private GestureData NormalizeClosedLines(GestureData data)
{
var result = new GestureData();
foreach (var line in data.lines)
{
var resultLine = new GestureLine()
{
points = line.points.ToList(),
closedLine = line.closedLine
};
if (line.closedLine)
{
resultLine.points.Add(Vector2.Lerp(resultLine.points.Last(), resultLine.points[0], 0.99f));
}
result.lines.Add(resultLine);
}
return(result);
}
private RecognitionResult findPattern(GestureData queryData)
{
var bestGesture = default(GesturePattern);
var bestScore = Score.MaxDistance;
var indexes = Enumerable.Range(0, queryData.lines.Count).ToList();
List <List <int> > permutIndexes = GenPermutations(indexes);
var permutations = permutIndexes.Select(e => makePermutation(e, queryData)).ToList();
for (int i = 0; i < patterns.Count; i++)
{
var gestureAsset = patterns [i];
var assetData = NormalizeData(gestureAsset.gesture);
if (assetData.lines.Count != queryData.lines.Count)
{
//ignore wrong sizes
continue;
}
foreach (var data in permutations)
{
var permutScore = CalcScore(assetData, data);
float pd = permutScore.positionDistance;
float cd = permutScore.curvatureDistance;
float ad = permutScore.angleDistance;
if (permutScore > bestScore)
{
bestScore = permutScore;
bestGesture = gestureAsset;
}
}
}
return(new RecognitionResult()
{
gesture = bestGesture, score = bestScore
});
}
private Score CalcScore(GestureData data1, GestureData data2, bool useLinesDirections)
{
if (data1.lines.Count == data2.lines.Count)
{
var lineScores = new List <Score> ();
for (int i = 0; i < data1.lines.Count; i++)
{
var line1 = data1.lines [i].points;
var line2Fwd = data2.lines [i].points; //forward
var scoreFwd = CalcListScore(line1, line2Fwd, data2.lines[i].closedLine);
if (useLinesDirections)
{
lineScores.Add(scoreFwd);
}
else
{
var line2Bwd = line2Fwd.AsEnumerable().Reverse().ToList(); //backwards
var scoreBwd = CalcListScore(line1, line2Bwd, data2.lines[i].closedLine);
//gets best score between forward and backwards
var score = scoreFwd > scoreBwd ? scoreFwd : scoreBwd;
lineScores.Add(score);
}
}
//mean score from lines
return(new Score()
{
positionDistance = lineScores.Select(e => e.positionDistance).Sum() / lineScores.Count,
angleDistance = lineScores.Select(e => e.angleDistance).Sum() / lineScores.Count,
curvatureDistance = lineScores.Select(e => e.curvatureDistance).Sum() / lineScores.Count
});
}
else
{
return(Score.MaxDistance);
}
}
IEnumerator RecognizeCoroutine(GestureData data, System.Action <RecognitionResult> callback, bool normalizeScale)
{
RecognitionResult result = null;
//runs recocnition in another thread to avoid framerate drop
var thread = new System.Threading.Thread(() => {
var normData = NormalizeData(data, normalizeScale);
var found = findPattern(normData, normalizeScale);
result = found;
});
thread.Start();
while (thread.IsAlive)
{
yield return(null);
}
callback.Invoke(result);
}
RecognitionResult SearchThroughPatterns(int beginIndex, int endIndex, GestureData queryData, bool normalizeScale, List <GestureData> permutations, List <GestureData> singlePermutation)
{
var bestGesture = default(GesturePattern);
var bestScore = Score.MaxDistance;
for (int i = beginIndex; i <= endIndex; i++)
{
var gestureAsset = patterns[i];
var assetData = NormalizeData(gestureAsset.gesture, normalizeScale);
if (assetData.lines.Count != queryData.lines.Count)
{
//ignore wrong sizes
continue;
}
//if useLinesOrder, dont calc permutations
var permutationsToLook = gestureAsset.useLinesOrder ? singlePermutation : permutations;
foreach (var data in permutationsToLook)
{
var permutScore = CalcScore(data, assetData, gestureAsset.useLinesDirections);
float pd = permutScore.positionDistance;
float cd = permutScore.curvatureDistance;
float ad = permutScore.angleDistance;
if (permutScore > bestScore)
{
bestScore = permutScore;
bestGesture = gestureAsset;
}
}
}
return(new RecognitionResult()
{
gesture = bestGesture, score = bestScore
});
}
public void OnEndDrag(PointerEventData eventData)
{
data.LastLine.points.Add(FixedPosition(eventData.position));
UpdateLines();
for (int size = data.lines.Count; size >= 1 && size >= minLines; size--)
{
//last [size] lines
var sizedData = new GestureData()
{
lines = data.lines.GetRange(data.lines.Count - size, size)
};
RecognitionResult result = recognizer.Recognize(sizedData);
result.gestureData = sizedData;
if (result.gesture != null && result.score.score >= scoreToAccept)
{
OnRecognize.Invoke(result);
if (clearNotRecognizedLines)
{
data = sizedData;
UpdateLines();
}
break;
}
else
{
OnRecognize.Invoke(RecognitionResult.Empty);
}
}
//Added: Stijn
if (clearImmediatly)
{
data.lines.Clear();
UpdateLines();
}
}
IEnumerator OnEndDragCoroutine(PointerEventData eventData)
{
if (!in3D)
{
var fixedPos = FixedPosition(eventData.position);
if (fixedPos.x != -999f && fixedPos.y != -999f)
{
data.LastLine.points.Add(fixedPos);
}
UpdateLines();
}
else
{
var fixedPos = FixedPosition3D(eventData.position);
if (fixedPos.x != -999f && fixedPos.y != -999f)
{
data.LastLine.points3D.Add(fixedPos);
if (player.transform.forward == Vector3.right || player.transform.forward == -Vector3.right)
{
data.LastLine.points.Add(new Vector2(fixedPos.z, fixedPos.y));
}
else
{
data.LastLine.points.Add(new Vector2(fixedPos.x, fixedPos.y));
}
}
UpdateLines();
}
for (int size = data.lines.Count; size >= 1 && size >= minLines; size--)
{
//last [size] lines
var sizedData = new GestureData()
{
lines = data.lines.GetRange(data.lines.Count - size, size)
};
var sizedNormalizedData = sizedData;
//Fixed Area is never going to be used, so that line below is commented out as it throws an unnecessary error
if (fixedArea)
{
var rect = this.rectTransform.rect;
sizedNormalizedData = new GestureData()
{
lines = sizedData.lines.Select(line => new GestureLine()
{
closedLine = line.closedLine,
//points = line.points.Select(p => Rect.PointToNormalized(rect, this.rectTransform.InverseTransformPoint(p))).ToList()
}).ToList()
};
}
RecognitionResult result = null;
//run in another thread
var thread = new System.Threading.Thread(() =>
{
result = recognizer.Recognize(sizedNormalizedData, normalizeScale: !fixedArea);
});
thread.Start();
while (thread.IsAlive)
{
yield return(null);
}
if (result.gesture != null && result.score.score >= scoreToAccept)
{
OnRecognize.Invoke(result, this);
if (clearNotRecognizedLines)
{
data = sizedData;
UpdateLines();
}
break;
}
else
{
if (data.lines.Count > 1)
{
OnRecognize.Invoke(RecognitionResult.Empty, this);
}
}
}
yield return(null);
}
public void RecognizeAsync(GestureData data, System.Action <RecognitionResult> callback, bool normalizeScale = true)
{
StartCoroutine(RecognizeCoroutine(data, callback, normalizeScale));
}
private RecognitionResult findPattern(GestureData queryData, bool normalizeScale)
{
//UnityEngine.Profiling.Profiler.BeginSample ("Recognize.FindPattern.1");
var bestGesture = default(GesturePattern);
var bestScore = Score.MaxDistance;
var indexes = Enumerable.Range(0, queryData.lines.Count).ToList();
List <List <int> > permutIndexes = GenPermutations(indexes);
var permutations = permutIndexes.Select(e => makePermutation(e, queryData)).ToList();
var singlePermutation = permutations.GetRange(0, 1);
//UnityEngine.Profiling.Profiler.EndSample ();
//UnityEngine.Profiling.Profiler.BeginSample ("Recognize.FindPattern.2");
for (int i = 0; i < patterns.Count; i++)
{
//UnityEngine.Profiling.Profiler.BeginSample ("Recognize.FindPattern.2.1");
var gestureAsset = patterns [i];
var assetData = NormalizeData(gestureAsset.gesture, normalizeScale);
//UnityEngine.Profiling.Profiler.EndSample ();
//UnityEngine.Profiling.Profiler.BeginSample ("Recognize.FindPattern.2.2");
if (assetData.lines.Count != queryData.lines.Count)
{
//ignore wrong sizes
continue;
}
//if useLinesOrder, dont calc permutations
var permutationsToLook = gestureAsset.useLinesOrder ? singlePermutation : permutations;
foreach (var data in permutationsToLook)
{
//UnityEngine.Profiling.Profiler.BeginSample ("Recognize.FindPattern.2.2.1");
var permutScore = CalcScore(data, assetData, gestureAsset.useLinesDirections);
//UnityEngine.Profiling.Profiler.EndSample ();
float pd = permutScore.positionDistance;
float cd = permutScore.curvatureDistance;
float ad = permutScore.angleDistance;
if (permutScore > bestScore)
{
bestScore = permutScore;
bestGesture = gestureAsset;
}
}
//UnityEngine.Profiling.Profiler.EndSample ();
}
//UnityEngine.Profiling.Profiler.EndSample ();
return(new RecognitionResult()
{
gesture = bestGesture, score = bestScore
});
}
IEnumerator OnEndDragCoroutine(List <PointerEventData> eventData)
{
//Debug.Log("OnEndDragRoutine");
// zeichnet eine letzte linie an der mausposition
// deaktiviert da erkennung verzögert wird
//data.LastLine.points.Add(FixedPosition(eventData.position));
//UpdateLines();
for (int size = data.lines.Count; size >= 1 && size >= minLines; size--)
{
//last [size] lines
var sizedData = new GestureData()
{
lines = data.lines.GetRange(data.lines.Count - size, size)
};
var sizedNormalizedData = sizedData;
if (fixedArea)
{
var rect = this.rectTransform.rect;
sizedNormalizedData = new GestureData()
{
lines = sizedData.lines.Select(line => new GestureLine()
{
closedLine = line.closedLine,
points = line.points.Select(p => Rect.PointToNormalized(rect, this.rectTransform.InverseTransformPoint(p))).ToList()
}).ToList()
};
}
RecognitionResult result = null;
//run in another thread
var thread = new System.Threading.Thread(() =>
{
result = recognizer.Recognize(sizedNormalizedData, normalizeScale: !fixedArea);
});
thread.Start();
while (thread.IsAlive)
{
yield return(null);
}
if (result.gesture != null && result.score.score >= scoreToAccept)
{
recognized = true;
clearCounter = 0;
OnRecognize.Invoke(result, sizedNormalizedData);
if (clearNotRecognizedLines)
{
data = sizedData;
UpdateLines();
}
break;
}
else
{
OnRecognize.Invoke(RecognitionResult.Empty, null);
}
}
yield return(null);
}
private GestureData NormalizeData(GestureData data)
{
return(NormalizeDistribution(NormalizeScale(data), Detail));
}
IEnumerator OnEndDragCoroutine(PointerEventData eventData)
{
data.LastLine.points.Add(FixedPosition(eventData.position));
UpdateLines();
for (int size = data.lines.Count; size >= 1 && size >= minLines; size--)
{
//last [size] lines
var sizedData = new GestureData()
{
lines = data.lines.GetRange(data.lines.Count - size, size)
};
var sizedNormalizedData = sizedData;
if (fixedArea)
{
var rect = this.rectTransform.rect;
sizedNormalizedData = new GestureData()
{
lines = sizedData.lines.Select(line => new GestureLine()
{
closedLine = line.closedLine,
points = line.points.Select(p => Rect.PointToNormalized(rect, this.rectTransform.InverseTransformPoint(p))).ToList()
}).ToList()
};
}
RecognitionResult result = null;
//run in another thread
var thread = new System.Threading.Thread(() => {
result = recognizer.Recognize(sizedNormalizedData, normalizeScale: !fixedArea);
});
thread.Start();
while (thread.IsAlive)
{
yield return(null);
}
//USE THIS TO SNATCH THE SCORE to turn on boolean thing that allowws to go to next page!!! omg
if (result.gesture != null && result.score.score >= scoreToAccept)
{
OnRecognize.Invoke(result);
puzzleSolved = true;
// pm.canTurnPage[pm.page] = true;
if (clearNotRecognizedLines)
{
data = sizedData;
UpdateLines();
}
break;
}
else
{
OnRecognize.Invoke(RecognitionResult.Empty);
}
}
yield return(null);
}
private RecognitionResult findPattern(GestureData queryData, bool normalizeScale)
{
var bestGesture = default(GesturePattern);
var bestScore = Score.MaxDistance;
var indexes = Enumerable.Range(0, queryData.lines.Count).ToList();
List <List <int> > permutIndexes = GenPermutations(indexes);
var permutations = permutIndexes.Select(e => makePermutation(e, queryData)).ToList();
var singlePermutation = permutations.GetRange(0, 1);
int n_threads = Mathf.Min(this.numberOfThreads, patterns.Count);
var threads = new List <Thread>();
for (int threadIndex = 0; threadIndex < n_threads; threadIndex++)
{
int beginIndex = threadIndex * patterns.Count / n_threads;
int endIndex = (threadIndex + 1) * patterns.Count / n_threads - 1;
threads.Add(new Thread(() =>
{
var result = SearchThroughPatterns(beginIndex, endIndex, queryData, normalizeScale, permutations, singlePermutation);
lock (this)
{
if (result.score > bestScore)
{
bestScore = result.score;
bestGesture = result.gesture;
}
}
}));
}
for (int i = 0; i < threads.Count; i++)
{
threads[i].Start();
}
for (int i = 0; i < threads.Count; i++)
{
threads[i].Join();
}
// for (int i = 0; i < patterns.Count; i++)
// {
// var gestureAsset = patterns[i];
// var assetData = NormalizeData(gestureAsset.gesture, normalizeScale);
// if (assetData.lines.Count != queryData.lines.Count)
// {
// //ignore wrong sizes
// continue;
// }
// //if useLinesOrder, dont calc permutations
// var permutationsToLook = gestureAsset.useLinesOrder ? singlePermutation : permutations;
// foreach (var data in permutationsToLook)
// {
// var permutScore = CalcScore(data, assetData, gestureAsset.useLinesDirections);
// float pd = permutScore.positionDistance;
// float cd = permutScore.curvatureDistance;
// float ad = permutScore.angleDistance;
// if (permutScore > bestScore)
// {
// bestScore = permutScore;
// bestGesture = gestureAsset;
// }
// }
// }
return(new RecognitionResult()
{
gesture = bestGesture, score = bestScore
});
}
