def clouds(input_filename):
tensor = tf.image.convert_image_dtype(load(input_filename), tf.float32)
pre_shape = [SMALL_Y, SMALL_X, 1]
post_shape = [LARGE_Y, LARGE_X, 1]
control_kwargs = {
"freq": FREQ * 2,
"lattice_drift": 1,
"octaves": OCTAVES,
"ridges": True,
"shape": pre_shape,
"warp_freq": 3,
"warp_range": .25,
"warp_octaves": 2,
}
control = generators.multires(**control_kwargs)
layer_0 = tf.ones(pre_shape)
layer_1 = tf.zeros(pre_shape)
combined = effects.blend_layers(control, pre_shape, 1.0, layer_0, layer_1)
shadow = effects.offset(combined, pre_shape, random.randint(-15, 15),
random.randint(-15, 15))
shadow = tf.minimum(shadow * 2.5, 1.0)
shadow = effects.convolve(effects.ConvKernel.blur, shadow, pre_shape)
shadow = effects.convolve(effects.ConvKernel.blur, shadow, pre_shape)
shadow = effects.convolve(effects.ConvKernel.blur, shadow, pre_shape)
shadow = effects.resample(shadow, post_shape)
combined = effects.resample(combined, post_shape)
tensor = effects.blend(tensor, tf.zeros(post_shape), shadow * .5)
tensor = effects.blend(tensor, tf.ones(post_shape), combined)
post_shape = [LARGE_Y, LARGE_X, 3]
tensor = effects.shadow(tensor, post_shape, alpha=.25)
tensor = effects.bloom(tensor, post_shape, .333)
tensor = recipes.dither(tensor, post_shape, .075)
combined = tf.image.adjust_contrast(combined, 1.125)
with tf.Session().as_default():
save(tensor, FINAL_FILENAME)
def _control():
shape = [SMALL_Y, SMALL_X, 1]
control = generators.multires(shape=shape, freq=FREQ, octaves=OCTAVES, refract_range=.5)
erode_kwargs = {
"alpha": .025,
"density": 40,
"iterations": 20,
"inverse": True,
}
iterations = 5
for i in range(iterations):
control = effects.erode(control, shape, **erode_kwargs)
control = effects.convolve(constants.ValueMask.conv2d_blur, control, shape)
post_shape = [LARGE_Y, LARGE_X, 1]
control = effects.resample(control, post_shape)
iterations = 2
for i in range(iterations):
control = effects.erode(control, post_shape, **erode_kwargs)
control = effects.convolve(constants.ValueMask.conv2d_blur, control, post_shape)
control = effects.convolve(constants.ValueMask.conv2d_sharpen, control, post_shape)
control = effects.normalize(control)
with tf.Session().as_default():
save(control, CONTROL_FILENAME)
def interference(tensor, shape, time=0.0, speed=1.0):
"""
"""
height, width, channels = shape
value_shape = [height, width, 1]
distortion = basic(2,
value_shape,
time=time,
speed=speed,
distribution=ValueDistribution.simplex,
corners=True)
scan_noise = basic([2, 1], [2, 1, 1],
time=time,
speed=speed,
distribution=ValueDistribution.simplex)
scan_noise = tf.tile(scan_noise, [random.randint(32, 128), width, 1])
scan_noise = effects.resample(scan_noise, value_shape, spline_order=0)
scan_noise = effects.refract(scan_noise,
value_shape,
1,
reference_x=distortion)
tensor = 1.0 - (1.0 - tensor) * scan_noise
return tensor
def main(ctx, name, input_filename):
tensor = tf.image.convert_image_dtype(load(input_filename), tf.float32)
max_height = 1024
max_width = 1024
with tf.Session().as_default():
height, width, channels = tf.shape(tensor).eval()
need_resample = False
if height != width:
length = min(height, width)
height = length
width = length
tensor = tf.image.resize_image_with_crop_or_pad(tensor, length, length)
if height > max_height:
need_resample = True
width = int(width * (max_height / height))
height = max_height
if width > max_width:
need_resample = True
height = int(height * (max_width / width))
width = max_width
shape = [height, width, channels]
if need_resample:
tensor = effects.resample(tensor, shape)
save(tensor, name)
def clouds(tensor, shape, time=0.0, speed=1.0):
"""Top-down cloud cover effect"""
pre_shape = [int(shape[0] * .25) or 1, int(shape[1] * .25) or 1, 1]
control_kwargs = {
"freq": random.randint(2, 4),
"lattice_drift": 1,
"octaves": 8,
"ridges": True,
"speed": speed,
"shape": pre_shape,
"time": time,
"warp_freq": 3,
"warp_range": .125,
"warp_octaves": 2,
"distrib": ValueDistribution.simplex,
}
control = multires(**control_kwargs)
layer_0 = tf.ones(pre_shape)
layer_1 = tf.zeros(pre_shape)
combined = effects.blend_layers(control, pre_shape, 1.0, layer_0, layer_1)
shadow = effects.offset(combined, pre_shape, random.randint(-15, 15),
random.randint(-15, 15))
shadow = tf.minimum(shadow * 2.5, 1.0)
for _ in range(3):
shadow = effects.convolve(ValueMask.conv2d_blur, shadow, pre_shape)
post_shape = [shape[0], shape[1], 1]
shadow = effects.resample(shadow, post_shape)
combined = effects.resample(combined, post_shape)
tensor = effects.blend(tensor, tf.zeros(shape), shadow * .75)
tensor = effects.blend(tensor, tf.ones(shape), combined)
tensor = effects.shadow(tensor, shape, alpha=.5)
return tensor
def main(ctx, seed, name, preset_name, input_filename):
generators.set_seed(seed)
tensor = tf.image.convert_image_dtype(load(input_filename), tf.float32)
max_height = 1024
max_width = 1024
with tf.Session().as_default():
height, width, channels = tf.shape(tensor).eval()
need_resample = False
# Some presets only like square images. Work around for now by cropping.
if height != width:
length = min(height, width)
height = length
width = length
tensor = tf.image.resize_image_with_crop_or_pad(tensor, length, length)
if height > max_height:
need_resample = True
width = int(width * (max_height / height))
height = max_height
if width > max_width:
need_resample = True
height = int(height * (max_width / width))
width = max_width
shape = [height, width, channels]
if need_resample:
tensor = effects.resample(tensor, shape)
kwargs, preset_name = presets.load(preset_name, presets.EFFECTS_PRESETS())
kwargs["shape"] = [height, width, channels]
if "freq" not in kwargs:
kwargs["freq"] = [3, 3]
if "octaves" not in kwargs:
kwargs["octaves"] = 1
if "ridges" not in kwargs:
kwargs["ridges"] = False
tensor = effects.post_process(tensor, **kwargs)
tensor = recipes.post_process(tensor, **kwargs)
print(preset_name)
save(tensor, name)
def crt(tensor, shape):
"""
Apply vintage CRT snow and scanlines.
:param Tensor tensor:
:param list[int] shape:
"""
height, width, channels = shape
value_shape = [height, width, 1]
distortion = basic(3, value_shape)
distortion_amount = .25
white_noise = basic(int(height * .75), value_shape, spline_order=0) - .5
white_noise = effects.center_mask(
white_noise,
effects.refract(white_noise,
value_shape,
distortion_amount,
reference_x=distortion), value_shape)
white_noise2 = basic([int(height * .5), int(width * .25)], value_shape)
white_noise2 = effects.center_mask(
white_noise2,
effects.refract(white_noise2,
value_shape,
distortion_amount,
reference_x=distortion), value_shape)
tensor = effects.blend_cosine(tensor, white_noise, white_noise2 * .25)
scan_noise = tf.tile(basic([2, 1], [2, 1, 1]),
[int(height * .333), width, 1])
scan_noise = effects.resample(scan_noise, value_shape)
scan_noise = effects.center_mask(
scan_noise,
effects.refract(scan_noise,
value_shape,
distortion_amount,
reference_x=distortion), value_shape)
tensor = effects.blend_cosine(tensor, scan_noise, 0.25)
if channels <= 2:
return tensor
tensor = tf.image.random_hue(tensor, .125)
tensor = tf.image.adjust_saturation(tensor, 1.25)
return tensor
def frame(tensor, shape, time=0.0, speed=1.0):
"""
"""
half_shape = [int(shape[0] * .5), int(shape[1] * .5), shape[2]]
half_value_shape = [half_shape[0], half_shape[1], 1]
noise = multires(64,
half_value_shape,
time=time,
speed=speed,
distrib=ValueDistribution.simplex,
octaves=8)
black = tf.zeros(half_value_shape)
white = tf.ones(half_value_shape)
mask = effects.singularity(None,
half_value_shape,
1,
dist_func=3,
inverse=True)
mask = effects.normalize(mask + noise * .005)
mask = effects.blend_layers(tf.sqrt(mask), half_value_shape, 0.0125, white,
black, black, black)
faded = effects._downsample(tensor, shape, half_shape)
faded = tf.image.adjust_brightness(faded, .1)
faded = tf.image.adjust_contrast(faded, .75)
faded = effects.light_leak(faded, half_shape, .125)
faded = effects.vignette(faded, half_shape, 0.05, .75)
edge_texture = white * .9 + effects.shadow(noise, half_value_shape,
1.0) * .1
out = effects.blend(faded, edge_texture, mask)
out = effects.aberration(out, half_shape, .00666)
out = grime(out, half_shape)
out = tf.image.adjust_saturation(out, .5)
out = tf.image.random_hue(out, .05)
out = effects.resample(out, shape)
out = scratches(out, shape)
out = stray_hair(out, shape)
return out
def main(ctx, name, retro_upscale, input_filename):
shape = effects.shape_from_file(input_filename)
tensor = tf.image.convert_image_dtype(load(input_filename, channels=3),
tf.float32)
if retro_upscale:
shape = [shape[0] * 2, shape[1] * 2, shape[2]]
tensor = effects.resample(tensor, shape, spline_order=0)
tensor = effects.square_crop_and_resize(tensor, shape, 1024)
with tf.Session().as_default():
save(tensor, name)
def basic(ctx, width, height, input_dir, name):
shape = [height, width, 3]
filenames = [
f for f in os.listdir(input_dir)
if f.endswith(".png") or f.endswith(".jpg")
]
collage_count = min(random.randint(3, 5), len(filenames))
collage_images = []
for i in range(collage_count + 1):
index = random.randint(0, len(filenames) - 1)
collage_input = tf.image.convert_image_dtype(util.load(
os.path.join(input_dir, filenames[index])),
dtype=tf.float32)
collage_images.append(effects.resample(collage_input, shape))
base = generators.basic(freq=random.randint(2, 5),
shape=shape,
lattice_drift=random.randint(0, 1),
hue_range=random.random())
control = effects.value_map(collage_images.pop(), shape, keep_dims=True)
tensor = effects.blend_layers(control, shape,
random.random() * .5, *collage_images)
tensor = effects.blend(tensor, base, .125 + random.random() * .125)
tensor = effects.bloom(tensor, shape, alpha=.25 + random.random() * .125)
tensor = effects.shadow(tensor, shape, alpha=.25 + random.random() * .125)
tensor = tf.image.adjust_brightness(tensor, .05)
tensor = tf.image.adjust_contrast(tensor, 1.25)
with tf.Session().as_default():
save(tensor, name)
print(name)
def crt(tensor, shape, time=0.0, speed=1.0):
"""
Apply vintage CRT snow and scanlines.
:param Tensor tensor:
:param list[int] shape:
"""
height, width, channels = shape
value_shape = [height, width, 1]
# Horizontal scanlines
scan_noise = tf.tile(
basic([2, 1], [2, 1, 1],
time=time,
speed=speed,
distrib=ValueDistribution.simplex,
spline_order=0), [int(height * .125) or 1, width, 1])
scan_noise = effects.resample(scan_noise, value_shape)
scan_noise = lens_warp(scan_noise, value_shape, time=time, speed=speed)
tensor = effects.normalize(
effects.blend(tensor, (tensor + scan_noise) * scan_noise, 0.05))
if channels == 3:
tensor = effects.aberration(tensor, shape,
.0075 + random.random() * .0075)
tensor = tf.image.random_hue(tensor, .125)
tensor = tf.image.adjust_saturation(tensor, 1.25)
tensor = tf.image.adjust_contrast(tensor, 1.25)
tensor = effects.vignette(tensor,
shape,
brightness=0,
alpha=random.random() * .175)
return tensor
def render(ctx, glitch, vhs, crt, scan_error, snow, dither, aberration, bloom, name, input_filename):
tensor = tf.image.convert_image_dtype(load(input_filename), tf.float32)
freq = [3, 3]
max_height = 1024
max_width = 1024
with tf.Session().as_default():
height, width, channels = tf.shape(tensor).eval()
need_resample = False
if height > max_height:
need_resample = True
width = int(width * (max_height / height))
height = max_height
if width > max_width:
need_resample = True
height = int(height * (max_width / width))
width = max_width
shape = [height, width, channels]
if need_resample:
tensor = effects.resample(tensor, shape)
tensor = effects.post_process(tensor, shape=shape, freq=freq, with_bloom=bloom, with_aberration=aberration)
tensor = recipes.post_process(tensor, shape=shape, freq=freq, with_glitch=glitch, with_vhs=vhs, with_crt=crt,
with_scan_error=scan_error, with_snow=snow, with_dither=dither)
save(tensor, name)
print(name)
def spooky_ticker(tensor, shape):
"""
"""
if random.random() > .75:
tensor = on_screen_display(tensor, shape)
_masks = [
ValueMask.arecibo_nucleotide,
ValueMask.arecibo_num,
ValueMask.bank_ocr,
ValueMask.bar_code,
ValueMask.bar_code_short,
ValueMask.emoji,
ValueMask.fat_lcd_hex,
ValueMask.hex,
ValueMask.iching,
ValueMask.ideogram,
ValueMask.invaders,
ValueMask.lcd,
ValueMask.letters,
ValueMask.matrix,
ValueMask.numeric,
ValueMask.script,
ValueMask.white_bear,
]
bottom_padding = 2
rendered_mask = tf.zeros(shape)
for _ in range(random.randint(1, 3)):
mask = _masks[random.randint(0, len(_masks) - 1)]
mask_shape = masks.mask_shape(mask)
multiplier = 1 if mask != ValueMask.script and (
mask_shape[1] == 1 or mask_shape[1] >= 10) else 2
width = int(shape[1] / multiplier) or 1
width = mask_shape[1] * int(
width /
mask_shape[1]) # Make sure the mask divides evenly into width
freq = [mask_shape[0], width]
this_mask = basic(freq, [mask_shape[0], width, 1],
corners=True,
spline_order=0,
distrib=ValueDistribution.ones,
mask=mask)
this_mask = effects.resample(this_mask,
[mask_shape[0] * multiplier, shape[1]],
spline_order=1)
rendered_mask += tf.pad(
this_mask,
tf.stack([[
shape[0] - mask_shape[0] * multiplier - bottom_padding,
bottom_padding
], [0, 0], [0, 0]]))
bottom_padding += mask_shape[0] * multiplier + 2
alpha = .5 + random.random() * .25
# shadow
tensor = effects.blend(
tensor, tensor * 1.0 - effects.offset(rendered_mask, shape, -1, -1),
alpha * .333)
return effects.blend(tensor, tf.maximum(rendered_mask, tensor), alpha)
def basic(ctx, width, height, input_dir, name, control_filename,
retro_upscale):
shape = [height, width,
3] # Any shape you want, as long as it's [1024, 1024, 3]
filenames = []
for root, _, files in os.walk(input_dir):
for filename in files:
if filename.endswith(('.png', '.jpg')):
filenames.append(os.path.join(root, filename))
collage_count = min(random.randint(4, 6), len(filenames))
collage_images = []
for i in range(collage_count + 1):
index = random.randint(0, len(filenames) - 1)
input_filename = os.path.join(input_dir, filenames[index])
collage_input = tf.image.convert_image_dtype(util.load(input_filename,
channels=3),
dtype=tf.float32)
input_shape = effects.shape_from_file(input_filename)
if retro_upscale:
input_shape = [
input_shape[0] * 2, input_shape[1] * 2, input_shape[2]
]
collage_input = effects.resample(collage_input,
input_shape,
spline_order=0)
collage_input = effects.square_crop_and_resize(collage_input,
input_shape, 1024)
collage_images.append(collage_input)
base = generators.basic(freq=random.randint(2, 5),
shape=shape,
lattice_drift=random.randint(0, 1),
hue_range=random.random())
if control_filename:
control = tf.image.convert_image_dtype(util.load(control_filename,
channels=1),
dtype=tf.float32)
control = effects.square_crop_and_resize(
control, effects.shape_from_file(control_filename), 1024)
control = effects.value_map(control, shape, keep_dims=True)
else:
control = effects.value_map(collage_images.pop(),
shape,
keep_dims=True)
control = effects.convolve(effects.ValueMask.conv2d_blur, control,
[height, width, 1])
with tf.Session().as_default():
# sort collage images by brightness
collage_images = [
j[1] for j in sorted([(tf.reduce_sum(i).eval(), i)
for i in collage_images])
]
tensor = effects.blend_layers(control, shape,
random.random() * .5, *collage_images)
tensor = effects.blend(tensor, base, .125 + random.random() * .125)
tensor = effects.bloom(tensor,
shape,
alpha=.25 + random.random() * .125)
tensor = effects.shadow(tensor,
shape,
alpha=.25 + random.random() * .125,
reference=control)
tensor = tf.image.adjust_brightness(tensor, .05)
tensor = tf.image.adjust_contrast(tensor, 1.25)
save(tensor, name)
print('mashup')
def values(freq,
shape,
distrib=ValueDistribution.normal,
corners=False,
mask=None,
mask_inverse=False,
spline_order=3,
wavelet=False,
time=0.0,
speed=1.0):
"""
"""
initial_shape = freq + [shape[-1]]
if isinstance(distrib, int):
distrib = ValueDistribution(distrib)
elif isinstance(distrib, str):
distrib = ValueDistribution[distrib]
if isinstance(mask, int):
mask = ValueMask(mask)
elif isinstance(mask, str):
mask = ValueMask[mask]
if distrib == ValueDistribution.ones:
tensor = tf.ones(initial_shape)
elif distrib == ValueDistribution.mids:
tensor = tf.ones(initial_shape) * .5
elif distrib == ValueDistribution.normal:
tensor = tf.random_normal(initial_shape)
elif distrib == ValueDistribution.uniform:
tensor = tf.random_uniform(initial_shape)
elif distrib == ValueDistribution.exp:
tensor = tf.cast(tf.stack(np.random.exponential(size=initial_shape)),
tf.float32)
elif distrib == ValueDistribution.laplace:
tensor = tf.cast(tf.stack(np.random.laplace(size=initial_shape)),
tf.float32)
elif distrib == ValueDistribution.lognormal:
tensor = tf.cast(tf.stack(np.random.lognormal(size=initial_shape)),
tf.float32)
elif distrib == ValueDistribution.column_index:
tensor = tf.expand_dims(
tf.cast(effects.normalize(effects.column_index(initial_shape)),
tf.float32), -1) * tf.ones(initial_shape, tf.float32)
elif distrib == ValueDistribution.row_index:
tensor = tf.expand_dims(
tf.cast(effects.normalize(effects.row_index(initial_shape)),
tf.float32), -1) * tf.ones(initial_shape, tf.float32)
elif distrib == ValueDistribution.simplex:
tensor = simplex.simplex(initial_shape, time=time, speed=speed)
elif distrib == ValueDistribution.simplex_exp:
tensor = tf.pow(simplex.simplex(initial_shape, time=time, speed=speed),
4)
else:
raise ValueError("%s (%s) is not a ValueDistribution" %
(distrib, type(distrib)))
if mask:
atlas = None
if mask == ValueMask.truetype:
from noisemaker.glyphs import load_glyphs
atlas = load_glyphs([15, 15, 1])
if not atlas:
mask = ValueMask.numeric # Fall back to canned values
channel_shape = freq + [1]
mask_values, _ = masks.mask_values(mask,
channel_shape,
atlas=atlas,
inverse=mask_inverse,
time=time,
speed=speed)
tensor *= mask_values
if wavelet:
tensor = effects.wavelet(tensor, initial_shape)
tensor = effects.resample(tensor, shape, spline_order=spline_order)
if (not corners and (freq[0] % 2) == 0) or (corners and
(freq[0] % 2) == 1):
tensor = effects.offset(tensor,
shape,
x=int((shape[1] / freq[1]) * .5),
y=int((shape[0] / freq[0]) * .5))
return tensor
