to_space = [lab, srgb, cam, camucs]
space_back = [lab_back, srgb_back, cam_back, camucs_back]
name = ['lab', 'srgb', 'cam', 'camucs']
for current_iteration in range(N):
print('CURRENT_ITERATION:', current_iteration)
np.random.seed(SEED + current_iteration)
start_rgb = color.hex_2_rgb(START_COLOR)
end_rgb = color.hex_2_rgb(END_COLOR)
start_conv = to_space[current_iteration](start_rgb)
end_conv = to_space[current_iteration](end_rgb)
num_steps = 50
interp_j = np.linspace(start_conv[0], end_conv[0], num_steps)
interp_c = np.linspace(start_conv[1], end_conv[1], num_steps)
interp_h = np.linspace(start_conv[2], end_conv[2], num_steps)
gradient = np.stack((interp_j, interp_c, interp_h),
axis=1).reshape(1, num_steps, 3)
gradient = space_back[current_iteration](gradient)
gradient = np.clip(gradient, 0, 255)
img = data.upscale_nearest(gradient, ny=1000, nx=20).astype('uint8')
print(img.shape)
file.export_image(name[current_iteration] + '%d_%d' %
(current_iteration, int(round(time.time() * 1000))),
img,
format='png')
p1, p2 = PS[current_iteration]
print(p1, p2)
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)
if START_SERVER is True:
viz.start_image_server(generate_full_image, COLOR_STRING,
SEED + current_iteration)
break
elif SAVE_IMAGES is True:
image = generate_full_image(COLOR_STRING, SEED + current_iteration)
file.export_image('%d_%d_%d' %
(current_iteration, SEED + current_iteration,
int(round(time.time() * 1000))),
image,
format='png')
else:
generate_full_image(COLOR_STRING, SEED + current_iteration)
num_tiles=TILING_OPTIONS)
basic_tiles += [pats]
basic_tiles = m.SimpleProgression(values=basic_tiles, child_lengths=1)
r = [m.SimpleProgression(values=i) for i in [2, 3, 10, 11, 12]]
random_tiles = m.Processor(m.SimpleProgression(
values=m.RandomMarkovModel(values=r, child_lengths=[1, 2, 3, 4]),
child_lengths=TILE_HEIGHT * TILE_WIDTH // 2),
num_tiles=1,
length_limit=TILE_HEIGHT * TILE_WIDTH)
parent = m.RandomMarkovModel(values=[basic_tiles, random_tiles],
child_lengths=1)
img = gen_channel(parent)[:, :, 0]
print(img.shape)
print(np.min(img))
print(np.max(img))
colored_image = color.replace_indices_with_colors(img, COLOR_DICT)
print(colored_image.shape)
if N == 1:
viz.show_image(colored_image)
else:
file.export_image('%d_%d' %
(current_iteration, int(round(time.time() * 1000))),
colored_image.astype('uint8'),
format='png')