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 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, 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(load(input_filename,
channels=3),
dtype=tf.float32)
input_shape = shape_from_file(input_filename)
if retro_upscale:
input_shape = [
input_shape[0] * 2, input_shape[1] * 2, input_shape[2]
]
collage_input = value.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(load(control_filename,
channels=1),
dtype=tf.float32)
control = effects.square_crop_and_resize(
control, shape_from_file(control_filename), 1024)
control = value.value_map(control, shape, keepdims=True)
else:
control = value.value_map(collage_images.pop(), shape, keepdims=True)
control = effects.convolve(kernel=effects.ValueMask.conv2d_blur,
tensor=control,
shape=[height, width, 1])
with tf.compat.v1.Session().as_default():
# sort collage images by brightness
collage_images = [
j[1] for j in sorted([(tf.reduce_sum(i).numpy(), i)
for i in collage_images])
]
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)
save(tensor, name)
print('mashup')