private void Form2_Load(object sender, EventArgs e)
{
CheckData();
int width = 512, height = 512;
Image<Bgr, Byte> img = new Image<Bgr, byte>(width, height);
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
Matrix<float> sampleMat = new Matrix<float>(1, 2);
sampleMat[0, 0] = i / (float)width;
sampleMat[0, 1] = j / (float)height;
//sampleMat[0, 0] = i;
//sampleMat[0, 1] = j;
//Mat sampleMat = new Mat(1, 2, Emgu.CV.CvEnum.DepthType.Cv32S, 1);
//sampleMat.SetTo<float>(new float[] { i / (float)width, j / (float)height });
Matrix<float> responseMat = new Matrix<float>(1, 1);
bp.Predict(sampleMat, responseMat);
if (responseMat[0, 0] >= 0.5)
img[i, j] = new Bgr(Color.Green.B, Color.Green.G, Color.Green.R);
else
img[i, j] = new Bgr(Color.Blue.B, Color.Blue.G, Color.Blue.R);
}
}
pictureBox1.Image = img.Bitmap;
}
// Predict on loaded Dataset
public void Predict()
{
Matrix <float> prediction = new Matrix <float>(1, 1);
Matrix <float> sample = new Matrix <float>(1, InputLayers);
int tp = 0;
try
{
Console.WriteLine("______________ Testing Begins Here_____________");
for (int i = 0; i < testData.Rows; i++)
{
for (int j = 0; j < testData.Cols; j++)
{
sample[0, j] = testData[i, j];
}
nnet.Predict(sample, prediction);
predictedClasses[i, 0] = GetCloseValue(prediction.Data[0, 0]);
if (predictedClasses[i, 0] == testClasses[i, 0])
{
tp = tp + 1;
}
Console.WriteLine("Actual : " + testClasses[i, 0] + " Predicted: " + predictedClasses[i, 0]);
}
accuracy = (float)tp / (float)testClasses.Rows;
Console.WriteLine("Accuracy: " + accuracy);
}
catch (Exception ex) {
Console.WriteLine("Error occured Predicting..." + ex.Message);
}
}
public void TrainANNModel()
{
LoadImageGray();
// the output layer must know the number of classes.
//var numberOfClasses = imageBindingModel.ConvertAll(x => x.Label).Distinct().Count();
var numberOfClasses = 1;
var numberInput = resolutionImage.Width * resolutionImage.Height;
int trainSampleCount = imageBindingModel.Count;
Matrix <float> trainData = new Matrix <float>(trainSampleCount, numberInput);
Matrix <float> trainClasses = new Matrix <float>(trainSampleCount, numberOfClasses);
for (int i = 0; i < imageBindingModel.Count; i++)
{
for (int j = 0; j < numberInput; j++)
{
trainData[i, j] = imageBindingModel[i].Image.Bytes[j];
trainClasses[i, 0] = (float)imageBindingModel[i].Id;
}
}
Matrix <int> layerSize = new Matrix <int>(new int[] { numberInput, numberInput + numberInput, numberOfClasses });
MCvANN_MLP_TrainParams parameters = new MCvANN_MLP_TrainParams();
parameters.term_crit = new MCvTermCriteria(100, 1.0e-8);
parameters.train_method = Emgu.CV.ML.MlEnum.ANN_MLP_TRAIN_METHOD.BACKPROP;
parameters.bp_dw_scale = 0.1;
parameters.bp_moment_scale = 0.1;
using (ANN_MLP network = new ANN_MLP(layerSize, Emgu.CV.ML.MlEnum.ANN_MLP_ACTIVATION_FUNCTION.SIGMOID_SYM, 1.0, 1.0))
{
network.Train(trainData, trainClasses, null, null, parameters, Emgu.CV.ML.MlEnum.ANN_MLP_TRAINING_FLAG.DEFAULT);
Matrix <float> sample = new Matrix <float>(1, numberInput);
Matrix <float> prediction = new Matrix <float>(1, numberOfClasses);
int recog_true = 0;
for (int i = 0; i < imageBindingModel.Count; i++)
{
for (int j = 0; j < numberInput; j++)
{
sample[0, j] = trainData[i, j];
}
network.Predict(sample, prediction);
var response = prediction.Data[0, 0];
if (Math.Abs(response - imageBindingModel[i].Id) < 0.5)
{
recog_true++;
}
Console.WriteLine($"recoge : {response} -- target: {imageBindingModel[i].Id} -- result: {Math.Abs(response - imageBindingModel[i].Id) < 0.5}");
}
}
}
public Matrix <float> testingMLP(Matrix <float> testData, string modelName, int hiddenLayers = 2, ANN_MLP.AnnMlpActivationFunction activationType = ANN_MLP.AnnMlpActivationFunction.SigmoidSym)
{
Matrix <float> finalResult = null;
layerSize = new Matrix <int>(new int[] { testData.Cols, hiddenLayers, 1 });
try
{
using (ANN_MLP network1 = new ANN_MLP()) // Testing trainned Data
{
network1.SetActivationFunction(activationType);
network1.SetLayerSizes(layerSize);
network1.Read(new FileStorage(modelName + ".xml", FileStorage.Mode.Read).GetFirstTopLevelNode()); // Load trainned ANN weights
IInputArray Sample_test = testData;
IOutputArray Result = new Matrix <float>(1, 1);
network1.Predict(Sample_test, Result); //Start Network prediction
finalResult = (Matrix <float>)Result;
return(finalResult);
}
}
catch (Exception ee)
{
return(finalResult);
}
}
private Rectangle[] PredictBP(Bitmap img)
{
Rectangle[] tmpR = new Rectangle[] { };
Bitmap tmpImg = ZoomImg(img, bpWidth, bpHeight, ref tmpR);
List <Rectangle> retRects = new List <Rectangle>();
Image <Bgr, float> trainingData = new Image <Bgr, float>(img);
Matrix <float> trainingDataMats = new Matrix <float>(1, bpWidth * bpHeight);
for (int i = 0; i < bpWidth * bpHeight; i++)
{
trainingDataMats[0, i] = Color.FromArgb(
(int)trainingData.Data[i / bpWidth, i % bpHeight, 2],
(int)trainingData.Data[i / bpWidth, i % bpHeight, 1],
(int)trainingData.Data[i / bpWidth, i % bpHeight, 0]
).ToArgb() / (float)0xFFFFFF;
}
Matrix <float> labelsMats = new Matrix <float>(1, bpRectangleCount * 4);
bp.Predict(trainingDataMats, labelsMats);
for (int i = 0; i < bpRectangleCount * 4; i += 4)
{
Rectangle r = new Rectangle();
r.X = (int)(labelsMats[0, i] * (float)bpWidth);
r.Y = (int)(labelsMats[0, i + 1] * (float)bpHeight);
r.Width = (int)(labelsMats[0, i + 2] * (float)bpWidth);
r.Height = (int)(labelsMats[0, i + 3] * (float)bpHeight);
if (r.X >= 0 && r.Y >= 0 && r.Width > 0 && r.Height > 0)
{
retRects.Add(r);
}
}
return(retRects.ToArray());
}
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());
}
public static bool IsAnswer(Image <Gray, byte> img)
{
if (network == null)
{
return(false);
}
Matrix <float> sample = new Matrix <float>(1, width * height);
Matrix <float> prediction = new Matrix <float>(1, 1);
var testData = getImgData(img);
for (int j = 0; j < testData.Count; j++)
{
sample[0, j] = testData[j];
}
network.Predict(sample, prediction);
float response = prediction.Data[0, 0];
return(response > 0.5);
}
private void Btn_reg_Click(object sender, EventArgs e)
{
using (Matrix <int> layerSize = new Matrix <int>(new int[] { 2, 5, 1 }))
using (Mat layerSizeMat = layerSize.Mat)
using (ANN_MLP network = new ANN_MLP())
{
network.Load(annFileName);
//network.SetLayerSizes(layerSizeMat);
//network.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym, 0, 0);
//network.TermCriteria = new MCvTermCriteria(10, 1.0e-8);
//network.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Backprop, 0.1, 0.1);
float[,] testData = new float[1, 2] {
{ float.Parse(this.txb_percent.Text), float.Parse(this.txb_avg.Text) }
};
Matrix <float> sample = new Matrix <float>(testData);
Matrix <float> prediction = new Matrix <float>(1, 1);
network.Predict(sample, prediction);
float response = prediction.Data[0, 0];
MessageBox.Show($"判断结果：{response}");
}
}
public void ANN()
{
int trainSampleCount = 100;
#region Generate the traning data and classes
Matrix <float> trainData = new Matrix <float>(trainSampleCount, 2);
Matrix <float> trainClasses = new Matrix <float>(trainSampleCount, 1);
Image <Bgr, Byte> img = new Image <Bgr, byte>(500, 500);
Matrix <float> sample = new Matrix <float>(1, 2);
Matrix <float> prediction = new Matrix <float>(1, 1);
Matrix <float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix <float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix <float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix <float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion
Matrix <int> layerSize = new Matrix <int>(new int[] { 2, 5, 1 });
MCvANN_MLP_TrainParams parameters = new MCvANN_MLP_TrainParams();
parameters.term_crit = new MCvTermCriteria(10, 1.0e-8);
parameters.train_method = Emgu.CV.ML.MlEnum.ANN_MLP_TRAIN_METHOD.BACKPROP;
parameters.bp_dw_scale = 0.1;
parameters.bp_moment_scale = 0.1;
using (ANN_MLP network = new ANN_MLP(layerSize, Emgu.CV.ML.MlEnum.ANN_MLP_ACTIVATION_FUNCTION.SIGMOID_SYM, 1.0, 1.0))
{
network.Train(trainData, trainClasses, null, null, parameters, Emgu.CV.ML.MlEnum.ANN_MLP_TRAINING_FLAG.DEFAULT);
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = j;
sample.Data[0, 1] = i;
network.Predict(sample, prediction);
// estimates the response and get the neighbors' labels
float response = prediction.Data[0, 0];
// highlight the pixel depending on the accuracy (or confidence)
img[i, j] = response < 1.5 ? new Bgr(90, 0, 0) : new Bgr(0, 90, 0);
}
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++)
{
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF((int)trainData2[i, 0], (int)trainData2[i, 1]);
img.Draw(new CircleF(p2, 2), new Bgr(100, 255, 100), -1);
}
Emgu.CV.UI.ImageViewer.Show(img);
}
public void TestANN_MLP()
{
int trainSampleCount = 100;
#region Generate the traning data and classes
Matrix <float> trainData = new Matrix <float>(trainSampleCount, 2);
Matrix <float> trainClasses = new Matrix <float>(trainSampleCount, 1);
Image <Bgr, Byte> img = new Image <Bgr, byte>(500, 500);
Matrix <float> sample = new Matrix <float>(1, 2);
Matrix <float> prediction = new Matrix <float>(1, 1);
Matrix <float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix <float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix <float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix <float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion
using (Matrix <int> layerSize = new Matrix <int>(new int[] { 2, 5, 1 }))
using (Mat layerSizeMat = layerSize.Mat)
using (TrainData td = new TrainData(trainData, MlEnum.DataLayoutType.RowSample, trainClasses))
using (ANN_MLP network = new ANN_MLP())
{
network.SetLayerSizes(layerSizeMat);
network.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym, 0, 0);
network.TermCriteria = new MCvTermCriteria(10, 1.0e-8);
network.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Backprop, 0.1, 0.1);
network.Train(td, (int)Emgu.CV.ML.MlEnum.AnnMlpTrainingFlag.Default);
#if !NETFX_CORE
String fileName = Path.Combine(Path.GetTempPath(), "ann_mlp_model.xml");
network.Save(fileName);
if (File.Exists(fileName))
{
File.Delete(fileName);
}
#endif
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = j;
sample.Data[0, 1] = i;
network.Predict(sample, prediction);
// estimates the response and get the neighbors' labels
float response = prediction.Data[0, 0];
// highlight the pixel depending on the accuracy (or confidence)
img[i, j] = response < 1.5 ? new Bgr(90, 0, 0) : new Bgr(0, 90, 0);
}
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++)
{
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF((int)trainData2[i, 0], (int)trainData2[i, 1]);
img.Draw(new CircleF(p2, 2), new Bgr(100, 255, 100), -1);
}
//Emgu.CV.UI.ImageViewer.Show(img);
}
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());
}
}
}
public void TestANN_MLP()
{
int trainSampleCount = 100;
#region Generate the traning data and classes
Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix<float> trainClasses = new Matrix<float>(trainSampleCount, 1);
Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);
Matrix<float> sample = new Matrix<float>(1, 2);
Matrix<float> prediction = new Matrix<float>(1, 1);
Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion
Matrix<int> layerSize = new Matrix<int>(new int[] { 2, 5, 1 });
MCvANN_MLP_TrainParams parameters = new MCvANN_MLP_TrainParams();
parameters.term_crit = new MCvTermCriteria(10, 1.0e-8);
parameters.train_method = Emgu.CV.ML.MlEnum.ANN_MLP_TRAIN_METHOD.BACKPROP;
parameters.bp_dw_scale = 0.1;
parameters.bp_moment_scale = 0.1;
using (ANN_MLP network = new ANN_MLP(layerSize, Emgu.CV.ML.MlEnum.ANN_MLP_ACTIVATION_FUNCTION.SIGMOID_SYM, 1.0, 1.0))
{
network.Train(trainData, trainClasses, null, null, parameters, Emgu.CV.ML.MlEnum.ANN_MLP_TRAINING_FLAG.DEFAULT);
network.Save("ann_mlp_model.xml");
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = j;
sample.Data[0, 1] = i;
network.Predict(sample, prediction);
// estimates the response and get the neighbors' labels
float response = prediction.Data[0,0];
// highlight the pixel depending on the accuracy (or confidence)
img[i, j] = response < 1.5 ? new Bgr(90, 0, 0) : new Bgr(0, 90, 0);
}
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++)
{
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF((int)trainData2[i, 0], (int)trainData2[i, 1]);
img.Draw(new CircleF(p2, 2), new Bgr(100, 255, 100), -1);
}
}
private void btn_ANNReg_Click(object sender, EventArgs e)
{
var regPath = txbregPath.Text;
if (string.IsNullOrEmpty(regPath))
{
MessageBox.Show("待识别文件夹不能空");
return;
}
var isAct = ckbIsAct.Checked;
var files = Directory.GetFiles(regPath);
var testData = new List <List <float> >();
for (int i = 0; i < files.Length; i++)
{
var path = files[i];
Image <Gray, byte> img = new Image <Gray, byte>(path);
testData.Add(getImgData(img));
}
using (ANN_MLP network = new ANN_MLP())
{
network.Load(annFileName);
int colCount = width * height;
Matrix <float> sample = new Matrix <float>(1, colCount);
Matrix <float> prediction = new Matrix <float>(1, 1);
//1测试数据
var testCount = testData.Count;
var rightCount = 0;//正确act识别数量
for (int i = 0; i < testCount; i++)
{
var testColData = testData[i];
for (int j = 0; j < testColData.Count; j++)
{
sample[0, j] = testColData[j];
}
network.Predict(sample, prediction);
float response = prediction.Data[0, 0];
if (isAct && response > 0.5)
{
rightCount++;
Console.WriteLine($"该数据是涂答的，正确识别{response}");
}
else if (isAct && response <= 0.5)
{
Console.WriteLine($"该数据是涂答的，错误识别{response}");
File.Copy(files[i], Path.Combine(actRegErrorDir, Path.GetFileName(files[i])), true);
}
else if (!isAct && response <= 0.5)
{
rightCount++;
Console.WriteLine($"该数据是未涂答的，正确识别{response}");
}
else if (!isAct && response > 0.5)
{
Console.WriteLine($"该数据是未涂答的，错误识别{response}");
File.Copy(files[i], Path.Combine(negRegErrorDir, Path.GetFileName(files[i])), true);
}
else
{
Console.WriteLine("未知识别结果");
}
}
var result = $"测试数量：{testCount},正确数量：{rightCount},正确率：{rightCount * 1.0 / testCount}";
Console.WriteLine(result);
MessageBox.Show(result);
}
}
private void Btn_ann2_Click(object sender, EventArgs e)
{
this.prepareData();
if (trainNegData?.Count == 0 || trainActData?.Count == 0)
{
MessageBox.Show("训练数据不能为空");
return;
}
int trainSampleCount = trainActData.Count + trainNegData.Count;
int colCount = width * height;
Matrix <float> trainData = new Matrix <float>(trainSampleCount, colCount);
Matrix <float> trainClasses = new Matrix <float>(trainSampleCount, 1);
Matrix <float> sample = new Matrix <float>(1, colCount);
Matrix <float> prediction = new Matrix <float>(1, 1);
//准备正面数据
var actCount = trainActData.Count;
//Matrix<float> trainActDataMatr = new Matrix<float>(actCount, width * height);
//Matrix<float> trainActClassesMatr = new Matrix<float>(actCount, 1);
for (int i = 0; i < actCount; i++)
{
var colData = trainActData[i];
var colCount1 = colData.Count;
for (int j = 0; j < colCount1; j++)
{
trainData.Data[i, j] = trainActData[i][j];
}
trainClasses.Data[i, 0] = 1;
//trainClasses.Data[i, 1] = 0;
}
//准备未涂答数据
var negCount = trainNegData.Count;
//Matrix<float> trainNegDataMatr = new Matrix<float>(negCount, width * height);
//Matrix<float> trainNegClassesMatr = new Matrix<float>(negCount, 1);
for (int i = 0; i < negCount; i++)
{
var colData = trainNegData[i];
var colCount1 = colData.Count;
for (int j = 0; j < colCount1; j++)
{
trainData.Data[i + actCount, j] = trainNegData[i][j];
}
trainClasses.Data[i + actCount, 0] = 0;
//trainClasses.Data[i + actCount, 1] = 1;
}
//训练
using (Matrix <int> layerSize = new Matrix <int>(new int[] { 286, 10, 10, 1 }))
using (Mat layerSizeMat = layerSize.Mat)
using (TrainData td = new TrainData(trainData, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, trainClasses))
using (ANN_MLP network = new ANN_MLP())
{
network.SetLayerSizes(layerSizeMat);
network.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym, 0, 0);
network.TermCriteria = new MCvTermCriteria(10, 1.0e-8);
network.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Backprop, 0.01, 0.01);
network.Train(td, (int)Emgu.CV.ML.MlEnum.AnnMlpTrainingFlag.Default);
//String fileName = "ann_mlp_model.xml"; //Path.Combine(Path.GetTempPath(), "ann_mlp_model.xml");
network.Save(annFileName);
//if (File.Exists(fileName))
// File.Delete(fileName);
//测试
//1测试正面数据
var testActCount = testActData.Count;
var rightActCount = 0;//正确act识别数量
for (int i = 0; i < testActCount; i++)
{
var testData = testActData[i];
for (int j = 0; j < testData.Count; j++)
{
sample[0, j] = testData[j];
}
network.Predict(sample, prediction);
float response = prediction.Data[0, 0];
if (response > 0.5)
{
rightActCount++;
Console.WriteLine($"该数据是涂答的，正确识别{response}");
}
else
{
Console.WriteLine($"该数据是涂答的，错误识别{response}");
}
}
//2测试负面数据
var testNegCount = testNegData.Count;
var rightNegCount = 0;//正确neg识别数量
for (int i = 0; i < testNegCount; i++)
{
var testData = testNegData[i];
for (int j = 0; j < testData.Count; j++)
{
sample[0, j] = testData[j];
}
network.Predict(sample, prediction);
float response = prediction.Data[0, 0];
if (response <= 0.5)
{
rightNegCount++;
Console.WriteLine($"该数据是未涂答的，正确识别{response}");
}
else
{
Console.WriteLine($"该数据是未涂答的，错误识别{response}");
}
}
MessageBox.Show("训练完毕，并测试");
}
}
private void Btn_CNN2_Click(object sender, EventArgs e)
{
var positiveData = GetPositiveData();
var negativeData = GetNegativeData();
if (positiveData?.Count == 0 || negativeData?.Count == 0)
{
MessageBox.Show("训练数据不能为空");
return;
}
int trainSampleCount = positiveData.Count + negativeData.Count;
Matrix <float> trainData = new Matrix <float>(trainSampleCount, 2);
Matrix <float> trainClasses = new Matrix <float>(trainSampleCount, 1);
Matrix <float> sample = new Matrix <float>(1, 2);
Matrix <float> prediction = new Matrix <float>(1, 1);
for (int i = 0; i < positiveData.Count; i++)
{
var item = positiveData[i];
trainData.Data[i, 0] = item.Percent;
trainData.Data[i, 1] = item.Avg;
trainClasses.Data[i, 0] = 1;
}
for (int i = 0; i < negativeData.Count; i++)
{
var item = negativeData[i];
int row = positiveData.Count + i;
trainData.Data[row, 0] = item.Percent;
trainData.Data[row, 1] = item.Avg;
trainClasses.Data[row, 0] = 0;
}
Image <Bgr, Byte> img = new Image <Bgr, byte>(765, 300);
using (Matrix <int> layerSize = new Matrix <int>(new int[] { 2, 5, 1 }))
using (Mat layerSizeMat = layerSize.Mat)
using (TrainData td = new TrainData(trainData, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, trainClasses))
using (ANN_MLP network = new ANN_MLP())
{
network.SetLayerSizes(layerSizeMat);
network.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym, 0, 0);
network.TermCriteria = new MCvTermCriteria(10, 1.0e-8);
network.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Backprop, 0.1, 0.1);
network.Train(td, (int)Emgu.CV.ML.MlEnum.AnnMlpTrainingFlag.Default);
//String fileName = "ann_mlp_model.xml"; //Path.Combine(Path.GetTempPath(), "ann_mlp_model.xml");
network.Save(annFileName);
//if (File.Exists(fileName))
// File.Delete(fileName);
//画图
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = i * 1.0f / (100 * 3);
sample.Data[0, 1] = 255 - j * 1.0f / 3.0f;
network.Predict(sample, prediction);
// estimates the response and get the neighbors' labels
float response = prediction.Data[0, 0];
// highlight the pixel depending on the accuracy (or confidence)
img[i, j] = response < 0.5 ? new Bgr(90, 0, 0) : new Bgr(0, 90, 0);
}
}
}
// display the original training samples
for (int i = 0; i < positiveData.Count; i++)
{
var d = positiveData[i];
PointF p1 = new PointF((255 - d.Avg) * 3, d.Percent * 300);
img.Draw(new CircleF(p1, 2), new Bgr(255, 100, 100), -1);
}
for (int i = 0; i < negativeData.Count; i++)
{
var d = negativeData[i];
PointF p1 = new PointF((255 - d.Avg) * 3, d.Percent * 300);
img.Draw(new CircleF(p1, 2), new Bgr(100, 255, 100), -1);
}
this.ib_result.Image = img;
MessageBox.Show("训练完毕");
}
private void button1_Click(object sender, EventArgs e)
{
int trainSampleCount = 100;
#region Generate the traning data and classes
Matrix <float> trainData = new Matrix <float>(trainSampleCount, 2);
Matrix <float> trainClasses = new Matrix <float>(trainSampleCount, 1);
Image <Bgr, Byte> img = new Image <Bgr, byte>(500, 500);
Matrix <float> sample = new Matrix <float>(1, 2);
Matrix <float> prediction = new Matrix <float>(1, 1);
Matrix <float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix <float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix <float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix <float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion
using (Matrix <int> layerSize = new Matrix <int>(new int[] { 2, 10, 2 }))
using (Mat layerSizeMat = layerSize.Mat)
using (TrainData td = new TrainData(trainData, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, trainClasses))
using (ANN_MLP network = new ANN_MLP())
{
network.SetLayerSizes(layerSizeMat);
network.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym);
network.TermCriteria = new MCvTermCriteria(10000, 1.0e-8);
network.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Backprop, 0.1, 0.1);
network.Train(td);
network.Save("temp.txt");
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = j;
sample.Data[0, 1] = i;
network.Predict(sample, prediction);
// estimates the response and get the neighbors' labels
float response = prediction.Data[0, 0];
// highlight the pixel depending on the accuracy (or confidence)
if (response < 1.5)
{
img[i, j] = new Bgr(90, 0, 0);
}
else
{
img[i, j] = new Bgr(0, 90, 0);
}
}
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++)
{
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF((int)trainData2[i, 0], (int)trainData2[i, 1]);
img.Draw(new CircleF(p2, 2), new Bgr(100, 255, 100), -1);
}
pictureBox1.Image = img.ToBitmap();
// Emgu.CV.UI.ImageViewer.Show(img);
}
