寻找复杂背景下物体的轮廓（OpenCV / C++

一、问题提出

这是一个来自"answerOpenCV"（http://answers.opencv.org/question/200422/opencv-c-filling-holes/）整编如下：

title：OpenCV / C++ - Filling holes

content:

Hello there,

For a personnel projet, I'm trying to detect object and there shadow. These are the result I have for now: Original:

题，原始问题

Object:

Shadow:

The external contours of the object are quite good, but as you can see, my object is not full. Same for the shadow. I would like to get full contours, filled, for the object and its shadow, and I don't know how to get better than this (I juste use "dilate" for the moment). Does someone knows a way to obtain a better result please? Regards.

二、问题分析

从原始图片上来看，这张图片的拍摄的背景比较复杂，此外光照也存在偏光现象；而提问者虽然提出的是“将缝隙合并”的要求，实际上他还是想得到目标物体的准确轮廓。

三、问题解决

基于现有经验，和OpenCV，GOCVhelper等工具，能够很快得出以下结果

h通道：

去光差：

阈值：

标注：

四、算法关键

这套算法首先解决了这个问题，而且我认为也是稳健鲁棒的。其中，算法中除了经典的“hsv分解->ostu阈值->最大轮廓标注”外，最为关键的算法为顶帽去光差。这个算法来自于冈萨雷斯《数字图像处理教程》形态学篇章，完全按照书本建议实现，体现良好作用。

//answerOpenCV OpenCV / C++ - Filling holes

#include "stdafx.h"

#include <iostream>

#include <vector>

using namespace cv;

using namespace std;

//find the biggest contour

vector<Point> FindBigestContour(Mat src){

int imax = 0;

int imaxcontour = -1;

std::vector<std::vector<Point> >contours;

findContours(src,contours,CV_RETR_LIST,CV_CHAIN_APPROX_SIMPLE);

for (int i=0;i<contours.size();i++){

int itmp = contourArea(contours[i]);

if (imaxcontour < itmp ){

imax = i;

imaxcontour = itmp;

}

return contours[imax];

}

//remove Light difference by using top hat

Mat moveLightDiff(Mat src,int radius){

Mat dst;

Mat srcclone = src.clone();

Mat mask = Mat::zeros(radius*2,radius*2,CV_8U);

circle(mask,Point(radius,radius),radius,Scalar(255),-1);

//top hat

erode(srcclone,srcclone,mask);

dilate(srcclone,srcclone,mask);

dst = src - srcclone;

return dst;

}

int main( void )

{

Mat src = imread("e:/sandbox/question.png");

Mat src_hsv;

Mat bin;

Mat src_h;

cvtColor(src,src_hsv,COLOR_BGR2HSV);

vector<Mat> rgb_planes;

split(src_hsv, rgb_planes );

src_h = rgb_planes[0]; // h channel is useful

src_h = moveLightDiff(src_h,40);

threshold(src_h,bin,100,255,THRESH_OTSU);

//find and draw the biggest contour

vector<Point> bigestcontrour = FindBigestContour(bin);

vector<vector<Point> > controus;

controus.push_back(bigestcontrour);

cv::drawContours(src,controus,0,Scalar(0,0,255),3);

waitKey();

return 0;

}

五、经验小结

解决这个问题我只用了10分钟的时间，写博客10分钟。能够快速解决问题并书写出来的关键为：

1、积累维护的代码库:GOCVHelper(https://github.com/jsxyhelu/GOCvHelper）

2、不断阅读思考实践的习惯；

感谢阅读至此，希望有所帮助!

目前方向：图像拼接融合、图像识别联系方式：jsxyhelu@foxmail.com