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tanks2nerf.py
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tanks2nerf.py
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import os
import numpy as np
import math
import json
import argparse
# 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 Tanks&Temple dataset (contains rgb/, pose/, intrinsics.txt)")
opt = parser.parse_args()
print(f'[INFO] process {opt.path}')
# load data
intrinsics = np.loadtxt(os.path.join(opt.path, "intrinsics.txt"))
fl_x = intrinsics[0, 0]
fl_y = intrinsics[1, 1]
cx = intrinsics[0, 2]
cy = intrinsics[1, 2]
H = 1080
W = 1920
pose_files = sorted(os.listdir(os.path.join(opt.path, 'pose')))
img_files = sorted(os.listdir(os.path.join(opt.path, 'rgb')))
# read in all poses, and do transform
poses = []
for pose_f in pose_files:
pose = np.loadtxt(os.path.join(opt.path, 'pose', pose_f)) # [4, 4]
poses.append(pose)
poses = np.stack(poses, axis=0) # [N, 4, 4]
N = poses.shape[0]
# 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}')
# process three splits
for split, prefix in zip(['train', 'val', 'test'], ['0_', '1_', '2_']):
print(f'[INFO] process split = {split}')
split_poses = [poses[i] for i, x in enumerate(pose_files) if x.startswith(prefix)]
split_images = [x for x in img_files if x.startswith(prefix)]
if len(split_poses) == 0:
print(f'[INFO] No test data found, use valid as test')
split_poses = [poses[i] for i, x in enumerate(pose_files) if x.startswith('1_')]
split_images = [x for x in img_files if x.startswith('1_')]
print(f'[INFO] loaded {len(split_images)} images, {len(split_poses)} poses.')
assert len(split_poses) == len(split_images)
# construct a transforms.json
frames = []
for image, pose in zip(split_images, split_poses):
frames.append({
'file_path': os.path.join('rgb', image),
'transform_matrix': pose.tolist(),
})
transforms = {
'w': W,
'h': H,
'fl_x': fl_x,
'fl_y': fl_y,
'cx': cx,
'cy': cy,
'aabb_scale': 2,
'frames': frames,
}
# write
output_path = os.path.join(opt.path, f'transforms_{split}.json')
print(f'[INFO] write to {output_path}')
with open(output_path, 'w') as f:
json.dump(transforms, f, indent=2)