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206 lines
7.3 KiB
Python
206 lines
7.3 KiB
Python
"""
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Some ray datastructures.
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"""
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import random
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from dataclasses import dataclass
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from typing import Optional
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import torch
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from torchtyping import TensorType
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from mattport.utils.misc import is_not_none
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@dataclass
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class PointSamples:
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"""Samples in space."""
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positions: TensorType[..., 3] = None # XYZ locations
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directions: TensorType[..., 3] = None # Unit direction vector
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camera_indices: TensorType[..., 1] = None # Camera index
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valid_mask: TensorType[..., 1] = None # Rays that are valid
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@dataclass
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class RaySamples:
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"""Samples along a ray"""
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positions: TensorType[..., 3] = None # XYZ locations
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directions: TensorType[..., 3] = None # Unit direction vector
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camera_indices: TensorType[..., 1] = None # Camera index
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valid_mask: TensorType[..., 1] = None # Rays that are valid
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ts: TensorType[..., 1] = None # "time steps", distances along ray
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deltas: TensorType[..., 1] = None # "width" of each sample
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def to_point_samples(self) -> PointSamples:
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"""Convert to PointSamples instance and return."""
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# TODO: make this more interpretable
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return PointSamples(positions=self.positions, directions=self.directions, valid_mask=self.valid_mask)
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def get_weights(self, densities: TensorType[..., "num_samples", 1]) -> TensorType[..., "num_samples"]:
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"""Return weights based on predicted densities
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Args:
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densities (TensorType[..., "num_samples", 1]): Predicted densities for samples along ray
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Returns:
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TensorType[..., "num_samples"]: Weights for each sample
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"""
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delta_density = self.deltas * densities[..., 0]
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alphas = 1 - torch.exp(-delta_density)
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transmittance = torch.cumsum(delta_density[..., :-1], dim=-1)
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transmittance = torch.cat(
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[torch.zeros((*transmittance.shape[:1], 1)).to(densities.device), transmittance], axis=-1
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)
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transmittance = torch.exp(-transmittance) # [..., "num_samples"]
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weights = alphas * transmittance # [..., "num_samples"]
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return weights
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def set_valid_mask(self, valid_mask: TensorType[..., "num_samples"]) -> None:
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"""Sets valid mask"""
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self.valid_mask = valid_mask
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@dataclass
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class RayBundle:
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"""A bundle of ray parameters."""
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origins: TensorType["num_rays", 3] # Ray origins
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directions: TensorType["num_rays", 3] #
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camera_indices: Optional[TensorType["num_rays"]] = None
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nears: Optional[TensorType["num_rays"]] = None
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fars: Optional[TensorType["num_rays"]] = None
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valid_mask: Optional[TensorType["num_rays"]] = None
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def to_camera_ray_bundle(self, image_height, image_width) -> "CameraRayBundle":
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"""Returns a CameraRayBundle from this object."""
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camera_ray_bundle = CameraRayBundle(
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origins=self.origins.view(image_height, image_width, 3),
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directions=self.directions.view(image_height, image_width, 3),
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camera_indices=self.camera_indices.view(image_height, image_width)
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if not isinstance(self.camera_indices, type(None))
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else None,
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)
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return camera_ray_bundle
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def move_to_device(self, device):
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"""Move to a device."""
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self.origins = self.origins.to(device)
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self.directions = self.directions.to(device)
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if not isinstance(self.camera_indices, type(None)):
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self.camera_indices = self.camera_indices.to(device)
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def __len__(self):
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num_rays = self.origins.shape[0]
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return num_rays
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def sample(self, num_rays: int):
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"""Returns a RayBundle as a subset of rays.
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Args:
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num_rays (int):
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Returns:
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RayBundle: _description_
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"""
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assert num_rays <= len(self)
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indices = random.sample(range(len(self)), k=num_rays)
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return RayBundle(
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origins=self.origins[indices],
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directions=self.directions[indices],
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camera_indices=self.camera_indices[indices],
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)
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def get_masked_ray_bundle(self, valid_mask):
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"""Return a masked instance of the ray bundle."""
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return RayBundle(
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origins=self.origins[valid_mask],
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directions=self.directions[valid_mask],
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camera_indices=self.camera_indices[valid_mask] if is_not_none(self.camera_indices) else None,
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nears=self.nears[valid_mask] if is_not_none(self.nears) else None,
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fars=self.fars[valid_mask] if is_not_none(self.fars) else None,
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valid_mask=self.valid_mask[valid_mask] if is_not_none(self.valid_mask) else None,
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)
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def get_ray_samples(self, ts: TensorType["num_rays", "num_samples"]) -> RaySamples:
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"""
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Args:
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ts (TensorType["num_rays", "num_samples"]): _description_
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Returns:
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RaySamples: _description_
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"""
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positions = self.origins[:, None] + ts[:, :, None] * self.directions[:, None]
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directions = self.directions.unsqueeze(1).repeat(1, positions.shape[1], 1)
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valid_mask = torch.ones_like(ts, dtype=torch.bool)
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dists = ts[..., 1:] - ts[..., :-1]
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dists = torch.cat([dists, dists[..., -1:]], -1) # [N_rays, N_samples]
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deltas = dists * torch.norm(self.directions[..., None, :], dim=-1)
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if self.camera_indices is not None:
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camera_indices = self.camera_indices.unsqueeze(1).repeat(1, positions.shape[1])
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else:
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camera_indices = None
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ray_samples = RaySamples(
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positions=positions,
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directions=directions,
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camera_indices=camera_indices,
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valid_mask=valid_mask,
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ts=ts,
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deltas=deltas,
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)
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return ray_samples
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@dataclass
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class CameraRayBundle:
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"""_summary_"""
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origins: TensorType["image_height", "image_width", 3]
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directions: TensorType["image_height", "image_width", 3]
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camera_indices: Optional[TensorType["image_height", "image_width", 3]] = None
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camera_index: int = None
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def __post_init__(self):
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if not isinstance(self.camera_index, type(None)):
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self.set_camera_indices(self.camera_index)
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def set_camera_indices(self, camera_index: int):
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"""Sets the camera indices for a specific camera index."""
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self.camera_index = camera_index
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self.camera_indices = torch.ones_like(self.origins[..., 0]).long() * camera_index
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def get_num_rays(self):
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"""Return the number of rays in this bundle."""
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image_height, image_width = self.origins.shape[:2]
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num_rays = image_height * image_width
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return num_rays
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def to_ray_bundle(self) -> RayBundle:
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"""_summary_
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Returns:
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RayBundle: _description_
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"""
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# TODO(ethan): handle camera_index
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ray_bundle = RayBundle(origins=self.origins.view(-1, 3), directions=self.directions.view(-1, 3))
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return ray_bundle
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def get_row_major_sliced_ray_bundle(self, start_idx, end_idx):
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"""Return a RayBundle"""
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camera_indices = (
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self.camera_indices.view(-1)[start_idx:end_idx] if not isinstance(self.camera_indices, type(None)) else None
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)
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return RayBundle(
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origins=self.origins.view(-1, 3)[start_idx:end_idx],
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directions=self.directions.view(-1, 3)[start_idx:end_idx],
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camera_indices=camera_indices,
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)
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