Spaces:
Running
on
Zero
Running
on
Zero
File size: 13,965 Bytes
2ac1c2d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 |
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
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from PIL import Image
from step1x3d_geometry.utils.typing import *
@dataclass
class BaseDataModuleConfig:
root_dir: str = None
batch_size: int = 4
num_workers: int = 8
################################# General argumentation #################################
random_flip: bool = (
False # whether to randomly flip the input point cloud and the input images
)
################################# Geometry part #################################
load_geometry: bool = True # whether to load geometry data
with_sharp_data: bool = False
geo_data_type: str = "sdf" # occupancy, sdf
# for occupancy or sdf supervision
n_samples: int = 4096 # number of points in input point cloud
upsample_ratio: int = 1 # upsample ratio for input point cloud
sampling_strategy: Optional[str] = (
"random" # sampling strategy for input point cloud
)
scale: float = 1.0 # scale of the input point cloud and target supervision
noise_sigma: float = 0.0 # noise level of the input point cloud
rotate_points: bool = (
False # whether to rotate the input point cloud and the supervision, for VAE aug.
)
load_geometry_supervision: bool = False # whether to load supervision
supervision_type: str = "sdf" # occupancy, sdf, tsdf, tsdf_w_surface
n_supervision: int = 10000 # number of points in supervision
tsdf_threshold: float = (
0.01 # threshold for truncating sdf values, used when input is sdf
)
################################# Image part #################################
load_image: bool = False # whether to load images
image_type: str = "rgb" # rgb, normal, rgb_or_normal
image_file_type: str = "png" # png, jpeg
image_type_ratio: float = (
1.0 # ratio of rgb for each dataset when image_type is "rgb_or_normal"
)
crop_image: bool = True # whether to crop the input image
random_color_jitter: bool = (
False # whether to randomly color jitter the input images
)
random_rotate: bool = (
False # whether to randomly rotate the input images, default [-10 deg, 10 deg]
)
random_mask: bool = False # whether to add random mask to the input image
background_color: Tuple[int, int, int] = field(
default_factory=lambda: (255, 255, 255)
)
idx: Optional[List[int]] = None # index of the image to load
n_views: int = 1 # number of views
foreground_ratio: Optional[float] = 0.90
################################# Caption part #################################
load_caption: bool = False # whether to load captions
load_label: bool = False # whether to load labels
class BaseDataset(Dataset):
def __init__(self, cfg: Any, split: str) -> None:
super().__init__()
self.cfg: BaseDataModuleConfig = cfg
self.split = split
self.uids = json.load(open(f"{cfg.root_dir}/{split}.json"))
print(f"Loaded {len(self.uids)} {split} uids")
# add ColorJitter transforms for input images
if self.cfg.random_color_jitter:
self.color_jitter = transforms.ColorJitter(
brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2
)
# add RandomRotation transforms for input images
if self.cfg.random_rotate:
self.rotate = transforms.RandomRotation(
degrees=10, fill=(*self.cfg.background_color, 0.0)
) # by default 10 deg
def __len__(self):
return len(self.uids)
def _load_shape_from_occupancy_or_sdf(self, index: int) -> Dict[str, Any]:
if self.cfg.geo_data_type == "sdf":
data = np.load(f"{self.cfg.root_dir}/surfaces/{self.uids[index]}.npz")
# for input point cloud
surface = data["surface"]
if self.cfg.with_sharp_data:
sharp_surface = data["sharp_surface"]
else:
raise NotImplementedError(
f"Data type {self.cfg.geo_data_type} not implemented"
)
# random sampling
if self.cfg.sampling_strategy == "random":
rng = np.random.default_rng()
ind = rng.choice(
surface.shape[0],
self.cfg.upsample_ratio * self.cfg.n_samples,
replace=True,
)
surface = surface[ind]
if self.cfg.with_sharp_data:
sharp_surface = sharp_surface[ind]
elif self.cfg.sampling_strategy == "fps":
import fpsample
kdline_fps_samples_idx = fpsample.bucket_fps_kdline_sampling(
surface[:, :3], self.cfg.n_samples, h=5
)
surface = surface[kdline_fps_samples_idx]
if self.cfg.with_sharp_data:
kdline_fps_samples_idx = fpsample.bucket_fps_kdline_sampling(
sharp_surface[:, :3], self.cfg.n_samples, h=5
)
sharp_surface = sharp_surface[kdline_fps_samples_idx]
else:
raise NotImplementedError(
f"sampling strategy {self.cfg.sampling_strategy} not implemented"
)
# rescale data
surface[:, :3] = surface[:, :3] * self.cfg.scale # target scale
if self.cfg.with_sharp_data:
sharp_surface[:, :3] = sharp_surface[:, :3] * self.cfg.scale # target scale
ret = {
"uid": self.uids[index].split("/")[-1],
"surface": surface.astype(np.float32),
"sharp_surface": sharp_surface.astype(np.float32),
}
else:
ret = {
"uid": self.uids[index].split("/")[-1],
"surface": surface.astype(np.float32),
}
return ret
def _load_shape_supervision_occupancy_or_sdf(self, index: int) -> Dict[str, Any]:
# for supervision
ret = {}
if self.cfg.geo_data_type == "sdf":
data = np.load(f"{self.cfg.root_dir}/surfaces/{self.uids[index]}.npz")
data = np.concatenate(
[data["volume_rand_points"], data["near_surface_points"]], axis=0
)
rand_points, sdfs = data[:, :3], data[:, 3:]
else:
raise NotImplementedError(
f"Data type {self.cfg.geo_data_type} not implemented"
)
# random sampling
rng = np.random.default_rng()
ind = rng.choice(rand_points.shape[0], self.cfg.n_supervision, replace=False)
rand_points = rand_points[ind]
rand_points = rand_points * self.cfg.scale
ret["rand_points"] = rand_points.astype(np.float32)
if self.cfg.geo_data_type == "sdf":
if self.cfg.supervision_type == "sdf":
ret["sdf"] = sdfs[ind].flatten().astype(np.float32)
elif self.cfg.supervision_type == "occupancy":
ret["occupancies"] = np.where(sdfs[ind].flatten() < 1e-3, 0, 1).astype(
np.float32
)
elif self.cfg.supervision_type == "tsdf":
ret["sdf"] = (
sdfs[ind]
.flatten()
.astype(np.float32)
.clip(-self.cfg.tsdf_threshold, self.cfg.tsdf_threshold)
/ self.cfg.tsdf_threshold
)
else:
raise NotImplementedError(
f"Supervision type {self.cfg.supervision_type} not implemented"
)
return ret
def _load_image(self, index: int) -> Dict[str, Any]:
def _process_img(image, background_color=(255, 255, 255), foreground_ratio=0.9):
alpha = image.getchannel("A")
background = Image.new("RGBA", image.size, (*background_color, 255))
image = Image.alpha_composite(background, image)
image = image.crop(alpha.getbbox())
new_size = tuple(int(dim * foreground_ratio) for dim in image.size)
resized_image = image.resize(new_size)
padded_image = Image.new("RGBA", image.size, (*background_color, 255))
paste_position = (
(image.width - resized_image.width) // 2,
(image.height - resized_image.height) // 2,
)
padded_image.paste(resized_image, paste_position)
# Expand image to 1:1
max_dim = max(padded_image.size)
image = Image.new("RGBA", (max_dim, max_dim), (*background_color, 255))
paste_position = (
(max_dim - padded_image.width) // 2,
(max_dim - padded_image.height) // 2,
)
image.paste(padded_image, paste_position)
image = image.resize((512, 512))
return image.convert("RGB"), alpha
ret = {}
if self.cfg.image_type == "rgb" or self.cfg.image_type == "normal":
assert (
self.cfg.n_views == 1
), "Only single view is supported for single image"
sel_idx = random.choice(self.cfg.idx)
ret["sel_image_idx"] = sel_idx
if self.cfg.image_type == "rgb":
img_path = (
f"{self.cfg.root_dir}/images/"
+ "/".join(self.uids[index].split("/")[-2:])
+ f"/{'{:04d}'.format(sel_idx)}_rgb.{self.cfg.image_file_type}"
)
elif self.cfg.image_type == "normal":
img_path = (
f"{self.cfg.root_dir}/images/"
+ "/".join(self.uids[index].split("/")[-2:])
+ f"/{'{:04d}'.format(sel_idx)}_normal.{self.cfg.image_file_type}"
)
image = Image.open(img_path).copy()
# add random color jitter
if self.cfg.random_color_jitter:
rgb = self.color_jitter(image.convert("RGB"))
image = Image.merge("RGBA", (*rgb.split(), image.getchannel("A")))
# add random rotation
if self.cfg.random_rotate:
image = self.rotate(image)
# add crop
if self.cfg.crop_image:
background_color = (
torch.randint(0, 256, (3,))
if self.cfg.background_color is None
else torch.as_tensor(self.cfg.background_color)
)
image, alpha = _process_img(
image, background_color, self.cfg.foreground_ratio
)
else:
alpha = image.getchannel("A")
background = Image.new("RGBA", image.size, background_color)
image = Image.alpha_composite(background, image).convert("RGB")
ret["image"] = torch.from_numpy(np.array(image) / 255.0)
ret["mask"] = torch.from_numpy(np.array(alpha) / 255.0).unsqueeze(0)
else:
raise NotImplementedError(
f"Image type {self.cfg.image_type} not implemented"
)
return ret
def _get_data(self, index):
ret = {"uid": self.uids[index]}
# random flip
flip = np.random.rand() < 0.5 if self.cfg.random_flip else False
# load geometry
if self.cfg.load_geometry:
if self.cfg.geo_data_type == "occupancy" or self.cfg.geo_data_type == "sdf":
# load shape
ret = self._load_shape_from_occupancy_or_sdf(index)
# load supervision for shape
if self.cfg.load_geometry_supervision:
ret.update(self._load_shape_supervision_occupancy_or_sdf(index))
else:
raise NotImplementedError(
f"Geo data type {self.cfg.geo_data_type} not implemented"
)
if flip: # random flip the input point cloud and the supervision
for key in ret.keys():
if key in ["surface", "sharp_surface"]: # N x (xyz + normal)
ret[key][:, 0] = -ret[key][:, 0]
ret[key][:, 3] = -ret[key][:, 3]
elif key in ["rand_points"]:
ret[key][:, 0] = -ret[key][:, 0]
# load image
if self.cfg.load_image:
ret.update(self._load_image(index))
if flip: # random flip the input image
for key in ret.keys():
if key in ["image"]: # random flip the input image
ret[key] = torch.flip(ret[key], [2])
if key in ["mask"]: # random flip the input image
ret[key] = torch.flip(ret[key], [2])
# load caption
meta = None
if self.cfg.load_caption:
with open(f"{self.cfg.root_dir}/metas/{self.uids[index]}.json", "r") as f:
meta = json.load(f)
ret.update({"caption": meta["caption"]})
# load label
if self.cfg.load_label:
if meta is None:
with open(
f"{self.cfg.root_dir}/metas/{self.uids[index]}.json", "r"
) as f:
meta = json.load(f)
ret.update({"label": [meta["label"]]})
return ret
def __getitem__(self, index):
try:
return self._get_data(index)
except Exception as e:
print(f"Error in {self.uids[index]}: {e}")
return self.__getitem__(np.random.randint(len(self)))
def collate(self, batch):
from torch.utils.data._utils.collate import default_collate_fn_map
return torch.utils.data.default_collate(batch)
|