def gen_portion(parent, height, width, tile_height=None, tile_width=None):
img = m.paint_linearly_markov_hierarchy(markov_tree=parent,
width=width,
height=height,
tile_width=tile_width,
tile_height=tile_height)
return img.reshape(img.shape[0], img.shape[1])
def gen_channel(seed):
img = markov.paint_linearly_markov_hierarchy(markov_tree=parent,
width=WIDTH,
height=HEIGHT,
seed=seed)
img = data.upscale_nearest(img, UPSCALE_FACTOR)
return img.reshape(HEIGHT * UPSCALE_FACTOR, WIDTH * UPSCALE_FACTOR, 1)
def gen_coherent_noise(height,width):
vs = np.linspace(-0.2, 0.2, num=width)
px_1 = markov.FuzzyProgression(
values=vs,
positive_shifts=3, negative_shifts=3,
repeat_factor=4)
np.random.seed(120)
values = []
for i in range(3):
mask = np.random.binomial(1, p=0.5, size=width // 10)
vs = ['0', '1','2' ]
pattern = ''.join([vs[i] for i in mask])
print(pattern)
values += [
markov.SimplePattern(pattern=pattern, candidates=[-0.5,0,0.5])]
black_white = markov.RandomMarkovModel(
values=values,
child_lengths=[width * i for i in range(1, 2)])
varied = markov.SimpleProgression(
values=[px_1],
child_lengths=[width * i for i in range(10, 15)])
parent = markov.RandomMarkovModel(
values=[varied, black_white],
child_lengths=[1, 2])
img = markov.paint_linearly_markov_hierarchy(
markov_tree=parent, width=width, height=height, seed=20)
return img
def gen_channel(parent):
img = m.paint_linearly_markov_hierarchy(markov_tree=parent,
width=WIDTH,
height=HEIGHT,
tile_height=TILE_HEIGHT,
tile_width=TILE_WIDTH)
img = data.upscale_nearest(img, UPSCALE_FACTOR)
# img = data.upscale_with_circle(img,UPSCALE_FACTOR,bg=0)
# img = data.upscale_with_shape(img,line,bg=0)
return img.reshape(img.shape[0], img.shape[1], 1)
]
color_patterns += [
m.SPat(pattern=p, self_length=len(p), candidates=skips,lenghts=r.choice([5,10,14,15]))
for p in color_patterns_2
for i in range(2)
]
color_column_left = m.Proc(
m.SProg(values=color_patterns, self_length=WIDTH // 3), length_limit=WIDTH)
color_column_right = m.Proc(
m.SProg(values=color_patterns, self_length=WIDTH // 3), length_limit=WIDTH)
color_parent = m.SProg(values=[color_column_left, color_column_right],start_probs=0)
colors_deriv = m.paint_linearly_markov_hierarchy(
markov_tree=color_parent, width=WIDTH, height=2)
colors_acum = np.cumsum(colors_deriv, axis=1)
# generating the row transitions
patterns = [
m.SPat(pattern=p,candidates=skips,start_probs=[0,1],lenghts=r.choice([1,2,3,4,5]),self_length=len(p))
for p in patterns_1
for _ in range(3)
]
patterns += [
m.SPat(pattern=p,candidates=skips,start_probs=[0,1],lenghts=r.choice([5,10,15]*2+[30,40,50,60]),self_length=len(p))
for p in patterns_2
for _ in range(3)
]