def render(ctx, width, height, input_dir, voronoi_func, voronoi_nth,
point_freq, point_distrib, point_drift, name):
shape = [height, width, 3]
x, y = points.point_cloud(point_freq,
distrib=point_distrib,
shape=shape,
drift=point_drift)
base = generators.basic(freq=random.randint(2, 4),
shape=shape,
lattice_drift=random.randint(0, 1),
hue_range=random.random())
tensor = effects.voronoi(base,
shape,
diagram_type=effects.VoronoiDiagramType.collage,
xy=(x, y, len(x)),
nth=voronoi_nth,
input_dir=input_dir,
alpha=.333 + random.random() * .333)
tensor = effects.bloom(tensor, shape, alpha=.333 + random.random() * .333)
with tf.Session().as_default():
save(tensor, name)
print('mashup')
def mashup(ctx, input_dir, filename, control_filename, time, speed, seed):
filenames = []
for root, _, files in os.walk(input_dir):
for f in files:
if f.endswith(('.png', '.jpg')):
filenames.append(os.path.join(root, f))
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)
collage_images.append(collage_input)
if control_filename:
control_shape = util.shape_from_file(control_filename)
control = tf.image.convert_image_dtype(util.load(control_filename, channels=control_shape[2]), dtype=tf.float32)
else:
control = collage_images.pop()
shape = tf.shape(control) # All images need to be the same size!
control = value.value_map(control, shape, keepdims=True)
base = generators.basic(freq=random.randint(2, 5), shape=shape, lattice_drift=random.randint(0, 1), hue_range=random.random(),
seed=seed, time=time, speed=speed)
value_shape = value.value_shape(shape)
control = value.convolve(kernel=effects.ValueMask.conv2d_blur, tensor=control, shape=value_shape)
with tf.compat.v1.Session().as_default():
tensor = effects.blend_layers(control, shape, random.random() * .5, *collage_images)
tensor = value.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, filename)
print('mashup')
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 frame(ctx, input_dir, frame, seed, filename, width, height):
value.set_seed(seed)
shape = [height, width, 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, dirname))) if f.endswith('.png')]
if not filenames:
continue
input_filename = os.path.join(input_dir, dirname, 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(), seed=seed, time=frame/30.0, speed=0.125)
control = value.value_map(collage_images.pop(), shape, keepdims=True)
control = value.convolve(kernel=effects.ValueMask.conv2d_blur, tensor=control, shape=[shape[0], shape[1], 1])
with tf.compat.v1.Session().as_default():
tensor = effects.blend_layers(control, shape, random.random() * .5, *collage_images)
tensor = value.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, filename)
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 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')