OpenCV 真圆度测量

最近一个项目需要在图像上测量一些小孔的真圆度。因此专门研究了一下真圆度计算问题。

对于一个轮廓，我们可以求出这个轮廓的外接圆和内切圆。这两个圆的半径差定义为真圆度。这个数值越小，表示这个圆越标准。

外接圆在 OpenCV 中有现成的函数来计算，但是内切圆是没有的。去算内切圆难度还是蛮大的。

为此，我采用了个变通的方法。对这个轮廓先计算最小二乘拟合圆。之后计算轮廓上的各个点到圆心的距离。最大距离减去最小距离的差可以作为真圆度的一个近似的计算。

真圆度的计算代码封装到了一个类里。这个类的头文件如下：

#ifndef TRUEROUNDNESS_H
#define TRUEROUNDNESS_H#include <opencv2/opencv.hpp>/*** @brief The TrueRoundness class 真圆度计算。只实现了一种最基本的算法。*/
class TrueRoundness
{
public:/*** @brief TrueRoundness 真圆度计算的类的构造函数。考虑到除去噪声影响，可以排除部分数据点。* @param top 计算真圆度时，最大直径排除前 top 个点。* @param bottom 计算真圆度时，最小直径排除最后 bottom 个点。*/TrueRoundness(unsigned int top = 0, unsigned int bottom = 0);//可以去除两头的一部分数据/*** @brief TrueRoundness 真圆度计算的类的构造函数。考虑到除去噪声影响，可以排除部分数据点。* @param top_percent 计算真圆度时，最大直径排除前 top_percent % 的点。* @param bottom_percent 计算真圆度时，最小直径排除最后 bottom_percent % 的点。*/TrueRoundness(double top_percent, double bottom_percent); //去除两头一定比例的数据/*** @brief operator ()计算真圆度。首先计算出拟合圆，然后计算轮廓上到点到圆心的最远距离和最近距离。之差就是真圆度。* @param [in] contour 轮廓上的点。* @param [out] r_min* @param [out] r_max* @return  返回真圆度。如果结果小于 0 ，则表示计算失败。通常是拟合圆失败。*/double operator() (const std::vector<cv::Point2i> &contour, double &r_min, double &r_max);double operator() (const std::vector<cv::Point2f> &contour, double &r_min, double &r_max);/*** @brief circleLeastFit 最小二乘法拟合圆。这个函数是辅助函数，通常不需要使用。* @param points 圆的轮廓上的点* @param center_x 计算出的圆心坐标 x* @param center_y 计算出的圆心坐标 y* @param radius 计算出的半径值* @return true 表示拟合成功。false 表示失败。*/bool circleLeastFit(const std::vector<cv::Point2i> &points, double &center_x, double &center_y, double &radius);bool circleLeastFit(const std::vector<cv::Point2f> &points, double &center_x, double &center_y, double &radius);/*** @brief sort 辅助函数，对边缘数据进行排序，离圆心远的数据排在前面。* @param contour 轮廓上的点* @param center_x 圆心坐标 x* @param center_y 圆心坐标 y*/void sort( std::vector<cv::Point2i> &contour, double center_x, double center_y );void sort( std::vector<cv::Point2f> &contour, double center_x, double center_y );/*** @brief distance 辅助函数，计算一个点到圆心的距离* @param p* @param center_x* @param center_y* @return*/double distance(cv::Point2i p, double center_x, double center_y);double distance(cv::Point2f p, double center_x, double center_y);
private:int m_type = 0;int m_top = 0;int m_bottom = 0;double m_top_percent = 0;double m_bottom_percent = 0;
};#endif // TRUEROUNDNESS_H

实现代码如下：

#include "TrueRoundness.h"TrueRoundness::TrueRoundness(unsigned int top, unsigned int bottom)
{m_type = 0;m_top = top;m_bottom = bottom;
}TrueRoundness::TrueRoundness(double top_percent, double bottom_percent)
{m_type = 1;m_top_percent = top_percent;m_bottom_percent = bottom_percent;
}double TrueRoundness::operator()(const std::vector<cv::Point2i> &contour, double &r_min, double &r_max)
{double center_x;double center_y;double radius;bool ret = circleLeastFit(contour, center_x, center_y, radius);if(!ret) return -1;std::vector<cv::Point2i> newcontour = contour;sort(newcontour, center_x, center_y);int N = newcontour.size();if(m_type == 0){if(m_top > N || m_bottom > N){return -2;}int N1 = m_top;int N2 = N - 1 - m_bottom;if(distance(newcontour[N1], center_x, center_y) < radius || distance(newcontour[N2], center_x, center_y) > radius){return -3;}r_max = distance(newcontour[N1], center_x, center_y);r_min = distance(newcontour[N2], center_x, center_y);return  r_max - r_min ;}else if(m_type == 1){if(m_top_percent >= 1 || m_bottom_percent >= 1) return -4;int N1 = 0, N2 = 0;for(int i = 0; i < N; i++){double r = distance(newcontour[i], center_x, center_y);if(r > radius) N1 ++;if(r < radius) N2 ++;}N1 = N1 * (m_top_percent);N2 = N - 1 - N2 * (m_bottom_percent);r_max = distance(newcontour[N1], center_x, center_y);r_min = distance(newcontour[N2], center_x, center_y);return  r_max - r_min ;}return -5;
}double TrueRoundness::operator()(const std::vector<cv::Point2f> &contour, double &r_min, double &r_max)
{double center_x;double center_y;double radius;bool ret = circleLeastFit(contour, center_x, center_y, radius);if(!ret) return -1;std::vector<cv::Point2f> newcontour = contour;sort(newcontour, center_x, center_y);int N = newcontour.size();if(m_type == 0){if(m_top > N || m_bottom > N){return -2;}int N1 = m_top;int N2 = N - 1 - m_bottom;if(distance(newcontour[N1], center_x, center_y) < radius || distance(newcontour[N2], center_x, center_y) > radius){return -3;}r_max = distance(newcontour[N1], center_x, center_y);r_min = distance(newcontour[N2], center_x, center_y);return  r_max - r_min ;}else if(m_type == 1){if(m_top_percent >= 1 || m_bottom_percent >= 1) return -4;int N1 = 0, N2 = 0;for(int i = 0; i < N; i++){double r = distance(newcontour[i], center_x, center_y);if(r > radius) N1 ++;if(r < radius) N2 ++;}N1 = N1 * (m_top_percent);N2 = N - 1 - N2 * (m_bottom_percent);r_max = distance(newcontour[N1], center_x, center_y);r_min = distance(newcontour[N2], center_x, center_y);return  r_max - r_min ;}return -5;
}bool TrueRoundness::circleLeastFit(const std::vector<cv::Point2i> &points, double &center_x, double &center_y, double &radius)
{center_x = 0.0;center_y = 0.0;radius = 0.0;if (points.size() < 3){return false;}double sum_x = 0.0, sum_y = 0.0;double sum_x2 = 0.0, sum_y2 = 0.0;double sum_x3 = 0.0, sum_y3 = 0.0;double sum_xy = 0.0, sum_x1y2 = 0.0, sum_x2y1 = 0.0;int N = points.size();for (int i = 0; i < N; i++){double x = points[i].x;double y = points[i].y;double x2 = x * x;double y2 = y * y;sum_x += x;sum_y += y;sum_x2 += x2;sum_y2 += y2;sum_x3 += x2 * x;sum_y3 += y2 * y;sum_xy += x * y;sum_x1y2 += x * y2;sum_x2y1 += x2 * y;}double C, D, E, G, H;double a, b, c;C = N * sum_x2 - sum_x * sum_x;D = N * sum_xy - sum_x * sum_y;E = N * sum_x3 + N * sum_x1y2 - (sum_x2 + sum_y2) * sum_x;G = N * sum_y2 - sum_y * sum_y;H = N * sum_x2y1 + N * sum_y3 - (sum_x2 + sum_y2) * sum_y;a = (H * D - E * G) / (C * G - D * D);b = (H * C - E * D) / (D * D - G * C);c = -(a * sum_x + b * sum_y + sum_x2 + sum_y2) / N;center_x = a / (-2);center_y = b / (-2);radius = sqrt(a * a + b * b - 4 * c) / 2;return true;
}bool TrueRoundness::circleLeastFit(const std::vector<cv::Point2f> &points, double &center_x, double &center_y, double &radius)
{center_x = 0.0;center_y = 0.0;radius = 0.0;if (points.size() < 3){return false;}double sum_x = 0.0, sum_y = 0.0;double sum_x2 = 0.0, sum_y2 = 0.0;double sum_x3 = 0.0, sum_y3 = 0.0;double sum_xy = 0.0, sum_x1y2 = 0.0, sum_x2y1 = 0.0;int N = points.size();for (int i = 0; i < N; i++){double x = points[i].x;double y = points[i].y;double x2 = x * x;double y2 = y * y;sum_x += x;sum_y += y;sum_x2 += x2;sum_y2 += y2;sum_x3 += x2 * x;sum_y3 += y2 * y;sum_xy += x * y;sum_x1y2 += x * y2;sum_x2y1 += x2 * y;}double C, D, E, G, H;double a, b, c;C = N * sum_x2 - sum_x * sum_x;D = N * sum_xy - sum_x * sum_y;E = N * sum_x3 + N * sum_x1y2 - (sum_x2 + sum_y2) * sum_x;G = N * sum_y2 - sum_y * sum_y;H = N * sum_x2y1 + N * sum_y3 - (sum_x2 + sum_y2) * sum_y;a = (H * D - E * G) / (C * G - D * D);b = (H * C - E * D) / (D * D - G * C);c = -(a * sum_x + b * sum_y + sum_x2 + sum_y2) / N;center_x = a / (-2);center_y = b / (-2);radius = sqrt(a * a + b * b - 4 * c) / 2;return true;
}void TrueRoundness::sort(std::vector<cv::Point2i> &contour, double center_x, double center_y)
{auto cmp = [center_x, center_y](cv::Point2i p1, cv::Point2i p2){double r1 = hypot(p1.x - center_x, p1.y - center_y);double r2 = hypot(p2.x - center_x, p2.y - center_y);return r1 > r2;};std::sort( contour.begin(), contour.end(), cmp );}void TrueRoundness::sort(std::vector<cv::Point2f> &contour, double center_x, double center_y)
{auto cmp = [center_x, center_y](cv::Point2i p1, cv::Point2i p2){double r1 = hypot(p1.x - center_x, p1.y - center_y);double r2 = hypot(p2.x - center_x, p2.y - center_y);return r1 > r2;};std::sort( contour.begin(), contour.end(), cmp );
}double TrueRoundness::distance(cv::Point2i p, double center_x, double center_y)
{return hypot(p.x - center_x, p.y - center_y);
}double TrueRoundness::distance(cv::Point2f p, double center_x, double center_y)
{return hypot(p.x - center_x, p.y - center_y);
}

下面是个简单的测试用例。首先我们需要个测试图片：

下面是测试代码：

#include <QCoreApplication>
#include <QDebug>
#include <opencv2/opencv.hpp>#include "TrueRoundness.h"
bool circleLeastFit(const std::vector<cv::Point2i> &points, double &center_x, double &center_y, double &radius);
int main(int argc, char *argv[])
{QCoreApplication a(argc, argv);cv::Mat image;image = cv::imread("E:\\testimage\\round.png", cv::IMREAD_COLOR);cv::Mat gray;cv::cvtColor(image, gray, cv::COLOR_BGRA2GRAY);cv::Canny(gray ,gray, 40, 120);std::vector<std::vector<cv::Point2i>> contours;std::vector<cv::Vec4i> hierarchy;cv::findContours(gray, contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE);double center_x, center_y;double radius, r_min, r_max;TrueRoundness tr(1U, 1U);tr.circleLeastFit(contours[0], center_x, center_y, radius);double t = tr(contours[0], r_min, r_max);cv::circle(image, cv::Point(center_x, center_y), r_min, cv::Scalar(0, 255, 0), 1);cv::circle(image, cv::Point(center_x, center_y), r_max, cv::Scalar(0, 0, 255), 1);qDebug() << t;cv::imshow("circle", image);return a.exec();
}

输出的结果如下：

OpenCV 真圆度测量相关推荐

用 Python 和 OpenCV 来测量相机到目标的距离
用 Python 和 OpenCV 来测量相机到目标的距离 http://python.jobbole.com/84378/ 几天前,一个叫 Cameron 的 PyImageSearch 读者发来邮 ...
python opencv卡尺测量边缘距离
opencv 卡尺法测量边缘距离参考来源 :https://github.com/crackwitz/metrology-demo 前言一.测量方法二.测量步骤 1.获取直线的像素 2.高斯滤 ...
python opencv实时显示测量数据_python OpenCV 宽度测量
机器视觉第六次实验一.实验目的通过OpenCV第六次进行实验,对图片进行宽度测量. 二.实验内容对图片进行宽度测量. 三.实验过程我使用的是python语言+openCV对图片进行宽度测量的功 ...
python 机器视觉测量_python OpenCV 宽度测量
机器视觉第六次实验一.实验目的通过OpenCV第六次进行实验,对图片进行宽度测量. 二.实验内容对图片进行宽度测量. 三.实验过程我使用的是python语言+openCV对图片进行宽度测量的功 ...
pythonopencv测距_如何在opencv中测量两点之间的距离（像素到厘米）
你好,我有一个代码,可以测量两点之间的距离,但我认为结果是以像素为单位的,所以想得到距离,但以厘米为单位. 我使用的是微软LifecamHD-3000摄像头,我真的不知道在代码的这一点上该怎么做. T ...
OpenCV测量物体的尺寸技能 get～
点击上方"Python爬虫与数据挖掘",进行关注回复"书籍"即可获赠Python从入门到进阶共10本电子书今日鸡汤盛年不重来,一日难再晨. 及时当勉 ...
基于OpenCV的单目摄像机测距
点击上方"小白学视觉",选择加"星标"或"置顶" 重磅干货,第一时间送达本文转自|新机器视觉我的论文方向目前是使用单目摄像头实现机器人对人 ...
opencv 通过网络连接工业相机_单目摄像机测距（python+opencv）
点击上方"新机器视觉",选择加"星标"或"置顶" 重磅干货,第一时间送达我的论文方向目前是使用单目摄像头实现机器人对人的跟随,首先单目摄像 ...
单目摄像机测距（python+opencv）（转载）
我的论文方向目前是使用单目摄像头实现机器人对人的跟随,首先单目摄像头与kinect等深度摄像头最大的区别是无法有效获取深度信息,那就首先从这方面入手,尝试通过图像获取摄像头与人的距离. 在网上看了几天 ...

OpenCV 真圆度测量

OpenCV 真圆度测量

OpenCV 真圆度测量相关推荐

最新文章

热门文章