# -*- coding: utf-8 -*- import cv2 import sys import numpy as np # from matplotlib import pyplot as plt def watershed(src): # Change color to gray scale gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) # Use the Otsu's binarization thresh,bin_img = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) # print(thresh) # print threshold # Noise removal kernel = np.ones((3,3), np.uint8) opening = cv2.morphologyEx(bin_img,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 ret, 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 # Apply watershed markers = cv2.watershed(src,markers) src[markers == -1] = [255,0,0] # Check marker (If check markers, please import matplotlib) # plt.imshow(markers) # plt.show() # Check markers data # print(np.unique(markers,return_counts=True)) return markers, src if __name__ == '__main__': param = sys.argv if (len(param) != 2): print ("Usage: $ python " + param[0] + " sample.jpg") quit() # open image file try: input_img = cv2.imread(param[1]) except: print ('faild to load %s' % param[1]) quit() if input_img is None: print ('faild to load %s' % param[1]) quit() markers, img = watershed(input_img) cv2.imwrite("watershed_markers_" + param[1], markers) cv2.imwrite("watershed_image_" + param[1], img)