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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 [email protected]
#
import json
import numpy as np
import torch
from scene.cameras import Camera, MiniCam
from utils.general import PILtoTorch
from utils.graphics import fov2focal, focal2fov, getWorld2View, getProjectionMatrix
WARNED = False
def load_json(path, H, W):
cams = []
with open(path) as json_file:
contents = json.load(json_file)
FoVx = contents["camera_angle_x"]
FoVy = focal2fov(fov2focal(FoVx, W), H)
zfar = 100.0
znear = 0.01
frames = contents["frames"]
for idx, frame in enumerate(frames):
# NeRF 'transform_matrix' is a camera-to-world transform
c2w = np.array(frame["transform_matrix"])
# change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward)
c2w[:3, 1:3] *= -1
if c2w.shape[0] == 3:
one = np.zeros((1, 4))
one[0, -1] = 1
c2w = np.concatenate((c2w, one), axis=0)
# get the world-to-camera transform and set R, T
w2c = np.linalg.inv(c2w)
R = np.transpose(w2c[:3, :3]) # R is stored transposed due to 'glm' in CUDA code
T = w2c[:3, 3]
w2c = torch.as_tensor(getWorld2View(R, T)).T.cuda()
proj = getProjectionMatrix(znear, zfar, FoVx, FoVy).T.cuda()
cams.append(MiniCam(W, H, FoVx, FoVy, znear, zfar, w2c, w2c @ proj))
return cams
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, ...]
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)
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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