import numpy as np
import cv2

# START of original watershed example
# from https://docs.opencv.org/3.4/d3/db4/tutorial_py_watershed.html

img = cv2.imread('water_coins.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, 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
marker_count, 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)

# END of original watershed example

# Iterate over all non-background labels
for i in range(2, marker_count + 1):
    mask = np.uint8(np.where(markers==i, 255, 0))
    x,y,w,h = cv2.boundingRect(mask)
    area = cv2.countNonZero(mask)
    print "Label %d at (%d, %d) size (%d x %d) area %d pixels" % (i,x,y,w,h,area)