#!/usr/bin/env python ''' Watershed segmentation ========= This program demonstrates the watershed segmentation algorithm in OpenCV: watershed(). Usage ----- watershed.py [image filename] Keys ---- 1-7 - switch marker color SPACE - update segmentation r - reset a - toggle autoupdate ESC - exit ''' # Python 2/3 compatibility from __future__ import print_function import numpy as np import cv2 as cv from common import Sketcher class App: def __init__(self, fn): self.img = cv.imread(fn) if self.img is None: raise Exception('Failed to load image file: %s' % fn) h, w = self.img.shape[:2] self.markers = np.zeros((h, w), np.int32) self.markers_vis = self.img.copy() self.cur_marker = 1 self.colors = np.int32( list(np.ndindex(2, 2, 2)) ) * 255 self.auto_update = True self.sketch = Sketcher('img', [self.markers_vis, self.markers], self.get_colors) def get_colors(self): return list(map(int, self.colors[self.cur_marker])), self.cur_marker def watershed(self): m = self.markers.copy() cv.watershed(self.img, m) overlay = self.colors[np.maximum(m, 0)] vis = cv.addWeighted(self.img, 0.5, overlay, 0.5, 0.0, dtype=cv.CV_8UC3) cv.imshow('watershed', vis) def run(self): while cv.getWindowProperty('img', 0) != -1 or cv.getWindowProperty('watershed', 0) != -1: ch = cv.waitKey(50) if ch == 27: break if ch >= ord('1') and ch <= ord('7'): self.cur_marker = ch - ord('0') print('marker: ', self.cur_marker) if ch == ord(' ') or (self.sketch.dirty and self.auto_update): self.watershed() self.sketch.dirty = False if ch in [ord('a'), ord('A')]: self.auto_update = not self.auto_update print('auto_update if', ['off', 'on'][self.auto_update]) if ch in [ord('r'), ord('R')]: self.markers[:] = 0 self.markers_vis[:] = self.img self.sketch.show() cv.destroyAllWindows() if __name__ == '__main__': import sys try: fn = sys.argv[1] except: fn = 'fruits.jpg' print(__doc__) App(cv.samples.findFile(fn)).run()