Python实现图片查找轮廓、多边形拟合、最小外接矩形代码

 import cv2 import numpy as np img = cv2.imread('/home/yasin/coffe.jpg-600') img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) _, contours, hierarchy = cv2.findContours(img_gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cv2.drawContours(img, contours, -1, (255, 0, 255), 1) cv2.namedWindow('Result of drawContours', 0) cv2.imshow('Result of drawContours', img) cv2.waitKey() cnt = 0 for i in range(len(contours)): arclen = cv2.arcLength(contours[i], True) epsilon = max(3, int(arclen * 0.02)) # 拟合出的多边形与原轮廓最大距离，可以自己设置，这里根据轮廓周长动态设置 approx = cv2.approxPolyDP(contours[i], epsilon, False) # 轮廓的多边形拟合 area = cv2.contourArea(contours[i]) # 计算面积 rect = cv2.minAreaRect(contours[i]) box = np.int0(cv2.boxPoints(rect)) # 计算最小外接矩形顶点 h = int(rect[1][0]) w = int(rect[1][1]) if min(h, w) == 0: ration = 0 else: ration = max(h,w) /min(h,w) # 长宽比 # 对长宽比，轮廓面积，拟合出的多边形顶点数做筛选 if ration <10 and area> 20 and area <4000 and approx.shape[0]> 3 : # 对满足条件的轮廓画出轮廓的拟合多边形 cv2.polylines(img, [approx], True, (0, 255, 0), 1) cv2.namedWindow('Result of filtered', 0) cv2.imshow('Result of filtered', img) cv2.waitKey() 

 import cv2 import numpy as np def main(): # 1.导入图片 img_src = cv2.imread("cc.jpg-600") # 2.灰度化,二值化 img_gray = cv2.cvtColor(img_src, cv2.COLOR_BGR2GRAY) ret, img_bin = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY) # 3.连通域分析 img_bin, contours, hierarchy = cv2.findContours(img_bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) # 4.获取最小外接圆 圆心 半径 center, radius = cv2.minEnclosingCircle(contours[0]) center = np.int0(center) # 5.绘制最小外接圆 img_result = img_src.copy() cv2.circle(img_result, tuple(center), int(radius), (255, 255, 255), 2) # 6.显示结果图片 cv2.imshow("img_src", img_src) cv2.imshow("img_result", img_result) cv2.waitKey() cv2.destroyAllWindows() if __name__ == '__main__': main()