private List <Rect> GetSymbolRects(Mat plateNum)
{
Mat edgedPlate = new Mat();
var blPlate = MakePictureBlurred(plateNum);
Cv2.Canny(blPlate, edgedPlate, 50, 200);
var contours = new Mat[50];
Mat hierarchy = new Mat();
Cv2.FindContours(edgedPlate, out contours, hierarchy, RetrievalModes.List, ContourApproximationModes.ApproxSimple);
List <Rect> symbols = new List <Rect>();
foreach (var cnt in contours)
{
var rect = Cv2.BoundingRect(cnt);
if (rect.Width < 15 || rect.Width > 40 || rect.Height < 30 || rect.Height > 60)
{
continue;
}
symbols.Add(rect);
}
return(symbols);
}
static Mat matematika_tiesei(Mat color_img, out Mat edges)
{
Mat gray = new Mat();
Cv2.CvtColor(color_img, gray, ColorConversion.RgbToGray);
edges = new Mat();
Cv2.Canny(gray, edges, 50, 50);
//Cv2.Threshold(gray, edges, 100, 255, ThresholdType.BinaryInv);
CvLineSegmentPolar[] lines = Cv2.HoughLines(edges, 1, Cv.PI / 360, 200, 0, 0); //houghline p greitesnis
foreach (CvLineSegmentPolar line in lines)
{
double a = Math.Cos(line.Theta);
double b = Math.Sin(line.Theta);
double x0 = a * line.Rho;
double y0 = b * line.Rho;
Point first = new Point(x0 + (1000 * (-b)), y0 + 1000 * a);
Point second = new Point(x0 - (1000 * (-b)), y0 - 1000 * a);
//Cv2.Line(color_img, first, second, Scalar.Red, 1);
// if ((line.Theta >= 0) && (line.Theta < 0.5))
Cv2.Line(color_img, first, second, Scalar.Green, 5);
//if ((line.Theta > Cv.PI / 180 * 89) && (line.Theta < Cv.PI / 180 * 91))
Cv2.Line(color_img, first, second, Scalar.Green, 5);
}
return(color_img);
}
private static Point[][] GetContours(Mat srcNumberOrg, out HierarchyIndex[] hierarchyIndexesOrg)
{
var dstGray = new Mat();
Cv2.Threshold(srcNumberOrg, dstGray, 80, 80, ThresholdTypes.Binary);
var hhh = srcFilename.Split('.');
var saveTo = path + hhh[0] + "_gray.jpg";
Cv2.ImWrite(saveTo, dstGray);
var dstNumberOrg = new Mat();
Cv2.Canny(srcNumberOrg, dstNumberOrg, 80, 80); //src
// todo mb sobel könnte deutlich besser funktionieren?! siehe bild von aforge.net
//Cv2.Sobel(srcNumberOrg, dstNumberOrg, MatType.CV_16S, 80, 80);
Point[][] contoursOrg;
// HierarchyIndex[] hierarchyIndexesOrg;
Cv2.FindContours(
dstNumberOrg,
out contoursOrg,
out hierarchyIndexesOrg,
mode: RetrievalModes.External, // nur die äußeren
method: ContourApproximationModes.ApproxNone);
Console.WriteLine("Conturs found: " + contoursOrg.Length);
if (contoursOrg.Length == 0)
{
throw new NotSupportedException("Couldn't find any object in the image.");
}
return(contoursOrg);
}
public static Mat DetectEdges(Mat src)
{
var dst = new Mat();
Cv2.Canny(src, dst, 50, 200);
return(dst);
}
private OpenCvSharp.Point getOMRImage(String path)
{
int offset = 100;
Mat src, gray, binary, canny;
src = Cv2.ImRead(path);
Rect rect = new Rect(offset, offset, src.Width - offset * 2, src.Height - offset * 2);
src = src.SubMat(rect);
gray = new Mat();
binary = new Mat();
canny = new Mat();
Cv2.CvtColor(src, gray, ColorConversionCodes.BGR2GRAY);
Cv2.Threshold(gray, binary, 150, 255, ThresholdTypes.Binary);
Cv2.Canny(binary, canny, 0, 0, 3);
// width, height
OpenCvSharp.Point pt = projectPerspective(src, canny);
src.Dispose();
gray.Dispose();
binary.Dispose();
canny.Dispose();
return(pt);
}
static void Main(string[] args)
{
Mat src = Cv2.ImRead("card.jpg");
Mat gray = new Mat();
Mat binary = new Mat();
Mat morp = new Mat();
Mat canny = new Mat();
Mat dst = src.Clone();
Mat kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(3, 3));
Cv2.CvtColor(src, gray, ColorConversionCodes.BGR2GRAY);
Cv2.Threshold(gray, binary, 150, 255, ThresholdTypes.Binary);
Cv2.Dilate(binary, morp, kernel, new Point(-1, -1));
Cv2.Erode(morp, morp, kernel, new Point(-1, -1), 3);
Cv2.Dilate(morp, morp, kernel, new Point(-1, -1), 2);
Cv2.Canny(morp, canny, 0, 0, 3);
LineSegmentPoint[] lines = Cv2.HoughLinesP(canny, 1, Cv2.PI / 180, 140, 50, 10);
for (int i = 0; i < lines.Length; i++)
{
Cv2.Line(dst, lines[i].P1, lines[i].P2, Scalar.Yellow, 2);
}
Cv2.ImShow("dst", dst);
Cv2.WaitKey(0);
Cv2.DestroyAllWindows();
}
void f1()
{
Mat mainMat = new Mat(ttexture.height, ttexture.width, MatType.CV_8UC3);
Mat grayMat = new Mat();
mainMat = OpenCvSharp.Unity.TextureToMat(ttexture);
Cv2.CvtColor(mainMat, grayMat, ColorConversionCodes.BGR2GRAY);
Cv2.GaussianBlur(grayMat, grayMat, new Size(5, 5), 0);
Cv2.Canny(grayMat, grayMat, 10.0, 70.0);
Cv2.FastNlMeansDenoising(grayMat, grayMat, 3, 7, 21);
Cv2.Threshold(grayMat, grayMat, 70.0, 255.0, ThresholdTypes.BinaryInv);
texture1 = OpenCvSharp.Unity.MatToTexture(grayMat);
Color[] pixels = texture1.GetPixels(0, 0, texture1.width, texture1.height, 0);
for (int p = 0; p < pixels.Length; p++)
{
if (pixels[p].Equals(new Color(1, 1, 1, 1)))
{
pixels[p] = new Color(0, 0, 0, 0);
}
}
texture1.SetPixels(0, 0, texture1.width, texture1.height, pixels, 0);
texture1.Apply();
m1.mainTexture = texture1;
//m_RawImage.texture = texture1;
}
// Our sketch generation function
protected override bool ProcessTexture(WebCamTexture input, ref Texture2D output)
{
Mat img = Unity.TextureToMat(input, TextureParameters);
//Convert image to grayscale
Mat imgGray = new Mat();
Cv2.CvtColor(img, imgGray, ColorConversionCodes.BGR2GRAY);
// Clean up image using Gaussian Blur
Mat imgGrayBlur = new Mat();
Cv2.GaussianBlur(imgGray, imgGrayBlur, new Size(5, 5), 0);
//Extract edges
Mat cannyEdges = new Mat();
Cv2.Canny(imgGrayBlur, cannyEdges, 70.0, 1.0);
//Do an invert binarize the image
Mat mask = new Mat();
Cv2.Threshold(cannyEdges, mask, 70.0, 255.0, ThresholdTypes.Binary);
// result, passing output texture as parameter allows to re-use it's buffer
// should output texture be null a new texture will be created
output = Unity.MatToTexture(mask, output);
return(true);
}
public static void cv_12() // 边缘检测系列之 Canny算子
{
//Canny算法中，先在 X 和 Y 方向求得一阶导数，
//然后将它们组合成4个方向 的导数。
//其中方向导数是局部最大值的点是组成边缘的候选项。
//Canny算法最明显的创新，就是将单个的边缘候选像素加入轮廓。
Mat srcImg = Cv2.ImRead(@"G:\\pics\\6.jpg");
Mat CannyImg = new Mat();
Mat gussImage = new Mat();
Cv2.GaussianBlur(srcImg, gussImage, new OpenCvSharp.Size(3, 3), 0, 0, BorderTypes.Default); //高斯模糊
Mat grayImage = new Mat();
Cv2.CvtColor(gussImage, grayImage, ColorConversionCodes.RGB2GRAY); //灰度图
Cv2.Canny(grayImage, CannyImg, 50, 150, 3, true);
//cannny算子。参数：1，8 bit 输入图像；2，输出边缘图像，一般是二值图像，背景是黑色；
//3，低阈值。值越大，找到的边缘越少；4，高阈值；5，表示应用Sobel算子的孔径大小，其有默认值3；6，计算图像梯度幅值的标识，有默认值false。
//低于阈值1的像素点会被认为不是边缘；
//高于阈值2的像素点会被认为是边缘；
//在阈值1和阈值2之间的像素点,若与一阶偏导算子计算梯度得到的边缘像素点相邻，则被认为是边缘，否则被认为不是边缘。
Cv2.ImShow("srcImg", srcImg);
Cv2.ImShow("gussImage", gussImage);
Cv2.ImShow("CannyImg", CannyImg);
Cv2.WaitKey();
}
public static void cv_06() // 边缘轮廓的检测/查找
{
Mat srcImage = Cv2.ImRead(@"G:\\pics\\123.jpg", ImreadModes.Color);
Mat src_gray = new Mat();
Cv2.CvtColor(srcImage, src_gray, ColorConversionCodes.RGB2GRAY); //转换为灰度图
Cv2.Blur(src_gray, src_gray, new OpenCvSharp.Size(2, 2)); //滤波
Mat canny_Image = new Mat();
Cv2.Canny(src_gray, canny_Image, 100, 200); //Canny边缘检测
OpenCvSharp.Point[][] contours;
HierarchyIndex[] hierarchly;
Cv2.FindContours(canny_Image, out contours, out hierarchly, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple, new OpenCvSharp.Point(0, 0)); //获得轮廓
Mat dst_Image = Mat.Zeros(canny_Image.Size(), srcImage.Type()); // 图片像素值归零
Random rnd = new Random();
for (int i = 0; i < contours.Length; i++)
{
Scalar color = new Scalar(rnd.Next(0, 255), rnd.Next(0, 255), rnd.Next(0, 255));
Cv2.DrawContours(dst_Image, contours, i, color, 2, LineTypes.Link8, hierarchly); //画出轮廓
}
//return dst_Image; //返回结果
Cv2.ImShow("dst_Image", dst_Image);
Cv2.ImShow("canny_Image", canny_Image);
Cv2.WaitKey();
//return canny_Image;
}
private void timer1_Tick(object sender, EventArgs e)
{
try
{
Console.WriteLine("Tick");
cap.Read(frame);
OpenCvSharp.Size newsize = new OpenCvSharp.Size(pictureBox1.Width, pictureBox1.Height);
frame = frame.Resize(newsize);
Cv2.Canny(frame, dstframe, 20, 100);
Bitmap tempimage1 = BitmapConverter.ToBitmap(frame);
Bitmap tempimage2 = BitmapConverter.ToBitmap(dstframe);
pictureBox1.Image = tempimage1;
pictureBox2.Image = tempimage2;
//Cv2.WaitKey(10);
}
catch (Exception extdf)
{
Console.WriteLine(extdf.ToString());
}
}
private Mat Canny(Mat picture, Mat result)
{
Data_th th_data = th.Get_Data();
Cv2.Canny(picture, result, th_data.Get_Cth1(), th_data.Get_Cth2()); // 임계값 1이하에 포함되는 가장자리는 제외, 2이상 포함되는 가장자리는 가장자리로 간주.
return(result);
}
public static Mat CannyEdge(Mat mat, int lowerThreshold = 50, int higherThreshold = 200)
{
Mat cannyMat = new Mat();
Cv2.Canny(mat, cannyMat, lowerThreshold, higherThreshold);
return(cannyMat);
}
private void bn_Canny_Click(object sender, RoutedEventArgs e)
{
if (listImage.Count > 0)
{
SubWindow.Win_Canny win = new SubWindow.Win_Canny();
if (win.ShowDialog() == true)
{
string strTitle = listImage[_nSelWin].Title;
Mat matSrc = listImage[_nSelWin].fn_GetImage();
Mat matDst = new Mat();
double dSigma = 0.0;
double dLowTh = 0.0;
double dHighTh = 0.0;
double.TryParse(win.tb_Sigma.Text, out dSigma);
double.TryParse(win.tb_LowTh.Text, out dLowTh);
double.TryParse(win.tb_HighTh.Text, out dHighTh);
int width = matSrc.Cols;
int height = matSrc.Rows;
timeStart = DateTime.Now;
Cv2.GaussianBlur(matSrc, matDst, new OpenCvSharp.Size(3, 3), dSigma);
Cv2.Canny(matDst, matDst, dLowTh, dHighTh);
fn_WriteLog($"[Canny] {strTitle} ({(DateTime.Now - timeStart).TotalMilliseconds} ms)");
fn_NewImage(matDst, $"{strTitle}_Canny");
}
}
}
public static void WriteVideo()
{
Size dsize = new Size(640, 480);
// Opens a camera device
using (VideoCapture capture = new VideoCapture(0))
// Read movie frames and write them to VideoWriter
using (VideoWriter writer = new VideoWriter("out.avi", -1, capture.Fps, dsize))
using (Mat frame = new Mat())
using (Mat gray = new Mat())
using (Mat canny = new Mat())
using (Mat dst = new Mat())
{
Console.WriteLine("Converting each movie frames...");
while (true)
{
// Read image
capture.Read(frame);
if (frame.Empty())
{
break;
}
//Console.CursorLeft = 0;
//Console.Write("{0} / {1}", capture.PosFrames, capture.FrameCount);
// grayscale -> canny -> resize
Cv2.CvtColor(frame, gray, ColorConversionCodes.BGR2GRAY);
Cv2.Canny(gray, canny, 100, 180);
Cv2.Resize(canny, dst, dsize, 0, 0, InterpolationFlags.Linear);
// Write mat to VideoWriter
writer.Write(dst);
}
}
}
private void mnuFilterEdgeCanny_Click(object sender, EventArgs e)
{
// Canny Edge
Cv2.Canny(_matDisp, _matDisp, 100.0, 150.0);
// 画像の描画
DrawMatImage(_matDisp);
}
public dynamic PreProcessImage(ref Mat image, Mat sourceImage)
{
var copy = new Mat();
try
{
Cv2.BilateralFilter(image, copy, 9, 75, 75);
Cv2.AdaptiveThreshold(copy, copy, 255, AdaptiveThresholdTypes.GaussianC, ThresholdTypes.Binary, 115, 4);
Cv2.MedianBlur(copy, copy, 11);
Cv2.CopyMakeBorder(copy, copy, 5, 5, 5, 5, BorderTypes.Constant, Scalar.Black);
// TODO: Dispose new Mat()
var otsu = Cv2.Threshold(copy, new Mat(), 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
Cv2.Canny(copy, copy, otsu, otsu * 2, 3, true);
}
catch
{
copy.Dispose();
throw;
}
image.Dispose();
image = copy;
return(null);
}
private void ProcImage5(ref System.Drawing.Bitmap src, ref System.Drawing.Bitmap srcB, out System.Drawing.Bitmap dst)
{
dst = null;
Mat tmp = new Mat();
var element = Cv2.GetStructuringElement(
MorphShapes.Rect,
new OpenCvSharp.Size(2 * 2 + 1, 2 * 2 + 1),
new OpenCvSharp.Point(2, 2));
Mat srcImg = BitmapConverter.ToMat(src);
Cv2.CvtColor(srcImg, srcImg, ColorConversionCodes.BGRA2BGR);
Mat edge = new Mat();
Cv2.Canny(srcImg, edge, 16, 50);
//Cv2.MorphologyEx(edge, tmp, MorphTypes.Open, element);
Cv2.MorphologyEx(edge, tmp, MorphTypes.Close, element, null, 25);
Cv2.MorphologyEx(tmp, edge, MorphTypes.Open, element, null, 3);
Cv2.MorphologyEx(edge, tmp, MorphTypes.Close, element, null, 10);
Mat srcImgB = BitmapConverter.ToMat(srcB);
Cv2.CvtColor(srcImgB, srcImgB, ColorConversionCodes.BGRA2BGR);
Mat edgeB = new Mat();
Cv2.Canny(srcImgB, edgeB, 16, 50);
Mat mask = new Mat();
//Cv2.Absdiff(edge, edgeB, mask);
dst = BitmapConverter.ToBitmap(tmp);
}
private Mat FindContours(Mat srcImage)
{
Mat src_gray = new Mat();
Cv2.CvtColor(srcImage, src_gray, ColorConversionCodes.RGB2GRAY);
Cv2.Blur(src_gray, src_gray, new OpenCvSharp.Size(2, 2));
Mat cannyImage = new Mat();
Cv2.Canny(src_gray, cannyImage, 100, 200);
OpenCvSharp.Point[][] contours;
HierarchyIndex[] hierarchly;
Cv2.FindContours(cannyImage, out contours, out hierarchly, RetrievalModes.Tree,
ContourApproximationModes.ApproxSimple, new OpenCvSharp.Point(0, 0));
Mat dstImage = Mat.Zeros(cannyImage.Size(), srcImage.Type());
Random rnd = new Random();
for (int i = 0; i < contours.Length; i++)
{
Scalar color = new Scalar(rnd.Next(0, 255), rnd.Next(0, 255), rnd.Next(0, 255));
Cv2.DrawContours(dstImage, contours, i, color, 2, LineTypes.Link8, hierarchly);
}
return(dstImage);
}
public void Run()
{
const string OutVideoFile = "out.avi";
// Opens MP4 file (ffmpeg is probably needed)
VideoCapture capture = new VideoCapture(FilePath.Bach);
// Read movie frames and write them to VideoWriter
Size dsize = new Size(640, 480);
using (VideoWriter writer = new VideoWriter(OutVideoFile, -1, capture.Fps, dsize))
{
Console.WriteLine("Converting each movie frames...");
Mat frame = new Mat();
while (true)
{
// Read image
capture.Read(frame);
if (frame.Empty())
{
break;
}
Console.CursorLeft = 0;
Console.Write("{0} / {1}", capture.PosFrames, capture.FrameCount);
// grayscale -> canny -> resize
Mat gray = new Mat();
Mat canny = new Mat();
Mat dst = new Mat();
Cv2.CvtColor(frame, gray, ColorConversion.BgrToGray);
Cv2.Canny(gray, canny, 100, 180);
Cv2.Resize(canny, dst, dsize, 0, 0, Interpolation.Linear);
// Write mat to VideoWriter
writer.Write(dst);
}
Console.WriteLine();
}
// Watch result movie
using (VideoCapture capture2 = new VideoCapture(OutVideoFile))
using (Window window = new Window("result"))
{
int sleepTime = (int)(1000 / capture.Fps);
Mat frame = new Mat();
while (true)
{
capture2.Read(frame);
if (frame.Empty())
{
break;
}
window.ShowImage(frame);
Cv2.WaitKey(sleepTime);
}
}
}
public static Mat EdgeDetector(this Mat image)
{
// Mat src = Cv2.ImRead("lenna.png", ImreadModes.GrayScale);
Mat dst = new Mat();
Cv2.Canny(image, dst, 50, 200);
return(dst);
}
private Bitmap CannyFilter(Bitmap originalBitmap)
{
var originalMat = BitmapConverter.ToMat(originalBitmap);
var cannyMat = new Mat();
Cv2.Canny(originalMat, cannyMat, 50, 100);
return(BitmapConverter.ToBitmap(cannyMat));
}
public Mat GetConvertImage(Mat input)
{
Mat result = new Mat();
Cv2.Canny(input, result, _threshold1, _threshold2);
return(result);
}
public static Mat convertImage(Mat origional)
{
Mat newMat = new Mat();
origional.CopyTo(newMat);
Cv2.Canny(origional, newMat, 200, 300);
return(newMat);
}
static void Canny(Mat src, Mat dst)
{
Cv2.Canny(src, dst, 50, 200);
Cv2.ImShow("元画像", src);
Cv2.ImShow("dst", dst);
Cv2.WaitKey();
Cv2.DestroyAllWindows();
}
// https://docs.opencv.org/2.4/modules/imgproc/doc/filtering.html?highlight=sobel#sobel
//Canny filter
public static Mat C_Image_Canny(Mat image, double thres1)
{
Mat image_copy = new Mat();
Cv2.Canny(image, image_copy, thres1, thres1, 3, true);
//Cv2.Canny(image, image_copy, thres1, thres1*2,3,true);
return(image_copy);
}
public override Photo?Process(Photo photo, CannyThresholdParameter parameters)
{
var input = ImageConverter.Photo2Mat(photo);
var output = new Mat();
Cv2.Canny(input, output, parameters.ThresholdMin, parameters.ThresholdMax);
return(ImageConverter.Mat2Photo(output));
}
static void Main(string[] args)
{
using var src = new Mat("example.jpg", ImreadModes.Grayscale);
using var dst = new Mat();
Cv2.Canny(src, dst, 50, 200);
Cv2.ImWrite("example_canny.jpg", dst);
Console.WriteLine("Example done!");
}
public static void EdgeDetection(string fileName)
{
Mat src = new Mat(Path.Combine(INPUT_PATH, fileName), ImreadModes.AnyColor);
Mat dst = new Mat();
Cv2.Canny(src, dst, 500, 600);
Cv2.ImWrite(Path.Combine(EDGEDETECTION_PATH, fileName), dst);
}
public override void Actualizar(IEsCable nodo)
{
if (nodo == null)
{
return;
}
var matEntrante = nodo.MatOut();
if (matEntrante == null)
{
return;
}
if (apertureSize % 2 == 0)
{
apertureSize--;
}
if (apertureSize < 3)
{
apertureSize = 3;
}
else if (apertureSize > 7)
{
apertureSize = 7;
}
if (sobreescribirMat)
{
if (mat != matEntrante)
{
mat = matEntrante;
}
}
else
{
if (mat == null)
{
mat = new Mat(matEntrante.Size(), matEntrante.Type());
}
else
{
if (mat.Width != matEntrante.Width || mat.Height != matEntrante.Height)
{
if (mat.Width * mat.Height == matEntrante.Width * matEntrante.Height)
{
mat.Reshape(0, matEntrante.Rows);
}
else
{
mat = matEntrante.Clone();
}
}
}
}
Cv2.Canny(matEntrante, mat, umbralUno, umbralDos, apertureSize, L2gradient);
PropagarActualizacion();
}
