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'
)