这篇文章主要介绍了c#实现识别图片上的验证码数字的方法，本文给大家汇总了2种方法，有需要的小伙伴可以参考下。
正文
c#实现识别图片上的验证码数字
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public void imgdo(bitmap img) { //去色 bitmap btp = img; color c = new color(); int rr, gg, bb; for (int i = 0; i < btp.width; i++) { for (int j = 0; j < btp.height; j++) { //取图片当前的像素点 c = btp.getpixel(i, j); rr = c.r; gg = c.g; bb = c.b; //改变颜色 if (rr == 102 && gg == 0 && bb == 0) { //重新设置当前的像素点 btp.setpixel(i, j, color.fromargb(255, 255, 255, 255)); } if (rr == 153 && gg == 0 && bb == 0) { //重新设置当前的像素点 btp.setpixel(i, j, color.fromargb(255, 255, 255, 255)); } if (rr == 153 && gg == 0 && bb == 51) { //重新设置当前的像素点 btp.setpixel(i, j, color.fromargb(255, 255, 255, 255)); } if (rr == 153 && gg == 43 && bb == 51) { //重新设置当前的像素点 btp.setpixel(i, j, color.fromargb(255, 255, 255, 255)); } if (rr == 255 && gg == 255 && bb == 0) { //重新设置当前的像素点 btp.setpixel(i, j, color.fromargb(255, 255, 255, 255)); } if (rr == 255 && gg == 255 && bb == 51) { //重新设置当前的像素点 btp.setpixel(i, j, color.fromargb(255, 255, 255, 255)); } } } btp.save("d:\\去除相关颜色.png"); picturebox2.image = image.fromfile("d:\\去除相关颜色.png"); //灰度 bitmap bmphd = btp; for (int i = 0; i < bmphd.width; i++) { for (int j = 0; j < bmphd.height; j++) { //取图片当前的像素点 var color = bmphd.getpixel(i, j); var gray = (int)(color.r * 0.001 + color.g * 0.700 + color.b * 0.250); //重新设置当前的像素点 bmphd.setpixel(i, j, color.fromargb(gray, gray, gray)); } } bmphd.save("d:\\灰度.png"); picturebox27.image = image.fromfile("d:\\灰度.png"); //二值化 bitmap erzhi = bmphd; bitmap orcbmp; int nn = 3; int w = erzhi.width; int h = erzhi.height; bitmapdata data = erzhi.lockbits(new rectangle(0, 0, w, h), imagelockmode.readonly, pixelformat.format24bpprgb); unsafe { byte* p = (byte*)data.scan0; byte[,] vsource = new byte[w, h]; int offset = data.stride - w * nn; for (int y = 0; y < h; y++) { for (int x = 0; x < w; x++) { vsource[x, y] = (byte)(((int)p[0] + (int)p[1] + (int)p[2]) / 3); p += nn; } p += offset; } erzhi.unlockbits(data); bitmap bmpdest = new bitmap(w, h, pixelformat.format24bpprgb); bitmapdata datadest = bmpdest.lockbits(new rectangle(0, 0, w, h), imagelockmode.writeonly, pixelformat.format24bpprgb); p = (byte*)datadest.scan0; offset = datadest.stride - w * nn; for (int y = 0; y < h; y++) { for (int x = 0; x < w; x++) { p[0] = p[1] = p[2] = (int)vsource[x, y] > 161 ? (byte)255 : (byte)0; //p[0] = p[1] = p[2] = (int)getaveragecolor(vsource, x, y, w, h) > 50 ? (byte)255 : (byte)0; p += nn; } p += offset; } bmpdest.unlockbits(datadest); orcbmp = bmpdest; orcbmp.save("d:\\二值化.png"); picturebox29.image = image.fromfile("d:\\二值化.png"); } //ocr的值 if (orcbmp != null) { string result = ocr(orcbmp); label32.text = result.replace("\n", "\r\n").replace(" ", ""); } } |
c#识别验证码图片通用类
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using system;using system.collections.generic;using system.text;using system.collections;using system.drawing;using system.drawing.imaging;using system.runtime.interopservices; namespace ballotaiying2{ class uncodebase { public bitmap bmpobj; public uncodebase(bitmap pic) { bmpobj = new bitmap(pic); //转换为format32bpprgb } /// <summary> /// 根据rgb，计算灰度值 /// </summary> /// <param name="posclr">color值</param> /// <returns>灰度值，整型</returns> private int getgraynumcolor(system.drawing.color posclr) { return (posclr.r * 19595 + posclr.g * 38469 + posclr.b * 7472) >> 16; } /// <summary> /// 灰度转换,逐点方式 /// </summary> public void graybypixels() { for (int i = 0; i < bmpobj.height; i++) { for (int j = 0; j < bmpobj.width; j++) { int tmpvalue = getgraynumcolor(bmpobj.getpixel(j, i)); bmpobj.setpixel(j, i, color.fromargb(tmpvalue, tmpvalue, tmpvalue)); } } } /// <summary> /// 去图形边框 /// </summary> /// <param name="borderwidth"></param> public void clearpicborder(int borderwidth) { for (int i = 0; i < bmpobj.height; i++) { for (int j = 0; j < bmpobj.width; j++) { if (i < borderwidth || j < borderwidth || j > bmpobj.width - 1 - borderwidth || i > bmpobj.height - 1 - borderwidth) bmpobj.setpixel(j, i, color.fromargb(255, 255, 255)); } } } /// <summary> /// 灰度转换,逐行方式 /// </summary> public void graybyline() { rectangle rec = new rectangle(0, 0, bmpobj.width, bmpobj.height); bitmapdata bmpdata = bmpobj.lockbits(rec, imagelockmode.readwrite, bmpobj.pixelformat);// pixelformat.format32bpppargb); // bmpdata.pixelformat = pixelformat.format24bpprgb; intptr scan0 = bmpdata.scan0; int len = bmpobj.width * bmpobj.height; int[] pixels = new int[len]; marshal.copy(scan0, pixels, 0, len); //对图片进行处理 int grayvalue = 0; for (int i = 0; i < len; i++) { grayvalue = getgraynumcolor(color.fromargb(pixels)); pixels = (byte)(color.fromargb(grayvalue, grayvalue, grayvalue)).toargb(); //color转byte } bmpobj.unlockbits(bmpdata); } /// <summary> /// 得到有效图形并调整为可平均分割的大小 /// </summary> /// <param name="dggrayvalue">灰度背景分界值</param> /// <param name="charscount">有效字符数</param> /// <returns></returns> public void getpicvalidbyvalue(int dggrayvalue, int charscount) { int posx1 = bmpobj.width; int posy1 = bmpobj.height; int posx2 = 0; int posy2 = 0; for (int i = 0; i < bmpobj.height; i++) //找有效区 { for (int j = 0; j < bmpobj.width; j++) { int pixelvalue = bmpobj.getpixel(j, i).r; if (pixelvalue < dggrayvalue) //根据灰度值 { if (posx1 > j) posx1 = j; if (posy1 > i) posy1 = i; if (posx2 < j) posx2 = j; if (posy2 < i) posy2 = i; }; }; }; // 确保能整除 int span = charscount - (posx2 - posx1 + 1) % charscount; //可整除的差额数 if (span < charscount) { 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 if (posx1 > leftspan) posx1 = posx1 - leftspan; if (posx2 + span - leftspan < bmpobj.width) posx2 = posx2 + span - leftspan; } //复制新图 rectangle clonerect = new rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1); bmpobj = bmpobj.clone(clonerect, bmpobj.pixelformat); } /// <summary> /// 得到有效图形,图形为类变量 /// </summary> /// <param name="dggrayvalue">灰度背景分界值</param> /// <param name="charscount">有效字符数</param> /// <returns></returns> public void getpicvalidbyvalue(int dggrayvalue) { int posx1 = bmpobj.width; int posy1 = bmpobj.height; int posx2 = 0; int posy2 = 0; for (int i = 0; i < bmpobj.height; i++) //找有效区 { for (int j = 0; j < bmpobj.width; j++) { int pixelvalue = bmpobj.getpixel(j, i).r; if (pixelvalue < dggrayvalue) //根据灰度值 { if (posx1 > j) posx1 = j; if (posy1 > i) posy1 = i; if (posx2 < j) posx2 = j; if (posy2 < i) posy2 = i; }; }; }; //复制新图 rectangle clonerect = new rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1); bmpobj = bmpobj.clone(clonerect, bmpobj.pixelformat); } /// <summary> /// 得到有效图形,图形由外面传入 /// </summary> /// <param name="dggrayvalue">灰度背景分界值</param> /// <param name="charscount">有效字符数</param> /// <returns></returns> public bitmap getpicvalidbyvalue(bitmap singlepic, int dggrayvalue) { int posx1 = singlepic.width; int posy1 = singlepic.height; int posx2 = 0; int posy2 = 0; for (int i = 0; i < singlepic.height; i++) //找有效区 { for (int j = 0; j < singlepic.width; j++) { int pixelvalue = singlepic.getpixel(j, i).r; if (pixelvalue < dggrayvalue) //根据灰度值 { if (posx1 > j) posx1 = j; if (posy1 > i) posy1 = i; if (posx2 < j) posx2 = j; if (posy2 < i) posy2 = i; }; }; }; //复制新图 rectangle clonerect = new rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1); return singlepic.clone(clonerect, singlepic.pixelformat); } /// <summary> /// 平均分割图片 /// </summary> /// <param name="rownum">水平上分割数</param> /// <param name="colnum">垂直上分割数</param> /// <returns>分割好的图片数组</returns> public bitmap [] getsplitpics(int rownum,int colnum) { if (rownum == 0 || colnum == 0) return null; int singw = bmpobj.width / rownum; int singh = bmpobj.height / colnum; bitmap [] picarray=new bitmap[rownum*colnum]; rectangle clonerect; for (int i = 0; i < colnum; i++) //找有效区 { for (int j = 0; j < rownum; j++) { clonerect = new rectangle(j*singw, i*singh, singw , singh); picarray[i*rownum+j]=bmpobj.clone(clonerect, bmpobj.pixelformat);//复制小块图 } } return picarray; } /// <summary> /// 返回灰度图片的点阵描述字串，1表示灰点，0表示背景 /// </summary> /// <param name="singlepic">灰度图</param> /// <param name="dggrayvalue">背前景灰色界限</param> /// <returns></returns> public string getsinglebmpcode(bitmap singlepic, int dggrayvalue) { color piexl; string code = ""; for (int posy = 0; posy < singlepic.height; posy++) for (int posx = 0; posx < singlepic.width; posx++) { piexl = singlepic.getpixel(posx, posy); if (piexl.r < dggrayvalue) // color.black ) code = code + "1"; else code = code + "0"; } return code; } }} |
以上2则都是使用c#实现的orc识别的代码，希望对大家学习c#有所帮助。
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