public void GridDetection()
{
// convert to gray-scaler image
Mat image = originalImage.Mat.Clone();
// blur the image
CvInvoke.GaussianBlur(image, image, new Size(11, 11), 0);
// threshold the image
CvInvoke.AdaptiveThreshold(image, image, 255, AdaptiveThresholdType.MeanC, ThresholdType.Binary, 5, 2);
CvInvoke.BitwiseNot(image, image);
Mat kernel = new Mat(new Size(3, 3), DepthType.Cv8U, 1);
Marshal.Copy(new byte[] { 0, 1, 0, 1, 1, 1, 0, 1, 0 }, 0, kernel.DataPointer, 9);
CvInvoke.Dilate(image, image, kernel, new Point(-1, -1), 1, BorderType.Default, new MCvScalar(255));
FindOuterGridByFloorFill(image);
CvInvoke.Erode(image, image, kernel, new Point(-1, -1), 1, BorderType.Default, new MCvScalar(255));
ImageShowCase.ShowImage(image, "biggest blob");
VectorOfPointF lines = new VectorOfPointF();
CvInvoke.HoughLines(image, lines, 1, Math.PI / 180, 200);
// merging lines
PointF[] linesArray = lines.ToArray();
//MergeLines(linesArray, image);
lines = RemoveUnusedLine(linesArray);
Mat harrisResponse = new Mat(image.Size, DepthType.Cv8U, 1);
CvInvoke.CornerHarris(image, harrisResponse, 5);
DrawLines(lines.ToArray(), image);
ImageShowCase.ShowImage(image, "corners");
}
private void btnHarrisApply_Click(object sender, EventArgs e)
{
try
{
Image <Gray, float> response = new Image <Gray, float>(img.Size); //we store in the response image
CvInvoke.CornerHarris(img, response, 2, 3, 0.04); //calling Harris Corner Detector
double max = 0;
double min = 0;
Point maxLoc = new Point(0, 0);
Point minLoc = new Point(0, 0);
CvInvoke.MinMaxLoc(response, ref min, ref max, ref minLoc, ref maxLoc); //Locating the maximum response
Bitmap result = (Bitmap)picOriginal.Image.Clone();
double tresh = 0.01 * max;
//For the i, j locations where the response[i,j] > tresh, we will set the pixel(i,j) to Red.
for (int i = 0; i < result.Width; i++)
{
for (int j = 0; j < result.Height; j++)
{
double r = response.Data[i, j, 0];
if (r > tresh)
{
//Setting to Red
result.SetPixel(j, i, Color.FromArgb(255, 0, 0));
}
}
}
picResult.Image = result;
}catch (Exception ex)
{
MessageBox.Show("Please load image first.");
}
}
private void button_CornerHarris_Click(object sender, EventArgs e)
{
// imageBox1.Image = imagebgr;
// Image<Gray, byte> image_CornerHarris = imagetest;
Mat imtstoimat = imagetest.Mat;
Mat cornmat = imagetest.Mat;
//CvInvoke.CvtColor(imtstoimat, cornmat, ColorConversion.Bgr2Gray);
//Mat scr = new Mat(@"D:\image_CornerHarris.jpg", ImreadModes.AnyColor);//加载图像图片。
// Mat gray_scr = new Mat(@"D:\image_CornerHarris.jpg", ImreadModes.Grayscale);//加载灰度 图像。
////CvInvoke.Threshold(gray_scr, gray_scr, 150, 255, ThresholdType.Otsu);//二值 化图像。
////CvInvoke.CornerHarris(gray_scr, gray_scr, 3);//二值化图像角点检测。
////CvInvoke.Canny(gray_scr, gray_scr, 120, 150);//canny 处理。
////CvInvoke.CornerHarris(gray_scr, gray_scr, 3);//轮廓角点检测。
//CvInvoke.CornerHarris(gray_scr, gray_scr, 3);//灰度图像角点检测
//CvInvoke.Normalize(gray_scr, gray_scr, 0, 255, NormType.MinMax);//进行映 射到【0，255】区域中。
// gray_scr = gray_scr.ToImage<Gray, byte>().Mat;//把 gray 类型转成 Byte 类型。
//imageBox1.Image = scr;//显示输入图像。
//imageBox2.Image = gray_scr;//显示角点检测图像
CvInvoke.CornerHarris(cornmat, cornmat, 3); //灰度图像角点检测
CvInvoke.Normalize(cornmat, cornmat, 0, 255, NormType.MinMax); //进行映 射到【0，255】区域中。
cornmat = cornmat.ToImage <Gray, byte>().Mat;
imageBox1.Image = imagemat; //显示输入图像。
imageBox2.Image = cornmat; //显示角点检测图像
}
private static Image <Gray, float> GetRawCornerImage(Image <Gray, byte> contourImage)
{
var cornerImage = new Image <Gray, float>(contourImage.Size);
CvInvoke.CornerHarris(contourImage, cornerImage, 7);
return(cornerImage);
}
static void DoHarris(string imagePath)
{
var srcImage = new Image <Gray, byte>(imagePath);
var cornerImage = new Image <Gray, float>(srcImage.Size);
var thresholdImage = new Image <Gray, byte>(srcImage.Size);
CvInvoke.CornerHarris(srcImage, cornerImage, 5, 5, 0.01);
CvInvoke.Threshold(cornerImage, thresholdImage, 0.0001, 255.0, Emgu.CV.CvEnum.ThresholdType.BinaryInv);
thresholdImage.Save($"corner-{imagePath}");
}
private void button20_Click(object sender, EventArgs e)
{
// crear una imagen para contener las esquinas
Image <Gray, float> ImgFuentes = new Image <Gray, float>(PictureAnalizer.ImagenEntrada);
Image <Gray, float> ImgEsquinas = new Image <Gray, float>(PictureAnalizer.ImagenEntrada);
CvInvoke.CornerHarris(ImgFuentes, ImgEsquinas, 3, 3, 0.01);
PictureAnalizer.ImagenEntrada = ImgEsquinas.ToBitmap();
ImagenEntrada.Image = PictureAnalizer.ImagenEntrada;
}
private void processFrame(object sender, EventArgs arg)
{
cam.Retrieve(inputImage, 3);
CvInvoke.CvtColor(inputImage, inputGrayImage, ColorConversion.Bgr2Gray, 0);
CvInvoke.CornerHarris(inputGrayImage, outputCornerImage, 3);
CvInvoke.Normalize(outputCornerImage, outputCornerImage, 0, 255, NormType.MinMax, DepthType.Cv32F);
imageBoxInput.Image = inputGrayImage;
imageBoxOutput.Image = outputCornerImage;
}
private Mat PrepareImage(Mat Image)
{
Mat inputBinary = new Mat();
CvInvoke.Threshold(Image, inputBinary, 0, 255, ThresholdType.Binary | ThresholdType.Otsu);
_input_thinned = Skelatanize(inputBinary.Bitmap).Mat;
Mat harris_corners = Mat.Zeros(_input_thinned.Rows, _input_thinned.Cols, DepthType.Cv32F, 3);
CvInvoke.CornerHarris(_input_thinned, harris_corners, 2, 3, 0.04, BorderType.Default);
Mat harris_normalised = new Mat();
CvInvoke.Normalize(harris_corners, harris_normalised, 0, 255, NormType.MinMax, DepthType.Cv32F);
return(harris_normalised);
}
int max_thresh = 175; //最大阈值
private void button2_Click(object sender, EventArgs e)
{
//---------------------------【1】定义一些局部变量-----------------------------
Image <Gray, float> dstImage = new Image <Gray, float>(src.Size); //目标图
Mat normImage = new Mat(); //归一化后的图
Image <Gray, byte> scaledImage = new Image <Gray, byte>(src.Size); //线性变换后的八位无符号整型的图
//---------------------------【2】初始化---------------------------------------
//置零当前需要显示的两幅图，即清除上一次调用此函数时他们的值
Image <Gray, byte> g_srcImage1 = src.Clone();
//---------------------------【3】正式检测-------------------------------------
//进行角点检测 点检测传出的为Float类型的数据
CvInvoke.CornerHarris(src, dstImage, 2);
// 归一化与转换
CvInvoke.Normalize(dstImage, normImage, 0, 255, Emgu.CV.CvEnum.NormType.MinMax);
double min = 0, max = 0;
Point minp = new Point(0, 0);
Point maxp = new Point(0, 0);
CvInvoke.MinMaxLoc(normImage, ref min, ref max, ref minp, ref maxp);
double scale = 255 / (max - min);
double shift = min * scale;
CvInvoke.ConvertScaleAbs(normImage, scaledImage, scale, shift);//将归一化后的图线性变换成8位无符号整型
//---------------------------【4】进行绘制-------------------------------------
// 将检测到的，且符合阈值条件的角点绘制出来
byte[] data = scaledImage.Bytes;
for (int j = 0; j < normImage.Rows; j++)
{
for (int i = 0; i < normImage.Cols; i++)
{
int k = i * src.Width + j;
if (k < data.Length)
{
if (data[k] > thresh)
{
CvInvoke.Circle(g_srcImage1, new Point(i, j), 5, new MCvScalar(10, 10, 255), 2);
CvInvoke.Circle(scaledImage, new Point(i, j), 5, new MCvScalar(0, 10, 255), 2);
}
}
}
}
imageBox1.Image = g_srcImage1;
imageBox2.Image = scaledImage;
}
public override void Process(Image <Bgr, byte> image, out Image <Bgr, byte> annotatedImage, out List <object> data)
{
base.Process(image, out annotatedImage, out data);
// create image for the corners
var corners = new Image <Gray, float>(image.Size);
// run the Harris corner detector against the image
CvInvoke.CornerHarris(
image.Convert <Gray, byte>(),
corners,
_blockSize,
_apertureSize);
// normalize the image
CvInvoke.Normalize(corners, corners);
// optionally show the corners image
if (_viewCorners)
{
annotatedImage = corners.Convert <Bgr, byte>();
return;
}
// crate gray byte image from corners image
var gray = corners.Convert <Gray, byte>();
data = new List <object>();
// for each pixel annotate the corner if
// the inensity is beyond the threshold
for (var j = 0; j < gray.Rows; j++)
{
for (var i = 0; i < gray.Cols; i++)
{
if (!(gray[j, i].Intensity > _threshold))
{
continue;
}
var circle = new CircleF(new PointF(i, j), 1);
annotatedImage.Draw(circle, new Bgr(_annoColor.Color()), _lineThick);
data.Add(new Circle(circle));
}
}
}
static void Main(string[] args)
{
var img = new Image <Bgr, Byte>(DataDir.Contours("multipieces.jpg"));
var grey = new Image <Gray, byte>(img.Bitmap);
var smooth = grey.SmoothGaussian(7);
var thresholded = smooth.ThresholdBinary(new Gray(160), new Gray(256));
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
// Build list of contours
CvInvoke.FindContours(thresholded, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
var contourAreas = Enumerable.Range(0, contours.Size).Select(i => CvInvoke.ContourArea(contours[i]))
.ToArray();
var filteredAreas = contourAreas.Where(a => a > 500).OrderBy(a => a).ToArray();
var median = filteredAreas.Skip(filteredAreas.Length / 2).FirstOrDefault();
const double deviation = 0.2;
var from = median * (1 - deviation);
var to = median * (1 + deviation);
for (int i = 0; i < contours.Size; i++)
{
var area = contourAreas[i];
if (area < from || area > to)
{
continue;
}
var contour = contours[i];
CvInvoke.Polylines(img, contour, true, new Bgr(Color.Red).MCvScalar, 4);
}
}
var cornerImage = new Image <Gray, float>(thresholded.Size);
CvInvoke.CornerHarris(thresholded, cornerImage, 3);
var cornersThresholded = cornerImage.ThresholdBinaryInv(new Gray(160), new Gray(256));
cornersThresholded.Bitmap.Save(DataDir.Contours("cornerImage.bmp"), ImageFormat.Bmp);
img.Bitmap.Save(DataDir.Contours("markedContours.bmp"), ImageFormat.Bmp);
}
/// <summary>
/// Compute Harris Conrner
/// </summary>
/// <param name="img">Source Image</param>
public void Detect(Image <Gray, Byte> img)
{
this._CornerStrength = new Image <Gray, float>(img.Size);
//Harris computation
CvInvoke.CornerHarris(
img,
this._CornerStrength,
this._Aperture,
this._Neighborhood,
this._K);
//internal threshold computation
double[] maxStrength;
double[] minStrength; //not used
Point[] minPoints; //not used
Point[] maxPoints; //not used
this._CornerStrength.MinMax(out minStrength, out maxStrength, out minPoints, out maxPoints);
this._MaxStrength = maxStrength[0];
}
/// <summary>
/// Compute Harris corners
/// </summary>
/// <param name="image">source image</param>
public void Detect(Image <Gray, Byte> image)
{
this._CornerStrength = new Image <Gray, float>(image.Size);
//Harris computation
CvInvoke.CornerHarris(
image, //source image
this._CornerStrength, //result image
this._Neighborhood, //neighborhood size
this._Aperture, //aperture size
this._K); //Harris parameter
//internal threshold computation
double[] maxStrength;
double[] minStrength; //not used
Point[] minPoints; //not used
Point[] maxPoints; //not used
this._CornerStrength.MinMax(out minStrength, out maxStrength, out minPoints, out maxPoints);
this._MaxStrength = maxStrength[0];
}
public void CornerDetection(VisionProfile profile, Mat img)
{
var output = new Mat();
var outputNormalized = new Mat();
var outputNormalizedScaled = new Mat();
CvInvoke.CornerHarris(img, output, profile.HarrisCornerBlockSize, profile.HarrisCornerAperture, profile.HarrisCornerK, BorderType.Default);
CvInvoke.Normalize(output, outputNormalized, 0, 255, NormType.MinMax, DepthType.Cv32F, null);
CvInvoke.ConvertScaleAbs(outputNormalized, outputNormalizedScaled, 5, 5);
for (int j = 0; j < outputNormalized.Rows; j++)
{
for (int i = 0; i < outputNormalized.Cols; i++)
{
// if ((int)outputNormalized.GetData(j,i) > profile.HarrisCornerThreshold)
{
// circle(outputNormalizedScaled, Point(i, j), 5, Scalar(0), 2, 8, 0);
}
}
}
}
public List <Vector2> GetCornerPoints()
{
Image <Gray, float> cornerimg = new Image <Gray, float>(this.img.Size);
Image <Gray, Byte> cornerthrimg = new Image <Gray, Byte>(this.img.Size);
Image <Gray, Byte> cannyimg = this.img.Canny(60, 100);
CvInvoke.CornerHarris(cannyimg, cornerimg, 3, 3, 0.04);
//CvInvoke.cvNormalize(cornerimg, cornerimg, 0, 255, Emgu.CV.CvEnum.NORM_TYPE.CV_MINMAX, IntPtr.Zero); //标准化处理
double min = 0, max = 0;
System.Drawing.Point minp = new System.Drawing.Point(0, 0);
System.Drawing.Point maxp = new System.Drawing.Point(0, 0);
CvInvoke.MinMaxLoc(cornerimg, ref min, ref max, ref minp, ref maxp);
double scale = 255 / (max - min);
double shift = min * scale;
CvInvoke.ConvertScaleAbs(cornerimg, cornerthrimg, scale, shift);//进行缩放，转化为byte类型
byte[] data = cornerthrimg.Bytes;
List <Vector2> corners = new List <Vector2>();
List <Vector3> corners_3 = new List <Vector3>();
for (int i = 0; i < cornerimg.Height; i++)
{
for (int j = 0; j < cornerimg.Width; j++)
{
int k = i * cornerthrimg.Width + j;
if (data[k] > 80) //通过阈值判断
{
corners.Add(new Vector2(j, i));
corners_3.Add(m_projector.ImageToWorld(corners.Last()));
}
}
}
m_renderEngine.DrawPoints(corners_3);
return(corners);
}
private void ApplyHarisCorner(int threshold = 200)
{
try
{
if (imgList["Input"] == null)
{
return;
}
var img = imgList["Input"].Clone();
var gray = img.Convert <Gray, byte>();
var corners = new Mat();
CvInvoke.CornerHarris(gray, corners, 2);
CvInvoke.Normalize(corners, corners, 255, 0, Emgu.CV.CvEnum.NormType.MinMax);
Matrix <float> matrix = new Matrix <float>(corners.Rows, corners.Cols);
corners.CopyTo(matrix);
for (int i = 0; i < matrix.Rows; i++)
{
for (int j = 0; j < matrix.Cols; j++)
{
if (matrix[i, j] > threshold)
{
CvInvoke.Circle(img, new Point(j, i), 5, new MCvScalar(0, 0, 255), 3);
}
}
}
imageBoxEx1.Image = img.AsBitmap();
}
catch (Exception ex)
{
throw new Exception(ex.Message);
}
}
public Image <Gray, Byte> harrisCornerDetection()
{
// original source image as grayscale
Image <Gray, Byte> m_SourceImage = null;
// raw corner strength image (must be 32-bit float)
Image <Gray, float> m_CornerImage = null;
// inverted thresholded corner strengths (for display)
Image <Gray, Byte> m_ThresholdImage = null;
// create and show source image as grayscale
m_SourceImage = this.preProcessedImageInGrayScale;
// create corner strength image and do Harris
m_CornerImage = new Image <Gray, float>(m_SourceImage.Size);
CvInvoke.CornerHarris(m_SourceImage, m_CornerImage, 3, 3, 0.01);
// create and show inverted threshold image
m_ThresholdImage = new Image <Gray, Byte>(m_SourceImage.Size);
CvInvoke.Threshold(m_CornerImage, m_ThresholdImage, 0.0001,
255.0, ThresholdType.BinaryInv);
m_ThresholdImage.Save("harrisCornerDetection.jpg");
return(m_ThresholdImage);
}
private void button3_Click(object sender, EventArgs e)
{
harrisImage = new Image <Gray, float>(img.Size);
CvInvoke.CornerHarris(img, harrisImage, 2, 3, 0.01);
picProcImage.BackgroundImage = harrisImage.ToBitmap();
}
public AlgorithmResult DetectCornerHarris(
string filename,
byte threshold,
int blockSize,
int apertureSize,
double k,
HarrisBorderType borderType)
{
AlgorithmResult result = new AlgorithmResult();
Image <Bgr, byte> image = ImageHelper.GetImage(filename);
var resultImage = new Image <Bgr, byte>(filename);
// Create new (gray, float) image for corner
var corners = new Image <Gray, float>(image.Size);
// Harris corner with GrayScale
CvInvoke.CornerHarris(
image.Convert <Gray, byte>(),
corners,
blockSize,
apertureSize,
k,
GetBorderType(borderType));
// Normalize
CvInvoke.Normalize(corners, corners);
// Set resultImage after normalizing
resultImage = corners.Convert <Bgr, byte>();
// Crate new (gray, byte) image for corner
var gray = corners.Convert <Gray, byte>();
result.CircleDatas = new List <CirclePointModel>();
// for each pixel annotate the corner
// if inensity is beyond the threshold
for (var j = 0; j < gray.Rows; j++)
{
for (var i = 0; i < gray.Cols; i++)
{
if (!(gray[j, i].Intensity > threshold))
{
continue;
}
var circle = new CircleF(new PointF(i, j), 1);
resultImage.Draw(circle, new Bgr(Color.FromArgb(255, 77, 77)), 3);
result.CircleDatas.Add(new CirclePointModel()
{
CenterX = circle.Center.X,
CenterY = circle.Center.Y,
Radius = circle.Radius,
Area = circle.Area
});
}
}
result.ImageArray = ImageHelper.SetImage(resultImage);
return(result);
}
public static void processImage()
{
IImage image;
string finalFileName;
string finalFileNameForANN;
string imgNumber;
Mat grayscaleImg = new Mat();
Mat histImg = new Mat();
Mat downScaledImg = new Mat();
Mat smoothedImg = new Mat();
Mat sobelImg = new Mat();
Mat cannyImg = new Mat();
Mat eigenImg = new Mat();
int xorder = 0;
int yorder = 1;
string folderResizedImgFaces = @"testImages/ResizedFaces";
string folderProcessedImgFaces = @"testImages/ProcessedFaces/";
string folderProcessedSobel = @"testImages/ProcessedSobel/";
string folderEasyRecog = @"testImages/ProcessedSmoothEasyRecog/";
string folderCannyRecog = @"testImages/ProcessedCannyRecog/";
string imgExtension = ".bmp";
int number = 0;
//Read all files in the source image folder
var sourceImgFiles = Directory.GetFiles(folderResizedImgFaces, "*.bmp", SearchOption.AllDirectories); //List<string> imgFiles = new List<string>();
foreach (string fileName in sourceImgFiles)
{
// convert source image to UMat format (an array class)
image = new UMat(fileName, ImreadModes.Color);
// turns color image to grayscale
CvInvoke.CvtColor(image, grayscaleImg, ColorConversion.Bgr2Gray);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
grayscaleImg.Save(finalFileName);
number = number + 1;
// normalizes brightness and contrast
CvInvoke.EqualizeHist(grayscaleImg, histImg);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
grayscaleImg.Save(finalFileName);
number = number + 1;
// downsamples and rejects even rows and columns
CvInvoke.PyrDown(grayscaleImg, downScaledImg);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
downScaledImg.Save(finalFileName);
number = number + 1;
// upsamples and injects zero rows and columns ( smoothed image)
CvInvoke.PyrUp(downScaledImg, smoothedImg);
imgNumber = number.ToString();
finalFileNameForANN = folderEasyRecog + imgNumber + imgExtension;
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
smoothedImg.Save(finalFileName);
smoothedImg.Save(finalFileNameForANN);
number = number + 1;
// perform Sobel operation on the img
// save to Sobel folder
CvInvoke.Sobel(smoothedImg, sobelImg, DepthType.Default, xorder, yorder);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
finalFileNameForANN = folderProcessedSobel + imgNumber + imgExtension;
sobelImg.Save(finalFileName);
sobelImg.Save(finalFileNameForANN);
number = number + 1;
// perform Canny edge detection on the img
CvInvoke.Canny(smoothedImg, cannyImg, 100, 60);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
finalFileNameForANN = folderCannyRecog + imgNumber + imgExtension;
cannyImg.Save(finalFileName);
cannyImg.Save(finalFileNameForANN);
number = number + 1;
// perform Corner Harris detection on the img
CvInvoke.CornerHarris(smoothedImg, eigenImg, 3, 3, 0.04, BorderType.Default);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
eigenImg.Save(finalFileName);
number = number + 1;
}
}
int symm_rad_range = 5; //100D : 66 //indu 20 //hospi 35
public Particle_parameter_for_fullimg(Image <Bgr, byte> img)
{
width = img.Width;
height = img.Height;
grayimg = img.Convert <Gray, byte>();
//**Gradient
//horizontal filter
sobelX = grayimg.Sobel(1, 0, 3);
//vertical filter
sobelY = grayimg.Sobel(0, 1, 3);
//**Saturation
saturation = grayimg.CopyBlank();
for (int j = 0; j < height; ++j)
{
for (int i = 0; i < width; ++i)
{
saturation[j, i] = new Gray(Color.FromArgb((int)img[j, i].Red, (int)img[j, i].Green, (int)img[j, i].Blue).GetSaturation() * 256);
}
}
Image <Gray, Byte> imageL = saturation.Not();
CvInvoke.MedianBlur(imageL.Mat, saturation.Mat, 3);
saturation._EqualizeHist();
double otsu;
otsu = CvInvoke.Threshold(saturation, imageL, 0, 255, ThresholdType.Otsu);
CvInvoke.Threshold(saturation, saturation, (otsu + 255) * 0.6, 255, ThresholdType.Binary);
//**frst
Mat input = grayimg.Mat;
Mat output;
fullfrst(ref input, alpha_symm, 0.1);
//frst2d(ref input, out output, rad_symm, alpha_symm, 0.1);
//frst = output.Clone();
//for (int i = 0; i < contours.Size; i++)
//{
// img.Draw(new CircleF(mc[i], 2), new Bgr(Color.Green), 0);
//}
//**harris
m_SourceImage = grayimg.Clone();
// create corner strength image and do Harris
m_CornerImage = new Image <Gray, float>(m_SourceImage.Width, m_SourceImage.Height);
//CvInvoke.CornerHarris(m_SourceImage, m_CornerImage, 3, 3);
// create and show inverted threshold image
//m_ThresholdImage = new Image<Gray, Byte>(m_SourceImage.Size);
//CvInvoke.Threshold(m_CornerImage, m_ThresholdImage, 0.0001,
// 255.0, Emgu.CV.CvEnum.ThresholdType.BinaryInv);
//Image<Gray, byte> c = new Image<Gray, byte>(m_SourceImage.Width, m_SourceImage.Height);
CvInvoke.CornerHarris(m_SourceImage, m_CornerImage, HarrisBlockSize); //注意：角点检测传出的为Float类型的数据
CvInvoke.Normalize(m_CornerImage, m_CornerImage, 0, 255, NormType.MinMax, DepthType.Cv32F); //标准化处理
double min = 0, max = 0;
Point minp = new Point(0, 0);
Point maxp = new Point(0, 0);
CvInvoke.MinMaxLoc(m_CornerImage, ref min, ref max, ref minp, ref maxp);
double scale = 255 / (max - min);
double shift = min * scale;
CvInvoke.ConvertScaleAbs(m_CornerImage, m_SourceImage, scale, shift);//进行缩放，转化为byte类型
//c.Save("harris.bmp");
//byte[] data = c.Bytes;
//for (int i = 0; i < m_CornerImage.Height; i++)
//{
// for (int j = 0; j < m_CornerImage.Width; j++)
// {
// int k = i * m_SourceImage.Width + j;
// if (data[k] > 100) //通过阈值判断
// {
// CvInvoke.Circle(m_SourceImage, new Point(j, i), 1, new MCvScalar(0, 0, 255, 255), 2);
// }
// }
//}
}
public static void processImage(string imgPath)
{
IImage image;
string finalFileName;
string imgNumber;
string imgToProcess = imgPath;
Mat grayscaleImg = new Mat();
Mat histImg = new Mat();
Mat downScaledImg = new Mat();
Mat smoothedImg = new Mat();
Mat sobelImg = new Mat();
Mat cannyImg = new Mat();
Mat eigenImg = new Mat();
int xorder = 0;
int yorder = 1;
string folderProcessedImgFaces = @"testImages/ShowFaces/pro";
string imgExtension = ".bmp";
int number = 0;
// convert source image to UMat format (an array class)
image = new UMat(imgToProcess, ImreadModes.Color);
// turns color image to grayscale
CvInvoke.CvtColor(image, grayscaleImg, ColorConversion.Bgr2Gray);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
grayscaleImg.Save(finalFileName);
number = number + 1;
// normalizes brightness and contrast
CvInvoke.EqualizeHist(grayscaleImg, histImg);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
grayscaleImg.Save(finalFileName);
number = number + 1;
// downsamples and rejects even rows and columns
CvInvoke.PyrDown(grayscaleImg, downScaledImg);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
downScaledImg.Save(finalFileName);
number = number + 1;
// upsamples and injects zero rows and columns ( smoothed image)
CvInvoke.PyrUp(downScaledImg, smoothedImg);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
smoothedImg.Save(finalFileName);
number = number + 1;
// perform Sobel operation on the img
CvInvoke.Sobel(smoothedImg, sobelImg, DepthType.Default, xorder, yorder);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
sobelImg.Save(finalFileName);
number = number + 1;
// perform Canny edge detection on the img
CvInvoke.Canny(smoothedImg, cannyImg, 100, 60);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
cannyImg.Save(finalFileName);
number = number + 1;
// perform Canny edge detection on the img
CvInvoke.CornerHarris(smoothedImg, eigenImg, 3, 3, 0.04, BorderType.Default);
imgNumber = number.ToString();
finalFileName = folderProcessedImgFaces + imgNumber + imgExtension;
eigenImg.Save(finalFileName);
number = number + 1;
}
public Point WVPF(String Identifier)
{
#region setting
if (Identifier.Last().ToString() == "R")
{
img = CornerR.Clone();
this.row = CornerR.Height;
this.col = CornerR.Width;
rowMean = new double[row];
colMean = new double[col];
col_row_Mean_Black();
}
if (Identifier.Last().ToString() == "L")
{
img = CornerL.Clone();
this.row = CornerL.Height;
this.col = CornerL.Width;
rowMean = new double[row];
colMean = new double[col];
col_row_Mean_Black();
}
#endregion
Dictionary <int, Double> vVPF = new Dictionary <int, double>();
Dictionary <int, Double> hVPF = new Dictionary <int, double>();
#region harris
Image <Gray, float> CornerL_harris = img.Convert <Gray, float>().CopyBlank();
Image <Gray, float> CornerR_harris = img.Convert <Gray, float>().CopyBlank();
double min = 0, max = 0;
Point minP = new Point();
Point maxP = new Point();
if (Identifier.First().ToString() == "R" && Identifier.Last().ToString() == "L")// Patient's right eye inner corner
{
CornerL_harris = CornerL.Convert <Gray, float>();
CvInvoke.CornerHarris(CornerL, CornerL_harris, 3);
CvInvoke.Normalize(CornerL_harris, CornerL_harris, 0, 255, NormType.MinMax, DepthType.Cv32F);
CornerL_harris = CornerL_harris.AbsDiff(new Gray(0));
CvInvoke.MinMaxLoc(CornerL_harris, ref min, ref max, ref minP, ref maxP);
vVPF = calculateVPF(vVPF, "Vertical", CornerR_harris);
hVPF = calculateVPF(hVPF, "Horizontal", CornerR_harris);
}
else if (Identifier.First().ToString() == "L" && Identifier.Last().ToString() == "R")// Patient's left eye inner corner
{
CornerR_harris = CornerR.Convert <Gray, float>();
CvInvoke.CornerHarris(CornerR, CornerR_harris, 3);
CvInvoke.Normalize(CornerR_harris, CornerR_harris, 0, 255, NormType.MinMax, DepthType.Cv32F);
CornerR_harris = CornerR_harris.AbsDiff(new Gray(0));
CvInvoke.MinMaxLoc(CornerR_harris, ref min, ref max, ref minP, ref maxP);
vVPF = calculateVPF(vVPF, "Vertical", CornerL_harris);
hVPF = calculateVPF(hVPF, "Horizontal", CornerL_harris);
}
else
{
vVPF = calculateVPF(vVPF, "Vertical");
hVPF = calculateVPF(hVPF, "Horizontal");
}
#endregion
//Draw the variance for visualization
Image <Gray, byte> variance = img.Clone();
// Calculate the diffrience between the point of variance
Point[] p = new Point[col];
Dictionary <int, int> diffMax = new Dictionary <int, int>();
diffMax = VerticalDiffMax(p, diffMax, vVPF);
var dicSort = from objDic in diffMax orderby objDic.Value descending select objDic;
int CornerX = 0;
if (Identifier.Last().ToString() == "L")
{
CornerX = dicSort.ElementAt(0).Key + CornerL.ROI.X;
}
if (Identifier.Last().ToString() == "R")
{
CornerX = dicSort.ElementAt(0).Key + CornerR.ROI.X;
}
variance.Draw(new LineSegment2D(new Point(dicSort.ElementAt(0).Key, 0), new Point(dicSort.ElementAt(0).Key, variance.Height)), new Gray(0), 1);
variance.DrawPolyline(p, false, new Gray(255));
variance.Save(Identifier.First().ToString() + "\\varianceVertical" + Identifier.Last().ToString() + ".jpg");
// Calculate the diffrience between the point of variance
p = new Point[row];
diffMax = new Dictionary <int, int>();
diffMax = HorizontalDiffMax(p, diffMax, hVPF);
dicSort = from objDic in diffMax orderby objDic.Value descending select objDic;
int CornerY = 0;
if (Identifier.Last().ToString() == "L")
{
CornerY = dicSort.ElementAt(0).Key + CornerL.ROI.Y;
}
else if (Identifier.Last().ToString() == "R")
{
CornerY = dicSort.ElementAt(0).Key + CornerR.ROI.Y;
}
// Draw a line to segment the CORNER area
variance.Draw(new LineSegment2D(new Point(0, dicSort.ElementAt(0).Key), new Point(variance.Width, dicSort.ElementAt(0).Key)), new Gray(0), 1);
variance.DrawPolyline(p, false, new Gray(255));
variance.Save(Identifier.First().ToString() + "\\varianceCross" + Identifier.Last().ToString() + ".jpg");
variance = img.Clone();
variance.Draw(new LineSegment2D(new Point(0, dicSort.ElementAt(0).Key), new Point(variance.Width, dicSort.ElementAt(0).Key)), new Gray(0), 1);
variance.DrawPolyline(p, false, new Gray(255));
variance.Save(Identifier.First().ToString() + "\\varianceHorizontal" + Identifier.Last().ToString() + ".jpg");
if (Identifier.First().ToString() == "R" && Identifier.Last().ToString() == "L")// Patient's right eye inner corner
{
return(maxP);
}
else if (Identifier.First().ToString() == "L" && Identifier.Last().ToString() == "R")// Patient's left eye inner corner
{
return(maxP);
}
else
{
return(new Point(CornerX, CornerY));
}
}
private void harris_btn_Click(object sender, EventArgs e)
{
harrisImage = new Image <Gray, float>(img.Size);
CvInvoke.CornerHarris(img, harrisImage, 2, 3, 0.02);
modified_img.BackgroundImage = harrisImage.ToBitmap();
}