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 
#include 
#include 

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 > contours;
    vector 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(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。