Opencv-Python求单个/多个图像重心

import cv2


def get_one_center(image):
    img = cv2.imread(image, 0)

    # convert the grayscale image to binary image
    ret, thresh = cv2.threshold(img, 127, 255, 0)

    # calculate moments of binary image
    M = cv2.moments(thresh)

    # calculate x,y coordinate of center
    cX = int(M["m10"] / M["m00"])
    cY = int(M["m01"] / M["m00"])

    # put text and highlight the center
    cv2.circle(img, (cX, cY), 5, (0, 0, 0), -1)
    cv2.putText(img, "centroid", (cX - 25, cY - 25), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)

    # display the image
    cv2.imshow("Image", img)
    cv2.waitKey(0)


def get_multi_center(image):
    # read image through command line
    img = cv2.imread(image, 0)

    # convert the grayscale image to binary image
    ret, thresh = cv2.threshold(img, 127, 255, 0)

    # find contours in the binary image
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    for c in contours:
        # calculate moments for each contour
        M = cv2.moments(c)

        # calculate x,y coordinate of center
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])
        cv2.circle(img, (cX, cY), 5, (0, 0, 0), -1)
        cv2.putText(img, "centroid", (cX - 25, cY - 25), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)

    # display the image
    cv2.imshow("Image", img)
    cv2.waitKey(0)


get_one_center('0001.jpg')
get_multi_center('0009.jpg')