mirror of
https://github.com/graphdeco-inria/gaussian-splatting
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72 lines
3.0 KiB
Python
72 lines
3.0 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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import torch
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from torch import nn
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import numpy as np
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from utils.graphics_utils import getWorld2View2, getProjectionMatrix
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class Camera(nn.Module):
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def __init__(self, colmap_id, R, T, FoVx, FoVy, image, gt_alpha_mask,
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image_name, uid,
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trans=np.array([0.0, 0.0, 0.0]), scale=1.0, data_device = "cuda"
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):
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super(Camera, self).__init__()
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self.uid = uid
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self.colmap_id = colmap_id
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self.R = R # 相机到世界的 C2W
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self.T = T # 世界到相机的 W2C
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self.FoVx = FoVx
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self.FoVy = FoVy
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self.image_name = image_name
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try:
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self.data_device = torch.device(data_device)
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except Exception as e:
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print(e)
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print(f"[Warning] Custom device {data_device} failed, fallback to default cuda device" )
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self.data_device = torch.device("cuda")
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self.original_image = image.clamp(0.0, 1.0).to(self.data_device) # tensor, 归一化的 C H W
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self.image_width = self.original_image.shape[2]
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self.image_height = self.original_image.shape[1]
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if gt_alpha_mask is not None:
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self.original_image *= gt_alpha_mask.to(self.data_device)
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else:
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self.original_image *= torch.ones((1, self.image_height, self.image_width), device=self.data_device)
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self.zfar = 100.0
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self.znear = 0.01
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self.trans = trans
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self.scale = scale
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self.world_view_transform = torch.tensor(getWorld2View2(R, T, trans, scale)).transpose(0, 1).cuda() # C2W 相机到世界的变换矩阵
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self.projection_matrix = getProjectionMatrix(znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy).transpose(0,1).cuda() # 生成了一个投影矩阵，用于将视图坐标投影到图像平面
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self.full_proj_transform = (self.world_view_transform.unsqueeze(0).bmm(self.projection_matrix.unsqueeze(0))).squeeze(0) # 使用 bmm（批量矩阵乘法）将世界到视图变换矩阵和投影矩阵相乘，生成完整的投影变换矩阵
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self.camera_center = self.world_view_transform.inverse()[3, :3] # 通过求逆变换矩阵获取相机在世界坐标系中的位置（相机中心）
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class MiniCam:
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def __init__(self, width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform):
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self.image_width = width
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self.image_height = height
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self.FoVy = fovy
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self.FoVx = fovx
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self.znear = znear
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self.zfar = zfar
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self.world_view_transform = world_view_transform
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self.full_proj_transform = full_proj_transform
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view_inv = torch.inverse(self.world_view_transform)
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self.camera_center = view_inv[3][:3]
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