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Random Bézier Walk in a Random Neural Network
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| #!/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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