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78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
# Copyright 2022 The Nerfstudio Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Shaders for rendering."""
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from typing import Optional
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from torch import nn
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from torchtyping import TensorType
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class LambertianShader(nn.Module):
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"""Calculate Lambertian shading."""
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@classmethod
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def forward(
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cls,
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rgb: TensorType["bs":..., 3],
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normals: TensorType["bs":..., 3],
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light_direction: TensorType["bs":..., 3],
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shading_weight: float = 1.0,
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detach_normals=True,
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):
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"""Calculate Lambertian shading.
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Args:
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rgb: Accumulated rgb along a ray.
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normals: Accumulated normals along a ray.
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light_direction: Direction of light source.
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shading_weight: Lambertian shading (1.0) vs. ambient lighting (0.0) ratio
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detach_normals: Detach normals from the computation graph when computing shading.
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Returns:
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Textureless Lambertian shading, Lambertian shading
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"""
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if detach_normals:
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normals = normals.detach()
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lambertian = (1 - shading_weight) + shading_weight * (normals @ light_direction).clamp(min=0)
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shaded = lambertian.unsqueeze(-1).repeat(1, 3)
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shaded_albedo = rgb * lambertian.unsqueeze(-1)
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return shaded, shaded_albedo
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class NormalsShader(nn.Module):
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"""Calculate shading for normals."""
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@classmethod
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def forward(
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cls,
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normals: TensorType["bs":..., 3],
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weights: Optional[TensorType["bs":..., 1]] = None,
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):
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"""Applies a rainbow colormap to the normals.
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Args:
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normals: Normalized 3D vectors.
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weights: Optional weights to scale to the normal colors. (Can be used for masking)
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Returns:
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Colored normals
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"""
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normals = (normals + 1) / 2
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if weights is not None:
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normals = normals * weights
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return normals
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