def grime(tensor, shape):
"""
"""
value_shape = [shape[0], shape[1], 1]
mask = multires(5,
value_shape,
distrib="exp",
octaves=8,
refract_range=1.0,
deriv=3,
deriv_alpha=.5)
dusty = effects.blend(tensor, .25, tf.square(mask) * .125)
specks = basic(
[int(shape[0] * .25), int(shape[1] * .25)],
value_shape,
distrib="exp",
refract_range=.1)
specks = 1.0 - tf.sqrt(effects.normalize(tf.maximum(specks - .5, 0.0)))
dusty = effects.blend(dusty, basic([shape[0], shape[1]], value_shape),
.125) * specks
return effects.blend(tensor, dusty, mask)
def glitch(tensor, shape, time=0.0, speed=1.0):
"""
Apply a glitch effect.
:param Tensor tensor:
:param list[int] shape:
:return: Tensor
"""
height, width, channels = shape
tensor = effects.normalize(tensor)
base = multires(2,
shape,
time=time,
speed=speed,
distrib=ValueDistribution.simplex,
octaves=random.randint(2, 5),
spline_order=0,
refract_range=random.random())
stylized = effects.normalize(
effects.color_map(base,
tensor,
shape,
horizontal=True,
displacement=2.5))
jpegged = effects.color_map(base,
stylized,
shape,
horizontal=True,
displacement=2.5)
if channels in (1, 3):
jpegged = effects.jpeg_decimate(jpegged, shape)
# Offset a single color channel
separated = [stylized[:, :, i] for i in range(channels)]
x_index = (effects.row_index(shape) + random.randint(1, width)) % width
index = tf.cast(tf.stack([effects.column_index(shape), x_index], 2),
tf.int32)
channel = random.randint(0, channels - 1)
separated[channel] = effects.normalize(
tf.gather_nd(separated[channel], index) % random.random())
stylized = tf.stack(separated, 2)
combined = effects.blend(tf.multiply(stylized, 1.0), jpegged, base)
combined = effects.blend(tensor, combined, tf.maximum(base * 2 - 1, 0))
combined = effects.blend(combined, effects.pixel_sort(combined, shape),
1.0 - base)
combined = tf.image.adjust_contrast(combined, 1.75)
return combined
def glitch(tensor, shape):
"""
Apply a glitch effect.
:param Tensor tensor:
:param list[int] shape:
:return: Tensor
"""
height, width, channels = shape
tensor = effects.normalize(tensor)
base = multires(2,
shape,
octaves=random.randint(2, 5),
spline_order=0,
refract_range=random.random())
stylized = effects.normalize(
effects.color_map(base,
tensor,
shape,
horizontal=True,
displacement=2.5))
jpegged = effects.jpeg_decimate(
effects.color_map(base,
stylized,
shape,
horizontal=True,
displacement=2.5), shape)
# Offset a single color channel
separated = [stylized[:, :, i] for i in range(channels)]
x_index = (effects.row_index(shape) + random.randint(1, width)) % width
index = tf.cast(tf.stack([effects.column_index(shape), x_index], 2),
tf.int32)
channel = random.randint(0, channels - 1)
separated[channel] = effects.normalize(
tf.gather_nd(separated[channel], index) % random.random())
channel = random.randint(0, channels - 1)
top, _ = tf.nn.top_k(effects.value_map(tensor, shape), k=width)
separated[channel] += top
stylized = tf.stack(separated, 2)
combined = effects.blend(tf.multiply(stylized, 1.0), jpegged, base)
combined = effects.blend(tensor, combined, tf.maximum(base * 2 - 1, 0))
return combined
def blended():
shape = [LARGE_Y, LARGE_X, 3]
erode_kwargs = {
"alpha": .025,
"density": 250,
"iterations": 50,
"inverse": True,
}
control = tf.image.convert_image_dtype(load(CONTROL_FILENAME), tf.float32)
water = tf.ones(shape) * tf.stack([.05, .2, .333])
water = effects.blend(water, control * 4.0, .125)
low = tf.image.convert_image_dtype(load(LOW_FILENAME), tf.float32)
mid = tf.image.convert_image_dtype(load(MID_FILENAME), tf.float32)
high = tf.image.convert_image_dtype(load(HIGH_FILENAME), tf.float32)
blend_control = generators.multires(shape=shape,
freq=FREQ * 4,
ridges=True,
octaves=4)
blend_control = 1.0 - effects.value_map(
blend_control, shape, keep_dims=True) * .5
combined_land = effects.blend_layers(control, shape, blend_control,
control * 2, low, mid, high)
combined_land = effects.erode(combined_land,
shape,
xy_blend=.25,
**erode_kwargs)
combined_land = effects.erode(combined_land, shape, **erode_kwargs)
combined_land_0 = effects.shadow(combined_land, shape, alpha=1.0)
combined_land_1 = effects.shadow(combined_land,
shape,
alpha=1.0,
reference=control)
combined_land = effects.blend(combined_land_0, combined_land_1, .5)
combined = effects.blend_layers(control, shape, .01, water, combined_land,
combined_land, combined_land)
combined = effects.blend(combined_land, combined, .625)
combined = tf.image.adjust_brightness(combined, .1)
combined = tf.image.adjust_contrast(combined, .75)
combined = tf.image.adjust_saturation(combined, .625)
with tf.Session().as_default():
save(combined, BLENDED_FILENAME)
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 fibers(tensor, shape, time=0.0, speed=1.0):
"""
"""
value_shape = [shape[0], shape[1], 1]
for i in range(4):
mask = basic(
4,
value_shape,
with_worms=4,
worms_alpha=1,
worms_density=.05 + random.random() * .00125,
worms_duration=1,
worms_kink=random.randint(5, 10),
worms_stride=.75,
worms_stride_deviation=.125,
time=time,
speed=speed,
distrib=ValueDistribution.simplex,
)
brightness = basic(128,
shape,
time=time,
speed=speed,
distrib=ValueDistribution.simplex,
saturation=2.0)
tensor = effects.blend(tensor, brightness, mask * .5)
return tensor
def stray_hair(tensor, shape, time=0.0, speed=1.0):
"""
"""
value_shape = [shape[0], shape[1], 1]
mask = basic(
4,
value_shape,
with_worms=4,
worms_alpha=1,
worms_density=.0025 + random.random() * .00125,
worms_duration=random.randint(8, 16),
worms_kink=random.randint(5, 50),
worms_stride=.5,
worms_stride_deviation=.25,
time=time,
speed=speed,
distrib=ValueDistribution.simplex,
)
brightness = basic(32,
value_shape,
time=time,
speed=speed,
distrib=ValueDistribution.simplex)
return effects.blend(tensor, brightness * .333, mask * .666)
def scanline_error(tensor, shape):
"""
"""
height, width, channels = shape
value_shape = [height, width, 1]
error_line = tf.maximum(
basic([int(height * .75), 1],
value_shape,
distrib=ValueDistribution.exp) - .5, 0)
error_swerve = tf.maximum(
basic([int(height * .01), 1],
value_shape,
distrib=ValueDistribution.exp) - .5, 0)
error_line *= error_swerve
error_swerve *= 2
white_noise = basic([int(height * .75), 1], value_shape)
white_noise = effects.blend(0, white_noise, error_swerve)
error = error_line + white_noise
y_index = effects.column_index(shape)
x_index = (effects.row_index(shape) - tf.cast(
effects.value_map(error, value_shape) * width * .025,
tf.int32)) % width
return tf.minimum(
tf.gather_nd(tensor, tf.stack([y_index, x_index], 2)) +
error_line * white_noise * 4, 1)
def dither(tensor, shape, amount):
"""
"""
height, width, channels = shape
white_noise = basic([height, width], [height, width, 1])
return effects.blend(tensor, white_noise, amount)
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 dither(tensor, shape, amount, time=0.0, speed=1.0):
"""
"""
height, width, channels = shape
white_noise = basic([height, width], [height, width, 1],
time=time,
speed=speed,
distrib=ValueDistribution.simplex)
return effects.blend(tensor, white_noise, amount)
def snow(tensor, shape, amount):
"""
"""
height, width, channels = shape
white_noise_1 = basic([height, width], [height, width, 1],
wavelet=True,
refract_range=10)
white_noise_2 = tf.maximum(
basic([int(height * .75), int(width * .75)], [height, width, 1]) -
(1 - amount), 0) * 2
return effects.blend(tensor, white_noise_1, white_noise_2)
def grime(tensor, shape, time=0.0, speed=1.0):
"""
"""
value_shape = [shape[0], shape[1], 1]
mask = multires(5,
value_shape,
time=time,
speed=speed,
distrib=ValueDistribution.simplex_exp,
octaves=8,
refract_range=1.0,
refract_y_from_offset=True,
deriv=3,
deriv_alpha=.5)
dusty = effects.blend(tensor, .25, tf.square(mask) * .125)
specks = basic(
[int(shape[0] * .25), int(shape[1] * .25)],
value_shape,
time=time,
speed=speed,
distrib=ValueDistribution.simplex_exp,
refract_range=.1)
specks = 1.0 - tf.sqrt(effects.normalize(tf.maximum(specks - .5, 0.0)))
dusty = effects.blend(
dusty,
basic([shape[0], shape[1]],
value_shape,
time=time,
speed=speed,
distrib=ValueDistribution.simplex), .125) * specks
return effects.blend(tensor, dusty, mask)
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 snow(tensor, shape, amount, time=0.0, speed=1.0):
"""
"""
height, width, channels = shape
static = basic([height, width], [height, width, 1],
time=time,
speed=speed * 100,
distrib=ValueDistribution.simplex,
spline_order=0)
static_limiter = basic([height, width], [height, width, 1],
time=time,
speed=speed * 100,
distrib=ValueDistribution.simplex_exp,
spline_order=0) * amount
return effects.blend(tensor, static, static_limiter)
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 stray_hair(tensor, shape):
"""
"""
value_shape = [shape[0], shape[1], 1]
mask = basic(
4,
value_shape,
with_worms=4,
worms_alpha=1,
worms_density=.0025 + random.random() * .00125,
worms_duration=random.randint(8, 16),
worms_kink=random.randint(5, 50),
worms_stride=.5,
worms_stride_deviation=.25,
)
brightness = basic(32, value_shape)
return effects.blend(tensor, brightness * .333, mask * .666)
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 vhs(tensor, shape, time=0.0, speed=1.0):
"""
Apply a bad VHS tracking effect.
:param Tensor tensor:
:param list[int] shape:
:return: Tensor
"""
height, width, channels = shape
# Generate scan noise
scan_noise = basic(
[int(height * .5) + 1, int(width * .05) + 1], [height, width, 1],
time=time,
speed=speed,
spline_order=1,
distrib=ValueDistribution.simplex)
# Create horizontal offsets
grad = basic([int(random.random() * 10) + 5, 1], [height, width, 1],
time=time,
speed=speed,
distrib=ValueDistribution.simplex)
grad = tf.maximum(grad - .5, 0)
grad = tf.minimum(grad * 2, 1)
x_index = effects.row_index(shape)
x_index -= tf.squeeze(
tf.cast(scan_noise * width * tf.square(grad), tf.int32))
x_index = x_index % width
tensor = effects.blend(tensor, scan_noise, grad)
identity = tf.stack([effects.column_index(shape), x_index], 2)
tensor = tf.gather_nd(tensor, identity)
return tensor
def watermark(tensor, shape, time=0.0, speed=1.0):
"""
"""
value_shape = [int(shape[0] * .5), int(shape[1] * .5), 1]
value_shape = [shape[0], shape[1], 1]
mask = basic(240,
value_shape,
spline_order=0,
distrib=ValueDistribution.ones,
mask="numeric")
mask = crt(mask, value_shape)
mask = effects.warp(mask,
value_shape, [2, 4],
octaves=1,
displacement=.5,
time=time,
speed=speed)
mask *= tf.square(
basic(2,
value_shape,
time=time,
speed=speed,
distrib=ValueDistribution.simplex))
value_shape = [shape[0], shape[1], 1]
brightness = basic(16,
value_shape,
time=time,
speed=speed,
distrib=ValueDistribution.simplex)
return effects.blend(tensor, brightness, mask * .125)
def on_screen_display(tensor, shape):
glyph_count = random.randint(3, 6)
_masks = [
ValueMask.bank_ocr,
ValueMask.hex,
ValueMask.numeric,
]
mask = _masks[random.randint(0, len(_masks) - 1)]
mask_shape = masks.mask_shape(mask)
width = int(shape[1] / 24)
width = mask_shape[1] * int(
width / mask_shape[1]) # Make sure the mask divides evenly
height = mask_shape[0] * int(width / mask_shape[1])
width *= glyph_count
freq = [mask_shape[0], mask_shape[1] * glyph_count]
this_mask = basic(freq, [height, width, shape[2]],
corners=True,
spline_order=0,
distrib=ValueDistribution.ones,
mask=mask)
rendered_mask = tf.pad(
this_mask,
tf.stack([[25, shape[0] - height - 25], [shape[1] - width - 25, 25],
[0, 0]]))
alpha = .5 + random.random() * .25
return effects.blend(tensor, tf.maximum(rendered_mask, tensor), alpha)
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 frame(ctx, input_dir, frame, seed, name):
generators.set_seed(seed)
shape = [512, 512, 3]
dirnames = [
d for d in os.listdir(input_dir)
if os.path.isdir(os.path.join(input_dir, d))
]
if not dirnames:
click.echo("Couldn't determine directory names inside of input dir " +
input_dir)
sys.exit(1)
collage_count = min(random.randint(4, 6), len(dirnames))
collage_images = []
for i in range(collage_count + 1):
index = random.randint(0, len(dirnames) - 1)
dirname = dirnames[index]
filenames = [
f for f in sorted(
os.listdir(os.path.join(input_dir, dirnames[index])))
if f.endswith('.png')
]
if not filenames:
continue
input_filename = os.path.join(input_dir, dirnames[index],
filenames[frame])
collage_images.append(
tf.image.convert_image_dtype(util.load(input_filename, channels=3),
dtype=tf.float32))
base = generators.basic(freq=random.randint(2, 4),
shape=shape,
hue_range=random.random(),
time=frame / 30.0,
speed=0.125,
distrib="simplex")
control = effects.value_map(collage_images.pop(), shape, keep_dims=True)
control = effects.convolve(effects.ValueMask.conv2d_blur, control,
[512, 512, 1])
with tf.Session().as_default():
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)
util.save(tensor, name)
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')
