static MLearner()
{
if (!File.Exists(annFileName))
{
return;
}
network = new ANN_MLP();
network.Load(annFileName);
}
// Loading the Trained Model
public void LoadTrainedModel(String ModelFileName)
{
#if !NETFX_CORE
// Loading the Trained Model from File
try
{
nnet.Load(ModelFileName);
}
catch (Exception ex) {
Console.WriteLine(ex.ToString());
}
#endif
}
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($"判断结果：{response}");
}
}
private void btn_ANNReg_Click(object sender, EventArgs e)
{
var regPath = txbregPath.Text;
if (string.IsNullOrEmpty(regPath))
{
MessageBox.Show("待识别文件夹不能空");
return;
}
var isAct = ckbIsAct.Checked;
var files = Directory.GetFiles(regPath);
var testData = new List <List <float> >();
for (int i = 0; i < files.Length; i++)
{
var path = files[i];
Image <Gray, byte> img = new Image <Gray, byte>(path);
testData.Add(getImgData(img));
}
using (ANN_MLP network = new ANN_MLP())
{
network.Load(annFileName);
int colCount = width * height;
Matrix <float> sample = new Matrix <float>(1, colCount);
Matrix <float> prediction = new Matrix <float>(1, 1);
//1测试数据
var testCount = testData.Count;
var rightCount = 0;//正确act识别数量
for (int i = 0; i < testCount; i++)
{
var testColData = testData[i];
for (int j = 0; j < testColData.Count; j++)
{
sample[0, j] = testColData[j];
}
network.Predict(sample, prediction);
float response = prediction.Data[0, 0];
if (isAct && response > 0.5)
{
rightCount++;
Console.WriteLine($"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);
}
}
