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 main(ctx, name, retro_upscale, input_filename):
shape = 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 = value.resample(tensor, shape, spline_order=0)
tensor = effects.square_crop_and_resize(tensor, shape, 1024)
with tf.compat.v1.Session().as_default():
save(tensor, name)
def main(ctx, seed, name, no_resize, overrides, time, preset_name, input_filename):
presets.bake_presets(seed)
input_shape = effects.shape_from_file(input_filename)
input_shape[2] = min(input_shape[2], 3)
tensor = tf.image.convert_image_dtype(load(input_filename, channels=input_shape[2]), dtype=tf.float32)
if preset_name == 'random':
preset_name = 'random-effect'
kwargs = presets.preset(preset_name)
print(kwargs['name'])
kwargs['time'] = time
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
if no_resize:
kwargs['shape'] = input_shape
else:
kwargs['shape'] = [1024, 1024, input_shape[2]]
tensor = effects.square_crop_and_resize(tensor, input_shape, kwargs['shape'][0])
if overrides:
kwargs.update(json.loads(overrides))
tensor = effects.post_process(tensor, **kwargs)
tensor = recipes.post_process(tensor, **kwargs)
with tf.Session().as_default():
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')
