Spaces:
Running
Running
File size: 13,045 Bytes
9223079 4d4dd90 9223079 e15a186 9223079 e15a186 9223079 e15a186 9223079 e15a186 9223079 e15a186 9223079 8004049 e15a186 9223079 8004049 9223079 8004049 9223079 42dde81 9223079 4d4dd90 68a65da 9223079 e15a186 9223079 |
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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 |
import argparse
from typing import Union, Optional, Dict, List, Tuple
from pathlib import Path
import pprint
from queue import Queue
from threading import Thread
from functools import partial
from tqdm import tqdm
import h5py
import torch
from . import matchers, logger
from .utils.base_model import dynamic_load
from .utils.parsers import names_to_pair, names_to_pair_old, parse_retrieval
import numpy as np
"""
A set of standard configurations that can be directly selected from the command
line using their name. Each is a dictionary with the following entries:
- output: the name of the match file that will be generated.
- model: the model configuration, as passed to a feature matcher.
"""
confs = {
"superglue": {
"output": "matches-superglue",
"model": {
"name": "superglue",
"weights": "outdoor",
"sinkhorn_iterations": 50,
"match_threshold": 0.2,
},
"preprocessing": {
"grayscale": True,
"resize_max": 1024,
"dfactor": 8,
"force_resize": False,
},
},
"superglue-fast": {
"output": "matches-superglue-it5",
"model": {
"name": "superglue",
"weights": "outdoor",
"sinkhorn_iterations": 5,
"match_threshold": 0.2,
},
},
"superpoint-lightglue": {
"output": "matches-lightglue",
"model": {
"name": "lightglue",
"match_threshold": 0.2,
"width_confidence": 0.99, # for point pruning
"depth_confidence": 0.95, # for early stopping,
"features": "superpoint",
"model_name": "superpoint_lightglue.pth",
},
"preprocessing": {
"grayscale": True,
"resize_max": 1024,
"dfactor": 8,
"force_resize": False,
},
},
"disk-lightglue": {
"output": "matches-lightglue",
"model": {
"name": "lightglue",
"match_threshold": 0.2,
"width_confidence": 0.99, # for point pruning
"depth_confidence": 0.95, # for early stopping,
"features": "disk",
"model_name": "disk_lightglue.pth",
},
"preprocessing": {
"grayscale": True,
"resize_max": 1024,
"dfactor": 8,
"force_resize": False,
},
},
"sgmnet": {
"output": "matches-sgmnet",
"model": {
"name": "sgmnet",
"seed_top_k": [256, 256],
"seed_radius_coe": 0.01,
"net_channels": 128,
"layer_num": 9,
"head": 4,
"seedlayer": [0, 6],
"use_mc_seeding": True,
"use_score_encoding": False,
"conf_bar": [1.11, 0.1],
"sink_iter": [10, 100],
"detach_iter": 1000000,
"match_threshold": 0.2,
},
"preprocessing": {
"grayscale": True,
"resize_max": 1024,
"dfactor": 8,
"force_resize": False,
},
},
"NN-superpoint": {
"output": "matches-NN-mutual-dist.7",
"model": {
"name": "nearest_neighbor",
"do_mutual_check": True,
"distance_threshold": 0.7,
"match_threshold": 0.2,
},
},
"NN-ratio": {
"output": "matches-NN-mutual-ratio.8",
"model": {
"name": "nearest_neighbor",
"do_mutual_check": True,
"ratio_threshold": 0.8,
"match_threshold": 0.2,
},
},
"NN-mutual": {
"output": "matches-NN-mutual",
"model": {
"name": "nearest_neighbor",
"do_mutual_check": True,
"match_threshold": 0.2,
},
},
"Dual-Softmax": {
"output": "matches-Dual-Softmax",
"model": {
"name": "dual_softmax",
"match_threshold": 0.01,
"inv_temperature": 20,
},
},
"adalam": {
"output": "matches-adalam",
"model": {
"name": "adalam",
"match_threshold": 0.2,
},
},
}
class WorkQueue:
def __init__(self, work_fn, num_threads=1):
self.queue = Queue(num_threads)
self.threads = [
Thread(target=self.thread_fn, args=(work_fn,))
for _ in range(num_threads)
]
for thread in self.threads:
thread.start()
def join(self):
for thread in self.threads:
self.queue.put(None)
for thread in self.threads:
thread.join()
def thread_fn(self, work_fn):
item = self.queue.get()
while item is not None:
work_fn(item)
item = self.queue.get()
def put(self, data):
self.queue.put(data)
class FeaturePairsDataset(torch.utils.data.Dataset):
def __init__(self, pairs, feature_path_q, feature_path_r):
self.pairs = pairs
self.feature_path_q = feature_path_q
self.feature_path_r = feature_path_r
def __getitem__(self, idx):
name0, name1 = self.pairs[idx]
data = {}
with h5py.File(self.feature_path_q, "r") as fd:
grp = fd[name0]
for k, v in grp.items():
data[k + "0"] = torch.from_numpy(v.__array__()).float()
# some matchers might expect an image but only use its size
data["image0"] = torch.empty((1,) + tuple(grp["image_size"])[::-1])
with h5py.File(self.feature_path_r, "r") as fd:
grp = fd[name1]
for k, v in grp.items():
data[k + "1"] = torch.from_numpy(v.__array__()).float()
data["image1"] = torch.empty((1,) + tuple(grp["image_size"])[::-1])
return data
def __len__(self):
return len(self.pairs)
def writer_fn(inp, match_path):
pair, pred = inp
with h5py.File(str(match_path), "a", libver="latest") as fd:
if pair in fd:
del fd[pair]
grp = fd.create_group(pair)
matches = pred["matches0"][0].cpu().short().numpy()
grp.create_dataset("matches0", data=matches)
if "matching_scores0" in pred:
scores = pred["matching_scores0"][0].cpu().half().numpy()
grp.create_dataset("matching_scores0", data=scores)
def main(
conf: Dict,
pairs: Path,
features: Union[Path, str],
export_dir: Optional[Path] = None,
matches: Optional[Path] = None,
features_ref: Optional[Path] = None,
overwrite: bool = False,
) -> Path:
if isinstance(features, Path) or Path(features).exists():
features_q = features
if matches is None:
raise ValueError(
"Either provide both features and matches as Path"
" or both as names."
)
else:
if export_dir is None:
raise ValueError(
"Provide an export_dir if features is not"
f" a file path: {features}."
)
features_q = Path(export_dir, features + ".h5")
if matches is None:
matches = Path(
export_dir, f'{features}_{conf["output"]}_{pairs.stem}.h5'
)
if features_ref is None:
features_ref = features_q
match_from_paths(conf, pairs, matches, features_q, features_ref, overwrite)
return matches
def find_unique_new_pairs(pairs_all: List[Tuple[str]], match_path: Path = None):
"""Avoid to recompute duplicates to save time."""
pairs = set()
for i, j in pairs_all:
if (j, i) not in pairs:
pairs.add((i, j))
pairs = list(pairs)
if match_path is not None and match_path.exists():
with h5py.File(str(match_path), "r", libver="latest") as fd:
pairs_filtered = []
for i, j in pairs:
if (
names_to_pair(i, j) in fd
or names_to_pair(j, i) in fd
or names_to_pair_old(i, j) in fd
or names_to_pair_old(j, i) in fd
):
continue
pairs_filtered.append((i, j))
return pairs_filtered
return pairs
@torch.no_grad()
def match_from_paths(
conf: Dict,
pairs_path: Path,
match_path: Path,
feature_path_q: Path,
feature_path_ref: Path,
overwrite: bool = False,
) -> Path:
logger.info(
"Matching local features with configuration:"
f"\n{pprint.pformat(conf)}"
)
if not feature_path_q.exists():
raise FileNotFoundError(f"Query feature file {feature_path_q}.")
if not feature_path_ref.exists():
raise FileNotFoundError(f"Reference feature file {feature_path_ref}.")
match_path.parent.mkdir(exist_ok=True, parents=True)
assert pairs_path.exists(), pairs_path
pairs = parse_retrieval(pairs_path)
pairs = [(q, r) for q, rs in pairs.items() for r in rs]
pairs = find_unique_new_pairs(pairs, None if overwrite else match_path)
if len(pairs) == 0:
logger.info("Skipping the matching.")
return
device = "cuda" if torch.cuda.is_available() else "cpu"
Model = dynamic_load(matchers, conf["model"]["name"])
model = Model(conf["model"]).eval().to(device)
dataset = FeaturePairsDataset(pairs, feature_path_q, feature_path_ref)
loader = torch.utils.data.DataLoader(
dataset, num_workers=5, batch_size=1, shuffle=False, pin_memory=True
)
writer_queue = WorkQueue(partial(writer_fn, match_path=match_path), 5)
for idx, data in enumerate(tqdm(loader, smoothing=0.1)):
data = {
k: v if k.startswith("image") else v.to(device, non_blocking=True)
for k, v in data.items()
}
pred = model(data)
pair = names_to_pair(*pairs[idx])
writer_queue.put((pair, pred))
writer_queue.join()
logger.info("Finished exporting matches.")
def scale_keypoints(kpts, scale):
if np.any(scale != 1.0):
kpts *= kpts.new_tensor(scale)
return kpts
@torch.no_grad()
def match_images(model, feat0, feat1):
# forward pass to match keypoints
desc0 = feat0["descriptors"][0]
desc1 = feat1["descriptors"][0]
if len(desc0.shape) == 2:
desc0 = desc0.unsqueeze(0)
if len(desc1.shape) == 2:
desc1 = desc1.unsqueeze(0)
if isinstance(feat0["keypoints"], list):
feat0["keypoints"] = feat0["keypoints"][0][None]
if isinstance(feat1["keypoints"], list):
feat1["keypoints"] = feat1["keypoints"][0][None]
pred = model(
{
"image0": feat0["image"],
"keypoints0": feat0["keypoints"],
"scores0": feat0["scores"][0].unsqueeze(0),
"descriptors0": desc0,
"image1": feat1["image"],
"keypoints1": feat1["keypoints"],
"scores1": feat1["scores"][0].unsqueeze(0),
"descriptors1": desc1,
}
)
pred = {
k: v.cpu().detach()[0] if isinstance(v, torch.Tensor) else v
for k, v in pred.items()
}
kpts0, kpts1 = (
feat0["keypoints"][0].cpu().numpy(),
feat1["keypoints"][0].cpu().numpy(),
)
matches, confid = pred["matches0"], pred["matching_scores0"]
# Keep the matching keypoints.
valid = matches > -1
mkpts0 = kpts0[valid]
mkpts1 = kpts1[matches[valid]]
mconfid = confid[valid]
# rescale the keypoints to their original size
s0 = feat0["original_size"] / feat0["size"]
s1 = feat1["original_size"] / feat1["size"]
kpts0_origin = scale_keypoints(torch.from_numpy(kpts0 + 0.5), s0) - 0.5
kpts1_origin = scale_keypoints(torch.from_numpy(kpts1 + 0.5), s1) - 0.5
mkpts0_origin = scale_keypoints(torch.from_numpy(mkpts0 + 0.5), s0) - 0.5
mkpts1_origin = scale_keypoints(torch.from_numpy(mkpts1 + 0.5), s1) - 0.5
ret = {
"image0_orig": feat0["image_orig"],
"image1_orig": feat1["image_orig"],
"keypoints0": kpts0,
"keypoints1": kpts1,
"keypoints0_orig": kpts0_origin.numpy(),
"keypoints1_orig": kpts1_origin.numpy(),
"mkeypoints0": mkpts0,
"mkeypoints1": mkpts1,
"mkeypoints0_orig": mkpts0_origin.numpy(),
"mkeypoints1_orig": mkpts1_origin.numpy(),
"mconf": mconfid.numpy(),
}
del feat0, feat1, desc0, desc1, kpts0, kpts1, kpts0_origin, kpts1_origin
torch.cuda.empty_cache()
return ret
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--pairs", type=Path, required=True)
parser.add_argument("--export_dir", type=Path)
parser.add_argument(
"--features", type=str, default="feats-superpoint-n4096-r1024"
)
parser.add_argument("--matches", type=Path)
parser.add_argument(
"--conf", type=str, default="superglue", choices=list(confs.keys())
)
args = parser.parse_args()
main(confs[args.conf], args.pairs, args.features, args.export_dir)
|