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Zero
| # | |
| # 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 [email protected] | |
| # | |
| import torch | |
| from torch import nn | |
| import numpy as np | |
| from utils.graphics_utils import getWorld2View2, getProjectionMatrix | |
| from utils.general_utils import PILtoTorch | |
| import cv2 | |
| class Camera(nn.Module): | |
| def __init__(self, resolution, colmap_id, R, T, FoVx, FoVy, depth_params, image, invdepthmap, | |
| image_name, uid, | |
| trans=np.array([0.0, 0.0, 0.0]), scale=1.0, data_device = "cuda", | |
| train_test_exp = False, is_test_dataset = False, is_test_view = False | |
| ): | |
| 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") | |
| resized_image_rgb = PILtoTorch(image, resolution) | |
| gt_image = resized_image_rgb[:3, ...] | |
| self.alpha_mask = None | |
| if resized_image_rgb.shape[0] == 4: | |
| self.alpha_mask = resized_image_rgb[3:4, ...].to(self.data_device) | |
| else: | |
| self.alpha_mask = torch.ones_like(resized_image_rgb[0:1, ...].to(self.data_device)) | |
| if train_test_exp and is_test_view: | |
| if is_test_dataset: | |
| self.alpha_mask[..., :self.alpha_mask.shape[-1] // 2] = 0 | |
| else: | |
| self.alpha_mask[..., self.alpha_mask.shape[-1] // 2:] = 0 | |
| self.original_image = gt_image.clamp(0.0, 1.0).to(self.data_device) | |
| self.image_width = self.original_image.shape[2] | |
| self.image_height = self.original_image.shape[1] | |
| self.invdepthmap = None | |
| self.depth_reliable = False | |
| if invdepthmap is not None: | |
| self.depth_mask = torch.ones_like(self.alpha_mask) | |
| self.invdepthmap = cv2.resize(invdepthmap, resolution) | |
| self.invdepthmap[self.invdepthmap < 0] = 0 | |
| self.depth_reliable = True | |
| if depth_params is not None: | |
| if depth_params["scale"] < 0.2 * depth_params["med_scale"] or depth_params["scale"] > 5 * depth_params["med_scale"]: | |
| self.depth_reliable = False | |
| self.depth_mask *= 0 | |
| if depth_params["scale"] > 0: | |
| self.invdepthmap = self.invdepthmap * depth_params["scale"] + depth_params["offset"] | |
| if self.invdepthmap.ndim != 2: | |
| self.invdepthmap = self.invdepthmap[..., 0] | |
| self.invdepthmap = torch.from_numpy(self.invdepthmap[None]).to(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] | |