import cv2 import numpy as np # 函数cv2.pyrDown()是降低图像分辨率,变为原来一半 img = cv2.pyrDown(cv2.imread("G:/Python_code/OpenCVStudy/images/timg.jpg", cv2.IMREAD_UNCHANGED)) # 将图片转化为灰度,再进行二值化 ret, thresh = cv2.threshold(cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY), 127, 255, cv2.THRESH_BINARY) image, contours, hier = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for c in contours: # 边界框: # find bounding box coordinates # boundingRect()将轮廓转化成(x,y,w,h)的简单边框,cv2.rectangle()画出矩形[绿色(0, 255, 0)] x, y, w, h = cv2.boundingRect(c) cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) # 最小矩形区域: # 1 计算出最小矩形区域 2 计算这个的矩形顶点 3 由于计算出来的是浮点数,而像素是整型,所以进行转化 4 绘制轮廓[红色(0, 0, 255)] # find minimum area rect = cv2.minAreaRect(c) # calculate coordinates of the minimum area rectangle box = cv2.boxPoints(rect) # normalize coordinates to integers box = np.int0(box) # draw contours cv2.drawContours(img, [box], 0, (0, 0, 255), 3) # 最小闭圆的轮廓: # calculate center and radius of minimum enclosing circle[蓝色(255, 0, 0)] (x, y), radius = cv2.minEnclosingCircle(c) # cast to integers center = (int(x), int(y)) radius = int(radius) # draw the circle img = cv2.circle(img, center, radius, (255, 0, 0), 2) # 轮廓检测:绘制轮廓 cv2.drawContours(img, contours, -1, (255, 0, 0), 1) cv2.imshow("contours", img) cv2.waitKey() cv2.destroyAllWindows()
OpenCV轮廓、边界框、最小矩形、最小闭圆检测
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转载自blog.csdn.net/cinding/article/details/80037274
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