public static void ShowPlot(double[][] trainData)
{
//繪製視覺化
int RectSize = 56 * 2;
Mat img = new Mat(RectSize, RectSize, Emgu.CV.CvEnum.DepthType.Cv8U, 1);
img.SetTo(new Emgu.CV.Structure.MCvScalar(0x0, 0x0, 0x0));//清除，不然會有雜點
for (int i = 0; i < trainData.Length; i++)
{
MatExtension.SetValue(img, (int)(RectSize / 4 * trainData[i][1]) + RectSize / 2, (int)(trainData[i][0] * RectSize / 7), (byte)0xFF);
}
CvInvoke.Flip(img, img, Emgu.CV.CvEnum.FlipType.Vertical);
CvInvoke.Resize(img, img, new System.Drawing.Size(56 * 4, 56 * 4), 0, 0, Emgu.CV.CvEnum.Inter.Nearest);
CvInvoke.Imshow("img", img);
CvInvoke.WaitKey(1);
}
public static Mat Do(Mat srcImg, bool isMax, out double[] inputVector)
{
//Max Pooling
Mat poolingImg = new Mat(srcImg.Rows / 2, srcImg.Cols / 2, Emgu.CV.CvEnum.DepthType.Cv8U, 1);
inputVector = new double[poolingImg.Rows * poolingImg.Cols];
for (int j = 0; j < poolingImg.Rows; j++)
{
for (int i = 0; i < poolingImg.Cols; i++)
{
int[] PixelPool = new int[4];
PixelPool[0] = MatExtension.GetValue(srcImg, 2 * j, 2 * i);
PixelPool[1] = MatExtension.GetValue(srcImg, 2 * j + 1, 2 * i);
PixelPool[2] = MatExtension.GetValue(srcImg, 2 * j, 2 * i + 1);
PixelPool[3] = MatExtension.GetValue(srcImg, 2 * j + 1, 2 * i + 1);
MatExtension.SetValue(poolingImg, j, i, isMax ? (byte)PixelPool.Max() : (byte)PixelPool.Min());
inputVector[j * poolingImg.Cols + i] = Convert.ToDouble(isMax ? (byte)PixelPool.Max() : (byte)PixelPool.Min());
}
}
return(poolingImg);
}
