def generate_interpolation_video(run_id, snapshot=None, grid_size=[1,1], image_shrink=1, image_zoom=1, duration_sec=60.0, smoothing_sec=1.0, mp4=None, mp4_fps=30, mp4_codec='libx265', mp4_bitrate='16M', random_seed=1000, minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if mp4 is None:
mp4 = misc.get_id_string_for_network_pkl(network_pkl) + '-lerp.mp4'
num_frames = int(np.rint(duration_sec * mp4_fps))
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)))
# Frame generation func for moviepy.
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
# Generate video.
import moviepy.editor # pip install moviepy
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
moviepy.editor.VideoClip(make_frame, duration=duration_sec).write_videofile(os.path.join(result_subdir, mp4), fps=mp4_fps, codec='libx264', bitrate=mp4_bitrate)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=random_state)
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)
misc.save_image_grid(
images,
os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)),
[0, 255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_interpolation_video(run_id, snapshot=None, grid_size=[1,1], image_shrink=1, image_zoom=1, duration_sec=60.0, smoothing_sec=1.0, mp4=None, mp4_fps=30, mp4_codec='libx265', mp4_bitrate='16M', random_seed=1000, minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if mp4 is None:
mp4 = misc.get_id_string_for_network_pkl(network_pkl) + '-lerp.mp4'
num_frames = int(np.rint(duration_sec * mp4_fps))
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)))
# Frame generation func for moviepy.
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
# Generate video.
import moviepy.editor # pip install moviepy
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
moviepy.editor.VideoClip(make_frame, duration=duration_sec).write_videofile(os.path.join(result_subdir, mp4), fps=mp4_fps, codec='libx264', bitrate=mp4_bitrate)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_interpolation_video(run_id, snapshot=None, grid_size=[1,1], image_shrink=1, image_zoom=1, duration_sec=60.0, smoothing_sec=1.0, mp4=None, mp4_fps=30, mp4_codec='libx265', mp4_bitrate='16M', random_seed=1000, minibatch_size=8):
interpolate_dim=500
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if mp4 is None:
# mp4 = misc.get_id_string_for_network_pkl(network_pkl) + '-lerp.mp4'
mp4 = misc.get_id_string_for_network_pkl(network_pkl) + '-lerp' + '-interpolate-dim-' + str(interpolate_dim) + '.mp4'
num_frames = int(np.rint(duration_sec * mp4_fps))
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=[1800, 1, 512]; grid_size=[1, 1]; Gs.input_shape=[None, 512]
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)))
latent_dims=512 # TODO: Hardcoded
for frame in range(num_frames):
all_latents[frame][0][0:interpolate_dim]=-0.5
all_latents[frame][0][interpolate_dim+1:latent_dims]=-0.5
# Frame generation func for moviepy.
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
# Generate video.
import moviepy.editor # pip install moviepy
# result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc + '-interpolate-dim-' + str(interpolate_dim))
moviepy.editor.VideoClip(make_frame, duration=duration_sec).write_videofile(os.path.join(result_subdir, mp4), fps=mp4_fps, codec='libx264', bitrate=mp4_bitrate)
# aSk
# np.set_printoptions(threshold=np.inf)
# with open(os.path.join(result_subdir, 'all_latents_mod.txt'), 'w') as fp:
# fp.write(str(all_latents))
with open(os.path.join(result_subdir, 'latents_interpolate_dim_' + str(interpolate_dim) + '.txt'), 'w') as fp:
for frame in range(num_frames):
for latent_dim in range(latent_dims):
fp.write(str(all_latents[frame][0][latent_dim]) + ', ')
fp.write('\n')
# _aSk
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images_cond2(run_id, x, y, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=1):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
training_set = dataset.load_dataset(data_dir=config.data_dir, shuffle_mb=0, verbose=True, **config.dataset)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
real, label = training_set.get_minibatch_np(num_pngs)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
size=128
grid= np.zeros((3, x*256, y* 256))
image=[]
for i in range(1, 18):
filename = 'cond4/'+str(i)+'.png'
if os.path.isfile(filename):
im=Image.open(filename)
im.load()
im = np.asarray(im, dtype=np.float32 )
im=np.transpose(im, (2, 0, 1))
image.append(im)
print(image[i-1].shape)
print(len(image))
for i in range (1, x):
grid[:, (i)*256:(i)*256+256, 0:256]= image[i]
print(i)
for j in range (i, 16):
grid[:, 0:256, (j-i)*256:(j-i)*256+256]= image[j]
for j in range (128, y*256-128, 128):
for i in range (128, x*256-128, 128):
real = grid[:,i:i+256, j:j+256]
real1= real[:, :(size),:(size)]
real2= real[:, (size):,:(size)]
real3= real[:, :(size),(size):]
real1=(real1.astype(np.float32)-127.5)/127.5
real2=(real2.astype(np.float32)-127.5)/127.5
real3=(real3.astype(np.float32)-127.5)/127.5
latents = np.random.randn(3, 128, 128)
left = np.concatenate((real1, real2), axis=1)
print('left:'+str(left.shape))
right = np.concatenate((real3, latents), axis=1)
lat_and_cond = np.concatenate((left, right), axis=2)
lat_and_cond = lat_and_cond[np.newaxis]
fake_images_out_small = Gs.get_output_for(lat_and_cond, is_training=False)
fake_images_out_small = (fake_images_out_small.eval()*127.5)+127.5
print(fake_images_out_small.shape)
fake_images_out_small=fake_images_out_small[0, :,:,:]
grid[:,i+128:i+256, j+128:j+256]=fake_images_out_small
images = grid[np.newaxis]
misc.save_image_grid(images, os.path.join(result_subdir, 'grid.png'), [0,255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def fit_real_images(run_id,
snapshot=None,
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
latent = tf.get_variable('latent', shape=(1, 512), trainable=True)
label = tf.get_variable('label', shape=(1, 0), trainable=True)
images = Gs.fit(latent,
label,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus)
sess = tf.get_default_session()
target = tf.placeholder(tf.float32, name='target')
lr = tf.placeholder(tf.float32, name='lr')
#loss = tf.reduce_sum(tf.abs(images[0][0] - target))
loss = tf.nn.l2_loss(images[0][0] - target)
with tf.variable_scope('adam'):
opt = tf.train.AdamOptimizer(lr).minimize(loss, var_list=latent)
sess.run(tf.variables_initializer([latent, label]))
sess.run(tf.variables_initializer(tf.global_variables('adam')))
# real_path = '/vol/phoebe/3DMD_SCIENCE_MUSEUM/Colour_UV_maps'
# real_path = '/home/baris/data/mein3d_600x600'
real_path = '/media/gen/pca_alone'
save_path = '/media/gen/gan-pca'
#target_im = PIL.Image.open('/media/logs-nvidia/002-fake-images-0/000-pgan-mein3d_tf-preset-v2-2gpus-fp32-VERBOSE-HIST-network-final-000001.png')
for ind, real in enumerate(myutil.files(real_path)):
target_im = myutil.crop_im(
PIL.Image.open(os.path.join(real_path, real)))
for j in [0.1, 0.01, 0.001]:
for i in range(500):
l2, _ = sess.run([loss, opt], {
target: myutil.rgb2tf(target_im),
lr: j
})
if i % 100 == 0:
print(l2)
myutil.concat_image(np.asarray(target_im),
myutil.tf2rgb(sess.run(images))).save(
os.path.join(save_path, real))
sess.close()
def generate_fake_interpolate_midle_images(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8,
middle_img=10):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(num_pngs):
latents = misc.random_latents(middle_img + 2,
Gs,
random_state=random_state)
from_to_tensor = latents[middle_img + 1] - latents[0]
from_z = latents[0]
#between_x_list = [from_x]
counter = 0
for alpha in np.linspace(-0.5, 0.5, middle_img +
2): #np.linspace(0, 1, middle_img + 1):
print('alpha: ', alpha, 'counter= ', counter)
between_z = from_z + alpha * from_to_tensor
latents[counter] = between_z
counter += 1
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_size_1=[middle_img+2,1]
grid_size_1 = [middle_img + 1, 1]
#png_prefix=0
misc.save_image_grid(
images[1:, :, :, :],
os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)),
[0, 255], grid_size_1)
'''
def find_latent_with_query_image(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=4123, minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
# Create query image - tensorflow constant
query_image = cv2.imread('../../data/ACDC/training/patient001/cardiac_cycles/0/0.png')
query_image = cv2.resize(query_image, (256, 256))
print('Saving query image to "%s"...' % result_subdir)
cv2.imwrite(result_subdir+'/query_image.png', query_image)
query_image = query_image.transpose(2,0,1)
query_image = query_image[np.newaxis]
x = tf.constant(query_image, dtype=tf.float32, name='query_image')
# Create G(z) - tensorflow variable and label
latent = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
initial = tf.constant(latent, dtype=tf.float32)
z = tf.Variable(initial_value=initial, dtype=tf.float32, name='latent_space')
label = np.zeros([latent.shape[0], 5], np.float32)
label[:,4] = 1 # | 0 -> NOR | 1 -> DCM | 2 -> HCM | 3 -> MINF | 4 -> RV |
gz = Gs.run(latent, label, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.float32)
gz = tf.Variable(gz, dtype=tf.float32)
# Define a loss function
residual_loss = tf.losses.absolute_difference(x, gz)
# Define an optimizer
train_op = tf.train.AdamOptimizer(learning_rate=0.01).minimize(residual_loss)
zs, gzs, step = [], [], 1
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
_, loss_value = sess.run([train_op, residual_loss])
while (loss_value > 2e-04 and step <= 50000):
_, loss_value = sess.run([train_op, residual_loss])
step += 1
if step % 10000 == 0:
print('Step {}, Loss value: {}'.format(step, loss_value))
gzs.append(sess.run(gz))
zs.append(sess.run(z))
for png_idx, image in enumerate(gzs):
misc.save_image_grid(image, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
np.save(result_subdir+'/zs.npy', np.asarray(zs))
def generate_fake_images(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
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)
misc.save_image_grid(images, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
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)
def generate_fake_images_cond(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=1):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
training_set = dataset.load_dataset(data_dir=config.data_dir, verbose=True, **config.dataset)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
real, label = training_set.get_minibatch_np(num_pngs)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
size=128
for png_idx in range(num_pngs):
real1= real[png_idx,:, :(size),:(size)]
real2= real[png_idx,:, (size):,:(size)]
real3= real[png_idx,:, :(size),(size):]
real1=(real1.astype(np.float32)-127.5)/127.5
real2=(real2.astype(np.float32)-127.5)/127.5
real3=(real3.astype(np.float32)-127.5)/127.5
latents = np.random.randn(3, 128, 128)
left = np.concatenate((real1, real2), axis=1)
print('left:'+str(left.shape))
right = np.concatenate((real3, latents), axis=1)
lat_and_cond = np.concatenate((left, right), axis=2)
lat_and_cond = lat_and_cond[np.newaxis]
fake_images_out_small = Gs.get_output_for(lat_and_cond, is_training=False)
fake_images_out_small = (fake_images_out_small.eval()*127.5)+127.5
print(fake_images_out_small.shape)
fake_images_out_small=fake_images_out_small[0, :,:,:]
real1=(real1.astype(np.float32)*127.5)+127.5
real2=(real2.astype(np.float32)*127.5)+127.5
real3=(real3.astype(np.float32)*127.5)+127.5
fake_image_out_right =np.concatenate((real3, fake_images_out_small), axis=1)
fake_image_out_left = np.concatenate((real1, real2), axis=1)
images = np.concatenate((fake_image_out_left, fake_image_out_right), axis=2)
images = images[np.newaxis]
misc.save_image_grid(images, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
subdir=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if subdir is None:
subdir = misc.get_id_string_for_network_pkl(network_pkl)
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = "results/images/" + subdir
if not os.path.exists(result_subdir):
os.makedirs(result_subdir)
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = random_latents(np.prod(grid_size),
Gs,
random_state=random_state)
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents,
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)
misc.save_image_grid(
images, os.path.join(result_subdir, '%06d.png' % (png_idx)),
[0, 255], grid_size)
np.save(result_subdir + "/" + '%06d' % (png_idx), latents)
def get_generator(run_id, snapshot=None, image_shrink=1, minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
latent = tf.get_variable('latent', shape=(1, 512), trainable=True)
label = tf.get_variable('label',
shape=(1, 0),
trainable=True,
initializer=tf.zeros_initializer)
images = Gs.fit(latent,
label,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=0.5,
out_add=0.5,
out_shrink=image_shrink,
out_dtype=np.float32)
sess = tf.get_default_session()
sess.run(tf.variables_initializer([latent, label]))
return images, latent, sess
def find_dir_latent_with_query_image(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=4123, minibatch_size=8, dir_path='../../data/ACDC/latents/cleaned_testing/'):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
replicate_folder_structure(dir_path, result_subdir+'/')
train_patients = sorted_nicely(glob.glob(dir_path+'*'))
for patient in train_patients:
cardiac_cycles = sorted_nicely(glob.glob(patient+'/*/*/*.png'))
cfg = open(patient+'/Info.cfg')
label = condition_to_onehot(cfg.readlines()[2][7:])
cont = 0
for cycle in cardiac_cycles:
# Get folder containing the image
supfolder = sup_folder(cycle)
latent_subir = result_subdir + '/' + supfolder
# Create query image - tensorflow constant
query_image = cv2.imread(cycle) # read frame
query_image = cv2.resize(query_image, (256, 256))
query_image = query_image.transpose(2,0,1)
query_image = query_image[np.newaxis]
x = tf.constant(query_image, dtype=tf.float32, name='query_image')
# Create G(z) - tensorflow variable and label
latent = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
initial = tf.constant(latent, dtype=tf.float32)
z = tf.Variable(initial_value=initial, dtype=tf.float32, name='latent_space')
gz = Gs.run(latent, label, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.float32)
gz = tf.Variable(gz, dtype=tf.float32)
# Define a loss function
residual_loss = tf.losses.absolute_difference(x, gz)
# Define an optimizer
train_op = tf.train.AdamOptimizer(learning_rate=0.1).minimize(residual_loss)
zs, gzs, step = [], [], 1
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
_, loss_value = sess.run([train_op, residual_loss])
while (loss_value > 2e-04 and step <= 5000):
_, loss_value = sess.run([train_op, residual_loss])
step += 1
if step % 1000 == 0:
print('Step {}, Loss value: {}'.format(step, loss_value))
gzs.append(sess.run(gz))
zs.append(sess.run(z))
# save last image
print('Image saved at {}'.format(os.path.join(latent_subir, '%s.png' % (cont))))
misc.save_image_grid(gzs[-1], os.path.join(latent_subir, '%02d.png' % (cont)), [0,255], grid_size)
print('Latent vectors saved at {}'.format(os.path.join(latent_subir, 'latent_%02d.npy' % (cont))))
np.save(os.path.join(latent_subir, 'latent_%02d.npy' % (cont)), zs[-1])
print('Labels saved at {}'.format(os.path.join(latent_subir, 'label_%02d.npy' % (cont))))
np.save(os.path.join(latent_subir, 'label_%02d.npy' % (cont)), label)
cont+=1
cfg.close()
cont = 0
def generate_interpolation_images(run_id,
snapshot=None,
grid_size=[1, 1],
image_shrink=1,
image_zoom=1,
duration_sec=60.0,
smoothing_sec=1.0,
mp4=None,
mp4_fps=30,
mp4_codec='libx265',
mp4_bitrate='16M',
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if mp4 is None:
mp4 = misc.get_id_string_for_network_pkl(network_pkl) + '-lerp.mp4'
num_frames = int(np.rint(duration_sec * mp4_fps))
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)))
lsfm_model = m3io.import_lsfm_model(
'/home/baris/Projects/faceganhd/models/all_all_all.mat')
lsfm_tcoords = \
mio.import_pickle('/home/baris/Projects/team members/stelios/UV_spaces_V2/UV_dicts/full_face/512_UV_dict.pkl')[
'tcoords']
lsfm_params = []
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(int(num_frames / minibatch_size)):
start = time.time()
print('Generating png %d-%d / %d... in ' %
(png_idx * minibatch_size,
(png_idx + 1) * minibatch_size, num_frames),
end='')
latents = all_latents[png_idx * minibatch_size:(png_idx + 1) *
minibatch_size, 0]
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_shrink=image_shrink)
for i in range(minibatch_size):
texture = Image(np.clip(images[i, 0:3] / 2 + 0.5, 0, 1))
mesh_raw = from_UV_2_3D(Image(images[i, 3:6]))
normals = images[i, 6:9]
normals_norm = (normals - normals.min()) / (normals.max() -
normals.min())
mesh = lsfm_model.reconstruct(mesh_raw)
lsfm_params.append(lsfm_model.project(mesh_raw))
t_mesh = TexturedTriMesh(mesh.points, lsfm_tcoords.points, texture,
mesh.trilist)
m3io.export_textured_mesh(
t_mesh,
os.path.join(result_subdir,
'%06d.obj' % (png_idx * minibatch_size + i)),
texture_extension='.png')
mio.export_image(
Image(normals_norm),
os.path.join(result_subdir,
'%06d_nor.png' % (png_idx * minibatch_size + i)))
print('%0.2f seconds' % (time.time() - start))
mio.export_pickle(lsfm_params,
os.path.join(result_subdir, 'lsfm_params.pkl'))
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
batch_size=8,
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
lsfm_model = m3io.import_lsfm_model(
'/home/baris/Projects/faceganhd/models/all_all_all.mat')
lsfm_tcoords = \
mio.import_pickle('/home/baris/Projects/team members/stelios/UV_spaces_V2/UV_dicts/full_face/512_UV_dict.pkl')[
'tcoords']
lsfm_params = []
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(int(num_pngs / batch_size)):
start = time.time()
print('Generating png %d-%d / %d... in ' %
(png_idx * batch_size, (png_idx + 1) * batch_size, num_pngs),
end='')
latents = misc.random_latents(np.prod(grid_size) * batch_size,
Gs,
random_state=random_state)
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_shrink=image_shrink)
for i in range(batch_size):
if images.shape[1] == 3:
mio.export_pickle(
images[i],
os.path.join(
result_subdir,
'%s%06d.pkl' % (png_prefix, png_idx * batch_size + i)))
# misc.save_image(images[i], os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx*batch_size+i)), [0,255], grid_size)
elif images.shape[1] == 6:
mio.export_pickle(images[i][3:6],
os.path.join(
result_subdir, '%s%06d.pkl' %
(png_prefix, png_idx * batch_size + i)),
overwrite=True)
misc.save_image(
images[i][0:3],
os.path.join(
result_subdir,
'%s%06d.png' % (png_prefix, png_idx * batch_size + i)),
[-1, 1], grid_size)
elif images.shape[1] == 9:
texture = Image(np.clip(images[i, 0:3] / 2 + 0.5, 0, 1))
mesh_raw = from_UV_2_3D(Image(images[i, 3:6]))
normals = images[i, 6:9]
normals_norm = (normals - normals.min()) / (normals.max() -
normals.min())
mesh = lsfm_model.reconstruct(mesh_raw)
lsfm_params.append(lsfm_model.project(mesh_raw))
t_mesh = TexturedTriMesh(mesh.points, lsfm_tcoords.points,
texture, mesh.trilist)
m3io.export_textured_mesh(
t_mesh,
os.path.join(result_subdir,
'%06d.obj' % (png_idx * minibatch_size + i)),
texture_extension='.png')
mio.export_image(
Image(normals_norm),
os.path.join(
result_subdir,
'%06d_nor.png' % (png_idx * minibatch_size + i)))
shape = images[i, 3:6]
shape_norm = (shape - shape.min()) / (shape.max() -
shape.min())
mio.export_image(
Image(shape_norm),
os.path.join(
result_subdir,
'%06d_shp.png' % (png_idx * minibatch_size + i)))
mio.export_pickle(
t_mesh,
os.path.join(result_subdir,
'%06d.pkl' % (png_idx * minibatch_size + i)))
print('%0.2f seconds' % (time.time() - start))
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_keyframed_video(run_id,
latents_idx,
subdir=None,
snapshot=None,
grid_size=[1, 1],
image_shrink=1,
image_zoom=1,
transition_frames=120,
smoothing_sec=1.0,
mp4=None,
mp4_fps=30,
mp4_codec='libx265',
mp4_bitrate='16M',
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
if subdir is None:
subdir = misc.get_id_string_for_network_pkl(network_pkl)
keyframe_dir = "results/images/" + subdir
result_subdir = "results/videos/" + subdir
if not os.path.exists(result_subdir):
os.makedirs(result_subdir)
# codec mp4
# if mp4 is None:
# count = len(glob.glob(result_subdir + "/*.avi"))
# mp4 = str(count) + '-video.avi'
# codec AVI
if mp4 is None:
count = len(glob.glob(result_subdir + "/*.avi"))
mp4 = str(count) + '-video.avi'
files = [f for f in glob.glob(keyframe_dir + "/*.npy", recursive=True)]
for f in files:
print(f)
latents = list(map(lambda idx: np.load(files[idx]), latents_idx))
print('len(latents)', len(latents))
num_frames = transition_frames * len(latents)
duration_sec = num_frames / mp4_fps
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
# Generate video.
import moviepy.editor # pip install moviepy
moviepy.editor.VideoClip(make_frame,
duration=duration_sec).write_videofile(
os.path.join(result_subdir, mp4),
fps=mp4_fps,
codec='png',
bitrate=mp4_bitrate)
with open(os.path.join(result_subdir, mp4 + '-keyframes.txt'),
'w') as file:
file.write(str(latents_idx))
def __init__(self, api, save_dir):
########################
# INITIALIZE MODEL:
########################
misc.init_output_logging()
numpy.random.seed(config.random_seed)
print('Initializing TensorFlow...')
os.environ.update(config.env)
tfutil.init_tf(config.tf_config)
#-----------------
network_pkl = misc.locate_network_pkl(14, None)
print('Loading network from "%s"...' % network_pkl)
self.G, self.D, self.Gs = misc.load_network_pkl(14, None)
self.random_state = numpy.random.RandomState()
########################
# INITIALIZE API INFORMATION:
########################
# Azure storage information
self.table_account_name = '' # NEEDS TO BE COMPLETED WITH AZURE ACCOUNT
self.table_account_key = '' # NEEDS TO BE COMPLETED WITH AZURE ACCOUNT
self.block_blob_service = BlockBlobService(self.table_account_name, self.table_account_key)
self.https_prefix = '' # NEEDS TO BE COMPLETED WITH AZURE ACCOUNT
self.table_service = TableService(account_name=self.table_account_name, account_key=self.table_account_key)
self.container_name = '' # NEEDS TO BE COMPLETED WITH AZURE ACCOUNT
# Microsoft face detection
self.msft_face_detection_key = '' # NEEDS TO BE COMPLETED WITH MSFT API ACCOUNT
self.msft_face_detection_url = '' # NEEDS TO BE COMPLETED WITH MSFT API ACCOUNT
self.msft_face_attributes = 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise'
self.msft_headers = {'Ocp-Apim-Subscription-Key': self.msft_face_detection_key}
self.msft_params = {
'returnFaceId': 'true',
'returnFaceLandmarks': 'false',
'returnFaceAttributes': self.msft_face_attributes
}
# FacePlusPlus face detection
self.faceplusplus_http_url = 'https://api-us.faceplusplus.com/facepp/v3/detect'
self.faceplusplus_key = "" # NEEDS TO BE COMPLETED WITH FACE++ API ACCOUNT
self.faceplusplus_secret = "" # NEEDS TO BE COMPLETED WITH FACE++ API ACCOUNT
# IBM Watson face detection
self.IBM_visual_recognition = VisualRecognitionV3(
version='2018-03-19',
iam_api_key='', # NEEDS TO BE COMPLETED WITH IBM API ACCOUNT
url = 'https://gateway.watsonplatform.net/visual-recognition/api'
)
# Amazon AWS Rekognition face detection:
self.amazon_face_detection_id = '' # NEEDS TO BE COMPLETED WITH AMAZON API ACCOUNT
self.amazon_face_detection_key = '' # NEEDS TO BE COMPLETED WITH AMAZON API ACCOUNT
self.amazon_client = boto3.client('rekognition','us-east-1',aws_access_key_id=self.amazon_face_detection_id,aws_secret_access_key=self.amazon_face_detection_key)
# SightEngine:
self.SEclient = SightengineClient('', '') # NEEDS TO BE COMPLETED WITH SE API ACCOUNT
########################
# SET WHICH FACE API TO USE:
########################
self.faceAPI = api # or "FacePlusePlus" or "IBM" or "Google" or "Amazon" or "SE"
self.save_dir = save_dir+'\\images'
self.raw_results = save_dir+'\\raw_results.txt'
f = open(self.raw_results, 'a')
f.write("ImageLocation,Race_Int,Gender_Int,Race,Gender,Face_Detected,Gender_Prediction,Gender_Correct\n")
f.close()
self.image_count=0
def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
embeddings_contant = False
labels_constant = False
latents_constant = False
idx = random.randint(0, 56880)
df = pandas.read_csv('datasets/50k_sorted_tf/50k_index_sorted.csv')
print('embeddings_contant : ' + str(embeddings_contant))
print('labels_constant : ' + str(labels_constant))
print('latents_constant : ' + str(latents_constant))
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir + '/' + run_id,
config.desc)
if latents_constant:
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=None)
#embeddings = np.zeros([1, 300], dtype=np.float32)
#labels = np.zeros([1, 32], dtype=np.float32)
embeddings = np.load(
'datasets/50k_sorted_tf/sum_embedding_title.embeddings')
embeddings = embeddings.astype('float32')
labels = np.load(
'datasets/50k_sorted_tf/sum_embedding_category_average.labels')
labels = labels.astype('float32')
name1 = ''
if labels_constant:
label = labels[idx]
name1 = name1 + ' ' + df.at[idx, 'category1']
label = label.reshape(1, label.shape[0])
if embeddings_contant:
embedding = embeddings[idx]
title = df.at[idx, 'title']
name1 = name1 + ' ' + title[:10]
embedding = embedding.reshape(1, embedding.shape[0])
#print(latents.shape)
for png_idx in range(num_pngs):
name = ''
name = name + name1
print('Generating png %d / %d...' % (png_idx, num_pngs))
rand = random.randint(0, 56880)
#rand = png_idx * 1810
#labels = sess.run(classes[0])
if not latents_constant:
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=random_state)
if not labels_constant:
label = labels[rand]
label = label.reshape(1, label.shape[0])
name = name + ' ' + df.at[rand, 'category1']
if not embeddings_contant:
embedding = embeddings[rand]
title = df.at[rand, 'title']
name = name + ' ' + title[:10]
embedding = embedding.reshape(1, embedding.shape[0])
#print(labels.shape)
images = Gs.run(latents,
label,
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)
misc.save_image_grid(
images, os.path.join(result_subdir,
'%s%06d.png' % (name, png_idx)), [0, 255],
grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
batch_size=8,
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config_test.result_dir,
config_test.desc)
for png_idx in range(int(num_pngs / batch_size)):
start = time.time()
print('Generating png %d-%d / %d... in ' %
(png_idx * batch_size, (png_idx + 1) * batch_size, num_pngs),
end='')
latents = misc.random_latents(np.prod(grid_size) * batch_size,
Gs,
random_state=random_state)
labels = np.zeros([latents.shape[0], 7], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config_test.num_gpus,
out_shrink=image_shrink)
for i in range(batch_size):
if images.shape[1] == 3:
mio.export_pickle(
images[i],
os.path.join(
result_subdir,
'%s%06d.pkl' % (png_prefix, png_idx * batch_size + i)))
# misc.save_image(images[i], os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx*batch_size+i)), [0,255], grid_size)
elif images.shape[1] == 6:
mio.export_pickle(images[i][3:6],
os.path.join(
result_subdir, '%s%06d.pkl' %
(png_prefix, png_idx * batch_size + i)),
overwrite=True)
misc.save_image(
images[i][0:3],
os.path.join(
result_subdir,
'%s%06d.png' % (png_prefix, png_idx * batch_size + i)),
[-1, 1], grid_size)
elif images.shape[1] == 9:
mio.export_pickle(images[i][3:6],
os.path.join(
result_subdir, '%s%06d_shp.pkl' %
(png_prefix, png_idx * batch_size + i)),
overwrite=True)
mio.export_pickle(images[i][6:9],
os.path.join(
result_subdir, '%s%06d_nor.pkl' %
(png_prefix, png_idx * batch_size + i)),
overwrite=True)
misc.save_image(
images[i][0:3],
os.path.join(
result_subdir,
'%s%06d.png' % (png_prefix, png_idx * batch_size + i)),
[-1, 1], grid_size)
print('%0.2f seconds' % (time.time() - start))
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_interpolation_images(run_id,
snapshot=None,
grid_size=[1, 1],
image_shrink=1,
image_zoom=1,
duration_sec=60.0,
smoothing_sec=1.0,
mp4=None,
mp4_fps=30,
mp4_codec='libx265',
mp4_bitrate='16M',
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if mp4 is None:
mp4 = misc.get_id_string_for_network_pkl(network_pkl) + '-lerp.mp4'
num_frames = int(np.rint(duration_sec * mp4_fps))
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:][0] + Gs.input_shapes[1][1:][0]
] # [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)))
#10 10 10 10 5 3 10
# model = mio.import_pickle('../models/lsfm_shape_model_fw.pkl')
# facesoft_model = mio.import_pickle('../models/facesoft_id_and_exp_3d_face_model.pkl')['shape_model']
# lsfm_model = m3io.import_lsfm_model('/home/baris/Projects/faceganhd/models/all_all_all.mat')
# model_mean = lsfm_model.mean().copy()
# mask = mio.import_pickle('../UV_spaces_V2/mask_full_2_crop.pkl')
lsfm_tcoords = \
mio.import_pickle('512_UV_dict.pkl')['tcoords']
lsfm_params = []
result_subdir = misc.create_result_subdir(config_test.result_dir,
config_test.desc)
for png_idx in range(int(num_frames / minibatch_size)):
start = time.time()
print('Generating png %d-%d / %d... in ' %
(png_idx * minibatch_size,
(png_idx + 1) * minibatch_size, num_frames),
end='')
latents = all_latents[png_idx * minibatch_size:(png_idx + 1) *
minibatch_size, 0, :Gs.input_shape[1:][0]]
labels = all_latents[png_idx * minibatch_size:(png_idx + 1) *
minibatch_size, 0, Gs.input_shape[1:][0]:]
labels_softmax = softmax(labels) * np.array([10, 10, 10, 10, 5, 3, 10])
images = Gs.run(latents,
labels_softmax,
minibatch_size=minibatch_size,
num_gpus=config_test.num_gpus,
out_shrink=image_shrink)
for i in range(minibatch_size):
texture = Image(np.clip(images[i, 0:3] / 2 + 0.5, 0, 1))
img_shape = ndimage.gaussian_filter(images[i, 3:6],
sigma=(0, 3, 3),
order=0)
mesh_raw = from_UV_2_3D(Image(img_shape),
topology='full',
uv_layout='oval')
# model_mean.points[mask,:] = mesh_raw.points
normals = images[i, 6:9]
normals_norm = (normals - normals.min()) / (normals.max() -
normals.min())
mesh = mesh_raw #facesoft_model.reconstruct(model_mean).from_mask(mask)
# lsfm_params.append(lsfm_model.project(mesh_raw))
t_mesh = TexturedTriMesh(mesh.points, lsfm_tcoords.points, texture,
mesh.trilist)
m3io.export_textured_mesh(
t_mesh,
os.path.join(result_subdir,
'%06d.obj' % (png_idx * minibatch_size + i)),
texture_extension='.png')
fix_obj(
os.path.join(result_subdir,
'%06d.obj' % (png_idx * minibatch_size + i)))
mio.export_image(
Image(normals_norm),
os.path.join(result_subdir,
'%06d_nor.png' % (png_idx * minibatch_size + i)))
print('%0.2f seconds' % (time.time() - start))
mio.export_pickle(lsfm_params,
os.path.join(result_subdir, 'lsfm_params.pkl'))
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()