基于OpenCV實(shí)現(xiàn)矩形輪廓查找

1、基于 opencv 實(shí)現(xiàn)矩形輪廓查找1. 功能描述基于 opencv 實(shí)現(xiàn)背景圖像中矩形輪廓的查找，主要涉及的圖像處理步驟包括灰度化、二值化（自適應(yīng)閾值）、輪廓查找、幾何特征過(guò)濾等。2. 處理流程3. 源代碼包含兩個(gè)源文件，分別是visionmain.cpp和visionutil.cpp。/*visionmain.cpp*/#include <string> #include <stdlib.h>#include <opencv2/core/core.hpp> #include <opencv2/imgproc/imgproc.hpp> #inc

2、lude <stdio.h>#include “visionutil.h“ #include “threadedcapture.h“ #include <vector>using namespace cv;const char windowname = “rectangles“;static double matchacceptancelevel = 0.5;static double areaacceptancelevel = 0.074074074; static double rectangularacceptancelevel = 0.1;typedef str

3、uct cv:point2f centerofmass; double area;double matchlevel; double areaerror;double rectangularmatchlevel; matchedshape;void findrectangle(cv:mat &src, std:vector<matchedshape>& results) cv:mat dst;src.copyto(dst);cv:cvtcolor(dst, dst, cv:color_rgb2gray); cv:adaptivethreshold(dst, dst,

4、 255, cv_adaptive_thresh_mean_c,cv_thresh_binary_inv, 75, 10);std:vector<std:vector<cv:point> > contours; std:vector<std:vector<cv:point> > cleancontours; std:vector<std:vector<cv:point> > dirtycontours; std:vector<cv:vec4i> heirarchy;cv:findcontours(dst, co

5、ntours, heirarchy, cv_retr_tree, cv_chain_approx_simple);for (size_t i = 0; i < contours.size(); i+) double arc = cv:arclength(contoursi, true); std:vector<cv:point> newcontour; cv:approxpolydp(contoursi, newcontour, arc * 0.03, true); double contourarearaw = cv:contourarea(contoursi);if (c

6、ontourarearaw < 1000 | newcontour.size() != 4| !cv:iscontourconvex(newcontour) continue;dirtycontours.push_back(contoursi);/cv:rotatedrect r = cv:minarearect(newcontour); double newcontourarea = cv:contourarea(newcontour); double areadiff = newcontourarea - contourarearaw;if (fabs(areadiff) / new

7、contourarea > areaacceptancelevel) continue;double matchlevel = matchheuristic(newcontour, contoursi); if (matchlevel > matchacceptancelevel) continue;double rectangularlevel = twosidelengthsheuristic(newcontour);if (rectangularlevel > rectangularacceptancelevel) continue;cleancontours.push

8、_back(newcontour); matchedshape m;m.centerofmass = centerofmass(newcontour); m.centerofmass.x -= src.cols / 2; m.centerofmass.y -= src.rows / 2; m.centerofmass.x /= (float)src.cols; m.centerofmass.y /= (float)src.rows;m.area = newcontourarea;m.areaerror = fabs(areadiff) / newcontourarea; m.matchleve

9、l = matchlevel; m.rectangularmatchlevel = rectangularlevel; results.push_back(m);if (cleancontours.size() > 1) areaacceptancelevel /= 1.1;matchacceptancelevel /= 1.1;rectangularacceptancelevel /= 1.1;else if (cleancontours.size() = 0) areaacceptancelevel *= 1.1;matchacceptancelevel *= 1.1;rectang

10、ularacceptancelevel *= 1.1;cv:drawcontours(src, dirtycontours, -1, cv:scalar(255, 0, 0);cv:drawcontours(src, cleancontours, -1, cv:scalar(0, 0, 255); cv:imshow(windowname, src);waitkey(20);int main(int argc, char* argv)cv:namedwindow(windowname); std:vector<matchedshape> results; size_t i;cv:m

11、at temporarymaterial; results.clear(); cv:videocapture cap(0);while (1)mat img; cap >> img;findrectangle(img, results);printf(“found %d rectangles!n“, (int)results.size(); printf(“areatmxtmytmatchtrectangle matchn“); for (i = 0; i < results.size(); i+) printf(“%dt%ft%ft%ft%fn“, (int)results

12、i.area, resultsi.centerofmass.x, resultsi.centerofmass.y, resultsi.matchlevel, resultsi.rectangularmatchlevel);/*visionutil.cpp*/#include “visionutil.h“#include “opencv2/core/core.hpp“ #include “opencv2/imgproc/imgproc.hpp“double calcdistance(cv:point &a, cv:point &b) return (a.x - b.x) * (a

13、.x - b.x) + (a.y - b.y) * (a.y - b.y);cv:point2f centerofmass(std:vector<cv:point>& contour) cv:point2f pt;pt.x = pt.y = 0; size_t i;for (i = 0; i < contour.size(); i+) pt.x += contouri.x;pt.y += contouri.y;pt.x /= contour.size(); pt.y /= contour.size(); return pt;/ checks the point clo

14、ud against the contour using a perimeter comparison. double matchheuristic(std:vector<cv:point>& contour,std:vector<cv:point>& cloud) double matchdistance = cv:arclength(cloud, false); double mydistance = cv:arclength(contour, false); return fabs(matchdistance - mydistance) / myd

15、istance;/ looks for rectangular contoursdouble twosidelengthsheuristic(std:vector<cv:point>& contour) double sidelength4;double minlength = 1e100; double maxlength = 0; double totallength = 0;for (size_t i = 0; i < contour.size(); i+) double len = calcdistance(contouri,contour(i + 1) % contour.size(); sidelengthi = len;if (len < minlength) minlength = len;if (len > maxlength) maxlength = len;totallength += len;double total = 0, diffa, diffb;for (size_t