def generator(ctx, width, height, channels, time, speed, seed, filename,
preset_name):
if not seed:
seed = random.randint(1, MAX_SEED_VALUE)
value.set_seed(seed)
reload_presets(PRESETS)
if preset_name == "random":
preset_name = list(GENERATOR_PRESETS)[random.randint(
0,
len(GENERATOR_PRESETS) - 1)]
print(f"{preset_name} (seed: {seed})")
preset = GENERATOR_PRESETS[preset_name]
try:
preset.render(shape=[height, width, channels],
time=time,
speed=speed,
filename=filename)
except Exception as e:
util.logger.error(
f"preset.render() failed: {e}\nSeed: {seed}\nArgs: {preset.__dict__}"
)
raise
def effect(ctx, seed, filename, no_resize, time, speed, preset_name, input_filename):
if not seed:
seed = random.randint(1, MAX_SEED_VALUE)
value.set_seed(seed)
reload_presets(PRESETS)
input_shape = util.shape_from_file(input_filename)
input_shape[2] = min(input_shape[2], 3)
tensor = tf.image.convert_image_dtype(util.load(input_filename, channels=input_shape[2]), dtype=tf.float32)
if preset_name == "random":
preset_name = list(EFFECT_PRESETS)[random.randint(0, len(EFFECT_PRESETS) - 1)]
print(f"{preset_name} (seed: {seed})")
preset = EFFECT_PRESETS[preset_name]
if no_resize:
shape = input_shape
else:
shape = [1024, 1024, input_shape[2]]
tensor = effects.square_crop_and_resize(tensor, input_shape, shape[0])
try:
preset.render(tensor=tensor, shape=shape, time=time, speed=speed, filename=filename)
except Exception as e:
util.logger.error(f"preset.render() failed: {e}\nSeed: {seed}\nArgs: {preset.__dict__}")
raise
def animation(ctx, width, height, channels, seed, effect_preset, filename, save_frames, frame_count, watermark, preset_name):
if seed is None:
seed = random.randint(1, MAX_SEED_VALUE)
value.set_seed(seed)
reload_presets(PRESETS)
if preset_name == 'random':
preset_name = list(GENERATOR_PRESETS)[random.randint(0, len(GENERATOR_PRESETS) - 1)]
if effect_preset == 'random':
effect_preset = list(EFFECT_PRESETS)[random.randint(0, len(EFFECT_PRESETS) - 1)]
if effect_preset:
print(f"{preset_name} vs. {effect_preset} (seed: {seed})")
else:
print(f"{preset_name} (seed: {seed})")
preset = GENERATOR_PRESETS[preset_name]
with tempfile.TemporaryDirectory() as tmp:
for i in range(frame_count):
frame_filename = f'{tmp}/{i:04d}.png'
common_params = ['--seed', str(seed),
'--time', f'{i/frame_count:0.4f}',
'--filename', frame_filename]
util.check_call(['noisemaker', 'generator', preset_name,
'--speed', str(_use_reasonable_speed(preset, frame_count)),
'--height', str(height),
'--width', str(width)] + common_params)
if effect_preset:
util.check_call(['noisemaker', 'effect', effect_preset, frame_filename,
'--no-resize',
'--speed', str(_use_reasonable_speed(EFFECT_PRESETS[effect_preset], frame_count))] + common_params)
if save_frames:
shutil.copy(frame_filename, save_frames)
if watermark:
util.watermark(watermark, frame_filename)
if filename.endswith(".mp4"):
# when you want something done right
util.check_call(['ffmpeg',
'-y', # overwrite existing
'-framerate', '30',
'-i', f'{tmp}/%04d.png',
'-c:v', 'libx264', # because this is what twitter wants
'-pix_fmt', 'yuv420p', # because this is what twitter wants
'-b:v', '1700000', # maximum allowed bitrate for 720x720 (2048k), minus some encoder overhead
'-s', '720x720', # a twitter-recommended size
filename])
else:
util.magick(f'{tmp}/*png', filename)
import click
import tensorflow as tf
from noisemaker.composer import EFFECT_PRESETS, GENERATOR_PRESETS, reload_presets
from noisemaker.constants import ValueDistribution
from noisemaker.presets import PRESETS
import noisemaker.cli as cli
import noisemaker.generators as generators
import noisemaker.effects as effects
import noisemaker.util as util
import noisemaker.value as value
MAX_SEED_VALUE = 2**16
reload_presets(PRESETS)
@click.group(help="""
Noisemaker - Let's make generative art with noise
https://github.com/aayars/py-noisemaker
""",
context_settings=cli.CLICK_CONTEXT_SETTINGS)
def main():
pass
@main.command(help="Generate a .png or .jpg from preset")
@cli.width_option(default=2048)
@cli.height_option()
