import os, sys
import math
import json
import importlib
from pathlib import Path
import cv2
import random
import numpy as np
from PIL import Image
import webdataset as wds
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torchvision import transforms
from src.utils.train_util import instantiate_from_config
from src.utils.camera_util import (
FOV_to_intrinsics,
center_looking_at_camera_pose,
get_surrounding_views,
)
class DataModuleFromConfig(pl.LightningDataModule):
def __init__(
self,
batch_size=8,
num_workers=4,
train=None,
validation=None,
test=None,
**kwargs,
):
super().__init__()
self.batch_size = batch_size
self.num_workers = num_workers
self.dataset_configs = dict()
if train is not None:
self.dataset_configs['train'] = train
if validation is not None:
self.dataset_configs['validation'] = validation
if test is not None:
self.dataset_configs['test'] = test
def setup(self, stage):
if stage in ['fit']:
self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs)
else:
raise NotImplementedError
def train_dataloader(self):
sampler = DistributedSampler(self.datasets['train'])
return wds.WebLoader(self.datasets['train'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, sampler=sampler)
def val_dataloader(self):
sampler = DistributedSampler(self.datasets['validation'])
return wds.WebLoader(self.datasets['validation'], batch_size=1, num_workers=self.num_workers, shuffle=False, sampler=sampler)
def test_dataloader(self):
return wds.WebLoader(self.datasets['test'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False)
class ObjaverseData(Dataset):
def __init__(self,
root_dir='objaverse/',
meta_fname='valid_paths.json',
input_image_dir='rendering_random_32views',
target_image_dir='rendering_random_32views',
input_view_num=6,
target_view_num=2,
total_view_n=32,
fov=50,
camera_rotation=True,
validation=False,
):
self.root_dir = Path(root_dir)
self.input_image_dir = input_image_dir
self.target_image_dir = target_image_dir
self.input_view_num = input_view_num
self.target_view_num = target_view_num
self.total_view_n = total_view_n
self.fov = fov
self.camera_rotation = camera_rotation
with open(os.path.join(root_dir, meta_fname)) as f:
filtered_dict = json.load(f)
paths = filtered_dict['good_objs']
self.paths = paths
self.depth_scale = 4.0
total_objects = len(self.paths)
print('============= length of dataset %d =============' % len(self.paths))
def __len__(self):
return len(self.paths)
def load_im(self, path, color):
'''
replace background pixel with random color in rendering
'''
pil_img = Image.open(path)
image = np.asarray(pil_img, dtype=np.float32) / 255.
alpha = image[:, :, 3:]
image = image[:, :, :3] * alpha + color * (1 - alpha)
image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float()
alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float()
return image, alpha
def __getitem__(self, index):
# load data
while True:
input_image_path = os.path.join(self.root_dir, self.input_image_dir, self.paths[index])
target_image_path = os.path.join(self.root_dir, self.target_image_dir, self.paths[index])
indices = np.random.choice(range(self.total_view_n), self.input_view_num + self.target_view_num, replace=False)
input_indices = indices[:self.input_view_num]
target_indices = indices[self.input_view_num:]
'''background color, default: white'''
bg_white = [1., 1., 1.]
bg_black = [0., 0., 0.]
image_list = []
alpha_list = []
depth_list = []
normal_list = []
pose_list = []
try:
input_cameras = np.load(os.path.join(input_image_path, 'cameras.npz'))['cam_poses']
for idx in input_indices:
image, alpha = self.load_im(os.path.join(input_image_path, '%03d.png' % idx), bg_white)
normal, _ = self.load_im(os.path.join(input_image_path, '%03d_normal.png' % idx), bg_black)
depth = cv2.imread(os.path.join(input_image_path, '%03d_depth.png' % idx), cv2.IMREAD_UNCHANGED) / 255.0 * self.depth_scale
depth = torch.from_numpy(depth).unsqueeze(0)
pose = input_cameras[idx]
pose = np.concatenate([pose, np.array([[0, 0, 0, 1]])], axis=0)
image_list.append(image)
alpha_list.append(alpha)
depth_list.append(depth)
normal_list.append(normal)
pose_list.append(pose)
target_cameras = np.load(os.path.join(target_image_path, 'cameras.npz'))['cam_poses']
for idx in target_indices:
image, alpha = self.load_im(os.path.join(target_image_path, '%03d.png' % idx), bg_white)
normal, _ = self.load_im(os.path.join(target_image_path, '%03d_normal.png' % idx), bg_black)
depth = cv2.imread(os.path.join(target_image_path, '%03d_depth.png' % idx), cv2.IMREAD_UNCHANGED) / 255.0 * self.depth_scale
depth = torch.from_numpy(depth).unsqueeze(0)
pose = target_cameras[idx]
pose = np.concatenate([pose, np.array([[0, 0, 0, 1]])], axis=0)
image_list.append(image)
alpha_list.append(alpha)
depth_list.append(depth)
normal_list.append(normal)
pose_list.append(pose)
except Exception as e:
print(e)
index = np.random.randint(0, len(self.paths))
continue
break
images = torch.stack(image_list, dim=0).float() # (6+V, 3, H, W)
alphas = torch.stack(alpha_list, dim=0).float() # (6+V, 1, H, W)
depths = torch.stack(depth_list, dim=0).float() # (6+V, 1, H, W)
normals = torch.stack(normal_list, dim=0).float() # (6+V, 3, H, W)
w2cs = torch.from_numpy(np.stack(pose_list, axis=0)).float() # (6+V, 4, 4)
c2ws = torch.linalg.inv(w2cs).float()
normals = normals * 2.0 - 1.0
normals = F.normalize(normals, dim=1)
normals = (normals + 1.0) / 2.0
normals = torch.lerp(torch.zeros_like(normals), normals, alphas)
# random rotation along z axis
if self.camera_rotation:
degree = np.random.uniform(0, math.pi * 2)
rot = torch.tensor([
[np.cos(degree), -np.sin(degree), 0, 0],
[np.sin(degree), np.cos(degree), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
]).unsqueeze(0).float()
c2ws = torch.matmul(rot, c2ws)
# rotate normals
N, _, H, W = normals.shape
normals = normals * 2.0 - 1.0
normals = torch.matmul(rot[:, :3, :3], normals.view(N, 3, -1)).view(N, 3, H, W)
normals = F.normalize(normals, dim=1)
normals = (normals + 1.0) / 2.0
normals = torch.lerp(torch.zeros_like(normals), normals, alphas)
# random scaling
if np.random.rand() < 0.5:
scale = np.random.uniform(0.8, 1.0)
c2ws[:, :3, 3] *= scale
depths *= scale
# instrinsics of perspective cameras
K = FOV_to_intrinsics(self.fov)
Ks = K.unsqueeze(0).repeat(self.input_view_num + self.target_view_num, 1, 1).float()
data = {
'input_images': images[:self.input_view_num], # (6, 3, H, W)
'input_alphas': alphas[:self.input_view_num], # (6, 1, H, W)
'input_depths': depths[:self.input_view_num], # (6, 1, H, W)
'input_normals': normals[:self.input_view_num], # (6, 3, H, W)
'input_c2ws': c2ws_input[:self.input_view_num], # (6, 4, 4)
'input_Ks': Ks[:self.input_view_num], # (6, 3, 3)
# lrm generator input and supervision
'target_images': images[self.input_view_num:], # (V, 3, H, W)
'target_alphas': alphas[self.input_view_num:], # (V, 1, H, W)
'target_depths': depths[self.input_view_num:], # (V, 1, H, W)
'target_normals': normals[self.input_view_num:], # (V, 3, H, W)
'target_c2ws': c2ws[self.input_view_num:], # (V, 4, 4)
'target_Ks': Ks[self.input_view_num:], # (V, 3, 3)
'depth_available': 1,
}
return data
class ValidationData(Dataset):
def __init__(self,
root_dir='objaverse/',
input_view_num=6,
input_image_size=256,
fov=50,
):
self.root_dir = Path(root_dir)
self.input_view_num = input_view_num
self.input_image_size = input_image_size
self.fov = fov
self.paths = sorted(os.listdir(self.root_dir))
print('============= length of dataset %d =============' % len(self.paths))
cam_distance = 2.5
azimuths = np.array([30, 90, 150, 210, 270, 330])
elevations = np.array([30, -20, 30, -20, 30, -20])
azimuths = np.deg2rad(azimuths)
elevations = np.deg2rad(elevations)
x = cam_distance * np.cos(elevations) * np.cos(azimuths)
y = cam_distance * np.cos(elevations) * np.sin(azimuths)
z = cam_distance * np.sin(elevations)
cam_locations = np.stack([x, y, z], axis=-1)
cam_locations = torch.from_numpy(cam_locations).float()
c2ws = center_looking_at_camera_pose(cam_locations)
self.c2ws = c2ws.float()
self.Ks = FOV_to_intrinsics(self.fov).unsqueeze(0).repeat(6, 1, 1).float()
render_c2ws = get_surrounding_views(M=8, radius=cam_distance)
render_Ks = FOV_to_intrinsics(self.fov).unsqueeze(0).repeat(render_c2ws.shape[0], 1, 1)
self.render_c2ws = render_c2ws.float()
self.render_Ks = render_Ks.float()
def __len__(self):
return len(self.paths)
def load_im(self, path, color):
'''
replace background pixel with random color in rendering
'''
pil_img = Image.open(path)
pil_img = pil_img.resize((self.input_image_size, self.input_image_size), resample=Image.BICUBIC)
image = np.asarray(pil_img, dtype=np.float32) / 255.
if image.shape[-1] == 4:
alpha = image[:, :, 3:]
image = image[:, :, :3] * alpha + color * (1 - alpha)
else:
alpha = np.ones_like(image[:, :, :1])
image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float()
alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float()
return image, alpha
def __getitem__(self, index):
# load data
input_image_path = os.path.join(self.root_dir, self.paths[index])
'''background color, default: white'''
# color = np.random.uniform(0.48, 0.52)
bkg_color = [1.0, 1.0, 1.0]
image_list = []
alpha_list = []
for idx in range(self.input_view_num):
image, alpha = self.load_im(os.path.join(input_image_path, f'{idx:03d}.png'), bkg_color)
image_list.append(image)
alpha_list.append(alpha)
images = torch.stack(image_list, dim=0).float() # (6+V, 3, H, W)
alphas = torch.stack(alpha_list, dim=0).float() # (6+V, 1, H, W)
data = {
'input_images': images, # (6, 3, H, W)
'input_alphas': alphas, # (6, 1, H, W)
'input_c2ws': self.c2ws, # (6, 4, 4)
'input_Ks': self.Ks, # (6, 3, 3)
'render_c2ws': self.render_c2ws,
'render_Ks': self.render_Ks,
}
return data