复制代码
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)
{
//test
image_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);
}
}
}
