Open Source Computer Vision Library https://opencv.org/
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#!/usr/bin/env python
'''
Multiscale Turing Patterns generator
====================================
Inspired by http://www.jonathanmccabe.com/Cyclic_Symmetric_Multi-Scale_Turing_Patterns.pdf
'''
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
if PY3:
xrange = range
import numpy as np
import cv2
from common import draw_str
import getopt, sys
from itertools import count
help_message = '''
USAGE: turing.py [-o <output.avi>]
Press ESC to stop.
'''
if __name__ == '__main__':
print(help_message)
w, h = 512, 512
args, args_list = getopt.getopt(sys.argv[1:], 'o:', [])
args = dict(args)
out = None
if '-o' in args:
fn = args['-o']
out = cv2.VideoWriter(args['-o'], cv2.VideoWriter_fourcc(*'DIB '), 30.0, (w, h), False)
print('writing %s ...' % fn)
a = np.zeros((h, w), np.float32)
cv2.randu(a, np.array([0]), np.array([1]))
def process_scale(a_lods, lod):
d = a_lods[lod] - cv2.pyrUp(a_lods[lod+1])
for i in xrange(lod):
d = cv2.pyrUp(d)
v = cv2.GaussianBlur(d*d, (3, 3), 0)
return np.sign(d), v
scale_num = 6
for frame_i in count():
a_lods = [a]
for i in xrange(scale_num):
a_lods.append(cv2.pyrDown(a_lods[-1]))
ms, vs = [], []
for i in xrange(1, scale_num):
m, v = process_scale(a_lods, i)
ms.append(m)
vs.append(v)
mi = np.argmin(vs, 0)
a += np.choose(mi, ms) * 0.025
a = (a-a.min()) / a.ptp()
if out:
out.write(a)
vis = a.copy()
draw_str(vis, (20, 20), 'frame %d' % frame_i)
cv2.imshow('a', vis)
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()