效果

项目

代码

using OpenCvSharp;
using System;
using System.Drawing;
using System.Text;
using System.Windows.Forms;namespace OpenCvSharp_Demo
{public partial class frmMain : Form{public frmMain(){InitializeComponent();}string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";string image_path = "";DateTime dt1 = DateTime.Now;DateTime dt2 = DateTime.Now;Mat image;Mat result_image;StringBuilder sb = new StringBuilder();private void button1_Click(object sender, EventArgs e){OpenFileDialog ofd = new OpenFileDialog();ofd.Filter = fileFilter;if (ofd.ShowDialog() != DialogResult.OK) return;pictureBox1.Image = null;pictureBox2.Image = null;textBox1.Text = "";image_path = ofd.FileName;pictureBox1.Image = new Bitmap(image_path);image = new Mat(image_path);}private void Form1_Load(object sender, EventArgs e){//testimage_path = "test_img/1.jpg";image = new Mat(image_path);pictureBox1.Image = new Bitmap(image_path);}private void button2_Click(object sender, EventArgs e){if (image_path == ""){return;}textBox1.Text = "检测中，请稍等……";pictureBox2.Image = null;Application.DoEvents();result_image = image.Clone();//二值化操作Mat grayimg = new Mat();Cv2.CvtColor(image, grayimg, ColorConversionCodes.BGR2GRAY);Mat BinaryImg = new Mat();Cv2.Threshold(grayimg, BinaryImg, 240, 255, ThresholdTypes.Binary);//Cv2.ImShow("二值化", BinaryImg);//腐蚀Mat kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(15, 15));Mat morhImage = new Mat();Cv2.Dilate(BinaryImg, morhImage, kernel, null, 2);//Cv2.ImShow("morphology", morhImage);//距离变换：用于二值化图像中的每一个非零点距自己最近的零点的距离，距离变换图像上越亮的点，代表了这一点距离零点的距离越远Mat dist = new Mat();Cv2.BitwiseNot(morhImage, morhImage);/*OpenCV中，函数distanceTransform()用于计算图像中每一个非零点像素与其最近的零点像素之间的距离，输出的是保存每一个非零点与最近零点的距离信息，图像上越亮的点，代表了离零点的距离越远。用途：可以根据距离变换的这个性质，经过简单的运算，用于细化字符的轮廓和查找物体质心（中心）。*//*距离变换的处理图像通常都是二值图像，而二值图像其实就是把图像分为两部分，即背景和物体两部分，物体通常又称为前景目标。通常我们把前景目标的灰度值设为255（即白色），背景的灰度值设为0（即黑色）。所以定义中的非零像素点即为前景目标，零像素点即为背景。所以图像中前景目标中的像素点距离背景越远，那么距离就越大，如果我们用这个距离值替换像素值，那么新生成的图像中这个点越亮。*///User：用户自定义//L1：  曼哈顿距离//L2：  欧式距离//C：   棋盘距离Cv2.DistanceTransform(morhImage, dist, DistanceTypes.L1, DistanceTransformMasks.Mask3);Cv2.Normalize(dist, dist, 0, 1.0, NormTypes.MinMax);   //范围在0~1之间//Cv2.ImShow("distance", dist);//形态学处理Mat MorphImg = new Mat();dist.ConvertTo(MorphImg, MatType.CV_8U);Cv2.Threshold(MorphImg, MorphImg, 0.99, 255, ThresholdTypes.Binary);  //上图像素值在0~1之间kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(7, 3), new OpenCvSharp.Point(-1, -1));Cv2.MorphologyEx(MorphImg, MorphImg, MorphTypes.Open, kernel);  //开操作//Cv2.ImShow("t-distance", MorphImg);//找到种子的轮廓区域OpenCvSharp.Point[][] contours;HierarchyIndex[] hierarchly;Cv2.FindContours(MorphImg, out contours, out hierarchly, RetrievalModes.External, ContourApproximationModes.ApproxSimple, new OpenCvSharp.Point(0, 0));Mat markers = Mat.Zeros(image.Size(), MatType.CV_8UC3);int x, y, w, h;Rect rect;for (int i = 0; i < contours.Length; i++){// Cv2.DrawContours(markers, contours, i, Scalar.RandomColor(), 2, LineTypes.Link8, hierarchly);rect = Cv2.BoundingRect(contours[i]);x = rect.X;y = rect.Y;w = rect.Width;h = rect.Height;Cv2.Circle(result_image, x + w / 2, y + h / 2, 20, new Scalar(0, 0, 255), -1);if (i >= 9){Cv2.PutText(result_image, (i + 1).ToString(), new OpenCvSharp.Point(x + w / 2 - 18, y + h / 2 + 8), HersheyFonts.HersheySimplex, 0.8, new Scalar(0, 255, 0), 2);}else{Cv2.PutText(result_image, (i + 1).ToString(), new OpenCvSharp.Point(x + w / 2 - 8, y + h / 2 + 8), HersheyFonts.HersheySimplex, 0.8, new Scalar(0, 255, 0), 2);}}textBox1.Text = "number of corns: " + contours.Length;pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());}private void pictureBox2_DoubleClick(object sender, EventArgs e){Common.ShowNormalImg(pictureBox2.Image);}private void pictureBox1_DoubleClick(object sender, EventArgs e){Common.ShowNormalImg(pictureBox1.Image);}}
}

下载

Demo下载