def make_frame(t):
frame_idx = int(np.clip(np.round(t * mp4_fps), 0, num_frames - 1))
section = frame_idx // transition_frames
start = latents[section]
end = latents[(section + 1) % len(latents)]
transition_i = frame_idx - section * transition_frames
maxindex = transition_frames - 1.0
# mu1 = min(max(0, (transition_i*1.0/maxindex) ), 1) # linear interpolation
# mu1 = min(max(0, (transition_i*1.0/maxindex)*(transition_i*1.0/maxindex) ), 1) # quadratic interpolation
mu1 = min(max(0, 1 - math.cos(math.pi * transition_i / maxindex)),
2) / 2 # sine interpolation
lat = np.multiply(start, 1.0 - mu1) + np.multiply(end, mu1)
labels = np.zeros([lat.shape[0], 0], np.float32)
images = Gs.run(lat,
labels,
minibatch_size=minibatch_size,
num_gpus=1,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8,
randomize_noise=False)
grid = misc.create_image_grid(images, grid_size).transpose(1, 2,
0) # HWC
if image_zoom > 1:
grid = scipy.ndimage.zoom(grid, [image_zoom, image_zoom, 1],
order=0)
if grid.shape[2] == 1:
grid = grid.repeat(3, 2) # grayscale => RGB
return grid
def make_frame(t):
frame_idx = int(np.clip(np.round(t * mp4_fps), 0, num_frames - 1))
latents = all_latents[frame_idx]
labels = np.zeros([latents.shape[0], 0], np.float32)
classes = list(range(0, 32))
classes = tf.one_hot(classes, 32)
sess = tf.Session()
labels = sess.run(classes[30])
labels = labels.reshape(1, labels.shape[0])
sess.close()
embeddings = np.load('datasets/50k_sorted_tf-rxx.embeddings')
embeddings = embeddings.astype('float32')
embedding = embeddings[54200]
embedding = embedding.reshape(1, embedding.shape[0])
images = Gs.run(latents,
labels,
embedding,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
grid = misc.create_image_grid(images, grid_size).transpose(1, 2,
0) # HWC
if image_zoom > 1:
grid = scipy.ndimage.zoom(grid, [image_zoom, image_zoom, 1],
order=0)
if grid.shape[2] == 1:
grid = grid.repeat(3, 2) # grayscale => RGB
return grid
def make_frame(t):
frame_idx = int(np.clip(np.round(t * mp4_fps), 0, num_frames - 1))
latents = all_latents[frame_idx]
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents, labels, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.uint8)
grid = misc.create_image_grid(images, grid_size).transpose(1, 2, 0) # HWC
if image_zoom > 1:
grid = scipy.ndimage.zoom(grid, [image_zoom, image_zoom, 1], order=0)
if grid.shape[2] == 1:
grid = grid.repeat(3, 2) # grayscale => RGB
return grid
def make_frame(t):
frame_idx = int(np.clip(np.round(t * mp4_fps), 0, num_frames - 1))
latents = all_latents[frame_idx]
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents, labels, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.uint8)
grid = misc.create_image_grid(images, grid_size).transpose(1, 2, 0) # HWC
if image_zoom > 1:
grid = scipy.ndimage.zoom(grid, [image_zoom, image_zoom, 1], order=0)
if grid.shape[2] == 1:
grid = grid.repeat(3, 2) # grayscale => RGB
return grid
def generate_test_image_with_corresponding_z(run_id,
snapshot=None,
grid_size=[1, 1],
image_shrink=1,
image_zoom=1,
num_frames=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if num_frames is None:
num_frames = 4
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
print('Generating latent vectors...')
shape = [num_frames, np.prod(grid_size)
] + Gs.input_shape[1:] # [frame, image, channel, component]
all_latents = random_state.randn(*shape).astype(np.float32)
#all_latents = scipy.ndimage.gaussian_filter(all_latents, [smoothing_sec * mp4_fps] + [0] * len(Gs.input_shape), mode='wrap')
all_latents /= np.sqrt(np.mean(np.square(all_latents)))
# Generate images.
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for idx, latent in enumerate(all_latents):
labels = np.zeros([latent.shape[0], 0], np.float32)
images = Gs.run(latent,
labels,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
grid = misc.create_image_grid(images, grid_size).transpose(1, 2,
0) # HWC
if image_zoom > 1:
grid = scipy.ndimage.zoom(grid, [image_zoom, image_zoom, 1],
order=0)
if grid.shape[2] == 1:
grid = grid.repeat(3, 2) # grayscale => RGB
np.save(os.path.join(result_subdir, 'z_' + str(idx)), latent)
filename = 'im_' + str(idx) + '.png'
scipy.misc.imsave(os.path.join(result_subdir, filename), grid)
