/// <summary>
/// 単体の特徴量構造体を配列に直す 要検討
/// </summary>
/// <param name="feature"></param>
/// <param name="value_array"></param>
private void SetFeatureToArray(FaceFeature.FeatureValue feature, ref double[] value_array)
{
int idx = 0;
value_array[idx++] = (feature.LeftEyeValueL);
value_array[idx++] = (feature.LeftEyeValueR);
value_array[idx++] = (feature.RightEyeValueL);
value_array[idx++] = (feature.RightEyeValueR);
value_array[idx++] = (feature.NoseLValueL);
value_array[idx++] = (feature.NoseLValueR);
value_array[idx++] = (feature.MouthLValueL);
value_array[idx++] = (feature.MouthLValueR);
}
//SVM判定
public int SVMPredict(FaceFeature.FeatureValue feature)
{
double[] feature_array = new double[2];
SetFeatureToArray(feature, ref feature_array);
CvMat dataMat = new CvMat(1, 2, MatrixType.F32C1, feature_array, true);
//学習ファイルを読み込んでいなかったらロード
if (this.LoadFlag == false)
{
svm.Load(@"SvmLearning.xml");
this.LoadFlag = true;
}
return((int)this.svm.Predict(dataMat));
}
//SVM判定
public int SVMPredict(FaceFeature.FeatureValue feature)
{
//学習ファイルを読み込んでいなかったらロード
if (this.LoadFlag == false)
{
this.libSVM_model = SVM.LoadModel(@"model_FaceFeature.xml");
this.LoadFlag = true;
}
//スケーリングファイルを読み込む あれば
if (this.LoadScaleFlag == false && JudgeGUII.APPSetting.NORMALIZE_USE)
{
this.LoadScaleFlag = ReadScaleFile(@"out/normalize_scale.csv");
}
double[] feature_array = new double[FEATURE_COUNT];
int answer = 0;
{
SetFeatureToArray(feature, ref feature_array);
//ここでスケーリングのデータを読み込んでいたら使う
if (this.LoadScaleFlag == true && JudgeGUII.APPSetting.NORMALIZE_USE)
{
execNormalize(ref feature_array);
}
//問題を作成
SVMNode[] node_array = new SVMNode[FEATURE_COUNT];
for (int i = 0; i < FEATURE_COUNT; i++)
{
node_array[i] = new SVMNode(i + 1, feature_array[i]);
}
answer = (int)SVM.Predict(libSVM_model, node_array);
return(answer);
}
}