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#!/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__':
print(__doc__)
import sys
try:
fn = sys.argv[1]
except:
fn = 'fruits.jpg'
App(cv.samples.findFile(fn)).run()
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