Watershed Algorithm.py

#importing required libraries
import numpy as np
import cv2
import matplotlib.pyplot as plt

#reading the image
image = cv2.imread('coins.jpg')
#converting image to grayscale format
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
#apply thresholding
ret,thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
#get a kernel
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel,iterations = 2)
#extract the background from image
sure_bg = cv2.dilate(opening,kernel,iterations = 3)

dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret,sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)

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

ret,markers = cv2.connectedComponents(sure_fg)

markers = markers+1

markers[unknown==255] = 0

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

plt.imshow(sure_fg)