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neural_3D_dataset_NDC.py
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neural_3D_dataset_NDC.py
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import concurrent.futures
import gc
import glob
import os
import cv2
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms as T
from .ray_utils import get_ray_directions_blender, get_rays, ndc_rays_blender
def normalize(v):
"""Normalize a vector."""
return v / np.linalg.norm(v)
def average_poses(poses):
"""
Calculate the average pose, which is then used to center all poses
using @center_poses. Its computation is as follows:
1. Compute the center: the average of pose centers.
2. Compute the z axis: the normalized average z axis.
3. Compute axis y': the average y axis.
4. Compute x' = y' cross product z, then normalize it as the x axis.
5. Compute the y axis: z cross product x.
Note that at step 3, we cannot directly use y' as y axis since it's
not necessarily orthogonal to z axis. We need to pass from x to y.
Inputs:
poses: (N_images, 3, 4)
Outputs:
pose_avg: (3, 4) the average pose
"""
# 1. Compute the center
center = poses[..., 3].mean(0) # (3)
# 2. Compute the z axis
z = normalize(poses[..., 2].mean(0)) # (3)
# 3. Compute axis y' (no need to normalize as it's not the final output)
y_ = poses[..., 1].mean(0) # (3)
# 4. Compute the x axis
x = normalize(np.cross(z, y_)) # (3)
# 5. Compute the y axis (as z and x are normalized, y is already of norm 1)
y = np.cross(x, z) # (3)
pose_avg = np.stack([x, y, z, center], 1) # (3, 4)
return pose_avg
def center_poses(poses, blender2opencv):
"""
Center the poses so that we can use NDC.
See https://github.com/bmild/nerf/issues/34
Inputs:
poses: (N_images, 3, 4)
Outputs:
poses_centered: (N_images, 3, 4) the centered poses
pose_avg: (3, 4) the average pose
"""
poses = poses @ blender2opencv
pose_avg = average_poses(poses) # (3, 4)
pose_avg_homo = np.eye(4)
pose_avg_homo[
:3
] = pose_avg # convert to homogeneous coordinate for faster computation
pose_avg_homo = pose_avg_homo
# by simply adding 0, 0, 0, 1 as the last row
last_row = np.tile(np.array([0, 0, 0, 1]), (len(poses), 1, 1)) # (N_images, 1, 4)
poses_homo = np.concatenate(
[poses, last_row], 1
) # (N_images, 4, 4) homogeneous coordinate
poses_centered = np.linalg.inv(pose_avg_homo) @ poses_homo # (N_images, 4, 4)
# poses_centered = poses_centered @ blender2opencv
poses_centered = poses_centered[:, :3] # (N_images, 3, 4)
return poses_centered, pose_avg_homo
def viewmatrix(z, up, pos):
vec2 = normalize(z)
vec1_avg = up
vec0 = normalize(np.cross(vec1_avg, vec2))
vec1 = normalize(np.cross(vec2, vec0))
m = np.eye(4)
m[:3] = np.stack([-vec0, vec1, vec2, pos], 1)
return m
def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, N_rots=2, N=120):
render_poses = []
rads = np.array(list(rads) + [1.0])
for theta in np.linspace(0.0, 2.0 * np.pi * N_rots, N + 1)[:-1]:
c = np.dot(
c2w[:3, :4],
np.array([np.cos(theta), -np.sin(theta), -np.sin(theta * zrate), 1.0])
* rads,
)
z = normalize(c - np.dot(c2w[:3, :4], np.array([0, 0, -focal, 1.0])))
render_poses.append(viewmatrix(z, up, c))
return render_poses
def process_video(video_data_save, video_path, img_wh, downsample, transform):
"""
Load video_path data to video_data_save tensor.
"""
video_frames = cv2.VideoCapture(video_path)
count = 0
while video_frames.isOpened():
ret, video_frame = video_frames.read()
if ret:
video_frame = cv2.cvtColor(video_frame, cv2.COLOR_BGR2RGB)
video_frame = Image.fromarray(video_frame)
if downsample != 1.0:
img = video_frame.resize(img_wh, Image.LANCZOS)
img = transform(img)
video_data_save[count] = img.view(3, -1).permute(1, 0)
count += 1
else:
break
video_frames.release()
print(f"Video {video_path} processed.")
return None
# define a function to process all videos
def process_videos(videos, skip_index, img_wh, downsample, transform, num_workers=1):
"""
A multi-threaded function to load all videos fastly and memory-efficiently.
To save memory, we pre-allocate a tensor to store all the images and spawn multi-threads to load the images into this tensor.
"""
all_imgs = torch.zeros(len(videos) - 1, 300, img_wh[-1] * img_wh[-2], 3)
with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
# start a thread for each video
current_index = 0
futures = []
for index, video_path in enumerate(videos):
# skip the video with skip_index (eval video)
if index == skip_index:
continue
else:
future = executor.submit(
process_video,
all_imgs[current_index],
video_path,
img_wh,
downsample,
transform,
)
futures.append(future)
current_index += 1
return all_imgs
def get_spiral(c2ws_all, near_fars, rads_scale=1.0, N_views=120):
"""
Generate a set of poses using NeRF's spiral camera trajectory as validation poses.
"""
# center pose
c2w = average_poses(c2ws_all)
# Get average pose
up = normalize(c2ws_all[:, :3, 1].sum(0))
# Find a reasonable "focus depth" for this dataset
dt = 0.75
close_depth, inf_depth = near_fars.min() * 0.9, near_fars.max() * 5.0
focal = 1.0 / ((1.0 - dt) / close_depth + dt / inf_depth)
# Get radii for spiral path
zdelta = near_fars.min() * 0.2
tt = c2ws_all[:, :3, 3]
rads = np.percentile(np.abs(tt), 90, 0) * rads_scale
render_poses = render_path_spiral(
c2w, up, rads, focal, zdelta, zrate=0.5, N=N_views
)
return np.stack(render_poses)
class Neural3D_NDC_Dataset(Dataset):
def __init__(
self,
datadir,
split="train",
downsample=1.0,
is_stack=True,
cal_fine_bbox=False,
N_vis=-1,
time_scale=1.0,
scene_bbox_min=[-1.0, -1.0, -1.0],
scene_bbox_max=[1.0, 1.0, 1.0],
N_random_pose=1000,
bd_factor=0.75,
eval_step=1,
eval_index=0,
sphere_scale=1.0,
):
self.img_wh = (
int(1024 / downsample),
int(768 / downsample),
) # According to the neural 3D paper, the default resolution is 1024x768
self.root_dir = datadir
self.split = split
self.downsample = 2704 / self.img_wh[0]
self.is_stack = is_stack
self.N_vis = N_vis
self.time_scale = time_scale
self.scene_bbox = torch.tensor([scene_bbox_min, scene_bbox_max])
self.world_bound_scale = 1.1
self.bd_factor = bd_factor
self.eval_step = eval_step
self.eval_index = eval_index
self.blender2opencv = np.eye(4)
self.transform = T.ToTensor()
self.near = 0.0
self.far = 1.0
self.near_far = [self.near, self.far] # NDC near far is [0, 1.0]
self.white_bg = False
self.ndc_ray = True
self.depth_data = False
self.load_meta()
print("meta data loaded")
def load_meta(self):
"""
Load meta data from the dataset.
"""
# Read poses and video file paths.
poses_arr = np.load(os.path.join(self.root_dir, "poses_bounds.npy"))
poses = poses_arr[:, :-2].reshape([-1, 3, 5]) # (N_cams, 3, 5)
self.near_fars = poses_arr[:, -2:]
videos = glob.glob(os.path.join(self.root_dir, "cam*.mp4"))
videos = sorted(videos)
assert len(videos) == poses_arr.shape[0]
H, W, focal = poses[0, :, -1]
focal = focal / self.downsample
self.focal = [focal, focal]
poses = np.concatenate([poses[..., 1:2], -poses[..., :1], poses[..., 2:4]], -1)
poses, pose_avg = center_poses(
poses, self.blender2opencv
) # Re-center poses so that the average is near the center.
near_original = self.near_fars.min()
scale_factor = near_original * 0.75
self.near_fars /= (
scale_factor # rescale nearest plane so that it is at z = 4/3.
)
poses[..., 3] /= scale_factor
# Sample N_views poses for validation - NeRF-like camera trajectory.
N_views = 120
self.val_poses = get_spiral(poses, self.near_fars, N_views=N_views)
W, H = self.img_wh
self.directions = torch.tensor(
get_ray_directions_blender(H, W, self.focal)
) # (H, W, 3)
if self.split == "train":
# Loading all videos from this dataset requires around 50GB memory, and stack them into a tensor requires another 50GB.
# To save memory, we allocate a large tensor and load videos into it instead of using torch.stack/cat operations.
all_times = []
all_rays = []
count = 300
for index in range(0, len(videos)):
if (
index == self.eval_index
): # the eval_index(0 as default) is the evaluation one. We skip evaluation cameras.
continue
video_times = torch.tensor([i / (count - 1) for i in range(count)])
all_times += [video_times]
rays_o, rays_d = get_rays(
self.directions, torch.FloatTensor(poses[index])
) # both (h*w, 3)
rays_o, rays_d = ndc_rays_blender(H, W, focal, 1.0, rays_o, rays_d)
all_rays += [torch.cat([rays_o, rays_d], 1)]
print(f"video {index} is loaded")
gc.collect()
# load all video images
all_imgs = process_videos(
videos,
self.eval_index,
self.img_wh,
self.downsample,
self.transform,
num_workers=8,
)
all_times = torch.stack(all_times, 0)
all_rays = torch.stack(all_rays, 0)
breakpoint()
print("stack performed")
N_cam, N_time, N_rays, C = all_imgs.shape
self.image_stride = N_rays
self.cam_number = N_cam
self.time_number = N_time
self.all_rgbs = all_imgs
self.all_times = all_times.view(N_cam, N_time, 1)
self.all_rays = all_rays.reshape(N_cam, N_rays, 6)
self.all_times = self.time_scale * (self.all_times * 2.0 - 1.0)
self.global_mean_rgb = torch.mean(all_imgs, dim=1)
else:
index = self.eval_index
video_imgs = []
video_frames = cv2.VideoCapture(videos[index])
while video_frames.isOpened():
ret, video_frame = video_frames.read()
if ret:
video_frame = cv2.cvtColor(video_frame, cv2.COLOR_BGR2RGB)
video_frame = Image.fromarray(video_frame)
if self.downsample != 1.0:
img = video_frame.resize(self.img_wh, Image.LANCZOS)
img = self.transform(img)
video_imgs += [img.view(3, -1).permute(1, 0)]
else:
break
video_imgs = torch.stack(video_imgs, 0)
video_times = torch.tensor(
[i / (len(video_imgs) - 1) for i in range(len(video_imgs))]
)
video_imgs = video_imgs[0 :: self.eval_step]
video_times = video_times[0 :: self.eval_step]
rays_o, rays_d = get_rays(
self.directions, torch.FloatTensor(poses[index])
) # both (h*w, 3)
rays_o, rays_d = ndc_rays_blender(H, W, focal, 1.0, rays_o, rays_d)
all_rays = torch.cat([rays_o, rays_d], 1)
gc.collect()
N_time, N_rays, C = video_imgs.shape
self.image_stride = N_rays
self.time_number = N_time
self.all_rgbs = video_imgs.view(-1, N_rays, 3)
self.all_rays = all_rays
self.all_times = video_times
self.all_rgbs = self.all_rgbs.view(
-1, *self.img_wh[::-1], 3
) # (len(self.meta['frames]),h,w,3)
self.all_times = self.time_scale * (self.all_times * 2.0 - 1.0)
def __len__(self):
if self.split == "train" and self.is_stack is True:
return self.cam_number * self.time_number
else:
return len(self.all_rgbs)
def __getitem__(self, idx):
if self.split == "train": # use data in the buffers
if self.is_stack:
cam_idx = idx // self.time_number
time_idx = idx % self.time_number
sample = {
"rays": self.all_rays[cam_idx],
"rgbs": self.all_rgbs[cam_idx, time_idx],
"time": self.all_times[cam_idx, time_idx]
* torch.ones_like(self.all_rays[cam_idx][:, 0:1]),
}
else:
sample = {
"rays": self.all_rays[
idx // (self.time_number * self.image_stride),
idx % (self.image_stride),
],
"rgbs": self.all_rgbs[idx],
"time": self.all_times[
idx // (self.time_number * self.image_stride),
idx
% (self.time_number * self.image_stride)
// self.image_stride,
]
* torch.ones_like(self.all_rgbs[idx][:, 0:1]),
}
else: # create data for each image separately
if self.is_stack:
sample = {
"rays": self.all_rays,
"rgbs": self.all_rgbs[idx],
"time": self.all_times[idx]
* torch.ones_like(self.all_rays[:, 0:1]),
}
else:
sample = {
"rays": self.all_rays[idx % self.image_stride],
"rgbs": self.all_rgbs[idx],
"time": self.all_times[idx // self.image_stride]
* torch.ones_like(self.all_rays[:, 0:1]),
}
return sample
def get_val_pose(self):
render_poses = self.val_poses
render_times = torch.linspace(0.0, 1.0, render_poses.shape[0]) * 2.0 - 1.0
return render_poses, self.time_scale * render_times
def get_val_rays(self):
val_poses, val_times = self.get_val_pose() # get valitdation poses and times
rays_all = [] # initialize list to store [rays_o, rays_d]
for i in range(val_poses.shape[0]):
c2w = torch.FloatTensor(val_poses[i])
rays_o, rays_d = get_rays(self.directions, c2w) # both (h*w, 3)
if self.ndc_ray:
W, H = self.img_wh
rays_o, rays_d = ndc_rays_blender(
H, W, self.focal[0], 1.0, rays_o, rays_d
)
rays = torch.cat([rays_o, rays_d], 1) # (h*w, 6)
rays_all.append(rays)
return rays_all, torch.FloatTensor(val_times)