为了解决你的问题，你可以用这个片段。我在新的部分留下了评论，使它更容易理解

import cv2
import numpy as np

importedImage = 'shapes.png'
originalImg = cv2.imread(importedImage)

#filters image bilaterally and displays it
bilatImg = cv2.bilateralFilter(originalImg, 5, 175, 175)

#finds edges of bilaterally filtered image and displays it
edgeImg = cv2.Canny(bilatImg, 75, 200)

#gets contours (outlines) for shapes and sorts from largest area to smallest area
contours, hierarchy = cv2.findContours(edgeImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)

# drawing red contours on the image
for con in contours:
    cv2.drawContours(originalImg, con, -1, (0, 0, 255), 3)

# and double-checking the outcome
cv2.imshow("Contours check",originalImg)
cv2.waitKey()
cv2.destroyWindow("Contours check")

# find the perimeter of the first closed contour
perim = cv2.arcLength(contours[0], True)
# setting the precision
epsilon = 0.02*perim
# approximating the contour with a polygon
approxCorners = cv2.approxPolyDP(contours[0], epsilon, True)
# check how many vertices has the approximate polygon
approxCornersNumber = len(approxCorners)
print("Number of approximated corners: ", approxCornersNumber)

# can also be used to filter before moving on [if needed]
# i.e. if approxCornersNumber== 4:

# printing the position of the calculated corners
print("Coordinates of approximated corners:\n", approxCorners)

额外好处：如果你想检查哪个顶点是哪个，你可以简单地基于this answer追加最后一部分，其中(x, y)是上面为每个顶点提供的坐标

originalImg = cv2.circle(originalImg, (x,y), radius=10, color=(255, 255, 255), thickness=-1)
cv2.imshow("Vertex position",originalImg)
cv2.waitKey()
cv2.destroyWindow("Vertex position")