在OpenCV中查找椭圆的成本 [英] Find cost of ellipse in OpenCV

问题描述

我使用的代码取决于

您可以看到好的椭圆朝向绿色，而糟糕的结果朝向红色：

代码：

  include< opencv2 / opencv.hpp> 
 using namespace std; 
 using namespace cv; 
 
 int main（）
 {
 // Load image 
 Mat3b img = imread（path_to_image）; 
 
 //转换为灰度。如果需要，Binarize 
 Mat1b bin; 
 cvtColor（img，bin，COLOR_BGR2GRAY）; 
 
 //查找轮廓
 vector< vector< Point>>轮廓; 
 findContours（bin.clone（），contoururs，RETR_EXTERNAL，CHAIN_APPROX_NONE）; 
 
 //对于每个轮廓
 for（int i = 0; i  {
 //查找椭圆
 RotatedRect ell = fitEllipse（contoururs [i]）; 
 
 //绘制轮廓
 Mat1b maskContour（img.rows，img.cols，uchar（0））; 
 drawContours（maskContour，contoururs，i，Scalar（255），2）; 
 
 //绘制椭圆
 Mat1b maskEllipse（img.rows，img.cols，uchar（0））; 
 ellipse（maskEllipse，ell，Scalar（255），2）; 
 
 //相交
 Mat1b intersection = maskContour& maskEllipse; 
 
 //计算交集数量
 float cnz = countNonZero（intersection）; 
 //绘制轮廓中的像素的计数
 float n = countNonZero（maskContour）; 
 //计算你的度量
 float measure = cnz / n; 
 
 //绘制，颜色编码：好 - > gree，bad  - > red 
 ellipse（img，ell，Scalar（0，measure * 255，255  -  measure * 255），3）; 
 
} 
 
 imshow（Result，img）; 
 waitKey（）; 
 
 
 return 0; 
} 
  

I am using code based off of this example and wonder if there is any way to find out how 'good' the ellipse fit is. I have some ellipses which only very generally fit my data and I want to get rid of them while some of the ellipses are almost perfect.

I'd like to keep on the very good fits and get rid of the poor fits. How can I do this in opencv?

解决方案

There are several method you can find in literature, for example:

• check Dilip K. Prasad, Maylor K.H. Leung and Siu-Yeung Cho, "Edge curvature and convexity based ellipse detection method," Pattern Recognition, 2012. at Section 4.2

• check Fornaciari, Michele, Andrea Prati, and Rita Cucchiara. "A fast and effective ellipse detector for embedded vision applications." Pattern Recognition 47.11 (2014): 3693-3708. at Section 3.3.1

However, a very simple method can be the number of pixel that belong both to the contour and the ellipse. You can compute this, for example, drawing the contour and the ellipse on two separate black-initialized images, and counting the number of intersections, i.e. the number of white pixel of the logic AND of the two images.

To be more robust, you can draw both the contour and the ellipse with a line width of 2. This will account for slightly misaligned estimations, but still perceptually correct.

For example, given this input image:

You can see that good ellipses are toward green color, while bad results are toward red color:

Code:

#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;

int main()
{
    // Load image
    Mat3b img = imread("path_to_image");

    // Convert to grayscale. Binarize if needed
    Mat1b bin;
    cvtColor(img, bin, COLOR_BGR2GRAY);

    // Find contours
    vector<vector<Point>> contours;
    findContours(bin.clone(), contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);

    // For each contour
    for (int i = 0; i < contours.size(); ++i)
    {
        // Find ellipse
        RotatedRect ell = fitEllipse(contours[i]);

        // Draw contour
        Mat1b maskContour(img.rows, img.cols, uchar(0));
        drawContours(maskContour, contours, i, Scalar(255), 2);

        // Draw ellips
        Mat1b maskEllipse(img.rows, img.cols, uchar(0));
        ellipse(maskEllipse, ell, Scalar(255), 2);

        // Intersect
        Mat1b intersection = maskContour & maskEllipse;

        // Count amount of intersection
        float cnz = countNonZero(intersection);
        // Count number of pixels in the drawn contour
        float n = countNonZero(maskContour);
        // Compute your measure
        float measure = cnz / n;

        // Draw, color coded: good -> gree, bad -> red
        ellipse(img, ell, Scalar(0, measure*255, 255 - measure*255), 3);

    }

    imshow("Result", img);
    waitKey();


    return 0;
}

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