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Random Bézier Walk in a Random Neural Network
#!/usr/bin/env python
from __future__ import print_function
# Images can be converted to video with ffmpeg.
# > ffmpeg -pattern_type glob \
# -i "*.png" \
# -vcodec libx264 \
# output.avi
import os
import sys
from PIL import Image
import neuralart
import numpy as np
RENDER_SEED = 10
Z_SEED = 0
DEVICE = "cpu" # 'cpu' for CPU, 'cuda' for GPU
ITERATIONS = 10000
MIN_STEP_SIZE = .005
MAX_STEP_SIZE = .006
XRES = 2048
YRES = 2048
XLIM = np.array([-1.0, 1.0])
YLIM = XLIM * (float(YRES) / XRES)
DEPTH = 9
CHANNELS = 1
OUTPUT_STD = 1.5
HIDDEN_STD = 1.1
Z_DIMS = 4
Z_RANGE = (-1, 1)
RADIUS=True
if len(sys.argv) != 2:
sys.stderr.write("Usage: {} DIRECTORY\n".format(sys.argv[0]))
sys.exit(1)
directory = sys.argv[1]
if not os.path.exists(directory):
os.makedirs(directory)
rng = np.random.RandomState(seed=Z_SEED)
zfill = len(str(ITERATIONS - 1))
M = np.array([
[-1, 3, -3, 1],
[ 3, -6, 3, 0],
[-3, 3, 0, 0],
[ 1, 0, 0, 0]
])
P0 = rng.uniform(*Z_RANGE, size=Z_DIMS)
P1 = rng.uniform(*Z_RANGE, size=Z_DIMS)
P2 = rng.uniform(*Z_RANGE, size=Z_DIMS)
P3 = rng.uniform(*Z_RANGE, size=Z_DIMS)
count = 0
while count < ITERATIONS:
P0 = P3
P1 = 2 * P3 - P2
P2 = rng.uniform(*Z_RANGE, size=Z_DIMS)
P3 = rng.uniform(*Z_RANGE, size=Z_DIMS)
pos = P0
lo = 0.0
hi = 1.0
while np.linalg.norm(P3 - pos) > MIN_STEP_SIZE:
if count >= ITERATIONS:
break
t = (lo + hi) / 2.0
P = np.vstack((P0, P1, P2, P3)).T
C = P.dot(M).dot(np.array([t ** 3, t ** 2, t, 1]))
distance = np.linalg.norm(C - pos)
if distance < MIN_STEP_SIZE:
lo = t
continue
elif distance > MAX_STEP_SIZE:
hi = t
continue
pos = C
result = neuralart.render(
depth=DEPTH,
xres=XRES,
yres=YRES,
xlim=XLIM,
ylim=YLIM,
seed=RENDER_SEED,
channels=CHANNELS,
output_std=OUTPUT_STD,
hidden_std=HIDDEN_STD,
radius=RADIUS,
z=C,
device=DEVICE
)
file = os.path.join(directory, str(count).zfill(zfill) + ".png")
im = Image.fromarray(result.squeeze())
im.save(file, "png")
count += 1
lo = t
hi = 1.0
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