opencv学习(四十二)之多边形包围图像轮廓

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/keith_bb/article/details/70194821

首先介绍一个逼近多边形的函数approxPolyDP()，其定义如下：

void cv::approxPolyDP   (   InputArray  curve,
                            OutputArray     approxCurve,
                            double  epsilon,
                            bool    closed 
                        )   

函数作用是用指定精度逼近多边形曲线
curve:输入的二维点集，可以是vector类型或Mat类型
approxCurve:多边形逼近的结果，其类型与输入的点集类型一致
epsilon：逼近的精度，为原始曲线和逼近曲线间的最大值
closed:如果为true，逼近的曲线为封闭曲线，如果为false则逼近曲线不封闭

1.使用矩形和最小圆包围图像

使用boundingRect()函数计算包围轮廓的矩形框，使用minEnclosingCircle()函数计算包围轮廓的最小圆包围

boundingRect()
函数计算并返回点集最外面的矩形边界，其函数定义如下：

cv::boundingRect    (   InputArray  points  )   

minEnclosingCircle()
利用迭代算法，对给定的二维点集寻找计算可包围点集的最小圆形，其定义如下

void cv::minEnclosingCircle (   InputArray  points,
                                Point2f &   center,
                                float &     radius 
                            )

参数解释：

points:输入的二维点集，数据类型为vector<>或Mat类型
center:绘制圆的圆心坐标
radius:圆的半径

示例代码

#include <iostream>
#include <opencv2\core\core.hpp>
#include <opencv2\highgui\highgui.hpp>
#include <opencv2\imgproc\imgproc.hpp>

using namespace std;
using namespace cv;

//定义全局变量
Mat srcImage, grayImage;
int thresh = 100;
const int threshMaxValue = 255;
RNG rng(12345);

//声明回调函数
void thresh_callback(int, void*);

int main()
{
    srcImage = imread("bundingRect_1.jpg");

    //判断图像是否加载成功
    if (srcImage.empty())
    {
        cout << "图像加载失败!";
        return -1;
    }
    else
        cout << "图像加载成功!" << endl << endl;

    cvtColor(srcImage, grayImage, COLOR_BGR2GRAY);
    blur(grayImage, grayImage, Size(3, 3));

    namedWindow("原图像", WINDOW_AUTOSIZE);
    imshow("原图像", grayImage);

    //创建轨迹条
    createTrackbar("Thresh:", "原图像", &thresh, threshMaxValue, thresh_callback);
    thresh_callback(thresh, 0);

    waitKey(0);

    return 0;
}

void thresh_callback(int, void*)
{
    Mat threshold_output;
    vector<vector<Point>>contours;
    vector<Vec4i>hierarchy;

    //图像二值化检测边缘
    threshold(grayImage, threshold_output, thresh, 255, THRESH_BINARY);
    //寻找图像轮廓
    findContours(threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));

    //使用多边形逼近检测到的图像轮廓来寻找包围轮廓的矩形和最小圆
    vector<vector<Point>>contours_poly(contours.size());
    vector<Rect>boundRect(contours.size());
    vector<Point2f>center(contours.size());
    vector<float>radius(contours.size());

    for (int i = 0; i < contours.size(); i++)
    {
        approxPolyDP(Mat(contours[i]), contours_poly[i], 3, true);
        boundRect[i] = boundingRect(Mat(contours_poly[i]));
        minEnclosingCircle((Mat)contours_poly[i], center[i], radius[i]);
    }

    //绘制检测到的图像轮廓、矩形和最小包围圆
    Mat drawing = Mat::zeros(threshold_output.size(), CV_8UC3);
    for (int i = 0; i < contours.size(); i++)
    {
        Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
        drawContours(drawing, contours_poly, i, color, 1, 8, vector<Vec4i>(), 0, Point());
        /*Rect类中tl表示top_left即左上角的点，br表示bottom_right即右下方的点*/
        rectangle(drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0);
        circle(drawing, center[i], (int)radius[i], color, 2, 8, 0);
    }

    namedWindow("轮廓图", WINDOW_AUTOSIZE);
    imshow("轮廓图", drawing);
}

运行结果：

2.使用旋转的矩形和椭圆包围图像轮廓

minAreaRect()

cv::minAreaRect (   InputArray  points  )   

对于输入的二维点集，计算包围点集的最小矩形

fitEllipse()

cv::fitEllipse  (   InputArray  points  )   

根据输入的二维点集使用椭圆拟合方法包围二维点集

示例代码

#include <iostream>
#include <opencv2\core\core.hpp>
#include <opencv2\highgui\highgui.hpp>
#include <opencv2\imgproc\imgproc.hpp>

using namespace std;
using namespace cv;

//定义全局变量
Mat srcImage, grayImage;
int thresh = 100;
const int threshMaxValue = 255;
RNG rng(12345);

//声明回调函数
void thresh_callback(int, void*);

int main()
{
    srcImage = imread("bundingRect_1.jpg");

    //判断图像是否加载成功
    if (srcImage.empty())
    {
        cout << "图像加载失败!";
        return -1;
    }
    else
        cout << "图像加载成功!" << endl << endl;

    cvtColor(srcImage, grayImage, COLOR_BGR2GRAY);
    blur(grayImage, grayImage, Size(3, 3));

    namedWindow("原图像", WINDOW_AUTOSIZE);
    imshow("原图像", grayImage);

    //创建轨迹条
    createTrackbar("Thresh:", "原图像", &thresh, threshMaxValue, thresh_callback);
    thresh_callback(thresh, 0);

    waitKey(0);

    return 0;
}

void thresh_callback(int, void*)
{
    Mat threshold_output;
    vector<vector<Point>>contours;
    vector<Vec4i>hierarchy;

    //图像二值化检测边缘
    threshold(grayImage, threshold_output, thresh, 255, THRESH_BINARY);
    //寻找图像轮廓
    findContours(threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));

    //为每个轮廓寻找旋转的矩形和椭圆
    vector<RotatedRect>minRect(contours.size());
    vector<RotatedRect>minEllipse(contours.size());

    //如果轮廓上的点大于5个则用椭圆填充
    for (int i = 0; i < contours.size(); i++)
    {
        minRect[i] = minAreaRect(Mat(contours[i]));
        if (contours[i].size()>5)
        {
            minEllipse[i] = fitEllipse(Mat(contours[i]));
        }
    }

    //绘制检测到的图像轮廓、旋转矩形和椭圆
    Mat drawing = Mat::zeros(threshold_output.size(), CV_8UC3);
    for (int i = 0; i < contours.size(); i++)
    {
        Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
        drawContours(drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point());
        ellipse(drawing, minEllipse[i], color, 2, 8);
        //旋转矩形
        Point2f rect_points[4];
        minRect[i].points(rect_points);
        for (int j = 0; j < 4; j++)
        {
            line(drawing, rect_points[j], rect_points[(j + 1) % 4], color, 1, 8);
        }
    }

    namedWindow("轮廓图", WINDOW_AUTOSIZE);
    imshow("轮廓图", drawing);
}

运行结果：

此外还有最小三角包围轮廓函数minEnclosingTriangle()，请自行查阅