public static Mat AddNoise(this Mat image, double noiseLevel = 0.75)
{
if (noiseLevel < 0 || noiseLevel > 1.0)
{
throw new ArgumentException("noiseLevel must be between 0.0 and 1.0");
}
if (noiseLevel == 0)
{
return(image);
}
var mean = new MCvScalar(0);
var std = new MCvScalar(255);
const int gaussSize = 13;
var output = new Mat();
var noise = new Mat(image.Size, DepthType.Cv8U, image.NumberOfChannels);
using (ScalarArray scalarArray1 = new ScalarArray(mean))
using (ScalarArray scalarArray2 = new ScalarArray(std))
{
CvInvoke.Randn(noise, scalarArray1, scalarArray2);
}
CvInvoke.GaussianBlur(noise, noise, new Size(gaussSize, gaussSize), 0.0);
CvInvoke.AddWeighted(image, 1 - noiseLevel, noise, noiseLevel, 0, output, image.Depth);
return(output);
}
private void button_run_Click(object sender, EventArgs e)
{
tab.SelectedIndex = 2;
double a = (double)value_a.Value;
double b = (double)value_b.Value;
double p = (double)value_p.Value;
double t = (double)value_t.Value;
if (t == 0)
{
imageBox3.Image = left.origin;
return;
}
else if (t == 1)
{
imageBox3.Image = right.origin;
return;
}
list_left = left.LineList;
list_right = right.LineList;
List <SupLine> list_dest = new List <SupLine>();
for (int i = 0; i < list_left.Count; i++)
{
list_dest.Add(SupLine.Interpolation(list_left[i], list_right[i], t));
}
Image <Bgr, byte> warp_left = left.Warp(list_dest, a, b, p);
Image <Bgr, byte> warp_right = right.Warp(list_dest, a, b, p);
imageBox3.Image = new Image <Bgr, byte>(warp_left.Size);
CvInvoke.AddWeighted(warp_left, 1 - t, warp_right, t, 0, imageBox3.Image);
}
public static UMat getSharpened(UMat img, double sigma, double amount)
{
/// <summary>
/// Apply sharpening to the given UMat.
/// </summary>
/// <param name="img">The src UMat.</param>
/// <param name="sigma">Sigma value for shrpening.</param>
/// <param name="amount">Amount of sharpening required.</param>
/// <returns>
/// Sharpened UMat image.
/// </returns>
/// <remarks>
/// Used for image sharpening.
/// </remarks>
///
UMat dblImg = new UMat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
UMat dblBlurImg = new UMat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
UMat outImg = new UMat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
img.ConvertTo(dblImg, Emgu.CV.CvEnum.DepthType.Cv64F);
int k = 2 * (int)Math.Round(3.0 * sigma) + 1;
CvInvoke.GaussianBlur(dblImg, dblBlurImg, new Size(k, k), sigma, sigma);
CvInvoke.AddWeighted(dblImg, 1.0 + amount, dblBlurImg, -amount, 0, outImg);
dblImg.Dispose();
dblBlurImg.Dispose();
img.Dispose();
return(outImg);
}
private Image <Gray, byte> ThresholdImage(Image <Hsv, byte> original, Point[] ranges)
{
List <Image <Gray, byte> > images = new List <Image <Gray, byte> >();
for (int i = 0; i < ranges.Length; i++)
{
images.Add(original.InRange(new Hsv(ranges[i].X, lowSaturation, lowValue), new Hsv(ranges[i].Y, 255, 255)));
}
Image <Gray, byte> threshold = new Image <Gray, byte>(original.Size);
for (int j = 0; j < images.Count; j++)
{
CvInvoke.AddWeighted(images[j], 1, threshold, 1, 0, threshold);
}
threshold.SmoothGaussian(9);
Image <Gray, byte> temp = threshold.Clone();
var element = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Cross, new Size(3, 3), new Point(-1, -1));
// opening
CvInvoke.Erode(threshold, temp, element, new Point(-1, -1), 3, Emgu.CV.CvEnum.BorderType.Reflect, default(MCvScalar));
CvInvoke.Dilate(temp, threshold, element, new Point(-1, -1), 2, Emgu.CV.CvEnum.BorderType.Reflect, default(MCvScalar));
//// closing
//CvInvoke.Dilate(threshold, temp, element, new Point(-1, -1), 2, Emgu.CV.CvEnum.BorderType.Reflect, default(MCvScalar));
//CvInvoke.Erode(temp, threshold, element, new Point(-1, -1), 2, Emgu.CV.CvEnum.BorderType.Reflect, default(MCvScalar));
return(threshold);
}
public override bool Do()
{
try
{
int id1 = IntPropertys["Src1ID"].Value;
int id2 = IntPropertys["Src2ID"].Value;
for (int SourcesId = 0; SourcesId < Sources.Count; SourcesId++)
{
var srcDatas = Sources[SourcesId].GetOut();
if ((id1 < srcDatas.Count) && (id2 < srcDatas.Count))
{
Mat resImg = new Mat();
CvInvoke.AddWeighted(srcDatas[id1].Image, FloatPropertys["Src1"].Value,
srcDatas[id2].Image, FloatPropertys["Src2"].Value,
FloatPropertys["Gamma"].Value,
resImg, DepthType.Cv8U);
this.m_out.Add(new DataSrc(resImg, srcDatas[id1].Info, false));
}
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
return(false);
}
return(true);
}
public void SavePreprocess(List <CircleF> list_Pupil, String LorR)
{
if (img_Gray != null && img_Threshold != null && img_Edge != null)
{
img_Gray.Save(LorR + "\\" + "img_Gray_" + LorR + ".jpg");
img_Bgr.Save(LorR + "\\" + "img_Bgr_" + LorR + ".jpg");
img_Threshold.Save(LorR + "\\" + "img_Threshold" + LorR + ".jpg");
img_Edge.Save(LorR + "\\" + "img_Edge" + LorR + ".jpg");
img_Ada.Save(LorR + "\\" + "img_Ada7" + LorR + ".jpg");
img_Sobel.Save(LorR + "\\" + "img_Sobel" + LorR + ".jpg");
img_SobelX.Save(LorR + "\\" + "img_SobelX" + LorR + ".jpg");
img_SobelY.Save(LorR + "\\" + "img_SobelY" + LorR + ".jpg");
img_laplace.Save(LorR + "\\" + "img_laplace" + LorR + ".jpg");
img_laplaceByte.Save(LorR + "\\" + "img_laplaceByte" + LorR + ".jpg");
sobelImage.Save(LorR + "\\" + "sobelImage" + LorR + ".jpg");
img_EdgeText.Save(LorR + "\\" + "img_EdgeText" + LorR + ".jpg");
img_overlap = img_Gray.CopyBlank();
CvInvoke.AddWeighted(img_Edge, 0.3, img_Gray, 0.7, 0, img_overlap);
img_overlap.Save(LorR + "\\" + "img_overlap" + LorR + ".jpg");
}
Draw_cyedg_cybla(list_Pupil, LorR + "\\", img_Edge, img_Threshold);
}
/// <summary>
/// Suma una fila con un arreglo de floats row = row*alpha + values*beta + gamma
/// </summary>
/// <param name="RowIndex">Índice de la fila</param>
/// <param name="values">Valores a sumar a la fila, el largo debe ser igual a la cantidad de columnas de la matriz</param>
/// <param name="alpha">multiplicador de la fila</param>
/// <param name="beta">multiplicador de values</param>
/// <param name="gamma">valor sumado a todos los elementos</param>
public static void AddWeightedFloatsToRow(this Mat m, int RowIndex, float[] values, double alpha = 1, double beta = 1, double gamma = 0)
{
if (m.Depth != DepthType.Cv32F)
{
throw new FormatException("La matriz no es de floats");
}
if (RowIndex < 0 || RowIndex >= m.Rows)
{
throw new FieldAccessException("La fila " + RowIndex + " no existe en la matriz");
}
if (values.Length != m.Cols)
{
throw new FieldAccessException("Las columnas de la matriz (" + m.Cols + ") no coinciden con el largo del arreglo (" + values.Length + ")");
}
Mat rowmat = m.GetRowHeader(RowIndex);
Mat valmat = values.GetMatHeader();
CvInvoke.AddWeighted(rowmat, alpha, valmat, beta, gamma, rowmat);
rowmat.Dispose();
valmat.Dispose();
}
static void Main(string[] args)
{
var img = CvInvoke.Imread(Path.Join("resources", "ZeroSweater.jpg"));
var gray = new Mat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv16S, 1);
var gradX = new Mat(gray.Rows, gray.Cols, Emgu.CV.CvEnum.DepthType.Cv16S, 1);
var gradY = new Mat(gray.Rows, gray.Cols, Emgu.CV.CvEnum.DepthType.Cv16S, 1);
var absGradX = new Mat(gray.Rows, gray.Cols, Emgu.CV.CvEnum.DepthType.Cv8U, 1);
var absGradY = new Mat(gray.Rows, gray.Cols, Emgu.CV.CvEnum.DepthType.Cv8U, 1);
var sobelGrad = new Mat(gray.Rows, gray.Cols, Emgu.CV.CvEnum.DepthType.Cv8U, 1);
CvInvoke.CvtColor(img, gray, Emgu.CV.CvEnum.ColorConversion.Bgr2Gray);
CvInvoke.GaussianBlur(gray, gray, new System.Drawing.Size(3, 3), 0);
CvInvoke.Sobel(gray, gradX, Emgu.CV.CvEnum.DepthType.Cv8U, 1, 0, 3);
CvInvoke.Sobel(gray, gradY, Emgu.CV.CvEnum.DepthType.Cv8U, 0, 1, 3);
CvInvoke.ConvertScaleAbs(gradX, absGradX, 1, 0);
CvInvoke.ConvertScaleAbs(gradY, absGradY, 1, 0);
CvInvoke.AddWeighted(absGradX, .5, absGradY, .5, 0, sobelGrad);
CvInvoke.Imshow("sobel x", absGradX);
CvInvoke.Imshow("sobel Y", absGradY);
CvInvoke.Imshow("sobel", sobelGrad);
CvInvoke.Imwrite("sobelX.jpg", absGradX);
CvInvoke.Imwrite("sobelY.jpg", absGradY);
CvInvoke.Imwrite("sobel.jpg", sobelGrad);
CvInvoke.Imshow("gray", gray);
CvInvoke.WaitKey(0);
}
private void sobelToolStripMenuItem_Click(object sender, EventArgs e)
{
try
{
if (pictureBox1.Image == null)
{
return;
}
float[,] data =
{
{ -1, 0, 1 },
{ -2, 0, 2 },
{ -1, 0, 1 }
};
Matrix <float> SEx = new Matrix <float>(data);
Matrix <float> SEy = SEx.Transpose();
var img = new Bitmap(pictureBox1.Image)
.ToImage <Bgr, float>();
var Gx = new Mat();
var Gy = new Mat();
CvInvoke.Sobel(img, Gx, Emgu.CV.CvEnum.DepthType.Cv32F, 1, 0);
CvInvoke.Sobel(img, Gy, Emgu.CV.CvEnum.DepthType.Cv32F, 0, 1);
var gx = Gx.ToImage <Gray, float>();
var gy = Gy.ToImage <Gray, float>();
var Gxx = new Mat(Gx.Size, Emgu.CV.CvEnum.DepthType.Cv32F, 1);
var Gyy = new Mat(Gx.Size, Emgu.CV.CvEnum.DepthType.Cv32F, 1);
CvInvoke.ConvertScaleAbs(Gx, Gxx, 0, 0);
CvInvoke.ConvertScaleAbs(Gy, Gyy, 0, 0);
var mag = new Mat(Gx.Size, Emgu.CV.CvEnum.DepthType.Cv8U, 1);
CvInvoke.AddWeighted(Gxx, 0.5, Gyy, 0.5, 0, mag);
AddImage(mag.ToImage <Bgr, byte>(), "Mag Absolute");
gx._Mul(gx);
gy._Mul(gy);
var M = new Mat(gx.Size, Emgu.CV.CvEnum.DepthType.Cv8U, 1);
CvInvoke.Sqrt(gx + gy, M);
AddImage(M.ToImage <Bgr, byte>(), "Mag Squared");
//CvInvoke.Filter2D(img, Gx, SEx, new Point(-1, -1));
//CvInvoke.Filter2D(img, Gy, SEy, new Point(-1, -1));
pictureBox1.Image = M.ToBitmap();
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
private void ShowDetail()
{
int id = -1;
this.Dispatcher.Invoke(() =>
{
id = cbFOV.SelectedIndex;
});
System.Drawing.Rectangle ROI = mModel.Gerber.FOVs[id].ROI;
this.Dispatcher.Invoke(() =>
{
txtROIX.Text = ROI.X.ToString();
txtROIY.Text = ROI.Y.ToString();
txtROIWidth.Text = ROI.Width.ToString();
txtROIHeight.Text = ROI.Height.ToString();
});
if (mImage != null)
{
var modelFov = mModel.FOV;
System.Drawing.Rectangle ROIGerber = new System.Drawing.Rectangle(
mAnchorROIGerber[id].X - modelFov.Width / 2, mAnchorROIGerber[id].Y - modelFov.Height / 2,
modelFov.Width, modelFov.Height);
mImage.ROI = ROI;
using (Image <Bgr, byte> imgGerberBgr = new Image <Bgr, byte>(ROIGerber.Size))
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
for (int i = 0; i < mModel.Gerber.PadItems.Count; i++)
{
PadItem item = mModel.Gerber.PadItems[i];
if (item.FOVs.Count > 0)
{
if (item.FOVs[0] == id)
{
System.Drawing.Point[] cntPointSub = new System.Drawing.Point[item.ContourAdjust.Length];
for (int j = 0; j < cntPointSub.Length; j++)
{
cntPointSub[j] = new System.Drawing.Point(item.ContourAdjust[j].X - ROIGerber.X, item.ContourAdjust[j].Y - ROIGerber.Y);
}
contours.Push(new VectorOfPoint(cntPointSub));
}
}
}
CvInvoke.DrawContours(imgGerberBgr, contours, -1, new MCvScalar(255), -1);
CvInvoke.AddWeighted(imgGerberBgr, 0.5, mImage, 0.5, 1, imgGerberBgr);
this.Dispatcher.Invoke(() =>
{
BitmapSource bms = Utils.Convertor.Bitmap2BitmapSource(imgGerberBgr.Bitmap);
imb.Source = bms;
});
}
GC.Collect();
GC.WaitForPendingFinalizers();
}
else
{
mLog.Info(string.Format("Cant Capture image in FOV : {0}", id + 1));
}
}
/// <remarks>
/// // Converted Implementation of MSSIM from
/// https://docs.opencv.org/2.4/doc/tutorials/gpu/gpu-basics-similarity/gpu-basics-similarity.html
/// </remarks>
public static double MSSIMCpu(Mat i1, Mat i2, MSSIMCpuParam b, double C1 = defaultC1, double C2 = defaultC2)
{
try
{
var gaussianSize = new Size(3, 3);
i1.ConvertTo(b.I1, b.DepthType);
i2.ConvertTo(b.I2, b.DepthType);
CvInvoke.Multiply(b.I1, b.I1, b.I1_2);
CvInvoke.Multiply(b.I2, b.I2, b.I2_2);
CvInvoke.Multiply(b.I1, b.I2, b.I1_I2);
CvInvoke.GaussianBlur(b.I1, b.Mu1, gaussianSize, 1.5);
CvInvoke.GaussianBlur(b.I2, b.Mu2, gaussianSize, 1.5);
CvInvoke.Multiply(b.Mu1, b.Mu1, b.Mu1_2);
CvInvoke.Multiply(b.Mu2, b.Mu2, b.Mu2_2);
CvInvoke.Multiply(b.Mu1, b.Mu2, b.Mu1_Mu2);
CvInvoke.GaussianBlur(b.I1_2, b.Sigma1_2, gaussianSize, 1.5);
CvInvoke.GaussianBlur(b.I2_2, b.Sigma2_2, gaussianSize, 1.5);
CvInvoke.GaussianBlur(b.I1_I2, b.Sigma12, gaussianSize, 1.5);
//sigma1_2 = sigma1_2 - mu1_2
CvInvoke.AddWeighted(b.Sigma1_2, 1, b.Mu1_2, -1, 0, b.Sigma1_2);
//sigma2_2 = sigma2_2 - mu2_2
CvInvoke.AddWeighted(b.Sigma2_2, 1, b.Mu2_2, -1, 0, b.Sigma2_2);
//sigma12 = sigma12 - mu1_mu2
CvInvoke.AddWeighted(b.Sigma12, 1, b.Mu1_Mu2, -1, 0, b.Sigma12);
// t1 = 2 * mu1_mu2 + C1
CvInvoke.AddWeighted(b.Mu1_Mu2, 2, b.Ones, C1, 0, b.T1);
// t2 = 2 * sigma12 + C2
CvInvoke.AddWeighted(b.Sigma12, 2, b.Ones, C1, 0, b.T2);
CvInvoke.Multiply(b.T1, b.T2, b.T3);
//t1 = mu1_2 + mu2_2 + C1;
CvInvoke.AddWeighted(b.Mu1_2, 1, b.Mu2_2, 1, C1, b.T1);
//t2 = sigma1_2m + sigma2_2m + C2;
CvInvoke.AddWeighted(b.Sigma1_2, 1, b.Sigma2_2, 1, C2, b.T2);
CvInvoke.Multiply(b.T1, b.T2, b.T1);
CvInvoke.Divide(b.T3, b.T1, b.SSIM_map);
var mssim = CvInvoke.Mean(b.SSIM_map);
return(Math.Sqrt(
mssim.V0 * mssim.V0 +
mssim.V1 * mssim.V1 +
mssim.V2 * mssim.V2
));
}
catch (Exception)
{
throw;
}
}
/// <summary>
/// Sharp image with default values.
/// </summary>
/// <param name="input">input image</param>
/// <returns>sharpened image</returns>
public static UMat Sharp(UMat input)
{
UMat temp = new UMat();
CvInvoke.GaussianBlur(input, temp, new Size(0, 0), 3);
CvInvoke.AddWeighted(input, 1.5, temp, -0.5, 0, temp);
input.Dispose();
return(temp);
}
// 叠加图像
private Image <Bgr, byte> PicSubtraction(Image <Bgr, byte> pic1, Image <Bgr, byte> pic2)
{
Image <Bgr, byte> outpic = new Image <Bgr, byte>(pic1.Size);
pic1 = ContourFilling(ToBin(picture));
pic2 = ContourFilling2(ToBin(picture));
CvInvoke.AddWeighted(pic1, 0.5, pic2, 0.5, 1, outpic);
return(outpic);
}
public override bool Do()
{
try
{
int comparableID = 0;
BaseFilter betaSourceFilter = Graph.GetFilter(EnumPropertys["BetaSource"].Value);
for (int SourcesId = 0; SourcesId < Sources.Count; SourcesId++)
{
var AlphaDatas = Sources[SourcesId].GetOut();
var betaDatas = betaSourceFilter.GetOut();
for (int ImgId = 0; ImgId < AlphaDatas.Count; ImgId++)
{
Mat alphaImg = AlphaDatas[ImgId].Image;
Mat betaImg = null;
if (FilePropertys["BetaFileImg"].FileName.Length > 0)
{
betaImg = CvInvoke.Imread(FilePropertys["BetaFileImg"].FileName);
}
else
{
if (comparableID < AlphaDatas.Count)
{
betaImg = betaDatas[comparableID].Image.Clone();
}
else
{
betaImg = new Mat(alphaImg.Size, alphaImg.Depth, alphaImg.NumberOfChannels);
}
}
comparableID++;
Mat resImg = new Mat();
CvInvoke.AddWeighted(alphaImg, FloatPropertys["Alpha"].Value,
betaImg, FloatPropertys["Beta"].Value,
FloatPropertys["Gamma"].Value,
resImg, DepthType.Cv8U);
this.m_out.Add(new DataSrc(resImg, AlphaDatas[ImgId].Info, false));
}
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
return(false);
}
return(true);
}
/// <summary>
/// Multiplica los valores de la diagonal de una matriz por un factor
/// </summary>
/// <param name="factor">factor por el cual se multiplicará la diagonal</param>
public static void ScaleDiagonal(this Mat m, double factor)
{
Mat temp = new Mat(m.Rows, m.Cols, m.Depth, m.NumberOfChannels);
double f = factor - 1;
CvInvoke.SetIdentity(temp, new MCvScalar(f, f, f, f));
CvInvoke.AddWeighted(m, 1, temp, 1, 0, m);
temp.Dispose();
}
public static UMat getExposureCorrected(ref UMat img, double ev)
{
UMat dblImg = new UMat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
UMat outImg = new UMat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
img.ConvertTo(dblImg, Emgu.CV.CvEnum.DepthType.Cv64F);
//outImg = (UMat)ev*dblImg;
CvInvoke.AddWeighted(dblImg, ev, dblImg, 0, 0, outImg);
//CvInvoke.cvConvertScale(dblImg, outImg, ev,0);
dblImg.Dispose();
img.Dispose();
return(outImg);
}
public static UMat getContrastAdjusted(ref UMat img, double cont1, double cont2)
{
UMat dblImg = new UMat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
UMat outImg = new UMat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
img.ConvertTo(dblImg, Emgu.CV.CvEnum.DepthType.Cv64F);
//outImg = (UMat)ev*dblImg;
CvInvoke.AddWeighted(dblImg, cont1, dblImg, 0, cont1 * (-128) + cont2 + 128, outImg);
//CvInvoke.cvConvertScale(dblImg, outImg, ev,0);
dblImg.Dispose();
img.Dispose();
return(outImg);
}
public Image <Bgr, byte> getProcessed(Mat img)
{
Mat dblImg = new Mat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
Mat dblBlurImg = new Mat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
Mat outImg = new Mat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
img.ConvertTo(dblImg, Emgu.CV.CvEnum.DepthType.Cv64F);
int k = 2 * (int)Math.Round(3.0 * sigma) + 1;
CvInvoke.GaussianBlur(dblImg, dblBlurImg, new Size(k, k), sigma, sigma);
CvInvoke.AddWeighted(dblImg, 1.0 + amount, dblBlurImg, -amount, 0, outImg);
return(outImg.ToImage <Bgr, byte>());
}
// 填充缺陷轮廓
private Image <Bgr, byte> ContourFilling3(Image <Bgr, byte> pic)
{
Image <Bgr, byte> outpic = new Image <Bgr, byte>(pic.Size);
Image <Ycc, byte> ycc = pic.Convert <Ycc, byte>();
for (int i = 0; i < ycc.Height; i++)
{
for (int j = 0; j < ycc.Width; j++)
{
if (ycc[i, j].Cr > 35 && ycc[i, j].Cr < 148 &&
ycc[i, j].Cb > 48 && ycc[i, j].Cb < 141)
{
ycc[i, j] = new Ycc(0, 0, 0);
}
else
{
ycc[i, j] = new Ycc(255, 255, 255);
}
}
}
Image <Gray, byte> gray = ycc.Convert <Gray, byte>();
gray = gray.ThresholdBinary(new Gray(100), new Gray(255));
gray = gray.Canny(100, 60);
Image <Gray, byte> outcon = new Image <Gray, byte>(pic.Size);
VectorOfVectorOfPoint con = new VectorOfVectorOfPoint();
CvInvoke.FindContours(gray, con, outcon, RetrType.External, ChainApproxMethod.ChainApproxNone);
int n = 0;
for (int i = 0; i < con.Size; i++)
{
if (CvInvoke.ContourArea(con[i]) > 0)
{
n++;
}
}
textBox1.Text = "共" + n.ToString() + "个缺陷" + " " + "\n";
n = 0;
for (int i = 0; i < con.Size; i++)
{
if (CvInvoke.ContourArea(con[i]) > 0)
{
CvInvoke.DrawContours(outpic, con, i, new MCvScalar(0, 255, 0), 5);
textBox1.Text = textBox1.Text + "第" + (++n).ToString() + "个缺陷的面积为" + CvInvoke.ContourArea(con[i]) + " \n";
}
}
CvInvoke.AddWeighted(outpic, 0.5, picture, 0.5, 0, outpic);
return(outpic);
}
public static UMat getSaturationAdjusted(ref UMat img, double amount)
{
Image <Hsv, double> outImg = img.ToImage <Hsv, double>();
UMat dblImg = new UMat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
outImg = img.ToImage <Hsv, double>();
var colors = new VectorOfUMat(3);
CvInvoke.Split(outImg, colors);
double shift = (1 + amount) >= 0.0 ? 1 + amount : 0;
CvInvoke.AddWeighted(colors[1], shift, colors[1], 0, 0, colors[1]);
CvInvoke.Merge(colors, dblImg);
return(dblImg);
}
// *************************** 模块8 END ***********************************************************//
/*
* 模块9：LAD_resShow
* 功能： 显示最后效果
*/
// *************************** 模块9 BEGIN ***********************************************************//
public Mat LAD_resShow(Mat srcImage, Mat bianaryImage, Matrix <double> left_Matrix, Matrix <double> right_Matrix)
{
Mat image = new Mat();
bianaryImage.CopyTo(image);
Mat uphalf = LAD_getUpHalf(srcImage);
Mat bottomHalf = LAD_getROI(srcImage);
Mat warpImage = new Mat(bianaryImage.Size, DepthType.Cv8U, 3);
warpImage.SetTo(new Bgr(0, 0, 0).MCvScalar);
Mat warpCopy = warpImage.Clone();
int H = bianaryImage.Rows;
int W = bianaryImage.Cols;
Point[] left_fitted_points = new Point[H];
for (int x = 0; x < H; x++)
{
int y = (int)(left_Matrix.Data[0, 0] + left_Matrix.Data[1, 0] * x + left_Matrix.Data[2, 0] * Math.Pow(x, 2));
left_fitted_points[x] = new Point(y, x);
}
CvInvoke.Polylines(warpImage, left_fitted_points, false, new Bgr(255, 0, 0).MCvScalar, 5, LineType.EightConnected);
Point[] right_fitted_points = new Point[H];
for (int x = 0; x < H; x++)
{
int y = (int)(right_Matrix.Data[0, 0] + right_Matrix.Data[1, 0] * x + right_Matrix.Data[2, 0] * Math.Pow(x, 2));
right_fitted_points[x] = new Point(y, x);
}
CvInvoke.Polylines(warpImage, right_fitted_points, false, new Bgr(255, 0, 0).MCvScalar, 5, LineType.EightConnected);
Mat rev = LAD_reverseAffineTrans(warpImage);
Mat laneImage = new Mat();
CvInvoke.AddWeighted(bottomHalf, 0.5, rev, 0.5, 0, laneImage);
Mat tmp = new Mat();
CvInvoke.AddWeighted(uphalf, 0.5, warpCopy, 0.5, 0, tmp);
Mat dst = LAD_ImageStitch_2(tmp, laneImage);
return(dst);
}
private Image <Gray, Byte> processImage(Image <Bgr, Byte> image)
{
Mat hsvImg = new Mat();
CvInvoke.CvtColor(image, hsvImg, ColorConversion.Bgr2Hsv);
//Threshold image, keep only the red pixel
Mat lower_hue_range = new Mat();
Mat upper_hue_range = new Mat();
CvInvoke.InRange(hsvImg, new ScalarArray(new MCvScalar(0, 100, 30)), new ScalarArray(new MCvScalar(80, 255, 255)), lower_hue_range); //80(multi color) -> 10(only red)
CvInvoke.InRange(hsvImg, new ScalarArray(new MCvScalar(160, 100, 30)), new ScalarArray(new MCvScalar(179, 255, 255)), upper_hue_range);
//// Combine the above two images
CvInvoke.AddWeighted(lower_hue_range, 1.0, upper_hue_range, 1.0, 0.0, hsvImg);
//CvInvoke.MedianBlur(hue_image, hue_image, 9);
return(new Image <Gray, Byte>(hsvImg.Bitmap));
}
public override void Process(ref Image <Bgr, byte> image)
{
// the gradient image in x
var gradX = image.Sobel(_order, 0, _apertureSize).ConvertScale <byte>(1, 0);
// the gradient image in y
var gradY = image.Sobel(0, _order, _apertureSize).ConvertScale <byte>(1, 0);
var grad = new Image <Bgr, byte>(image.Width, image.Height);
// blend the gradient images
CvInvoke.AddWeighted(gradX, 0.5, gradY, 0.5, 1.0, grad);
// invert the image
if (_invert)
{
CvInvoke.BitwiseNot(grad, grad);
}
image = grad.Convert <Bgr, byte>();
}
static public Mat drawRect(Mat inputMat, Point pointStart, Point pointEnd)
{
Mat overlay = new Mat();
Mat outputMat = new Mat();
overlay = inputMat.Clone();
outputMat = inputMat.Clone();
Point rectPoint = new Point(pointStart.X < pointEnd.X ? pointStart.X : pointEnd.X, pointStart.Y < pointEnd.Y ? pointStart.Y : pointEnd.Y);
Size rectSize = new Size(Math.Abs(pointStart.X - pointEnd.X), Math.Abs(pointStart.Y - pointEnd.Y));
Rectangle rect = new Rectangle(rectPoint, rectSize);
CvInvoke.Rectangle(overlay, rect, new Bgr(Color.Cyan).MCvScalar, 2);
CvInvoke.AddWeighted(inputMat, 0.7, overlay, 0.3, 0, outputMat);
CvInvoke.Rectangle(outputMat, rect, new Bgr(Color.Cyan).MCvScalar, 1);
return(outputMat);
}
public static UMat getColorAdjusted(ref UMat img, double redshift, double greenshift, double blueshift)
{
double shift;
UMat dblImg = new UMat(img.Rows, img.Cols, Emgu.CV.CvEnum.DepthType.Cv64F, img.NumberOfChannels);
img.ConvertTo(dblImg, Emgu.CV.CvEnum.DepthType.Cv64F);
var colors = new VectorOfUMat(3);
CvInvoke.Split(img, colors);
shift = (1 + redshift) > 0 ? (1 + redshift) : 0;
CvInvoke.AddWeighted(colors[2], shift, colors[2], 0, 0, colors[2]);
shift = (1 + greenshift) > 0 ? (1 + greenshift) : 0;
CvInvoke.AddWeighted(colors[1], shift, colors[1], 0, 0, colors[1]);
shift = (1 + blueshift) > 0 ? (1 + blueshift) : 0;
CvInvoke.AddWeighted(colors[0], shift, colors[0], 0, 0, colors[0]);
CvInvoke.Merge(colors, dblImg);
img.Dispose();
return(dblImg);
}
public static Image <Bgr, byte> MaskOverlay(Image <Bgr, byte> Image, Image <Gray, Byte> Mask)
{
Image <Bgr, byte> greenImage = Image.CopyBlank();
greenImage.SetValue(new Bgr(0, 255, 0), Mask);
Image <Bgr, byte> redImage = Image.CopyBlank();
Mask._Not();
redImage.SetValue(new Bgr(0, 0, 255), Mask);
var colorMask = redImage.Add(greenImage);
var blendedImage = Image.CopyBlank();
CvInvoke.AddWeighted(Image, 0.8, colorMask, 0.2, 0, blendedImage);
redImage.Dispose();
greenImage.Dispose();
colorMask.Dispose();
return(blendedImage);
}
private void AddNoise(Mat image)
{
const double noiseLevel = 0.75;
var mean = new MCvScalar(0);
var std = new MCvScalar(255);
const int gaussSize = 13;
const double scale = 0.5;
const double shift = 100;
var noise = new Mat(image.Size, DepthType.Cv8U, 1);
using (ScalarArray scalarArray1 = new ScalarArray(mean))
using (ScalarArray scalarArray2 = new ScalarArray(std))
{
CvInvoke.Randn(noise, scalarArray1, scalarArray2);
}
CvInvoke.GaussianBlur(noise, noise, new Size(gaussSize, gaussSize), 0.0);
CvInvoke.AddWeighted(image, 1 - noiseLevel, noise, noiseLevel, 0, image, image.Depth);
CvInvoke.ConvertScaleAbs(image, image, scale, shift);
}
public static Mat sobelEdgeDetection(ref Mat src_roi)
{
Mat roi_gray = new Mat();
Mat grad_x = new Mat();
Mat grad_y = new Mat();
Mat grad_absx = new Mat();
Mat grad_absy = new Mat();
Mat roi_soble = new Mat();
CvInvoke.CvtColor(src_roi, roi_gray, ColorConversion.Rgb2Gray);
CvInvoke.Sobel(roi_gray, grad_x, DepthType.Cv16S, 1, 0, 3, 1, 1, BorderType.Default);//x方向的sobel检测
CvInvoke.ConvertScaleAbs(grad_x, grad_absx, 1, 0);
CvInvoke.Sobel(roi_gray, grad_y, DepthType.Cv16S, 0, 1, 3, 1, 1, BorderType.Default);//y方向的sobel检测
CvInvoke.ConvertScaleAbs(grad_y, grad_absy, 1, 0);
CvInvoke.AddWeighted(grad_absx, 0.5, grad_absy, 0.5, 0, roi_soble);
return(roi_soble);
}
/**
* \brief Draws a bounding box on the viewport.
* \param void
* \return void
*/
private void drawBoundingBox()
{
if (mainImage != null)
{
Image <Bgr, byte> overlay = mainImage.Copy();
if (showingBox)
{
CvInvoke.Rectangle(overlay, new Rectangle(
boxStart.X,
boxStart.Y,
(boxEnd.X - boxStart.X),
(boxEnd.Y - boxStart.Y)),
new MCvScalar(255, 200, 10), -1);
CvInvoke.AddWeighted(overlay, 0.3, mainImage, 0.7, 0, boxImage);
}
viewport.Image = boxImage.Bitmap;
}
}
//Save the circle & eye overlapping image for debugging purpose , Optional
private void Draw_cyedg_cybla(List <CircleF> list_Pupil, String Directory, Image <Gray, byte> Edge, Image <Gray, byte> Black)
{
int count = 0;
foreach (CircleF cy in list_Pupil)
{
Image <Gray, byte> cyedg = new Image <Gray, byte>(img_Edge.Width, img_Edge.Height, new Gray(255));
//cyedg.Draw(cy, new Gray(0), cyedg_thickness);
for (double theta = 0.0; theta < 2.0; theta += 0.01)
{
//if ((theta > 0.25 && theta < 0.75) || theta > 1.0 && theta < 1.99) continue;
//if ( theta > 1.0 && theta < 1.99) continue;
double rx = cy.Center.X + cy.Radius * Math.Cos(theta * Math.PI) + 0.5;
double ry = cy.Center.Y + cy.Radius * Math.Sin(theta * Math.PI) + 0.5;
cyedg.Draw(new CircleF(new PointF((int)rx, (int)ry), 0), new Gray(0), 0);
rx = cy.Center.X + (cy.Radius + 1) * Math.Cos(theta * Math.PI) + 0.5;
ry = cy.Center.Y + (cy.Radius + 1) * Math.Sin(theta * Math.PI) + 0.5;
cyedg.Draw(new CircleF(new PointF((int)rx, (int)ry), 0), new Gray(0), 0);
rx = cy.Center.X + (2) * Math.Cos(theta * Math.PI) + 0.5;
ry = cy.Center.Y + (2) * Math.Sin(theta * Math.PI) + 0.5;
cyedg.Draw(new CircleF(new PointF((int)rx, (int)ry), 0), new Gray(0), 0);
}
CvInvoke.AddWeighted(img_SobelX.Convert <Gray, byte>(), 0.5, cyedg, 0.5, 0, cyedg);
cyedg.Save(Directory + "cyedg" + count + ".jpg");
Image <Gray, byte> cybla = new Image <Gray, byte>(img_Edge.Width, img_Edge.Height, new Gray(255));
cybla.Draw(cy, new Gray(0), cyble_thickness);
CvInvoke.AddWeighted(Black, 0.5, cybla, 0.5, 0, cybla);
cybla.Save(Directory + "cybla" + count + ".jpg");
count++;
}
}
