File size: 12,432 Bytes
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
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",
            "do_mutual_check": True,
            "match_threshold": 0.2,  # TODO
        },
    },
    "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)
    pred = model(
        {
            "image0": feat0["image"],
            "keypoints0": feat0["keypoints"][0],
            "scores0": feat0["scores"][0].unsqueeze(0),
            "descriptors0": desc0,
            "image1": feat1["image"],
            "keypoints1": feat1["keypoints"][0],
            "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(mkpts0 + 0.5), s0) - 0.5
    kpts1_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(),
        "mconf": mconfid,
    }
    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)