Upload ./convert_gt.py with huggingface_hub
Browse files- convert_gt.py +445 -0
convert_gt.py
ADDED
@@ -0,0 +1,445 @@
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1 |
+
from pathlib import Path
|
2 |
+
import os
|
3 |
+
from utils import load_json, write_json, dir_of_this_file, load_csv
|
4 |
+
import torch
|
5 |
+
# import numpy as np
|
6 |
+
from tqdm import tqdm
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7 |
+
|
8 |
+
|
9 |
+
sn_2_imgdir = {
|
10 |
+
e[0]: Path("/your_path/colmap_results/data/") / e[1]
|
11 |
+
for e in load_csv(dir_of_this_file(__file__) / "seed_db.csv")
|
12 |
+
}
|
13 |
+
|
14 |
+
|
15 |
+
SAVE_ROOT = dir_of_this_file(__file__) / "gt_cams"
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16 |
+
|
17 |
+
|
18 |
+
def write_cams(sn, all_cams):
|
19 |
+
output_fn = SAVE_ROOT / f"{sn}.json"
|
20 |
+
write_json(output_fn, all_cams)
|
21 |
+
print(sn, end=',')
|
22 |
+
print(output_fn)
|
23 |
+
|
24 |
+
|
25 |
+
def list_scene_fnames(sn):
|
26 |
+
return list(sorted(os.listdir(sn_2_imgdir[sn])))
|
27 |
+
|
28 |
+
|
29 |
+
def break_scenes(raw):
|
30 |
+
raw = raw.strip().split('\n')
|
31 |
+
return [e.strip() for e in raw]
|
32 |
+
|
33 |
+
|
34 |
+
def strip_sn_prefix(sn_name):
|
35 |
+
parts = sn_name.split("_")[1:]
|
36 |
+
return "_".join(parts)
|
37 |
+
|
38 |
+
|
39 |
+
def invert_trans(trans_T):
|
40 |
+
assert trans_T.shape == (4, 4)
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41 |
+
R = trans_T[0:3, 0:3]
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42 |
+
t = trans_T[0:3, 3:4]
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43 |
+
new_T = torch.eye(4, dtype=trans_T.dtype, device=trans_T.device)
|
44 |
+
new_T[0:3, 0:3] = R.T
|
45 |
+
new_T[0:3, 3:4] = -R.T @ t
|
46 |
+
return new_T
|
47 |
+
|
48 |
+
|
49 |
+
def hike():
|
50 |
+
''' # these are problematic scenes
|
51 |
+
hike_garden2: cams without their images!
|
52 |
+
'''
|
53 |
+
|
54 |
+
scenes = '''
|
55 |
+
hike_forest1
|
56 |
+
hike_forest2
|
57 |
+
hike_forest3
|
58 |
+
hike_garden3
|
59 |
+
hike_indoor
|
60 |
+
hike_playground
|
61 |
+
hike_university1
|
62 |
+
hike_university2
|
63 |
+
hike_university3
|
64 |
+
hike_university4
|
65 |
+
'''
|
66 |
+
scenes = break_scenes(scenes)
|
67 |
+
root = Path("/your_path/colmap_results/data/statichike")
|
68 |
+
|
69 |
+
# for sn in scenes:
|
70 |
+
# gt_path = f"/your_path/colmap_results/data/statichike/{strip_sn_prefix(sn)}/sparse"
|
71 |
+
# gt_path = Path(gt_path)
|
72 |
+
# assert not (gt_path / "1").is_dir()
|
73 |
+
# print(sn, end=',')
|
74 |
+
# print(str(gt_path / "0"))
|
75 |
+
# return
|
76 |
+
|
77 |
+
for sn in scenes:
|
78 |
+
img_fnames = list_scene_fnames(sn)
|
79 |
+
|
80 |
+
raw = load_json(
|
81 |
+
root / strip_sn_prefix(sn) / "transforms.json"
|
82 |
+
)
|
83 |
+
frames = list(sorted(raw['frames'], key=lambda x: x['file_path']))
|
84 |
+
|
85 |
+
cam_dir = root / strip_sn_prefix(sn) / "sparse"
|
86 |
+
assert not (cam_dir / "1").is_dir()
|
87 |
+
|
88 |
+
fr_fnames = [Path(fr['file_path']).name for fr in frames]
|
89 |
+
|
90 |
+
c2ws_b = torch.tensor(
|
91 |
+
[fr['transform_matrix'] for fr in frames],
|
92 |
+
dtype=torch.float64, device="cuda"
|
93 |
+
)
|
94 |
+
# from opengl to opencv
|
95 |
+
c2ws_b[:, :, 1] *= -1
|
96 |
+
c2ws_b[:, :, 2] *= -1
|
97 |
+
|
98 |
+
try:
|
99 |
+
from metrics import load_colmap_db_cams, pose_stats_suite
|
100 |
+
# from read_colmap_model import read_colmap_w2c
|
101 |
+
# names, intrs, Rs, ts = read_colmap_w2c(cam_dir / "0")
|
102 |
+
names, _, c2ws_a = load_colmap_db_cams(cam_dir / "0", ".bin", return_all=True)
|
103 |
+
assert fr_fnames == names
|
104 |
+
res = pose_stats_suite(c2ws_a, c2ws_b)
|
105 |
+
assert res['ate'] < 1e-5
|
106 |
+
assert res['auc_p'][0] > 99.99
|
107 |
+
del names, c2ws_a, res
|
108 |
+
'''
|
109 |
+
the c2w in frames are globally shifted for some reason.
|
110 |
+
check that after alignment, error is small.
|
111 |
+
'''
|
112 |
+
except FileNotFoundError as e:
|
113 |
+
print(e)
|
114 |
+
|
115 |
+
# some imgs are discarded in gt cams
|
116 |
+
assert set(fr_fnames).issubset(set(img_fnames))
|
117 |
+
# if len(fr_fnames) != len(img_fnames):
|
118 |
+
# print(f"{sn} img {len(img_fnames)} vs cam {len(fr_fnames)}")
|
119 |
+
|
120 |
+
c2ws_b = c2ws_b.cpu().float().tolist()
|
121 |
+
all_cams = []
|
122 |
+
for i in range(len(frames)):
|
123 |
+
all_cams.append({
|
124 |
+
'fname': fr_fnames[i],
|
125 |
+
'c2w': c2ws_b[i]
|
126 |
+
})
|
127 |
+
|
128 |
+
write_cams(sn, all_cams)
|
129 |
+
|
130 |
+
|
131 |
+
def process_meganerf_cam(cam):
|
132 |
+
c2w = cam['c2w'] # [3, 4] opengl: x-right, y-up, z-back
|
133 |
+
x, y, z, t = torch.unbind(c2w, dim=1)
|
134 |
+
c2w = torch.stack([x, -y, -z, t], dim=-1) # opengl -> opencv
|
135 |
+
full_c2w = torch.eye(4)
|
136 |
+
full_c2w[0:3] = c2w
|
137 |
+
return full_c2w
|
138 |
+
|
139 |
+
|
140 |
+
def mill19():
|
141 |
+
scenes = """
|
142 |
+
mill19_building
|
143 |
+
mill19_rubble
|
144 |
+
"""
|
145 |
+
scenes = break_scenes(scenes)
|
146 |
+
|
147 |
+
for sn in scenes:
|
148 |
+
img_fnames = list_scene_fnames(sn)
|
149 |
+
cam_dir = Path(f"/your_path/colmap_results/data/mill19/{strip_sn_prefix(sn)}-pixsfm/train/metadata")
|
150 |
+
all_cams = []
|
151 |
+
for im in tqdm(img_fnames):
|
152 |
+
cam_file = cam_dir / Path(im).with_suffix(".pt")
|
153 |
+
assert cam_file.is_file()
|
154 |
+
cam = torch.load(cam_file, weights_only=True)
|
155 |
+
c2w = process_meganerf_cam(cam)
|
156 |
+
all_cams.append({
|
157 |
+
'fname': im,
|
158 |
+
'c2w': c2w.tolist()
|
159 |
+
})
|
160 |
+
|
161 |
+
write_cams(sn, all_cams)
|
162 |
+
|
163 |
+
|
164 |
+
def urban_scene():
|
165 |
+
from string import Template
|
166 |
+
|
167 |
+
scenes = '''
|
168 |
+
urbn_Campus
|
169 |
+
urbn_Residence
|
170 |
+
urbn_Sci-Art
|
171 |
+
'''
|
172 |
+
scenes = break_scenes(scenes)
|
173 |
+
for sn in scenes:
|
174 |
+
_sn = strip_sn_prefix(sn).lower()
|
175 |
+
lns = load_csv(
|
176 |
+
f"/your_path/colmap_results/data/urbanscene3d_meganerf/{_sn}-pixsfm/mappings.txt"
|
177 |
+
)
|
178 |
+
cam_dir_template = Template(
|
179 |
+
"/your_path/colmap_results/data/urbanscene3d_meganerf/${sn}-pixsfm/${split}/metadata"
|
180 |
+
)
|
181 |
+
|
182 |
+
im_2_camfn = {e[0]: e[1] for e in lns}
|
183 |
+
all_cams = []
|
184 |
+
keys = list(sorted(im_2_camfn.keys()))
|
185 |
+
for k in tqdm(keys):
|
186 |
+
# default assumes it's under train/
|
187 |
+
camfn = Path(cam_dir_template.substitute(sn=_sn, split="train")) / im_2_camfn[k]
|
188 |
+
if not camfn.is_file():
|
189 |
+
camfn = Path(cam_dir_template.substitute(sn=_sn, split="val")) / im_2_camfn[k]
|
190 |
+
assert camfn.is_file()
|
191 |
+
|
192 |
+
cam = torch.load(camfn, weights_only=True)
|
193 |
+
c2w = process_meganerf_cam(cam)
|
194 |
+
all_cams.append({
|
195 |
+
'fname': k,
|
196 |
+
'c2w': c2w.tolist()
|
197 |
+
})
|
198 |
+
|
199 |
+
write_cams(sn, all_cams)
|
200 |
+
|
201 |
+
|
202 |
+
def nerf_osr():
|
203 |
+
scenes = """
|
204 |
+
nosr_europa
|
205 |
+
nosr_lk2
|
206 |
+
nosr_lwp
|
207 |
+
nosr_rathaus
|
208 |
+
nosr_schloss
|
209 |
+
nosr_st
|
210 |
+
nosr_stjacob
|
211 |
+
nosr_stjohann
|
212 |
+
"""
|
213 |
+
scenes = break_scenes(scenes)
|
214 |
+
|
215 |
+
for sn in scenes:
|
216 |
+
img_fnames = list_scene_fnames(sn)
|
217 |
+
raw = load_json(
|
218 |
+
f"/your_path/colmap_results/data/nerfosr_original/{strip_sn_prefix(sn)}/final/kai_cameras.json"
|
219 |
+
)
|
220 |
+
all_cams = []
|
221 |
+
for im in img_fnames:
|
222 |
+
cam = raw[im]
|
223 |
+
w2c = torch.tensor(cam['W2C'], dtype=torch.float64).reshape(4, 4)
|
224 |
+
c2w = invert_trans(w2c)
|
225 |
+
all_cams.append({
|
226 |
+
'fname': im,
|
227 |
+
'c2w': c2w.tolist()
|
228 |
+
})
|
229 |
+
|
230 |
+
write_cams(sn, all_cams)
|
231 |
+
|
232 |
+
|
233 |
+
def drone_deploy():
|
234 |
+
# ruin1 has missing images. ignore that scene
|
235 |
+
scenes = """
|
236 |
+
dploy_house1
|
237 |
+
dploy_house2
|
238 |
+
dploy_house3
|
239 |
+
dploy_house4
|
240 |
+
dploy_pipes1
|
241 |
+
dploy_ruins1
|
242 |
+
dploy_ruins2
|
243 |
+
dploy_ruins3
|
244 |
+
dploy_tower1
|
245 |
+
dploy_tower2
|
246 |
+
"""
|
247 |
+
scenes = break_scenes(scenes)
|
248 |
+
for sn in scenes:
|
249 |
+
img_fnames = list_scene_fnames(sn)
|
250 |
+
raw = load_json(
|
251 |
+
f"/your_path/colmap_results/data/dronedeploy/{strip_sn_prefix(sn)}/cameras.json"
|
252 |
+
)
|
253 |
+
# keys: 'frames', 'fl_x', 'fl_y', 'k1', 'k2', 'p1', 'p2', 'k3', 'k4', 'k5', 'k6', 'cx', 'cy', 'w', 'h',
|
254 |
+
# 'camera_angle_x', 'camera_angle_y', 'aabb_scale'
|
255 |
+
frames = raw['frames']
|
256 |
+
frames = list(sorted(frames, key=lambda x: x['file_path']))
|
257 |
+
|
258 |
+
# print(f"{sn}, {len(img_fnames)} vs {len(frames)}")
|
259 |
+
_fnames = [
|
260 |
+
Path(e['file_path']).name
|
261 |
+
for e in frames
|
262 |
+
]
|
263 |
+
|
264 |
+
has_missing_img = False
|
265 |
+
for e in _fnames:
|
266 |
+
if e not in img_fnames:
|
267 |
+
has_missing_img = True
|
268 |
+
# print(f"warn! img for {e} missing")
|
269 |
+
|
270 |
+
if has_missing_img:
|
271 |
+
# ruin1 has missing images. ignore that scene
|
272 |
+
continue
|
273 |
+
|
274 |
+
# some imgs don't have gt cam
|
275 |
+
# assert img_fnames == _fnames
|
276 |
+
|
277 |
+
all_cams = []
|
278 |
+
for fr in frames:
|
279 |
+
c2w = torch.tensor(fr['transform_matrix'])
|
280 |
+
x, y, z, t = torch.unbind(c2w, dim=1)
|
281 |
+
c2w = torch.stack([x, -y, -z, t], dim=-1) # opengl -> opencv
|
282 |
+
all_cams.append({
|
283 |
+
'fname': Path(fr['file_path']).name,
|
284 |
+
'c2w': c2w.tolist()
|
285 |
+
})
|
286 |
+
|
287 |
+
write_cams(sn, all_cams)
|
288 |
+
|
289 |
+
|
290 |
+
def mipnerf360():
|
291 |
+
scenes = """
|
292 |
+
m360_flowers
|
293 |
+
m360_room
|
294 |
+
m360_counter
|
295 |
+
m360_stump
|
296 |
+
m360_kitchen
|
297 |
+
m360_garden
|
298 |
+
m360_bicycle
|
299 |
+
m360_bonsai
|
300 |
+
m360_treehill
|
301 |
+
"""
|
302 |
+
scenes = break_scenes(scenes)
|
303 |
+
for sn in scenes:
|
304 |
+
path = f"/your_path/nerfbln_dset/mipnerf360/{strip_sn_prefix(sn)}/sparse/0"
|
305 |
+
print(sn, end=',')
|
306 |
+
print(path)
|
307 |
+
|
308 |
+
|
309 |
+
def eyeful():
|
310 |
+
scenes = """
|
311 |
+
eft_apartment
|
312 |
+
eft_kitchen
|
313 |
+
"""
|
314 |
+
|
315 |
+
# def make_filter_f(sensor_prefix):
|
316 |
+
# return lambda fr: fr['cameraId'].split('/')[0] != sensor_prefix
|
317 |
+
|
318 |
+
scenes = break_scenes(scenes)
|
319 |
+
for sn in scenes:
|
320 |
+
frames = load_json(
|
321 |
+
Path(f"/your_path/colmap_results/data/eyefultower/{strip_sn_prefix(sn)}/cameras.json")
|
322 |
+
)['KRT']
|
323 |
+
frames = sorted(frames, key=lambda x: x['cameraId'])
|
324 |
+
|
325 |
+
# # filter low overlap cameras
|
326 |
+
# prefix_to_discard = {
|
327 |
+
# 'eft_apartment': '31',
|
328 |
+
# 'eft_kitchen': '28'
|
329 |
+
# }[sn]
|
330 |
+
# n_before = len(frames)
|
331 |
+
# frames = list(filter(make_filter_f(prefix_to_discard), frames))
|
332 |
+
# n_after = len(frames)
|
333 |
+
# print(f"{n_before} vs {n_after}")
|
334 |
+
|
335 |
+
all_cams = []
|
336 |
+
for fr in tqdm(frames):
|
337 |
+
w2c = torch.tensor(fr['T']).T # note the transpose. col_major -> row major
|
338 |
+
c2w = invert_trans(w2c)
|
339 |
+
all_cams.append({
|
340 |
+
'fname': f"{fr['cameraId']}.jpg",
|
341 |
+
'c2w': c2w.tolist()
|
342 |
+
})
|
343 |
+
|
344 |
+
write_cams(sn, all_cams)
|
345 |
+
|
346 |
+
# I renamed the gt jsons that discarded low overlap cams as
|
347 |
+
# eft_apartment_remove_31.json
|
348 |
+
# eft_kitchen_remove_28.json
|
349 |
+
# they are created on 25.03.10 14:54
|
350 |
+
# the other gt files are made from 25.02.23 - 23.02.26
|
351 |
+
|
352 |
+
|
353 |
+
def tnt():
|
354 |
+
scenes = '''
|
355 |
+
tnt_advn_Auditorium
|
356 |
+
tnt_advn_Ballroom
|
357 |
+
tnt_advn_Courtroom
|
358 |
+
tnt_advn_Museum
|
359 |
+
tnt_advn_Palace
|
360 |
+
tnt_advn_Temple
|
361 |
+
tnt_intrmdt_Family
|
362 |
+
tnt_intrmdt_Francis
|
363 |
+
tnt_intrmdt_Horse
|
364 |
+
tnt_intrmdt_Lighthouse
|
365 |
+
tnt_intrmdt_M60
|
366 |
+
tnt_intrmdt_Panther
|
367 |
+
tnt_intrmdt_Playground
|
368 |
+
tnt_intrmdt_Train
|
369 |
+
tnt_trng_Barn
|
370 |
+
tnt_trng_Caterpillar
|
371 |
+
tnt_trng_Church
|
372 |
+
tnt_trng_Courthouse
|
373 |
+
tnt_trng_Ignatius
|
374 |
+
tnt_trng_Meetingroom
|
375 |
+
tnt_trng_Truck
|
376 |
+
'''
|
377 |
+
scenes = break_scenes(scenes)
|
378 |
+
for sn in scenes:
|
379 |
+
_sn = sn.split('_')[-1].lower()
|
380 |
+
gt_cam_path = f"/your_path/nerfbln_dset/tnt/{_sn}/sparse" # no 0/
|
381 |
+
print(sn, end=',')
|
382 |
+
print(gt_cam_path)
|
383 |
+
|
384 |
+
|
385 |
+
def eth3d_dslr():
|
386 |
+
scenes = '''
|
387 |
+
eth3d_dslr_botanical_garden
|
388 |
+
eth3d_dslr_boulders
|
389 |
+
eth3d_dslr_bridge
|
390 |
+
eth3d_dslr_courtyard
|
391 |
+
eth3d_dslr_delivery_area
|
392 |
+
eth3d_dslr_door
|
393 |
+
eth3d_dslr_electro
|
394 |
+
eth3d_dslr_exhibition_hall
|
395 |
+
eth3d_dslr_facade
|
396 |
+
eth3d_dslr_kicker
|
397 |
+
eth3d_dslr_lecture_room
|
398 |
+
eth3d_dslr_living_room
|
399 |
+
eth3d_dslr_lounge
|
400 |
+
eth3d_dslr_meadow
|
401 |
+
eth3d_dslr_observatory
|
402 |
+
eth3d_dslr_office
|
403 |
+
eth3d_dslr_old_computer
|
404 |
+
eth3d_dslr_pipes
|
405 |
+
eth3d_dslr_playground
|
406 |
+
eth3d_dslr_relief
|
407 |
+
eth3d_dslr_relief_2
|
408 |
+
eth3d_dslr_statue
|
409 |
+
eth3d_dslr_terrace
|
410 |
+
eth3d_dslr_terrace_2
|
411 |
+
eth3d_dslr_terrains
|
412 |
+
'''
|
413 |
+
scenes = break_scenes(scenes)
|
414 |
+
|
415 |
+
# # used to edit db_mapping.csv
|
416 |
+
# for sn in scenes:
|
417 |
+
# db_path = f"/your_path/sfm_workspace/runs_db/d_{sn}/database.db"
|
418 |
+
# assert Path(db_path).is_file()
|
419 |
+
# print(sn, end=',')
|
420 |
+
# print(db_path)
|
421 |
+
# return
|
422 |
+
|
423 |
+
for sn in scenes:
|
424 |
+
_sn = sn[len('eth3d_dslr_'):]
|
425 |
+
gt_cam_path = f"/your_path/colmap_results/data/eth3d_dslr/{_sn}/dslr_calibration_undistorted"
|
426 |
+
assert Path(gt_cam_path).is_dir()
|
427 |
+
print(sn, end=',')
|
428 |
+
print(gt_cam_path)
|
429 |
+
|
430 |
+
|
431 |
+
def main():
|
432 |
+
# hike()
|
433 |
+
# mill19()
|
434 |
+
# nerf_osr()
|
435 |
+
# mipnerf360()
|
436 |
+
# eyeful()
|
437 |
+
# tnt()
|
438 |
+
# urban_scene()
|
439 |
+
# drone_deploy()
|
440 |
+
# eth3d_dslr()
|
441 |
+
pass
|
442 |
+
|
443 |
+
|
444 |
+
if __name__ == "__main__":
|
445 |
+
main()
|