public static async Task <GetRecognisedUserViewModel> IdentifyInPersonGroup(IFaceClient client, List <InPutDictlist> personDictionary, string url, string url2, string sourceImageFileName, string recognitionModel)
{
GetRecognisedUserViewModel getRecognisedUserViewModel = new GetRecognisedUserViewModel();
string OutPut = "Initializing..... \n Creating new Personal Model \n ";
// Create a dictionary for all your images, grouping similar ones under the same key.
// A group photo that includes some of the persons you seek to identify from your dictionary.
// string sourceImageFileName = "identification1.jpg";
// Create a person group.
// Console.WriteLine($"Create a person group ({personGroupId}).");
OutPut = OutPut + "person group identity: " + personGroupId + ". \n";
await client.PersonGroup.CreateAsync(personGroupId, personGroupId, recognitionModel : recognitionModel);
// The similar faces will be grouped into a single person group person.
foreach (var groupedFace in personDictionary)
{
// Limit TPS
await Task.Delay(250);
Microsoft.Azure.CognitiveServices.Vision.Face.Models.Person person = await client.PersonGroupPerson.CreateAsync(personGroupId, groupedFace.Name);
// Console.WriteLine($"Create a person group person '{groupedFace}'.");
// Add face to the person group person.
foreach (var similarImage in groupedFace.ImageName)
{
OutPut = OutPut + "Add face to the person group person( " + groupedFace + ") from image `" + similarImage + "` \n ";
string urlzz = url + similarImage;
PersistedFace face = await client.PersonGroupPerson.AddFaceFromUrlAsync(personGroupId, person.PersonId, urlzz, similarImage);
}
}
// Start to train the person group.
OutPut = OutPut + "\n \n Now training Person Group: " + personGroupId + "\n";
await client.PersonGroup.TrainAsync(personGroupId);
// Wait until the training is completed.
while (true)
{
await Task.Delay(1000);
var trainingStatus = await client.PersonGroup.GetTrainingStatusAsync(personGroupId);
OutPut = OutPut + "Training status: " + trainingStatus.Status + ". \n";
if (trainingStatus.Status == TrainingStatusType.Succeeded)
{
break;
}
}
Console.WriteLine();
List <Guid> sourceFaceIds = new List <Guid>();
// Detect faces from source image url.
List <DetectedFace> detectedFaces = await DetectFaceRecognize(client, $"{url2}{sourceImageFileName}", recognitionModel);
// Add detected faceId to sourceFaceIds.
foreach (var detectedFace in detectedFaces)
{
sourceFaceIds.Add(detectedFace.FaceId.Value);
}
// Identify the faces in a person group.
var identifyResults = await client.Face.IdentifyAsync(sourceFaceIds, personGroupId);
double numberstream = 0.0;
Microsoft.Azure.CognitiveServices.Vision.Face.Models.Person outputperson = new Microsoft.Azure.CognitiveServices.Vision.Face.Models.Person();
foreach (var identifyResult in identifyResults)
{
Microsoft.Azure.CognitiveServices.Vision.Face.Models.Person person = await client.PersonGroupPerson.GetAsync(personGroupId, identifyResult.Candidates[0].PersonId);
OutPut = OutPut + "Person: '" + person.Name + "' is identified for face in: " + sourceImageFileName + " - " + identifyResult.FaceId + " \n";
List <Guid> gruildie = new List <Guid>();
gruildie.Add(identifyResult.FaceId);
if (identifyResult.Candidates[0].Confidence > numberstream)
{
outputperson.Name = person.Name;
outputperson.PersonId = person.PersonId;
outputperson.PersistedFaceIds = gruildie;
getRecognisedUserViewModel.ConfidenceLevel = identifyResult.Candidates[0].Confidence;
}
}
await DeletePersonGroup(client, personGroupId);
OutPut = OutPut + "\n \n Person Id group deleted.";
getRecognisedUserViewModel.Answers = OutPut;
getRecognisedUserViewModel.highestperson = outputperson;
// At end, delete person groups in both regions (since testing only)
return(getRecognisedUserViewModel);
}
private async void Detect_Person_Button_Click(object sender, RoutedEventArgs e)
{
var fileinfo = new FileInfo(imagepathHolder);
BlobConstructors.UploadFile(fileinfo, BlobConstructors.Blobconnectionstring, BlobConstructors.containername2);
IFaceClient client = CompareFaceConstructor.Authenticate(ENDPOINT, SUBSCRIPTION_KEY);
// Find Similar - find a similar face from a list of faces.
// var result = await CompareFaceConstructor.FindSimilar(client, BlobConstructors.blobBaseUrl, imageName, BlobConstructors.blobBaseUrl2, RecognitionModel.Recognition04, imageNames);
string _dbPath = Database_Connection._dbpath;
//Verify(client, IMAGE_BASE_URL, RECOGNITION_MODEL4).Wait();
//// Identify - recognize a face(s) in a person group (a person group is created in this example).
///
GetRecognisedUserViewModel Identity = new GetRecognisedUserViewModel();
try
{
Identity = await CompareFaceConstructor.IdentifyInPersonGroup(client, personDictionary, BlobConstructors.blobBaseUrl, BlobConstructors.blobBaseUrl2, imageName, RECOGNITION_MODEL4);
string Message = "";
var db = new SQLiteConnection(_dbPath);
var dz = new SQLiteConnection(_dbPath);
db.CreateTable <Models.Person>();
db.CreateTable <Models.Attendance>();
//var p = db.Table<Models.Person>().Where(m => m.ImageName == result.Result).FirstOrDefault();
int way = int.Parse(Identity.highestperson.Name);
var p = db.Table <Models.Person>().Where(m => m.Id == way).FirstOrDefault();
var Count = db.Table <Models.Attendance>().Count();
//Books_Label.Text = result.Output.ToString();
//readText(result.ToString());
if (Identity.ConfidenceLevel > 0.7)
{
Message = "Welcome back " + p.LastName + " " + p.FirstName + " , You have been logged in. Have a pleasant day!";
Books_Label.Text = Identity.Answers;
Attendance attendance = new Attendance()
{
Id = Count + 1,
FirstName = p.FirstName,
Date_Signed_In_Date_and_Time = DateTime.UtcNow,
LastName = p.LastName,
User_ID = p.Id
};
db.Insert(attendance);
readText(Message);
}
else
{
var confipercentage = Identity.ConfidenceLevel * 100;
Message = "I am not comfortable this is who i think this is. i am only " + confipercentage + " percent sure, that this is" + p.FirstName + " " + p.LastName + ".";
readText(Message);
}
}
catch
{
readText("Face not detected from Image or Server cannot be reached");
}
//// LargePersonGroup - create, then get data.
//LargePersonGroup(client, IMAGE_BASE_URL, RECOGNITION_MODEL4).Wait();
//// Group faces - automatically group similar faces.
//Group(client, IMAGE_BASE_URL, RECOGNITION_MODEL4).Wait();
}
