/// <summary>
/// Callback for when the <see cref="PredictEmotionButton"/> is clicked. This launches a new task which calls <see cref="MachineLearning.PredictEmotion"/> with the
/// <see cref="currentImageFileInfo"/> and <see cref="selectedFaceDataType"/>, which results are then used to update <see cref="PredictedEmotionMainLabel"/> and
/// <see cref="PredictedEmotionsListBox"/>.
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private async void PredictEmotionButton_Click(object sender, RoutedEventArgs e)
{
var originalLabel = PredictEmotionButton.Content;
PredictedEmotionMainLabel.Content = "Predicting...";
PredictedEmotionsListBox.Items.Clear();
PredictEmotionButton.Content = (string)PredictEmotionButton.Content + " (Predicting...)";
PredictEmotionButton.IsEnabled = false;
FaceOutput predictedEmotion = null;
Dictionary <string, float> predictedEmotionsWithAllLabels = null;
MachineLearning.DrawPointsOfLandmarks(currentImageFileInfo);
await Task.Run(() =>
{
MachineLearning.PredictEmotion(currentImageFileInfo, selectedFaceDataType, out var prediction, out var predictionWithLabels);
predictedEmotion = prediction;
predictedEmotionsWithAllLabels = predictionWithLabels;
});
PredictedEmotionMainLabel.Content = $"{FirstLetterToUpper(predictedEmotion.PredictedEmotion)}" +
$" ({FloatToPercent(predictedEmotionsWithAllLabels[predictedEmotion.PredictedEmotion])})";
PredictedEmotionsListBox.Items.Clear();
predictedEmotionsWithAllLabels = predictedEmotionsWithAllLabels.OrderByDescending(c => c.Value).ToDictionary(i => i.Key, i => i.Value);
foreach (var predictedEmotionLabelAndScore in predictedEmotionsWithAllLabels)
{
PredictedEmotionsListBox.Items.Add(
$"{FirstLetterToUpper(predictedEmotionLabelAndScore.Key)}: {FloatToPercent(predictedEmotionLabelAndScore.Value)}");
}
PredictedEmotionsListBox.Items.RemoveAt(0);
PredictEmotionButton.Content = originalLabel;
PredictEmotionButton.IsEnabled = true;
}
/// <summary>
/// Predict the emotion of an image.
/// </summary>
/// <param name="imageFileInfo"><see cref="FileInfo"/> of the image file.</param>
/// <param name="TFaceData">Type of face data that the parameters should be used.</param>
/// <param name="predictedEmotion">The emotion that was predicted.</param>
/// <param name="predictedEmotionWithAllLabels">All the other emotions with their scores appended after.</param>
public static void PredictEmotion(FileInfo imageFileInfo, Type TFaceData, out FaceOutput predictedEmotion, out Dictionary <string, float> predictedEmotionWithAllLabels)
{
if (mlContext == null)
{
mlContext = new MLContext();
}
if (model == null)
{
model = mlContext.Model.Load(GetModelZipFileName(TFaceData), out var dataView);
}
predictedEmotion = null;
predictedEmotionWithAllLabels = null;
// Not using generics because different function calls are required anyway.
if (TFaceData == typeof(FaceData1))
{
using (var predictor = mlContext.Model.CreatePredictionEngine <FaceData1, FaceOutput>(model))
{
using (var fd = Dlib.GetFrontalFaceDetector())
using (var sp = ShapePredictor.Deserialize(GetFile(ShapePredictorFileName).FullName))
{
var faceDataFromImage = GetFaceData1FromImage(imageFileInfo, sp, fd, false);
faceDataFromImage.Emotion = ""; // Get rid of label, as this is what we want to know.
predictedEmotion = predictor.Predict(faceDataFromImage);
}
// Prediction with all labels.
predictedEmotionWithAllLabels = new Dictionary <string, float>();
var slotNames = new VBuffer <ReadOnlyMemory <char> >();
predictor.OutputSchema.GetColumnOrNull("Label")?.GetKeyValues(ref slotNames);
var names = new string[slotNames.Length];
var num = 0;
foreach (var denseValue in slotNames.DenseValues())
{
predictedEmotionWithAllLabels.Add(denseValue.ToString(), predictedEmotion.Scores[num++]);
}
Console.WriteLine("Predicted Emotion: " + predictedEmotion.PredictedEmotion);
Console.WriteLine($"Scores: {string.Join(" ", predictedEmotion.Scores)}");
}
}
else if (TFaceData == typeof(FaceData2))
{
using (var predictor = mlContext.Model.CreatePredictionEngine <FaceData2, FaceOutput>(model))
{
using (var fd = Dlib.GetFrontalFaceDetector())
using (var sp = ShapePredictor.Deserialize(GetFile(ShapePredictorFileName).FullName))
{
var faceDataFromImage = GetFaceData2FromImage(imageFileInfo, sp, fd, false);
faceDataFromImage.Emotion = ""; // Get rid of label, as this is what we want to know.
predictedEmotion = predictor.Predict(faceDataFromImage);
}
// Prediction with all labels.
predictedEmotionWithAllLabels = new Dictionary <string, float>();
var slotNames = new VBuffer <ReadOnlyMemory <char> >();
predictor.OutputSchema.GetColumnOrNull("Label")?.GetKeyValues(ref slotNames);
var names = new string[slotNames.Length];
var num = 0;
foreach (var denseValue in slotNames.DenseValues())
{
predictedEmotionWithAllLabels.Add(denseValue.ToString(), predictedEmotion.Scores[num++]);
}
Console.WriteLine("Predicted Emotion: " + predictedEmotion.PredictedEmotion);
Console.WriteLine($"Scores: {string.Join(" ", predictedEmotion.Scores)}");
}
}
else if (TFaceData == typeof(FaceData3))
{
using (var predictor = mlContext.Model.CreatePredictionEngine <FaceData3, FaceOutput>(model))
{
using (var fd = Dlib.GetFrontalFaceDetector())
using (var sp = ShapePredictor.Deserialize(GetFile(ShapePredictorFileName).FullName))
{
var faceDataFromImage = GetFaceData3FromImage(imageFileInfo, sp, fd, false);
faceDataFromImage.Emotion = ""; // Get rid of label, as this is what we want to know.
predictedEmotion = predictor.Predict(faceDataFromImage);
}
// Prediction with all labels.
predictedEmotionWithAllLabels = new Dictionary <string, float>();
var slotNames = new VBuffer <ReadOnlyMemory <char> >();
predictor.OutputSchema.GetColumnOrNull("Label")?.GetKeyValues(ref slotNames);
var names = new string[slotNames.Length];
var num = 0;
foreach (var denseValue in slotNames.DenseValues())
{
predictedEmotionWithAllLabels.Add(denseValue.ToString(), predictedEmotion.Scores[num++]);
}
Console.WriteLine("Predicted Emotion: " + predictedEmotion.PredictedEmotion);
Console.WriteLine($"Scores: {string.Join(" ", predictedEmotion.Scores)}");
}
}
}
