C#图像处理2---Otsu二值化算法

最新推荐文章于 2025-06-02 15:33:01 发布

Mrs.Gril

最新推荐文章于 2025-06-02 15:33:01 发布

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分类专栏： C#图像处理算法文章标签： c# 算法

本文链接：https://blog.youkuaiyun.com/zhangdaoliang1/article/details/120234851

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这篇博客介绍了如何使用Otsu算法对图像进行二值化处理，通过C#代码展示了OpenFileDialog选择图片后，如何将图像转为灰度并应用Otsu算法划分阈值，最终实现图像的黑白转换。

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Otsu算法：

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;

namespace 图像_Oust算法
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }
        Bitmap bt;
        int iw, ih;
        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            if (ofd.ShowDialog() == DialogResult.OK)
            {
                bt = new Bitmap(ofd.FileName);
                pictureBox1.Image = bt;
            }
            iw = bt.Width;
            ih = bt.Height;
        }

        private void button2_Click(object sender, EventArgs e)
        {
            Bitmap bm = new Bitmap(pictureBox1.Image);

            int wh = iw * ih;
            int[,] inIm = new int[iw, ih];

            int i, j, t;
            int L = 256;
            double[] p = new double[L];

            //转为灰度图像in_image[,]            
            for (j = 0; j < ih; j++)
                for (i = 0; i < iw; i++)
                    inIm[i, j] =( bm.GetPixel(i, j).R + bm.GetPixel(i, j).G + bm.GetPixel(i, j).B)/3;//采用平均值法

            for (i = 0; i < L; i++)//初始化
                p[i] = 0;

            //统计各灰度级出现的次数
            for (j = 0; j < ih; j++)
                for (i = 0; i < iw; i++)
                    p[inIm[i, j]]++;

            //计算各灰度级出现的概率
            for (int m = 0; m < L; m++)
                p[m] = p[m] / wh;

            double[] sigma = new double[L];
            for (t = 0; t < L; t++)
            {
                double w0 = 0;
                for (int m = 0; m < t + 1; m++)
                    w0 += p[m];
                double w1 = 1 - w0;

                double u0 = 0;
                for (int m = 0; m < t + 1; m++)
                    u0 += m * p[m] / w0;

                double u1 = 0;
                for (int m = t; m < L; m++)
                    u1 += m * p[m] / w1;

                sigma[t] = w0 * w1 * (u0 - u1) * (u0 - u1);
            }

            double max = 0.0;
            int T = 0;
            for (i = 0; i < L; i++)
            {
                if (max < sigma[i])
                {
                    max = sigma[i];
                    T = i;
                }
            }
            pictureBox2.Refresh();
            pictureBox2.Image = threshSeg(inIm, iw, ih, T); ;
        }

        Bitmap threshSeg(int[,] in_image, int w, int h, int t)
        {
            Bitmap bm = new Bitmap(w, h);
            Color c;
            for (int j = 0; j < h; j++)
            {
                for (int i = 0; i < w; i++)
                {
                    if (in_image[i, j] > t)
                        c = Color.FromArgb(255, 255, 255);//设置RGB值
                    else
                        c = Color.FromArgb(0, 0, 0);
                    bm.SetPixel(i, j, c);
                }
            }
            return bm;
        }
    }
}