import cv2
import numpy as np

OPENCV_MAJOR_VERSION = int(cv2.__version__.split('.')[0])

img = cv2.pyrDown(cv2.imread("../images/hammer.jpg"))

ret, thresh = cv2.threshold(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),
                            127, 255, cv2.THRESH_BINARY)

if OPENCV_MAJOR_VERSION >= 4:
    # OpenCV 4 or a later version is being used.
    contours, hier = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
                                      cv2.CHAIN_APPROX_SIMPLE)
else:
    # OpenCV 3 or an earlier version is being used.
    # cv2.findContours has an extra return value.
    # The extra return value is the thresholded image, which (in
    # OpenCV 3.1 or an earlier version) may have been modified, but
    # we can ignore it.
    _, contours, hier = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
                                         cv2.CHAIN_APPROX_SIMPLE)

for c in contours:
    # find bounding box coordinates
    x, y, w, h = cv2.boundingRect(c)
    cv2.rectangle(img, (x,y), (x+w, y+h), (0, 255, 0), 2)

    # 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)

    # calculate center and radius of minimum enclosing circle
    (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, (0, 255, 0), 2)

cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
cv2.imshow("contours", img)

cv2.waitKey()
cv2.destroyAllWindows()