def image(
w,
h=None,
batch=None,
sd=None,
decorrelate=True,
fft=True,
alpha=False,
channels=None,
init_val=None,
):
h = h or w
batch = batch or 1
ch = channels or (4 if alpha else 3)
shape = [batch, h, w, ch]
param_f = fft_image if fft else pixel_image
t = param_f(shape, sd=sd, init_val=init_val)
if channels:
output = tf.nn.sigmoid(t)
else:
output = to_valid_rgb(t[..., :3],
decorrelate=decorrelate,
sigmoid=True)
if alpha:
a = tf.nn.sigmoid(t[..., 3:])
output = tf.concat([output, a], -1)
return output
def image(w, h=None, batch=None, sd=None, decorrelate=True, fft=True, alpha=False, init_val=None):
h = h or w
batch = batch or 1
channels = 4 if alpha else 3
shape = [batch, w, h, channels]
if init_val is not None:
t = tf.Variable( init_val )
rgb = to_valid_rgb( t[..., :3], decorrelate=True, sigmoid=True )
else:
t = fft_image( shape, sd=sd ) if fft else pixel_image( shape, sd=sd )
rgb = to_valid_rgb( t[..., :3], decorrelate=decorrelate, sigmoid=True )
if alpha:
a = tf.nn.sigmoid(t[..., 3:])
return tf.concat([rgb, a], -1)
return rgb
def image(w, h=None, batch=None, sd=None, decorrelate=True, fft=True, alpha=False):
h = h or w
batch = batch or 1
channels = 4 if alpha else 3
shape = [batch, w, h, channels]
param_f = fft_image if fft else naive
t = param_f(shape, sd=sd)
rgb = to_valid_rgb(t[..., :3], decorrelate=decorrelate, sigmoid=True)
if alpha:
a = tf.nn.sigmoid(t[..., 3:])
return tf.concat([rgb, a], -1)
return rgb
def image(w,
h=None,
batch=None,
sd=None,
decorrelate=True,
fft=True,
alpha=False,
gray=False,
init_val=None,
decay_power=None):
h = h or w
batch = batch or 1
if gray:
channels = 1
else:
if alpha:
channels = 4
else:
channels = 3
if decay_power is not None:
fft_image_func = partial(fft_image, decay_power=decay_power)
else:
fft_image_func = fft_image
shape = [batch, w, h, channels]
param_f = fft_image_func if fft else pixel_image
t = param_f(shape, sd=sd, init_val=init_val)
if gray:
rgb = tf.tile(t, [1, 1, 1, 3])
return to_valid_rgb(rgb[..., :3], decorrelate=False, sigmoid=True)
else:
rgb = to_valid_rgb(t[..., :3], decorrelate=decorrelate, sigmoid=True)
if alpha:
a = tf.nn.sigmoid(t[..., 3:])
return tf.concat([rgb, a], -1)
return rgb
def test_param_can_fit_image(param, test_image, maxsteps=1000):
shape = (1, ) + test_image.shape
with tf.Session() as sess:
image_param_t = param(shape)
image_t = to_valid_rgb(image_param_t)
loss_t = tf.reduce_mean((image_t - test_image)**2)
dist_t = tf.reduce_mean(tf.abs(image_t - test_image))
optimizer = tf.train.AdamOptimizer(0.05)
optimize_op = optimizer.minimize(loss_t)
tf.global_variables_initializer().run()
for step in range(maxsteps):
mean_distance, _ = sess.run([dist_t, optimize_op])
if mean_distance < 0.01:
break
assert mean_distance < 0.01
def image_sample(shape, decorrelate=True, sd=None, decay_power=1):
raw_spatial = rand_fft_image(shape, sd=sd, decay_power=decay_power)
return to_valid_rgb(raw_spatial, decorrelate=decorrelate)
def video_sample(shape, decorrelate=True, sd=None, decay_power=1):
raw_spatial = fft_video(shape, sd=sd, decay_power=decay_power, rand=True)
return to_valid_rgb(raw_spatial, decorrelate=decorrelate)
