def generate_full_image(color_string, seed):
r.init_def_generator(seed)
rkey = r.bind_generator()
lp = len(PS)
guard_size = 450
image = np.zeros((HEIGHT + guard_size * 2, WIDTH + guard_size * 2))
# num_circles = r.choice_from(rkey,[5,10,15,20,25])
# num_circles = r.choice_from(rkey,[30,40,50,60,70,80,90,100])
# num_circles = r.choice_from(rkey,[80,120,140])
# num_circles = r.choice_from(rkey,[200,220,240])
num_circles = 10
ps = r.choice_from(rkey, PS, lp)
for i in range(num_circles):
loopkey = r.bind_generator_from(rkey)
band_size = r.choice_from(loopkey, [10] + [15])
circle = gradient_circle(band_size, PS[::-1] + PS[::2] + PS[1::2],
r.bind_generator_from(loopkey))
cheight, cwidth = circle.shape
xstart = r.choice_from(loopkey, np.arange(WIDTH + 250))
ystart = r.choice_from(loopkey, np.arange(HEIGHT + 250))
image[ystart:ystart + cheight, xstart:xstart + cwidth] += circle
image = image[guard_size:HEIGHT + guard_size,
guard_size:WIDTH + guard_size]
image /= np.max(image)
return data.upscale_nearest(data.prepare_image_for_export(image * 255),
ny=UPSCALE_FACTOR_Y,
nx=UPSCALE_FACTOR_X)
def generate_full_image(color_string, seed):
r.init_def_generator(seed)
rkey = r.bind_generator()
p = PS[current_iteration]
image = r.binomial_from(rkey, 1, p, size=(HEIGHT, WIDTH)) * 255
return data.upscale_nearest(data.prepare_image_for_export(image),
ny=UPSCALE_FACTOR_Y,
nx=UPSCALE_FACTOR_X)
def generate_full_image(color_string, seed):
r.init_def_generator(seed)
rkey = r.bind_generator()
image = np.zeros((HEIGHT, WIDTH))
for i in range(NUM_SEGMENTS):
start = i * SEGMENT_LENGTH
end = (i + 1) * SEGMENT_LENGTH
image[:,
start:end] = r.binomial_from(rkey,
1,
PS[i],
size=(HEIGHT, SEGMENT_LENGTH))
return data.upscale_nearest(data.prepare_image_for_export(image * 255),
ny=UPSCALE_FACTOR_Y,
nx=UPSCALE_FACTOR_X)
def generate_full_image(color_string,seed):
r.init_def_generator(seed)
rkey = r.bind_generator()
image = np.zeros((HEIGHT,WIDTH))
p1,p2 = PS[current_iteration]
image[:,:WIDTH//2] = r.binomial_from(rkey,1,p1,size=(HEIGHT,WIDTH//2))
image[:,WIDTH//2:] = r.binomial_from(rkey,1,p2,size=(HEIGHT,WIDTH//2))
return data.upscale_nearest(
data.prepare_image_for_export(image*255),
ny=UPSCALE_FACTOR_Y,
nx=UPSCALE_FACTOR_X
)
def generate_full_image(color_string, seed):
r.init_def_generator(seed)
rkey = r.bind_generator()
lp = len(PS)
template_height = lp * BAND_LEN
image = np.zeros((template_height, template_height))
for i, p in enumerate(PS):
cheight = (lp - i) * BAND_LEN
c = gen.circle((template_height, template_height), cheight // 2)
p = r.binomial_from(rkey,
1,
p,
size=(template_height, template_height))
mask = c == 1
image[mask] = p[mask]
return data.upscale_nearest(data.prepare_image_for_export(image * 255),
ny=UPSCALE_FACTOR_Y,
nx=UPSCALE_FACTOR_X)
def generate_full_image(color_string, seed):
r.init_def_generator(seed)
rkey = r.bind_generator()
template_height = NUM_BANDS * BAND_LEN
image = np.zeros((template_height, template_height))
for i, p in enumerate(range(NUM_BANDS)):
cheight = (NUM_BANDS - i) * BAND_LEN
c = gen.circle((template_height, template_height), cheight // 2)
p = r.poisson_from(rkey,
i + 1.1,
size=(template_height, template_height))
mask = c == 1
image[mask] = p[mask]
image = image / np.max(image)
return data.upscale_nearest(data.prepare_image_for_export(image * 255),
ny=UPSCALE_FACTOR_Y,
nx=UPSCALE_FACTOR_X)
def generate_full_image(color_string,seed):
r.init_def_generator(seed)
rkey = r.bind_generator()
guard_size = 200
image = np.zeros((HEIGHT+guard_size*2,WIDTH+guard_size*2))
num_circles = r.choice_from(rkey,[5,10,15,20,25])
# num_circles = r.choice_from(rkey,[30,40,50,60,70,80,90,100])
# num_circles = r.choice_from(rkey,[400,500,600,700,800])
for i in range(num_circles):
loopkey = r.bind_generator_from(rkey)
band_size = r.choice_from(loopkey,np.arange(5,10))
circle = gradient_circle(
band_size,
r.bind_generator_from(loopkey)
)
cheight,cwidth = circle.shape
xstart = r.choice_from(loopkey,np.arange(WIDTH+100))
ystart = r.choice_from(loopkey,np.arange(HEIGHT+100))
image[ystart:ystart+cheight,xstart:xstart+cwidth] += circle
image /= np.max(image)
image = image[
guard_size:HEIGHT+guard_size,
guard_size:WIDTH+guard_size]
return data.upscale_nearest(
data.prepare_image_for_export(image*255),
ny=UPSCALE_FACTOR_Y,
nx=UPSCALE_FACTOR_X
)
