opencv学习(四十二)之多边形包围图像轮廓
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
#include
#include
#include
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>contours;
vectorhierarchy;
//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>contours_poly(contours.size());
vectorboundRect(contours.size());
vectorcenter(contours.size());
vectorradius(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(), 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
#include
#include
#include
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>contours;
vectorhierarchy;
//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
vectorminRect(contours.size());
vectorminEllipse(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(), 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