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import os
import json
import math
import numpy as np
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader, IterableDataset
import torchvision.transforms.functional as TF
from torchvision.utils import make_grid, save_image
from einops import rearrange
import pytorch_lightning as pl
import datasets
from models.ray_utils import get_ray_directions
from utils.misc import get_rank
from datasets.ortho import (
inv_RT,
camNormal2worldNormal,
RT_opengl2opencv,
normal_opengl2opencv,
)
from utils.dpt import DPT
def blender2midas(img):
"""Blender: rub
midas: lub
"""
img[..., 0] = -img[..., 0]
img[..., 1] = -img[..., 1]
img[..., -1] = -img[..., -1]
return img
def midas2blender(img):
"""Blender: rub
midas: lub
"""
img[..., 0] = -img[..., 0]
img[..., 1] = -img[..., 1]
img[..., -1] = -img[..., -1]
return img
class BlenderDatasetBase:
def setup(self, config, split):
self.config = config
self.rank = get_rank()
self.has_mask = True
self.apply_mask = True
dpt = DPT(device=self.rank, mode="normal")
with open(
os.path.join(
self.config.root_dir, self.config.scene, f"transforms_train.json"
),
"r",
) as f:
meta = json.load(f)
if "w" in meta and "h" in meta:
W, H = int(meta["w"]), int(meta["h"])
else:
W, H = 800, 800
if "img_wh" in self.config:
w, h = self.config.img_wh
assert round(W / w * h) == H
elif "img_downscale" in self.config:
w, h = W // self.config.img_downscale, H // self.config.img_downscale
else:
raise KeyError("Either img_wh or img_downscale should be specified.")
self.w, self.h = w, h
self.img_wh = (self.w, self.h)
# self.near, self.far = self.config.near_plane, self.config.far_plane
self.focal = (
0.5 * w / math.tan(0.5 * meta["camera_angle_x"])
) # scaled focal length
# ray directions for all pixels, same for all images (same H, W, focal)
self.directions = get_ray_directions(
self.w, self.h, self.focal, self.focal, self.w // 2, self.h // 2
).to(
self.rank
) # (h, w, 3)
self.all_c2w, self.all_images, self.all_fg_masks = [], [], []
for i, frame in enumerate(meta["frames"]):
c2w = torch.from_numpy(np.array(frame["transform_matrix"])[:3, :4])
self.all_c2w.append(c2w)
img_path = os.path.join(
self.config.root_dir,
self.config.scene,
f"{frame['file_path']}.png",
)
img = Image.open(img_path)
img = img.resize(self.img_wh, Image.BICUBIC)
img = TF.to_tensor(img).permute(1, 2, 0) # (4, h, w) => (h, w, 4)
self.all_fg_masks.append(img[..., -1]) # (h, w)
self.all_images.append(img[..., :3])
self.all_c2w, self.all_images, self.all_fg_masks = (
torch.stack(self.all_c2w, dim=0).float().to(self.rank),
torch.stack(self.all_images, dim=0).float().to(self.rank),
torch.stack(self.all_fg_masks, dim=0).float().to(self.rank),
)
self.normals = dpt(self.all_images)
self.all_masks = self.all_fg_masks.cpu().numpy() > 0.1
self.normals = self.normals * 2.0 - 1.0
self.normals = midas2blender(self.normals).cpu().numpy()
# self.normals = self.normals.cpu().numpy()
self.normals[..., 0] *= -1
self.normals[~self.all_masks] = [0, 0, 0]
normals = rearrange(self.normals, "b h w c -> b c h w")
normals = normals * 0.5 + 0.5
normals = torch.from_numpy(normals)
save_image(make_grid(normals, nrow=4), "tmp/normals.png")
# exit(0)
(
self.all_poses,
self.all_normals,
self.all_normals_world,
self.all_w2cs,
self.all_color_masks,
) = ([], [], [], [], [])
for c2w_opengl, normal in zip(self.all_c2w.cpu().numpy(), self.normals):
RT_opengl = inv_RT(c2w_opengl)
RT_opencv = RT_opengl2opencv(RT_opengl)
c2w_opencv = inv_RT(RT_opencv)
self.all_poses.append(c2w_opencv)
self.all_w2cs.append(RT_opencv)
normal = normal_opengl2opencv(normal)
normal_world = camNormal2worldNormal(inv_RT(RT_opencv)[:3, :3], normal)
self.all_normals.append(normal)
self.all_normals_world.append(normal_world)
self.directions = torch.stack([self.directions] * len(self.all_images))
self.origins = self.directions
self.all_poses = np.stack(self.all_poses)
self.all_normals = np.stack(self.all_normals)
self.all_normals_world = np.stack(self.all_normals_world)
self.all_w2cs = np.stack(self.all_w2cs)
self.all_c2w = torch.from_numpy(self.all_poses).float().to(self.rank)
self.all_images = self.all_images.to(self.rank)
self.all_fg_masks = self.all_fg_masks.to(self.rank)
self.all_rgb_masks = self.all_fg_masks.to(self.rank)
self.all_normals_world = (
torch.from_numpy(self.all_normals_world).float().to(self.rank)
)
# normals = rearrange(self.all_normals_world, "b h w c -> b c h w")
# normals = normals * 0.5 + 0.5
# # normals = torch.from_numpy(normals)
# save_image(make_grid(normals, nrow=4), "tmp/normals_world.png")
# # exit(0)
# # normals = (normals + 1) / 2.0
# # for debug
# index = [0, 9]
# self.all_poses = self.all_poses[index]
# self.all_c2w = self.all_c2w[index]
# self.all_normals_world = self.all_normals_world[index]
# self.all_w2cs = self.all_w2cs[index]
# self.rgb_masks = self.all_rgb_masks[index]
# self.fg_masks = self.all_fg_masks[index]
# self.all_images = self.all_images[index]
# self.directions = self.directions[index]
# self.origins = self.origins[index]
# images = rearrange(self.all_images, "b h w c -> b c h w")
# normals = rearrange(normals, "b h w c -> b c h w")
# save_image(make_grid(images, nrow=4), "tmp/images.png")
# save_image(make_grid(normals, nrow=4), "tmp/normals.png")
# breakpoint()
# self.normals = self.normals * 2.0 - 1.0
class BlenderDataset(Dataset, BlenderDatasetBase):
def __init__(self, config, split):
self.setup(config, split)
def __len__(self):
return len(self.all_images)
def __getitem__(self, index):
return {"index": index}
class BlenderIterableDataset(IterableDataset, BlenderDatasetBase):
def __init__(self, config, split):
self.setup(config, split)
def __iter__(self):
while True:
yield {}
@datasets.register("videonvs")
class BlenderDataModule(pl.LightningDataModule):
def __init__(self, config):
super().__init__()
self.config = config
def setup(self, stage=None):
if stage in [None, "fit"]:
self.train_dataset = BlenderIterableDataset(
self.config, self.config.train_split
)
if stage in [None, "fit", "validate"]:
self.val_dataset = BlenderDataset(self.config, self.config.val_split)
if stage in [None, "test"]:
self.test_dataset = BlenderDataset(self.config, self.config.test_split)
if stage in [None, "predict"]:
self.predict_dataset = BlenderDataset(self.config, self.config.train_split)
def prepare_data(self):
pass
def general_loader(self, dataset, batch_size):
sampler = None
return DataLoader(
dataset,
num_workers=os.cpu_count(),
batch_size=batch_size,
pin_memory=True,
sampler=sampler,
)
def train_dataloader(self):
return self.general_loader(self.train_dataset, batch_size=1)
def val_dataloader(self):
return self.general_loader(self.val_dataset, batch_size=1)
def test_dataloader(self):
return self.general_loader(self.test_dataset, batch_size=1)
def predict_dataloader(self):
return self.general_loader(self.predict_dataset, batch_size=1)
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