private void btnSave_Click(object sender, EventArgs e)
{
SaveFileDialog sfd = new SaveFileDialog();
sfd.Filter = "*.cnn|*.cnn";
if (sfd.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
CnnHelper.SaveCnn(cnn, sfd.FileName);
//CnnHelper.SaveCnnGroup(cnn, sfd.FileName);
}
}
private void btnLoad_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = "*.cnn|*.cnn";
if (ofd.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
cnn = CnnHelper.LoadCnn(ofd.FileName);
//cnn = CnnHelper.LoadCnnGroup(ofd.FileName);
}
}
private void btnPic_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = "*.jpg|*.jpg";
if (ofd.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
Bitmap img = new Bitmap(ofd.FileName);
//img = CnnHelper.ZoomImg(img, 28, 28);
Image <Bgr, float> trainingData = new Image <Bgr, float>(img);
double[,] input = new double[28, 28];
for (int w = 0; w < 28; w++)
{
for (int h = 0; h < 28; h++)
{
input[w, h] = Color.FromArgb(0,
(int)trainingData.Data[h, w, 2],
(int)trainingData.Data[h, w, 1],
(int)trainingData.Data[h, w, 0]
).ToArgb() / (double)0xFFFFFF;
}
}
input = CnnHelper.MatrixExpand(input, 2, 2, 0);
double[] labels = cnn.Predict(input);
double[] labelsTrue = new double[10];
double maxtype = labels[0], max = 0;
for (int n = 0; n < 10; n++)
{
if (maxtype < labels[n])
{
max = n;
maxtype = labels[n];
}
Console.Write(labels[n] + " ");
}
Console.WriteLine("");
//Console.WriteLine(i + ":" + label + "," + max);
//CnnHelper.ShowChange(labels, labelsTrue, 10000);
this.Invoke(new Action(() =>
{
lblResult.Text = max.ToString();
pbImage.Image = img;
}));
}
}
private void button1_Click(object sender, EventArgs e)
{
if (threadCnn != null && threadCnn.ThreadState == ThreadState.Running)
{
return;
}
learningRate = (double)numLearningRate.Value;
threadCnn = new Thread(() =>
{
try
{
trainCount = 0;
//int retry = 0;
while (CnnHelper.TruePercent != 1)
{
trainCount++;
CnnHelper.SumCount = 0;
CnnHelper.TrueCount = 0;
if (!chkHandwritten.Checked)
{
#region MNIST库
using (FileStream fs = new FileStream("data/MNIST/train-labels.idx1-ubyte", FileMode.Open))
{
using (FileStream fsImages = new FileStream("data/MNIST/train-images.idx3-ubyte", FileMode.Open))
{
byte[] bytes4 = new byte[4];
fsImages.Seek(4, SeekOrigin.Current);
fs.Seek(8, SeekOrigin.Current);
fsImages.Read(bytes4, 0, 4);
int count = ToInt32(bytes4);
fsImages.Read(bytes4, 0, 4);
int height = ToInt32(bytes4);
fsImages.Read(bytes4, 0, 4);
int width = ToInt32(bytes4);
for (int i = 0; i < count; i++)
{
Bitmap img = GetImage(fsImages, width, height);
byte label = GetLable(fs);
double[] labels = new double[10];
for (int i2 = 0; i2 < 10; i2++)
{
labels[i2] = 0;
}
labels[label] = 1;
Image <Bgr, float> trainingData = new Image <Bgr, float>(img);;
double[,] input = new double[width, height];
for (int w = 0; w < width; w++)
{
for (int h = 0; h < height; h++)
{
input[w, h] = Color.FromArgb(0,
(int)trainingData.Data[h, w, 2],
(int)trainingData.Data[h, w, 1],
(int)trainingData.Data[h, w, 0]
).ToArgb() / (double)0xFFFFFF;
}
}
int left = CnnHelper.RandomObj.Next(4), top = CnnHelper.RandomObj.Next(4);
input = CnnHelper.MatrixExpand(input, left, top, 4 - left, 4 - top, 0);
double[] forwardOutputFull = null;
cnn.Train(input, labels, learningRate, ref forwardOutputFull);
CnnHelper.ShowChange(forwardOutputFull, labels, 60000);
this.Invoke(new Action(() =>
{
lblInfo.Text = String.Format("训练周期:{0} 训练次数:{1}/{2} 正确率:{3:00.####%}", trainCount, CnnHelper.TrueCount, CnnHelper.SumCount, CnnHelper.TrueCount / (double)CnnHelper.SumCount);
if (i % 20 == 0)
{
lblResult.Text = CnnHelper.LabelsNum + " " + CnnHelper.ResultNum;
pbImage.Image = img;
}
//if (CnnHelper.LabelsNum != CnnHelper.ResultNum && retry < 5)
//{
// i--;
// retry++;
//}
//else
//{
// retry = 0;
//}
}));
//img.Save("imgs/" + i + "_" + label + ".jpg", System.Drawing.Imaging.ImageFormat.Jpeg);
}
fsImages.Close();
fs.Close();
}
}
#endregion
}
else
{
#region 手写字体集
foreach (var file in Directory.GetFiles("img", "*.jpg"))
{
using (Bitmap img = new Bitmap(file))
{
byte label = Convert.ToByte(file.Substring(file.LastIndexOf('\\') + 1, 1));
double[] labels = new double[10];
for (int i2 = 0; i2 < 10; i2++)
{
labels[i2] = 0;
}
labels[label] = 1;
//he
Image <Bgr, float> trainingData = new Image <Bgr, float>(img);
double[,] input = new double[img.Width, img.Height];
for (int w = 0; w < img.Width; w++)
{
for (int h = 0; h < img.Height; h++)
{
input[w, h] = Color.FromArgb(0,
(int)trainingData.Data[h, w, 2],
(int)trainingData.Data[h, w, 1],
(int)trainingData.Data[h, w, 0]
).ToArgb() / (double)0xFFFFFF;
}
}
int left = CnnHelper.RandomObj.Next(4), top = CnnHelper.RandomObj.Next(4);
input = CnnHelper.MatrixExpand(input, left, top, 4 - left, 4 - top, 0);
double[] forwardOutputFull = null;
cnn.Train(input, labels, learningRate, ref forwardOutputFull);
CnnHelper.ShowChange(forwardOutputFull, labels, 60000);
this.Invoke(new Action(() =>
{
lblInfo.Text = String.Format("训练周期:{0} 训练次数:{1}/{2} 正确率:{3:00.####%}", trainCount, CnnHelper.TrueCount, CnnHelper.SumCount, CnnHelper.TrueCount / (double)CnnHelper.SumCount);
lblResult.Text = CnnHelper.LabelsNum + " " + CnnHelper.ResultNum;
pbImage.Image = new Bitmap(img);
}));
}
}
#endregion
}
}
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
});
threadCnn.Start();
}
private void button2_Click(object sender, EventArgs e)
{
if (threadCnn != null && threadCnn.ThreadState == ThreadState.Running)
{
return;
}
threadCnn = new Thread(() =>
{
CnnHelper.SumCount = 0;
CnnHelper.TrueCount = 0;
if (!chkHandwritten.Checked)
{
#region MNIST库
using (FileStream fs = new FileStream("t10k-labels.idx1-ubyte", FileMode.Open))
{
using (FileStream fsImages = new FileStream("t10k-images.idx3-ubyte", FileMode.Open))
{
byte[] bytes4 = new byte[4];
fsImages.Seek(4, SeekOrigin.Current);
fs.Seek(8, SeekOrigin.Current);
fsImages.Read(bytes4, 0, 4);
int count = ToInt32(bytes4);
fsImages.Read(bytes4, 0, 4);
int height = ToInt32(bytes4);
fsImages.Read(bytes4, 0, 4);
int width = ToInt32(bytes4);
for (int i = 0; i < count; i++)
{
Bitmap img = GetImage(fsImages, width, height);
byte label = GetLable(fs);
Image <Bgr, float> trainingData = new Image <Bgr, float>(img);;
double[,] input = new double[width, height];
for (int w = 0; w < width; w++)
{
for (int h = 0; h < height; h++)
{
input[w, h] = Color.FromArgb(0,
(int)trainingData.Data[h, w, 2],
(int)trainingData.Data[h, w, 1],
(int)trainingData.Data[h, w, 0]
).ToArgb() / (double)0xFFFFFF;
}
}
input = CnnHelper.MatrixExpand(input, 2, 2, 0);
double[] labels = cnn.Predict(input);
double[] labelsTrue = new double[10];
double maxtype = labels[0], max = 0;
for (int n = 0; n < 10; n++)
{
if (maxtype < labels[n])
{
max = n;
maxtype = labels[n];
}
if (label == n)
{
labelsTrue[n] = 1;
}
}
//Console.WriteLine(i + ":" + label + "," + max);
CnnHelper.ShowChange(labels, labelsTrue, 10000);
this.Invoke(new Action(() =>
{
lblInfo.Text = String.Format("识别次数:{0}/{1} 正确率:{2:00.####%}", CnnHelper.TrueCount, CnnHelper.SumCount, CnnHelper.TrueCount / (double)CnnHelper.SumCount);
if (i % 20 == 0)
{
lblResult.Text = CnnHelper.LabelsNum + " " + CnnHelper.ResultNum;
pbImage.Image = img;
}
}));
//img.Save("imgs/" + i + "_" + label + ".jpg");
}
fsImages.Close();
fs.Close();
}
}
#endregion
}
else
{
#region 手写字体集
foreach (var file in Directory.GetFiles("img2", "*.jpg"))
{
using (Bitmap img = new Bitmap(file))
{
byte label = Convert.ToByte(file.Substring(file.LastIndexOf('\\') + 1, 1));
Image <Bgr, float> trainingData = new Image <Bgr, float>(img);;
double[,] input = new double[img.Width, img.Height];
for (int w = 0; w < img.Width; w++)
{
for (int h = 0; h < img.Height; h++)
{
input[w, h] = Color.FromArgb(0,
(int)trainingData.Data[h, w, 2],
(int)trainingData.Data[h, w, 1],
(int)trainingData.Data[h, w, 0]
).ToArgb() / (double)0xFFFFFF;
}
}
input = CnnHelper.MatrixExpand(input, 2, 2, 0);
double[] labels = cnn.Predict(input);
double[] labelsTrue = new double[10];
double maxtype = labels[0], max = 0;
for (int n = 0; n < 10; n++)
{
if (maxtype < labels[n])
{
max = n;
maxtype = labels[n];
}
if (label == n)
{
labelsTrue[n] = 1;
}
}
//Console.WriteLine(i + ":" + label + "," + max);
CnnHelper.ShowChange(labels, labelsTrue, 10000);
this.Invoke(new Action(() =>
{
lblInfo.Text = String.Format("识别次数:{0}/{1} 正确率:{2:00.####%}", CnnHelper.TrueCount, CnnHelper.SumCount, CnnHelper.TrueCount / (double)CnnHelper.SumCount);
lblResult.Text = CnnHelper.LabelsNum + " " + CnnHelper.ResultNum;
pbImage.Image = new Bitmap(img);
}));
}
}
#endregion
}
});
threadCnn.Start();
}
private void button1_Click(object sender, EventArgs e)
{
if (threadCnn != null && threadCnn.ThreadState == ThreadState.Running)
{
return;
}
learningRate = (double)numLearningRate.Value;
threadCnn = new Thread(() =>
{
try
{
trainCount = 0;
//int retry = 0;
string path = "F:\\迅雷下载\\lfw\\";
StreamReader sr = new StreamReader(path + "pairsDevTrain.txt");
int trueCount = Convert.ToInt32(sr.ReadLine()); //正确数
string nowLine = sr.ReadLine(); //当前行
List <string[]> dataList = new List <string[]>();
while (!String.IsNullOrEmpty(nowLine))
{
string[] values = nowLine.Split('\t');
string[] values2 = new string[4];
if (values.Length == 3)
{
values2[0] = values[0];
values2[1] = values[1];
values2[2] = values[0];
values2[3] = values[2];
}
else
{
values2[0] = values[0];
values2[1] = values[1];
values2[2] = values[2];
values2[3] = values[3];
}
dataList.Add(values2);
nowLine = sr.ReadLine();//当前行
}
while (CnnHelper.TruePercent != 1)
{
trainCount++;
CnnHelper.SumCount = 0;
CnnHelper.TrueCount = 0;
#region LFW人脸集
List <string[]> randomList = new List <string[]>();
foreach (var data in dataList)//随机排序
{
randomList.Insert(CnnHelper.RandomObj.Next(randomList.Count + 1), data);
}
foreach (var data in randomList)
{
string file1 = path + "lfw\\" + data[0] + "\\" + data[0] + "_" + Convert.ToInt32(data[1]).ToString("0000") + ".jpg";
string file2 = path + "lfw\\" + data[2] + "\\" + data[2] + "_" + Convert.ToInt32(data[3]).ToString("0000") + ".jpg";
using (Bitmap img1 = new Bitmap(file1))
{
using (Bitmap img2 = new Bitmap(file2))
{
double[] labels = new double[1];
if (dataList.IndexOf(data) < trueCount)
{
labels[0] = 1;
}
Image <Bgr, float> trainingData1 = new Image <Bgr, float>(img1);
Image <Bgr, float> trainingData2 = new Image <Bgr, float>(img2);
double[,] input = new double[img1.Width * 2 + 8, img1.Height + 2];
for (int w = 2; w < img1.Width; w++)
{
for (int h = 1; h < img1.Height; h++)
{
input[w, h] = Color.FromArgb(0,
(int)trainingData1.Data[h, w, 2],
(int)trainingData1.Data[h, w, 1],
(int)trainingData1.Data[h, w, 0]
).ToArgb() / (double)0xFFFFFF;
input[img1.Width + 4 + w, h] = Color.FromArgb(0,
(int)trainingData2.Data[h, w, 2],
(int)trainingData2.Data[h, w, 1],
(int)trainingData2.Data[h, w, 0]
).ToArgb() / (double)0xFFFFFF;
}
}
double[] forwardOutputFull = null;
cnn.Train(input, labels, learningRate, ref forwardOutputFull);
CnnHelper.ShowChange2(forwardOutputFull, labels);
this.Invoke(new Action(() =>
{
lblInfo.Text = String.Format("训练周期:{0} 训练次数:{1}/{2} 正确率:{3:00.####%}", trainCount, CnnHelper.TrueCount, CnnHelper.SumCount, CnnHelper.TrueCount / (double)CnnHelper.SumCount);
lblResult.Text = labels[0].ToString();
pbImage1.Image = new Bitmap(img1);
pbImage2.Image = new Bitmap(img2);
}));
}
}
}
#endregion
}
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
});
threadCnn.Start();
}