问题:一张输入图片,图片上有两条平行线,求出这两条平行线之间的距离
解决思路:
1. 对图像中的直线进行细化
2. 提取直线的轮廓坐标
3. 对轮廓上的坐标进行直线集合,从而得到直线方程
4. 计算两条直线之间的距离
参考:
问题来源 http://www.opencvchina.com/thread-854-1-1.html
图像细化 http://blog.csdn.net/qianchenglenger/article/details/19332011
图像轮廓提取 http://blog.csdn.net/augusdi/article/details/9000893
直线拟合 http://blog.csdn.net/zhuoyue08/article/details/6803040
两条直线之间的距离公式3:http://zhidao.baidu.com/link?url=ef_DHNkjyq1qq7VgubX3afL2KIUQIB4ukd3zHGp0zz8iPPKC046azyvG5ltHR-i0WaLI72eO7j0sOJI4wZSE4q
工具:
opencv 2.4.8 + VS2013
代码:
1.头文件 ProcessImage.h
//ProcessImage.h #pragma once #include <opencv2/highgui/highgui.hpp> /* 对输入图像进行细化 * src为输入图像,用cvThreshold函数处理过的8位灰度图像格式,元素中只有0与1,1代表有元素,0代表为空白 * dst为对src细化后的输出图像,格式与src格式相同,调用前需要分配空间,元素中只有0与1,1代表有元素,0代表为空白 * maxIterations限制迭代次数,如果不进行限制,默认为-1,代表不限制迭代次数,直到获得最终结果 */ void thinImage(IplImage* src, IplImage* dst, int maxIterations = -1);
//ProcessImage.cpp #include "ProcessImage.h" #include <utility> #include <vector> void thinImage(IplImage* src, IplImage* dst, int maxIterations) { using namespace cv; CvSize size = cvGetSize(src); cvCopy(src, dst);//将src中的内容拷贝到dst中 int count = 0; //记录迭代次数 while (true) { count++; if (maxIterations != -1 && count > maxIterations) //限制次数并且迭代次数到达 break; //std::cout << count << ' ';输出迭代次数 std::vector<std::pair<int, int> > mFlag; //用于标记需要删除的点 //对点标记 for (int i = 0; i<size.height; ++i) { for (int j = 0; j<size.width; ++j) { //如果满足四个条件,进行标记 // p9 p2 p3 // p8 p1 p4 // p7 p6 p5 int p1 = CV_IMAGE_ELEM(dst, uchar, i, j); int p2 = (i == 0) ? 0 : CV_IMAGE_ELEM(dst, uchar, i - 1, j); int p3 = (i == 0 || j == size.width - 1) ? 0 : CV_IMAGE_ELEM(dst, uchar, i - 1, j + 1); int p4 = (j == size.width - 1) ? 0 : CV_IMAGE_ELEM(dst, uchar, i, j + 1); int p5 = (i == size.height - 1 || j == size.width - 1) ? 0 : CV_IMAGE_ELEM(dst, uchar, i + 1, j + 1); int p6 = (i == size.height - 1) ? 0 : CV_IMAGE_ELEM(dst, uchar, i + 1, j); int p7 = (i == size.height - 1 || j == 0) ? 0 : CV_IMAGE_ELEM(dst, uchar, i + 1, j - 1); int p8 = (j == 0) ? 0 : CV_IMAGE_ELEM(dst, uchar, i, j - 1); int p9 = (i == 0 || j == 0) ? 0 : CV_IMAGE_ELEM(dst, uchar, i - 1, j - 1); if ((p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) >= 2 && (p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) <= 6) { int ap = 0; if (p2 == 0 && p3 == 1) ++ap; if (p3 == 0 && p4 == 1) ++ap; if (p4 == 0 && p5 == 1) ++ap; if (p5 == 0 && p6 == 1) ++ap; if (p6 == 0 && p7 == 1) ++ap; if (p7 == 0 && p8 == 1) ++ap; if (p8 == 0 && p9 == 1) ++ap; if (p9 == 0 && p2 == 1) ++ap; if (ap == 1) { if (p2*p4*p6 == 0) { if (p4*p6*p8 == 0) { //标记 mFlag.push_back(std::make_pair(i, j)); } } } } } } //将标记的点删除 for (std::vector<std::pair<int, int> >::iterator i = mFlag.begin(); i != mFlag.end(); ++i) { CV_IMAGE_ELEM(dst, uchar, i->first, i->second) = 0; } //直到没有点满足,算法结束 if (mFlag.size() == 0) { break; } else { mFlag.clear();//将mFlag清空 } //对点标记 for (int i = 0; i<size.height; ++i) { for (int j = 0; j<size.width; ++j) { //如果满足四个条件,进行标记 // p9 p2 p3 // p8 p1 p4 // p7 p6 p5 int p1 = CV_IMAGE_ELEM(dst, uchar, i, j); if (p1 != 1) continue; int p2 = (i == 0) ? 0 : CV_IMAGE_ELEM(dst, uchar, i - 1, j); int p3 = (i == 0 || j == size.width - 1) ? 0 : CV_IMAGE_ELEM(dst, uchar, i - 1, j + 1); int p4 = (j == size.width - 1) ? 0 : CV_IMAGE_ELEM(dst, uchar, i, j + 1); int p5 = (i == size.height - 1 || j == size.width - 1) ? 0 : CV_IMAGE_ELEM(dst, uchar, i + 1, j + 1); int p6 = (i == size.height - 1) ? 0 : CV_IMAGE_ELEM(dst, uchar, i + 1, j); int p7 = (i == size.height - 1 || j == 0) ? 0 : CV_IMAGE_ELEM(dst, uchar, i + 1, j - 1); int p8 = (j == 0) ? 0 : CV_IMAGE_ELEM(dst, uchar, i, j - 1); int p9 = (i == 0 || j == 0) ? 0 : CV_IMAGE_ELEM(dst, uchar, i - 1, j - 1); if ((p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) >= 2 && (p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) <= 6) { int ap = 0; if (p2 == 0 && p3 == 1) ++ap; if (p3 == 0 && p4 == 1) ++ap; if (p4 == 0 && p5 == 1) ++ap; if (p5 == 0 && p6 == 1) ++ap; if (p6 == 0 && p7 == 1) ++ap; if (p7 == 0 && p8 == 1) ++ap; if (p8 == 0 && p9 == 1) ++ap; if (p9 == 0 && p2 == 1) ++ap; if (ap == 1) { if (p2*p4*p8 == 0) { if (p2*p6*p8 == 0) { //标记 mFlag.push_back(std::make_pair(i, j)); } } } } } } //删除 for (std::vector<std::pair<int, int> >::iterator i = mFlag.begin(); i != mFlag.end(); ++i) { CV_IMAGE_ELEM(dst, uchar, i->first, i->second) = 0; } //直到没有点满足,算法结束 if (mFlag.size() == 0) { break; } else { mFlag.clear();//将mFlag清空 } } }3.主函数所在文件 Source.cpp
//Source.cpp #include "ProcessImage.h" #include <iostream> #include <opencv2/opencv.hpp> #define _TEST using namespace cv; int main(int argc, char * argv[]) { //判断输入是否满足要求 if (argc != 2) { std::cout << "argument error!"; return -1; } IplImage *pSrc = cvLoadImage(argv[1], CV_LOAD_IMAGE_GRAYSCALE); if (!pSrc) { std::cout << "read file failed!"; return -1; } //显示原图 namedWindow("原图", CV_WINDOW_AUTOSIZE); cvShowImage("原图", pSrc); IplImage *pTemp = cvCreateImage(cvGetSize(pSrc), pSrc->depth, pSrc->nChannels); IplImage *pDst = cvCreateImage(cvGetSize(pSrc), pSrc->depth, pSrc->nChannels); //将原图像转换为二值图像 cvThreshold(pSrc, pTemp, 128, 1, CV_THRESH_BINARY_INV); //细化 thinImage(pTemp, pDst); #ifdef _TEST //显示细化后的图像 IplImage *pThinImage = cvCreateImage(cvGetSize(pSrc), pSrc->depth, pSrc->nChannels); cvCopy(pDst, pThinImage); cvThreshold(pThinImage, pThinImage, 0.5, 255,CV_THRESH_BINARY); namedWindow("1 图像细化的结果", CV_WINDOW_AUTOSIZE); cvShowImage("1 图像细化的结果", pThinImage); cvReleaseImage(&pThinImage); #endif //求轮廓 CvMemStorage* storage = cvCreateMemStorage(0); CvSeq* contours = 0; cvFindContours(pDst , storage, &contours, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_NONE, cvPoint(0, 0)); #ifdef _TEST //将轮廓画出来 IplImage *pDrawing1 = cvCreateImage(cvGetSize(pSrc),8,3); cvZero(pDrawing1); cvDrawContours(pDrawing1, contours, Scalar(255, 0, 0), Scalar(0, 0, 255), 1, 2, 8, cvPoint(0, 0)); namedWindow("2 求轮廓", CV_WINDOW_AUTOSIZE); cvShowImage("2 求轮廓", pDrawing1); cvReleaseImage(&pDrawing1); #endif //轮廓已经寻找到,均在contours中存放,我们需要对轮廓进行拟合 //FitLine函数的用法: // 二维空间点拟合时 是 float[4] // 三位空间点拟合时 是 float[6] float *line1 = new float[4]; float *line2 = new float[4]; // 第一个参数: 存储点序列 // 第二个参数: 拟合算法,其中 CV_DIST_L2 就是平常的最小二乘法 // 第三,第四,第五参数推荐值是 0, 0.01, 0.01, // 第六参数: line中存储返回值 // 二维空间时: line[0--3] 分别为 (vx, vy, x0, y0) // 其中 vx, vy 是正规化之后的斜率向量。 x0,y0 是直线经过的点。 // 三维空间时: line[0--5] 分别是 (vx, vy, vz, x0, y0, z0) 。意义同上 cvFitLine(contours, CV_DIST_L2, 0, 0.01, 0.01, line1); cvFitLine(contours->h_next, CV_DIST_L2, 0, 0.01, 0.01, line2); //输出四个点 std::cout << "第一条线: " << line1[0] << " " << line1[1] << " " << line1[2] << " " << line1[3] << std::endl; std::cout << "第二条线: " << line2[0] << " " << line2[1] << " " << line2[2] << " " << line2[3] << std::endl; #ifdef _TEST //根据直线方程公式,我们从直线上取点,并画出来 IplImage *pDrawing2 = cvCreateImage(cvGetSize(pSrc), 8, 3); cvZero(pDrawing2); cvLine(pDrawing2, cvPoint(0, (int)(line1[3] - line1[1] / line1[0] * line1[2])), cvPoint(pDrawing2->width - 1, (int)((pDrawing2->width - 1 - line1[2])*line1[1] / line1[0] + line1[3])), cvScalar(255, 0, 0)); cvLine(pDrawing2, cvPoint(0, (int)(line2[3] - line2[1] / line2[0] * line2[2])), cvPoint(pDrawing2->width - 1, (int)((pDrawing2->width - 1 - line2[2])*line2[1] / line2[0] + line2[3])), cvScalar(0, 0, 255)); namedWindow("3 直线拟合", CV_WINDOW_AUTOSIZE); cvShowImage("3 直线拟合", pDrawing2); cvReleaseImage(&pDrawing2); #endif //我们根据距离方程,求出两条直线的距离 double distance = abs(line1[0] * (line2[3]-line1[3]) - line1[1] * (line2[2]-line1[2])); //注意,vx,vy已经正规化了 std::cout << "两条直线之间的距离为: " << distance << std::endl; delete[] line1; delete[] line2; cvReleaseMemStorage(&storage); cvReleaseImage(&pSrc); cvReleaseImage(&pTemp); cvReleaseImage(&pDst); waitKey(0); return 0; }运行效果:
输入:
输出: