test1.py

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('img_test/1.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

# noise removal
kernel = np.ones((2, 2), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=50)

# sure background area
sure_bg = cv2.dilate(opening, kernel, iterations=10)

# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
_, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)

# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)

# Marker labelling
_, markers = cv2.connectedComponents(sure_fg)

# Add one to all labels so that sure background is not 0, but 1
markers = markers + 1

# Now, mark the region of unknown with zero
markers[unknown == 255] = 0

markers = cv2.watershed(img, markers)
img[markers == -1] = [255, 0, 0]

plt.subplot(131), plt.imshow(sure_bg), plt.title("Background")
plt.subplot(132), plt.imshow(sure_fg), plt.title("Foreground")
plt.subplot(133), plt.imshow(img), plt.title("Image with markers")
plt.show()