How to locate and read Data Matrix code with python

It turns out that the Harris corner detector (B) finds the round elements very well with proper settings.

After thresholding (C) we detect contours of the resulting areas. We select the largest contour (D) and find a minimal bounding box (E).

import matplotlib.pyplot as plt
import numpy as np
import cv2
import PIL
from urllib.request import urlopen

well = np.array(PIL.Image.open(urlopen('https://i.stack.imgur.com/kqHkw.png')))
well = cv2.cvtColor(well, cv2.COLOR_BGRA2GRAY)
plt.subplot(151); plt.title('A')
plt.imshow(well)

harris = cv2.cornerHarris(well,4, 1,0.00)
plt.subplot(152); plt.title('B')
plt.imshow(harris)

x, thr = cv2.threshold(harris, 0.1 * harris.max(), 255, cv2.THRESH_BINARY)
thr = thr.astype('uint8')
plt.subplot(153); plt.title('C')
plt.imshow(thr)

contours, hierarchy = cv2.findContours(thr, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
areas = [cv2.contourArea(cv2.convexHull(x)) for x in contours]
max_i = areas.index(max(areas))
d = cv2.drawContours(np.zeros_like(thr), contours, max_i, 255, 1)
plt.subplot(154); plt.title('D')
plt.imshow(d)

rect = cv2.minAreaRect(contours[max_i])
box = cv2.boxPoints(rect)
box = np.int0(box)
e = cv2.drawContours(well,[box],0,1,1)
plt.subplot(155); plt.title('E')
plt.imshow(e)

plt.show()