486 lines
19 KiB
Python
486 lines
19 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 os
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import sys
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from PIL import Image
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from typing import NamedTuple
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from scene.colmap_loader import read_extrinsics_text, read_intrinsics_text, qvec2rotmat, \
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read_extrinsics_binary, read_intrinsics_binary, read_points3D_binary, read_points3D_text
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from scene.hyper_loader import Load_hyper_data, format_hyper_data
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import torchvision.transforms as transforms
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from utils.graphics_utils import getWorld2View2, focal2fov, fov2focal
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import numpy as np
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import torch
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import json
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from pathlib import Path
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from plyfile import PlyData, PlyElement
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from utils.sh_utils import SH2RGB
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from scene.gaussian_model import BasicPointCloud
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from tqdm import tqdm
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class CameraInfo(NamedTuple):
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uid: int
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R: np.array
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T: np.array
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FovY: np.array
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FovX: np.array
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image: np.array
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image_path: str
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image_name: str
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width: int
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height: int
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time : float
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class SceneInfo(NamedTuple):
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point_cloud: BasicPointCloud
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train_cameras: list
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test_cameras: list
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video_cameras: list
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nerf_normalization: dict
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ply_path: str
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maxtime: int
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def getNerfppNorm(cam_info):
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def get_center_and_diag(cam_centers):
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cam_centers = np.hstack(cam_centers)
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avg_cam_center = np.mean(cam_centers, axis=1, keepdims=True)
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center = avg_cam_center
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dist = np.linalg.norm(cam_centers - center, axis=0, keepdims=True)
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diagonal = np.max(dist)
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return center.flatten(), diagonal
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cam_centers = []
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for cam in cam_info:
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W2C = getWorld2View2(cam.R, cam.T)
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C2W = np.linalg.inv(W2C)
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cam_centers.append(C2W[:3, 3:4])
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center, diagonal = get_center_and_diag(cam_centers)
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radius = diagonal * 1.1
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translate = -center
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return {"translate": translate, "radius": radius}
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def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):
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cam_infos = []
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for idx, key in enumerate(cam_extrinsics):
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sys.stdout.write('\r')
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# the exact output you're looking for:
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sys.stdout.write("Reading camera {}/{}".format(idx+1, len(cam_extrinsics)))
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sys.stdout.flush()
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extr = cam_extrinsics[key]
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intr = cam_intrinsics[extr.camera_id]
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height = intr.height
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width = intr.width
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uid = intr.id
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R = np.transpose(qvec2rotmat(extr.qvec))
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T = np.array(extr.tvec)
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if intr.model in ["SIMPLE_PINHOLE", "SIMPLE_RADIAL"]:
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focal_length_x = intr.params[0]
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FovY = focal2fov(focal_length_x, height)
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FovX = focal2fov(focal_length_x, width)
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elif intr.model=="PINHOLE":
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focal_length_x = intr.params[0]
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focal_length_y = intr.params[1]
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FovY = focal2fov(focal_length_y, height)
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FovX = focal2fov(focal_length_x, width)
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else:
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assert False, "Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!"
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image_path = os.path.join(images_folder, os.path.basename(extr.name))
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image_name = os.path.basename(image_path).split(".")[0]
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image = Image.open(image_path)
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cam_info = CameraInfo(uid=uid, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
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image_path=image_path, image_name=image_name, width=width, height=height,
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time = 0)
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cam_infos.append(cam_info)
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sys.stdout.write('\n')
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return cam_infos
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def fetchPly(path):
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plydata = PlyData.read(path)
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vertices = plydata['vertex']
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positions = np.vstack([vertices['x'], vertices['y'], vertices['z']]).T
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colors = np.vstack([vertices['red'], vertices['green'], vertices['blue']]).T / 255.0
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normals = np.vstack([vertices['nx'], vertices['ny'], vertices['nz']]).T
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return BasicPointCloud(points=positions, colors=colors, normals=normals)
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def storePly(path, xyz, rgb):
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# Define the dtype for the structured array
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dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'),
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('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'),
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('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]
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normals = np.zeros_like(xyz)
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elements = np.empty(xyz.shape[0], dtype=dtype)
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attributes = np.concatenate((xyz, normals, rgb), axis=1)
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elements[:] = list(map(tuple, attributes))
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# Create the PlyData object and write to file
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vertex_element = PlyElement.describe(elements, 'vertex')
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ply_data = PlyData([vertex_element])
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ply_data.write(path)
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def readColmapSceneInfo(path, images, eval, llffhold=8):
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try:
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cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.bin")
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cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.bin")
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cam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file)
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cam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file)
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except:
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cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.txt")
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cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.txt")
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cam_extrinsics = read_extrinsics_text(cameras_extrinsic_file)
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cam_intrinsics = read_intrinsics_text(cameras_intrinsic_file)
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reading_dir = "images" if images == None else images
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cam_infos_unsorted = readColmapCameras(cam_extrinsics=cam_extrinsics, cam_intrinsics=cam_intrinsics, images_folder=os.path.join(path, reading_dir))
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cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : x.image_name)
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if eval:
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train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0]
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test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0]
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else:
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train_cam_infos = cam_infos
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test_cam_infos = []
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nerf_normalization = getNerfppNorm(train_cam_infos)
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ply_path = os.path.join(path, "sparse/0/points3D.ply")
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bin_path = os.path.join(path, "sparse/0/points3D.bin")
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txt_path = os.path.join(path, "sparse/0/points3D.txt")
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if not os.path.exists(ply_path):
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print("Converting point3d.bin to .ply, will happen only the first time you open the scene.")
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try:
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xyz, rgb, _ = read_points3D_binary(bin_path)
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except:
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xyz, rgb, _ = read_points3D_text(txt_path)
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storePly(ply_path, xyz, rgb)
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try:
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pcd = fetchPly(ply_path)
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except:
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pcd = None
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scene_info = SceneInfo(point_cloud=pcd,
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train_cameras=train_cam_infos,
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test_cameras=test_cam_infos,
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video_cameras=train_cam_infos,
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maxtime=0,
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nerf_normalization=nerf_normalization,
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ply_path=ply_path)
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return scene_info
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def generateCamerasFromTransforms(path, template_transformsfile, extension, maxtime):
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trans_t = lambda t : torch.Tensor([
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[1,0,0,0],
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[0,1,0,0],
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[0,0,1,t],
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[0,0,0,1]]).float()
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rot_phi = lambda phi : torch.Tensor([
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[1,0,0,0],
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[0,np.cos(phi),-np.sin(phi),0],
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[0,np.sin(phi), np.cos(phi),0],
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[0,0,0,1]]).float()
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rot_theta = lambda th : torch.Tensor([
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[np.cos(th),0,-np.sin(th),0],
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[0,1,0,0],
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[np.sin(th),0, np.cos(th),0],
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[0,0,0,1]]).float()
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def pose_spherical(theta, phi, radius):
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c2w = trans_t(radius)
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c2w = rot_phi(phi/180.*np.pi) @ c2w
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c2w = rot_theta(theta/180.*np.pi) @ c2w
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c2w = torch.Tensor(np.array([[-1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]])) @ c2w
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return c2w
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cam_infos = []
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# generate render poses and times
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render_poses = torch.stack([pose_spherical(angle, -30.0, 4.0) for angle in np.linspace(-180,180,40+1)[:-1]], 0)
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render_times = torch.linspace(0,maxtime,render_poses.shape[0])
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with open(os.path.join(path, template_transformsfile)) as json_file:
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template_json = json.load(json_file)
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fovx = template_json["camera_angle_x"]
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# load a single image to get image info.
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for idx, frame in enumerate(template_json["frames"]):
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cam_name = os.path.join(path, frame["file_path"] + extension)
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image_path = os.path.join(path, cam_name)
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image_name = Path(cam_name).stem
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image = Image.open(image_path)
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im_data = np.array(image.convert("RGBA"))
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break
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# format infomation
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for idx, (time, poses) in enumerate(zip(render_times,render_poses)):
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time = time/maxtime
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matrix = np.linalg.inv(np.array(poses))
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R = -np.transpose(matrix[:3,:3])
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R[:,0] = -R[:,0]
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T = -matrix[:3, 3]
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fovy = focal2fov(fov2focal(fovx, image.size[0]), image.size[1])
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FovY = fovy
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FovX = fovx
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cam_infos.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
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image_path=None, image_name=None, width=image.size[0], height=image.size[1],
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time = time))
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return cam_infos
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def readCamerasFromTransforms(path, transformsfile, white_background, extension=".png", mapper = {}):
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cam_infos = []
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with open(os.path.join(path, transformsfile)) as json_file:
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contents = json.load(json_file)
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fovx = contents["camera_angle_x"]
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frames = contents["frames"]
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for idx, frame in enumerate(frames):
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cam_name = os.path.join(path, frame["file_path"] + extension)
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time = mapper[frame["time"]]
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matrix = np.linalg.inv(np.array(frame["transform_matrix"]))
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R = -np.transpose(matrix[:3,:3])
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R[:,0] = -R[:,0]
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T = -matrix[:3, 3]
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image_path = os.path.join(path, cam_name)
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image_name = Path(cam_name).stem
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image = Image.open(image_path)
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im_data = np.array(image.convert("RGBA"))
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bg = np.array([1,1,1]) if white_background else np.array([0, 0, 0])
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norm_data = im_data / 255.0
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arr = norm_data[:,:,:3] * norm_data[:, :, 3:4] + bg * (1 - norm_data[:, :, 3:4])
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image = Image.fromarray(np.array(arr*255.0, dtype=np.byte), "RGB")
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fovy = focal2fov(fov2focal(fovx, image.size[0]), image.size[1])
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FovY = fovy
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FovX = fovx
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cam_infos.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
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image_path=image_path, image_name=image_name, width=image.size[0], height=image.size[1],
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time = time))
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return cam_infos
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def read_timeline(path):
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with open(os.path.join(path, "transforms_train.json")) as json_file:
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train_json = json.load(json_file)
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with open(os.path.join(path, "transforms_test.json")) as json_file:
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test_json = json.load(json_file)
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time_line = [frame["time"] for frame in train_json["frames"]] + [frame["time"] for frame in test_json["frames"]]
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time_line = set(time_line)
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time_line = list(time_line)
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time_line.sort()
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timestamp_mapper = {}
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max_time_float = max(time_line)
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for index, time in enumerate(time_line):
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# timestamp_mapper[time] = index
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timestamp_mapper[time] = time/max_time_float
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return timestamp_mapper, max_time_float
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def readNerfSyntheticInfo(path, white_background, eval, extension=".png"):
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timestamp_mapper, max_time = read_timeline(path)
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print("Reading Training Transforms")
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train_cam_infos = readCamerasFromTransforms(path, "transforms_train.json", white_background, extension, timestamp_mapper)
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print("Reading Test Transforms")
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test_cam_infos = readCamerasFromTransforms(path, "transforms_test.json", white_background, extension, timestamp_mapper)
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print("Generating Video Transforms")
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video_cam_infos = generateCamerasFromTransforms(path, "transforms_train.json", extension, max_time)
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if not eval:
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train_cam_infos.extend(test_cam_infos)
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test_cam_infos = []
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nerf_normalization = getNerfppNorm(train_cam_infos)
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ply_path = os.path.join(path, "points3d.ply")
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if not os.path.exists(ply_path):
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# Since this data set has no colmap data, we start with random points
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num_pts = 100_000
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print(f"Generating random point cloud ({num_pts})...")
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# We create random points inside the bounds of the synthetic Blender scenes
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xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3
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shs = np.random.random((num_pts, 3)) / 255.0
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pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)))
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storePly(ply_path, xyz, SH2RGB(shs) * 255)
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try:
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pcd = fetchPly(ply_path)
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except:
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pcd = None
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scene_info = SceneInfo(point_cloud=pcd,
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train_cameras=train_cam_infos,
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test_cameras=test_cam_infos,
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video_cameras=video_cam_infos,
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nerf_normalization=nerf_normalization,
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ply_path=ply_path,
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maxtime=max_time
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)
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return scene_info
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def format_infos(dataset,split):
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# loading
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cameras = []
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image = dataset[0][0]
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if split == "train":
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for idx in tqdm(range(len(dataset))):
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image_path = None
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image_name = f"{idx}"
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time = dataset.image_times[idx]
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# matrix = np.linalg.inv(np.array(pose))
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R,T = dataset.load_pose(idx)
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FovX = focal2fov(dataset.focal[0], image.shape[1])
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FovY = focal2fov(dataset.focal[0], image.shape[2])
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cameras.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
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image_path=image_path, image_name=image_name, width=image.shape[2], height=image.shape[1],
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time = time))
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return cameras
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def readHyperDataInfos(datadir,use_bg_points,eval):
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data_class = Load_hyper_data(datadir,0.25,use_bg_points,add_cam=True)
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train_cam_infos = format_hyper_data(data_class,"train")
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test_cam_infos = format_hyper_data(data_class,"test")
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max_time = data_class.max_time
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video_cam_infos = format_render_poses(train_cam_infos.val_poses, train_cam_infos)
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ply_path = os.path.join(datadir, "points3d.ply")
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# ply_path = os.path.join(datadir, "points.npy")
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# if not os.path.exists(ply_path):
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# Since this data set has no colmap data, we start with random points
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num_pts = 100_000
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print(f"Generating random point cloud ({num_pts})...")
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# from scene.ray_utils import compute_bbox_by_cam_frustrm_hyper
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# We create random points inside the bounds of the synthetic Blender scenes
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# xyz_min, xyz_max = compute_bbox_by_cam_frustrm_hyper(data_class)
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bounds = 10
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xyz_min = np.array([-bounds,-bounds,-bounds])
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xyz_max = np.array([bounds,bounds,bounds])
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print("scene bounding box:",xyz_min, xyz_max)
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center = (xyz_min + xyz_max)/2
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xyz = (np.random.random((num_pts, 3)))* (np.array(xyz_max) - np.array(xyz_min)) + np.array(center)
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shs = np.random.random((xyz.shape[0], 3)) / 255.0
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pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((xyz.shape[0], 3)))
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storePly(ply_path, xyz, SH2RGB(shs) * 255)
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# try:
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#
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# pcd = fetchPly(ply_path)
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# except:
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# pcd = None
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nerf_normalization = getNerfppNorm(train_cam_infos)
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scene_info = SceneInfo(point_cloud=pcd,
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train_cameras=train_cam_infos,
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test_cameras=test_cam_infos,
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video_cameras=video_cam_infos,
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nerf_normalization=nerf_normalization,
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ply_path=ply_path,
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maxtime=max_time
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)
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return scene_info
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def format_render_poses(poses,data_infos):
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cameras = []
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tensor_to_pil = transforms.ToPILImage()
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len_poses = len(poses)
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times = [i/len_poses for i in range(len_poses)]
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image = data_infos[0][0]
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for idx, p in tqdm(enumerate(poses)):
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# image = None
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image_path = None
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image_name = f"{idx}"
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time = times[idx]
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pose = np.eye(4)
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pose[:3,:] = p[:3,:]
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# matrix = np.linalg.inv(np.array(pose))
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R = pose[:3,:3]
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R = - R
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R[:,0] = -R[:,0]
|
|
T = -pose[:3,3].dot(R)
|
|
FovX = focal2fov(data_infos.focal[0], image.shape[2])
|
|
FovY = focal2fov(data_infos.focal[0], image.shape[1])
|
|
cameras.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
|
|
image_path=image_path, image_name=image_name, width=image.shape[2], height=image.shape[1],
|
|
time = time))
|
|
return cameras
|
|
def read3DVideoInfo(datadir,use_bg_points,eval):
|
|
# loading all the data follow hexplane format
|
|
ply_path = os.path.join(datadir, "points3d.ply")
|
|
|
|
from scene.neural_3D_dataset_NDC import Neural3D_NDC_Dataset
|
|
train_dataset = Neural3D_NDC_Dataset(
|
|
datadir,
|
|
"train",
|
|
2.0,
|
|
time_scale=1,
|
|
scene_bbox_min=[-2.5, -2.0, -1.0],
|
|
scene_bbox_max=[2.5, 2.0, 1.0],
|
|
eval_index=0,
|
|
)
|
|
test_dataset = Neural3D_NDC_Dataset(
|
|
datadir,
|
|
"test",
|
|
2.0,
|
|
time_scale=1,
|
|
scene_bbox_min=[-2.5, -2.0, -1.0],
|
|
scene_bbox_max=[2.5, 2.0, 1.0],
|
|
eval_index=0,
|
|
)
|
|
train_cam_infos = format_infos(train_dataset,"train")
|
|
|
|
# test_cam_infos = format_infos(test_dataset,"test")
|
|
val_cam_infos = format_render_poses(test_dataset.val_poses,test_dataset)
|
|
nerf_normalization = getNerfppNorm(train_cam_infos)
|
|
# create pcd
|
|
# if not os.path.exists(ply_path):
|
|
# Since this data set has no colmap data, we start with random points
|
|
num_pts = 2000
|
|
print(f"Generating random point cloud ({num_pts})...")
|
|
threshold = 2.5
|
|
xyz_max = np.array([threshold, threshold, threshold])
|
|
xyz_min = np.array([-threshold, -threshold, -threshold])
|
|
center = (xyz_max - xyz_min)/2
|
|
# We create random points inside the bounds of the synthetic Blender scenes
|
|
xyz = np.random.random((num_pts, 3)) * (xyz_max-xyz_min) -center
|
|
print("point cloud initialization:",xyz.max(axis=0),xyz.min(axis=0))
|
|
shs = np.random.random((num_pts, 3)) / 255.0
|
|
pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)))
|
|
storePly(ply_path, xyz, SH2RGB(shs) * 255)
|
|
try:
|
|
# xyz = np.load
|
|
pcd = fetchPly(ply_path)
|
|
except:
|
|
pcd = None
|
|
scene_info = SceneInfo(point_cloud=pcd,
|
|
train_cameras=train_dataset,
|
|
test_cameras=test_dataset,
|
|
video_cameras=val_cam_infos,
|
|
nerf_normalization=nerf_normalization,
|
|
ply_path=ply_path,
|
|
maxtime=300
|
|
)
|
|
return scene_info
|
|
sceneLoadTypeCallbacks = {
|
|
"Colmap": readColmapSceneInfo,
|
|
"Blender" : readNerfSyntheticInfo,
|
|
"hyper" : readHyperDataInfos,
|
|
"3dvideo" : read3DVideoInfo,
|
|
|
|
} |