Random Bézier Walk in a Random Neural Network

bezier_neural_walk.py

#!/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