DUMP_PREVIOUS_EXPORTS = False
SHOW_DEBUG_DATA = False
START_SERVER = False
SHOW_IMAGES = True
SAVE_IMAGES = True
TRAIN_NNET = True
CONTINUE_TRAINING = False
SAVE_INTERMEDIARY = False
EXPLORE_LAYER = 'o_0'
NUM_VIZ_ROWS = 1
N = 1
SEED = config.get('seed', 325)
HEIGHT = 500
WIDTH = 500
TRAINING_UPSAMPLE = 2
### NEURAL NET CONSTANTS
LEARNING_RATE = 0.005
NUM_TRAINING_EPOCHS = 150
BATCH_SIZE = 1024
FIRST_DENSE = 5
HIDDEN_STRUCTURE = [
('sparse', False, 32, 2, False),
('bottleneck', False, 200, 16),
# ('bottleneck',32,16),
('sparse', False, 44, 4, False),
import constants as c
import script_config as config
import numpy as np
import cv2
import utils.generate_utils as gen
import random_manager as r
import utils.file_utils as file
import utils.data_utils as data
import utils.markov_utils as m
import utils.color_utils as color
import utils.viz_utils as viz
### DATA/INPUT/SHARED by all runs section
print('PREPARING DATA SECTION')
N = 30
SEED = config.get('seed',0)
HEIGHT = 500
WIDTH = HEIGHT
UPSCALE_FACTOR = c.INSTA_SIZE // HEIGHT
STRING_COLORS = config.get(
'colors','aaaaaa-aaaaaa-aaaaaa-aaaaaa-aaaaaa')
COLOR_DICT = {
i:j
for i,j in enumerate(STRING_COLORS.split('-'))
}
COLOR_DICT = {
import constants as c
import script_config as config
import numpy as np
import cv2
import utils.generate_utils as gen
import random_manager as r
import utils.file_utils as file
import utils.data_utils as data
import utils.markov_utils as m
import utils.color_utils as color
import utils.viz_utils as viz
### DATA/INPUT/SHARED by all runs section
print('PREPARING DATA SECTION')
N = 1
SEED = config.get('seed', 0)
HEIGHT = 100
WIDTH = HEIGHT
UPSCALE_FACTOR = c.INSTA_SIZE // HEIGHT
NUM_COLORS = 10
UPSCALE_COLORS = HEIGHT // (NUM_COLORS * 2)
STRING_COLORS_1 = config.get('colors_1', 'aaaaaa-aaaaaa-aaaaaa-aaaaaa-aaaaaa')
STRING_COLORS_2 = config.get('colors_2', 'aaaaaa-aaaaaa-aaaaaa-aaaaaa-aaaaaa')
COLOR_DICT_1 = {i: j for i, j in enumerate(STRING_COLORS_1.split('-'))}
COLOR_DICT_2 = {10 + i: j for i, j in enumerate(STRING_COLORS_2.split('-'))}
COLOR_DICT = {
import constants as c
import script_config as config
import numpy as np
import cv2
import utils.generate_utils as gen
import random_manager as r
import utils.file_utils as file
import utils.data_utils as data
import utils.markov_utils as m
import utils.color_utils as color
import utils.viz_utils as viz
### DATA/INPUT/SHARED by all runs section
print('PREPARING DATA SECTION')
N = 100
SEED = config.get('seed', 0)
HEIGHT = 1000
WIDTH = HEIGHT
UPSCALE_FACTOR = c.INSTA_SIZE // HEIGHT
COLOR_DICT_1 = color.build_palette_from_state(
config.get('string-color-3', c.DEFAULT_COLOR_STR_1))
COLOR_DICT_2 = color.build_palette_from_state(
config.get('string-color-4', c.DEFAULT_COLOR_STR_1))
### SETUP section
print('SETUP SECTION')
if N > 1:
file.clear_export_folder()
import random_manager as r
import utils.file_utils as file
import utils.data_utils as data
import utils.markov_utils as m
import utils.color_utils as color
import utils.viz_utils as viz
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter
### DATA/INPUT/SHARED by all runs section
print('PREPARING DATA SECTION')
DUMP_PREVIOUS_EXPORTS = True
START_SERVER = False
SAVE_IMAGES = True
N = 9
SEED = config.get('seed', 296)
HEIGHT = 500
WIDTH = 500
SEGMENT_LENGTH = 40
PS = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
UPSCALE_FACTOR_Y = c.INSTA_SIZE // HEIGHT
UPSCALE_FACTOR_X = c.INSTA_SIZE // WIDTH
COLOR_STRING = config.get(
'color-string',
'First:0/2bff4b/3-1/00ffe1/3-2/00618e/3-3/bc09b6/3,Second:0/ff7b60/-1/ffe175/-2/f6ffa5/-3/ff002e/'
)
### SETUP section
def generate_full_image(color_string,seed):
r.init_def_generator(seed)
image = np.zeros((HEIGHT,WIDTH,3))
plots = []
loop_key = r.bind_generator()
setup_key = r.bind_generator()
p1 = m.MarkovModel(
values=[0.1,-0.1],
preference_matrix=data.str2mat('1 5, 5 1'),
self_length=SEGMENT_LENGTH,
parent_rkey=r.bind_generator_from(setup_key)
)
p2 = m.MarkovModel(
values=[-0.1, 0.1],
preference_matrix=data.str2mat('1 5, 5 1'),
self_length=SEGMENT_LENGTH,
parent_rkey=r.bind_generator_from(setup_key)
)
num_coefs = 12
vs = np.sin(np.linspace(0, 1, num_coefs) * np.pi * 2) * 0.1
p3 = m.SimpleProgression(
values=vs,
start_probs=0,
self_length=[num_coefs],
parent_rkey=r.bind_generator_from(loop_key)
)
p = m.MarkovModel(
values=[p1, p2, p3],
start_probs=2,
preference_matrix=data.str2mat(
'0 1 2, 1 0 2, 1 1 4'),
self_length=HEIGHT//SEGMENT_LENGTH+1,
parent_rkey=r.bind_generator_from(setup_key)
)
num_samples_1 = HEIGHT//2
sample_scale_1 = m.sample_markov_hierarchy(p, num_samples_1)
sample_2 = m.sample_markov_hierarchy(p, num_samples_1)
sample_3 = m.sample_markov_hierarchy(p, num_samples_1)
# interpolation_h_1 = integrate_and_normalize(sample_scale_1,2)
# interpolation_h_2 = integrate_and_normalize(sample_2,2)
interpolation_color = integrate_and_normalize(sample_3,2)
color_repo = color.build_color_repository(color_string)
meta = color.get_meta_from_palette(
color_repo['First'],
keys=[0,1,2,3],
meta_cast_function=int)
print(meta)
color_lines = compute_color_lines(color_repo,interpolation_color)
print(color_lines.shape)
# plt.plot(interpolation_h_1)
# plt.plot(interpolation_h_2)
# plt.plot(interpolation_color)
# plt.show()
scale_1_freq = r.choice_from(setup_key,config.get('scale-1-freq-options',[0.025]))
scale_2_freq = r.choice_from(setup_key,config.get('scale-2-freq-options',[0.02]))
scale_1_scale = r.choice_from(setup_key,config.get('scale-1-scale-options',[0.02]))
scale_2_scale = r.choice_from(setup_key,config.get('scale-2-scale-options',[0.02]))
num_sin_coeffs = r.choice_from(setup_key,config.get('num-sin-coefficients-options',[18]))
f1_scale = r.choice_from(setup_key,config.get('f1-scale-options',[0.2]))
f2_scale = r.choice_from(setup_key,config.get('f2-scale-options',[0.4]))
f3_scale = r.choice_from(setup_key,config.get('f3-scale-options',[0.15]))
for current_row in range(HEIGHT):
loop_key = r.reset_key(loop_key)
# self_length = SEGMENT_LENGTH+int(10*np.sin(np.pi*i*0.01))
self_length = SEGMENT_LENGTH
# scale_1 = 0.1 * (1 - interpolation_h_1[current_row]) + 0.15 * interpolation_h_1[current_row]
scale_1 = 0.1 + scale_1_scale * np.sin(np.pi * current_row * scale_1_freq )
scale_2 = 0.1 + scale_2_scale * np.sin(np.pi * current_row * scale_2_freq )
p1 = m.MarkovModel(
values=[scale_1, -scale_2],
preference_matrix=data.str2mat('1 5, 5 1'),
self_length=self_length,
parent_rkey=r.bind_generator_from(loop_key)
)
p2 = m.MarkovModel(
values=[-scale_1, scale_2],
preference_matrix=data.str2mat('1 5, 5 1'),
self_length=self_length,
parent_rkey=r.bind_generator_from(loop_key)
)
zeros = m.MarkovModel(
values=[0,0],
preference_matrix=data.str2mat('1 1, 1 1'),
self_length=self_length*3,
parent_rkey=r.bind_generator_from(loop_key)
)
jumps = m.MarkovModel(
values=[-0.5, 0.5],
preference_matrix=data.str2mat('1 1, 1 1'),
self_length=1,
parent_rkey=r.bind_generator_from(loop_key)
)
num_coefs = num_sin_coeffs
vs = np.sin(np.linspace(0, 1, num_coefs) * np.pi * 2)*0.1
p3 = m.SimpleProgression(
values=vs,
start_probs=0,
self_length=[num_coefs],
parent_rkey=r.bind_generator_from(loop_key)
)
p = m.MarkovModel(
values=[p1, p2, p3, jumps, zeros],
start_probs=2,
preference_matrix=data.str2mat(
'0 1 2 2 1, 1 0 2 2 1, 1 1 4 2 2, 1 1 2 0 0, 1 1 1 1 2'),
self_length=WIDTH//SEGMENT_LENGTH+1,
parent_rkey=r.bind_generator_from(loop_key)
)
num_samples_1 = WIDTH//4
num_samples_2 = WIDTH//3
sample_x_up = m.sample_markov_hierarchy(p, num_samples_1)
sample_x_down = m.sample_markov_hierarchy(p, num_samples_2)
sample_x_up_int = data.integrate_series(sample_x_up,2,mean_influence=1)
sample_x_down_int = data.integrate_series(sample_x_down,2,mean_influence=1)
f1 = 0.5 + f1_scale * np.sin(np.pi * current_row * 0.002 )
f2 = -1 - f2_scale * np.sin(np.pi * current_row * 0.002 )
f3 = 0.3 + f3_scale * np.sin(np.pi * current_row * 0.001 )
sample_x_up_int = data.concat_signals(
[sample_x_up_int]*4,
[f1,f2,f1,f2])
sample_x_down_int = data.concat_signals(
[sample_x_down_int,sample_x_down_int,sample_x_down_int],
[f3, f1, f3])
sample_x_down_int = np.r_[sample_x_down_int[0],sample_x_down_int]
# roll_distance = 500 + int((interpolation_h_2[current_row]-0.5)*250)
# roll_distance = 500 + int(current_row)
# print(roll_distance)
# sample_x_down_int = np.roll(sample_x_down_int, roll_distance)
sample_x = sample_x_up_int + sample_x_down_int
interpolation_sequence = sample_x[:HEIGHT]
interpolation_sequence = gaussian_filter(interpolation_sequence,sigma=1)
interpolation_sequence -= np.min(interpolation_sequence)
interpolation_sequence /= np.max(interpolation_sequence)
# interpolation_sequence = data.ease_inout_sin(interpolation_sequence)
interpolation_sequence *= 3
# interpolation_sequence *= 2
# print(interpolation_sequence)
gradient = data.interpolate(
color_lines[:,current_row,:],
interpolation_sequence,
value_influences=meta
)
gradient = color.cam02_2_srgb(gradient)
image[current_row] = gradient
plots += [np.copy(interpolation_sequence)]
image = data.upscale_nearest(image,ny=UPSCALE_FACTOR_Y,nx=UPSCALE_FACTOR_X)
image[image<0] = 0
image[image>255] = 255
if SHOW_DEBUG_DATA is True:
viz.animate_plots_y(plots)
return image
import time
import constants as c
import script_config as config
import numpy as np
import cv2
import utils.generate_utils as gen
import utils.file_utils as file
import utils.data_utils as data
import utils.markov_utils as m
import utils.color_utils as color
import utils.viz_utils as viz
### DATA/INPUT/SHARED by all runs section
print('PREPARING DATA SECTION')
N = 4
SEED = config.get('seed', 0)
HEIGHT = 100
WIDTH = HEIGHT
UPSCALE_FACTOR = c.INSTA_SIZE // HEIGHT
START_COLOR = 'ff0000'
END_COLOR = '0000ff'
### SETUP section
print('SETUP SECTION')
if N > 1:
file.clear_export_folder()
### FUNCTIONS section
print('FUNCTIONS SETUP')
import constants as c
import script_config as config
import numpy as np
import cv2
import utils.generate_utils as gen
import random_manager as r
import utils.file_utils as file
import utils.data_utils as data
import utils.markov_utils as m
import utils.color_utils as color
import utils.viz_utils as viz
### DATA/INPUT/SHARED by all runs section
print('PREPARING DATA SECTION')
N = 200
SEED = config.get('seed', 0)
HEIGHT = 500
WIDTH = HEIGHT
UPSCALE_FACTOR = c.INSTA_SIZE // HEIGHT
STRING_COLORS_1 = config.get('colors_1', 'b3b4d4-e4f097-edd1ad-e9a6b4-ffe572')
STRING_COLORS_2 = config.get('colors_2', 'b3b4d4-e4f097-edd1ad-e9a6b4-ffe572')
STRING_COLORS_3 = config.get('colors_3', 'b3b4d4-e4f097-edd1ad-e9a6b4-ffe572')
COLOR_DICT_1 = {i: j for i, j in enumerate(STRING_COLORS_1.split('-'))}
COLOR_DICT_2 = {i: j for i, j in enumerate(STRING_COLORS_2.split('-'))}
COLOR_DICT_3 = {i: j for i, j in enumerate(STRING_COLORS_3.split('-'))}
def generate_patch(height, width, color_dict, rkey):
patch = np.zeros((height, width, 3), dtype='float64')
color_start_lengths = np.array(
[int(i) for i in color.get_meta_from_palette(color_dict)])
num_color_samples = width // np.min(color_start_lengths) + 20
color_codes = color.get_keys_from_palette(color_dict)
pattern = m.FuzzyProgression(values=color_codes,
positive_shifts=3,
negative_shifts=3,
self_length=num_color_samples,
parent_rkey=r.bind_generator_from(rkey))
sample_raw_start = m.sample_markov_hierarchy(
pattern, num_color_samples).astype('int32')
sample_raw_down_start = m.sample_markov_hierarchy(
pattern, num_color_samples).astype('int32')
# print(sample_raw_start)
sample_raw_end = m.sample_markov_hierarchy(
pattern, num_color_samples).astype('int32')
sample_raw_down_end = m.sample_markov_hierarchy(
pattern, num_color_samples).astype('int32')
sample_raw_backup = m.sample_markov_hierarchy(
pattern, num_color_samples).astype('int32')
# making the probability of same color used smaller
replace_mask = sample_raw_start == sample_raw_end
sample_raw_end[replace_mask] = sample_raw_backup[replace_mask]
sample_start = color.replace_indices_with_colors(sample_raw_start,
color_dict)
sample_end = color.replace_indices_with_colors(sample_raw_end, color_dict)
sample_down_start = color.replace_indices_with_colors(
sample_raw_down_start, color_dict)
sample_down_end = color.replace_indices_with_colors(
sample_raw_down_end, color_dict)
switch_key = r.bind_generator_from(rkey)
switch = np.array([
r.choice_from(switch_key, [0, 1], replace=False, size=(2, ))
for i in range(sample_start.shape[0])
])
sample_start_t = np.where(switch[:, 0][:, None], sample_start, sample_end)
sample_end_t = np.where(switch[:, 1][:, None], sample_start, sample_end)
sample_start = sample_start_t
sample_end = sample_end_t
start_lengths = color.get_meta_for_each_sample(sample_raw_start,
color_dict)
start_lengths = np.array([int(i) for i in start_lengths])
start_lengths = np.cumsum(start_lengths)
num_vertical_reps = 2
num_vertical_samples = height // num_vertical_reps + 3
model = m.MarkovModel(
values=np.arange(0, 41, 10) - 20,
preference_matrix=data.str2mat(
'0 1 5 1 0, 1 2 5 1 0, 0 1 10 1 0, 0 1 5 2 1, 0 1 5 1 0'),
self_length=num_vertical_samples,
parent_rkey=r.bind_generator_from(rkey))
offsets = np.stack([
m.sample_markov_hierarchy(model, num_vertical_samples)
for _ in range(num_color_samples)
],
axis=1)
offsets = np.repeat(offsets,
repeats=r.choice_from(
rkey, [num_vertical_reps + i for i in range(1)],
size=(num_vertical_samples, )),
axis=0)
offsets = np.cumsum(offsets, axis=0)
offsets += start_lengths
offsets = np.hstack([np.zeros((offsets.shape[0], 1)), offsets])
i = 0
offset_index = 0
transition = np.linspace(0, 1, num_vertical_samples)
sample_start_gradient = sample_start[:, :, None] * (
1 - transition) + sample_down_start[:, :, None] * transition
sample_end_gradient = sample_end[:, :, None] * (
1 - transition) + sample_down_end[:, :, None] * transition
multiples_choices = r.choice_from(rkey,
config.get('multiples-choices',
[20, 30, 40, 50]),
size=(6, ))
# print('multiples-choices',multiples_choices)
while i < height:
loop_key = r.bind_generator_from(rkey)
current_lengths = offsets[offset_index]
acum_max = np.maximum.accumulate(current_lengths)
mask = acum_max == current_lengths
diff = np.diff(current_lengths[mask])
samples_start_masked = sample_start[mask[1:]]
samples_end_masked = sample_end[mask[1:]]
#
# samples_start_masked = sample_start_gradient[:,:,i//num_vertical_reps][mask[1:]]
# samples_end_masked = sample_end_gradient[:,:,i//num_vertical_reps][mask[1:]]
p_switch = config.get('gradient-switch-p', 0.5)
switch = r.choice_from(loop_key, [0, 1],
size=samples_start_masked.shape[0],
p=[p_switch, 1 - p_switch])
switch = np.stack((switch, 1 - switch), axis=1)
sample_start_switched = np.where(switch[:, 0][:, None],
samples_start_masked,
samples_end_masked)
sample_end_switched = np.where(switch[:, 1][:, None],
samples_start_masked,
samples_end_masked)
multiples = r.choice_from(loop_key, multiples_choices)
gradient = generate_gradient(sample_start_switched,
sample_end_switched, diff)[:width]
patch[i:i + multiples] = gradient[None, :]
i += multiples
offset_index += 1
patch[patch < 0] = 0
patch[patch > 255] = 255
return patch
import constants as c
import script_config as config
import numpy as np
import cv2
import utils.generate_utils as gen
import random_manager as r
import utils.file_utils as file
import utils.data_utils as data
import utils.markov_utils as m
import utils.color_utils as color
import utils.viz_utils as viz
### DATA/INPUT/SHARED by all runs section
print('PREPARING DATA SECTION')
N = 20
SEED = config.get('seed', 0)
HEIGHT = 1000
WIDTH = HEIGHT
UPSCALE_FACTOR = c.INSTA_SIZE // HEIGHT
COLOR_STRING = config.get(
'color-string-3',
'Yellows:0/ffdbc9/20-1/ffebc4/20-2/f2ffcc/20-3/def9b8/20-4/ffdcba/20,Violets:10/d62a55/20-11/f97cbd/20-12/f48e7f/20-13/ffa29b/20-14/fc8c58/20,Blue:5/2098f9/20-6/2332ff/20-7/1980e8/20-8/0742f2/20-9/21f3ff/20'
)
### SETUP section
print('SETUP SECTION')
if N > 1:
file.clear_export_folder()