def main(ctx, width, height, channels, seed, name, preset_name):
generators.set_seed(seed)
kwargs, preset_name = presets.load(preset_name)
print(preset_name)
kwargs["shape"] = [height, width, channels]
# print(kwargs)
if "freq" not in kwargs:
kwargs["freq"] = 3
if "octaves" not in kwargs:
kwargs["octaves"] = 1
if "ridges" not in kwargs:
kwargs["ridges"] = False
tensor = generators.multires(**kwargs)
tensor = recipes.post_process(tensor, **kwargs)
with tf.Session().as_default():
save(tensor, name)
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 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 main(ctx, width, height, channels, time, clut, seed, overrides, name,
preset_name):
presets.bake_presets(seed)
if preset_name == 'random':
preset_name = 'random-preset'
kwargs = presets.preset(preset_name)
print(kwargs['name'])
kwargs['shape'] = [height, width, channels]
kwargs['time'] = time
if 'freq' not in kwargs:
kwargs['freq'] = 3
if 'octaves' not in kwargs:
kwargs['octaves'] = 1
if 'ridges' not in kwargs:
kwargs['ridges'] = False
if clut:
kwargs['clut'] = clut
kwargs['clut_horizontal'] = True
if overrides:
kwargs.update(json.loads(overrides))
# print(json.dumps(kwargs, sort_keys=True, indent=4, default=str))
tensor = generators.multires(**kwargs)
tensor = recipes.post_process(tensor, **kwargs)
with tf.Session().as_default():
save(tensor, name)
def run_preset(preset_name, shape, filename, tensor=None):
kwargs = presets.preset(preset_name)
kwargs['shape'] = shape
if 'freq' not in kwargs:
kwargs['freq'] = 3
if 'octaves' not in kwargs:
kwargs['octaves'] = 1
if 'ridges' not in kwargs:
kwargs['ridges'] = False
kwargs['post_brightness'] = .125
if tensor is None:
tensor = generators.multires(**kwargs)
tensor = recipes.post_process(tensor, **kwargs)
with tf.Session().as_default():
save(tensor, filename)
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 main(ctx, freq, width, height, channels, octaves, ridges, post_ridges, sin,
wavelet, lattice_drift, vortex, warp, warp_octaves, warp_interp,
warp_freq, reflect, refract, reindex, reverb, reverb_iterations,
post_reindex, post_reflect, post_refract, clut, clut_horizontal,
clut_range, ripple, ripple_freq, ripple_kink, worms, worms_density,
worms_duration, worms_stride, worms_stride_deviation, worms_alpha,
worms_kink, wormhole, wormhole_kink, wormhole_stride, sobel, outline,
normals, post_deriv, deriv, deriv_alpha, interp, distrib, corners,
mask, mask_inverse, posterize, erosion_worms, voronoi, voronoi_func,
voronoi_nth, voronoi_alpha, voronoi_refract, voronoi_inverse, glitch,
vhs, crt, scan_error, snow, dither, aberration, light_leak, vignette,
vignette_brightness, pop, bloom, rgb, hue_range, hue_rotation,
saturation, saturation_distrib, post_hue_rotation, post_saturation,
brightness_distrib, input_dir, dla, dla_padding, point_freq,
point_distrib, point_corners, point_generations, point_drift, shadow,
density, seed, name, **convolve_kwargs):
generators.set_seed(seed)
shape = [height, width, channels]
tensor = generators.multires(freq=freq,
shape=shape,
octaves=octaves,
ridges=ridges,
post_ridges=post_ridges,
sin=sin,
wavelet=wavelet,
lattice_drift=lattice_drift,
reflect_range=reflect,
refract_range=refract,
reindex_range=reindex,
with_reverb=reverb,
reverb_iterations=reverb_iterations,
post_reindex_range=post_reindex,
post_reflect_range=post_reflect,
post_refract_range=post_refract,
ripple_range=ripple,
ripple_freq=ripple_freq,
ripple_kink=ripple_kink,
clut=clut,
clut_horizontal=clut_horizontal,
clut_range=clut_range,
with_worms=worms,
worms_density=worms_density,
worms_duration=worms_duration,
worms_stride=worms_stride,
worms_stride_deviation=worms_stride_deviation,
worms_alpha=worms_alpha,
worms_kink=worms_kink,
with_wormhole=wormhole,
wormhole_kink=wormhole_kink,
wormhole_stride=wormhole_stride,
with_erosion_worms=erosion_worms,
with_voronoi=voronoi,
voronoi_func=voronoi_func,
voronoi_nth=voronoi_nth,
voronoi_alpha=voronoi_alpha,
voronoi_refract=voronoi_refract,
voronoi_inverse=voronoi_inverse,
with_dla=dla,
dla_padding=dla_padding,
point_freq=point_freq,
point_distrib=point_distrib,
point_corners=point_corners,
point_generations=point_generations,
point_drift=point_drift,
with_outline=outline,
with_sobel=sobel,
with_normal_map=normals,
post_deriv=post_deriv,
deriv=deriv,
deriv_alpha=deriv_alpha,
spline_order=interp,
distrib=distrib,
corners=corners,
mask=mask,
mask_inverse=mask_inverse,
warp_range=warp,
warp_octaves=warp_octaves,
warp_interp=warp_interp,
warp_freq=warp_freq,
posterize_levels=posterize,
vortex_range=vortex,
rgb=rgb,
hue_range=hue_range,
hue_rotation=hue_rotation,
saturation=saturation,
post_hue_rotation=post_hue_rotation,
post_saturation=post_saturation,
brightness_distrib=brightness_distrib,
saturation_distrib=saturation_distrib,
input_dir=input_dir,
with_aberration=aberration,
with_bloom=bloom,
with_pop=pop,
with_light_leak=light_leak,
with_vignette=vignette,
vignette_brightness=vignette_brightness,
with_shadow=shadow,
with_density_map=density,
**convolve_kwargs)
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)
with tf.Session().as_default():
save(tensor, name)
print(name)