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Open Source Computer Vision Library
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85 lines
2.3 KiB
85 lines
2.3 KiB
#!/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 as cv |
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from common import Sketcher |
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class App: |
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def __init__(self, fn): |
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self.img = cv.imread(fn) |
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if self.img is None: |
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raise Exception('Failed to load image file: %s' % fn) |
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h, w = self.img.shape[:2] |
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self.markers = np.zeros((h, w), np.int32) |
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self.markers_vis = self.img.copy() |
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self.cur_marker = 1 |
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self.colors = np.int32( list(np.ndindex(2, 2, 2)) ) * 255 |
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self.auto_update = True |
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self.sketch = Sketcher('img', [self.markers_vis, self.markers], self.get_colors) |
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def get_colors(self): |
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return list(map(int, self.colors[self.cur_marker])), self.cur_marker |
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def watershed(self): |
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m = self.markers.copy() |
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cv.watershed(self.img, m) |
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overlay = self.colors[np.maximum(m, 0)] |
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vis = cv.addWeighted(self.img, 0.5, overlay, 0.5, 0.0, dtype=cv.CV_8UC3) |
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cv.imshow('watershed', vis) |
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def run(self): |
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while cv.getWindowProperty('img', 0) != -1 or cv.getWindowProperty('watershed', 0) != -1: |
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ch = cv.waitKey(50) |
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if ch == 27: |
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break |
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if ch >= ord('1') and ch <= ord('7'): |
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self.cur_marker = ch - ord('0') |
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print('marker: ', self.cur_marker) |
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if ch == ord(' ') or (self.sketch.dirty and self.auto_update): |
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self.watershed() |
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self.sketch.dirty = False |
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if ch in [ord('a'), ord('A')]: |
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self.auto_update = not self.auto_update |
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print('auto_update if', ['off', 'on'][self.auto_update]) |
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if ch in [ord('r'), ord('R')]: |
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self.markers[:] = 0 |
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self.markers_vis[:] = self.img |
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self.sketch.show() |
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cv.destroyAllWindows() |
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if __name__ == '__main__': |
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print(__doc__) |
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import sys |
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try: |
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fn = sys.argv[1] |
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except: |
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fn = 'fruits.jpg' |
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App(cv.samples.findFile(fn)).run()
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