OpenCV函数：提取轮廓相关函数使用方法

opencv中提供findContours()函数来寻找图像中物体的轮廓，并结合drawContours()函数将找到的轮廓绘制出。首先看一下findContours(),opencv中提供了两种定义形式

官网：https://docs.opencv.org/3.3.1/d3/dc0/group__imgproc__shape.html#ga17ed9f5d79ae97bd4c7cf18403e1689a 

void cv::findContours   (   InputOutputArray    image,
                            OutputArrayOfArrays     contours,
                            OutputArray     hierarchy,
                            int     mode,
                            int     method,
                            Point   offset = Point() 
                        )   

　　

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
#include <stdlib.h>

using namespace cv;
using namespace std;

Mat src; Mat src_gray;
int thresh = 100;
int max_thresh = 255;
RNG rng(12345);

/// Function header
void thresh_callback(int, void*);

/** @function main */
int main(int argc, char** argv)
{
	/// 加载源图像
	src = imread("E:\VS2015Opencv\vs2015\project\picture\07.jpg", 1);

	/// 转成灰度并模糊化降噪
	cvtColor(src, src_gray, CV_BGR2GRAY);
	blur(src_gray, src_gray, Size(3, 3));

	/// 创建窗体
	char* source_window = "Source";
	namedWindow(source_window, CV_WINDOW_AUTOSIZE);
	imshow(source_window, src);

	createTrackbar(" Canny thresh:", "Source", &thresh, max_thresh, thresh_callback);
	thresh_callback(0, 0);

	waitKey(0);
	return(0);
}

/** @function thresh_callback */
void thresh_callback(int, void*)
{
	Mat canny_output;
	vector<vector<Point> > contours;
	vector<Vec4i> hierarchy;

	/// 用Canny算子检测边缘
	Canny(src_gray, canny_output, thresh, thresh * 2, 3);
	/// 寻找轮廓
	findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

	/// 绘出轮廓
	Mat drawing = Mat::zeros(canny_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, 2, 8, hierarchy, 0, Point());
	}

	/// 在窗体中显示结果
	namedWindow("Contours", CV_WINDOW_AUTOSIZE);
	imshow("Contours", drawing);
}

　　

OpenCV提取轮廓之后，还可以进行许多操作：

ArcLength()                计算轮廓长度 
ContourArea()            计算轮廓区域的面积 
BoundingRect()          轮廓的外包矩形 
ConvexHull()              提取轮廓的凸包 
IsContourConvex()     测试轮廓的凸性 
MinAreaRect()            轮廓的最小外包矩形 
MinEnclosingCircle()  轮廓的最小外包圆
fitEllipse()                   用椭圆拟合二维点集
approxPolyDP()          逼近多边形曲线

boundingRect函数简介

boundingRect函数是用来计算轮廓的最小外接矩形，通常与findContours函数组合使用，findContours函数用来查找图像的轮廓，boundingRect获取轮廓的最小外接矩形！

Rect boundingRect( InputArray array );

(1) 第一个参数，InputArray array，一般为findContours函数查找的轮廓，包含轮廓的点集或者Mat；

(2) 返回值，Rect，返回值为最小外接矩形的Rect，即左上点与矩形的宽度和高度；

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
#include <stdlib.h>

using namespace cv;
using namespace std;

Mat src; Mat src_gray;
int thresh = 100;
int max_thresh = 255;
RNG rng(12345);

/// 函数声明
void thresh_callback(int, void*);

/** @主函数 */
int main(int argc, char** argv)
{
	/// 载入原图像, 返回3通道图像
	src = imread("E:\VS2015Opencv\vs2015\project\picture\1.1.jpg", 1);

	/// 转化成灰度图像并进行平滑
	cvtColor(src, src_gray, CV_BGR2GRAY);
	blur(src_gray, src_gray, Size(3, 3));

	/// 创建窗口
	char* source_window = "Source";
	namedWindow(source_window, CV_WINDOW_AUTOSIZE);
	imshow(source_window, src);

	createTrackbar(" Threshold:", "Source", &thresh, max_thresh, thresh_callback);
	thresh_callback(0, 0);

	waitKey(0);
	return(0);
}

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

	/// 使用Threshold检测边缘
	threshold(src_gray, threshold_output, thresh, 255, THRESH_BINARY);
	/// 找到轮廓
	findContours(threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

	/// 多边形逼近轮廓 + 获取矩形和圆形边界框
	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(contours_poly[i], center[i], radius[i]);
	}


	/// 画多边形轮廓 + 包围的矩形框 + 圆形框
	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());
		rectangle(drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0);
		circle(drawing, center[i], (int)radius[i], color, 2, 8, 0);
	}

	/// 显示在一个窗口
	namedWindow("Contours", CV_WINDOW_AUTOSIZE);
	imshow("Contours", drawing);
}

　

minAreaRect()

作用：找到一个能包围输入二维点集的面积最小的任意方向矩形。

形式：minAreaRect(InputArray points);

参数：points：输入二维点集，并用std::vector or Mat存储；

fitEllipse()

作用：寻找一个适合的围绕二维点集的椭圆。

形式：fitEllipse(InputArray points)；

参数：points：输入二维点集，并用std::vector or Mat存储；

ellipse()

作用：画一个简单的或明显的椭圆弧，或填充一个椭圆部分。

形式：void ellipse(Mat& img, const RotatedRect& box, const Scalar& color, int thickness=1, int lineType=8)；

或void ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, const Scalar& color, int thickness=1, int lineType=8, int shift=0)；

参数：

img：输入的图像；

box：通过RotatedRect or CvBox2D选择椭圆代表，也就是在任意方向矩阵中镶嵌一个椭圆；

后边三个参数分别是：颜色、边线粗细、边线的类型；

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
#include <stdlib.h>

using namespace cv;
using namespace std;

Mat src; Mat src_gray;
int thresh = 100;
int max_thresh = 255;
RNG rng(12345);

/// Function header
void thresh_callback(int, void*);

/** @function main */
int main(int argc, char** argv)
{
    /// 加载源图像
    src = imread("E:\VS2015Opencv\vs2015\project\picture\01.jpg");

    /// 转为灰度图并模糊化
    cvtColor(src, src_gray, CV_BGR2GRAY);
    blur(src_gray, src_gray, Size(3, 3));

    /// 创建窗体
    char* source_window = "Source";
    namedWindow(source_window, CV_WINDOW_AUTOSIZE);
    imshow(source_window, src);

    createTrackbar(" Threshold:", "Source", &thresh, max_thresh, thresh_callback);
    thresh_callback(0, 0);

    waitKey(0);
    return(0);
}

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

    /// 阈值化检测边界
    threshold(src_gray, threshold_output, thresh, 255, THRESH_BINARY);
    /// 寻找轮廓
    findContours(threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

    /// 对每个找到的轮廓创建可倾斜的边界框和椭圆
    vector<RotatedRect> minRect(contours.size());
    vector<RotatedRect> minEllipse(contours.size());

    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]));
        }
    }

    /// 绘出轮廓及其可倾斜的边界框和边界椭圆
    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));
        // contour
        drawContours(drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point());
        // ellipse
        ellipse(drawing, minEllipse[i], color, 2, 8);
        // rotated rectangle
        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);
    }

    /// 结果在窗体中显示
    namedWindow("Contours", CV_WINDOW_AUTOSIZE);
    imshow("Contours", drawing);
}

 本文参考：OpenCV函数：提取轮廓相关函数使用方法