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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
#
from scene.cameras import Camera
import numpy as np
from utils.general_utils import PILtoTorch, DepthMaptoTorch, ObjectPILtoTorch
from utils.graphics_utils import fov2focal
import torch
WARNED = False
def loadCam(args, id, cam_info, resolution_scale):
orig_w, orig_h = cam_info.image.size
if args.resolution in [1, 2, 4, 8]:
resolution = round(orig_w/(resolution_scale * args.resolution)), round(orig_h/(resolution_scale * args.resolution))
else: # should be a type that converts to float
if args.resolution == -1:
if orig_w > 1600:
global WARNED
if not WARNED:
print("[ INFO ] Encountered quite large input images (>1.6K pixels width), rescaling to 1.6K.\n "
"If this is not desired, please explicitly specify '--resolution/-r' as 1")
WARNED = True
global_down = orig_w / 1600
else:
global_down = 1
else:
global_down = orig_w / args.resolution
scale = float(global_down) * float(resolution_scale)
resolution = (int(orig_w / scale), int(orig_h / scale))
resized_image_rgb = PILtoTorch(cam_info.image, resolution)
gt_image = resized_image_rgb[:3, ...]
loaded_mask = None
if resized_image_rgb.shape[1] == 4:
loaded_mask = resized_image_rgb[3:4, ...]
# for waymo
sky_mask = None
if cam_info.sky_mask is not None:
sky_mask = PILtoTorch(cam_info.sky_mask, resolution)
depth_map = None
if cam_info.depth_map is not None:
depth_map = DepthMaptoTorch(cam_info.depth_map)
semantic_mask = None
if cam_info.semantic_mask is not None:
semantic_mask = ObjectPILtoTorch(cam_info.semantic_mask, resolution)
instance_mask = None
if cam_info.instance_mask is not None:
instance_mask = ObjectPILtoTorch(cam_info.instance_mask, resolution)
sam_mask = None
if cam_info.sam_mask is not None:
sam_mask = ObjectPILtoTorch(cam_info.sam_mask, resolution)
feat_map = None
if cam_info.feat_map is not None:
feat_map = cam_info.feat_map
dynamic_mask = None
if cam_info.dynamic_mask is not None:
dynamic_mask = ObjectPILtoTorch(cam_info.dynamic_mask, resolution)
intrinsic = None
if cam_info.intrinsic is not None:
intrinsic = torch.from_numpy(cam_info.intrinsic).to(dtype=torch.float32)
c2w = None
if cam_info.c2w is not None:
c2w = torch.from_numpy(cam_info.c2w).to(dtype=torch.float32)
return Camera(colmap_id=cam_info.uid, R=cam_info.R, T=cam_info.T,
FoVx=cam_info.FovX, FoVy=cam_info.FovY,
image=gt_image, gt_alpha_mask=loaded_mask,
image_name=cam_info.image_name, uid=id, data_device=args.data_device,
# for waymo
sky_mask = sky_mask, depth_map = depth_map,
semantic_mask = semantic_mask, instance_mask = instance_mask,
sam_mask = sam_mask,
dynamic_mask = dynamic_mask,
feat_map = feat_map,
objects=torch.from_numpy(np.array(cam_info.objects)) if cam_info.objects is not None else None,
intrinsic = intrinsic,
c2w = c2w,
time = cam_info.time
)
def cameraList_from_camInfos(cam_infos, resolution_scale, args):
camera_list = []
for id, c in enumerate(cam_infos):
camera_list.append(loadCam(args, id, c, resolution_scale))
return camera_list
def camera_to_JSON(id, camera : Camera):
Rt = np.zeros((4, 4))
Rt[:3, :3] = camera.R.transpose()
Rt[:3, 3] = camera.T
Rt[3, 3] = 1.0
W2C = np.linalg.inv(Rt)
pos = W2C[:3, 3]
rot = W2C[:3, :3]
serializable_array_2d = [x.tolist() for x in rot]
camera_entry = {
'id' : id,
'img_name' : camera.image_name,
'width' : camera.width,
'height' : camera.height,
'position': pos.tolist(),
'rotation': serializable_array_2d,
'fy' : fov2focal(camera.FovY, camera.height),
'fx' : fov2focal(camera.FovX, camera.width)
}
return camera_entry
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