扫描图像二维码抠图（倾斜校正 去黑边）

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由于要识别扫描仪得到的图片，直接将得到的图片进行识别，并不能得到识别结果，笔者使用的是zbar类，只能正常识别只含有二维码图像的图片。于是将二维码从图中扣出来就成了工作中的一个需求。
（网上有一些收费的控件具有图像预处理的功能，可以进行很好的识别）

简单的使用现有的图像边缘检测和连通域算法，并不能得到很好的效果：

例如canny边沿检测处理结果：

不过观察不难发现二维码的特点，接近于正方形的一个小方块，于是设想是否能够通过简单的画框得到该二维码的区域。

另外由于扫描是可能会产生倾斜，会导致二维码区域变成非矩形的区域（其实二维码的识别是360度的，也许并不需要进行倾斜校正）。

笔者在网上找到倾斜校正的算法：

参见大神博客：http://johnhany.net/2013/11/dft-based-text-rotation-correction/

人为增加一点倾斜，如图：

使用大神的算法后得到：

然后在通过边缘检测算法得到：

为了方便画框，首先要去掉周围的白线。大致思路是获取最大的黑色连通区域，为了做到这一点，笔者使用横竖画框，就是如果一个50*50的区域内没有白点，则进行画框，初步的结果如下：

然后将相邻的白框进行和并，之后取最大的连通域框，并将周围的区域都变成黑色：

之后将所有的白框置黑：

至此便得到了一个比较干净的图片。下一步就是进行画框，笔者使用的思路是，从左往右进行探测如果10个像素点内出现了白点，则往右移动。出现一个白点，则往右往下延展10个像素点。从上往下 从左往右 依次探测，如此会生成很多个小白框：

同样利用合并的思路，需要进行框的合并，左右合并，上下合并，需要遍历进行两次得到最后的矩形框：

看到二维码周围的那个白框，就看到了预期的结果。在此需要将它和其他的非二维码进行区分，可以设定条件，例如宽高比和宽高像素等。最后将二维码的白框向左上扩大10个像素点，根据这个框的坐标取原图像取对应的图像：

大功告成！

当然其中的算法也存在很多漏洞，例如已经发现的有，如果最大的连通区域不是中间的，那么就需要进行取舍。如果图中含有其他一些黑边什么的会影响最后的画框也不行。针对不同的图形，去白边和画框的参数需要重新设定。

这只是作为图像处理菜鸟的简单算法。对于opencv和cximage都不是很熟悉，也不知道该用什么函数，所以代码很臃肿，但是就图像的本质来说，只不过是二维的矩阵，熟练的掌握了二维数组，那么处理数据也并不是那么困难。

以下是源码，希望大神多多指点：

</pre><pre name="code" class="cpp"><span style="font-size:18px;">/**  原倾斜校正作者！*    Author: John Hany*  Website: http://johnhany.net*   Source code updates: https://github/johnhany/textRotCorrect*    If you have any advice, you could contact me at: johnhany@163.com* Need OpenCV environment!**/#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"#include <iostream>
#include <algorithm>
#include <stdio.h>
#include "opencv/cv.h"
#include "opencv/cxcore.h"
#include "opencv2/highgui/highgui_c.h"
#include "direct.h"#define  BOX_WIDTH  50
#define  BLACK  0
#define  WHITE 255#define  RATE  0.2
// #pragma comment(lib, "ml.lib")
// #pragma comment(lib, "cv.lib")
// #pragma comment(lib, "cvaux.lib")
// #pragma comment(lib, "cvcam.lib")
// #pragma comment(lib, "cxcore.lib")
// #pragma comment(lib, "cxts.lib")
// #pragma comment(lib, "highgui.lib")
// #pragma comment(lib, "cvhaartraining.lib
using namespace cv;
using namespace std;#define GRAY_THRESH 150
#define HOUGH_VOTE 100//#define DEGREE 27
//图像的轮廓检测下
//By MoreWindows (http://blog.csdn.net/MoreWindows)char szCurrentPath[MAX_PATH]; string getFilePath( const char * szBuf)
{string str;str = szCurrentPath;str += "\\";str += szBuf;//删除已经存在的文件DeleteFile(str.c_str());return str;
}string strOrigin;
string strSave1;
string strSave2;
string strSave3;
string strSave4;
string strSave5;
string strSave6_0;
string strSave6_1;
string strSave6;
string strSave7;
string strSave8;//#pragma comment(linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"")
IplImage *g_pGrayImage = NULL;
const char *pstrWindowsBinaryTitle = "二值图(http://blog.csdn.net/MoreWindows)";
const char *pstrWindowsOutLineTitle = "轮廓图(http://blog.csdn.net/MoreWindows)";
CvSeq *g_pcvSeq = NULL;void on_trackbar(int pos)
{// 转为二值图IplImage *pBinaryImage = cvCreateImage(cvGetSize(g_pGrayImage), IPL_DEPTH_8U, 1);cvThreshold(g_pGrayImage, pBinaryImage, pos, 255, CV_THRESH_BINARY);// 显示二值图cvShowImage(pstrWindowsBinaryTitle, pBinaryImage);CvMemStorage* cvMStorage = cvCreateMemStorage();// 检索轮廓并返回检测到的轮廓的个数cvFindContours(pBinaryImage,cvMStorage, &g_pcvSeq);IplImage *pOutlineImage = cvCreateImage(cvGetSize(g_pGrayImage), IPL_DEPTH_8U, 3);int _levels = 5;cvZero(pOutlineImage);cvDrawContours(pOutlineImage, g_pcvSeq, CV_RGB(255,0,0), CV_RGB(0,255,0), _levels);cvShowImage(pstrWindowsOutLineTitle, pOutlineImage);cvReleaseMemStorage(&cvMStorage);cvReleaseImage(&pBinaryImage);cvReleaseImage(&pOutlineImage);
}//调用opencv 轮廓检测
int outLinePic()
{const char *pstrWindowsSrcTitle = "原图(http://blog.csdn.net/MoreWindows)";const char *pstrWindowsToolBarName = "二值化";// 从文件中加载原图IplImage *pSrcImage = cvLoadImage("003.jpg", CV_LOAD_IMAGE_UNCHANGED);// 显示原图cvNamedWindow(pstrWindowsSrcTitle, CV_WINDOW_AUTOSIZE);cvShowImage(pstrWindowsSrcTitle, pSrcImage);// 转为灰度图g_pGrayImage =  cvCreateImage(cvGetSize(pSrcImage), IPL_DEPTH_8U, 1);cvCvtColor(pSrcImage, g_pGrayImage, CV_BGR2GRAY);// 创建二值图和轮廓图窗口cvNamedWindow(pstrWindowsBinaryTitle, CV_WINDOW_AUTOSIZE);cvNamedWindow(pstrWindowsOutLineTitle, CV_WINDOW_AUTOSIZE);// 滑动条   int nThreshold = 0;cvCreateTrackbar(pstrWindowsToolBarName, pstrWindowsBinaryTitle, &nThreshold, 254, on_trackbar);on_trackbar(1);cvWaitKey(0);cvDestroyWindow(pstrWindowsSrcTitle);cvDestroyWindow(pstrWindowsBinaryTitle);cvDestroyWindow(pstrWindowsOutLineTitle);cvReleaseImage(&pSrcImage);cvReleaseImage(&g_pGrayImage);return 0;
}int outLinePic2()
{Mat src = imread(strSave1.c_str());Mat dst;//输入图像//输出图像//输入图像颜色通道数//x方向阶数//y方向阶数Sobel(src,dst,src.depth(),1,1);//imwrite("sobel.jpg",dst);//输入图像//输出图像//输入图像颜色通道数Laplacian(src,dst,src.depth());//imwrite("laplacian.jpg",dst);//输入图像//输出图像//彩色转灰度cvtColor(src,src,CV_BGR2GRAY);  //canny只处理灰度图//输入图像//输出图像//低阈值//高阈值，opencv建议是低阈值的3倍//内部sobel滤波器大小Canny(src,dst,50,150,3);imwrite(strSave2.c_str(),dst);//imshow("dst",dst);//waitKey();return 0;}
//连通域分割
int ConnectDomain()
{IplImage* src;  src=cvLoadImage("imageText_D.jpg",CV_LOAD_IMAGE_GRAYSCALE);  IplImage* dst = cvCreateImage( cvGetSize(src), 8, 3 );  CvMemStorage* storage = cvCreateMemStorage(0);  CvSeq* contour = 0;  cvThreshold( src, src,120, 255, CV_THRESH_BINARY );//二值化   cvNamedWindow( "Source", 1 );  cvShowImage( "Source", src );  //提取轮廓   cvFindContours( src, storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );  cvZero( dst );//清空数组   CvSeq* _contour =contour;   double maxarea=0;  double minarea=100;  int n=-1,m=0;//n为面积最大轮廓索引，m为迭代索引   for( ; contour != 0; contour = contour->h_next )  {  double tmparea=fabs(cvContourArea(contour));  if(tmparea < minarea)   {  cvSeqRemove(contour,0); //删除面积小于设定值的轮廓   continue;  }  CvRect aRect = cvBoundingRect( contour, 0 );   if ((aRect.width/aRect.height)<1)  {  cvSeqRemove(contour,0); //删除宽高比例小于设定值的轮廓   continue;  }  if(tmparea > maxarea)  {  maxarea = tmparea;  n=m;  }  m++;  //  CvScalar color = CV_RGB( rand()&255, rand()&255, rand()&255 );//创建一个色彩值   CvScalar color = CV_RGB( 0, 255,255 );  //max_level 绘制轮廓的最大等级。如果等级为0，绘制单独的轮廓。如果为1，绘制轮廓及在其后的相同的级别下轮廓。   //如果值为2，所有的轮廓。如果等级为2，绘制所有同级轮廓及所有低一级轮廓，诸此种种。   //如果值为负数，函数不绘制同级轮廓，但会升序绘制直到级别为abs(max_level)-1的子轮廓。    cvDrawContours( dst, contour, color, color, -1, 1, 8 );//绘制外部和内部的轮廓   }  contour =_contour; /*int k=0;*/  int count=0;  for( ; contour != 0; contour = contour->h_next )  {  count++;  double tmparea=fabs(cvContourArea(contour));  if (tmparea==maxarea /*k==n*/)  {  CvScalar color = CV_RGB( 255, 0, 0);  cvDrawContours( dst, contour, color, color, -1, 1, 8 );  }  /*k++;*/  }  printf("The total number of contours is:%d",count);  cvNamedWindow( "Components", 1 );  cvShowImage( "Components", dst ); cvSaveImage("imageText_ConnectDomain.jpg",dst);cvWaitKey(0);  cvDestroyWindow( "Source" );  cvReleaseImage(&src);  cvDestroyWindow( "Components" );  cvReleaseImage(&dst);  return 0;
}struct MYBOX{BOOL bIsBlack;Rect rect;MYBOX(){bIsBlack = FALSE;}
};struct CONNECT_ZON{vector<RECT> rectList;int nBox;CONNECT_ZON(){nBox = 0;}
};//画框线为一个颜色
void DrowBoxColor(Mat &srcImg, std::vector<RECT> &boxList, int nColor)
{int nResultSize = boxList.size();for (int i = 0; i < nResultSize; i ++){RECT tempRect = boxList[i];//上下边线int y1 = tempRect.top;int y2 = tempRect.bottom;for (int x = tempRect.left;  x <= tempRect.right; x ++){*(srcImg.data + srcImg.step[1] * x + srcImg.step[0] * y1) = nColor;*(srcImg.data + srcImg.step[1] * x + srcImg.step[0] * y2) = nColor;}//左右边线int x1 = tempRect.left;int x2 = tempRect.right;for (int y = tempRect.top; y <= tempRect.bottom; y ++){*(srcImg.data + srcImg.step[1] * x1 + srcImg.step[0] * y) = nColor;*(srcImg.data + srcImg.step[1] * x2 + srcImg.step[0] * y) = nColor;}}
}//计算两个白框是否临近
BOOL IsRectNear(RECT rect1, RECT rect2)
{if (  sqrt(1.0*(rect1.top - rect2.top)* (rect1.top - rect2.top) + 1.0*(rect1.left - rect2.left)*(rect1.left - rect2.left) )  == BOX_WIDTH){return TRUE;}else{return FALSE;}
}
//计算两个框是否相交
BOOL IsRectIntersect(RECT rect1, RECT rect2)
{int nWidth1 = rect1.right - rect1.left;int nHeight1 = rect1.bottom - rect1.top;int nWidth2 = rect2.right - rect2.left;int nHeight2 = rect2.bottom - rect2.top;if ( rect1.left <= rect2.left){if(rect2.left - rect1.left <= nWidth1){if (rect1.top < rect2.top){if (rect2.top - rect1.top <= nHeight1){return TRUE;}else{return FALSE;}}else{if (rect1.top - rect2.top <= nHeight2){return TRUE;}else{return FALSE;}}}else{return FALSE;}}else{if(rect1.left - rect2.left <= nWidth2){if (rect1.top < rect2.top){if (rect2.top - rect1.top <= nHeight1){return TRUE;}else{return FALSE;}}else{if (rect1.top - rect2.top <= nHeight2){return TRUE;}else{return FALSE;}}}else{return FALSE;}}
}//两个区域是否临近
BOOL IsZonNear(CONNECT_ZON zon1, CONNECT_ZON zon2)
{for(int i = 0; i < zon1.rectList.size(); i++){RECT rect1 = zon1.rectList[i];for(int j = 0; j < zon2.rectList.size(); j ++){RECT rect2 = zon2.rectList[j];if (IsRectNear(rect1,rect2) == TRUE){return TRUE;}}}return FALSE;
}
//将两个区域合并
void MergZon(CONNECT_ZON& zon1, CONNECT_ZON& zon2)
{if (zon1.nBox >= zon2.nBox){for(int i = 0; i < zon2.nBox; i ++){zon1.rectList.push_back(zon2.rectList[i]);}zon1.nBox += zon2.nBox;zon2.rectList.clear();zon2.nBox = 0;}else{for(int i = 0; i < zon1.nBox; i ++){zon2.rectList.push_back(zon1.rectList[i]);}zon2.nBox += zon1.nBox;zon1.rectList.clear();zon1.nBox = 0;}
}
//自定义排序函数
BOOL SortByM1( const RECT &v1, const RECT &v2)//注意：本函数的参数的类型一定要与vector中元素的类型一致
{  if (v1.top < v2.top )   //按列升序排列{return TRUE;}else if (v1.top  == v2.top && v1.left < v2.left  ){return TRUE;}else{//如果a==b，则返回的应该是false，如果返回的是true，则会出上面的错。return FALSE;}
}
//自定义排序函数
BOOL SortByM2( const CONNECT_ZON &v1, const CONNECT_ZON &v2)//注意：本函数的参数的类型一定要与vector中元素的类型一致
{  if (v1.nBox > v2.nBox){return TRUE;}else{return FALSE;}
} //将一部分区域变成黑色或者白色
void SetRectColor(Mat& srcImg, RECT rect, int nColor)
{for (int col = rect.top; col < rect.bottom ; col ++){for (int row = rect.left; row < rect.right; row++){(*(srcImg.data + srcImg.step[0] * col + srcImg.step[1] * row)) = nColor;}}
}
//选择一个内部的联通域 第一个返回ture 第二个返回false
BOOL IsInnerZon(CONNECT_ZON &zon1, CONNECT_ZON &zon2)
{RECT rect1 = zon1.rectList[0];RECT rect2 = zon2.rectList[0];//获取两个区域的左上 右下坐标for (int i = 1; i < zon1.rectList.size(); i ++){RECT tempRect = zon1.rectList[i];if (tempRect.left < rect1.left){rect1.left  = tempRect.left;}if (tempRect.right > rect1.right){rect1.right = tempRect.right;}if (tempRect.top < rect1.top){rect1.top = tempRect.top;}if (tempRect.bottom > rect1.bottom){rect1.bottom = tempRect.bottom;}}for (int i = 1; i < zon2.rectList.size(); i ++){RECT tempRect = zon2.rectList[i];if (tempRect.left < rect2.left){rect2.left  = tempRect.left;}if (tempRect.right > rect2.right){rect2.right = tempRect.right;}if (tempRect.top < rect2.top){rect2.top = tempRect.top;}if (tempRect.bottom > rect2.bottom){rect2.bottom = tempRect.bottom;}}//评分 int nPoint1 = 0;int nPoint2 = 0;if (rect1.left < rect2.left){nPoint1 ++;}else{nPoint2 ++;}if (rect1.right > rect2.right){nPoint1 ++;}else{nPoint2 ++;}if (rect1.top < rect2.top){nPoint1 ++;}else{nPoint2 ++;}if (rect1.bottom > rect2.bottom){nPoint1 ++;}else{nPoint2 ++;}if (nPoint1 > nPoint2){return FALSE;}else{return TRUE;}
}
//清理图像的边缘，只保留中间的部分
int ClearEdge()
{//IplImage* src = cvLoadImage("imageText_D.jpg",CV_LOAD_IMAGE_GRAYSCALE); Mat srcImg = imread(strSave2.c_str(), CV_LOAD_IMAGE_GRAYSCALE);//int nWidth = src->width;//int nHeight = src->height;int nWidth = srcImg.cols;int nHeight = srcImg.rows;int nUp = 210;int nLeft = 140;int nRight = 170;int nDown = 210;//先确定上边界int nRectSize = 50;vector<RECT > BoxList;for(int i = 0; i < nHeight - nRectSize; i += nRectSize){for (int j = 0; j < nWidth - nRectSize; j += nRectSize){//看这个box中的像素是否都为黑 允许一个杂点BOOL bBlack = TRUE;int nWhite = 0;for (int col = j; col < j + nRectSize; col ++){for (int row = i; row < i + nRectSize; row++){int nPixel = (int)(*(srcImg.data + srcImg.step[0] * row + srcImg.step[1] * col));if ( nPixel == 255){nWhite ++;if (nWhite >= 0){bBlack = FALSE;}}}if (bBlack == FALSE){break;}}if (bBlack){RECT  temRect = {j,i, j + nRectSize, i + nRectSize};BoxList.push_back(temRect);}}}//将box的边线都编程白色。int nSize = BoxList.size();for (int i = 0; i < nSize; i ++){RECT tempRect = BoxList[i];//上下边线int y1 = tempRect.top;int y2 = tempRect.bottom;for (int x = tempRect.left;  x <= tempRect.right; x ++){*(srcImg.data + srcImg.step[1] * x + srcImg.step[0] * y1) = WHITE;*(srcImg.data + srcImg.step[1] * x + srcImg.step[0] * y2) = WHITE;}//左右边线int x1 = tempRect.left;int x2 = tempRect.right;for (int y = tempRect.top; y <= tempRect.bottom; y ++){*(srcImg.data + srcImg.step[1] * x1 + srcImg.step[0] * y) = WHITE;*(srcImg.data + srcImg.step[1] * x2 + srcImg.step[0] * y) = WHITE;}}imwrite(strSave3.c_str(),srcImg);vector<CONNECT_ZON> g_ConnectZon;//获取白框最大的联通域for(int i = 0; i < nSize; i++){RECT tempRect = BoxList[i];if (g_ConnectZon.empty()){CONNECT_ZON connectTemp;connectTemp.rectList.push_back(tempRect);connectTemp.nBox ++;g_ConnectZon.push_back(connectTemp);}else{BOOL bInList = FALSE;for(int j = 0; j < g_ConnectZon.size(); j ++){CONNECT_ZON connectZon = g_ConnectZon[j];for (int k = 0; k < connectZon.rectList.size(); k ++){if (IsRectNear(tempRect, connectZon.rectList[k])){g_ConnectZon[j].rectList.push_back(tempRect);g_ConnectZon[j].nBox ++;bInList = TRUE;break;}if (bInList){break;}}}//没有相邻则加入新的if (bInList == FALSE){CONNECT_ZON connectTemp;connectTemp.rectList.push_back(tempRect);connectTemp.nBox ++;g_ConnectZon.push_back(connectTemp);}}}//检查任意两个连通域中是否有相邻的框for (int i = 0; i < g_ConnectZon.size(); i ++){for(int j = i + 1; j < g_ConnectZon.size(); j ++){BOOL bZonNear = IsZonNear(g_ConnectZon[i], g_ConnectZon[j]);if (bZonNear){//相邻则把小的加入到大的之中//MergZon(g_ConnectZon[i], g_ConnectZon[j]);if (g_ConnectZon[i].nBox >= g_ConnectZon[j].nBox){for(int k = 0; k < g_ConnectZon[j].nBox; k ++){g_ConnectZon[i].rectList.push_back(g_ConnectZon[j].rectList[k]);}g_ConnectZon[i].nBox += g_ConnectZon[j].nBox;g_ConnectZon[j].rectList.clear();g_ConnectZon[j].nBox = 0;}else{for(int k = 0; k < g_ConnectZon[i].nBox; k ++){g_ConnectZon[j].rectList.push_back(g_ConnectZon[i].rectList[k]);}g_ConnectZon[j].nBox += g_ConnectZon[i].nBox;g_ConnectZon[i].rectList.clear();g_ConnectZon[i].nBox = 0;}}}}//取最大的联通域boxListint nMaxSize = 0;//如果有两个较大的联通域，则取里面的一个。std::sort(g_ConnectZon.begin(),g_ConnectZon.end(),SortByM2);CONNECT_ZON maxConnect = g_ConnectZon[0];//出现另外一个if (g_ConnectZon.size() > 1){if (g_ConnectZon[1].nBox > 0){CONNECT_ZON maxConnectOther = g_ConnectZon[1];BOOL bInner = IsInnerZon(maxConnect, maxConnectOther);if (!bInner){maxConnect = maxConnectOther;}}}//将box进行排序，按照从左到右 从上到下。std::sort(maxConnect.rectList.begin(),maxConnect.rectList.end(),SortByM1);//将之分成多个行。vector<CONNECT_ZON> LineConnect;int nIndexOfLine = -1;int nLine = -1;for (int i = 0; i < maxConnect.rectList.size(); i ++){RECT tempRect = maxConnect.rectList[i];if (nLine != tempRect.top){CONNECT_ZON tempConnect;tempConnect.rectList.push_back(tempRect);tempConnect.nBox ++;nIndexOfLine ++;LineConnect.push_back(tempConnect);nLine = tempRect.top;}else{LineConnect[nIndexOfLine].rectList.push_back(tempRect);LineConnect[nIndexOfLine].nBox ++;}//从左往右 从上往下。}//如果没有白色联通域则直接保存结果if (maxConnect.rectList.size() == 0){IplImage* src;  IplImage* dst;src = cvLoadImage(strSave7.c_str(),1);  if(!src)  {return 0;}cvSetImageROI(src,cvRect(0,0,nWidth, nHeight));  dst = cvCreateImage(cvSize(nWidth, nHeight),  IPL_DEPTH_8U,  src->nChannels);  cvCopy(src,dst,0);  cvResetImageROI(src);  //cvNamedWindow("操作后的图像",1);  //cvShowImage("操作后的图像",dst); cvSaveImage(strSave8.c_str(), dst);return 0;}//将最大联通域周边的区域都变成黑色 0//将每一行的左右两边都变成黑色//上面部分。RECT rectFirst = LineConnect[0].rectList[0];RECT rectTop = {0,0,nWidth ,rectFirst.bottom};SetRectColor(srcImg,rectTop, BLACK);//中间各行for (int i = 0; i < LineConnect.size(); i ++){CONNECT_ZON tempConnect = LineConnect[i];RECT tempRect = tempConnect.rectList[0];RECT leftRect = {0, tempRect.top, tempRect.right, tempRect.bottom};SetRectColor(srcImg, leftRect,BLACK);tempRect = tempConnect.rectList[tempConnect.rectList.size() - 1];RECT rightRect = {tempRect.left, tempRect.top, nWidth, tempRect.bottom };SetRectColor(srcImg, rightRect, BLACK);}//最下面部分RECT rectLast = LineConnect[LineConnect.size() - 1].rectList[0];RECT rectBottom = {0, rectLast.bottom, nWidth, nHeight};SetRectColor(srcImg, rectBottom,BLACK);imwrite(strSave3.c_str(),srcImg);//将所有的框线都置黑DrowBoxColor(srcImg, maxConnect.rectList, BLACK);imwrite(strSave4.c_str(),srcImg);//探测矩形框int nMaxBlackNum = 10;int nMinBoxWidth = 50;int nBlack = 0;vector<RECT > ResultBoxList;int nBoxIndex = -1;BOOL bStartBox = FALSE;int nWhite = 0;for (int col = 0; col < nHeight; col += 1){for (int row = 0; row < nWidth; row++){int nPixel = (int)(*(srcImg.data + srcImg.step[1] * row + srcImg.step[0] * col));if (nPixel == BLACK && bStartBox == FALSE){nBlack = 0;continue;}//碰到第一个白色像素点开始探测矩形框。else if ( nPixel == WHITE && bStartBox == FALSE){//不能超过右 下边界RECT rectTemp = {row, col, min(row + nMaxBlackNum, nWidth), min(col + nMaxBlackNum, nHeight)};bStartBox = TRUE;ResultBoxList.push_back(rectTemp);nBoxIndex ++;}else if(nPixel == WHITE && bStartBox == TRUE){//第二个仍然是白色 宽度加1 或者中间有黑色像素if (ResultBoxList[nBoxIndex].right < nWidth - 1){if (nBlack == 0){ResultBoxList[nBoxIndex].right += 1;}else{ResultBoxList[nBoxIndex].right += nBlack;nBlack = 0;}}}else if(nPixel == BLACK && bStartBox == TRUE){//碰到黑色 nBlack ++;//连续碰到10个黑点则结束boxif (nBlack > nMaxBlackNum){//框的大小如果小于50 则不计入
//                  int nWidth = ResultBoxList[nBoxIndex].right - ResultBoxList[nBoxIndex].left;
//                  if (nWidth < nMinBoxWidth)
//                  {
//                      ResultBoxList.erase(ResultBoxList.end() - 1);
//                  }bStartBox = FALSE;}}}}//画框int nResultSize = ResultBoxList.size();
//   Mat Img5 = srcImg;
//   DrowBoxColor(Img5,ResultBoxList, WHITE);
//   imwrite(strSave5.c_str(),Img5);//合并框vector<RECT> mergResultList;int nIndexOfResultList = -1;for(int i = 0; i < nResultSize; i++){RECT tempRect = ResultBoxList[i];if (mergResultList.empty()){mergResultList.push_back(tempRect);nIndexOfResultList ++;}else{BOOL bInList = FALSE;for(int j = 0; j < mergResultList.size(); j ++){BOOL bIntersect = IsRectIntersect(mergResultList[j], tempRect);if (bIntersect){//相交则合并。mergResultList[j].left = min(tempRect.left, mergResultList[j].left);mergResultList[j].top = min(tempRect.top, mergResultList[j].top);mergResultList[j].right = max(tempRect.right, mergResultList[j].right);mergResultList[j].bottom = max(tempRect.bottom, mergResultList[j].bottom);bInList = TRUE;}}//没有相邻则加入新的if (bInList == FALSE){mergResultList.push_back(tempRect);nIndexOfResultList ++;}}}//第二次合并Mat Img6 = srcImg;// DrowBoxColor(Img6, mergResultList, WHITE);// imwrite(strSave6_0.c_str(),Img6);for(int i = 0; i < mergResultList.size(); i ++){if (mergResultList[i].left == 0 && mergResultList[i].right == 0){continue;}for(int j = i + 1; j < mergResultList.size(); j ++){BOOL bIntersect = IsRectIntersect(mergResultList[i], mergResultList[j]);if (bIntersect){//相交则合并。mergResultList[i].left = min(mergResultList[j].left, mergResultList[i].left);mergResultList[i].top = min(mergResultList[j].top, mergResultList[i].top);mergResultList[i].right = max(mergResultList[j].right, mergResultList[i].right);mergResultList[i].bottom = max(mergResultList[j].bottom, mergResultList[i].bottom);//被合并的清空mergResultList[j].left = 0;mergResultList[j].top = 0;mergResultList[j].right = 0;mergResultList[j].bottom = 0;}}}DrowBoxColor(srcImg, mergResultList, WHITE);imwrite(strSave6_1.c_str(),srcImg);//去除宽高比大鱼1.5 和长宽绝对值小于80的RECT destRect;BOOL bHaveOne = FALSE;for(int i = 0;i < mergResultList.size(); i ++){int nTempWidth = mergResultList[i].right - mergResultList[i].left;int nTempHeight = mergResultList[i].bottom - mergResultList[i].top;BOOL bRelative = abs(nTempWidth - nTempHeight) < RATE * min(nTempWidth,nTempHeight);if (nTempHeight < 80 || nTempWidth < 80 || !bRelative){mergResultList[i].left = 0;mergResultList[i].right = 0;mergResultList[i].top = 0;mergResultList[i].bottom = 0;}else{destRect = mergResultList[i];bHaveOne = TRUE;}}if (bHaveOne == FALSE){cout<<"can not find one QRCode!";return 0;}DrowBoxColor(srcImg, mergResultList, WHITE);imwrite(strSave6.c_str(),srcImg);//将box内容取出来。//Mat sourceImg = imread("imageText_D.bmp", CV_LOAD_IMAGE_GRAYSCALE);//Mat roi_img = sourceImg(Range(destRect.left,destRect.right),Range(destRect.top,destRect.bottom));//Rect rect(destRect.left, destRect.right, destRect.top, destRect.bottom);//Mat image_roi = sourceImg(rect);IplImage* src;  IplImage* dst;src = cvLoadImage(strSave7.c_str(),1);  if(!src)  {return 0;}// cvNamedWindow("源图像",1);  //cvShowImage("源图像",src);  //往左上移动10个点destRect.left -= 10;if (destRect.left < 0){destRect.left = 0;}destRect.top -= 10;if (destRect.top < 0){destRect.top = 0;}cvSetImageROI(src,cvRect(destRect.left,destRect.top ,destRect.right - destRect.left, destRect.bottom - destRect.top));  dst = cvCreateImage(cvSize(destRect.right - destRect.left, destRect.bottom - destRect.top),  IPL_DEPTH_8U,  src->nChannels);  cvCopy(src,dst,0);  cvResetImageROI(src);  //cvNamedWindow("操作后的图像",1);  //cvShowImage("操作后的图像",dst); strSave8 = getFilePath("imageText_Clear4.jpg");cvSaveImage(strSave8.c_str(), dst);return 0;
}
//倾斜校正
void imageCorrect()
{Mat srcImg = imread(strOrigin.c_str(), CV_LOAD_IMAGE_GRAYSCALE);if(srcImg.empty())return ;//imshow("source", srcImg);Point center(srcImg.cols/2, srcImg.rows/2);#ifdef DEGREE//Rotate source imageMat rotMatS = getRotationMatrix2D(center, DEGREE, 1.0);warpAffine(srcImg, srcImg, rotMatS, srcImg.size(), 1, 0, Scalar(255,255,255));//imshow("RotatedSrc", srcImg);//imwrite("imageText_R.jpg",srcImg);
#endif//Expand image to an optimal size, for faster processing speed//Set widths of borders in four directions//If borderType==BORDER_CONSTANT, fill the borders with (0,0,0)Mat padded;int opWidth = getOptimalDFTSize(srcImg.rows);int opHeight = getOptimalDFTSize(srcImg.cols);copyMakeBorder(srcImg, padded, 0, opWidth-srcImg.rows, 0, opHeight-srcImg.cols, BORDER_CONSTANT, Scalar::all(0));Mat planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)};Mat comImg;//Merge into a double-channel imagemerge(planes,2,comImg);//Use the same image as input and output,//so that the results can fit in Mat welldft(comImg, comImg);//Compute the magnitude//planes[0]=Re(DFT(I)), planes[1]=Im(DFT(I))//magnitude=sqrt(Re^2+Im^2)split(comImg, planes);magnitude(planes[0], planes[1], planes[0]);//Switch to logarithmic scale, for better visual results//M2=log(1+M1)Mat magMat = planes[0];magMat += Scalar::all(1);log(magMat, magMat);//Crop the spectrum//Width and height of magMat should be even, so that they can be divided by 2//-2 is 11111110 in binary system, operator & make sure width and height are always evenmagMat = magMat(Rect(0, 0, magMat.cols & -2, magMat.rows & -2));//Rearrange the quadrants of Fourier image,//so that the origin is at the center of image,//and move the high frequency to the cornersint cx = magMat.cols/2;int cy = magMat.rows/2;Mat q0(magMat, Rect(0, 0, cx, cy));Mat q1(magMat, Rect(0, cy, cx, cy));Mat q2(magMat, Rect(cx, cy, cx, cy));Mat q3(magMat, Rect(cx, 0, cx, cy));Mat tmp;q0.copyTo(tmp);q2.copyTo(q0);tmp.copyTo(q2);q1.copyTo(tmp);q3.copyTo(q1);tmp.copyTo(q3);//Normalize the magnitude to [0,1], then to[0,255]normalize(magMat, magMat, 0, 1, CV_MINMAX);Mat magImg(magMat.size(), CV_8UC1);magMat.convertTo(magImg,CV_8UC1,255,0);//imshow("magnitude", magImg);//imwrite("imageText_mag.jpg",magImg);//Turn into binary imagethreshold(magImg,magImg,GRAY_THRESH,255,CV_THRESH_BINARY);//imshow("mag_binary", magImg);//imwrite("imageText_bin.jpg",magImg);//Find lines with Hough Transformationvector<Vec2f> lines;float pi180 = (float)CV_PI/180;Mat linImg(magImg.size(),CV_8UC3);HoughLines(magImg,lines,1,pi180,HOUGH_VOTE,0,0);int numLines = lines.size();for(int l=0; l<numLines; l++){float rho = lines[l][0], theta = lines[l][1];Point pt1, pt2;double a = cos(theta), b = sin(theta);double x0 = a*rho, y0 = b*rho;pt1.x = cvRound(x0 + 1000*(-b));pt1.y = cvRound(y0 + 1000*(a));pt2.x = cvRound(x0 - 1000*(-b));pt2.y = cvRound(y0 - 1000*(a));line(linImg,pt1,pt2,Scalar(255,0,0),3,8,0);}//imshow("lines",linImg);//imwrite("imageText_line.jpg",linImg);//if(lines.size() == 3){//  cout << "found three angels:" << endl;//  cout << lines[0][1]*180/CV_PI << endl << lines[1][1]*180/CV_PI << endl << lines[2][1]*180/CV_PI << endl << endl;//}//Find the proper angel from the three found angelsfloat angel=0;float piThresh = (float)CV_PI/90;float pi2 = CV_PI/2;for(int l=0; l<numLines; l++){float theta = lines[l][1];if(abs(theta) < piThresh || abs(theta-pi2) < piThresh)continue;else{angel = theta;break;}}//Calculate the rotation angel//The image has to be square,//so that the rotation angel can be calculate rightangel = angel<pi2 ? angel : angel-CV_PI;if(angel != pi2){float angelT = srcImg.rows*tan(angel)/srcImg.cols;angel = atan(angelT);}float angelD = angel*180/(float)CV_PI;//cout << "the rotation angel to be applied:" << endl << angelD << endl << endl;//Rotate the image to recoverMat rotMat = getRotationMatrix2D(center,angelD,1.0);Mat dstImg = Mat::ones(srcImg.size(),CV_8UC3);warpAffine(srcImg,dstImg,rotMat,srcImg.size(),1,0,Scalar(255,255,255));//imshow("result",dstImg);imwrite(strSave1,dstImg);
}
int main(int argc, char **argv)
{if(argc < 2) return(1);//获取当前目录_getcwd(szCurrentPath,MAX_PATH);strOrigin = getFilePath("imageText.jpg");strSave1 = getFilePath("imageText_D.jpg");strSave2 = getFilePath("canny.jpg");strSave3 = getFilePath("imageText_Clear0.jpg");strSave4 = getFilePath("imageText_Clear1.jpg");strSave5 = getFilePath("imageText_Clear2.jpg");strSave6_0 = getFilePath("imageText_Clear3_0.jpg");strSave6_1 = getFilePath("imageText_Clear3_1.jpg");strSave6 = getFilePath("imageText_Clear3.jpg");strSave7 = getFilePath("imageText_D.jpg");strSave8 = getFilePath("imageText_Clear4.jpg");CopyFile(argv[1], strOrigin.c_str(), FALSE);imageCorrect();outLinePic2();ClearEdge();return 0;//ConnectDomain();//Read a single-channel image}
</span>

需要注意的是，程序需要opencv的环境，需要自己先安装和设置。需要opencv_imgproc2410d.lib opencv_core2410d.lib opencv_highgui2410d.lib三个lib。 中间的2410是对应的版本号，不同的版本应该也可以，另外别忘了对应的dll。

其中有一些函数并没有用到，只是作为边缘检测效果实验用。

最后保存二维码图片时，不知道该用什么函数来将一个RECT的图像复制出来，mat应该也有对应的函数吧。

扫描图像二维码抠图（倾斜校正 去黑边）

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原文：http://blog.csdn.net/u200814342a/article/details/51324366