opencv 最大内接矩形笔记
python 最小外接矩形:
- 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(ps: cv2.boxPoints(rect) for OpenCV 3.x)
- box = np.int0(box)
- cv2.drawContours(img, [box], 0, (255, 0, 0), 1)
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])
一下内容转自:
python-opencv 图像捕捉多个不规则轮廓,与轮廓内接区域(圆/矩形)思路-持续更新编辑中(会附上详细的思路解释和图片) - Lorzen - 博客园
- 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, y2
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<opencv2opencv.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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