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# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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
import kiui
from core.options import Options
from core.utils import get_rays, grid_distortion, orbit_camera_jitter
from kiui.cam import orbit_camera
import tarfile
from io import BytesIO
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
def load_np_array_from_tar(tar, path):
array_file = BytesIO()
array_file.write(tar.extractfile(path).read())
array_file.seek(0)
return np.load(array_file)
class ObjaverseDataset(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, evaluating=False):
self.opt = opt
self.training = training
self.evaluating = evaluating
self.items = []
with open(self.opt.datalist, 'r') as f:
for line in f.readlines():
self.items.append(line.strip())
anim_map = {}
for x in self.items:
k = x.split('-')[1]
if k in anim_map:
anim_map[k] += '|'+x
else:
anim_map[k] = x
self.items = list(anim_map.values())
# naive split
if self.training:
self.items = self.items[:-self.opt.batch_size]
elif self.evaluating:
self.items = self.items[::1000]
else:
self.items = self.items[-self.opt.batch_size:]
# 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 _get_batch(self, idx):
if self.training:
uid = random.choice(self.items[idx].split('|'))
else:
uid = self.items[idx].split('|')[0]
results = {}
# load num_views images
images = []
masks = []
cam_poses = []
if self.training and self.opt.shuffle_input:
vids = np.random.permutation(np.arange(32, 48))[:self.opt.num_input_views].tolist() + np.random.permutation(32).tolist()
else:
vids = np.arange(32, 48, 4).tolist() + np.arange(32).tolist()
random_tar_name = 'random_clip/' + uid
fixed_16_tar_name = 'fixed_16_clip/' + uid
local_random_tar_name = os.environ["DATA_HOME"] + random_tar_name.replace('/', '-')
local_fixed_16_tar_name = os.environ["DATA_HOME"] + fixed_16_tar_name.replace('/', '-')
tar_random = tarfile.open(local_random_tar_name)
tar_fixed = tarfile.open(local_fixed_16_tar_name)
T = self.opt.num_frames
for t_idx in range(T):
t = t_idx
vid_cnt = 0
for vid in vids:
if vid >= 32:
vid = vid % 32
tar = tar_fixed
else:
tar = tar_random
image_path = os.path.join('.', f'{vid:03d}/img', f'{t:03d}.jpg')
mask_path = os.path.join('.', f'{vid:03d}/mask', f'{t:03d}.png')
elevation_path = os.path.join('.', f'{vid:03d}/camera', f'elevation.npy')
rotation_path = os.path.join('.', f'{vid:03d}/camera', f'rotation.npy')
image = np.frombuffer(tar.extractfile(image_path).read(), np.uint8)
image = torch.from_numpy(cv2.imdecode(image, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255) # [512, 512, 4] in [0, 1]
azi = load_np_array_from_tar(tar, rotation_path)[t, None]
elevation = load_np_array_from_tar(tar, elevation_path)[t, None] * -1 # to align with pretrained LGM
azi = float(azi)
elevation = float(elevation)
c2w = torch.from_numpy(orbit_camera(elevation, azi, radius=1.5, opengl=True))
image = image.permute(2, 0, 1) # [4, 512, 512]
mask = np.frombuffer(tar.extractfile(mask_path).read(), np.uint8)
mask = torch.from_numpy(cv2.imdecode(mask, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255).unsqueeze(0) # [512, 512, 4] in [0, 1]
image = F.interpolate(image.unsqueeze(0), size=(512, 512), mode='nearest').squeeze(0)
mask = F.interpolate(mask.unsqueeze(0), size=(512, 512), mode='nearest').squeeze(0)
image = image[:3] * mask + (1 - mask) # [3, 512, 512], to white bg
image = image[[2,1,0]].contiguous() # bgr to rgb
images.append(image)
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
masks = masks + [masks[-1]] * n
cam_poses = cam_poses + [cam_poses[-1]] * n
images = torch.stack(images, dim=0) # [V, C, 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)
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]
images_input = F.interpolate(images.reshape(T, self.opt.num_views, *images.shape[1:])[:, :self.opt.num_input_views].reshape(-1, *images.shape[1:]).clone(), size=(self.opt.input_size, self.opt.input_size), mode='bilinear', align_corners=False) # [V, C, H, W]
cam_poses_input = cam_poses.reshape(T, self.opt.num_views, *cam_poses.shape[1:])[:, :self.opt.num_input_views].reshape(-1, *cam_poses.shape[1:]).clone()
# data augmentation
if self.training:
images_input = images_input.reshape(T, self.opt.num_input_views, *images_input.shape[1:])
cam_poses_input = cam_poses_input.reshape(T, self.opt.num_input_views, *cam_poses.shape[1:])
# apply random grid distortion to simulate 3D inconsistency
if random.random() < self.opt.prob_grid_distortion:
for t in range(T):
images_input[t, 1:] = grid_distortion(images_input[t, 1:])
# apply camera jittering (only to input!)
if random.random() < self.opt.prob_cam_jitter:
for t in range(T):
cam_poses_input[t, 1:] = orbit_camera_jitter(cam_poses_input[t, 1:])
images_input = images_input.reshape(-1, *images_input.shape[2:])
cam_poses_input = cam_poses_input.reshape(-1, *cam_poses.shape[1:])
# masking other views
images_input = images_input.reshape(T, self.opt.num_input_views, *images_input.shape[1:])
images_input[1:, 1:] = images_input[0:1, 1:]
images_input = images_input.reshape(-1, *images_input.shape[2:])
cam_poses_input = cam_poses_input.reshape(T, self.opt.num_input_views, *cam_poses.shape[1:])
cam_poses_input[1:, 1:] = cam_poses_input[0:1, 1:]
cam_poses_input = cam_poses_input.reshape(-1, *cam_poses.shape[1:])
images_input = TF.normalize(images_input, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
# resize render ground-truth images, range still in [0, 1]
results['images_output'] = F.interpolate(images, size=(self.opt.output_size, self.opt.output_size), mode='bilinear', align_corners=False) # [V, C, output_size, output_size]
results['masks_output'] = F.interpolate(masks.unsqueeze(1), size=(self.opt.output_size, self.opt.output_size), mode='bilinear', align_corners=False) # [V, 1, output_size, output_size]
# build rays for input views
rays_embeddings = []
for i in range(self.opt.num_input_views * T):
rays_o, rays_d = get_rays(cam_poses_input[i], self.opt.input_size, self.opt.input_size, self.opt.fovy) # [h, w, 3]
rays_plucker = torch.cat([torch.cross(rays_o, rays_d, dim=-1), rays_d], dim=-1) # [h, w, 6]
rays_embeddings.append(rays_plucker)
rays_embeddings = torch.stack(rays_embeddings, dim=0).permute(0, 3, 1, 2).contiguous() # [V, 6, h, w]
final_input = torch.cat([images_input, rays_embeddings], dim=1) # [V=4, 9, H, W]
results['input'] = final_input
# opengl to colmap camera for gaussian renderer
cam_poses[:, :3, 1:3] *= -1 # invert up & forward direction
# cameras needed by gaussian rasterizer
cam_view = torch.inverse(cam_poses).transpose(1, 2) # [V, 4, 4]
cam_view_proj = cam_view @ self.proj_matrix # [V, 4, 4]
cam_pos = - cam_poses[:, :3, 3] # [V, 3]
results['cam_view'] = cam_view
results['cam_view_proj'] = cam_view_proj
results['cam_pos'] = cam_pos
return results
def __getitem__(self, idx):
while True:
try:
results = self._get_batch(idx)
break
except Exception as e:
print(f"{e}")
idx = random.randint(0, len(self.items) - 1)
return results |