def _generate_rays(self):
"""Generate normalized device coordinate rays for llff."""
if self.split == "test":
n_render_poses = self.render_poses.shape[0]
self.camtoworlds = np.concatenate([self.render_poses, self.camtoworlds],
axis=0)
super()._generate_rays()
if not self.spherify:
ndc_origins, ndc_directions = convert_to_ndc(self.rays.origins,
self.rays.directions,
self.focal, self.w, self.h)
self.rays = utils.Rays(
origins=ndc_origins,
directions=ndc_directions,
viewdirs=self.rays.viewdirs)
# Split poses from the dataset and generated poses
if self.split == "test":
self.camtoworlds = self.camtoworlds[n_render_poses:]
split = [np.split(r, [n_render_poses], 0) for r in self.rays]
split0, split1 = zip(*split)
self.render_rays = utils.Rays(*split0)
self.rays = utils.Rays(*split1)
def _generate_rays(self):
"""Generating rays for all images."""
x, y = np.meshgrid( # pylint: disable=unbalanced-tuple-unpacking
np.arange(self.w, dtype=np.float32), # X-Axis (columns)
np.arange(self.h, dtype=np.float32), # Y-Axis (rows)
indexing="xy")
camera_dirs = np.stack([(x - self.w * 0.5) / self.focal,
-(y - self.h * 0.5) / self.focal, -np.ones_like(x)],
axis=-1)
directions = ((camera_dirs[None, Ellipsis, None, :] *
self.camtoworlds[:, None, None, :3, :3]).sum(axis=-1))
origins = np.broadcast_to(self.camtoworlds[:, None, None, :3, -1],
directions.shape)
viewdirs = directions / np.linalg.norm(directions, axis=-1, keepdims=True)
self.rays = utils.Rays(
origins=origins, directions=directions, viewdirs=viewdirs)