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3718945 98e7453 3718945 98e7453 3718945 98e7453 3718945 98e7453 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 | import math
import os
import json
import re
import cv2
from dataclasses import dataclass, field
import random
import imageio
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
# from PIL import Image
import PIL
from diffusionGS.utils.typing import *
from torch.utils.data._utils.collate import default_collate_fn_map
@dataclass
class BaseDataModuleConfig:
local_dir: str = ''
local_eval_dir: str = ''
view_idx_file_path: str = 'extra_files/evaluation_index_re10k.json'
batch_size: int = 32
eval_batch_size: int = 1
eval_subset: int = -1
num_workers: int = 0
num_workers_val: int = 0
training_res: Optional[List[int]] = field(default_factory=lambda:[256,256])
patch_size: int = 8
sel_views_train: int = 4
sel_views: int = 4 # first view is the input image
scene_scale_factor: float = 1.35
################################# Image part #################################
square_crop: bool = True
load_image: bool = True
class BaseDataset(Dataset):
def __init__(self, cfg: Any, split: str) -> None:
super().__init__()
self.cfg: BaseDataModuleConfig = cfg
self.split = split
if self.split == 'train':
with open(cfg.local_dir, 'r') as f:
all_scene_paths = f.read().splitlines()
all_scene_paths = [path for path in all_scene_paths if path.strip()]
self.uids = all_scene_paths#[10:]
else:
with open(cfg.local_eval_dir, 'r') as f:
all_scene_paths = f.read().splitlines()
all_scene_paths = [path for path in all_scene_paths if path.strip()]
self.view_idx_list = dict()
if self.cfg.view_idx_file_path != '':
if os.path.exists(self.cfg.view_idx_file_path):
with open(self.cfg.view_idx_file_path, 'r') as f:
self.view_idx_list = json.load(f)
# filter out None values, i.e. scenes that don't have specified input and targetviews
self.view_idx_list_filtered = [k for k, v in self.view_idx_list.items() if v is not None]
filtered_scene_paths = []
for scene in all_scene_paths:
file_name = scene.split("/")[-1]
scene_name = file_name.split(".")[0]
if scene_name in self.view_idx_list_filtered:
filtered_scene_paths.append(scene)
all_scene_paths = filtered_scene_paths
if self.cfg.eval_subset > 0:
all_scene_paths = all_scene_paths[:self.cfg.eval_subset]
self.uids = all_scene_paths
# load anything
if not self.uids:
raise ValueError(f"No {split} scenes found; check the scene list and evaluation index")
print(f"Loaded {len(self.uids)} {split} uids")
# breakpoint()
def __len__(self):
return len(self.uids)
def preprocess_frames(self, frames_chosen, image_paths_chosen):
resize_h = self.cfg.training_res[0]
patch_size = self.cfg.patch_size
square_crop = self.cfg.square_crop
images = []
intrinsics = []
for cur_frame, cur_image_path in zip(frames_chosen, image_paths_chosen):
image = PIL.Image.open(cur_image_path)
original_image_w, original_image_h = image.size
resize_w = int(resize_h / original_image_h * original_image_w)
resize_w = int(round(resize_w / patch_size) * patch_size)
# if torch.distributed.get_rank() == 0:
# import ipdb; ipdb.set_trace()
image = image.resize((resize_w, resize_h), resample=PIL.Image.LANCZOS)
if square_crop:
min_size = min(resize_h, resize_w)
start_h = (resize_h - min_size) // 2
start_w = (resize_w - min_size) // 2
image = image.crop((start_w, start_h, start_w + min_size, start_h + min_size))
image = np.array(image) / 255.0
image = torch.from_numpy(image).permute(2, 0, 1).float()
fxfycxcy = np.array(cur_frame["fxfycxcy"])
resize_ratio_x = resize_w / original_image_w
resize_ratio_y = resize_h / original_image_h
fxfycxcy *= (resize_ratio_x, resize_ratio_y, resize_ratio_x, resize_ratio_y)
if square_crop:
fxfycxcy[2] -= start_w
fxfycxcy[3] -= start_h
fxfycxcy = torch.from_numpy(fxfycxcy).float()
images.append(image)
intrinsics.append(fxfycxcy)
images = torch.stack(images, dim=0)
intrinsics = torch.stack(intrinsics, dim=0)
w2cs = np.stack([np.array(frame["w2c"]) for frame in frames_chosen])
c2ws = np.linalg.inv(w2cs) # (num_frames, 4, 4)
c2ws = torch.from_numpy(c2ws).float()
return images, intrinsics, c2ws
def preprocess_poses(
self,
in_c2ws: torch.Tensor,
scene_scale_factor=1.35,
):
"""
Preprocess the poses to:
1. translate and rotate the scene to align the average camera direction and position
2. rescale the whole scene to a fixed scale
"""
# Translation and Rotation
# align coordinate system (OpenCV coordinate) to the mean camera
# center is the average of all camera centers
# average direction vectors are computed from all camera direction vectors (average down and forward)
center = in_c2ws[:, :3, 3].mean(0)
avg_forward = F.normalize(in_c2ws[:, :3, 2].mean(0), dim=-1) # average forward direction (z of opencv camera)
avg_down = in_c2ws[:, :3, 1].mean(0) # average down direction (y of opencv camera)
avg_right = F.normalize(torch.cross(avg_down, avg_forward, dim=-1), dim=-1) # (x of opencv camera)
avg_down = F.normalize(torch.cross(avg_forward, avg_right, dim=-1), dim=-1) # (y of opencv camera)
avg_pose = torch.eye(4, device=in_c2ws.device) # average c2w matrix
avg_pose[:3, :3] = torch.stack([avg_right, avg_down, avg_forward], dim=-1)
avg_pose[:3, 3] = center
avg_pose = torch.linalg.inv(avg_pose) # average w2c matrix
in_c2ws = avg_pose @ in_c2ws
# Rescale the whole scene to a fixed scale
scene_scale = torch.max(torch.abs(in_c2ws[:, :3, 3]))
scene_scale = (scene_scale_factor * scene_scale).clamp_min(1e-8)
in_c2ws[:, :3, 3] /= scene_scale
return in_c2ws
def _get_data(self, index):
uid = self.uids[index]
scene_path = uid.strip()
data_json = json.load(open(scene_path, 'r'))
frames = data_json["frames"]
scene_name = data_json["scene_name"]
#### conduct view selecting ####
if self.split != 'train' and scene_name in self.view_idx_list:
current_view_idx = self.view_idx_list[scene_name]
image_indices= current_view_idx["context"][:1] + current_view_idx["target"] # only take first frame as input
# breakpoint()
else:
# breakpoint()
image_indices = random.sample(range(len(frames)), self.cfg.sel_views + self.cfg.sel_views_train)
######### load data #########
image_paths_chosen = [frames[ic]["image_path"] for ic in image_indices]
frames_chosen = [frames[ic] for ic in image_indices]
input_images, input_intrinsics, input_c2ws = self.preprocess_frames(frames_chosen, image_paths_chosen)
# centerize and scale the poses (for unbounded scenes)
scene_scale_factor = self.cfg.scene_scale_factor
input_c2ws = self.preprocess_poses(input_c2ws, scene_scale_factor)
image_indices = torch.tensor(image_indices).long().unsqueeze(-1) # [v, 1]
scene_indices = torch.full_like(image_indices, index) # [v, 1]
indices = torch.cat([image_indices, scene_indices], dim=-1) # [v, 2]
# breakpoint()
ret ={
'rgbs': input_images,
'c2ws': input_c2ws,
'fxfycxcys': input_intrinsics,
'image_indices': image_indices,
'scene_indices': scene_indices,
'masks': torch.ones_like(input_images[:, :1]),
'indices': indices,
'uid': scene_name
}
# breakpoint()
ret['rgbs_input'] = ret['rgbs'][:self.cfg.sel_views+1]
ret['c2ws_input'] = ret['c2ws'][:self.cfg.sel_views+1]
ret['masks_input'] = ret['masks'][:self.cfg.sel_views+1]
ret['fxfycxcys_input'] = ret['fxfycxcys'][:self.cfg.sel_views+1]
# breakpoint()
return ret
def __getitem__(self, index):
try:
return self._get_data(index)
except Exception as e:
raise RuntimeError(f"Could not load scene {self.uids[index]}: {e}") from e
def collate(self, batch):
from torch.utils.data._utils.collate import default_collate_fn_map
ret = {}
for key, value in batch[0].items():
if isinstance(value, str):
ret[key] = [b[key] for b in batch]
elif isinstance(value, torch.Tensor):
ret[key] = torch.stack([b[key] for b in batch])
elif isinstance(value, np.ndarray):
ret[key] = torch.stack([torch.from_numpy(b[key]) for b in batch])
else:
print(key)
ret[key] = default_collate_fn_map[type(batch[0][key])](batch)
return ret
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