/// <summary> Function which submits a frame to the Face API. </summary>
/// <param name="frame"> The video frame to submit. </param>
/// <returns> A <see cref="Task{LiveCameraResult}"/> representing the asynchronous API call,
/// and containing the faces returned by the API. </returns>
private async Task <LiveCameraResult> FacesAnalysisFunction(VideoFrame frame)
{
// Encode image.
var jpg = frame.Image.ToMemoryStream(".jpg", s_jpegParams);
// Submit image to API.
var attrs = new List <FaceAttributeType> {
FaceAttributeType.Age,
FaceAttributeType.Gender, FaceAttributeType.HeadPose
};
var faces = await _faceClient.DetectAsync(jpg, returnFaceAttributes : attrs);
// Count the API call.
Properties.Settings.Default.FaceAPICallCount++;
// Output.
LiveCameraResult _result = new LiveCameraResult
{
Faces = faces,
TimeStamp = DateTime.Now,
SelectedCamera = camo
};
TotalAPIResults.Add(_result);
ApiResult = _result;
analysisLog.SaveData(_result);
return(_result);
}
public Dictionary <Guid, int> ComputeFrameScorePerPlayer(LiveCameraResult apiResult)
{
var scoresDictionary = new Dictionary <Guid, int>();
if (apiResult.Identities != null && apiResult.Identities.Count > 0)
{
KeyValuePair <string, float> currDominantEmotion;
Guid personId;
foreach (var item in apiResult.Identities)
{
personId = item.Key;
currDominantEmotion = getDominantEmotion(apiResult.Identities[personId].FaceAttributes.Emotion);
double delta = Math.Abs(currDominantEmotion.Value - this.targetScore);
if (currDominantEmotion.Key == this.targetEmotion.ToString() &&
delta <= Delta)
{
scoresDictionary[personId] = 10 * (int)Math.Round(1 + 10 * (Delta - delta), 1);
}
else
{
scoresDictionary[personId] = 0;
}
}
}
return(scoresDictionary);
}
/// <summary> Function which submits a frame to the Face API. </summary>
/// <param name="frame"> The video frame to submit. </param>
/// <returns> A <see cref="Task{LiveCameraResult}"/> representing the asynchronous API call,
/// and containing the faces returned by the API. </returns>
private async Task <LiveCameraResult> FacesAnalysisFunction(VideoFrame frame)
{
// Encode image.
var jpg = frame.Image.ToMemoryStream(".jpg", s_jpegParams);
//TODO Hackathon:
// Submit image to API.
var result = new LiveCameraResult();
try
{
var attrs = new List <FaceAttributeType> {
FaceAttributeType.Age, FaceAttributeType.Gender, FaceAttributeType.Glasses, FaceAttributeType.FacialHair, FaceAttributeType.Emotion, FaceAttributeType.Smile
};
result.Faces = await _faceClient.DetectAsync(jpg, returnFaceAttributes : attrs);
}
catch (FaceAPIException faceException)
{
MessageBox.Show(faceException.ErrorMessage, faceException.ErrorCode);
}
// Count the API call.
Properties.Settings.Default.FaceAPICallCount++;
// Output.
return(result);
}
private async Task <string> IdentifyPerson(string personGroupId)
{
string writeBack = "";
IList <IdentifyResult> result = new List <IdentifyResult>();
LiveCameraResult lc = new LiveCameraResult();
writeBack = $"Face ID: {lc.UserFace.ToString()}";
//IList<Guid> faceId = lc.UserFace.Select(face => face.FaceId.GetValueOrDefault()).ToList();
//var results = await faceClient.Face.IdentifyAsync(faceId, personGroupId);
//foreach (var identifyResult in results)
//{
// writeBack += ($"Result of face: {identifyResult.FaceId} ");
// if (identifyResult.Candidates.Count == 0)
// {
// writeBack += "No one identified ";
// }
// else
// {
// // Get top 1 among all candidates returned
// var candidateId = identifyResult.Candidates[0].PersonId;
// var person = await faceClient.PersonGroupPerson.GetAsync(personGroupId, candidateId);
// writeBack += ($"Identified as {person.Name} ");
// }
//}
return(writeBack);
}
/// <summary> Function which submits a frame to the Computer Vision API for celebrity
/// detection. </summary>
/// <param name="frame"> The video frame to submit. </param>
/// <returns> A <see cref="Task{LiveCameraResult}"/> representing the asynchronous API call,
/// and containing the celebrities returned by the API. </returns>
private async Task <LiveCameraResult> CelebrityAnalysisFunction(VideoFrame frame)
{
// Encode image.
var jpg = frame.Image.ToMemoryStream(".jpg", s_jpegParams);
// Submit image to API.
var result = await _visionClient.AnalyzeImageInDomainAsync(jpg, "celebrities");
// Count the API call.
Properties.Settings.Default.VisionAPICallCount++;
// Output.
var celebs = JsonConvert.DeserializeObject <CelebritiesResult>(result.Result.ToString()).Celebrities;
LiveCameraResult _result = new LiveCameraResult
{
TimeStamp = DateTime.Now,
// Extract face rectangles from results.
Faces = celebs.Select(c => CreateFace(c.FaceRectangle)).ToArray(),
// Extract celebrity names from results.
CelebrityNames = celebs.Select(c => c.Name).ToArray()
};
TotalAPIResults.Add(_result);
ApiResult = _result;
analysisLog.SaveData(_result);
return(_result);
}
private void SavePlayerImages(BitmapSource image, LiveCameraResult result)
{
if (result == null || result.Identities == null || this.gameState != GameState.Game)
{
return;
}
if (DateTime.Now.AddSeconds(-playerImagesTimeOffsetSec) > this.lastPlayerImagesTime)
{
this.groupImages.Add(image);
SaveImageToFile(image);
foreach (var player in result.Identities)
{
int offset = 0;
Int32Rect faceRectangle = new Int32Rect(player.Value.FaceRectangle.Left + offset, player.Value.FaceRectangle.Top + offset, player.Value.FaceRectangle.Width + offset, player.Value.FaceRectangle.Height + offset);
CroppedBitmap playerImage = new CroppedBitmap(image, faceRectangle);
if (playerImages.ContainsKey(player.Key))
{
playerImages[player.Key].Add(playerImage);
}
else
{
playerImages[player.Key] = new List <CroppedBitmap>()
{
playerImage
};
}
lastPlayerImagesTime = DateTime.Now;
}
}
}
private BitmapSource VisualizeResult(VideoFrame frame)
{
// Draw any results on top of the image.
BitmapSource visImage = frame.Image.ToBitmapSource();
LiveCameraResult result = _latestResultsToDisplay;
if (result != null)
{
// See if we have local face detections for this image.
var clientFaces = (OpenCvSharp.Rect[])frame.UserData;
if (clientFaces != null && result.Faces != null)
{
// If so, then the analysis results might be from an older frame. We need to match
// the client-side face detections (computed on this frame) with the analysis
// results (computed on the older frame) that we want to display.
MatchAndReplaceFaceRectangles(result.Faces, clientFaces);
}
if (this.gameState == GameState.Explain)
{
this.Dispatcher.BeginInvoke((Action)(() =>
{
RightImage.Source = ImageProvider.Instructions;
//visImage = Visualization.DrawExplain(visImage);
}));
}
else if (this.gameState == GameState.RoundBegin)
{
visImage = VisualizeStartRound(frame);
}
else if (this.gameState == GameState.RoundEnd)
{
visImage = VisualizeEndRound(frame);
}
else if (this.gameState == GameState.Game)
{
// Compute round score
Dictionary <Guid, int> scores = round.ComputeFrameScorePerPlayer(result);
scoringSystem.AddToCurrentRound(scores);
visImage = Visualization.DrawFaces(visImage, round, result.Identities, scoringSystem, _mode);
SavePlayerImages(frame.Image.ToBitmapSource(), result);
}
else if (this.gameState == GameState.Participants)
{
visImage = Visualization.DrawParticipants(visImage, result.Faces);
}
else if (this.gameState == GameState.GameEnd)
{
_grabber.StopProcessingAsync();
visImage = VisualizeEndGame(frame);
}
}
return(visImage);
}
public MainWindow()
{
InitializeComponent();
// Create grabber.
_grabber = new FrameGrabber <LiveCameraResult>();
// Set up a listener for when the client receives a new frame.
_grabber.NewFrameProvided += (s, e) =>
{
// The callback may occur on a different thread, so we must use the
// MainWindow.Dispatcher when manipulating the UI.
this.Dispatcher.BeginInvoke((Action)(() =>
{
// Display the image in the left pane.
LeftImage.Source = e.Frame.Image.ToBitmapSource();
}));
// See if auto-stop should be triggered.
if (Properties.Settings.Default.AutoStopEnabled && (DateTime.Now - _startTime) > Properties.Settings.Default.AutoStopTime)
{
_grabber.StopProcessingAsync();
}
};
// Set up a listener for when the client receives a new result from an API call.
_grabber.NewResultAvailable += (s, e) =>
{
this.Dispatcher.BeginInvoke((Action)(() =>
{
if (e.TimedOut)
{
MessageArea.Text = "API call timed out.";
}
else if (e.Exception != null)
{
string apiName = "";
string message = e.Exception.Message;
var faceEx = e.Exception as FaceAPIException;
if (faceEx != null)
{
apiName = "Face";
message = faceEx.ErrorMessage;
}
MessageArea.Text = string.Format("{0} API call failed on frame {1}. Exception: {2}", apiName, e.Frame.Metadata.Index, message);
}
else
{
_latestResultsToDisplay = e.Analysis;
RightImage.Source = VisualizeResult(e.Frame);
}
}));
};
// Create local face detector.
_localFaceDetector.Load("Data/haarcascade_frontalface_alt2.xml");
}
/// <summary> Function which submits a frame to the Emotion API. </summary>
/// <param name="frame"> The video frame to submit. </param>
/// <returns> A <see cref="Task{LiveCameraResult}"/> representing the asynchronous API call,
/// and containing the emotions returned by the API. </returns>
private async Task <LiveCameraResult> EmotionAnalysisFunction(VideoFrame frame)
{
// Encode image.
var jpg = frame.Image.ToMemoryStream(".jpg", s_jpegParams);
// Submit image to API.
Emotion[] emotions = null;
// See if we have local face detections for this image.
var localFaces = (OpenCvSharp.Rect[])frame.UserData;
if (localFaces == null)
{
// If localFaces is null, we're not performing local face detection.
// Use Cognigitve Services to do the face detection.
Properties.Settings.Default.EmotionAPICallCount++;
emotions = await _emotionClient.RecognizeAsync(jpg);
}
else if (localFaces.Count() > 0)
{
// If we have local face detections, we can call the API with them.
// First, convert the OpenCvSharp rectangles.
var rects = localFaces.Select(
f => new Microsoft.ProjectOxford.Common.Rectangle
{
Left = f.Left,
Top = f.Top,
Width = f.Width,
Height = f.Height
});
Properties.Settings.Default.EmotionAPICallCount++;
emotions = await _emotionClient.RecognizeAsync(jpg, rects.ToArray());
}
else
{
// Local face detection found no faces; don't call Cognitive Services.
emotions = new Emotion[0];
}
// Output.
LiveCameraResult _result = new LiveCameraResult
{
TimeStamp = DateTime.Now,
Faces = emotions.Select(e => CreateFace(e.FaceRectangle)).ToArray(),
// Extract emotion scores from results.
EmotionScores = emotions.Select(e => e.Scores).ToArray()
};
TotalAPIResults.Add(_result); ApiResult = _result;
analysisLog.SaveData(_result);
return(_result);
}
/// <summary> Function which submits a frame to the Face API. </summary>
/// <param name="frame"> The video frame to submit. </param>
/// <returns> A <see cref="Task{LiveCameraResult}"/> representing the asynchronous API call,
/// and containing the faces returned by the API. </returns>
private async Task <LiveCameraResult> FacesAnalysisFunction(VideoFrame frame)
{
// Encode image.
var jpg = frame.Image.ToMemoryStream(".jpg", s_jpegParams);
// Submit image to API.
var attrs = new List <FaceAPI.FaceAttributeType> {
FaceAPI.FaceAttributeType.Age,
FaceAPI.FaceAttributeType.Gender,
FaceAPI.FaceAttributeType.HeadPose,
FaceAPI.FaceAttributeType.Glasses,
FaceAPI.FaceAttributeType.FacialHair,
};
var faces = await _faceClient.DetectAsync(jpg, returnFaceAttributes : attrs);
// Count the API call.
Properties.Settings.Default.FaceAPICallCount++;
//IRUEL GET
LiveCameraResult result = new LiveCameraResult {
Faces = faces
};
Microsoft.ProjectOxford.Face.Contract.Face xx = new Microsoft.ProjectOxford.Face.Contract.Face();
xx = result.Faces[0];
Microsoft.ProjectOxford.Face.Contract.FaceAttributes dd = new Microsoft.ProjectOxford.Face.Contract.FaceAttributes();
dd = xx.FaceAttributes;
Trace.WriteLine(dd.Age.ToString() + ' ' + dd.Gender.ToString() + ' ' + dd.Glasses.ToString());
//return new LiveCameraResult
//{
// // Extract face rectangles from results.
// Faces = celebs.Select(c => CreateFace(c.FaceRectangle)).ToArray(),
// // Extract celebrity names from results.
// CelebrityNames = celebs.Select(c => c.Name).ToArray()
//};
// Output.
return(new LiveCameraResult {
Faces = faces
});
}
private async Task <LiveCameraResult> DescriptionAnalysisFunction(VideoFrame frame)
{
// Encode image.
var jpg = frame.Image.ToMemoryStream(".jpg", s_jpegParams);
// Submit image to API
var result = await _visionClient.DescribeAsync(jpg);
// Count the API call.
Properties.Settings.Default.VisionAPICallCount++;
// Output.
LiveCameraResult cResult = new LiveCameraResult();
cResult.Caption = result.Description.Captions;
return(new LiveCameraResult {
Caption = result.Description.Captions
});
}
public async Task <bool> FindSimilar(LiveCameraResult liveCameraResult)
{
try
{
if (string.IsNullOrWhiteSpace(DocumentImagePath))
{
return(false);
}
IList <Guid?> targetFaceIds = new List <Guid?>();
using (var jpg = File.OpenRead(DocumentImagePath))
{
// Detect faces from load image.
var detectWithStreamCmd = new DetectWithStreamCmd();
var faces = await detectWithStreamCmd.DetectWithStreamAsync(jpg);
//// Add detected faceId to list of GUIDs.
if (faces.Count <= 0)
{
MessageArea.Text = $"No se detectaron rostros en la imagen.";
return(false);
}
targetFaceIds.Add(faces[0].FaceId.Value);
}
var verifyFaceToFaceCmd = new VerifyFaceToFaceCmd();
var similarResults = await verifyFaceToFaceCmd.VerifyFaceToFaceAsync(liveCameraResult.Faces.First().FaceId.Value, targetFaceIds.First().Value);
if (similarResults.IsIdentical)
{
RightImage.Source = VisualizeResult(liveCameraResult.VideoFrame);
MessageArea.Text = $"Los rostros son similares con una confianza de: {similarResults.Confidence}.";
return(true);
}
else
{
MessageArea.Text = $"Los rostros no son identicos.";
return(true);
}
}
catch (Exception ex)
{
MessageArea.Text = $"Se ha presentado un error: {ex.Message}";
return(false);
}
}
/// <summary> Function which submits a frame to the Computer Vision API for tagging. </summary>
/// <param name="frame"> The video frame to submit. </param>
/// <returns> A <see cref="Task{LiveCameraResult}"/> representing the asynchronous API call,
/// and containing the tags returned by the API. </returns>
private async Task <LiveCameraResult> TaggingAnalysisFunction(VideoFrame frame)
{
// Encode image.
var jpg = frame.Image.ToMemoryStream(".jpg", s_jpegParams);
// Submit image to API.
var analysis = await _visionClient.GetTagsAsync(jpg);
// Count the API call.
Properties.Settings.Default.VisionAPICallCount++;
// Output.
LiveCameraResult _result = new LiveCameraResult {
Tags = analysis.Tags, TimeStamp = DateTime.Now
};
TotalAPIResults.Add(_result);
ApiResult = _result;
analysisLog.SaveData(_result);
return(_result);
}
public Dictionary <Guid, int> ComputeFrameScorePerPlayer(LiveCameraResult apiResult)
{
var scoresDictionary = new Dictionary <Guid, int>();
if (apiResult.Identities != null && apiResult.Identities.Count > 0 && apiResult.Identities.ContainsKey(leader))
{
KeyValuePair <string, float> currDominantEmotion;
Guid personId;
KeyValuePair <string, float> leaderEmotion;
int mimicPlayersCount = 0;
leaderEmotion = RoundEmotion.getDominantEmotion(apiResult.Identities[leader].FaceAttributes.Emotion);
foreach (var item in apiResult.Identities)
{
personId = item.Key;
if (personId == leader)
{
continue;
}
currDominantEmotion = RoundEmotion.getDominantEmotion(apiResult.Identities[personId].FaceAttributes.Emotion);
double delta = Math.Abs(currDominantEmotion.Value - leaderEmotion.Value);
if (currDominantEmotion.Key == leaderEmotion.Key &&
delta <= Delta)
{
scoresDictionary[personId] = 10 * (int)Math.Round(1 + 10 * (Delta - delta), 1);
mimicPlayersCount++;
}
else
{
scoresDictionary[personId] = 0;
}
}
int totalPlayersCount = apiResult.Identities.Count;
// handle leader scoring
scoresDictionary[leader] = (totalPlayersCount - mimicPlayersCount) * 10;
}
return(scoresDictionary);
}
/// <summary> Function which submits a frame to the Emotion API. </summary>
/// <param name="frame"> The video frame to submit. </param>
/// <returns> A <see cref="Task{LiveCameraResult}"/> representing the asynchronous API call,
/// and containing the emotions returned by the API. </returns>
private async Task <LiveCameraResult> AnalysisFunction(VideoFrame frame)
{
var jpg = frame.Image.ToMemoryStream(".jpg", s_jpegParams);
var attrs = new List <FaceAttributeType> {
FaceAttributeType.Age, FaceAttributeType.Emotion
};
Face[] faces = await _faceClient.DetectAsync(jpg, returnFaceAttributes : attrs);
Guid[] faceIds = faces.Select(face => face.FaceId).ToArray();
var liveCameraResult = new LiveCameraResult
{
Faces = faces,
EmotionScores = faces.Select(f => f.FaceAttributes.Emotion).ToArray()
};
try
{
IdentifyResult[] identities = await _faceClient.IdentifyAsync(currentGroupId, faceIds);
var identityDict = new Dictionary <Guid, Face>();
foreach (var identity in identities)
{
if (identity.Candidates.Length > 0 && identity.Candidates[0].Confidence > 0.6)
{
identityDict[identity.Candidates[0].PersonId] = faces.First(f => f.FaceId == identity.FaceId);
}
}
liveCameraResult.Identities = identityDict;
}
catch (Exception e)
{
}
return(liveCameraResult);
}
/// <summary> Function which submits a frame to the Computer Vision API for tagging. </summary>
/// <param name="frame"> The video frame to submit. </param>
/// <returns> A <see cref="Task{LiveCameraResult}"/> representing the asynchronous API call,
/// and containing the tags returned by the API. </returns>
private async Task <LiveCameraResult> TaggingAnalysisFunction(VideoFrame frame)
{
// Encode image.
var jpg = frame.Image.ToMemoryStream(".jpg", s_jpegParams);
//TODO Hackathon:
// Submit image to API.
// Count the API call.
// Output. return null is just a dummy.
var result = new LiveCameraResult();
try
{
var tagsResult = await _visionClient.GetTagsAsync(jpg);
result.Tags = tagsResult.Tags;
}
catch (Exception e)
{
MessageBox.Show(e.ToString());
}
return(result);
}
public Dictionary <Guid, int> ComputeFrameScorePerPlayer(LiveCameraResult apiResult)
{
var scoresDictionary = new Dictionary <Guid, int>();
if (apiResult.Identities != null && apiResult.Identities.Count > 0)
{
Guid personId;
double personAverageAge;
double deltaFromAverage;
double age;
foreach (var item in apiResult.Identities)
{
personId = item.Key;
if (!agesCount.ContainsKey(personId))
{
agesCount[personId] = 0;
agesSum[personId] = 0.0;
agesAverage[personId] = 0.0;
}
age = apiResult.Identities[personId].FaceAttributes.Age;
personAverageAge = this.agesAverage[personId];
deltaFromAverage = age - personAverageAge;
if (deltaFromAverage > 0 && personAverageAge > 0)
{
scoresDictionary[item.Key] = 10 * (int)Math.Round(deltaFromAverage / 2);
}
this.agesCount[personId]++;
this.agesSum[personId] += age;
this.agesAverage[personId] = this.agesSum[personId] / this.agesCount[personId];
}
}
return(scoresDictionary);
}
public MainWindow()
{
InitializeComponent();
// Create grabber.
_grabber = new FrameGrabber <LiveCameraResult>();
// Set up a listener for when the client receives a new frame.
_grabber.NewFrameProvided += (s, e) =>
{
if (_mode == AppMode.EmotionsWithClientFaceDetect)
{
// Local face detection.
var rects = _localFaceDetector.DetectMultiScale(e.Frame.Image);
// Attach faces to frame.
e.Frame.UserData = rects;
}
// The callback may occur on a different thread, so we must use the
// MainWindow.Dispatcher when manipulating the UI.
this.Dispatcher.BeginInvoke((Action)(() =>
{
// Display the image in the left pane.
LeftImage.Source = e.Frame.Image.ToBitmapSource();
// If we're fusing client-side face detection with remote analysis, show the
// new frame now with the most recent analysis available.
if (_fuseClientRemoteResults)
{
RightImage.Source = VisualizeResult(e.Frame);
}
}));
// See if auto-stop should be triggered.
if (Properties.Settings.Default.AutoStopEnabled && (DateTime.Now - _startTime) > Properties.Settings.Default.AutoStopTime)
{
_grabber.StopProcessingAsync();
}
};
// Set up a listener for when the client receives a new result from an API call.
_grabber.NewResultAvailable += (s, e) =>
{
this.Dispatcher.BeginInvoke((Action)(() =>
{
if (e.TimedOut)
{
MessageArea.Text = "API call timed out.";
}
else if (e.Exception != null)
{
string apiName = "";
string message = e.Exception.Message;
var faceEx = e.Exception as FaceAPI.FaceAPIException;
var emotionEx = e.Exception as Common.ClientException;
var visionEx = e.Exception as VisionAPI.ClientException;
if (faceEx != null)
{
apiName = "Face";
message = faceEx.ErrorMessage;
}
else if (emotionEx != null)
{
apiName = "Emotion";
message = emotionEx.Error.Message;
}
else if (visionEx != null)
{
apiName = "Computer Vision";
message = visionEx.Error.Message;
}
MessageArea.Text = string.Format("{0} API call failed on frame {1}. Exception: {2}", apiName, e.Frame.Metadata.Index, message);
}
else
{
_latestResultsToDisplay = e.Analysis;
// Display the image and visualization in the right pane.
if (!_fuseClientRemoteResults)
{
RightImage.Source = VisualizeResult(e.Frame);
}
}
}));
};
// Create local face detector.
_localFaceDetector.Load("Data/haarcascade_frontalface_alt2.xml");
}
public MainWindow()
{
InitializeComponent();
int onetime = 0;
// Create grabber.
_grabber = new FrameGrabber <LiveCameraResult>();
//DB call
string queryString =
"SELECT imageurl from API_transnet_Secondary";
string connectionString = "Server=tcp:transnetserver.database.windows.net,1433;Initial Catalog=API_transnet;Persist Security Info=False;User ID=admin_server;Password=romir123123@;MultipleActiveResultSets=False;Encrypt=True;TrustServerCertificate=False;Connection Timeout=30;";
string image_link = "";
using (SqlConnection connection =
new SqlConnection(connectionString))
{
// Create the Command and Parameter objects.
SqlCommand command = new SqlCommand(queryString, connection);
try
{
connection.Open();
SqlDataReader reader = command.ExecuteReader();
int x = 0;
while (reader.Read())
{
image_link = reader[0].ToString();
byte[] binaryUrl = StrToByteArray(image_link);
System.Drawing.Image imageUrl = CreateImage(binaryUrl);
imageUrl.Save("C:\\Users\\romir\\Documents\\Visual Studio 2017\\Projects\\Transnetwork\\Secondary_DB_images\\THEBUYER" + x + ".bmp");
//NOW WE SHOULD COMPARE BETWEEN THE IMAGE URL (PIC FROM APP) AND STAION URL (PIC FROM STATION) USING AZURE API
//MakeAnalysisRequest("C:\\Users\\romir\\Documents\\Visual Studio 2017\\Windows\\VideoFrameAnalyzer\\THEBUYER.bmp");
//MakeAnalysisRequest(stationUrl);
x++;
}
reader.Close();
}
catch (Exception ex)
{
throw new Exception();
}
}
//DB call ended
// Set up a listener for when the client receives a new frame.
_grabber.NewFrameProvided += (s, e) =>
{
if (_mode == AppMode.EmotionsWithClientFaceDetect)
{
// Local face detection.
var rects = _localFaceDetector.DetectMultiScale(e.Frame.Image);
// Attach faces to frame.
e.Frame.UserData = rects;
}
// The callback may occur on a different thread, so we must use the
// MainWindow.Dispatcher when manipulating the UI.
this.Dispatcher.BeginInvoke((Action)(() =>
{
// Display the image in the left pane.
LeftImage.Source = e.Frame.Image.ToBitmapSource();
// If we're fusing client-side face detection with remote analysis, show the
// new frame now with the most recent analysis available.
if (_fuseClientRemoteResults)
{
RightImage.Source = VisualizeResult(e.Frame);
}
}));
// See if auto-stop should be triggered.
if (Properties.Settings.Default.AutoStopEnabled && (DateTime.Now - _startTime) > Properties.Settings.Default.AutoStopTime)
{
_grabber.StopProcessingAsync();
}
};
// Set up a listener for when the client receives a new result from an API call.
_grabber.NewResultAvailable += (s, e) =>
{
this.Dispatcher.BeginInvoke((Action)(async() =>
{
if (e.TimedOut)
{
MessageArea.Text = "API call timed out.";
}
else if (e.Exception != null)
{
string apiName = "";
string message = e.Exception.Message;
var faceEx = e.Exception as FaceAPIException;
var emotionEx = e.Exception as Microsoft.ProjectOxford.Common.ClientException;
var visionEx = e.Exception as Microsoft.ProjectOxford.Vision.ClientException;
if (faceEx != null)
{
apiName = "Face";
message = faceEx.ErrorMessage;
}
else if (emotionEx != null)
{
apiName = "Emotion";
message = emotionEx.Error.Message;
}
else if (visionEx != null)
{
apiName = "Computer Vision";
message = visionEx.Error.Message;
}
MessageArea.Text = string.Format("{0} API call failed on frame {1}. Exception: {2}", apiName, e.Frame.Metadata.Index, message);
}
else
{
_latestResultsToDisplay = e.Analysis;
double x = 2;
double y = 2;
// Display the image and visualization in the right pane.
if (!_fuseClientRemoteResults)
{
//onetime++;
RightImage.Source = VisualizeResult(e.Frame);
var image = e.Frame.Image;
image.SaveImage("C:\\Users\\romir\\Documents\\Visual Studio 2017\\Projects\\Transnetwork\\Person\\NEW" + x + ".bmp");
//onetime++;
// faceServiceClient = new FaceServiceClient("23c757e5234948b4a67015d008f287b1");
y++;
string personGroupId = "romir" + y;
y++;
await faceServiceClient.CreatePersonGroupAsync(personGroupId, "My Friends");
// Define Anna
CreatePersonResult friend1 = await faceServiceClient.CreatePersonAsync(
// Id of the person group that the person belonged to
personGroupId,
// Name of the person
"Anna" + y
);
const string friend1ImageDir = @"C:\Users\romir\Documents\Visual Studio 2017\Projects\Transnetwork\Person\";
foreach (string imagePath in Directory.GetFiles(friend1ImageDir, "*.jpg"))
{
using (Stream si = File.OpenRead(imagePath))
{
// Detect faces in the image and add to Anna
await faceServiceClient.AddPersonFaceAsync(
personGroupId, friend1.PersonId, si);
}
}
await faceServiceClient.TrainPersonGroupAsync(personGroupId);
TrainingStatus trainingStatus = null;
while (true)
{
trainingStatus = await faceServiceClient.GetPersonGroupTrainingStatusAsync(personGroupId);
if (trainingStatus.Status.ToString() != "running")
{
break;
}
await Task.Delay(5000);
}
//call API
bool authenticated = false;
const string friend2ImageDir = @"C:\Users\romir\Documents\Visual Studio 2017\Projects\Transnetwork\Secondary_DB_images\";
foreach (string imagePath in Directory.GetFiles(friend2ImageDir, "*.jpg"))
{
string testImageFile = imagePath;
using (Stream si = File.OpenRead(testImageFile))
{
var faces = await faceServiceClient.DetectAsync(si);
var faceIds = faces.Select(face => face.FaceId).ToArray();
var results = await faceServiceClient.IdentifyAsync(personGroupId, faceIds);
foreach (var identifyResult in results)
{
Console.WriteLine("Result of face: {0}", identifyResult.FaceId);
if (identifyResult.Candidates.Length == 0)
{
Console.WriteLine("No one identified");
}
else
{
// Get top 1 among all candidates returned
authenticated = true;
}
}
}
if (authenticated)
{
throw new Exception();
//alert
}
}
x++;
}
}
}));
};
// Create local face detector.
_localFaceDetector.Load("Data/haarcascade_frontalface_alt2.xml");
}
public MainWindow()
{
currentGroupId = currentGroupName;
InitializeComponent();
StartTimer();
this.backgroundMusic = SoundProvider.Ukulele;
this.backgroundMusic.Volume = 0.05;
this.backgroundMusic.MediaEnded += new EventHandler((object sender, EventArgs e) => {
this.backgroundMusic.Position = TimeSpan.Zero;
this.backgroundMusic.Play();
});
this.backgroundMusic.Play();
t.Elapsed += T_Elapsed;
// Create grabber.
_grabber = new FrameGrabber <LiveCameraResult>();
updateMode(AppMode.Participants);
// Set up a listener for when the client receives a new frame.
_grabber.NewFrameProvided += (s, e) =>
{
if (_mode == AppMode.EmotionsWithClientFaceDetect)
{
// Local face detection.
var rects = _localFaceDetector.DetectMultiScale(e.Frame.Image);
// Attach faces to frame.
e.Frame.UserData = rects;
}
// The callback may occur on a different thread, so we must use the
// MainWindow.Dispatcher when manipulating the UI.
this.Dispatcher.BeginInvoke((Action)(() =>
{
// Display the image in the left pane.
LeftImage.Source = e.Frame.Image.ToBitmapSource();
// If we're fusing client-side face detection with remote analysis, show the
// new frame now with the most recent analysis available.
if (_fuseClientRemoteResults)
{
RightImage.Source = VisualizeResult(e.Frame);
}
}));
if (DateTime.Now - currentTimeTaskStart > currentTimerTask)
{
if (gameState == GameState.Explain)
{
roundStart = DateTime.Now;
nextRound();
}
else if (gameState == GameState.RoundBegin)
{
currentTimerTask = TimeSpan.FromSeconds(15);
currentTimeTaskStart = DateTime.Now;
gameState = GameState.Game;
roundStart = DateTime.Now;
}
else if (gameState == GameState.Game)
{
currentTimerTask = TimeSpan.FromSeconds(6);
currentTimeTaskStart = DateTime.Now;
gameState = GameState.RoundEnd;
scoringSystem.AddRoundToGameScore();
}
else if (gameState == GameState.RoundEnd)
{
if (roundNumber == NumOfRounds)
{
this.sound = SoundProvider.TheWinner;
this.sound.Play();
currentTimerTask = TimeSpan.FromSeconds(3);
gameState = GameState.GameEnd;
this.Dispatcher.BeginInvoke((Action)(() =>
{
StartEndImages();
button.Visibility = Visibility.Visible;
}));
}
else
{
nextRound();
roundStart = DateTime.Now;
}
}
}
};
// Set up a listener for when the client receives a new result from an API call.
_grabber.NewResultAvailable += (s, e) =>
{
this.Dispatcher.BeginInvoke((Action)(() =>
{
if (e.TimedOut)
{
MessageArea.Text = "API call timed out.";
}
else if (e.Exception != null)
{
string apiName = "";
string message = e.Exception.Message;
var faceEx = e.Exception as FaceAPIException;
var emotionEx = e.Exception as Microsoft.ProjectOxford.Common.ClientException;
var visionEx = e.Exception as Microsoft.ProjectOxford.Vision.ClientException;
if (faceEx != null)
{
apiName = "Face";
message = faceEx.ErrorMessage;
}
else if (emotionEx != null)
{
apiName = "Emotion";
message = emotionEx.Error.Message;
}
else if (visionEx != null)
{
apiName = "Computer Vision";
message = visionEx.Error.Message;
}
MessageArea.Text = string.Format("{0} API call failed on frame {1}. Exception: {2}", apiName, e.Frame.Metadata.Index, message);
}
else
{
_latestResultsToDisplay = e.Analysis;
// Display the image and visualization in the right pane.
if (!_fuseClientRemoteResults)
{
RightImage.Source = VisualizeResult(e.Frame);
}
if (gameState == GameState.Game || gameState == GameState.RoundBegin)
{
bool drawIndicator = false;
if (gameState == GameState.Game)
{
drawIndicator = true;
}
RightImage.Source = VisualizeTimer(drawIndicator);
}
}
}));
};
// Create local face detector.
_localFaceDetector.Load("Data/haarcascade_frontalface_alt2.xml");
}
public void SaveData(LiveCameraResult AnalysisFunction)
{
LogHelper.Log(LogTarget.Binary, AnalysisFunction, filePath);
}
public MainWindow()
{
InitializeComponent();
LeftT.Visibility = Visibility.Collapsed;
RightT.Visibility = Visibility.Collapsed;
VideoT.Visibility = Visibility.Collapsed;
Emo.Visibility = Visibility.Collapsed;
// Create grabber.
_grabber = new FrameGrabber <LiveCameraResult>();
// Set up a listener for when the client receives a new frame.
_grabber.NewFrameProvided += (s, e) =>
{
if (_mode == AppMode.EmotionsWithClientFaceDetect)
{
// Local face detection.
var rects = _localFaceDetector.DetectMultiScale(e.Frame.Image);
// Attach faces to frame.
e.Frame.UserData = rects;
}
// The callback may occur on a different thread, so we must use the
// MainWindow.Dispatcher when manipulating the UI.
this.Dispatcher.BeginInvoke((Action)(() =>
{
// Display the image in the left pane.
LeftImage.Source = e.Frame.Image.ToBitmapSource();
// If we're fusing client-side face detection with remote analysis, show the
// new frame now with the most recent analysis available.
if (_fuseClientRemoteResults)
{
RightImage.Source = VisualizeResult(e.Frame);
}
}));
// See if auto-stop should be triggered.
if (Properties.Settings.Default.AutoStopEnabled && (DateTime.Now - _startTime) > Properties.Settings.Default.AutoStopTime)
{
_grabber.StopProcessingAsync();
}
};
// Set up a listener for when the client receives a new result from an API call.
_grabber.NewResultAvailable += (s, e) =>
{
this.Dispatcher.BeginInvoke((Action)(() =>
{
if (e.TimedOut)
{
MessageArea.Text = "API call timed out.";
}
else if (e.Exception != null)
{
string apiName = "";
string message = e.Exception.Message;
var faceEx = e.Exception as FaceAPIException;
var emotionEx = e.Exception as Microsoft.ProjectOxford.Common.ClientException;
var visionEx = e.Exception as Microsoft.ProjectOxford.Vision.ClientException;
if (faceEx != null)
{
apiName = "Face";
message = faceEx.ErrorMessage;
}
else if (emotionEx != null)
{
apiName = "Emotion";
message = emotionEx.Error.Message;
}
else if (visionEx != null)
{
apiName = "Computer Vision";
message = visionEx.Error.Message;
}
MessageArea.Text = string.Format("{0} API call failed on frame {1}. Exception: {2}", apiName, e.Frame.Metadata.Index, message);
}
else
{
if (e.Analysis.EmotionScores.Count() > 0)
{
var emotion = e.Analysis.EmotionScores.First().ToRankedList().First();
emotions.Add(startTime - DateTime.Now, emotion);
Console.WriteLine($"{emotion.Key}: {emotion.Value}");
string src = @"C:\Users\Uddal\Downloads\Compressed\Cognitive-Samples-VideoFrameAnalysis-master\Windows\Emo\";
Emo.Visibility = Visibility.Visible;
if (emotion.Key.Equals("Angry"))
{
Uri uri = new Uri(src + "angry.png", UriKind.Absolute);
ImageSource imgSource = new BitmapImage(uri);
Emo.Source = imgSource;
}
else if (emotion.Key.Equals("Happiness"))
{
Uri uri = new Uri(src + "happy.png", UriKind.Absolute);
ImageSource imgSource = new BitmapImage(uri);
Emo.Source = imgSource;
}
else if (emotion.Key.Equals("Contempt"))
{
Uri uri = new Uri(src + "contempt.jpg", UriKind.Absolute);
ImageSource imgSource = new BitmapImage(uri);
Emo.Source = imgSource;
}
else if (emotion.Key.Equals("Disgust"))
{
Uri uri = new Uri(src + "disrupt.png", UriKind.Absolute);
ImageSource imgSource = new BitmapImage(uri);
Emo.Source = imgSource;
}
else if (emotion.Key.Equals("Fear"))
{
Uri uri = new Uri(src + "fear.png", UriKind.Absolute);
ImageSource imgSource = new BitmapImage(uri);
Emo.Source = imgSource;
}
else if (emotion.Key.Equals("Neutral"))
{
Uri uri = new Uri(src + "neutral.png", UriKind.Absolute);
ImageSource imgSource = new BitmapImage(uri);
Emo.Source = imgSource;
}
else if (emotion.Key.Equals("Sadness"))
{
Uri uri = new Uri(src + "sad.png", UriKind.Absolute);
ImageSource imgSource = new BitmapImage(uri);
Emo.Source = imgSource;
}
else if (emotion.Key.Equals("Surprise"))
{
Uri uri = new Uri(src + "surprise.png", UriKind.Absolute);
ImageSource imgSource = new BitmapImage(uri);
Emo.Source = imgSource;
}
}
_latestResultsToDisplay = e.Analysis;
// Display the image and visualization in the right pane.
if (!_fuseClientRemoteResults)
{
RightImage.Source = VisualizeResult(e.Frame);
}
/* int milliseconds = 2000;
* Thread.Sleep(milliseconds);*/
}
}));
};
// Create local face detector.
_localFaceDetector.Load("Data/haarcascade_frontalface_alt2.xml");
}
public MainWindow()
{
InitializeComponent();
ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12;
// Create grabber.
_grabber = new FrameGrabber <LiveCameraResult>();
// Set up a listener for when the client receives a new frame.
_grabber.NewFrameProvided += (s, e) =>
{
// The callback may occur on a different thread, so we must use the
// MainWindow.Dispatcher when manipulating the UI.
this.Dispatcher.BeginInvoke((Action)(() =>
{
// Display the image in the left pane.
LeftImage.Source = e.Frame.Image.ToBitmapSource();
}));
// See if auto-stop should be triggered.
if (Properties.Settings.Default.AutoStopEnabled && (DateTime.Now - _startTime) > Properties.Settings.Default.AutoStopTime)
{
_grabber.StopProcessingAsync().GetAwaiter().GetResult();
}
};
// Set up a listener for when the client receives a new result from an API call.
_grabber.NewResultAvailable += (s, e) =>
{
this.Dispatcher.BeginInvoke((Action)(async() =>
{
if (e.TimedOut)
{
MessageArea.Text = "API call timed out.";
}
else if (e.Exception != null)
{
string apiName = "";
string message = e.Exception.Message;
var faceEx = e.Exception as FaceAPI.Models.APIErrorException;
if (faceEx != null)
{
apiName = "Face";
message = faceEx.Message;
}
MessageArea.Text = string.Format("{0} API call failed on frame {1}. Exception: {2}", apiName, e.Frame.Metadata.Index, message);
}
else
{
_latestResultsToDisplay = e.Analysis;
if (_latestResultsToDisplay != null && _latestResultsToDisplay.Faces.Any())
{
if (!string.IsNullOrWhiteSpace(DocumentImagePath))
{
MessageArea.Text = "Verificando Rostro...";
if (await FindSimilar(_latestResultsToDisplay))
{
await _grabber.StopProcessingAsync();
}
}
else
{
MessageArea.Text = "Por favor seleccione una imagen";
}
}
}
}));
};
}
