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import numpy as np
import torch
import torch.nn.functional as F
from loguru import logger as guru
from nerfview import CameraState
from flow3d.scene_model import SceneModel
from flow3d.vis.utils import draw_tracks_2d_th, get_server
from flow3d.vis.viewer import DynamicViewer
class Renderer:
def __init__(
self,
model: SceneModel,
device: torch.device,
# Logging.
work_dir: str,
port: int | None = None,
):
self.device = device
self.model = model
self.num_frames = model.num_frames
self.work_dir = work_dir
self.global_step = 0
self.epoch = 0
self.viewer = None
if port is not None:
server = get_server(port=port)
self.viewer = DynamicViewer(
server, self.render_fn, model.num_frames, work_dir, mode="rendering"
)
self.tracks_3d = self.model.compute_poses_fg(
# torch.arange(max(0, t - 20), max(1, t), device=self.device),
torch.arange(self.num_frames, device=self.device),
inds=torch.arange(10, device=self.device),
)[0]
@staticmethod
def init_from_checkpoint(
path: str, device: torch.device, *args, **kwargs
) -> "Renderer":
guru.info(f"Loading checkpoint from {path}")
ckpt = torch.load(path)
state_dict = ckpt["model"]
model = SceneModel.init_from_state_dict(state_dict)
model = model.to(device)
renderer = Renderer(model, device, *args, **kwargs)
renderer.global_step = ckpt.get("global_step", 0)
renderer.epoch = ckpt.get("epoch", 0)
return renderer
@torch.inference_mode()
def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]):
if self.viewer is None:
return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8)
W, H = img_wh
focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item()
K = torch.tensor(
[[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]],
device=self.device,
)
w2c = torch.linalg.inv(
torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device)
)
t = (
int(self.viewer._playback_guis[0].value)
if not self.viewer._canonical_checkbox.value
else None
)
self.model.training = False
img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0]
if not self.viewer._render_track_checkbox.value:
img = (img.cpu().numpy() * 255.0).astype(np.uint8)
else:
assert t is not None
tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)]
tracks_2d = torch.einsum(
"ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0)
)
tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:]
img = draw_tracks_2d_th(img, tracks_2d)
return img
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