private void timer1_Tick(object sender, EventArgs e)
{
//移植元では同じ教育画像で教育しているが、より実践に近い学習に変更
if (this.counter < 11)
{
//ランダムに点が打たれた画像を生成
NdArray img_p = getRandomImage();
//目標とするフィルタで学習用の画像を出力
NdArray[] img_core = this.decon_core.Forward(img_p);
//未学習のフィルタで画像を出力
NdArray[] img_y = this.model.Forward(img_p);
//img_yを暗黙的にNdArrayとして使用
this.BackgroundImage = NdArrayConverter.NdArray2Image(img_y[0].GetSingleArray(0));
Real loss = this.meanSquaredError.Evaluate(img_y, img_core);
this.model.Backward(img_y);
this.model.Update();
this.Text = "[epoch" + this.counter + "] Loss : " + string.Format("{0:F4}", loss);
this.counter++;
}
else
{
this.timer1.Enabled = false;
}
}
private void timer1_Tick(object sender, EventArgs e)
{
//At the transplant source, we are educating with the same educational image, but changing to learning closer to practice
if (this.counter < 11)
{
//Generate random dotted images
NdArray img_p = getRandomImage();
//Output a learning image with a target filter
NdArray[] img_core = this.decon_core.Forward(img_p);
//Output an image with an unlearned filter
NdArray[] img_y = this.model.Forward(img_p);
//Implicitly use img_y as NdArray
this.BackgroundImage = NdArrayConverter.NdArray2Image(img_y[0].GetSingleArray(0));
Real loss = this.meanSquaredError.Evaluate(img_y, img_core);
this.model.Backward(img_y);
this.model.Update();
this.Text = "[epoch" + this.counter + "] Loss : " + string.Format("{0:F4}", loss);
this.counter++;
}
else
{
this.timer1.Enabled = false;
}
}
private void timer1_Tick(object sender, EventArgs e)
{
// I am educating with the same educational image at the transplanting source, but changing to learning closer to practice
if (counter < 11)
{
// Generate an image with randomly struck points
NdArray img_p = getRandomImage();
// Output a learning image with a target filter
NdArray[] img_core = decon_core?.Forward(true, img_p);
// Output an image with an unlearned filter
NdArray[] img_y = model?.Forward(true, img_p);
// implicitly use img_y as NdArray
BackgroundImage = NdArrayConverter.NdArray2Image(img_y[0].GetSingleArray(0));
Real loss = meanSquaredError.Evaluate(img_y, img_core);
model.Backward(true, img_y);
model.Update();
Text = "[epoch" + counter + "] Loss : " + $"{loss:F4}";
counter++;
}
else
{
timer1.Enabled = false;
}
}
private void button3_Click(object sender, EventArgs e)
{
SaveFileDialog sfd = new SaveFileDialog
{
Filter = "pngファイル(*.png)|*.png|すべてのファイル(*.*)|*.*",
FileName = "result.png"
};
if (sfd.ShowDialog() == DialogResult.OK)
{
Task.Factory.StartNew(() =>
{
//ネットワークへ入力する前に予め拡大しておく必要がある
Bitmap resultImage = new Bitmap(this._baseImage.Width * 2, this._baseImage.Height * 2, PixelFormat.Format24bppRgb);
Graphics g = Graphics.FromImage(resultImage);
//補間にニアレストネイバーを使用
g.InterpolationMode = InterpolationMode.NearestNeighbor;
//画像を拡大して描画する
g.DrawImage(this._baseImage, 0, 0, this._baseImage.Width * 2, this._baseImage.Height * 2);
g.Dispose();
NdArray image = NdArrayConverter.Image2NdArray(resultImage);
NdArray[] resultArray = this.nn.Predict(image);
resultImage = NdArrayConverter.NdArray2Image(resultArray[0].GetSingleArray(0));
resultImage.Save(sfd.FileName);
this.pictureBox1.Image = new Bitmap(resultImage);
}
).ContinueWith(_ =>
{
MessageBox.Show("変換完了");
});
MessageBox.Show("変換処理は開始されました。\n『変換完了』のメッセージが表示されるまで、しばらくお待ち下さい\n※非常に時間がかかります（64x64の画像で三分ほど）");
}
}
private void button3_Click(object sender, EventArgs e)
{
SaveFileDialog sfd = new SaveFileDialog
{
Filter = "png file (*. png) | *. png | all files (*. *) | *. *",
FileName = "result.png"
};
if (sfd.ShowDialog() == DialogResult.OK)
{
Task.Factory.StartNew(() =>
{
//It is necessary to enlarge in advance before entering the network
Bitmap resultImage = new Bitmap(this._baseImage.Width * 2, this._baseImage.Height * 2, PixelFormat.Format24bppRgb);
Graphics g = Graphics.FromImage(resultImage);
//Use nearest neighbor for interpolation
g.InterpolationMode = InterpolationMode.NearestNeighbor;
//Draw an image enlarged
g.DrawImage(this._baseImage, 0, 0, this._baseImage.Width * 2, this._baseImage.Height * 2);
g.Dispose();
NdArray image = NdArrayConverter.Image2NdArray(resultImage);
NdArray[] resultArray = this.nn.Predict(image);
resultImage = NdArrayConverter.NdArray2Image(resultArray[0].GetSingleArray(0));
resultImage.Save(sfd.FileName);
this.pictureBox1.Image = new Bitmap(resultImage);
}
).ContinueWith(_ =>
{
MessageBox.Show("Conversion complete");
});
MessageBox.Show("Conversion processing has been started. \n Please wait for a while until \"Conversion completed\" message is displayed \n * It will take a very long time (about three minutes with 64 x 64 images)");
}
}
private void button3_Click(object sender, EventArgs e)
{
SaveFileDialog sfd = new SaveFileDialog
{
Filter = "png Files(*.png)|*.png|All Files(*.*)|*.*",
FileName = "result.png"
};
if (sfd.ShowDialog() == DialogResult.OK)
{
Task.Factory.StartNew(() =>
{
// We need to enlarge in advance before entering the network
Bitmap resultImage = new Bitmap(_baseImage.Width * 2, _baseImage.Height * 2, PixelFormat.Format24bppRgb);
Graphics g = Graphics.FromImage(resultImage);
// use nearest neighbor for interpolation
g.InterpolationMode = InterpolationMode.NearestNeighbor;
// Enlarge and draw the image
g.DrawImage(_baseImage, 0, 0, _baseImage.Width * 2, _baseImage.Height * 2);
g.Dispose();
NdArray image = NdArrayConverter.Image2NdArray(resultImage);
NdArray[] resultArray = nn.Predict(true, image);
resultImage = NdArrayConverter.NdArray2Image(resultArray[0].GetSingleArray(0));
resultImage.Save(sfd.FileName);
pictureBox1.Image = new Bitmap(resultImage);
}
).ContinueWith(_ =>
{
MessageBox.Show("After the exchange finished");
});
MessageBox.Show("The conversion process has started. \n Please wait for a while until \"conversion complete\" message is displayed \n * It will take a very long time (about 3 minutes with 64 x 64 images)");
}
}