private void btnSaveBP_Click(object sender, EventArgs e)
{
SaveFileDialog sfd = new SaveFileDialog();
sfd.Filter = "XML文件(*.xml)|*.xml";
if (sfd.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
bp.Write(new FileStorage(sfd.FileName, FileStorage.Mode.Write));
MessageBox.Show("导出完成");
}
}
private void annTraining()
{
string finalOutput = "";
int features = 16;
int classes = 26;
Matrix <int> layers = new Matrix <int>(6, 1);
layers[0, 0] = features;
layers[1, 0] = classes * 16;
layers[2, 0] = classes * 8;
layers[3, 0] = classes * 4;
layers[4, 0] = classes * 2;
layers[5, 0] = classes;
FileStorage fileStorageRead = new FileStorage(@"ANN_Model.xml", FileStorage.Mode.Read);
ann.Read(fileStorageRead.GetRoot(0));
ann.SetLayerSizes(layers);
ann.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym, 0, 0);
ann.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Backprop, 0, 0);
ann.Train(allFeatureOfSample, DataLayoutType.RowSample, annAllResponse);
FileStorage fileStorageWrite = new FileStorage(@"ANN_Model.xml", FileStorage.Mode.Write);
ann.Write(fileStorageWrite);
Matrix <float> testSample = new Matrix <float>(1, 16);
for (int q = 0; q < 16; q++)
{
testSample[0, q] = allFeatureOfSample[12, q];
}
float real = ann.Predict(testSample);
finalOutput += labelArray[(int)real];
label5.Text = finalOutput.ToString();
SpeechSynthesizer reader1 = new SpeechSynthesizer();
if (label5.Text != " ")
{
reader1.Dispose();
reader1 = new SpeechSynthesizer();
reader1.SpeakAsync(finalOutput.ToString());
}
else
{
MessageBox.Show("No Text Present!");
}
System.IO.File.WriteAllText(@"ANNResult.txt", real.ToString());
}
bool Save(string filename)
{
FileStorage fs = new FileStorage(filename, FileStorage.Mode.Write);
if (!fs.IsOpened)
{
return(false);
}
ANN_MLP network = new ANN_MLP();
network.Write(fs);
fs.Write("values" + values);
fs.ReleaseAndGetString();
return(true);
}
private async void moduleFeatureExtraction(int first, int last)
{
string fghfh = "";
double[,] RawData = new double[16, 3780];
int mid = (first + last) / 2;
int low = mid - 8;;
int high = mid + 8;
for (int i = 0; i < 16; i++)
{
for (int j = 0; j < 26; j++)
{
if (j == adasas)
{
response[i, j] = 1;
}
if (j != adasas)
{
response[i, j] = 0;
}
}
}
adasas++;
if (low < first)
{
low++;
}
if (high > last)
{
low++;
}
int length = high - low;
for (int k = (low); k < (high); k++)
{
string frameName = "gesture//" + k + ".jpeg";
Image <Bgr, byte> featurExtractionInput = new Image <Bgr, byte>(frameName);
//pictureBox3.Image = featurExtractionInput.Bitmap;
//label4.Text = k.ToString();
await Task.Delay(1000 / Convert.ToInt32(2));
float[] desc = new float[3780];
desc = GetVector(featurExtractionInput);
int i = k - (low);
for (int j = 0; j < 3780; j++)
{
double val = Convert.ToDouble(desc[j]);
RawData.SetValue(val, i, j);
}
if (k == (high - 1))
{
Matrix <Double> DataMatrix = new Matrix <Double>(RawData);
Matrix <Double> Mean = new Matrix <Double>(1, 3780);
Matrix <Double> EigenValues = new Matrix <Double>(1, 3780);
Matrix <Double> EigenVectors = new Matrix <Double>(3780, 3780);
CvInvoke.PCACompute(DataMatrix, Mean, EigenVectors, 16);
Matrix <Double> result = new Matrix <Double>(16, 16);
CvInvoke.PCAProject(DataMatrix, Mean, EigenVectors, result);
String filePath = @"test.xml";
StringBuilder sb = new StringBuilder();
(new XmlSerializer(typeof(Matrix <double>))).Serialize(new StringWriter(sb), result);
XmlDocument xDoc = new XmlDocument();
xDoc.LoadXml(sb.ToString());
System.IO.File.WriteAllText(filePath, sb.ToString());
Matrix <double> matrix = (Matrix <double>)(new XmlSerializer(typeof(Matrix <double>))).Deserialize(new XmlNodeReader(xDoc));
string djf = null;
djf = System.IO.File.ReadAllText(@"g.txt");
djf += Environment.NewLine;
djf += Environment.NewLine;
for (int p = 0; p < 16; p++)
{
for (int q = 0; q < 16; q++)
{
djf += p + " , " + q + " " + matrix[p, q].ToString() + " ";
}
djf += Environment.NewLine;
}
Matrix <float> masjhdb = result.Convert <float>();
TrainData trainData = new TrainData(masjhdb, DataLayoutType.RowSample, response);
int features = 16;
int classes = 26;
Matrix <int> layers = new Matrix <int>(6, 1);
layers[0, 0] = features;
layers[1, 0] = classes * 16;
layers[2, 0] = classes * 8;
layers[3, 0] = classes * 4;
layers[4, 0] = classes * 2;
layers[5, 0] = classes;
ANN_MLP ann = new ANN_MLP();
FileStorage fileStorageRead = new FileStorage(@"abc.xml", FileStorage.Mode.Read);
ann.Read(fileStorageRead.GetRoot(0));
ann.SetLayerSizes(layers);
ann.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym, 0, 0);
ann.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Backprop, 0, 0);
ann.Train(masjhdb, DataLayoutType.RowSample, response);
FileStorage fileStorageWrite = new FileStorage(@"abc.xml", FileStorage.Mode.Write);
ann.Write(fileStorageWrite);
Matrix <float> hehe = new Matrix <float>(1, 16);
for (int q = 0; q < 16; q++)
{
hehe[0, q] = masjhdb[11, q];
}
float real = ann.Predict(hehe);
fghfh += array[(int)real];
SpeechSynthesizer reader = new SpeechSynthesizer();
if (richTextBox1.Text != " ")
{
reader.Dispose();
reader = new SpeechSynthesizer();
reader.SpeakAsync(fghfh.ToString());
}
else
{
MessageBox.Show("No Text Present!");
}
richTextBox1.Text = fghfh.ToString();
System.IO.File.WriteAllText(@"g.txt", real.ToString());
}
}
}
