EmgnCv进行轮廓寻找和计算物体凸包

http://blog.csdn.net/qq_22033759/article/details/48029493

一、轮廓寻找
用的是FindContours函数，在CvInvoke中
不过需要用到这个VectorOfVectorOfPoint，来代替c++中的Vector
还有就是FindContours函数中的第三个参数hierarchy，不知道作用是什么，填入的只要是符合IOutputArray类型的都可以运行
代码：

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Structure;
using Emgu.CV.Util;
using Emgu.Util;
using Emgu.CV.UI;

namespace EmguCVHist
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
            Image<Bgr, byte> a = new Image<Bgr, byte>("153_140703112619_1.jpg");
            Image<Gray, byte> b = new Image<Gray, byte>(a.Width, a.Height);
            Image<Gray, byte> c = new Image<Gray, byte>(a.Width, a.Height);
            Image<Bgr, byte> d = new Image<Bgr, byte>(a.Width, a.Height);
            CvInvoke.Canny(a, b, 100, 60);
            VectorOfVectorOfPoint con = new VectorOfVectorOfPoint();

            CvInvoke.FindContours(b, con, c, RetrType.Ccomp, ChainApproxMethod.ChainApproxSimple);
            for (int i = 0; i < con.Size; i++)
                CvInvoke.DrawContours(d, con, i, new MCvScalar(255, 0, 255, 255),2);
            imageBox1.Image = d;
        }
    }
}

这里写图片描述

二、凸包的计算
凸包的计算需要用到上面所求得的轮廓寻找的数据
ConvexHull函数的参数之前的不太一样，换成了PointF类型的，但所求的数据为VectorOfVectorOfPoint类型，所以就需要个转换，先把VectorOfVectorOfPoint转为Point的二维数组，在把二维数组转成PointF的二维数组，然后再挨个调用，
代码如下：

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Structure;
using Emgu.CV.Util;
using Emgu.Util;
using Emgu.CV.UI;

namespace EmguCVHist
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
            Image<Bgr, byte> a = new Image<Bgr, byte>("9660416_102608612175_2.jpg");
            Image <Gray, byte> b = new Image<Gray, byte>(a.Width, a.Height);
            Image<Gray, byte> c = new Image<Gray, byte>(a.Width, a.Height);
            Image<Bgr, byte> d = new Image<Bgr, byte>(a.Width, a.Height);
            CvInvoke.Canny(a, b, 100, 60);
            VectorOfVectorOfPoint con = new VectorOfVectorOfPoint();
            CvInvoke.FindContours(b, con, c, RetrType.Ccomp, ChainApproxMethod.ChainApproxSimple);

            Point[][] con1 = con.ToArrayOfArray();
            PointF[][] con2 = Array.ConvertAll<Point[], PointF[]>(con1, new Converter<Point[], PointF[]>(PointToPointF));
            for (int i = 0; i < con.Size; i++)
            {
                PointF[] hull = CvInvoke.ConvexHull(con2[i], true);
                for (int j = 0; j < hull.Length; j++)
                {
                    Point p1 = new Point((int)(hull[j].X + 0.5), (int)(hull[j].Y + 0.5));
                    Point p2;
                    if (j == hull.Length - 1)
                        p2 = new Point((int)(hull[0].X + 0.5), (int)(hull[0].Y + 0.5));
                    else
                        p2 = new Point((int)(hull[j + 1].X + 0.5), (int)(hull[j + 1].Y + 0.5));
                    CvInvoke.Circle(d, p1, 3, new MCvScalar(0, 255, 255, 255), 6);
                    CvInvoke.Line(d, p1, p2, new MCvScalar(255, 255, 0, 255), 3);
                }
            }
            for (int i = 0; i < con.Size; i++)
                CvInvoke.DrawContours(d, con, i, new MCvScalar(255, 0, 255, 255), 2);

            imageBox1.Image = a.ConcateVertical(d);
        }
        public static PointF[] PointToPointF(Point[] pf)
        {
            PointF[] aaa = new PointF[pf.Length];
            int num = 0;
            foreach(var point in pf)
            {
                aaa[num].X = (int)point.X;
                aaa[num++].Y = (int)point.Y;
            }
            return aaa;
        }
    }
}

运行图：（之前那个图片由于会被分为好多块，所以换了张图片）
这里写图片描述