Spaces:
Running
on
Zero
Running
on
Zero
File size: 12,547 Bytes
c1c0440 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 |
import os
import cv2
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms.functional as TF
from torch.utils.data import Dataset
from PIL import Image
import json
from torchvision.transforms import v2
import tarfile
import kiui
from core.options import Options
from core.utils import get_rays, grid_distortion, orbit_camera_jitter
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
os.environ["OPENCV_IO_ENABLE_OPENEXR"]="1"
class GobjaverseDataset(Dataset):
def _warn(self):
raise NotImplementedError('this dataset is just an example and cannot be used directly, you should modify it to your own setting! (search keyword TODO)')
def __init__(self, opt: Options, training=True):
self.opt = opt
self.training = training
if opt.over_fit:
data_list_path=opt.data_debug_list
else:
data_list_path=opt.data_list_path
# TODO: load the list of objects for training
self.items = []
with open(data_list_path, 'r') as f:
data = json.load(f)
for item in data:
self.items.append(item)
# naive split
if not opt.over_fit:
if self.training:
self.items = self.items[:-self.opt.batch_size]
else:
self.items = self.items[-self.opt.batch_size:]
else:
self.opt.batch_size=len(self.items)
self.opt.num_workers=0
# default camera intrinsics
self.tan_half_fov = np.tan(0.5 * np.deg2rad(self.opt.fovy))
self.proj_matrix = torch.zeros(4, 4, dtype=torch.float32)
self.proj_matrix[0, 0] = 1 / self.tan_half_fov
self.proj_matrix[1, 1] = 1 / self.tan_half_fov
self.proj_matrix[2, 2] = (self.opt.zfar + self.opt.znear) / (self.opt.zfar - self.opt.znear)
self.proj_matrix[3, 2] = - (self.opt.zfar * self.opt.znear) / (self.opt.zfar - self.opt.znear)
self.proj_matrix[2, 3] = 1
def __len__(self):
return len(self.items)
def __getitem__(self, idx):
uid = self.items[idx]
results = {}
# load num_views images
images = []
albedos = []
normals = []
depths = []
masks = []
cam_poses = []
vid_cnt = 0
# TODO: choose views, based on your rendering settings
if self.training:
# input views are in (36, 72), other views are randomly selected
if self.opt.mvdream_or_zero123:
vids = [0,30,12,36,27,6,33,18][:self.opt.num_input_views] + np.random.permutation(24).tolist()
else:
vids = [0,29,8,33,16,37,2,10][:self.opt.num_input_views] + np.random.permutation(24).tolist()
else:
# fixed views
if self.opt.mvdream_or_zero123:
vids = [0,30,12,36,27,6,33,18]#np.arange(0, 24, 6).tolist() + np.arange(27, 40, 3).tolist()
else:
vids = [0,29,8,33,16,37,2,10,18,28]
for vid in vids:
#try:
uid_last = uid.split('/')[1]
if self.opt.rar_data:
tar_path = os.path.join(self.opt.data_path, f"{uid}.tar")
image_path = os.path.join(uid_last, 'campos_512_v4', f"{vid:05d}/{vid:05d}.png")
meta_path = os.path.join(uid_last, 'campos_512_v4', f"{vid:05d}/{vid:05d}.json")
albedo_path = os.path.join(uid_last, 'campos_512_v4', f"{vid:05d}/{vid:05d}_albedo.png") # black bg...
# mr_path = os.path.join(uid_last, 'campos_512_v4', f"{vid:05d}/{vid:05d}_mr.png")
nd_path = os.path.join(uid_last, 'campos_512_v4', f"{vid:05d}/{vid:05d}_nd.exr")
with tarfile.open(tar_path, 'r') as tar:
with tar.extractfile(image_path) as f:
image = np.frombuffer(f.read(), np.uint8)
with tar.extractfile(albedo_path) as f:
albedo = np.frombuffer(f.read(), np.uint8)
with tar.extractfile(meta_path) as f:
meta = json.loads(f.read().decode())
with tar.extractfile(nd_path) as f:
nd = np.frombuffer(f.read(), np.uint8)
image = torch.from_numpy(cv2.imdecode(image, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255) # [512, 512, 4] in [0, 1]
albedo = torch.from_numpy(cv2.imdecode(albedo, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255) # [512, 512, 4] in [0, 1]
else:
image_path = os.path.join(self.opt.data_path,uid, f"{vid:05d}/{vid:05d}.png")
meta_path = os.path.join(self.opt.data_path,uid, f"{vid:05d}/{vid:05d}.json")
# albedo_path = os.path.join(uid_last, 'campos_512_v4', f"{vid:05d}/{vid:05d}_albedo.png") # black bg...
# mr_path = os.path.join(uid_last, 'campos_512_v4', f"{vid:05d}/{vid:05d}_mr.png")
nd_path = os.path.join(self.opt.data_path,uid, f"{vid:05d}/{vid:05d}_nd.exr")
albedo_path = os.path.join(self.opt.data_path,uid, f"{vid:05d}/{vid:05d}_albedo.png")
# 读取图片并转换为np.uint8类型的数组
with open(image_path, 'rb') as f:
image = np.frombuffer(f.read(), dtype=np.uint8)
with open(albedo_path, 'rb') as f:
albedo = np.frombuffer(f.read(), dtype=np.uint8)
# 读取JSON文件作为元数据
with open(meta_path, 'r') as f:
meta = json.load(f)
# 读取图片并转换为np.uint8类型的数组
with open(nd_path, 'rb') as f:
nd = np.frombuffer(f.read(), np.uint8)
image = torch.from_numpy(cv2.imdecode(image, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255) # [512, 512, 4] in [0, 1]
albedo = torch.from_numpy(cv2.imdecode(albedo, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255)
c2w = np.eye(4)
c2w[:3, 0] = np.array(meta['x'])
c2w[:3, 1] = np.array(meta['y'])
c2w[:3, 2] = np.array(meta['z'])
c2w[:3, 3] = np.array(meta['origin'])
c2w = torch.tensor(c2w, dtype=torch.float32).reshape(4, 4)
nd = cv2.imdecode(nd, cv2.IMREAD_UNCHANGED).astype(np.float32) # [512, 512, 4] in [-1, 1]
normal = nd[..., :3] # in [-1, 1], bg is [0, 0, 1]
depth = nd[..., 3] # in [0, +?), bg is 0
# rectify normal directions
normal = normal[..., ::-1]
normal[..., 0] *= -1
normal = torch.from_numpy(normal.astype(np.float32)).nan_to_num_(0) # there are nans in gt normal...
depth = torch.from_numpy(depth.astype(np.float32)).nan_to_num_(0)
# except Exception as e:
# # print(f'[WARN] dataset {uid} {vid}: {e}')
# continue
# blender world + opencv cam --> opengl world & cam
# world transform, 只要坐标系手系相同，不转不影响画图，会影响normal的着色
c2w[1] *= -1
c2w[[1, 2]] = c2w[[2, 1]]
# cam transform
c2w[:3, 1:3] *= -1 # invert up and forward direction
image = image.permute(2, 0, 1) # [4, 512, 512]
mask = image[3:4] # [1, 512, 512]
image = image[:3] * mask + (1 - mask) # [3, 512, 512], to white bg
image = image[[2,1,0]].contiguous() # bgr to rgb
# albdeo
albedo = albedo.permute(2, 0, 1) # [4, 512, 512]
albedo = albedo[:3] * mask + (1 - mask) # [3, 512, 512], to white bg
albedo = albedo[[2,1,0]].contiguous() # bgr to rgb
normal = normal.permute(2, 0, 1) # [3, 512, 512]
normal = normal * mask # to [0, 0, 0] bg
images.append(image)
albedos.append(albedo)
normals.append(normal)
depths.append(depth)
masks.append(mask.squeeze(0))
cam_poses.append(c2w)
vid_cnt += 1
if vid_cnt == self.opt.num_views:
break
if vid_cnt < self.opt.num_views:
print(f'[WARN] dataset {uid}: not enough valid views, only {vid_cnt} views found!')
n = self.opt.num_views - vid_cnt
images = images + [images[-1]] * n
normals = normals + [normals[-1]] * n
depths = depths + [depths[-1]] * n
masks = masks + [masks[-1]] * n
cam_poses = cam_poses + [cam_poses[-1]] * n
images = torch.stack(images, dim=0) # [V, 3, H, W]
albedos = torch.stack(albedos, dim=0) # [V, 3, H, W]
normals = torch.stack(normals, dim=0) # [V, 3, H, W]
depths = torch.stack(depths, dim=0) # [V, H, W]
masks = torch.stack(masks, dim=0) # [V, H, W]
cam_poses = torch.stack(cam_poses, dim=0) # [V, 4, 4]
# normalized camera feats as in paper (transform the first pose to a fixed position)
radius = torch.norm(cam_poses[0, :3, 3])
cam_poses[:, :3, 3] *= self.opt.cam_radius / radius
transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(cam_poses[0])
cam_poses = transform.unsqueeze(0) @ cam_poses # [V, 4, 4]
cam_poses_input = cam_poses[:self.opt.num_input_views].clone()
# 模拟的设定input size，原图512可以模拟输入320
images = F.interpolate(images, size=(self.opt.input_size, self.opt.input_size), mode='bilinear', align_corners=False) # [V, C, H, W]
albedos = F.interpolate(albedos, size=(self.opt.input_size, self.opt.input_size), mode='bilinear', align_corners=False)
target_images = v2.functional.resize(
images, self.opt.output_size, interpolation=3, antialias=True).clamp(0, 1)
target_albedos = v2.functional.resize(
albedos, self.opt.output_size, interpolation=3, antialias=True).clamp(0, 1)
# target_depths = v2.functional.resize(
# target_depths, render_size, interpolation=0, antialias=True)
target_alphas = v2.functional.resize(
masks.unsqueeze(1), self.opt.output_size, interpolation=0, antialias=True)
#target gt
results['images_output']=target_images
results['albedos_output']=target_albedos
results['masks_output']=target_alphas
# data augmentation condition input image
images_input = images[:self.opt.num_input_views].clone()
if self.training:
# apply random grid distortion to simulate 3D inconsistency
if random.random() < self.opt.prob_grid_distortion:
images_input[1:] = grid_distortion(images_input[1:])
# apply camera jittering (only to input!)
if random.random() < self.opt.prob_cam_jitter:
cam_poses_input[1:] = orbit_camera_jitter(cam_poses_input[1:])
#images_input = TF.normalize(images_input, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
results['input']=images_input #input view images, unused for tranformer based
#results['input'] = None # for gs based mesh
#for transformer hard code size
images_input_vit = F.interpolate(images_input, size=(224, 224), mode='bilinear', align_corners=False)
#images_input_vit = TF.normalize(images_input_vit, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
results['input_vit']=images_input_vit
cam_view = torch.inverse(cam_poses)#.transpose(1, 2) #w2c
cam_pos = - cam_poses[:, :3, 3]
results['w2c'] = cam_view
results['cam_pos'] = cam_pos
#lrm用的是内参和外参的混合，这里先直接用外参试下, 实验可行
results['source_camera']=cam_poses_input
return results |