotalMilliseconds) / 1000.0);
response.Add("matchlocations", matches);
response.Add("queryids", queryids);
response.Add("distances", distances);
socket.Send(response.ToString());
};
});
Console.WriteLine("Waiting for requests...");
var name = Console.ReadLine();
}
