C#图片验证码破解
最近在做模拟web登陆的时候碰到了图片验证码，这个时候就需要对验证码进行破解。
1 public class GetImageValue
2 {
3 //设定图片RGB字符串
4 string[] ArrayList = new string[]{
5 "00011100011111110110001111000001110000011100000111000001110000011100000111000001011000110111111100011100", //0
6 "00111000111110001111100000011000000110000001100000011000000110000001100000011000000110001111111111111111", //1
7 "01111100111111101000001100000011000000110000011000001100000110000011000001100000110000001111111111111111", //2
8 "01111100111111111000001100000011000001100111100001111110000001110000001100000011100001111111111001111100", //3
9 "00001100000111000001110000111100011011000110110010001100110011001111111110111111000011000000110000001100", //4
10 "11111111111111111100000011000000110000001111100011111110000001110000001100000011100001111111111001111100", //5
11 "00011110001111110110000101100000110000001101111011101111111000111100000101000001011000110110111100011110", //6
12 "01111010001111110000000100000000000000110000011000000100000011000000100000011000000110000011000000110000", //7
13 "00111110011111110110001101100011011100100011111000111110011001111100000111000001111000110111111100111110", //8
14 "00011100011110110110001111000001110000011010001101111111001111010000000100000001010000110101110000111100", //9
15 "00111000111111101000010010000011100000111000001110000011000000111000001110000011110001001111011000111000", //10
16 "00001100011111000111110000001100000011000000110000001100000011000000110000001100000011000111110101111111", //11
17 "11111000110111000000010000000110000001100000110000011000001100000110000011000000100000000101111011111110", //12
18 "10111000111111100000001000000110000011001111000011110100000011100000011000000110000011101111110011111000", //13
19 "00000110000011100000111000001110000101100011011000100110011001001111111111111111000001000000011000000110", //14
20 "11111110111111101000000010000000100000001111000011110100000011100000011000000110000011101111110011111000", //15
21 "00111100011111101100001011000000100000001011110011111110110001111000001110000011110001101111111000111100", //16
22 "11101111111111110000001100000010000001100000010000001000000110000001000000110000001100000110000001100000", //17
23 "01111100111111101100011011000110110001000111110001111100110011101000001110000011110001101101111001111100", //18
24 "01111000111111101100011010000011100000111100011111111111011110110000001100000100100001101111110001111000", //19
25 "00111100011111111100001110000001110000011110001101111111001101010000000000000011010000010111111000111100", //20=9
26 "00111000111111001100011010000011100000011000001100000011100000111000001010000011110001101111010000101000", //21=0
27 "00111000010111010110001111000001110000011110001101111111001111010000000100000011010000100111011000111100", //22=9
28 "00000110000010100000111000010110001101100011011001000110011001101011101111111111000001100000011000000110", //23=4
29 "00011110001011110110000101000000110000001101101011111101011000111100000110000001010000110101111000011110", //24=6
30 "00111100011111101110001101000001110000011110001101111111001111010000000100000011000000110101101000111100", //25=9
31 "11111000111100000000011000000100000000100000110000011000001100000110000001000000100000001111110011111110" //26=2
32 };
33
34 /// <summary>
35 /// 获取图片验证码数字
36 /// </summary>
37 /// <returns></returns>
38 public string GetImageValues()
39 {
40 string url = "http://xxxx.xxxx/image";
41 WebRequest myWebRequest = WebRequest.Create(url);
42 WebResponse myWebResponse = myWebRequest.GetResponse();
43 Stream ReceiveStream = myWebResponse.GetResponseStream();
44 Bitmap map = new Bitmap(ReceiveStream, false);
45 UnCodebase ucode = new UnCodebase(map);
46
47 ucode.GrayByPixels(); //灰度处理
48
49 Bitmap[] pics = ucode.readMap();
50 int[] gray = new int[4];
51 for (int j = 0; j < 4; j++)
52 {
53 gray[j] = ucode.GetSingleDgGrayValue(pics[j]);
54 }
55 string[] arr = new string[4];
56 for (int i = 0; i < 4; i++)
57 {
58 arr[i] = ucode.GetSingleBmpCode(pics[i], gray[i]);
59 }
60 string picnum = getPicnums(arr);
61 return picnum;
62 }
63
64 public string getPicnums(string[] arr)
65 {
66 string Code = "";
67 for (int i = 0; i < 4; i++)
68 {
69 string code = arr[i]; //得到代码串
70
71 for (int arrayIndex = 0; arrayIndex < ArrayList.Length; arrayIndex++)
72 {
73 //逐点判断特征码是否相同，允许误差!
74 char temp1, temp2;
75 int point = 0;
76 if (ArrayList[arrayIndex].Equals(code))
77 {
78 point = 0;
79 if (arrayIndex > 9)
80 {
81 if (arrayIndex == 20 || arrayIndex == 22 || arrayIndex == 25)
82 {
83 Code = Code + "9";
84 }
85 else if (arrayIndex == 21)
86 {
87 Code = Code + "0";
88 }
89 else if (arrayIndex == 23)
90 {
91 Code = Code + "4";
92 }
93 else if (arrayIndex == 24)
94 {
95 Code = Code + "6";
96 }
97 else if (arrayIndex == 26)
98 {
99 Code = Code + "2";
100 }
101 else
102 {
103 Code = Code + (arrayIndex - 10).ToString();
104 }
105 }
106 else
107 {
108 Code = Code + arrayIndex.ToString();
109 }
110 break;
111 }
112 else
113 {
114 //将字符串数组，直接转为单个字符进行对比，并记录不相同的点
115 for (int Comparison = 0; Comparison < code.Length; Comparison++)
116 {
117 temp1 = arr[i][Comparison];
118 temp2 = ArrayList[arrayIndex][Comparison];
119 if (temp1 != temp2)
120 {
121 point = point + 1;
122 }
123 }
124 }
125
126 //当不相同点的值小于10的时候，也就是说误差点小于10的时候则直接等于此数字，否则将跳出循环继续对下一个特征码进行判断
127 if (point < 10)
128 {
129 if (arrayIndex > 9)
130 {
131 if (arrayIndex == 20 || arrayIndex == 22 || arrayIndex == 25)
132 {
133 Code = Code + "9";
134 }
135 else if (arrayIndex == 21)
136 {
137 Code = Code + "0";
138 }
139 else if (arrayIndex == 23)
140 {
141 Code = Code + "4";
142 }
143 else if (arrayIndex == 24)
144 {
145 Code = Code + "6";
146 }
147 else if (arrayIndex == 26)
148 {
149 Code = Code + "2";
150 }
151 else
152 {
153 Code = Code + (arrayIndex - 10).ToString();
154 }
155 }
156 else
157 {
158 Code = Code + arrayIndex.ToString();
159 }
160 break;
161 }
162 }
163 }
164 return Code;
165 }
166
167
168 -------------------------图片处理类
169
170
171 class UnCodebase
172 {
173 public Bitmap bmpobj;
174 public UnCodebase(Bitmap pic)
175 {
176 bmpobj = new Bitmap(pic); //转换为Format32bppRgb
177 }
178
179 /**/
180 /// <summary>
181 /// 根据RGB，计算灰度值
182 /// </summary>
183 /// <param name="posClr">Color值</param>
184 /// <returns>灰度值，整型</returns>
185 private int GetGrayNumColor(System.Drawing.Color posClr)
186 {
187 return (posClr.R * 19595 + posClr.G * 38469 + posClr.B * 7472) >> 16;
188 }
189
190 /**/
191 /// <summary>
192 /// 灰度转换,逐点方式
193 /// </summary>
194 public void GrayByPixels()
195 {
196 for (int i = 0; i < bmpobj.Height; i++)
197 {
198 for (int j = 0; j < bmpobj.Width; j++)
199 {
200 int tmpValue = GetGrayNumColor(bmpobj.GetPixel(j, i));
201 bmpobj.SetPixel(j, i, Color.FromArgb(tmpValue, tmpValue, tmpValue));
202 }
203 }
204 }
205
206 /**/
207 /// <summary>
208 /// 去图形边框
209 /// </summary>
210 /// <param name="borderWidth"></param>
211 public void ClearPicBorder(int borderWidth)
212 {
213 for (int i = 0; i < bmpobj.Height; i++)
214 {
215 for (int j = 0; j < bmpobj.Width; j++)
216 {
217 if (i < borderWidth || j < borderWidth || j > bmpobj.Width - 1 - borderWidth || i > bmpobj.Height - 1 - borderWidth)
218 bmpobj.SetPixel(j, i, Color.FromArgb(255, 255, 255));
219 }
220 }
221 }
222
223 /**/
224 /// <summary>
225 /// 灰度转换,逐行方式
226 /// </summary>
227 public void GrayByLine()
228 {
229 Rectangle rec = new Rectangle(0, 0, bmpobj.Width, bmpobj.Height);
230 BitmapData bmpData = bmpobj.LockBits(rec, ImageLockMode.ReadWrite, bmpobj.PixelFormat);// PixelFormat.Format32bppPArgb);
231 // bmpData.PixelFormat = PixelFormat.Format24bppRgb;
232 IntPtr scan0 = bmpData.Scan0;
233 int len = bmpobj.Width * bmpobj.Height;
234 int[] pixels = new int[len];
235 Marshal.Copy(scan0, pixels, 0, len);
236
237 //对图片进行处理
238 int GrayValue = 0;
239 for (int i = 0; i < len; i++)
240 {
241 GrayValue = GetGrayNumColor(Color.FromArgb(pixels[i]));
242 pixels[i] = (byte)(Color.FromArgb(GrayValue, GrayValue, GrayValue)).ToArgb(); //Color转byte
243 }
244
245 bmpobj.UnlockBits(bmpData);
246 }
247
248 /**/
249 /// <summary>
250 /// 得到有效图形并调整为可平均分割的大小
251 /// </summary>
252 /// <param name="dgGrayValue">灰度背景分界值</param>
253 /// <param name="CharsCount">有效字符数</param>
254 /// <returns></returns>
255 public void GetPicValidByValue(int dgGrayValue, int CharsCount)
256 {
257 int posx1 = bmpobj.Width; int posy1 = bmpobj.Height;
258 int posx2 = 0; int posy2 = 0;
259 for (int i = 0; i < bmpobj.Height; i++) //找有效区
260 {
261 for (int j = 0; j < bmpobj.Width; j++)
262 {
263 int pixelValue = bmpobj.GetPixel(j, i).R;
264 if (pixelValue < dgGrayValue) //根据灰度值
265 {
266 if (posx1 > j) posx1 = j;
267 if (posy1 > i) posy1 = i;
268
269 if (posx2 < j) posx2 = j;
270 if (posy2 < i) posy2 = i;
271 };
272 };
273 };
274 // 确保能整除
275 int Span = CharsCount - (posx2 - posx1 + 1) % CharsCount; //可整除的差额数
276 if (Span < CharsCount)
277 {
278 int leftSpan = Span / 2; //分配到左边的空列 ，如span为单数,则右边比左边大1
279 if (posx1 > leftSpan)
280 posx1 = posx1 - leftSpan;
281 if (posx2 + Span - leftSpan < bmpobj.Width)
282 posx2 = posx2 + Span - leftSpan;
283 }
284 //复制新图
285 Rectangle cloneRect = new Rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1);
286 bmpobj = bmpobj.Clone(cloneRect, bmpobj.PixelFormat);
287 }
288
289 /**/
290 /// <summary>
291 /// 得到有效图形,图形为类变量
292 /// </summary>
293 /// <param name="dgGrayValue">灰度背景分界值</param>
294 /// <param name="CharsCount">有效字符数</param>
295 /// <returns></returns>
296 public void GetPicValidByValue(int dgGrayValue)
297 {
298 int posx1 = bmpobj.Width; int posy1 = bmpobj.Height;
299 int posx2 = 0; int posy2 = 0;
300 for (int i = 0; i < bmpobj.Height; i++) //找有效区
301 {
302 for (int j = 0; j < bmpobj.Width; j++)
303 {
304 int pixelValue = bmpobj.GetPixel(j, i).R;
305 if (pixelValue < dgGrayValue) //根据灰度值
306 {
307 if (posx1 > j) posx1 = j;
308 if (posy1 > i) posy1 = i;
309
310 if (posx2 < j) posx2 = j;
311 if (posy2 < i) posy2 = i;
312 };
313 };
314 };
315 //复制新图
316 Rectangle cloneRect = new Rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1);
317 bmpobj = bmpobj.Clone(cloneRect, bmpobj.PixelFormat);
318 }
319
320 /**/
321 /// <summary>
322 /// 得到有效图形,图形由外面传入
323 /// </summary>
324 /// <param name="dgGrayValue">灰度背景分界值</param>
325 /// <param name="CharsCount">有效字符数</param>
326 /// <returns></returns>
327 public Bitmap GetPicValidByValue(Bitmap singlepic, int dgGrayValue)
328 {
329 int posx1 = singlepic.Width; int posy1 = singlepic.Height;
330 int posx2 = 0; int posy2 = 0;
331 for (int i = 0; i < singlepic.Height; i++) //找有效区
332 {
333 for (int j = 0; j < singlepic.Width; j++)
334 {
335 int pixelValue = singlepic.GetPixel(j, i).R;
336 if (pixelValue < dgGrayValue) //根据灰度值
337 {
338 if (posx1 > j) posx1 = j;
339 if (posy1 > i) posy1 = i;
340
341 if (posx2 < j) posx2 = j;
342 if (posy2 < i) posy2 = i;
343 };
344 };
345 };
346 //复制新图
347 Rectangle cloneRect = new Rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1);
348 return singlepic.Clone(cloneRect, singlepic.PixelFormat);
349 }
350
351 /**/
352 /// <summary>
353 /// 平均分割图片
354 /// </summary>
355 /// <param name="RowNum">水平上分割数</param>
356 /// <param name="ColNum">垂直上分割数</param>
357 /// <returns>分割好的图片数组</returns>
358 public Bitmap[] GetSplitPics(int RowNum, int ColNum)
359 {
360 if (RowNum == 0 || ColNum == 0)
361 return null;
362 int singW = bmpobj.Width / RowNum;
363 int singH = bmpobj.Height / ColNum;
364 Bitmap[] PicArray = new Bitmap[RowNum * ColNum];
365
366 Rectangle cloneRect;
367 for (int i = 0; i < ColNum; i++) //找有效区
368 {
369 for (int j = 0; j < RowNum; j++)
370 {
371 cloneRect = new Rectangle(j * singW, i * singH, singW, singH);
372 PicArray[i * RowNum + j] = bmpobj.Clone(cloneRect, bmpobj.PixelFormat);//复制小块图
373 }
374 }
375 return PicArray;
376 }
377
378
379 public Bitmap[] readMap()
380 {
381 string str;
382 RectangleF[] block = new RectangleF[4];
383 block[0] = new Rectangle(7, 3, 8, 13);
384 block[1] = new Rectangle(20, 3, 8, 13);
385 block[2] = new Rectangle(33, 3, 8, 13);
386 block[3] = new Rectangle(47, 3, 8, 13);
387 //分别克隆图片的四个部分
388 Bitmap[] s = new Bitmap[4];
389 s[0] = bmpobj.Clone(block[0], PixelFormat.DontCare);
390 s[1] = bmpobj.Clone(block[1], PixelFormat.DontCare);
391 s[2] = bmpobj.Clone(block[2], PixelFormat.DontCare);
392 s[3] = bmpobj.Clone(block[3], PixelFormat.DontCare);
393 return s;
394 }
395
396 /**/
397 /// <summary>
398 /// 返回灰度图片的点阵描述字串，1表示灰点，0表示背景
399 /// </summary>
400 /// <param name="singlepic">灰度图</param>
401 /// <param name="dgGrayValue">背前景灰色界限</param>
402 /// <returns></returns>
403 public string GetSingleBmpCode(Bitmap singlepic, int dgGrayValue)
404 {
405 Color piexl;
406 string code = "";
407 for (int posy = 0; posy < singlepic.Height; posy++)
408 for (int posx = 0; posx < singlepic.Width; posx++)
409 {
410 piexl = singlepic.GetPixel(posx, posy);
411 if (piexl.R < dgGrayValue) // Color.Black )
412 code = code + "1";
413 else
414 code = code + "0";
415 }
416 return code;
417 }
418
419 /// <summary>
420 /// 得到单个灰度图像前景背景的临界值 最大类间方差法，yuanbao,2007.08
421 /// </summary>
422 /// <returns>前景背景的临界值</returns>
423 public int GetSingleDgGrayValue(Bitmap singlepic)
424 {
425 int[] pixelNum = new int[256]; //图象直方图，共256个点
426 int n, n1, n2;
427 int total; //total为总和，累计值
428 double m1, m2, sum, csum, fmax, sb; //sb为类间方差，fmax存储最大方差值
429 int k, t, q;
430 int threshValue = 1; // 阈值
431 int step = 1;
432 //生成直方图
433 for (int i = 0; i < singlepic.Width; i++)
434 {
435 for (int j = 0; j < singlepic.Height; j++)
436 {
437 //返回各个点的颜色，以RGB表示
438 pixelNum[singlepic.GetPixel(i, j).R]++; //相应的直方图加1
439 }
440 }
441 //直方图平滑化
442 for (k = 0; k <= 255; k++)
443 {
444 total = 0;
445 for (t = -2; t <= 2; t++) //与附近2个灰度做平滑化，t值应取较小的值
446 {
447 q = k + t;
448 if (q < 0) //越界处理
449 q = 0;
450 if (q > 255)
451 q = 255;
452 total = total + pixelNum[q]; //total为总和，累计值
453 }
454 pixelNum[k] = (int)((float)total / 5.0 + 0.5); //平滑化，左边2个+中间1个+右边2个灰度，共5个，所以总和除以5，后面加0.5是用修正值
455 }
456 //求阈值
457 sum = csum = 0.0;
458 n = 0;
459 //计算总的图象的点数和质量矩，为后面的计算做准备
460 for (k = 0; k <= 255; k++)
461 {
462 sum += (double)k * (double)pixelNum[k]; //x*f(x)质量矩，也就是每个灰度的值乘以其点数（归一化后为概率），sum为其总和
463 n += pixelNum[k]; //n为图象总的点数，归一化后就是累积概率
464 }
465
466 fmax = -1.0; //类间方差sb不可能为负，所以fmax初始值为-1不影响计算的进行
467 n1 = 0;
468 for (k = 0; k < 256; k++) //对每个灰度（从0到255）计算一次分割后的类间方差sb
469 {
470 n1 += pixelNum[k]; //n1为在当前阈值遍前景图象的点数
471 if (n1 == 0) { continue; } //没有分出前景后景
472 n2 = n - n1; //n2为背景图象的点数
473 if (n2 == 0) { break; } //n2为0表示全部都是后景图象，与n1=0情况类似，之后的遍历不可能使前景点数增加，所以此时可以退出循环
474 csum += (double)k * pixelNum[k]; //前景的“灰度的值*其点数”的总和
475 m1 = csum / n1; //m1为前景的平均灰度
476 m2 = (sum - csum) / n2; //m2为背景的平均灰度
477 sb = (double)n1 * (double)n2 * (m1 - m2) * (m1 - m2); //sb为类间方差
478 if (sb > fmax) //如果算出的类间方差大于前一次算出的类间方差
479 {
480 fmax = sb; //fmax始终为最大类间方差（otsu）
481 threshValue = k; //取最大类间方差时对应的灰度的k就是最佳阈值
482 }
483 }
484 return threshValue;
485 }
486
487 /// <summary>
488 /// 得到灰度图像前景背景的临界值 最大类间方差法，yuanbao,2007.08
489 /// </summary>
490 /// <returns>前景背景的临界值</returns>
491 public int GetDgGrayValue()
492 {
493 int[] pixelNum = new int[256]; //图象直方图，共256个点
494 int n, n1, n2;
495 int total; //total为总和，累计值
496 double m1, m2, sum, csum, fmax, sb; //sb为类间方差，fmax存储最大方差值
497 int k, t, q;
498 int threshValue = 1; // 阈值
499 int step = 1;
500 //生成直方图
501 for (int i = 0; i < bmpobj.Width; i++)
502 {
503 for (int j = 0; j < bmpobj.Height; j++)
504 {
505 //返回各个点的颜色，以RGB表示
506 pixelNum[bmpobj.GetPixel(i, j).R]++; //相应的直方图加1
507 }
508 }
509 //直方图平滑化
510 for (k = 0; k <= 255; k++)
511 {
512 total = 0;
513 for (t = -2; t <= 2; t++) //与附近2个灰度做平滑化，t值应取较小的值
514 {
515 q = k + t;
516 if (q < 0) //越界处理
517 q = 0;
518 if (q > 255)
519 q = 255;
520 total = total + pixelNum[q]; //total为总和，累计值
521 }
522 pixelNum[k] = (int)((float)total / 5.0 + 0.5); //平滑化，左边2个+中间1个+右边2个灰度，共5个，所以总和除以5，后面加0.5是用修正值
523 }
524 //求阈值
525 sum = csum = 0.0;
526 n = 0;
527 //计算总的图象的点数和质量矩，为后面的计算做准备
528 for (k = 0; k <= 255; k++)
529 {
530 sum += (double)k * (double)pixelNum[k]; //x*f(x)质量矩，也就是每个灰度的值乘以其点数（归一化后为概率），sum为其总和
531 n += pixelNum[k]; //n为图象总的点数，归一化后就是累积概率
532 }
533
534 fmax = -1.0; //类间方差sb不可能为负，所以fmax初始值为-1不影响计算的进行
535 n1 = 0;
536 for (k = 0; k < 256; k++) //对每个灰度（从0到255）计算一次分割后的类间方差sb
537 {
538 n1 += pixelNum[k]; //n1为在当前阈值遍前景图象的点数
539 if (n1 == 0) { continue; } //没有分出前景后景
540 n2 = n - n1; //n2为背景图象的点数
541 if (n2 == 0) { break; } //n2为0表示全部都是后景图象，与n1=0情况类似，之后的遍历不可能使前景点数增加，所以此时可以退出循环
542 csum += (double)k * pixelNum[k]; //前景的“灰度的值*其点数”的总和
543 m1 = csum / n1; //m1为前景的平均灰度
544 m2 = (sum - csum) / n2; //m2为背景的平均灰度
545 sb = (double)n1 * (double)n2 * (m1 - m2) * (m1 - m2); //sb为类间方差
546 if (sb > fmax) //如果算出的类间方差大于前一次算出的类间方差
547 {
548 fmax = sb; //fmax始终为最大类间方差（otsu）
549 threshValue = k; //取最大类间方差时对应的灰度的k就是最佳阈值
550 }
551 }
552 return threshValue;
553 }
554
555 }
556
557
2 {
3 //设定图片RGB字符串
4 string[] ArrayList = new string[]{
5 "00011100011111110110001111000001110000011100000111000001110000011100000111000001011000110111111100011100", //0
6 "00111000111110001111100000011000000110000001100000011000000110000001100000011000000110001111111111111111", //1
7 "01111100111111101000001100000011000000110000011000001100000110000011000001100000110000001111111111111111", //2
8 "01111100111111111000001100000011000001100111100001111110000001110000001100000011100001111111111001111100", //3
9 "00001100000111000001110000111100011011000110110010001100110011001111111110111111000011000000110000001100", //4
10 "11111111111111111100000011000000110000001111100011111110000001110000001100000011100001111111111001111100", //5
11 "00011110001111110110000101100000110000001101111011101111111000111100000101000001011000110110111100011110", //6
12 "01111010001111110000000100000000000000110000011000000100000011000000100000011000000110000011000000110000", //7
13 "00111110011111110110001101100011011100100011111000111110011001111100000111000001111000110111111100111110", //8
14 "00011100011110110110001111000001110000011010001101111111001111010000000100000001010000110101110000111100", //9
15 "00111000111111101000010010000011100000111000001110000011000000111000001110000011110001001111011000111000", //10
16 "00001100011111000111110000001100000011000000110000001100000011000000110000001100000011000111110101111111", //11
17 "11111000110111000000010000000110000001100000110000011000001100000110000011000000100000000101111011111110", //12
18 "10111000111111100000001000000110000011001111000011110100000011100000011000000110000011101111110011111000", //13
19 "00000110000011100000111000001110000101100011011000100110011001001111111111111111000001000000011000000110", //14
20 "11111110111111101000000010000000100000001111000011110100000011100000011000000110000011101111110011111000", //15
21 "00111100011111101100001011000000100000001011110011111110110001111000001110000011110001101111111000111100", //16
22 "11101111111111110000001100000010000001100000010000001000000110000001000000110000001100000110000001100000", //17
23 "01111100111111101100011011000110110001000111110001111100110011101000001110000011110001101101111001111100", //18
24 "01111000111111101100011010000011100000111100011111111111011110110000001100000100100001101111110001111000", //19
25 "00111100011111111100001110000001110000011110001101111111001101010000000000000011010000010111111000111100", //20=9
26 "00111000111111001100011010000011100000011000001100000011100000111000001010000011110001101111010000101000", //21=0
27 "00111000010111010110001111000001110000011110001101111111001111010000000100000011010000100111011000111100", //22=9
28 "00000110000010100000111000010110001101100011011001000110011001101011101111111111000001100000011000000110", //23=4
29 "00011110001011110110000101000000110000001101101011111101011000111100000110000001010000110101111000011110", //24=6
30 "00111100011111101110001101000001110000011110001101111111001111010000000100000011000000110101101000111100", //25=9
31 "11111000111100000000011000000100000000100000110000011000001100000110000001000000100000001111110011111110" //26=2
32 };
33
34 /// <summary>
35 /// 获取图片验证码数字
36 /// </summary>
37 /// <returns></returns>
38 public string GetImageValues()
39 {
40 string url = "http://xxxx.xxxx/image";
41 WebRequest myWebRequest = WebRequest.Create(url);
42 WebResponse myWebResponse = myWebRequest.GetResponse();
43 Stream ReceiveStream = myWebResponse.GetResponseStream();
44 Bitmap map = new Bitmap(ReceiveStream, false);
45 UnCodebase ucode = new UnCodebase(map);
46
47 ucode.GrayByPixels(); //灰度处理
48
49 Bitmap[] pics = ucode.readMap();
50 int[] gray = new int[4];
51 for (int j = 0; j < 4; j++)
52 {
53 gray[j] = ucode.GetSingleDgGrayValue(pics[j]);
54 }
55 string[] arr = new string[4];
56 for (int i = 0; i < 4; i++)
57 {
58 arr[i] = ucode.GetSingleBmpCode(pics[i], gray[i]);
59 }
60 string picnum = getPicnums(arr);
61 return picnum;
62 }
63
64 public string getPicnums(string[] arr)
65 {
66 string Code = "";
67 for (int i = 0; i < 4; i++)
68 {
69 string code = arr[i]; //得到代码串
70
71 for (int arrayIndex = 0; arrayIndex < ArrayList.Length; arrayIndex++)
72 {
73 //逐点判断特征码是否相同，允许误差!
74 char temp1, temp2;
75 int point = 0;
76 if (ArrayList[arrayIndex].Equals(code))
77 {
78 point = 0;
79 if (arrayIndex > 9)
80 {
81 if (arrayIndex == 20 || arrayIndex == 22 || arrayIndex == 25)
82 {
83 Code = Code + "9";
84 }
85 else if (arrayIndex == 21)
86 {
87 Code = Code + "0";
88 }
89 else if (arrayIndex == 23)
90 {
91 Code = Code + "4";
92 }
93 else if (arrayIndex == 24)
94 {
95 Code = Code + "6";
96 }
97 else if (arrayIndex == 26)
98 {
99 Code = Code + "2";
100 }
101 else
102 {
103 Code = Code + (arrayIndex - 10).ToString();
104 }
105 }
106 else
107 {
108 Code = Code + arrayIndex.ToString();
109 }
110 break;
111 }
112 else
113 {
114 //将字符串数组，直接转为单个字符进行对比，并记录不相同的点
115 for (int Comparison = 0; Comparison < code.Length; Comparison++)
116 {
117 temp1 = arr[i][Comparison];
118 temp2 = ArrayList[arrayIndex][Comparison];
119 if (temp1 != temp2)
120 {
121 point = point + 1;
122 }
123 }
124 }
125
126 //当不相同点的值小于10的时候，也就是说误差点小于10的时候则直接等于此数字，否则将跳出循环继续对下一个特征码进行判断
127 if (point < 10)
128 {
129 if (arrayIndex > 9)
130 {
131 if (arrayIndex == 20 || arrayIndex == 22 || arrayIndex == 25)
132 {
133 Code = Code + "9";
134 }
135 else if (arrayIndex == 21)
136 {
137 Code = Code + "0";
138 }
139 else if (arrayIndex == 23)
140 {
141 Code = Code + "4";
142 }
143 else if (arrayIndex == 24)
144 {
145 Code = Code + "6";
146 }
147 else if (arrayIndex == 26)
148 {
149 Code = Code + "2";
150 }
151 else
152 {
153 Code = Code + (arrayIndex - 10).ToString();
154 }
155 }
156 else
157 {
158 Code = Code + arrayIndex.ToString();
159 }
160 break;
161 }
162 }
163 }
164 return Code;
165 }
166
167
168 -------------------------图片处理类
169
170
171 class UnCodebase
172 {
173 public Bitmap bmpobj;
174 public UnCodebase(Bitmap pic)
175 {
176 bmpobj = new Bitmap(pic); //转换为Format32bppRgb
177 }
178
179 /**/
180 /// <summary>
181 /// 根据RGB，计算灰度值
182 /// </summary>
183 /// <param name="posClr">Color值</param>
184 /// <returns>灰度值，整型</returns>
185 private int GetGrayNumColor(System.Drawing.Color posClr)
186 {
187 return (posClr.R * 19595 + posClr.G * 38469 + posClr.B * 7472) >> 16;
188 }
189
190 /**/
191 /// <summary>
192 /// 灰度转换,逐点方式
193 /// </summary>
194 public void GrayByPixels()
195 {
196 for (int i = 0; i < bmpobj.Height; i++)
197 {
198 for (int j = 0; j < bmpobj.Width; j++)
199 {
200 int tmpValue = GetGrayNumColor(bmpobj.GetPixel(j, i));
201 bmpobj.SetPixel(j, i, Color.FromArgb(tmpValue, tmpValue, tmpValue));
202 }
203 }
204 }
205
206 /**/
207 /// <summary>
208 /// 去图形边框
209 /// </summary>
210 /// <param name="borderWidth"></param>
211 public void ClearPicBorder(int borderWidth)
212 {
213 for (int i = 0; i < bmpobj.Height; i++)
214 {
215 for (int j = 0; j < bmpobj.Width; j++)
216 {
217 if (i < borderWidth || j < borderWidth || j > bmpobj.Width - 1 - borderWidth || i > bmpobj.Height - 1 - borderWidth)
218 bmpobj.SetPixel(j, i, Color.FromArgb(255, 255, 255));
219 }
220 }
221 }
222
223 /**/
224 /// <summary>
225 /// 灰度转换,逐行方式
226 /// </summary>
227 public void GrayByLine()
228 {
229 Rectangle rec = new Rectangle(0, 0, bmpobj.Width, bmpobj.Height);
230 BitmapData bmpData = bmpobj.LockBits(rec, ImageLockMode.ReadWrite, bmpobj.PixelFormat);// PixelFormat.Format32bppPArgb);
231 // bmpData.PixelFormat = PixelFormat.Format24bppRgb;
232 IntPtr scan0 = bmpData.Scan0;
233 int len = bmpobj.Width * bmpobj.Height;
234 int[] pixels = new int[len];
235 Marshal.Copy(scan0, pixels, 0, len);
236
237 //对图片进行处理
238 int GrayValue = 0;
239 for (int i = 0; i < len; i++)
240 {
241 GrayValue = GetGrayNumColor(Color.FromArgb(pixels[i]));
242 pixels[i] = (byte)(Color.FromArgb(GrayValue, GrayValue, GrayValue)).ToArgb(); //Color转byte
243 }
244
245 bmpobj.UnlockBits(bmpData);
246 }
247
248 /**/
249 /// <summary>
250 /// 得到有效图形并调整为可平均分割的大小
251 /// </summary>
252 /// <param name="dgGrayValue">灰度背景分界值</param>
253 /// <param name="CharsCount">有效字符数</param>
254 /// <returns></returns>
255 public void GetPicValidByValue(int dgGrayValue, int CharsCount)
256 {
257 int posx1 = bmpobj.Width; int posy1 = bmpobj.Height;
258 int posx2 = 0; int posy2 = 0;
259 for (int i = 0; i < bmpobj.Height; i++) //找有效区
260 {
261 for (int j = 0; j < bmpobj.Width; j++)
262 {
263 int pixelValue = bmpobj.GetPixel(j, i).R;
264 if (pixelValue < dgGrayValue) //根据灰度值
265 {
266 if (posx1 > j) posx1 = j;
267 if (posy1 > i) posy1 = i;
268
269 if (posx2 < j) posx2 = j;
270 if (posy2 < i) posy2 = i;
271 };
272 };
273 };
274 // 确保能整除
275 int Span = CharsCount - (posx2 - posx1 + 1) % CharsCount; //可整除的差额数
276 if (Span < CharsCount)
277 {
278 int leftSpan = Span / 2; //分配到左边的空列 ，如span为单数,则右边比左边大1
279 if (posx1 > leftSpan)
280 posx1 = posx1 - leftSpan;
281 if (posx2 + Span - leftSpan < bmpobj.Width)
282 posx2 = posx2 + Span - leftSpan;
283 }
284 //复制新图
285 Rectangle cloneRect = new Rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1);
286 bmpobj = bmpobj.Clone(cloneRect, bmpobj.PixelFormat);
287 }
288
289 /**/
290 /// <summary>
291 /// 得到有效图形,图形为类变量
292 /// </summary>
293 /// <param name="dgGrayValue">灰度背景分界值</param>
294 /// <param name="CharsCount">有效字符数</param>
295 /// <returns></returns>
296 public void GetPicValidByValue(int dgGrayValue)
297 {
298 int posx1 = bmpobj.Width; int posy1 = bmpobj.Height;
299 int posx2 = 0; int posy2 = 0;
300 for (int i = 0; i < bmpobj.Height; i++) //找有效区
301 {
302 for (int j = 0; j < bmpobj.Width; j++)
303 {
304 int pixelValue = bmpobj.GetPixel(j, i).R;
305 if (pixelValue < dgGrayValue) //根据灰度值
306 {
307 if (posx1 > j) posx1 = j;
308 if (posy1 > i) posy1 = i;
309
310 if (posx2 < j) posx2 = j;
311 if (posy2 < i) posy2 = i;
312 };
313 };
314 };
315 //复制新图
316 Rectangle cloneRect = new Rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1);
317 bmpobj = bmpobj.Clone(cloneRect, bmpobj.PixelFormat);
318 }
319
320 /**/
321 /// <summary>
322 /// 得到有效图形,图形由外面传入
323 /// </summary>
324 /// <param name="dgGrayValue">灰度背景分界值</param>
325 /// <param name="CharsCount">有效字符数</param>
326 /// <returns></returns>
327 public Bitmap GetPicValidByValue(Bitmap singlepic, int dgGrayValue)
328 {
329 int posx1 = singlepic.Width; int posy1 = singlepic.Height;
330 int posx2 = 0; int posy2 = 0;
331 for (int i = 0; i < singlepic.Height; i++) //找有效区
332 {
333 for (int j = 0; j < singlepic.Width; j++)
334 {
335 int pixelValue = singlepic.GetPixel(j, i).R;
336 if (pixelValue < dgGrayValue) //根据灰度值
337 {
338 if (posx1 > j) posx1 = j;
339 if (posy1 > i) posy1 = i;
340
341 if (posx2 < j) posx2 = j;
342 if (posy2 < i) posy2 = i;
343 };
344 };
345 };
346 //复制新图
347 Rectangle cloneRect = new Rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1);
348 return singlepic.Clone(cloneRect, singlepic.PixelFormat);
349 }
350
351 /**/
352 /// <summary>
353 /// 平均分割图片
354 /// </summary>
355 /// <param name="RowNum">水平上分割数</param>
356 /// <param name="ColNum">垂直上分割数</param>
357 /// <returns>分割好的图片数组</returns>
358 public Bitmap[] GetSplitPics(int RowNum, int ColNum)
359 {
360 if (RowNum == 0 || ColNum == 0)
361 return null;
362 int singW = bmpobj.Width / RowNum;
363 int singH = bmpobj.Height / ColNum;
364 Bitmap[] PicArray = new Bitmap[RowNum * ColNum];
365
366 Rectangle cloneRect;
367 for (int i = 0; i < ColNum; i++) //找有效区
368 {
369 for (int j = 0; j < RowNum; j++)
370 {
371 cloneRect = new Rectangle(j * singW, i * singH, singW, singH);
372 PicArray[i * RowNum + j] = bmpobj.Clone(cloneRect, bmpobj.PixelFormat);//复制小块图
373 }
374 }
375 return PicArray;
376 }
377
378
379 public Bitmap[] readMap()
380 {
381 string str;
382 RectangleF[] block = new RectangleF[4];
383 block[0] = new Rectangle(7, 3, 8, 13);
384 block[1] = new Rectangle(20, 3, 8, 13);
385 block[2] = new Rectangle(33, 3, 8, 13);
386 block[3] = new Rectangle(47, 3, 8, 13);
387 //分别克隆图片的四个部分
388 Bitmap[] s = new Bitmap[4];
389 s[0] = bmpobj.Clone(block[0], PixelFormat.DontCare);
390 s[1] = bmpobj.Clone(block[1], PixelFormat.DontCare);
391 s[2] = bmpobj.Clone(block[2], PixelFormat.DontCare);
392 s[3] = bmpobj.Clone(block[3], PixelFormat.DontCare);
393 return s;
394 }
395
396 /**/
397 /// <summary>
398 /// 返回灰度图片的点阵描述字串，1表示灰点，0表示背景
399 /// </summary>
400 /// <param name="singlepic">灰度图</param>
401 /// <param name="dgGrayValue">背前景灰色界限</param>
402 /// <returns></returns>
403 public string GetSingleBmpCode(Bitmap singlepic, int dgGrayValue)
404 {
405 Color piexl;
406 string code = "";
407 for (int posy = 0; posy < singlepic.Height; posy++)
408 for (int posx = 0; posx < singlepic.Width; posx++)
409 {
410 piexl = singlepic.GetPixel(posx, posy);
411 if (piexl.R < dgGrayValue) // Color.Black )
412 code = code + "1";
413 else
414 code = code + "0";
415 }
416 return code;
417 }
418
419 /// <summary>
420 /// 得到单个灰度图像前景背景的临界值 最大类间方差法，yuanbao,2007.08
421 /// </summary>
422 /// <returns>前景背景的临界值</returns>
423 public int GetSingleDgGrayValue(Bitmap singlepic)
424 {
425 int[] pixelNum = new int[256]; //图象直方图，共256个点
426 int n, n1, n2;
427 int total; //total为总和，累计值
428 double m1, m2, sum, csum, fmax, sb; //sb为类间方差，fmax存储最大方差值
429 int k, t, q;
430 int threshValue = 1; // 阈值
431 int step = 1;
432 //生成直方图
433 for (int i = 0; i < singlepic.Width; i++)
434 {
435 for (int j = 0; j < singlepic.Height; j++)
436 {
437 //返回各个点的颜色，以RGB表示
438 pixelNum[singlepic.GetPixel(i, j).R]++; //相应的直方图加1
439 }
440 }
441 //直方图平滑化
442 for (k = 0; k <= 255; k++)
443 {
444 total = 0;
445 for (t = -2; t <= 2; t++) //与附近2个灰度做平滑化，t值应取较小的值
446 {
447 q = k + t;
448 if (q < 0) //越界处理
449 q = 0;
450 if (q > 255)
451 q = 255;
452 total = total + pixelNum[q]; //total为总和，累计值
453 }
454 pixelNum[k] = (int)((float)total / 5.0 + 0.5); //平滑化，左边2个+中间1个+右边2个灰度，共5个，所以总和除以5，后面加0.5是用修正值
455 }
456 //求阈值
457 sum = csum = 0.0;
458 n = 0;
459 //计算总的图象的点数和质量矩，为后面的计算做准备
460 for (k = 0; k <= 255; k++)
461 {
462 sum += (double)k * (double)pixelNum[k]; //x*f(x)质量矩，也就是每个灰度的值乘以其点数（归一化后为概率），sum为其总和
463 n += pixelNum[k]; //n为图象总的点数，归一化后就是累积概率
464 }
465
466 fmax = -1.0; //类间方差sb不可能为负，所以fmax初始值为-1不影响计算的进行
467 n1 = 0;
468 for (k = 0; k < 256; k++) //对每个灰度（从0到255）计算一次分割后的类间方差sb
469 {
470 n1 += pixelNum[k]; //n1为在当前阈值遍前景图象的点数
471 if (n1 == 0) { continue; } //没有分出前景后景
472 n2 = n - n1; //n2为背景图象的点数
473 if (n2 == 0) { break; } //n2为0表示全部都是后景图象，与n1=0情况类似，之后的遍历不可能使前景点数增加，所以此时可以退出循环
474 csum += (double)k * pixelNum[k]; //前景的“灰度的值*其点数”的总和
475 m1 = csum / n1; //m1为前景的平均灰度
476 m2 = (sum - csum) / n2; //m2为背景的平均灰度
477 sb = (double)n1 * (double)n2 * (m1 - m2) * (m1 - m2); //sb为类间方差
478 if (sb > fmax) //如果算出的类间方差大于前一次算出的类间方差
479 {
480 fmax = sb; //fmax始终为最大类间方差（otsu）
481 threshValue = k; //取最大类间方差时对应的灰度的k就是最佳阈值
482 }
483 }
484 return threshValue;
485 }
486
487 /// <summary>
488 /// 得到灰度图像前景背景的临界值 最大类间方差法，yuanbao,2007.08
489 /// </summary>
490 /// <returns>前景背景的临界值</returns>
491 public int GetDgGrayValue()
492 {
493 int[] pixelNum = new int[256]; //图象直方图，共256个点
494 int n, n1, n2;
495 int total; //total为总和，累计值
496 double m1, m2, sum, csum, fmax, sb; //sb为类间方差，fmax存储最大方差值
497 int k, t, q;
498 int threshValue = 1; // 阈值
499 int step = 1;
500 //生成直方图
501 for (int i = 0; i < bmpobj.Width; i++)
502 {
503 for (int j = 0; j < bmpobj.Height; j++)
504 {
505 //返回各个点的颜色，以RGB表示
506 pixelNum[bmpobj.GetPixel(i, j).R]++; //相应的直方图加1
507 }
508 }
509 //直方图平滑化
510 for (k = 0; k <= 255; k++)
511 {
512 total = 0;
513 for (t = -2; t <= 2; t++) //与附近2个灰度做平滑化，t值应取较小的值
514 {
515 q = k + t;
516 if (q < 0) //越界处理
517 q = 0;
518 if (q > 255)
519 q = 255;
520 total = total + pixelNum[q]; //total为总和，累计值
521 }
522 pixelNum[k] = (int)((float)total / 5.0 + 0.5); //平滑化，左边2个+中间1个+右边2个灰度，共5个，所以总和除以5，后面加0.5是用修正值
523 }
524 //求阈值
525 sum = csum = 0.0;
526 n = 0;
527 //计算总的图象的点数和质量矩，为后面的计算做准备
528 for (k = 0; k <= 255; k++)
529 {
530 sum += (double)k * (double)pixelNum[k]; //x*f(x)质量矩，也就是每个灰度的值乘以其点数（归一化后为概率），sum为其总和
531 n += pixelNum[k]; //n为图象总的点数，归一化后就是累积概率
532 }
533
534 fmax = -1.0; //类间方差sb不可能为负，所以fmax初始值为-1不影响计算的进行
535 n1 = 0;
536 for (k = 0; k < 256; k++) //对每个灰度（从0到255）计算一次分割后的类间方差sb
537 {
538 n1 += pixelNum[k]; //n1为在当前阈值遍前景图象的点数
539 if (n1 == 0) { continue; } //没有分出前景后景
540 n2 = n - n1; //n2为背景图象的点数
541 if (n2 == 0) { break; } //n2为0表示全部都是后景图象，与n1=0情况类似，之后的遍历不可能使前景点数增加，所以此时可以退出循环
542 csum += (double)k * pixelNum[k]; //前景的“灰度的值*其点数”的总和
543 m1 = csum / n1; //m1为前景的平均灰度
544 m2 = (sum - csum) / n2; //m2为背景的平均灰度
545 sb = (double)n1 * (double)n2 * (m1 - m2) * (m1 - m2); //sb为类间方差
546 if (sb > fmax) //如果算出的类间方差大于前一次算出的类间方差
547 {
548 fmax = sb; //fmax始终为最大类间方差（otsu）
549 threshValue = k; //取最大类间方差时对应的灰度的k就是最佳阈值
550 }
551 }
552 return threshValue;
553 }
554
555 }
556
557
