基于opencv计算星云图像的面积与周长

需求：以上图像是太空望远镜的星云图像，要求通过opencv计算出星云的面积与周长。

解决思路：通过二值分割+图像形态学+轮廓提取。

代码如下

#include <opencv2/opencv.hpp>
#include <iostream>
#include <math.h>

using namespace cv;
using namespace std;

int main()
{
Mat src_image = imread("1.jpg");
if(!src_image.data)
{
cout << "src image load failed!" << endl;
return -1;
}
namedWindow("src image", WINDOW_NORMAL);
imshow("src image", src_image);

/*此处高斯去燥有助于后面二值化处理的效果*/
Mat blur_image;
GaussianBlur(src_image, blur_image, Size(15, 15), 0, 0);
imshow("GaussianBlur", blur_image);

/*灰度变换与二值化*/
Mat gray_image, binary_image;
cvtColor(blur_image, gray_image, COLOR_BGR2GRAY);
threshold(gray_image, binary_image, 0, 255, THRESH_BINARY|THRESH_TRIANGLE);
imshow("binary", binary_image);

/*形态学闭操作*/
Mat morph_image;
Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
morphologyEx(binary_image, morph_image, MORPH_CLOSE, kernel, Point(-1, -1), 2);
imshow("morphology", morph_image);

/*查找外轮廓*/
vector< vector<Point> > contours;
vector<Vec4i> hireachy;
findContours(morph_image, contours, hireachy, CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point());
Mat result_image = Mat::zeros(src_image.size(), CV_8UC3);
for(size_t t = 0; t < contours.size(); t++)
{
/*过滤掉小的干扰轮廓*/
Rect rect = boundingRect(contours[t]);
if(rect.width < src_image.cols/2)
continue;
if(rect.width > (src_image.cols - 20))
continue;

/*计算面积与周长*/
double area = contourArea(contours[t]);
double len = arcLength(contours[t], true);

drawContours(result_image, contours, static_cast<int>(t), Scalar(0, 0, 255), 1, 8, hireachy);
cout << "area of start cloud: " << area << endl;
cout << "len of start cloud: " << len << endl;
}

imshow("result image", result_image);

waitKey(0);

return 0;
}  图像处理后的提取的轮廓图如下



  根据以上的轮廓图可以计算出星云的面积与周长，详见以上代码。