【OpenCV】透视变换-将图像由不规则四边形转换成矩形

本文是在点击打开链接基础上修改而成的，感谢原文博主提供的参考。

代码声明：近期在做身份证图像处理，由于采集问题，采集到的图像存在一定的角度偏差，成为了不规则的四边形，通过透视变换可将其转换成矩形，可以更好的进行分割。本文只提供相关函数和相关类。由于博主也是OpenCV新手，函数可能不那么完美，欢迎大家一起讨论。

class CPerspective{  private:   vector<Vec4i> lines;     double deltaRho;  double deltaTheta;  int minVote;     double minLength;     double maxGap;  public:  CPerspective() : deltaRho(1), deltaTheta(CV_PI/180), minVote(10), minLength(0.), maxGap(0.) {}   void setAccResolution(double dRho, double dTheta) {  deltaRho= dRho; deltaTheta= dTheta;  }    void setMinVote(int minv) {  minVote= minv;  }   void setLineLengthAndGap(double length, double gap) {  minLength= length;  maxGap= gap;  }  std::vector<Vec4i> findLines(Mat& binary) {  lines.clear();  HoughLinesP(binary,lines, deltaRho, deltaTheta, minVote, minLength, maxGap);  return lines;   }  vector<Vec4i> drawVerticalLines(Mat &image, Scalar color=Scalar(0,0,255)){  vector<Vec4i>::const_iterator iter=lines.begin();  vector<Vec4i> verline;while (iter!=lines.end()) {  Point pt1((*iter)[0],(*iter)[1]);  Point pt2((*iter)[2],(*iter)[3]); if(pt1.x == pt2.x || (pt2.y-pt1.y)/(pt2.x-pt1.x)>1 || (pt2.y-pt1.y)/(pt2.x-pt1.x)<-1)  {//line(image, pt1, pt2, color);  verline.push_back(*iter);++iter;  }else++iter;}  return verline; }  vector<Vec4i> drawHorizontalLines(Mat &image, Scalar color=Scalar(0,0,255)){  vector<Vec4i>::const_iterator iter1 = lines.begin();vector<Vec4i>::const_iterator iter2 = lines.begin();vector<Vec4i>::const_iterator top_iter = iter1;vector<Vec4i>::const_iterator bottom_iter = iter1;vector<Vec4i> horline;int miny = (*iter1)[1], maxy = (*iter1)[1];while (iter1!=lines.end()) {if(miny > (*iter1)[1]){miny = (*iter1)[1];    top_iter = iter1;iter1++;  }elseiter1++;}Point pt_top1((*top_iter)[0],(*top_iter)[1]);  Point pt_top2((*top_iter)[2],(*top_iter)[3]); //line(image, pt_top1, pt_top2, color);   horline.push_back(*top_iter);while (iter2!=lines.end()) {if(maxy < (*iter2)[1]){maxy = (*iter2)[1];    bottom_iter = iter2;iter2++;  }elseiter2++;}Point pt_bottom1((*bottom_iter)[0],(*bottom_iter)[1]);  Point pt_bottom2((*bottom_iter)[2],(*bottom_iter)[3]); //line(image, pt_bottom1, pt_bottom2, color); horline.push_back(*bottom_iter);return horline;  }  static CvPoint computeIntersect(Vec4i a, Vec4i b){int x1 = a[0], y1 = a[1], x2 = a[2], y2 = a[3];int x3 = b[0], y3 = b[1], x4 = b[2], y4 = b[3];if (float d = ((float)(x1-x2) * (y3-y4)) - ((y1-y2) * (x3-x4))){CvPoint pt;pt.x = ((x1*y2 - y1*x2) * (x3-x4) - (x1-x2) * (x3*y4 - y3*x4)) / d;pt.y = ((x1*y2 - y1*x2) * (y3-y4) - (y1-y2) * (x3*y4 - y3*x4)) / d;return pt;}}}; 

IplImage* mycvPerspectiveCorrectionImage(Mat& image){     Mat cannyMat;    Canny (image, cannyMat, 60, 220, 3);      CPerspective findVertex;      findVertex.setMinVote (90);      findVertex.setLineLengthAndGap (100,80);      findVertex.findLines (cannyMat);      vector<Vec4i> horline = findVertex.drawVerticalLines (image);  vector<Vec4i> verline = findVertex.drawHorizontalLines(image);CvPoint2D32f srcVertex[4], dstVertex[4];srcVertex[0].x = CPerspective::computeIntersect(horline[1], verline[0]).x; srcVertex[0].y = CPerspective::computeIntersect(horline[1], verline[0]).y;  srcVertex[1].x = CPerspective::computeIntersect(horline[0], verline[0]).x;  srcVertex[1].y = CPerspective::computeIntersect(horline[0], verline[0]).y;  srcVertex[2].x = CPerspective::computeIntersect(horline[1], verline[1]).x;  srcVertex[2].y = CPerspective::computeIntersect(horline[1], verline[1]).y;  srcVertex[3].x = CPerspective::computeIntersect(horline[0], verline[1]).x;  srcVertex[3].y = CPerspective::computeIntersect(horline[0], verline[1]).y; dstVertex[0].x = 0;dstVertex[0].y = 0;dstVertex[1].x = image.cols-1;dstVertex[1].y = 0;dstVertex[2].x = 0;dstVertex[2].y = image.rows-1;dstVertex[3].x = image.cols-1;dstVertex[3].y = image.rows-1;CvMat* warp_mat = cvCreateMat( 3, 3, CV_32FC1 ); cvGetPerspectiveTransform( srcVertex, dstVertex, warp_mat );  IplImage* srcImg = &IplImage(image);IplImage* dstImg = cvCloneImage(srcImg);cvWarpPerspective(srcImg, dstImg, warp_mat, CV_INTER_LINEAR, cvScalarAll(255));  //cvNamedWindow( "Perspective Warp" );      //cvShowImage( "Perspective Warp", dstImg );  //最终是输出dst  return dstImg;}

运行结果：原图像--目标图像