Spaces:
Sleeping
Sleeping
File size: 7,301 Bytes
ddefedb |
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 |
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
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
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):
self.opt = opt
self.training = training
# TODO: remove this barrier
self._warn()
# TODO: load the list of objects for training
self.items = []
with open('TODO: file containing the list', 'r') as f:
for line in f.readlines():
self.items.append(line.strip())
# naive split
if self.training:
self.items = self.items[:-self.opt.batch_size]
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 __getitem__(self, idx):
uid = self.items[idx]
results = {}
# load num_views images
images = []
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
vids = np.random.permutation(np.arange(36, 73))[:self.opt.num_input_views].tolist() + np.random.permutation(100).tolist()
else:
# fixed views
vids = np.arange(36, 73, 4).tolist() + np.arange(100).tolist()
for vid in vids:
image_path = os.path.join(uid, 'rgb', f'{vid:03d}.png')
camera_path = os.path.join(uid, 'pose', f'{vid:03d}.txt')
try:
# TODO: load data (modify self.client here)
image = np.frombuffer(self.client.get(image_path), np.uint8)
image = torch.from_numpy(cv2.imdecode(image, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255) # [512, 512, 4] in [0, 1]
c2w = [float(t) for t in self.client.get(camera_path).decode().strip().split(' ')]
c2w = torch.tensor(c2w, dtype=torch.float32).reshape(4, 4)
except Exception as e:
# print(f'[WARN] dataset {uid} {vid}: {e}')
continue
# TODO: you may have a different camera system
# blender world + opencv cam --> opengl world & cam
c2w[1] *= -1
c2w[[1, 2]] = c2w[[2, 1]]
c2w[:3, 1:3] *= -1 # invert up and forward direction
# scale up radius to fully use the [-1, 1]^3 space!
c2w[:3, 3] *= self.opt.cam_radius / 1.5 # 1.5 is the default scale
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
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[:self.opt.num_input_views].clone(), size=(self.opt.input_size, self.opt.input_size), mode='bilinear', align_corners=False) # [V, C, H, W]
cam_poses_input = cam_poses[:self.opt.num_input_views].clone()
# data augmentation
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)
# 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):
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 |