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https://github.com/graphdeco-inria/gaussian-splatting
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In some cases, calibration gives central point cx,cy != (0.5,0.5), or it can be decided to crop the input images. In those cases, it is necessary to split fovx to fovXleft,fovXright and fovy to fovYtop,fovYbottom Note that the export of cameras to cameras.json merges those values back to the basic fovx,fovy. This aims at avoiding the modification of diff_gaussian_rasterization branch used for SIBR_gaussianViewer_app. Signed-off-by: Matthieu Gendrin <matthieu.gendrin@orange.com>
74 lines
2.7 KiB
Python
74 lines
2.7 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, FovXleft, FovXright, FovYtop, FovYbottom, 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
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self.T = T
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self.FovXleft = FovXleft
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self.FovXright = FovXright
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self.FovYtop = FovYtop
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self.FovYbottom = FovYbottom
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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)
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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()
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self.projection_matrix = getProjectionMatrix(znear=self.znear, zfar=self.zfar, fovXleft=FovXleft, fovXright=FovXright, fovYtop=FovYtop, fovYbottom=FovYbottom).transpose(0,1).cuda()
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self.full_proj_transform = (self.world_view_transform.unsqueeze(0).bmm(self.projection_matrix.unsqueeze(0))).squeeze(0)
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self.camera_center = self.world_view_transform.inverse()[3, :3]
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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.FovYtop = fovy
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self.FovXleft = 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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