File size: 1,237 Bytes
0780cd9 |
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 |
#!/usr/bin/env python3
import numpy as np
import sys
import glob
import os
import json
import imageio
from tqdm import trange
arr = ['04-09_19_30_IMG_1192']*3
intrs = [np.loadtxt(os.path.join('./train/intrinsics', f+'.txt')) for f in arr]
poses = [np.loadtxt(os.path.join('./train/pose', f+'.txt')) for f in arr]
base = './static_path1'
os.makedirs(os.path.join(base, 'intrinsics'), exist_ok=True)
os.makedirs(os.path.join(base, 'pose'), exist_ok=True)
H, W = imageio.imread(os.path.join('./train/rgb', arr[0]+'.JPG')).shape[:2]
N = 100
obj = dict()
for i in range(N):
idxa = i*(len(intrs)-1)//N
idxb = idxa+1
blendratio = (i-idxa*N//(len(intrs)-1))/(N//(len(intrs)-1))
# intr = intrs[idxa]*(1-blendratio)+intrs[idxb]*blendratio
intr = intrs[0]
pose = poses[idxa]*(1-blendratio)+poses[idxb]*blendratio
print(i, idxa, idxb, blendratio)
fn = f'{i:06d}'
np.savetxt(os.path.join(base, 'intrinsics', fn+'.txt'), intr.reshape(1, -1))
np.savetxt(os.path.join(base, 'pose', fn+'.txt'), pose.reshape(1, -1))
obj[fn+'.png'] = {'K': list(intr.reshape(-1)), 'W2C': list(pose.reshape(-1)), 'img_size': [W, H]}
with open(os.path.join(base, 'cam_dict_norm.json'), 'w') as f:
json.dump(obj, f)
|