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import os
import cv2
import glob
import json
import tqdm
import random
import numpy as np
from scipy.spatial.transform import Slerp, Rotation
import trimesh
import torch
from torch.utils.data import DataLoader
from .utils import get_rays, safe_normalize
def visualize_poses(poses, size=0.1):
# poses: [B, 4, 4]
axes = trimesh.creation.axis(axis_length=4)
sphere = trimesh.creation.icosphere(radius=1)
objects = [axes, sphere]
for pose in poses:
# a camera is visualized with 8 line segments.
pos = pose[:3, 3]
a = pos + size * pose[:3, 0] + size * pose[:3, 1] + size * pose[:3, 2]
b = pos - size * pose[:3, 0] + size * pose[:3, 1] + size * pose[:3, 2]
c = pos - size * pose[:3, 0] - size * pose[:3, 1] + size * pose[:3, 2]
d = pos + size * pose[:3, 0] - size * pose[:3, 1] + size * pose[:3, 2]
segs = np.array([[pos, a], [pos, b], [pos, c], [pos, d], [a, b], [b, c], [c, d], [d, a]])
segs = trimesh.load_path(segs)
objects.append(segs)
trimesh.Scene(objects).show()
def get_view_direction(thetas, phis, overhead, front):
# phis [B,]; thetas: [B,]
# front = 0 [0, front)
# side (left) = 1 [front, 180)
# back = 2 [180, 180+front)
# side (right) = 3 [180+front, 360)
# top = 4 [0, overhead]
# bottom = 5 [180-overhead, 180]
res = torch.zeros(thetas.shape[0], dtype=torch.long)
# first determine by phis
res[(phis < front)] = 0
res[(phis >= front) & (phis < np.pi)] = 1
res[(phis >= np.pi) & (phis < (np.pi + front))] = 2
res[(phis >= (np.pi + front))] = 3
# override by thetas
res[thetas <= overhead] = 4
res[thetas >= (np.pi - overhead)] = 5
return res
def rand_poses(size, device, radius_range=[1, 1.5], theta_range=[0, 100], phi_range=[0, 360], return_dirs=False, angle_overhead=30, angle_front=60, jitter=False):
''' generate random poses from an orbit camera
Args:
size: batch size of generated poses.
device: where to allocate the output.
radius: camera radius
theta_range: [min, max], should be in [0, pi]
phi_range: [min, max], should be in [0, 2 * pi]
Return:
poses: [size, 4, 4]
'''
theta_range = np.deg2rad(theta_range)
phi_range = np.deg2rad(phi_range)
angle_overhead = np.deg2rad(angle_overhead)
angle_front = np.deg2rad(angle_front)
radius = torch.rand(size, device=device) * (radius_range[1] - radius_range[0]) + radius_range[0]
thetas = torch.rand(size, device=device) * (theta_range[1] - theta_range[0]) + theta_range[0]
phis = torch.rand(size, device=device) * (phi_range[1] - phi_range[0]) + phi_range[0]
centers = torch.stack([
radius * torch.sin(thetas) * torch.sin(phis),
radius * torch.cos(thetas),
radius * torch.sin(thetas) * torch.cos(phis),
], dim=-1) # [B, 3]
targets = 0
# jitters
if jitter:
centers = centers + (torch.rand_like(centers) * 0.2 - 0.1)
targets = targets + torch.randn_like(centers) * 0.2
# lookat
forward_vector = safe_normalize(targets - centers)
up_vector = torch.FloatTensor([0, -1, 0]).to(device).unsqueeze(0).repeat(size, 1)
right_vector = safe_normalize(torch.cross(forward_vector, up_vector, dim=-1))
if jitter:
up_noise = torch.randn_like(up_vector) * 0.02
else:
up_noise = 0
up_vector = safe_normalize(torch.cross(right_vector, forward_vector, dim=-1) + up_noise)
poses = torch.eye(4, dtype=torch.float, device=device).unsqueeze(0).repeat(size, 1, 1)
poses[:, :3, :3] = torch.stack((right_vector, up_vector, forward_vector), dim=-1)
poses[:, :3, 3] = centers
if return_dirs:
dirs = get_view_direction(thetas, phis, angle_overhead, angle_front)
else:
dirs = None
return poses, dirs
def circle_poses(device, radius=1.25, theta=60, phi=0, return_dirs=False, angle_overhead=30, angle_front=60):
theta = np.deg2rad(theta)
phi = np.deg2rad(phi)
angle_overhead = np.deg2rad(angle_overhead)
angle_front = np.deg2rad(angle_front)
thetas = torch.FloatTensor([theta]).to(device)
phis = torch.FloatTensor([phi]).to(device)
centers = torch.stack([
radius * torch.sin(thetas) * torch.sin(phis),
radius * torch.cos(thetas),
radius * torch.sin(thetas) * torch.cos(phis),
], dim=-1) # [B, 3]
# lookat
forward_vector = - safe_normalize(centers)
up_vector = torch.FloatTensor([0, -1, 0]).to(device).unsqueeze(0)
right_vector = safe_normalize(torch.cross(forward_vector, up_vector, dim=-1))
up_vector = safe_normalize(torch.cross(right_vector, forward_vector, dim=-1))
poses = torch.eye(4, dtype=torch.float, device=device).unsqueeze(0)
poses[:, :3, :3] = torch.stack((right_vector, up_vector, forward_vector), dim=-1)
poses[:, :3, 3] = centers
if return_dirs:
dirs = get_view_direction(thetas, phis, angle_overhead, angle_front)
else:
dirs = None
return poses, dirs
class NeRFDataset:
def __init__(self, opt, device, type='train', H=256, W=256, size=100):
super().__init__()
self.opt = opt
self.device = device
self.type = type # train, val, test
self.H = H
self.W = W
self.radius_range = opt.radius_range
self.fovy_range = opt.fovy_range
self.size = size
self.training = self.type in ['train', 'all']
self.cx = self.H / 2
self.cy = self.W / 2
# [debug] visualize poses
# poses, dirs = rand_poses(100, self.device, return_dirs=self.opt.dir_text, radius_range=self.radius_range)
# visualize_poses(poses.detach().cpu().numpy())
def collate(self, index):
B = len(index) # always 1
if self.training:
# random pose on the fly
poses, dirs = rand_poses(B, self.device, radius_range=self.radius_range, return_dirs=self.opt.dir_text, angle_overhead=self.opt.angle_overhead, angle_front=self.opt.angle_front, jitter=self.opt.jitter_pose)
# random focal
fov = random.random() * (self.fovy_range[1] - self.fovy_range[0]) + self.fovy_range[0]
focal = self.H / (2 * np.tan(np.deg2rad(fov) / 2))
intrinsics = np.array([focal, focal, self.cx, self.cy])
else:
# circle pose
phi = (index[0] / self.size) * 360
poses, dirs = circle_poses(self.device, radius=self.radius_range[1] * 1.2, theta=60, phi=phi, return_dirs=self.opt.dir_text, angle_overhead=self.opt.angle_overhead, angle_front=self.opt.angle_front)
# fixed focal
fov = (self.fovy_range[1] + self.fovy_range[0]) / 2
focal = self.H / (2 * np.tan(np.deg2rad(fov) / 2))
intrinsics = np.array([focal, focal, self.cx, self.cy])
# sample a low-resolution but full image for CLIP
rays = get_rays(poses, intrinsics, self.H, self.W, -1)
data = {
'H': self.H,
'W': self.W,
'rays_o': rays['rays_o'],
'rays_d': rays['rays_d'],
'dir': dirs,
}
return data
def dataloader(self):
loader = DataLoader(list(range(self.size)), batch_size=1, collate_fn=self.collate, shuffle=self.training, num_workers=0)
loader._data = self # an ugly fix... we need to access dataset in trainer.
return loader |