opencv 最大内接矩形笔记
时间：2023-02-01 12:00:00
python 最小外部矩形，
最小外部矩形的顶点坐标：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) # 获得最小外部矩形(中心(x,y), (宽,高), 旋转角度） box = cv2.boxPoints(rect) # 获得最小外部矩形的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最大内部矩形，从轮廓中的所有坐标中获得四个坐标：
python 获取过程如下：
转移：图像轮廓最大的内部矩形 - 奥布莱恩 - 博客园
def order_points(pts): # pts为轮廓坐标 # 列表中的存储元素分别是左上角、右上角、右下角和左下角 rect = np.zeros((4, 2), dtype = "float32") # 左上角的点有最小和，右下角的点有最大的和 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 # 右上角的点差最小， # 左下角的点差最大 diff = np.diff(pts, axis = 1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] # 返回排序坐标(左上右下左下) 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]
首先找出轮廓点 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() #内接矩形坐标为 print(xs[1],xs[2],ys[1],ys[2])一下内容转自：
python-opencv 图像捕捉多个不规则的轮廓，与轮廓内部区域(圆/矩形)的想法持续更新编辑(详细的想法解释和图片将附加) - Lorzen - 博客园
def drawInRectgle(img, cont, cX, cY, x_min, x_max, y_min, y_max): """绘制不规则的内部直接矩形""" # img 对应原图， 四个极值坐标对应四个最大的外矩形顶点 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轴比Y长；0:轮廓Y轴比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: # 取轴范围较长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: # 取轴范围较长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 # 暂设5，统一比例X:Y=5:1 因为会发现一些东西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方向延伸，假设X:Y=3:1，延长步进值X:Y=3:1 # 第二象限延伸 if flag_y1 == False: y1 -= 1 * radio_y # 假设X:Y=1:1，轮廓XY方向长度接近，可理解为延伸步进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: # 只在轮廓外进行y操作，说明y超出了范围 for count in range(0, radio_y - 1, 1): # 最长返回步进延伸 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，像素在轮廓内 p_x1y2 = v.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++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
#include
#include
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(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(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(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(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：
