51 lines
1.2 KiB
Python
51 lines
1.2 KiB
Python
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#!/usr/bin/env python
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'''
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Watershed segmentation
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=========
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This program demonstrates the watershed segmentation algorithm
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in OpenCV: watershed().
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Usage
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-----
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watershed.py [image filename]
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Keys
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----
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1-7 - switch marker color
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SPACE - update segmentation
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r - reset
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a - toggle autoupdate
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ESC - exit
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2
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from tests_common import NewOpenCVTests
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class watershed_test(NewOpenCVTests):
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def test_watershed(self):
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img = self.get_sample('cv/inpaint/orig.png')
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markers = self.get_sample('cv/watershed/wshed_exp.png', 0)
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refSegments = self.get_sample('cv/watershed/wshed_segments.png')
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if img == None or markers == None:
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self.assertEqual(0, 1, 'Missing test data')
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colors = np.int32( list(np.ndindex(3, 3, 3)) ) * 122
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cv2.watershed(img, np.int32(markers))
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segments = colors[np.maximum(markers, 0)]
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if refSegments == None:
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refSegments = segments.copy()
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cv2.imwrite(self.extraTestDataPath + '/cv/watershed/wshed_segments.png', refSegments)
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self.assertLess(cv2.norm(segments - refSegments, cv2.NORM_L1) / 255.0, 50)
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