private static void FeedFaceInfo(Face f, FaceSendInfo fsi, Emotion e)
{
fsi.faceId = f.FaceId.ToString();
fsi.age = f.FaceAttributes.Age;
fsi.faceRecHeight = f.FaceRectangle.Height;
fsi.faceRecLeft = f.FaceRectangle.Left;
fsi.faceRecTop = f.FaceRectangle.Top;
fsi.faceRecWidth = f.FaceRectangle.Width;
fsi.gender = f.FaceAttributes.Gender;
fsi.smile = f.FaceAttributes.Smile;
fsi.beard = f.FaceAttributes.FacialHair.Beard;
fsi.moustache = f.FaceAttributes.FacialHair.Moustache;
fsi.sideburns = f.FaceAttributes.FacialHair.Sideburns;
fsi.glasses = f.FaceAttributes.Glasses.ToString();
fsi.headYaw = f.FaceAttributes.HeadPose.Yaw;
fsi.headRoll = f.FaceAttributes.HeadPose.Roll;
fsi.headPitch = f.FaceAttributes.HeadPose.Pitch;
fsi.anger = e.Scores.Anger;
fsi.contempt = e.Scores.Contempt;
fsi.disgust = e.Scores.Disgust;
fsi.fear = e.Scores.Fear;
fsi.happiness = e.Scores.Happiness;
fsi.neutral = e.Scores.Neutral;
fsi.sadness = e.Scores.Sadness;
fsi.surprise = e.Scores.Surprise;
fsi.timeStamp = DateTime.Now;
}
public async Task <List <FaceSendInfo> > FindSimilarPersonWithEmotion()
{
var facesInfo = new List <FaceSendInfo>();
List <SimilarFaceMatch> result = new List <SimilarFaceMatch>();
//Loop thru all detected faces from previous steps and fill the facesInfo array
//For ease of processing in Azure Stream Analytics we create single level object
foreach (var f in this.DetectedFaces)
{
var fsi = new FaceSendInfo();
//Add emotions
var e = CoreUtil.FindEmotionForFace(f, this.DetectedEmotion);
FeedFaceInfo(f, fsi, e);
//We sen also info how many faces in total were recognized on the picture with current face
fsi.facesNo = this.DetectedFaces.Count();
Tuple <SimilarPersistedFace, string> similarPersistedFace = await FaceListManager.FindSimilarPersistedFaceAsync(await this.GetImageStreamCallback(), f.FaceId, f.FaceRectangle);
if (similarPersistedFace != null)
{
result.Add(new SimilarFaceMatch {
Face = f, SimilarPersistedFace = similarPersistedFace.Item1
});
fsi.canid = similarPersistedFace.Item1.PersistedFaceId.ToString();
fsi.canconf = similarPersistedFace.Item1.Confidence;
//In order to get also name we need to obtain Person
//p = await FaceServiceHelper.GetPersonAsync(groupId, can.PersonId);
fsi.canname = similarPersistedFace.Item1.PersistedFaceId.ToString();
}
facesInfo.Add(fsi);
}
SimilarFaceMatches = result;
return(facesInfo);
}
public async Task <List <FaceSendInfo> > IdentifyOrAddPersonWithEmotionsAsync(string groupName, ObservableCollection <IdentifiedFaces> identifiedPersonsIdCollection)
{
var facesInfo = new List <FaceSendInfo>();
//Loop thru all detected faces from previous steps and fill the facesInfo array
//For ease of processing in Azure Stream Analytics we create single level object
foreach (var f in this.DetectedFaces)
{
var fsi = new FaceSendInfo();
//Add emotions
var e = CoreUtil.FindEmotionForFace(f, this.DetectedEmotion);
FeedFaceInfo(f, fsi, e);
//We sen also info how many faces in total were recognized on the picture with current face
fsi.facesNo = this.DetectedFaces.Count();
facesInfo.Add(fsi);
}
//Now we proceed to face recognition/identification
//First we create group if it does not exist
try
{
var g = await FaceServiceHelper.CreatePersonGroupIfNoGroupExists(groupName);
groupId = g.PersonGroupId;
}
catch (Exception e)
{
// Catch errors with individual groups so we can continue looping through all groups. Maybe an answer will come from
// another one.
ErrorTrackingHelper.TrackException(e, "Problem creating group");
if (this.ShowDialogOnFaceApiErrors)
{
await ErrorTrackingHelper.GenericApiCallExceptionHandler(e, "Problem creating group");
}
}
//We need to find candidate for every face
try
{
IdentifyResult[] groupResults = await this.IdentifyFacesAsync(groupId);
//We loop thri all faces again in order to find candidate
foreach (var f in this.DetectedFaces)
{
bool needToRetrain = true;
var fi = facesInfo.Where(fin => fin.faceId == f.FaceId.ToString()).FirstOrDefault();
var newPersonID = Guid.NewGuid();
if (groupResults != null && groupResults.Where(gr => gr.FaceId == f.FaceId).Any() && groupResults.Where(gr => gr.FaceId == f.FaceId).FirstOrDefault().Candidates.Any())
{
var candidates = groupResults.Where(gr => gr.FaceId == f.FaceId).FirstOrDefault().Candidates.OrderByDescending(ca => ca.Confidence);
Person p = new Person();
var can = candidates.FirstOrDefault();
//If we have sufficient confidence, we add Face for person
if (can.Confidence >= SettingsHelper.Instance.Confidence)
{
fi.canid = can.PersonId.ToString();
fi.canconf = can.Confidence;
//In order to get also name we need to obtain Person
p = await FaceServiceHelper.GetPersonAsync(groupId, can.PersonId);
fi.canname = p.Name;
var identifiedPersonFromList = identifiedPersonsIdCollection.Where(ip => ip.Id == can.PersonId.ToString()).FirstOrDefault();
//Check whether we did not added too much photos lately, it is not neccesary to add photo for face every time
if (identifiedPersonFromList == null)
{
await AddFaceToPerson(f, p, can.PersonId);
}
else if (identifiedPersonFromList.NumOfAddedPhotosInLastPeriod < SettingsHelper.Instance.NumberOfPhotoAddsInPeriod)
{
await AddFaceToPerson(f, p, can.PersonId);
identifiedPersonFromList.NumOfAddedPhotosInLastPeriod++;
}
else if ((DateTime.Now - identifiedPersonFromList.FirstPhotoAddedInLastPeriod).Hours > SettingsHelper.Instance.PhotoAddPeriodSize)
{
identifiedPersonFromList.NumOfAddedPhotosInLastPeriod = 1;
identifiedPersonFromList.FirstPhotoAddedInLastPeriod = DateTime.Now;
await AddFaceToPerson(f, p, can.PersonId);
}
else
{
needToRetrain = false;
}
}
else
{
//if not sufficient confidence we also need to check whether there is similar face/ if not create new person
await CreatePrsonIfNoSimilarFaceExistsAsync(facesInfo, newPersonID, f);
}
}
else
{
//if no candidate we also need to check whether there is similar fac,e if not create new person
await CreatePrsonIfNoSimilarFaceExistsAsync(facesInfo, newPersonID, f);
}
try
{
//We need to train after operation on top of group (addition of photo, person etc.)
if (needToRetrain)
{
await FaceServiceHelper.TrainPersonGroupAsync(groupId);
}
}
catch (Exception e)
{
// Catch error with training of group
ErrorTrackingHelper.TrackException(e, "Problem training group");
if (this.ShowDialogOnFaceApiErrors)
{
await ErrorTrackingHelper.GenericApiCallExceptionHandler(e, "Problem training group");
}
}
//Handle the identified persons collection to which we locally save every identified person
if (!identifiedPersonsIdCollection.Where(ip => ip.Id == fi.canid).Any())
{
identifiedPersonsIdCollection.Add(new IdentifiedFaces()
{
Id = fi.canid
});
}
//Increase counter of identifications
else if (identifiedPersonsIdCollection.Where(ip => ip.Id == fi.canid).Any())
{
identifiedPersonsIdCollection.Where(ip => ip.Id == fi.canid).FirstOrDefault().NumberOfIdentifications++;
}
//Find faces which were wrongly learned (small number of identifications)
var tbd = new List <IdentifiedFaces>();
foreach (var ip in identifiedPersonsIdCollection)
{
if (ip.NumberOfIdentifications <= SettingsHelper.Instance.NeededFaceIdentNum && (ip.CreatedAt.AddSeconds(SettingsHelper.Instance.DeleteWindow) < DateTime.Now))
{
var g = (await FaceServiceHelper.GetPersonGroupsAsync()).Where(gr => gr.Name == groupName).FirstOrDefault();
Person pers = await FaceServiceHelper.GetPersonAsync(g.PersonGroupId, new Guid(ip.Id));
//if we saved insufficient number of faces than delete
if (pers.PersistedFaceIds.Length <= SettingsHelper.Instance.NeededFaceIdentNum)
{
await FaceServiceHelper.DeletePersonAsync(g.PersonGroupId, pers.PersonId);
string similarFaceId = "";
using (var db = new KioskDBContext())
{
var sfToDelete = db.SimilarFaces.Where(sf => sf.PersonId == pers.PersonId.ToString()).FirstOrDefault();
similarFaceId = sfToDelete.FaceId.ToString();
db.SimilarFaces.Remove(sfToDelete);
}
await FaceListManager.DeleteFaceFromFaceList(similarFaceId);
await FaceServiceHelper.TrainPersonGroupAsync(g.PersonGroupId);
tbd.Add(ip);
}
}
}
foreach (var iptodelete in tbd)
{
identifiedPersonsIdCollection.Remove(iptodelete);
}
}
}
catch (Exception e)
{
// Catch error with training of group
ErrorTrackingHelper.TrackException(e, "Problem with cognitive services");
if (this.ShowDialogOnFaceApiErrors)
{
await ErrorTrackingHelper.GenericApiCallExceptionHandler(e, "Problem with cognitive services");
}
}
return(facesInfo);
}
public async Task <List <FaceSendInfo> > IdentifyOrAddPersonWithEmotionsAsync(string groupName, double confidence)
{
var time = DateTime.Now;
//We also add emotions
var facesInfo = new List <FaceSendInfo>();
foreach (var f in this.DetectedFaces)
{
var fsi = new FaceSendInfo();
var e = CoreUtil.FindEmotionForFace(f, this.DetectedEmotion);
fsi.faceId = f.FaceId.ToString();
fsi.age = f.FaceAttributes.Age;
fsi.faceRecHeight = f.FaceRectangle.Height;
fsi.faceRecLeft = f.FaceRectangle.Left;
fsi.faceRecTop = f.FaceRectangle.Top;
fsi.faceRecWidth = f.FaceRectangle.Width;
fsi.gender = f.FaceAttributes.Gender;
fsi.smile = f.FaceAttributes.Smile;
fsi.beard = f.FaceAttributes.FacialHair.Beard;
fsi.moustache = f.FaceAttributes.FacialHair.Moustache;
fsi.sideburns = f.FaceAttributes.FacialHair.Sideburns;
fsi.glasses = f.FaceAttributes.Glasses.ToString();
fsi.headYaw = f.FaceAttributes.HeadPose.Yaw;
fsi.headRoll = f.FaceAttributes.HeadPose.Roll;
fsi.headPitch = f.FaceAttributes.HeadPose.Pitch;
fsi.anger = e.Scores.Anger;
fsi.contempt = e.Scores.Contempt;
fsi.disgust = e.Scores.Disgust;
fsi.fear = e.Scores.Fear;
fsi.happiness = e.Scores.Happiness;
fsi.neutral = e.Scores.Neutral;
fsi.sadness = e.Scores.Sadness;
fsi.surprise = e.Scores.Surprise;
fsi.timeStamp = time;
fsi.facesNo = this.DetectedFaces.Count();
facesInfo.Add(fsi);
}
var newPersonID = Guid.NewGuid();
//Move to initialization
try
{
var g = await FaceServiceHelper.CreatePersonGroupIfNoGroupExists(groupName);
groupId = g.PersonGroupId;
}
catch (Exception e)
{
// Catch errors with individual groups so we can continue looping through all groups. Maybe an answer will come from
// another one.
ErrorTrackingHelper.TrackException(e, "Problem creating group");
if (this.ShowDialogOnFaceApiErrors)
{
await ErrorTrackingHelper.GenericApiCallExceptionHandler(e, "Problem creating group");
}
}
List <IdentifiedPerson> ipresult = new List <IdentifiedPerson>();
// Compute Face Identification and Unique Face Ids
//We need to map detected faceID with actual personID (Identified face)
try
{
IdentifyResult[] groupResults = await this.IdentifyFacesAsync(groupId);
foreach (var f in this.DetectedFaces)
{
if (groupResults != null && groupResults.Where(gr => gr.FaceId == f.FaceId).Any() && groupResults.Where(gr => gr.FaceId == f.FaceId).FirstOrDefault().Candidates.Any())
{
var candidates = groupResults.Where(gr => gr.FaceId == f.FaceId).FirstOrDefault().Candidates.OrderByDescending(ca => ca.Confidence);
var fi = facesInfo.Where(fin => fin.faceId == f.FaceId.ToString()).FirstOrDefault();
int i = 0;
Person p = new Person();
foreach (var can in candidates)
{
switch (i)
{
case 0:
fi.can1id = can.PersonId.ToString();
fi.can1conf = can.Confidence;
p = await FaceServiceHelper.GetPersonAsync(groupId, can.PersonId);
fi.can1name = p.Name;
if (can.Confidence >= confidence)
{
ipresult.Add(new IdentifiedPerson()
{
Person = p, Confidence = can.Confidence, FaceId = f.FaceId
});
if (p.PersistedFaceIds.Length == 248)
{
Guid persistedFaceId = p.PersistedFaceIds.OrderBy(x => Guid.NewGuid()).FirstOrDefault();
await FaceServiceHelper.DeletePersonFaceAsync(groupId, can.PersonId, persistedFaceId);
}
try
{
await FaceServiceHelper.AddPersonFaceAsync(groupId, can.PersonId, await this.GetImageStreamCallback(), "", f.FaceRectangle);
}
catch (Exception e)
{
// Catch errors with individual groups so we can continue looping through all groups. Maybe an answer will come from
// another one.
ErrorTrackingHelper.TrackException(e, "Problem adding face to group");
if (this.ShowDialogOnFaceApiErrors)
{
await ErrorTrackingHelper.GenericApiCallExceptionHandler(e, "Problem adding face to group");
}
}
}
else
{
//create new Guy if confidence not sufficient
SimilarFaceMatch result = await GetSimilarFace(f);
//using (var db = new KioskDbContext())
//{
// Blogs.ItemsSource = db.Blogs.ToList();
//}
await CreatePerson(facesInfo, newPersonID, f);
}
break;
case 1:
fi.can2id = can.PersonId.ToString();
fi.can2conf = can.Confidence;
p = await FaceServiceHelper.GetPersonAsync(groupId, can.PersonId);
fi.can2name = p.Name;
break;
case 2:
fi.can3id = can.PersonId.ToString();
fi.can3conf = can.Confidence;
p = await FaceServiceHelper.GetPersonAsync(groupId, can.PersonId);
fi.can3name = p.Name;
break;
case 3:
fi.can4id = can.PersonId.ToString();
fi.can4conf = can.Confidence;
p = await FaceServiceHelper.GetPersonAsync(groupId, can.PersonId);
fi.can4name = p.Name;
break;
}
i++;
}
}
else
{
//if no candidate we also need to create new person
await CreatePerson(facesInfo, newPersonID, f);
}
try
{
await FaceServiceHelper.TrainPersonGroupAsync(groupId);
}
catch (Exception e)
{
// Catch error with training of group
ErrorTrackingHelper.TrackException(e, "Problem training group");
if (this.ShowDialogOnFaceApiErrors)
{
await ErrorTrackingHelper.GenericApiCallExceptionHandler(e, "Problem training group");
}
}
}
}
catch (Exception e)
{
// Catch error with training of group
ErrorTrackingHelper.TrackException(e, "Problem with cognitive services");
if (this.ShowDialogOnFaceApiErrors)
{
await ErrorTrackingHelper.GenericApiCallExceptionHandler(e, "Problem with cognitive services");
}
}
return(facesInfo);
}