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hyper2nerf.py
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hyper2nerf.py
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import os
import numpy as np
import math
import json
import argparse
import trimesh
def visualize_poses(poses, size=0.1):
# poses: [B, 4, 4]
axes = trimesh.creation.axis(axis_length=4)
box = trimesh.primitives.Box(extents=(2, 2, 2)).as_outline()
box.colors = np.array([[128, 128, 128]] * len(box.entities))
objects = [axes, box]
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]
dir = (a + b + c + d) / 4 - pos
dir = dir / (np.linalg.norm(dir) + 1e-8)
o = pos + dir * 3
segs = np.array([[pos, a], [pos, b], [pos, c], [pos, d], [a, b], [b, c], [c, d], [d, a], [pos, o]])
segs = trimesh.load_path(segs)
objects.append(segs)
trimesh.Scene(objects).show()
# returns point closest to both rays of form o+t*d, and a weight factor that goes to 0 if the lines are parallel
def closest_point_2_lines(oa, da, ob, db):
da = da / np.linalg.norm(da)
db = db / np.linalg.norm(db)
c = np.cross(da, db)
denom = np.linalg.norm(c)**2
t = ob - oa
ta = np.linalg.det([t, db, c]) / (denom + 1e-10)
tb = np.linalg.det([t, da, c]) / (denom + 1e-10)
if ta > 0:
ta = 0
if tb > 0:
tb = 0
return (oa+ta*da+ob+tb*db) * 0.5, denom
def rotmat(a, b):
a, b = a / np.linalg.norm(a), b / np.linalg.norm(b)
v = np.cross(a, b)
c = np.dot(a, b)
# handle exception for the opposite direction input
if c < -1 + 1e-10:
return rotmat(a + np.random.uniform(-1e-2, 1e-2, 3), b)
s = np.linalg.norm(v)
kmat = np.array([[0, -v[2], v[1]], [v[2], 0, -v[0]], [-v[1], v[0], 0]])
return np.eye(3) + kmat + kmat.dot(kmat) * ((1 - c) / (s ** 2 + 1e-10))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('path', type=str, help="root directory to the HyperNeRF dataset (contains camera/, rgb/, dataset.json, scene.json)")
parser.add_argument('--downscale', type=int, default=2, help="image size down scale, choose from [2, 4, 8, 16], e.g., 8")
parser.add_argument('--interval', type=int, default=4, help="used for interp dataset's train/val split, should > 2 and be even")
opt = parser.parse_args()
print(f'[INFO] process {opt.path}')
# load data
with open(os.path.join(opt.path, 'dataset.json'), 'r') as f:
json_dataset = json.load(f)
names = json_dataset['ids']
val_names = json_dataset['val_ids']
# data split mode following hypernerf (vrig / interp)
if len(val_names) > 0:
train_names = json_dataset['train_ids']
val_ids = []
train_ids = []
for i, name in enumerate(names):
if name in val_names:
val_ids.append(i)
elif name in train_names:
train_ids.append(i)
else:
all_ids = np.arange(len(names))
train_ids = all_ids[::opt.interval]
val_ids = (train_ids[:-1] + train_ids[1:]) // 2
print(f'[INFO] train_ids: {len(train_ids)}, val_ids: {len(val_ids)}')
with open(os.path.join(opt.path, 'scene.json'), 'r') as f:
json_scene = json.load(f)
scale = json_scene['scale']
center = json_scene['center']
with open(os.path.join(opt.path, 'metadata.json'), 'r') as f:
json_meta = json.load(f)
images = []
times = []
poses = []
H, W, f, cx, cy = None, None, None, None, None
for name in names:
# load image
images.append(os.path.join('rgb', f'{opt.downscale}x', f'{name}.png'))
# load time
times.append(json_meta[name]['time_id'])
# load pose
with open(os.path.join(opt.path, 'camera', f'{name}.json'), 'r') as f:
cam = json.load(f)
# TODO: we use a simplified pinhole camera model rather than the original openCV camera model... hope it won't influence results seriously...
pose = np.eye(4, 4)
pose[:3, :3] = np.array(cam['orientation']).T # it works...
#pose[:3, 3] = (np.array(cam['position']) - center) * scale * 4
pose[:3, 3] = np.array(cam['position'])
# CHECK: simply assume all intrinsic are same ?
W, H = cam['image_size'] # before scale
cx, cy = cam['principal_point']
fl = cam['focal_length']
poses.append(pose)
poses = np.stack(poses, axis=0) # [N, 4, 4]
times = np.asarray(times, dtype=np.float32) # [N]
times = times / times.max() # normalize to [0, 1]
N = len(images)
W = W // opt.downscale
H = H // opt.downscale
cx = cx / opt.downscale
cy = cy / opt.downscale
fl = fl / opt.downscale
print(f'[INFO] H = {H}, W = {W}, fl = {fl} (downscale = {opt.downscale})')
# visualize_poses(poses)
# the following stuff are from colmap2nerf...
poses[:, 0:3, 1] *= -1
poses[:, 0:3, 2] *= -1
poses = poses[:, [1, 0, 2, 3], :] # swap y and z
poses[:, 2, :] *= -1 # flip whole world upside down
up = poses[:, 0:3, 1].sum(0)
up = up / np.linalg.norm(up)
R = rotmat(up, [0, 0, 1]) # rotate up vector to [0,0,1]
R = np.pad(R, [0, 1])
R[-1, -1] = 1
poses = R @ poses
totw = 0.0
totp = np.array([0.0, 0.0, 0.0])
for i in range(N):
mf = poses[i, :3, :]
for j in range(i + 1, N):
mg = poses[j, :3, :]
p, w = closest_point_2_lines(mf[:,3], mf[:,2], mg[:,3], mg[:,2])
#print(i, j, p, w)
if w > 0.01:
totp += p * w
totw += w
totp /= totw
print(f'[INFO] totp = {totp}')
poses[:, :3, 3] -= totp
avglen = np.linalg.norm(poses[:, :3, 3], axis=-1).mean()
poses[:, :3, 3] *= 4.0 / avglen
print(f'[INFO] average radius = {avglen}')
# visualize_poses(poses)
# construct frames
frames_train = []
for i in train_ids:
frames_train.append({
'file_path': images[i],
'time': float(times[i]),
'transform_matrix': poses[i].tolist(),
})
frames_val = []
for i in val_ids:
frames_val.append({
'file_path': images[i],
'time': float(times[i]),
'transform_matrix': poses[i].tolist(),
})
def write_json(filename, frames):
# construct a transforms.json
out = {
'w': W,
'h': H,
'fl_x': fl,
'fl_y': fl,
'cx': cx,
'cy': cy,
'frames': frames,
}
# write
output_path = os.path.join(opt.path, filename)
print(f'[INFO] write {len(frames)} images to {output_path}')
with open(output_path, 'w') as f:
json.dump(out, f, indent=2)
write_json('transforms_train.json', frames_train)
write_json('transforms_val.json', frames_val[::10])
write_json('transforms_test.json', frames_val)