答案 0 :(得分:2)
一个简单的代码,只是为了给出一个想法。它适用于像你这样的图像。
(注意:我部分地使用了code)
#include "opencv2/opencv.hpp"
using namespace cv;
using namespace std;
int main( int argc, char** argv )
{
int largest_area=0;
int largest_contour_index=0;
cv::Rect bounding_rect;
Mat src = imread(argv[1]);
Mat edges;
cvtColor( src, edges, COLOR_BGR2GRAY ); //Convert to gray
GaussianBlur(edges, edges, Size(7,7), 1.5, 1.5);
Canny(edges, edges, 0, 50, 3);
dilate(edges,edges,Mat(),Point(-1,-1),3);
erode(edges,edges,Mat(),Point(-1,-1),3);
vector<vector<cv::Point> > contours; // Vector for storing contour
findContours( edges, contours,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE ); // Find the contours in the image
for( int i = 0; i< contours.size(); i++ ) // iterate through each contour.
{
double a=contourArea( contours[i],false); // Find the area of contour
if(a>largest_area)
{
largest_area=a;
largest_contour_index=i; //Store the index of largest contour
bounding_rect=boundingRect(contours[i]); // Find the bounding rectangle for biggest contour
}
}
// ------------
// makes border
bounding_rect.x -= 10;
bounding_rect.y -= 10;
bounding_rect.width += 20;
bounding_rect.height += 20;
bounding_rect = Rect(0,0,src.cols,src.rows) & bounding_rect;
// ------------
Mat biggest_contour_rect = src( bounding_rect ).clone();
imshow("biggest_contour_rect", biggest_contour_rect );
waitKey(0);
return 0;
}