寻找复杂背景下物体的轮廓（OpenCV / C++ - Filling holes）

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。

#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;
}