OpenCV contour, bounding box, minimum rectangle, minimum closed circle detection

import cv2
import numpy as np

# The function cv2.pyrDown() is to reduce the image resolution to half of the original
img = cv2.pyrDown(cv2.imread("G:/Python_code/OpenCVStudy/images/timg.jpg", cv2.IMREAD_UNCHANGED))

# Convert the image to grayscale and then binarize
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:
    # bounding box:
    # find bounding box coordinates
    # boundingRect() converts the outline into a simple border of (x, y, w, h), cv2.rectangle() draws a rectangle [green (0, 255, 0)]
    x, y, w, h = cv2.boundingRect(c)
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)

    # Minimum rectangular area:
    # 1 Calculate the minimum rectangular area 2 Calculate the rectangle vertex 3 Since the calculation is a floating point number, and the pixel is an integer, so convert it 4 Draw the outline [red (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)

    # Outline of the smallest closed circle:
    # 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)

# Contour detection: draw contours
cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
cv2.imshow("contours", img)

cv2.waitKey()
cv2.destroyAllWindows()