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95 lines
2.6 KiB
Python
95 lines
2.6 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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@dataclass
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class RayBundle:
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"""_summary_
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Returns:
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_type_: _description_
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"""
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origins: TensorType["num_rays", 3]
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directions: TensorType["num_rays", 3]
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camera_indices: Optional[TensorType["num_rays"]] = None
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def to_camera_ray_bundle(self, image_height, image_width) -> "CameraRayBundle":
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"""_summary_
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Args:
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image_height (_type_): _description_
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image_width (_type_): _description_
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Returns:
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CameraRayBundle: _description_
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"""
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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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)
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return camera_ray_bundle
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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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@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_index: int = None
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class RaySamples:
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"""_summary_"""
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def __init__(
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self,
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ts: TensorType["num_rays", "num_samples"],
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ray_bundle: RayBundle,
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) -> None:
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self.ray_bundle = ray_bundle
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self.ts = ts
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self.positions = self.get_positions(ray_bundle)
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self.directions = ray_bundle.directions.unsqueeze(1).repeat(1, self.positions.shape[1], 1)
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self.deltas = self.get_deltas()
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def get_positions(self, ray_bundle: RayBundle) -> TensorType["num_rays", "num_samples", 3]:
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"""Returns positions."""
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return ray_bundle.origins[:, None] + self.ts[:, :, None] * ray_bundle.directions[:, None]
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def get_deltas(self) -> TensorType[..., "num_samples"]:
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"""Returns deltas."""
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dists = self.ts[..., 1:] - self.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.ray_bundle.directions[..., None, :], dim=-1)
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return deltas
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