copilot/OpenSeeFace
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

It is now possible to train expression models with probability support. This however is a lot slower! Also, the C parameter for SVM training was set incorrectly. Now training works much better.

Browse source
This commit is contained in:
Emiliana 2020-02-13 22:24:41 +01:00
parent 9abc3029e7
commit bb845098ca
4 changed files with 46 additions and 20 deletions
Download patch file Download diff file Expand all files Collapse all files

18
Unity/OpenSeeExpression.cs
View file

@ -25,6 +25,8 @@ public class OpenSeeExpression : MonoBehaviour
public int overRecordingSkip = 5;
[Tooltip("This is the filename used for loading and saving expression data using the load and save flags.")]
public string filename = "";
[Tooltip("When enabled, training will be done in such a way that the model can output probabilities. This is a lot slower!")]
public bool enableProbabilityTraining = false;
[Header("Toggles")]
[Tooltip("When enabled, calibration data will be collected from the given OpenSee component.")]
public bool recording = false;
@ -54,6 +56,8 @@ public class OpenSeeExpression : MonoBehaviour
public float expressionTime = 0f;
[Tooltip("This is currently detected expression.")]
public string expression = "";
[Tooltip("These are the probabilities of all expressions. The labels in order can be retrieved through the GetClassLabels methods.")]
public double[] probabilities;
[Header("Training statistics")]
[Tooltip("This shows the accuracy result of the last training run.")]
public float accuracy = 0.0f;
@ -362,7 +366,10 @@ public class OpenSeeExpression : MonoBehaviour
i_test++;
}
}
model.TrainModel(X_train, y_train, rows_train, cols);
int probability = 0;
if (enableProbabilityTraining)
probability = 1;
model.TrainModel(X_train, y_train, rows_train, cols, probability);
confusionMatrix = model.ConfusionMatrix(X_test, y_test, i_test, out accuracy);
confusionMatrixString = SVMModel.FormatMatrix(confusionMatrix, classLabels);
for (int label = 0; label < classes; label++) {
@ -399,7 +406,7 @@ public class OpenSeeExpression : MonoBehaviour
float[] predictionData = new float[cols];
for (int i = 0; i < cols; i++)
predictionData[i] = faceData[indices[i]];
float[] prediction = model.Predict(predictionData, 1);
float[] prediction = model.Predict(predictionData, out probabilities, 1);
int predictedExpression = (int)Mathf.Round(prediction[0]);
if (predictedExpression == lastPrediction)
lastPredictionCount++;
@ -464,6 +471,13 @@ public class OpenSeeExpression : MonoBehaviour
return false;
}
}
public string[] GetClassLabels() {
List<string> labels = new List<string>();
foreach (string l in classLabels)
labels.Add(String.Copy(l));
return labels.ToArray();
}
public string[] GetExpressions() {
List<string> keys = new List<string>();