/// <summary>
/// Get contours from tresholded original image
/// </summary>
/// <param name="imgThresh">Treshold of original image</param>
/// <param name="errorCode">Error code</param>
/// <returns>Vector of vector of point contours</returns>
private static VectorOfVectorOfPoint GetImageContours(Mat imgThresh, ref int errorCode)
{
try
{
// Initiation of the image that will be used in this method
Mat imgCanny = new Mat();
// Initiate of vector of vector of point contours
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
// Find edges using Canny algorithm
CvInvoke.Canny(imgThresh, imgCanny, 150, 50, 7);
// Add all contours from canny image to contours variable
CvInvoke.FindContours(imgCanny, contours, null, RetrType.Ccomp, ChainApproxMethod.ChainApproxNone);
// Check if debug enabled
if (Properties.Settings.Default.debug)
{
// Show image of contours
CvInvoke.Imshow("Contours", imgCanny);
}
// Return the contours
return(contours);
}
catch (Exception ex)
{
Console.WriteLine(ex);
errorCode = 6;
return(new VectorOfVectorOfPoint());
}
}
private void Btn_findLine_Click(object sender, EventArgs e)
{
//LineSegment2D line1 = new LineSegment2D(new Point(1, 1), new Point(1, 20));
//LineSegment2D line2 = new LineSegment2D(new Point(1, 1), new Point(20, 1));
//MessageBox.Show(line1.GetExteriorAngleDegree(line2).ToString());
var bitmap = this.picSrc.GetFirstRegionRect();
var image = new Image <Bgr, byte>(bitmap);
UMat grayImage = new UMat();
CvInvoke.CvtColor(image, grayImage, ColorConversion.Bgr2Gray);
//使用高斯滤波去除噪声
//CvInvoke.GaussianBlur(grayImage, grayImage, new Size(5, 5), 3);
//CvInvoke.Imshow("Blur Image", grayImage);
UMat cannyEdges = new UMat();
CvInvoke.Canny(grayImage, cannyEdges, 100, 120);
CvInvoke.Imshow("Canny Image", cannyEdges);
CvInvoke.WaitKey(2);
var lines = CvInvoke.HoughLinesP(cannyEdges, 1, Math.PI / 40.0, 20, 8, 3);
for (int i = 0; i < lines.Length; i++)
{
var line = lines[i];
CvInvoke.Line(image, line.P1, line.P2, new MCvScalar(0, 0, 255));
}
this.picTarget.LoadImage(image.ToBitmap());
}
private Mat getCannyImage()
{
Mat GrayFrame = new Mat();
Mat BlurFrame = new Mat();
Mat CannyFrame = new Mat();
CvInvoke.CvtColor(ROIFrame, GrayFrame, Emgu.CV.CvEnum.ColorConversion.Bgr2Gray);
CvInvoke.MedianBlur(GrayFrame, BlurFrame, parameters.blurValue);
double CannyThresh2 = parameters.cannyThreshold1;
double CannyThresh1 = parameters.cannyThreshold2;
if (parameters.isAdaptive)
{
CannyThresh2 = CvInvoke.Threshold(BlurFrame, CannyFrame, 0, 255,
Emgu.CV.CvEnum.ThresholdType.Binary | Emgu.CV.CvEnum.ThresholdType.Otsu);
CannyThresh1 = 0.1 * CannyThresh2;
if (InvokeRequired)
{
Invoke(new MethodInvoker(() =>
{
cannyThreshold1.Value = (int)CannyThresh1;
cannyThreshold2.Value = (int)CannyThresh2;
}));
}
}
CvInvoke.Canny(BlurFrame, CannyFrame, CannyThresh1, CannyThresh2);
return(CannyFrame);
}
public VectorOfVectorOfPoint GetContours(Bitmap bitmap)
{
using var emguImage = bitmap.ToImage <Bgr, byte>();
// Grayscale
var grayScaleImage = emguImage.Convert <Gray, byte>();
// Applying GaussianBlur
//var blurredImage = grayScaleImage.SmoothGaussian(5, 5, 0, 0);
var blurredImage = new Image <Gray, byte>(grayScaleImage.Size);
CvInvoke.GaussianBlur(grayScaleImage, blurredImage, new Size(5, 5), 0);
// Applying Canny algorithm
using var cannyImage = new UMat();
CvInvoke.Canny(blurredImage, cannyImage, 50, 100);
//var name = @"C:\Users\aleks\Workspace\SmartSaver\ReceiptRecognizer\assets\Result\"
// + DateTime.Now.ToString("mm.ss.ff") + "canny.jpg";
//cannyImage.ToBitmap().Save(name, ImageFormat.Jpeg);
// Finding contours
var contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(cannyImage, contours, null, RetrType.Tree, ChainApproxMethod.ChainApproxSimple);
return(contours);
}
public void Can_use_indexes()
{
var sourceBitmap = Samples.sample13;
var w = sourceBitmap.Width;
var h = sourceBitmap.Height;
using var src = new UMat();
var srcMat = sourceBitmap.ToMat();
srcMat.CopyTo(src);
using var gray = new UMat();
using var resized = new UMat();
using var canny = new UMat();
CvInvoke.CvtColor(src, gray, ColorConversion.Bgra2Gray);
CvInvoke.Resize(gray, resized, new Size(w * 2, h * 2));
CvInvoke.Canny(resized, canny, 100, 40);
Run("samples/sample13.png");
canny.Save("canny.png");
Run("canny.png");
}
private void ProcessFrame(object sender, EventArgs arg)
{
if (_capture0 != null && _capture0.Ptr != IntPtr.Zero)
{
_capture0.Retrieve(_frame0, 0);
CvInvoke.CvtColor(_frame0, _grayFrame, ColorConversion.Bgr2Gray);
CvInvoke.PyrDown(_grayFrame, _smallGrayFrame);
CvInvoke.PyrUp(_smallGrayFrame, _smoothedGrayFrame);
CvInvoke.Canny(_smoothedGrayFrame, _cannyFrame, 100, 60);
captureImageBox.Image = _frame0;
//grayscaleImageBox.Image = _grayFrame;
smoothedGrayscaleImageBox.Image = _smoothedGrayFrame;
cannyImageBox.Image = _cannyFrame;
ILog log = log4net.LogManager.GetLogger("visClient.Logging");
log.Info(DateTime.Now.ToString("yyyy-MM-dd HH:mm:ss.fff") + "frame0 captured");
if (flag)
{
vw.Write(_frame0);
log.Info(DateTime.Now.ToString("yyyy-MM-dd HH:mm:ss.fff") + "video writing...");
}
}
}
private Mat Canny(Mat img)
{
Mat output = new Mat();
CvInvoke.Canny(img, output, 100, 50, 3);
return(output);
}
private void btnGetCanny_Click(object sender, EventArgs e)
{
Mat canny = new Mat();
CvInvoke.Canny(imgClone.Image, canny, 30, 200);
imgClone.Image = canny;
}
private void UpdateImage()
{
Mat displayImage = new Mat(m_image.Size, DepthType.Cv8U, 3);
CvInvoke.CvtColor(m_image, displayImage, ColorConversion.Gray2Bgr);
if (cbEnableCanny.IsChecked.Value)
{
CvInvoke.Canny(m_image, displayImage, scThresh1.Value, scThresh2.Value);
}
if (cbEnableCircles.IsChecked.Value)
{
try
{
var circs = CvInvoke.HoughCircles(m_image, HoughType.Gradient, 1, (int)scHoughDist.Value, (int)scThresh1.Value, (int)scThresh2.Value, (int)scHoughMin.Value, (int)scHoughMax.Value);
foreach (var circle in circs)
{
CvInvoke.Circle(displayImage, new System.Drawing.Point((int)circle.Center.X, (int)circle.Center.Y), (int)circle.Radius, new Emgu.CV.Structure.MCvScalar(0, 0, 255), 3);
}
}
catch (Exception exc)
{
Console.WriteLine("HoughCircle Exception: " + exc.Message);
}
}
imgPreview.Source = ConvertImage.ToBitmapSource(displayImage);
}
/// <summary>
/// sobel边缘检测
/// </summary>
/// <param name="mat"></param>
/// <returns></returns>
public static Mat Sobel(Mat mat)
{
Mat outMat = new Mat();
CvInvoke.Canny(mat, outMat, 90, 120, 3);
return(outMat);
}
/// <summary>
/// 移除与图像边缘联通的区域
/// </summary>
/// <param name="inMat">输入图片:Mat</param>
/// <param name="color">填充颜色</param>
/// <returns></returns>
public static Mat ClearBorder(Mat inMat, Color color)
{
Image <Gray, byte> src = new Image <Gray, byte>(inMat.Size);
src = inMat.ToImage <Gray, byte>();
Image <Gray, byte> cannyOut = new Image <Gray, byte>(src.Size);
CvInvoke.Canny(src, cannyOut, 5, 5 * 3);
//边缘延拓
Image <Gray, byte> mask = new Image <Gray, byte>(new Size(src.Width + 2, src.Height + 2));
CvInvoke.cvSetImageROI(mask, new Rectangle(1, 1, src.Width, src.Height));
cannyOut.CopyTo(mask);
CvInvoke.cvResetImageROI(mask);
Rectangle rect = new Rectangle();
CvInvoke.FloodFill(
src, //1 原图像
mask, //2 掩码
new Point(1, 1), //3 种子点
new MCvScalar(color.Blue, color.Green, color.Red), //4 填充颜色值
out rect, //5
new MCvScalar(25, 25, 25), //6
new MCvScalar(0, 0, 0), //7
Connectivity.EightConnected, //8 连通性设置
FloodFillType.FixedRange //9
);
src = src - cannyOut;
return(src.Mat);
}
/// <summary>
/// Prepare the image.
/// </summary>
/// <returns>The <see cref="UMat"/> instance.</returns>
private UMat PrepareImage()
{
// Resize image.
if (_image.Width > MaxSize && _image.Height > MaxSize)
{
_image = _image.Resize(MaxSize, MaxSize * _image.Width / _image.Height, Inter.Linear, true);
}
// Convert the image to gray-scale and filter out the noise.
using var uimage = new UMat();
CvInvoke.CvtColor(_image, uimage, ColorConversion.Bgr2Gray);
// Use image pyramid to remove noise.
using var pyrDown = new UMat();
CvInvoke.PyrDown(uimage, pyrDown);
CvInvoke.PyrUp(pyrDown, uimage);
var cannyEdges = new UMat();
CvInvoke.Canny(uimage, cannyEdges, ThresholdMin, ThresholdMax, l2Gradient: L2Gradient);
// Another way to process image, but worse. Use only one!
//CvInvoke.Threshold(uimage, cannyEdges, 50.0, 100.0, ThresholdType.Binary);
//CvInvoke.AdaptiveThreshold(uimage, cannyEdges, 50, AdaptiveThresholdType.MeanC, ThresholdType.Binary, 7, 1);
return cannyEdges;
}
public override void ImageGrabbedHandler(object sender, EventArgs e)
{
var frame = new Mat();
CameraCapture.Retrieve(frame);
var grayFrame = new Mat();
CvInvoke.CvtColor(frame, grayFrame, ColorConversion.Bgr2Gray);
var smallGrayFrame = new Mat();
CvInvoke.PyrDown(grayFrame, smallGrayFrame);
var smoothedGrayFrame = new Mat();
CvInvoke.PyrUp(smallGrayFrame, smoothedGrayFrame);
var cannyFrame = new Mat();
CvInvoke.Canny(smoothedGrayFrame, cannyFrame, 100, 60);
imageBoxCaptured.Image = frame;
imageBoxGray.Image = grayFrame;
imageBoxSmoothedGray.Image = smoothedGrayFrame;
imageBoxCanny.Image = cannyFrame;
NotifyStatus(string.Empty);
}
private void ProcessFrame(object sender, EventArgs arg)
{
Mat frame = new Mat();
_capture.Retrieve(frame, 0);
Mat grayFrame = new Mat();
CvInvoke.CvtColor(frame, grayFrame, ColorConversion.Bgr2Gray);
Mat smallGrayFrame = new Mat();
CvInvoke.PyrDown(grayFrame, smallGrayFrame);
Mat smoothedGrayFrame = new Mat();
CvInvoke.PyrUp(smallGrayFrame, smoothedGrayFrame);
Mat cannyFrame = new Mat();
CvInvoke.Canny(smoothedGrayFrame, cannyFrame, 100, 60);
this.ImgNormalZm.Image = frame;
this.ImgGrayZm.Image = grayFrame;
this.ImgSmoothedGrayZm.Image = smoothedGrayFrame;
this.ImgCannyZm.Image = cannyFrame;
}
public static void TestPreprocess()
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Multiselect = false;
if (!(ofd.ShowDialog() == DialogResult.OK))
{
return;
}
//简单图像处理
Mat img = CvInvoke.Imread(ofd.FileName, ImreadModes.Unchanged);
if (img.IsEmpty)
{
Console.WriteLine("can not load the image \n");
}
CvInvoke.Imshow("Image", img);
Mat grayImg = new Mat();
//转换为灰度图像
CvInvoke.CvtColor(img, grayImg, ColorConversion.Rgb2Gray);
CvInvoke.Imshow("Gray Image", grayImg);
//sobel
Mat sobelImg = new Mat();
CvInvoke.Sobel(grayImg, sobelImg, grayImg.Depth, 1, 0);
//使用canny算子查找边缘
Mat cannyImg = new Mat();
CvInvoke.Canny(grayImg, cannyImg, 20, 40);
CvInvoke.Imshow("Canny Image", cannyImg);
CvInvoke.WaitKey(0);
}
private void ProcessFrame(object sender, EventArgs arg)
{
Mat frame = new Mat();
_capture.Retrieve(frame, 0);
Mat grayFrame = new Mat();
CvInvoke.CvtColor(frame, grayFrame, ColorConversion.Bgr2Gray);
Mat smallGrayFrame = new Mat();
CvInvoke.PyrDown(grayFrame, smallGrayFrame);
Mat smoothedGrayFrame = new Mat();
CvInvoke.PyrUp(smallGrayFrame, smoothedGrayFrame);
//Image<Gray, Byte> smallGrayFrame = grayFrame.PyrDown();
//Image<Gray, Byte> smoothedGrayFrame = smallGrayFrame.PyrUp();
Mat cannyFrame = new Mat();
CvInvoke.Canny(smoothedGrayFrame, cannyFrame, 100, 60);
//Image<Gray, Byte> cannyFrame = smoothedGrayFrame.Canny(100, 60);
captureImageBox.Image = frame;
/*grayscaleImageBox.Image = grayFrame;
* smoothedGrayscaleImageBox.Image = smoothedGrayFrame;
* cannyImageBox.Image = cannyFrame;*/
}
private void cannyToolStripMenuItem_Click(object sender, EventArgs e)
{
Bitmap bm;
if (!processed)
{
bm = (Bitmap)srcPicBox.Image;
}
else
{
bm = (Bitmap)resPicBox.Image;
}
Image <Bgr, Byte> img = new Image <Bgr, Byte>(bm);
var uimage = new UMat();
CvInvoke.CvtColor(img, uimage, ColorConversion.Bgr2Gray);
var grayimage = new Image <Gray, byte>(bm);
CvInvoke.CvtColor(img, grayimage, ColorConversion.Bgr2Gray);
BlackBG(grayimage);
var cannyThreshold = GetKMeansThreshold(grayimage);
cannyThreshold = GetKMeansThreshold(grayimage);
var cannyEdges = new UMat();
CvInvoke.Canny(uimage, cannyEdges, cannyThreshold, cannyThreshold);
resPicBox.Image = cannyEdges.Bitmap;
processed = true;
}
public VectorOfVectorOfPoint Canny(ref UMat uimage, double cannyThreshold, double cannyThresholdLinking, double rho, int thresh, double minwidth, double gap)
{
Stopwatch watch = Stopwatch.StartNew();
UMat cannyEdges = new UMat();
CvInvoke.Canny(uimage, cannyEdges, cannyThreshold, cannyThresholdLinking);
lines = CvInvoke.HoughLinesP(
cannyEdges,
rho, //Distance resolution in pixel-related units
Math.PI / 45, //Angle resolution measured in radians.
// Math.PI / 45, //Angle resolution measured in radians.
thresh, //threshold 100
minwidth, //min Line width 2
gap); //gap between lines
// 20, //threshold
// 30, //min Line width
// 10); //gap between lines
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(cannyEdges, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
watch.Stop();
msgBuilder.Append(String.Format("Canny & Hough lines - {0} ms; ", watch.ElapsedMilliseconds));
// lineImageBox.Image = lineImage;
return(contours);
}
public void Setup()
{
var sourceBitmap = Samples.sample13;
_width = sourceBitmap.Width;
_height = sourceBitmap.Height;
_source = new UMat();
_gray = new UMat();
_resized = new UMat();
_canny = new UMat();
_dx = new UMat();
_dy = new UMat();
var source = sourceBitmap.ToMat();
source.CopyTo(_source);
CvInvoke.CvtColor(_source, _gray, ColorConversion.Bgra2Gray);
CvInvoke.Resize(_gray, _resized, new Size(_width * 2, _height * 2), interpolation: Inter.Linear);
CvInvoke.Sobel(_resized, _dx, DepthType.Cv16S, 1, 0);
CvInvoke.Sobel(_resized, _dy, DepthType.Cv16S, 0, 1);
CvInvoke.Canny(_dx, _dy, _canny, 80, 40);
_mser = new MSER(
minArea: 5, maxArea: 80, edgeBlurSize: 5);
}
public Image <Rgba, byte> Hough(ref Image <Rgb, byte> redImg2)
{
// WrongHough(redImg2);
Mat imageIn = redImg2.Mat;//.ImRead(filename, ImreadModes.GrayScale).Resize(new Size(800, 600));
Mat edges = new Mat();
CvInvoke.Canny(imageIn, edges, 95, 100);
//HoughLinesP
double theta = Math.PI / 180;
LineSegment2D[] segHoughP = CvInvoke.HoughLinesP(edges, 1, theta, 100, 100, 10);
Mat imageOutP = imageIn.Clone();
MCvScalar c = new MCvScalar(0);
foreach (LineSegment2D s in segHoughP)
{
CvInvoke.Line(imageOutP, s.P1, s.P2, c, 1, Emgu.CV.CvEnum.LineType.AntiAlias, 0);
}
// using (new Window("Edges", WindowMode.AutoSize, edges))
// using (new Window("HoughLinesP", WindowMode.AutoSize, imageOutP))
// {
// Window.WaitKey(0);
// }
// MessageBox.Show(hs.Count().ToString());
return(imageOutP.ToImage <Rgba, Byte>());
}
private void Canny_Click(object sender, RoutedEventArgs e)
{
Image <Bgr, Byte> sourceImg = new Image <Bgr, Byte>("C:\\Users\\Chase\\Desktop\\camoSample\\testSubjectOne.jpg");
Mat source = sourceImg.Mat;
Mat gray = new Mat();
CvInvoke.CvtColor(source, gray, Emgu.CV.CvEnum.ColorConversion.Bgr2Gray);
CvInvoke.Canny(gray, edges, 80, 250);
Image <Bgr, Byte> cannyEdges = edges.ToImage <Bgr, Byte>();
CvInvoke.Imwrite("C:\\Users\\Chase\\Desktop\\camoSample\\cannyEdges.jpg", cannyEdges);
MCvScalar m = new MCvScalar(0, 0, 255);
lines = CvInvoke.HoughLinesP(edges, 1, Math.PI / 180, 0, 0, 0);
Image <Bgr, Byte> lineImage = sourceImg.CopyBlank();
foreach (LineSegment2D line in lines)
{
lineImage.Draw(line, new Bgr(System.Drawing.Color.Red), 1);
sourceImg.Draw(line, new Bgr(System.Drawing.Color.Red), 1);
}
CvInvoke.Imwrite("C:\\Users\\Chase\\Desktop\\camoSample\\cannyHoughPattern.jpg", lineImage);
CvInvoke.Imwrite("C:\\Users\\Chase\\Desktop\\camoSample\\originalHoughPattern.jpg", sourceImg);
BitmapImage bitmapLineImage = new BitmapImage(new Uri("C:\\Users\\Chase\\Desktop\\camoSample\\cannyHoughPattern.jpg"));
imageControlFour.Source = bitmapLineImage;
}
public List <String> DetectLicensePlate(
IInputArray img,
List <IInputOutputArray> licensePlateImagesList,
List <IInputOutputArray> filteredLicensePlateImagesList,
List <RotatedRect> detectedLicensePlateRegionList)
{
List <String> licenses = new List <String>();
using (Mat gray = new Mat())
using (Mat canny = new Mat())
using (Mat gaussian = new Mat())
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.Imshow("Citra Berwarna", img);
CvInvoke.CvtColor(img, gray, ColorConversion.Bgr2Gray);
CvInvoke.Imshow("Citra Abu-abu", gray);
CvInvoke.GaussianBlur(gray, gaussian, new Size(5, 5), 1, 0, BorderType.Default);
CvInvoke.Imshow("Hasil Gaussian Filter", gaussian);
CvInvoke.Canny(gaussian, canny, 240, 100, 3, false);
CvInvoke.Imshow("Hasil Deteksi Tepi Canny", canny);
int[,] hierachy = CvInvoke.FindContourTree(canny, contours, ChainApproxMethod.ChainApproxSimple);
FindLicensePlate(contours, hierachy, 0, gray, canny, licensePlateImagesList, filteredLicensePlateImagesList, detectedLicensePlateRegionList, licenses);
}
return(licenses);
}
public void DetectStopSign(Mat img, List <Mat> stopSignList, List <Rectangle> boxList)
{
Mat smoothImg = new Mat();
CvInvoke.GaussianBlur(img, smoothImg, new Size(5, 5), 1.5, 1.5);
//Image<Bgr, Byte> smoothImg = img.SmoothGaussian(5, 5, 1.5, 1.5);
Mat smoothedRedMask = new Mat();
GetRedPixelMask(smoothImg, smoothedRedMask);
//Use Dilate followed by Erode to eliminate small gaps in some contour.
CvInvoke.Dilate(smoothedRedMask, smoothedRedMask, null, new Point(-1, -1), 1, BorderType.Constant, CvInvoke.MorphologyDefaultBorderValue);
CvInvoke.Erode(smoothedRedMask, smoothedRedMask, null, new Point(-1, -1), 1, BorderType.Constant, CvInvoke.MorphologyDefaultBorderValue);
using (Mat canny = new Mat())
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.Canny(smoothedRedMask, canny, 100, 50);
int[,] hierachy = CvInvoke.FindContourTree(canny, contours, ChainApproxMethod.ChainApproxSimple);
//Image<Gray, Byte> tmp = new Image<Gray, byte>(canny.Size);
//CvInvoke.DrawContours(tmp, contours, -1, new MCvScalar(255, 255, 255));
//Emgu.CV.UI.ImageViewer.Show(tmp);
if (hierachy.GetLength(0) > 0)
{
FindStopSign(img, stopSignList, boxList, contours, hierachy, 0);
}
}
}
private void button1_Click(object sender, EventArgs e)
{
OpenFileDialog Openfile = new OpenFileDialog();
if (Openfile.ShowDialog() == DialogResult.OK)
{
Image <Bgr, Byte> My_Image = new Image <Bgr, byte>(Openfile.FileName);
//Mat My_Image = CvInvoke.Imread(Openfile.FileName, Emgu.CV.CvEnum.ImreadModes.Unchanged);
// 高斯滤波
CvInvoke.GaussianBlur(My_Image, My_Image, new Size(3, 3), 0, 0);
CvInvoke.Imshow("GaussianBlur.", My_Image);
// 转换灰度图 二值化
CvInvoke.Threshold(My_Image, My_Image, 100, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
CvInvoke.Imshow("Gray.", My_Image);
// 腐蚀 膨胀核
Emgu.CV.Mat StructingElement = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Ellipse, new Size(7, 7), new Point(2, 2)); // 核
CvInvoke.Erode(My_Image, My_Image, StructingElement, new Point(-1, -1), 10, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar(0));
Image <Bgr, Byte> image1 = My_Image.Clone();
CvInvoke.Erode(My_Image, image1, StructingElement, new Point(-1, -1), 1, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar(0));
image1 = My_Image - image1;
CvInvoke.Imshow("GetStructuringElement.", image1);
CvInvoke.Canny(image1, image1, trackBar1.Value, trackBar1.Value * 3, 3);
imageBox1.Image = image1;
ScanBarCodeZbar(image1.ToBitmap());
}
}
private Mat CannyShapeDetection(Mat frame)
{
Mat returnImg = new Mat(frame.Rows, frame.Cols, frame.Depth, frame.NumberOfChannels);
CvInvoke.Canny(frame, returnImg, cannyThreshold, cannyThresholdLinking);
List <Triangle2DF> triangleList = new List <Triangle2DF>();
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint()) {
CvInvoke.FindContours(returnImg, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
int count = contours.Size;
for (int i = 0; i < count; i++)
{
using (VectorOfPoint contour = contours[i])
using (VectorOfPoint approxContour = new VectorOfPoint()) {
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.05, true);
if (CvInvoke.ContourArea(approxContour, false) > 250)
{
if (approxContour.Size == 3)
{
var pts = approxContour.ToArray();
triangleList.Add(new Triangle2DF(pts[0], pts[1], pts[2]));
}
}
}
}
}
foreach (var triangle in triangleList)
{
CvInvoke.Polylines(returnImg, Array.ConvertAll(triangle.GetVertices(), System.Drawing.Point.Round), true, new MCvScalar(255));
}
return(returnImg);
}
/// <summary>
/// Get the Canny Image Of the Gray Image
/// </summary>
public Mat GetCannyImage(Func <Mat> getCannyBase, double angle, CannyParam cannyParameter)
{
if (cannyParameter == null)
{
throw new ArgumentNullException("cannyParameter");
}
if (_cannyImage.image == null || _cannyImage.angle != angle || !_cannyImage.cannyParam.Equals(cannyParameter))
{
if (_cannyImage.image != null)
{
_cannyImage.image.Dispose();
_cannyImage.image = null;
}
using (Mat grayImage = getCannyBase())
{
using (Mat cannyImage = new Mat())
{
CvInvoke.Canny(grayImage, cannyImage,
threshold1: cannyParameter.Threshold1,
threshold2: cannyParameter.Threshold2,
apertureSize: cannyParameter.Aperture, l2Gradient: cannyParameter.L2Graident);
_cannyImage.image = cannyImage.Clone();
_cannyImage.angle = angle;
_cannyImage.cannyParam = cannyParameter;
}
}
}
return(_cannyImage.image.Clone());
}
void Canny()
{
UMat NB = new UMat();
CvInvoke.CvtColor(sourceImage, NB, ColorConversion.Bgr2Gray);
CvInvoke.Canny(NB, processedImage, 50, 150);
}
public List <String> DetectLicensePlate(
IInputArray img,
List <IInputOutputArray> licensePlateImagesList,
List <IInputOutputArray> filteredLicensePlateImagesList,
List <RotatedRect> detectedLicensePlateRegionList)
{
List <String> licenses = new List <String>();
using (Mat gray = new Mat())
using (Mat smallGray = new Mat())
using (Mat smoothedGray = new Mat())
using (Mat canny = new Mat())
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
try
{
CvInvoke.CvtColor(img, gray, ColorConversion.Bgr2Gray);
CvInvoke.Canny(gray, canny, 100, 50, 3, false);
int[,] hierachy = CvInvoke.FindContourTree(canny, contours, ChainApproxMethod.ChainApproxSimple);
FindLicensePlate(contours, hierachy, 0, gray, canny, licensePlateImagesList, filteredLicensePlateImagesList, detectedLicensePlateRegionList, licenses);
}
catch { }
}
return(licenses);
}
private void ProcessFrame(object sender, EventArgs arg)
{
if (_capture0 != null && _capture0.Ptr != IntPtr.Zero)
{
_capture0.Retrieve(_frame0, 0);
CvInvoke.CvtColor(_frame0, _grayFrame, ColorConversion.Bgr2Gray);
CvInvoke.PyrDown(_grayFrame, _smallGrayFrame);
CvInvoke.PyrUp(_smallGrayFrame, _smoothedGrayFrame);
CvInvoke.Canny(_smoothedGrayFrame, _cannyFrame, 100, 60);
captureImageBox.Image = _frame0;
//grayscaleImageBox.Image = _grayFrame;
smoothedGrayscaleImageBox.Image = _smoothedGrayFrame;
cannyImageBox.Image = _cannyFrame;
if (flag)
{
vw.Write(_frame0);
}
}
}
/// <summary>
/// 从给定的图像中检测字符
/// </summary>
/// <param name="img">给定图像</param>
/// <param name="PlateImagesList">存储检测到的字符串区域的图像列表</param>
/// <param name="filterePlateImagesList"></param>
/// <param name="detectedPlateRegionList">存储字符区域列表</param>
/// <returns>每个字符串的单词列表</returns>
public List <String> DetectPlate(
IInputArray img,
List <IInputOutputArray> PlateImagesList,
List <IInputOutputArray> filterePlateImagesList,
List <RotatedRect> detectedPlateRegionList
)
{
List <String> word = new List <string>();
using (Mat gray = img.GetInputArray().GetMat())
using (Mat canny = new Mat())
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
//CvInvoke.CvtColor(img, gray, ColorConversion.Bgr2Gray);
// 检测轮廓
CvInvoke.Canny(gray, canny, 100, 50, 3, false);
// 寻找边缘
// 得到层级关系
//hierachy [0，i，0] 是下一个轮廓的索引
//hierachy [0，i，1] 是同一层次结构级别上的先前轮廓的索引，或者 - 如果不存在则为-1。
//hierachy [0，i，2] code > 是轮廓 i 的子项的索引或 -如果不存在则为 - 1
//hierachy [0，i，3] 是轮廓父类的索引 i 或 - 1如果不存在
int[,] hierachy = CvInvoke.FindContourTree(canny, contours, ChainApproxMethod.ChainApproxSimple);
FindPlate(contours, hierachy, 0, gray, canny, PlateImagesList, filterePlateImagesList, detectedPlateRegionList, word);
}
return(word);
}