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# | |
# Copyright (C) 2023, Inria | |
# GRAPHDECO research group, https://team.inria.fr/graphdeco | |
# All rights reserved. | |
# | |
# This software is free for non-commercial, research and evaluation use | |
# under the terms of the LICENSE.md file. | |
# | |
# For inquiries contact george.drettakis@inria.fr | |
# | |
import numpy as np | |
import torch | |
from torch import nn | |
from utils.graphics import getWorld2View2, getProjectionMatrix | |
from utils.loss import image2canny | |
class Camera(nn.Module): | |
def __init__(self, colmap_id, R, T, FoVx, FoVy, image, gt_alpha_mask, | |
image_name, uid, | |
trans=np.array([0.0, 0.0, 0.0]), scale=1.0, data_device = "cuda" | |
): | |
super(Camera, self).__init__() | |
self.uid = uid | |
self.colmap_id = colmap_id | |
self.R = R | |
self.T = T | |
self.FoVx = FoVx | |
self.FoVy = FoVy | |
self.image_name = image_name | |
try: | |
self.data_device = torch.device(data_device) | |
except Exception as e: | |
print(e) | |
print(f"[Warning] Custom device {data_device} failed, fallback to default cuda device" ) | |
self.data_device = torch.device("cuda") | |
self.original_image = image.clamp(0.0, 1.0).to(self.data_device) | |
self.canny_mask = image2canny(self.original_image.permute(1,2,0), 50, 150, isEdge1=False).detach().to(self.data_device) | |
self.image_width = self.original_image.shape[2] | |
self.image_height = self.original_image.shape[1] | |
if gt_alpha_mask is not None: | |
self.original_image *= gt_alpha_mask.to(self.data_device) | |
else: | |
self.original_image *= torch.ones((1, self.image_height, self.image_width), device=self.data_device) | |
self.zfar = 100.0 | |
self.znear = 0.01 | |
self.trans = trans | |
self.scale = scale | |
self.world_view_transform = torch.tensor(getWorld2View2(R, T, trans, scale)).transpose(0, 1).cuda() | |
self.projection_matrix = getProjectionMatrix(znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy).transpose(0,1).cuda() | |
self.full_proj_transform = (self.world_view_transform.unsqueeze(0).bmm(self.projection_matrix.unsqueeze(0))).squeeze(0) | |
self.camera_center = self.world_view_transform.inverse()[3, :3] | |
class MiniCam: | |
def __init__(self, width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform): | |
self.image_width = width | |
self.image_height = height | |
self.FoVy = fovy | |
self.FoVx = fovx | |
self.znear = znear | |
self.zfar = zfar | |
self.world_view_transform = world_view_transform | |
self.full_proj_transform = full_proj_transform | |
view_inv = torch.inverse(self.world_view_transform) | |
self.camera_center = view_inv[3][:3] | |