参考：https://www.bbsmax.com/A/rV57LjWGdP/
https://blog.csdn.net/louislong007/article/details/47683035
简易验证码样例：
验证码识别流程：
首先进行图像获取：火狐浏览器，找到获取验证码地址，获取验证码图像，传递给类，直接获取到验证码！
验证码获取：
/// <summary>
/// 通过GET方式获取验证码
/// </summary>
/// <param name="Url">url</param>
/// <param name="postDataStr">GET数据</param>
/// <param name="cookie">GET容器</param>
/// <returns></returns>
public void SendDataByGET1(string Url, ref CookieContainer cookie)
{
HttpWebRequest request = (HttpWebRequest)WebRequest.Create(Url);
if (cookie.Count == 0)
{
request.CookieContainer = new CookieContainer();
cookie = request.CookieContainer;
}
else
{
request.CookieContainer = cookie;
}
request.Method = "GET";
request.ContentType = "text/html;charset=UTF-8";
HttpWebResponse response = (HttpWebResponse)request.GetResponse();
MemoryStream ms = null;
using (var stream = response.GetResponseStream())
{
Byte[] buffer = new Byte[response.ContentLength];
int offset = 0, actuallyRead = 0;
do
{
actuallyRead = stream.Read(buffer, offset, buffer.Length - offset);
offset += actuallyRead;
}
while (actuallyRead > 0);
ms = new MemoryStream(buffer);
}
b = new Bitmap(ms);
//aforge只接受像素格式为24/32bpp的像素格式图片，所以处理前，先进行格式转化
var bnew = new Bitmap(b.Width, b.Height,PixelFormat.Format24bppRgb);
Graphics g = Graphics.FromImage(bnew);
g.DrawImage(b, 0, 0);
g.Dispose();
pictureBox1.Image = bnew;
//b = new Threshold(50).Apply(b);
response.Close();
string strCookies = request.CookieContainer.GetCookieHeader(request.RequestUri); //把cookies转换成字符串
textBox2.Text=new VerificationCodeProcess().GetVerificationCode(bnew).ToString();
//Stream myResponseStream = response.GetResponseStream();
//StreamReader myStreamReader = new StreamReader(myResponseStream, Encoding.GetEncoding("utf-8"));
//string retString = myStreamReader.ReadToEnd();
//myStreamReader.Close();
//myResponseStream.Close();
//MessageBox.Show(strCookies);
}
验证码处理：
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Drawing;
using System.Drawing.Imaging;
using System.Drawing.Printing;
using System.Linq;
using System.Text;
using System.Windows.Forms;
//
using System.Net;
using System.IO;
using Model;
using BLL;
using Model;
using AForge;
using AForge.Imaging;
using AForge.MachineLearning;
using System.Drawing.Drawing2D;
using AForge.Imaging.Filters;
namespace EmsService
{
public class VerificationCodeProcess
{
public int GetVerificationCode(Bitmap bmp)
{
//灰度
bmp = ToGray(bmp);
// MessageBox.Show(b.PixelFormat.ToString());
//二进制//
// pictureBox3.Image = ConvertToBinaryImage(new Bitmap(pictureBox2.Image));
bmp = ConvertToBinaryImage(bmp);
// MessageBox.Show(b.PixelFormat.ToString());
//分割
List<Bitmap> bmList = ToResizeAndCenterIt(Crop_X(Crop_Y(bmp)));
//二进制化
StringBuilder sb = new StringBuilder();
List<string> lls = PP(bmList);
int top = Convert.ToInt32(lls[0]);
int last = Convert.ToInt32(lls[2]);
int result = 0;
if (lls[1] == "-")
{
result = top - last;
}
else
{
result = top + last;
}
return result;
}
/// <summary>
/// 灰度处理
/// </summary>
/// <param name="bmp"></param>
/// <returns></returns>
public Bitmap ToGray(Bitmap bmp)
{
Bitmap bm = new Bitmap(bmp.Width, bmp.Height);
for (int i = 0; i < bmp.Width; i++)
{
for (int j = 0; j < bmp.Height; j++)
{
// 获取该点的像素的RGB的颜色
Color color = bmp.GetPixel(i, j);
// 利用公式计算灰度值
// 根据YUV的颜色空间中，Y的分量的物理意义是点的亮度，由该值反映亮度等级，
// 根据RGB和YUV颜色空间的变化关系可建立亮度Y与R、G、B三个颜色分量的对应：
// Y=0.3R+0.59G+0.11B，以这个亮度值表达图像的灰度值
int gray = (int)(color.R * 0.3 + color.G * 0.59 + color.B * 0.11);
Color newColor = Color.FromArgb(gray, gray, gray);
bm.SetPixel(i, j, newColor);
}
}
return bm;
}
/// <summary>
/// 二进制化
/// </summary>
/// <param name="bmp"></param>
/// <returns></returns>
public Bitmap ConvertToBinaryImage(Bitmap bmp)
{
Bitmap bm = new Bitmap(bmp.Width, bmp.Height);
int average = 0;
for (int i = 0; i < bmp.Width; i++)
{
for (int j = 0; j < bmp.Height; j++)
{
Color color = bmp.GetPixel(i, j);
average += color.B;
}
}
average = 60;
for (int i = 0; i < bmp.Width; i++)
{
for (int j = 0; j < bmp.Height; j++)
{
//获取该点的像素的RGB的颜色
Color color = bmp.GetPixel(i, j);
int value = 255 - color.B;
Color newColor = value > average ? Color.FromArgb(0, 0, 0) : Color.FromArgb(255, 255, 255);
bm.SetPixel(i, j, newColor);
}
}
return bm;
}
/// <summary>
/// 重置图片的指定大小并且居中
/// </summary>
/// <param name="list"></param>
/// <returns></returns>
public List<Bitmap> ToResizeAndCenterIt(List<Bitmap> list, int w = 20, int h = 20)
{
List<Bitmap> resizeList = new List<Bitmap>();
for (int i = 0; i < list.Count; i++)
{
//MessageBox.Show(list[i].PixelFormat.ToString());
//反转一下图片
var bnew10 = new Bitmap(list[i].Width, list[i].Height, PixelFormat.Format24bppRgb);
Graphics g10 = Graphics.FromImage(bnew10);
g10.DrawImage(list[i], 0, 0);
g10.Dispose();
list[i] = bnew10;
list[i] = new Invert().Apply(list[i]);
int sw = list[i].Width;
int sh = list[i].Height;
Crop corpFilter = new Crop(new Rectangle(0, 0, w, h));
list[i] = corpFilter.Apply(list[i]);
//var bnew1 = new Bitmap(list[i].Width, list[i].Height, PixelFormat.Format24bppRgb);
//Graphics g1 = Graphics.FromImage(bnew1);
//g1.DrawImage(list[i], 0, 0);
//g1.Dispose();
//再反转回去
list[i] = new Invert().Apply(list[i]);
// //计算中心位置
int centerX = (w - sw) / 2;
int centerY = (h - sh) / 2;
var bnew2 = new Bitmap(list[i].Width, list[i].Height, PixelFormat.Format24bppRgb);
Graphics g2 = Graphics.FromImage(bnew2);
g2.DrawImage(list[i], 0, 0);
g2.Dispose();
list[i] = new CanvasMove(new AForge.IntPoint(centerX, centerY), Color.White).Apply(list[i]);
resizeList.Add(list[i]);
}
return resizeList;
}
/// <summary>
/// 按照 Y 轴线 切割
/// (丢弃等于号)
/// </summary>
/// <param name="?"></param>
/// <returns></returns>
public List<Bitmap> Crop_Y(Bitmap b)
{
var list = new List<Bitmap>();
//统计每一列的“1”的个数，方便切除
int[] cols = new int[b.Width];
/*
* 纵向切割
*/
for (int x = 0; x < b.Width; x++)
{
for (int y = 0; y < b.Height; y++)
{
//获取当前像素点像素
var pixel = b.GetPixel(x, y);
//说明是黑色点
if (pixel.R == 0)
{
cols[x] = ++cols[x];
}
}
}
int left = 0, right = 0;
for (int i = 0; i < cols.Length; i++)
{
//说明该列有像素值（为了防止像素干扰，去噪后出现空白的问题，所以多判断一下，防止切割成多个)
if (cols[i] > 0 || (i + 1 < cols.Length && cols[i + 1] > 0))
{
if (left == 0)
{
//切下来图片的横坐标left
left = i;
}
else
{
//切下来图片的横坐标right
right = i;
}
}
else
{
//说明已经有切割图了，下面我们进行切割处理
if ((left > 0 || right > 0))
{
Crop corp = new Crop(new Rectangle(left, 0, right - left + 1, b.Height));
var small = corp.Apply(b);
//居中，将图片放在20*50的像素里面
list.Add(small);
}
left = right = 0;
}
}
return list;
}
/// <summary>
/// 按照 X 轴线 切割
/// </summary>
/// <param name="b"></param>
/// <returns></returns>
public List<Bitmap> Crop_X(List<Bitmap> list)
{
var corplist = new List<Bitmap>();
//再对分割的图进行上下切割，取出上下的白边
foreach (var segb in list)
{
//统计每一行的“1”的个数，方便切除
int[] rows = new int[segb.Height];
/*
* 横向切割
*/
for (int y = 0; y < segb.Height; y++)
{
for (int x = 0; x < segb.Width; x++)
{
//获取当前像素点像素
var pixel = segb.GetPixel(x, y);
//说明是黑色点
if (pixel.R == 0)
{
rows[y] = ++rows[y];
}
}
}
int bottom = 0, top = 0;
for (int y = 0; y < rows.Length; y++)
{
//说明该行有像素值（为了防止像素干扰，去噪后出现空白的问题，所以多判断一下，防止切割成多个)
if (rows[y] > 0 || (y + 1 < rows.Length && rows[y + 1] > 0))
{
if (top == 0)
{
//切下来图片的top坐标
top = y;
}
else
{
//切下来图片的bottom坐标
bottom = y;
}
}
else
{
//说明已经有切割图了，下面我们进行切割处理
if ((top > 0 || bottom > 0) && bottom - top > 0)
{
Crop corp = new Crop(new Rectangle(0, top, segb.Width, bottom - top + 1));
var small = corp.Apply(segb);
corplist.Add(small);
}
top = bottom = 0;
}
}
}
return corplist;
}
//模式匹配
public List<string> PP(List<Bitmap> list)
{
var files = Directory.GetFiles(Environment.CurrentDirectory + "\\temp\\");
var templateList = files.Select(i => { return new Bitmap(i); }).ToList();
var templateListFileName = files.Select(i => { return i.Substring(i.Length - 5).Substring(0, 1); }).ToList();
var result = new List<string>();
ExhaustiveTemplateMatching templateMatching = new ExhaustiveTemplateMatching(0.9f);
//这里面有四张图片，进行四张图的模板匹配
for (int i = 0; i < 3; i++)
{
float max = 0;
int index = 0;
for (int j = 0; j < templateList.Count; j++)
{
var compare = templateMatching.ProcessImage(list[i], templateList[j]);
if (compare.Length > 0 && compare[0].Similarity > max)
{
//记录下最相似的
max = compare[0].Similarity;
index = j;
}
}
result.Add(templateListFileName[index]);
}
return result;
}
}
}
效果图：
/// <summary> /// 通过GET方式获取验证码 /// </summary> /// <param name="Url">url</param> /// <param name="postDataStr">GET数据</param> /// <param name="cookie">GET容器</param> /// <returns></returns> public void SendDataByGET1(string Url, ref CookieContainer cookie) { HttpWebRequest request = (HttpWebRequest)WebRequest.Create(Url); if (cookie.Count == 0) { request.CookieContainer = new CookieContainer(); cookie = request.CookieContainer; } else { request.CookieContainer = cookie; }
request.Method = "GET"; request.ContentType = "text/html;charset=UTF-8";
HttpWebResponse response = (HttpWebResponse)request.GetResponse();
MemoryStream ms = null; using (var stream = response.GetResponseStream()) { Byte[] buffer = new Byte[response.ContentLength]; int offset = 0, actuallyRead = 0; do { actuallyRead = stream.Read(buffer, offset, buffer.Length - offset); offset += actuallyRead; } while (actuallyRead > 0); ms = new MemoryStream(buffer); }
b = new Bitmap(ms);
//aforge只接受像素格式为24/32bpp的像素格式图片，所以处理前，先进行格式转化 var bnew = new Bitmap(b.Width, b.Height,PixelFormat.Format24bppRgb);
Graphics g = Graphics.FromImage(bnew);
g.DrawImage(b, 0, 0);
g.Dispose(); // // ////灰度 // Bitmap temp; // temp = AForge.Imaging.Image.Clone(b, b.PixelFormat); // b = new Grayscale(0.2125, 0.7154, 0.0721).Apply(b); // //二值化 // b = new Threshold(50).Apply(b); pictureBox1.Image = bnew; //b = new Threshold(50).Apply(b); response.Close(); string strCookies = request.CookieContainer.GetCookieHeader(request.RequestUri); //把cookies转换成字符串
textBox2.Text=new VerificationCodeProcess().GetVerificationCode(bnew).ToString();
//Stream myResponseStream = response.GetResponseStream(); //StreamReader myStreamReader = new StreamReader(myResponseStream, Encoding.GetEncoding("utf-8")); //string retString = myStreamReader.ReadToEnd(); //myStreamReader.Close(); //myResponseStream.Close();
//MessageBox.Show(strCookies); }
原文地址：https://www.cnblogs.com/wangzhenghua/p/8994305.html