def show_models():
TRANSITION_TIME = 2
viewer = MeshRenderer()
while True:
for sample_index in range(SAMPLE_COUNT):
try:
start = time.perf_counter()
end = start + TRANSITION_TIME
while time.perf_counter() < end:
progress = min(
(time.perf_counter() - start) / TRANSITION_TIME, 1.0)
if ROTATE_MODEL:
viewer.rotation = (
147 +
(sample_index + progress) / SAMPLE_COUNT * 360 * 6,
40)
code = torch.tensor(spline(float(sample_index) + progress),
dtype=torch.float32,
device=device)
viewer.set_mesh(
sdf_net.get_mesh(code,
voxel_resolution=64,
sphere_only=False,
level=SURFACE_LEVEL))
except KeyboardInterrupt:
viewer.stop()
return
def create_image_sequence():
ensure_directory('images')
frame_index = 0
viewer = MeshRenderer(size=1080, start_thread=False)
progress_bar = tqdm(total=SAMPLE_COUNT * TRANSITION_FRAMES)
for sample_index in range(SAMPLE_COUNT):
for step in range(TRANSITION_FRAMES):
code = torch.tensor(
spline(float(sample_index) + step / TRANSITION_FRAMES),
dtype=torch.float32,
device=device)
if ROTATE_MODEL:
viewer.rotation = (
147 + frame_index /
(SAMPLE_COUNT * TRANSITION_FRAMES) * 360 * 6, 40)
viewer.set_mesh(
sdf_net.get_mesh(code,
voxel_resolution=128,
sphere_only=False,
level=SURFACE_LEVEL))
image = viewer.get_image(flip_red_blue=True)
cv2.imwrite("images/frame-{:05d}.png".format(frame_index), image)
frame_index += 1
progress_bar.update()
print("\n\nUse this command to create a video:\n")
print(
'ffmpeg -framerate 30 -i images/frame-%05d.png -c:v libx264 -profile:v high -crf 19 -pix_fmt yuv420p video.mp4'
)
device=device)
indices[BATCH_SIZE // 2:] = torch.tensor(np.random.choice(
negative_indices, BATCH_SIZE // 2),
device=device)
sdf_net.zero_grad()
predicted_sdf = sdf_net(points[indices, :], latent_code)
batch_sdf = sdf[indices]
loss = torch.mean(torch.abs(predicted_sdf - batch_sdf))
loss.backward()
optimizer.step()
if loss.item() < error_targets[image_index]:
try:
viewer.set_mesh(
sdf_net.get_mesh(latent_code[0, :],
voxel_resolution=64,
raise_on_empty=True))
if save_images:
image = viewer.get_image(flip_red_blue=True)
cv2.imwrite("images/frame-{:05d}.png".format(image_index),
image)
image_index += 1
except ValueError:
pass
step += 1
print('Step {:04d}, Image {:04d}, loss: {:.6f}, target: {:.6f}'.format(
step, image_index, loss.item(), error_targets[image_index]))
except KeyboardInterrupt:
viewer.stop()
break
def create_tsne_plot(codes,
voxels=None,
labels=None,
filename="plot.pdf",
indices=None):
from sklearn.manifold import TSNE
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
width, height = 40, 52
print("Calculating t-sne embedding...")
tsne = TSNE(n_components=2)
embedded = tsne.fit_transform(codes)
print("Plotting...")
fig, ax = plt.subplots()
plt.axis('off')
margin = 0.0128
plt.margins(margin * height / width, margin)
x = embedded[:, 0]
y = embedded[:, 1]
x = np.interp(x, (x.min(), x.max()), (0, 1))
y = np.interp(y, (y.min(), y.max()), (0, 1))
ax.scatter(x, y, c=labels, s=40, cmap='Set1')
fig.set_size_inches(width, height)
if voxels is not None:
print("Creating images...")
from rendering import MeshRenderer
viewer = MeshRenderer(start_thread=False)
for i in tqdm(range(voxels.shape[0])):
viewer.set_voxels(voxels[i, :, :, :].cpu().numpy())
viewer.model_color = dataset.get_color(labels[i])
image = viewer.get_image(crop=True, output_size=128)
box = AnnotationBbox(OffsetImage(image, zoom=0.5, cmap='gray'),
(x[i], y[i]),
frameon=True)
ax.add_artist(box)
if indices is not None:
print("Creating images...")
dataset_directories = open('data/models.txt', 'r').readlines()
from rendering import MeshRenderer
viewer = MeshRenderer(start_thread=False)
import trimesh
import logging
logging.getLogger('trimesh').setLevel(1000000)
for i in tqdm(range(len(indices))):
mesh = trimesh.load(
os.path.join(dataset_directories[index].strip(),
'model_normalized.obj'))
viewer.set_mesh(mesh, center_and_scale=True)
viewer.model_color = dataset.get_color(labels[i])
image = viewer.get_image(crop=True, output_size=128)
box = AnnotationBbox(OffsetImage(image, zoom=0.5, cmap='gray'),
(x[i], y[i]),
frameon=True)
ax.add_artist(box)
print("Saving PDF...")
extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
plt.savefig(filename, bbox_inches=extent, dpi=200)
indices = []
for label in range(COUNT):
objects = (dataset.labels == label).nonzero()
indices.append(objects[random.randint(0, objects.shape[0] - 1)].item())
latent_codes = latent_codes[indices, :]
plot = ImageGrid(COUNT, 2, create_viewer=False)
dataset_directories = directories = open('data/models.txt',
'r').readlines()
for i in range(COUNT):
mesh = trimesh.load(
os.path.join(dataset_directories[index].strip(),
'model_normalized.obj'))
viewer.set_mesh(mesh, center_and_scale=True)
viewer.model_color = dataset.get_color(i)
image = viewer.get_image(crop=True)
plot.set_image(image, i, 0)
image = render_image(sdf_net,
latent_codes[i, :],
color=dataset.get_color(i),
crop=True)
plot.set_image(image, i, 1)
viewer.delete_buffers()
plot.save("plots/deepsdf-reconstruction-classes.pdf")
if "autoencoder" in sys.argv:
from dataset import dataset as dataset
dataset.load_voxels(device)
