最大内接矩形，从轮廓中所有坐标中获取其中4个坐标即可：

python 获取过程如下：

def order_points(pts):
    # pts为轮廓坐标
    # 列表中存储元素分别为左上角，右上角，右下角和左下角
    rect = np.zeros((4, 2), dtype = "float32")
    # 左上角的点具有最小的和，而右下角的点具有最大的和
    s = pts.sum(axis = 1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    # 计算点之间的差值
    # 右上角的点具有最小的差值,
    # 左下角的点具有最大的差值
    diff = np.diff(pts, axis = 1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    # 返回排序坐标(依次为左上右上右下左下)
    return rect
 

img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blurred = cv2.blur(gray, (9, 9))
_, thresh = cv2.threshold(blurred, 155, 255, cv2.THRESH_BINARY)
_, cnts, _ = cv2.findContours( thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]<br>先找出轮廓点

rect = order_points(c.reshape(c.shape[0], 2))
print(rect)
xs = [i[0] for i in rect]
ys = [i[1] for i in rect]
xs.sort()
ys.sort()
#内接矩形的坐标为
print(xs[1],xs[2],ys[1],ys[2])

c++版

OpenCVSharp 小练习 最大内接矩形_tfarcraw的博客-CSDN博客

​​​​​​opencv:求区域的内接矩形_cfqcfqcfqcfqcfq的博客-CSDN博客_opencv 内接矩形

#include<opencv2\opencv.hpp>
#include <iostream>
#include<vector>
 
using namespace cv;
using namespace std;
 
/**
* @brief expandEdge 扩展边界函数
* @param img:输入图像，单通道二值图，深度为8
* @param edge  边界数组，存放4条边界值
* @param edgeID 当前边界号
* @return 布尔值 确定当前边界是否可以扩展
*/
 
bool expandEdge(const Mat & img, int edge[], const int edgeID)
{
	//[1] --初始化参数
	int nc = img.cols;
	int nr = img.rows;
	switch (edgeID) {
	case 0:
		if (edge[0]>nr)
			return false;
		for (int i = edge[3]; i <= edge[1]; ++i)
		{
			if (img.at<uchar>(edge[0], i) == 255)//遇见255像素表明碰到边缘线
				return false;
		}
		edge[0]++;
		return true;
		break;
	case 1:
		if (edge[1]>nc)
			return false;
		for (int i = edge[2]; i <= edge[0]; ++i)
		{
			if (img.at<uchar>(i, edge[1]) == 255)//遇见255像素表明碰到边缘线
				return false;
		}
		edge[1]++;
		return true;
		break;
	case 2:
		if (edge[2]<0)
			return false;
		for (int i = edge[3]; i <= edge[1]; ++i)
		{
			if (img.at<uchar>(edge[2], i) == 255)//遇见255像素表明碰到边缘线
				return false;
		}
		edge[2]--;
		return true;
		break;
	case 3:
		if (edge[3]<0)
			return false;
		for (int i = edge[2]; i <= edge[0]; ++i)
		{
			if (img.at<uchar>(i, edge[3]) == 255)//遇见255像素表明碰到边缘线
				return false;
		}
		edge[3]--;
		return true;
		break;
	default:
		return false;
		break;
	}
 
}
 
/**
* @brief 求取连通区域内接矩
* @param img:输入图像，单通道二值图，深度为8
* @param center:最小外接矩的中心
* @return  最大内接矩形
* 基于中心扩展算法
*/
 
cv::Rect InSquare(Mat &img, const Point center)
{
	// --[1]参数检测
	if (img.empty() ||img.channels()>1|| img.depth()>8)
		return Rect();
	// --[2] 初始化变量
	int edge[4];
	edge[0] = center.y + 1;//top
	edge[1] = center.x + 1;//right
	edge[2] = center.y - 1;//bottom
	edge[3] = center.x - 1;//left
						   //[2]
						   // --[3]边界扩展(中心扩散法)
 
	bool EXPAND[4] = { 1,1,1,1 };//扩展标记位
	int n = 0;
	while (EXPAND[0] || EXPAND[1] || EXPAND[2] || EXPAND[3])
	{
		int edgeID = n % 4;
		EXPAND[edgeID] = expandEdge(img, edge, edgeID);
		n++;
	}
	//[3]
	//qDebug() << edge[0] << edge[1] << edge[2] << edge[3];
	Point tl = Point(edge[3], edge[0]);
	Point br = Point(edge[1], edge[2]);
	return Rect(tl, br);
}
 
 
 
 
int main()
{
 
	bool isExistence = false;
	float first_area = 0;
	/// 加载源图像
	Mat src;
	src = imread("cen.bmp", 1);
	//src = imread("C:\\Users\\Administrator\\Desktop\\测试图片\\xxx\\20190308152516.jpg",1);
	//src = imread("C:\\Users\\Administrator\\Desktop\\测试图片\\xx\\20190308151912.jpg",1);
	//src = imread("C:\\Users\\Administrator\\Desktop\\测试图像\\2\\BfImg17(x-247 y--91 z--666)-(492,280).jpg",1);
	cvtColor(src, src, CV_RGB2GRAY);
	threshold(src, src, 100, 255, THRESH_BINARY);
	Rect ccomp;
	Point center(src.cols / 2, src.rows / 2);
	//floodFill(src, center, Scalar(255, 255, 55), &ccomp, Scalar(20, 20, 20), Scalar(20, 20, 20));
	if (src.empty())
	{
		cout << "fali" << endl;
	}
	//resize(src, src, cv::Size(496, 460), cv::INTER_LINEAR);
	imshow("src", src);
	Rect rr = InSquare(src, center);
	rectangle(src, rr, Scalar(255), 1, 8);
	imshow("src2", src);
 
	waitKey(0);
	getchar();
	return 0;
}

原图和效果图：