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54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
"""
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Ray generator.
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"""
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from torch import nn
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from torchtyping import TensorType
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from mattport.structures.rays import RayBundle
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from mattport.structures.cameras import get_camera_model
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class RayGenerator(nn.Module):
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"""torch.nn Module for generating rays.
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This class will store the intrinsics and extrinsics parameters of the cameras."""
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def __init__(
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self,
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intrinsics: TensorType["num_cameras", "num_intrinsics_params"],
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camera_to_world: TensorType["num_cameras", 3, 4],
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) -> None:
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"""_summary_
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Args:
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intrinsics (TensorType["num_cameras", "num_intrinsics_params"]):
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The intrinsics parameters.
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camera_to_world (TensorType["num_cameras", 3, 4]): Camera to world transformation matrix.
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"""
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super().__init__()
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self.num_cameras, self.num_intrinsics_params = intrinsics.shape
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assert self.num_cameras >= 0
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self.intrinsics = nn.Parameter(intrinsics, requires_grad=False)
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self.camera_to_world = nn.Parameter(camera_to_world, requires_grad=False)
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# TODO(ethan): add learnable parameters that are deltas on the intrinsics and camera_to_world parameters
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def forward(self, ray_indices: TensorType["num_rays", 3]) -> RayBundle:
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"""Index into the cameras to generate the rays.
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Args:
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x (_type_): _description_
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"""
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c = ray_indices[:, 0] # camera indices
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i = ray_indices[:, 1] # row indices
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j = ray_indices[:, 2] # col indices
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intrinsics = self.intrinsics[c]
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camera_to_world = self.camera_to_world[c]
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# NOTE(ethan): we currently assume all images have the same height and width
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camera_index = 0
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camera_class = get_camera_model(self.num_intrinsics_params)
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camera = camera_class(*self.intrinsics[camera_index].tolist())
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image_coords = camera.get_image_coords()
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coords = image_coords[i, j]
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ray_bundle = camera_class.generate_rays(intrinsics=intrinsics, camera_to_world=camera_to_world, coords=coords)
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ray_bundle.camera_indices = c
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return ray_bundle
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