public User ValidateUser(User user, Skeleton skel)
{
if (skel == null) return user;
var actualHeight = PointDistance(skel.Joints[JointType.HipCenter].Position,
skel.Joints[JointType.Head].Position);
var actualWidth = PointDistance(skel.Joints[JointType.ShoulderLeft].Position,
skel.Joints[JointType.ShoulderRight].Position);
var actualLength = PointDistance(skel.Joints[JointType.ElbowLeft].Position,
skel.Joints[JointType.ShoulderLeft].Position);
var bestMatch = FindBestMatch(actualHeight, actualWidth, actualLength);
if(bestMatch.Name.Equals(user.Name))
{
user.Confidence = (user.FaceConfidence/100 + bestMatch.Confidence)/2;
user.HipHeight = bestMatch.HipHeight;
user.ShoulderWidth = bestMatch.ShoulderWidth;
user.ArmLength = bestMatch.ArmLength;
}else
{
user.Confidence = (user.FaceConfidence/100 - bestMatch.Confidence)/2;
user.HipHeight = bestMatch.HipHeight;
user.ShoulderWidth = bestMatch.ShoulderWidth;
user.ArmLength = bestMatch.ArmLength;
}
return user;
}
public void UserDetected(User user)
{
if (_comInterface != null && user != null) _comInterface.UserFound(user.Name,user.FaceConfidence,user.TrackingID);
}
void IKinect.UserLost(User user)
{
if (_comInterface != null && user != null) _comInterface.UserLost(user.Name);
}
private void LoadUsers()
{
var userData = FileLoader.LoadAllUsers();
if (userData == null) return;
foreach (var userInfo in userData)
{
var user = new User { Name = userInfo.Key, TrackingID = -1, IsActive = false };
if (userInfo.Value != null)
foreach (var feature in userInfo.Value)
{
if (feature.Key.Equals(FeatureType.ArmLength))
{
user.ArmLength = float.Parse(feature.Value);
}
else if (feature.Key.Equals(FeatureType.HipHeadHeight))
{
user.HipHeight = float.Parse(feature.Value);
}
else if (feature.Key.Equals(FeatureType.ShoulderWidth))
{
user.ShoulderWidth = float.Parse(feature.Value);
}
}
_users.Add(user);
}
}
private int FindCurrentUser(bool forceRecognition)
{
if (_recognitionEngine == null) return -1;
if (_skeletons == null) return -1;
foreach (var user in _users.Cast<User>().Where(user => user.IsActive))
{
foreach(var skel in _skeletons)
{
if(skel.TrackingId == user.TrackingID && skel.TrackingState == SkeletonTrackingState.Tracked)
{
return user.TrackingID;
}
}
user.IsActive = false;
user.TrackingID = -1;
return -1;
}
foreach(var skel in _skeletons)
{
if(skel.TrackingId == _activeTID && skel.TrackingState != SkeletonTrackingState.Tracked)
{
return -1;
}
}
if(_activeTID != -1 && _activeSkeleton != null && !_recognitionEngine.IsRecognizing)
{
if(_activeUser == null)
{
_activeUser = new User();
_activeUser.TrackingID = _activeTID;
_activeUser.Skeleton = _activeSkeleton;
}else if(_activeUser.TrackingID != _activeTID || _activeSkeleton.TrackingId != _activeUser.Skeleton.TrackingId)
{
_activeUser.TrackingID = _activeTID;
_activeUser.Skeleton = _activeSkeleton;
_activeUser.Attempts = 0;
}
if(_activeUser.Attempts >= User.MAX_ATTEMPTS && _activeUser.Attempts <= User.MAX_ATTEMPTS+1)
{
Log.Info("Gave up on recognition for user with id: " + _activeUser.TrackingID);
_activeUser.Attempts++;
return -1;
}
if(!_recSoundPlayed)
{
_recStart.Play();
_recSoundPlayed = true;
}
if(_recognitionEngine.Recognize(_activeSkeleton,Coding4Fun.Kinect.WinForm.BitmapExtensions.ToBitmap(_pixelData,640,480)))
{
if(_activeUser != null)
_activeUser.Attempts++;
}
return _activeTID;
}else if(_recognitionEngine.IsRecognizing){
return _activeTID;
}
return FindClosestSkeleton();
}
public void Train(string name, int skelID)
{
foreach(var skeleton in _skeletons)
{
if(skeleton.TrackingId == skelID && skeleton.TrackingState == SkeletonTrackingState.Tracked && skeleton.TrackingId == _activeTID)
{
var exists = _recognitionEngine.AddUser(name,skeleton);
_recognitionEngine.Train(name, skeleton, Coding4Fun.Kinect.WinForm.BitmapExtensions.ToBitmap(_pixelData, 640, 480));
if(!exists)
{
var u = new User {Name = name, IsActive = false, TrackingID = skeleton.TrackingId};
_users.Add(u);
}
}
}
}
public void RecognitionResult(User user)
{
if (user == null) return;
foreach(var skeleton in _skeletons)
{
if (skeleton.TrackingId != user.TrackingID ||
skeleton.TrackingState == SkeletonTrackingState.NotTracked) continue;
_kinectHandler.UserDetected(user);
foreach (User userL in _users)
{
if (!userL.Name.Equals(user.Name)) continue;
userL.TrackingID = user.TrackingID;
userL.Confidence = user.Confidence;
userL.FaceConfidence = user.FaceConfidence;
userL.IsActive = true;
userL.ShoulderWidth = user.ShoulderWidth;
userL.HipHeight = user.HipHeight;
userL.ArmLength = user.ArmLength;
_activeSkeleton = skeleton;
_recDone.Play();
_recSoundPlayed = false;
//zLog.Info("User skeleton found. Starting tracking of id : " + userL.TrackingID);
_activeTID = userL.TrackingID;
_kinectHandler.StartTracking(userL.TrackingID);
_kinectHandler.TrackingStarted(userL.TrackingID);
_activeUser = null;
}
}
}
void _recognizer_RecognitionCompletedEvent(System.ComponentModel.RunWorkerCompletedEventArgs e)
{
IsRecognizing = false;
if (e.Cancelled)
{
_trackingEngine.RecognitionResult(null);
return;
}
var users = e.Result as Dictionary<string, float>;
var bestMatch = "";
var bestConf = 50f;
foreach (var user in users)
{
if (user.Value > bestConf)
{
bestMatch = user.Key;
bestConf = user.Value;
}
}
if ("".Equals(bestMatch))
{
_trackingEngine.RecognitionResult(null);
return;
}
bestMatch = bestMatch.Replace("@homerKinect.com", "");
//Log.Info(bestMatch);
User userF = new User();
userF.Name = bestMatch;
userF.FaceConfidence = bestConf;
userF.TrackingID = _skelToMatch.TrackingId;
userF = _featureDetector.ValidateUser(userF, _skelToMatch);
_trackingEngine.RecognitionResult(userF);
}
private User FindBestMatch(float actualHeight, float actualWidth, float actualLength)
{
var bestUser = new User();
var bestConfidence = float.MinValue;
foreach (var storedFeatures in _users)
{
var userLabel = storedFeatures.Key;
float sumConfidence = 0;
foreach (var featureType in storedFeatures.Value)
{
switch (featureType.Key)
{
case FeatureType.HipHeadHeight:
{
var perDiff = Math.Abs(1 - (actualHeight/float.Parse(featureType.Value)));
var confidence = 1 - perDiff;
sumConfidence += confidence;
}
break;
case FeatureType.ShoulderWidth:
{
var perDiff = Math.Abs(1 - (actualWidth/float.Parse(featureType.Value)));
var confidence = 1 - perDiff;
sumConfidence += confidence;
}
break;
case FeatureType.ArmLength:
{
var perDiff = Math.Abs(1 - (actualLength/float.Parse(featureType.Value)));
var confidence = 1 - perDiff;
sumConfidence += confidence;
}
break;
}
}
sumConfidence = sumConfidence/3;
if (bestConfidence >= sumConfidence) continue;
bestConfidence = sumConfidence;
bestUser.Name = userLabel;
bestUser.HipHeight = actualHeight;
bestUser.ShoulderWidth = actualWidth;
bestUser.ArmLength = actualLength;
bestUser.Confidence = bestConfidence;
}
return bestUser;
}
