目录
python 最小外接矩形，
轮廓矩形框：
旋转矩形和最小包围矩形求解
python 获取最大内接矩形方法2：
c++ opencv获取最大内接矩形
python 最小外接矩形，
最小外接矩形的顶点坐标：cv2.boxPoints
cnt = np.array([[data_0_x, data_0_y], [data_1_x, data_1_y], [data_2_x, data_2_y], [data_3_x, data_3_y]]) # 必须是array数组的形式
rect = cv2.minAreaRect(cnt) # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度）
box = cv2.boxPoints(rect) # 获取最小外接矩形的4个顶点坐标
box = np.int0(box)
cv2.drawContours(img, [box], 0, (255, 0, 0), 1)轮廓矩形框：
cnt = np.array([[data_0_x, data_0_y], [data_1_x, data_1_y], [data_2_x, data_2_y], [data_3_x, data_3_y]]) # 必须是array数组的形式
ret=cv2.boundingRect(cnt)
x,y,w,h=ret旋转矩形和最小包围矩形求解
在 OpenCV 中，寻找一个多边形的最大内接矩形并不直接支持。但是你可以使用旋转矩形（RotatedRect）和最小包围矩形（minAreaRect）的概念来解决这个问题。
基本的思路是这样的：
- 找到多边形的最小包围矩形（这个矩形可能是旋转的）。
- 旋转多边形，使得最小包围矩形与坐标轴对齐。
- 在旋转后的多边形中找到最大的内接矩形。
这是一个基本的 Python 和 OpenCV 代码实现：
import cv2
import numpy as np
def max_area_rect_in_poly(poly):
# Find minimum area rectangle
rect = cv2.minAreaRect(poly)
box = cv2.boxPoints(rect)
box = np.int0(box)
# Rotate polygon to align minimum area rectangle with axis
angle = rect[-1]
if angle < -45:
angle = (90 + angle)
else:
angle = -angle
(h, w) = (poly.shape[0], poly.shape[1])
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
poly = cv2.warpAffine(poly, M, (w, h))
# Find maximum area rectangle in rotated polygon
# Here you can use a brute force method to find the maximum area rectangle.
# This can be a complex problem depending on the shape of your polygon.
return max_rect
# Example usage:
poly = np.array([[10,10], [50,10], [50,30], [30,50], [10,30]], dtype=np.int32)
max_rect = max_area_rect_in_poly(poly)python 获取最大内接矩形方法2：
原文链接：
import numpy as np
def get_max_inner_rectangles(matrix_np: np.ndarray, rectangle_bbox: list, area_value: int, result_list: list,
cur_area: float = float('inf')) -> list:
"""
递归获取空间的多个内接矩形
Args:
matrix_np: 包含空间的底图
rectangle_bbox: 空间的外接矩形
area_value: 最小面积阈值
result_list: 内接矩形列表
cur_area: 当前矩形的面积
Returns:
result_list: 内接矩形列表
"""
xmin, ymin, xmax, ymax = rectangle_bbox
crop_img = matrix_np[ymin:ymax, xmin:xmax] # 通过最大外接矩形，crop包含该空间的区域，优化速度
matrix_list = crop_img.tolist()
row = len(matrix_list)
col = len(matrix_list[0])
height = [0] * (col + 2)
res = 0 # 记录矩形内像素值相加后的最大值
bbox_rec = None # 最大内接矩形bbox
for i in range(row):
stack = [] # 利用栈的特性获取最大矩形区域
for j in range(col + 2):
if 1 <= j <= col:
if matrix_list[i][j - 1] == 255:
height[j] += 1
else:
height[j] = 0
# 精髓代码块 计算最大内接矩形 并计算最大值
while stack and height[stack[-1]] > height[j]:
cur = stack.pop()
if res < (j - stack[-1] - 1) * height[cur]:
res = (j - stack[-1] - 1) * height[cur]
bbox_rec = [stack[-1], i - height[cur], j, i]
stack.append(j)
# 递归停止条件，1.最大内接矩形面积小于阈值；2. 没有最大内接矩形
if cur_area < area_value or not bbox_rec:
return result_list
# 映射到原图中的位置
src_min_x = xmin + bbox_rec[0]
src_min_y = ymin + bbox_rec[1]
src_max_x = xmin + bbox_rec[2]
src_max_y = ymin + bbox_rec[3]
bbox_src_position = [src_min_x, src_min_y, src_max_x, src_max_y]
# 转成np格式，并将已经找到的最大内接矩形涂黑
bbox_cnt = [[bbox_src_position[0], bbox_src_position[1]],
[bbox_src_position[2], bbox_src_position[1]],
[bbox_src_position[2], bbox_src_position[3]],
[bbox_src_position[0], bbox_src_position[3]]]
contour_cur_np = np.array(bbox_cnt).reshape(-1, 1, 2)
cv2.polylines(matrix_np, [contour_cur_np], 1, 0)
cv2.fillPoly(matrix_np, [contour_cur_np], 0)
cur_area = (bbox_rec[2] - bbox_rec[0]) * (bbox_rec[3] - bbox_rec[1])
if cur_area > area_value:
result_list.append(bbox_src_position)
# 递归获取剩下的内接矩形
get_max_inner_rectangles(matrix_np, rectangle_bbox, area_value, result_list, cur_area)
return result_list
x, y, w, h = cv2.boundingRect(cnt_final.reshape(-1,1,2))
cnt_bbox = [x, y, x + w, y + h]
res_list = get_max_inner_rectangles(img, cnt_bbox, 100, [])
res_list = sorted(res_list, key=lambda _: (_[2] -_[0]) *(_[3] -_[1]), reverse=True)
res = res_list[0]
————————————————def drawInRectgle(img, cont, cX, cY, x_min, x_max, y_min, y_max):
"""绘制不规则最大内接正矩形"""
# img 对应的是原图, 四个极值坐标对应的是最大外接矩形的四个顶点
c = cont # 单个轮廓
# print(c)
range_x, range_y = x_max - x_min, y_max - y_min # 轮廓的X，Y的范围
x1, x2, y1, y2 = cX, cX, cY, cY # 中心扩散矩形的四个顶点x,y
cnt_range, radio = 0, 0
shape_flag = 1 # 1：轮廓X轴方向比Y长；0：轮廓Y轴方向比X长
if range_x > range_y: # 判断轮廓 X方向更长
radio, shape_flag = int(range_x / range_y), 1
range_x_left = cX - x_min
range_x_right = x_max - cX
if range_x_left >= range_x_right: # 取轴更长范围作for循环
cnt_range = int(range_x_left)
if range_x_left < range_x_right:
cnt_range = int(range_x_right)
else: # 判断轮廓 Y方向更长
radio, shape_flag = int(range_y / range_x), 0
range_y_top = cY - y_min
range_y_bottom = y_max - cY
if range_y_top >= range_y_bottom: # 取轴更长范围作for循环
cnt_range = int(range_y_top)
if range_y_top < range_y_bottom:
cnt_range = int(range_y_bottom)
print("X radio Y: %d " % radio)
print("---------new drawing range: %d-------------------------------------" % cnt_range)
flag_x1, flag_x2, flag_y1, flag_y2 = False, False, False, False
radio = 5 # 暂时设5，统一比例X:Y=5:1 因为发现某些会出现X:Y=4:1, 某些会出现X:Y=5:1
if shape_flag == 1:
radio_x = radio - 1
radio_y = 1
else:
radio_x = 1
radio_y = radio - 1
for ix in range(1, cnt_range, 1): # X方向延展，假设X:Y=3:1，那延展步进值X:Y=3:1
# 第二象限延展
if flag_y1 == False:
y1 -= 1 * radio_y # 假设X:Y=1:1，轮廓XY方向长度接近，可理解为延展步进X:Y=1:1
p_x1y1 = cv.pointPolygonTest(c, (x1, y1), False)
p_x2y1 = cv.pointPolygonTest(c, (x2, y1), False)
if p_x1y1 <= 0 or y1 <= y_min or p_x2y1 <= 0: # 在轮廓外，只进行y运算，说明y超出范围
for count in range(0, radio_y - 1, 1): # 最长返回步进延展
y1 += 1 # y超出, 步进返回
p_x1y1 = cv.pointPolygonTest(c, (x1, y1), False)
if p_x1y1 <= 0 or y1 <= y_min or p_x2y1 <= 0:
pass
else:
break
# print("y1 = %d, P=%d" % (y1, p_x1y1))
flag_y1 = True
if flag_x1 == False:
x1 -= 1 * radio_x
p_x1y1 = cv.pointPolygonTest(c, (x1, y1), False) # 满足第二象限的要求，像素都在轮廓内
p_x1y2 = cv.pointPolygonTest(c, (x1, y2), False) # 满足第三象限的要求，像素都在轮廓内
if p_x1y1 <= 0 or x1 <= x_min or p_x1y2 <= 0: # 若X超出轮廓范围
# x1 += 1 # x超出, 返回原点
for count in range(0, radio_x-1, 1): #
x1 += 1 # x超出, 步进返回
p_x1y1 = cv.pointPolygonTest(c, (x1, y1), False) # 满足第二象限的要求，像素都在轮廓内
p_x1y2 = cv.pointPolygonTest(c, (x1, y2), False) # 满足第三象限的要求，像素都在轮廓内
if p_x1y1 <= 0 or x1 <= x_min or p_x1y2 <= 0:
pass
else:
break
# print("x1 = %d, P=%d" % (x1, p_x1y1))
flag_x1 = True # X轴像左延展达到轮廓边界，标志=True
# 第三象限延展
if flag_y2 == False:
y2 += 1 * radio_y
p_x1y2 = cv.pointPolygonTest(c, (x1, y2), False)
p_x2y2 = cv.pointPolygonTest(c, (x2, y2), False)
if p_x1y2 <= 0 or y2 >= y_max or p_x2y2 <= 0: # 在轮廓外，只进行y运算，说明y超出范围
for count in range(0, radio_y - 1, 1): # 最长返回步进延展
y2 -= 1 # y超出, 返回原点
p_x1y2 = cv.pointPolygonTest(c, (x1, y2), False)
if p_x1y2 <= 0 or y2 >= y_max or p_x2y2 <= 0: # 在轮廓外，只进行y运算，说明y超出范围
pass
else:
break
# print("y2 = %d, P=%d" % (y2, p_x1y2))
flag_y2 = True # Y轴像左延展达到轮廓边界，标志=True
# 第一象限延展
if flag_x2 == False:
x2 += 1 * radio_x
p_x2y1 = cv.pointPolygonTest(c, (x2, y1), False) # 满足第一象限的要求，像素都在轮廓内
p_x2y2 = cv.pointPolygonTest(c, (x2, y2), False) # 满足第四象限的要求，像素都在轮廓内
if p_x2y1 <= 0 or x2 >= x_max or p_x2y2 <= 0:
for count in range(0, radio_x - 1, 1): # 最长返回步进延展
x2 -= 1 # x超出, 返回原点
p_x2y1 = cv.pointPolygonTest(c, (x2, y1), False) # 满足第一象限的要求，像素都在轮廓内
p_x2y2 = cv.pointPolygonTest(c, (x2, y2), False) # 满足第四象限的要求，像素都在轮廓内
if p_x2y1 <= 0 or x2 >= x_max or p_x2y2 <= 0:
pass
elif p_x2y2 > 0:
break
# print("x2 = %d, P=%d" % (x2, p_x2y1))
flag_x2 = True
if flag_y1 and flag_x1 and flag_y2 and flag_x2:
print("(x1,y1)=(%d,%d)" % (x1, y1))
print("(x2,y2)=(%d,%d)" % (x2, y2))
break
# cv.line(img, (x1,y1), (x2,y1), (255, 0, 0))
cv.rectangle(img, (x1, y1), (x2, y2), (255, 255, 255), 1, 8)
return x1, x2, y1, y2c++ opencv获取最大内接矩形
#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：
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