-
Notifications
You must be signed in to change notification settings - Fork 859
/
dataset.py
1021 lines (846 loc) · 39.7 KB
/
dataset.py
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
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# pyre-unsafe
import gzip
import json
import logging
import os
import pickle
from io import BytesIO
from typing import Dict, List, Tuple, Optional, IO, Any
import numpy as np
from opensfm import (
config,
features,
geo,
io,
pygeometry,
types,
pymap,
masking,
rig,
)
from opensfm.dataset_base import DataSetBase
from PIL.PngImagePlugin import PngImageFile
logger: logging.Logger = logging.getLogger(__name__)
class DataSet(DataSetBase):
"""Accessors to the main input and output data.
Data include input images, masks, and segmentation as well
temporary data such as features and matches and the final
reconstructions.
All data is stored inside a single folder with a specific subfolder
structure.
It is possible to store data remotely or in different formats
by subclassing this class and overloading its methods.
"""
io_handler: io.IoFilesystemBase = io.IoFilesystemDefault()
config = None
image_files: Dict[str, str] = {}
mask_files: Dict[str, str] = {}
image_list: List[str] = []
def __init__(self, data_path: str, io_handler=io.IoFilesystemDefault) -> None:
"""Init dataset associated to a folder."""
self.io_handler = io_handler
self.data_path = data_path
self.load_config()
self.load_image_list()
self.load_mask_list()
def _config_file(self) -> str:
return os.path.join(self.data_path, "config.yaml")
def load_config(self) -> None:
config_file_path = self._config_file()
if self.io_handler.isfile(config_file_path):
with self.io_handler.open(config_file_path) as f:
self.config = config.load_config_from_fileobject(f)
else:
self.config = config.default_config()
def _image_list_file(self) -> str:
return os.path.join(self.data_path, "image_list.txt")
def load_image_list(self) -> None:
"""Load image list from image_list.txt or list images/ folder."""
image_list_file = self._image_list_file()
image_list_path = os.path.join(self.data_path, "images")
if self.io_handler.isfile(image_list_file):
with self.io_handler.open_rt(image_list_file) as fin:
lines = fin.read().splitlines()
self._set_image_list(lines)
else:
self._set_image_path(image_list_path)
if self.data_path and not self.image_list:
raise IOError("No Images found in {}".format(image_list_path))
def images(self) -> List[str]:
"""List of file names of all images in the dataset."""
return self.image_list
def _image_file(self, image: str) -> str:
"""Path to the image file."""
return self.image_files[image]
def open_image_file(self, image: str) -> IO[Any]:
"""Open image file and return file object."""
return self.io_handler.open(self._image_file(image), "rb")
def load_image(
self,
image: str,
unchanged: bool = False,
anydepth: bool = False,
grayscale: bool = False,
) -> np.ndarray:
"""Load image pixels as numpy array.
The array is 3D, indexed by y-coord, x-coord, channel.
The channels are in RGB order.
"""
return self.io_handler.imread(
self._image_file(image),
unchanged=unchanged,
anydepth=anydepth,
grayscale=grayscale,
)
def image_size(self, image: str) -> Tuple[int, int]:
"""Height and width of the image."""
return self.io_handler.image_size(self._image_file(image))
def load_mask_list(self) -> None:
"""Load mask list from mask_list.txt or list masks/ folder."""
mask_list_file = os.path.join(self.data_path, "mask_list.txt")
if self.io_handler.isfile(mask_list_file):
with self.io_handler.open_rt(mask_list_file) as fin:
lines = fin.read().splitlines()
self._set_mask_list(lines)
else:
self._set_mask_path(os.path.join(self.data_path, "masks"))
def load_mask(self, image: str) -> Optional[np.ndarray]:
"""Load image mask if it exists, otherwise return None."""
if image in self.mask_files:
mask_path = self.mask_files[image]
mask = self.io_handler.imread(mask_path, grayscale=True)
if mask is None:
raise IOError(
"Unable to load mask for image {} "
"from file {}".format(image, mask_path)
)
else:
mask = None
return mask
def _instances_path(self) -> str:
return os.path.join(self.data_path, "instances")
def _instances_file(self, image: str) -> str:
return os.path.join(self._instances_path(), image + ".png")
def load_instances(self, image: str) -> Optional[np.ndarray]:
"""Load image instances file if it exists, otherwise return None."""
instances_file = self._instances_file(image)
if self.io_handler.isfile(instances_file):
instances = self.io_handler.imread(instances_file, grayscale=True)
else:
instances = None
return instances
def _segmentation_path(self) -> str:
return os.path.join(self.data_path, "segmentations")
def _segmentation_file(self, image: str) -> str:
return os.path.join(self._segmentation_path(), image + ".png")
def segmentation_labels(self) -> List[Any]:
return []
def load_segmentation(self, image: str) -> Optional[np.ndarray]:
"""Load image segmentation if it exists, otherwise return None."""
segmentation_file = self._segmentation_file(image)
if self.io_handler.isfile(segmentation_file):
with self.io_handler.open(segmentation_file, "rb") as fp:
with PngImageFile(fp) as png_image:
# TODO: We do not write a header tag in the metadata. Might be good safety check.
data = np.array(png_image)
if data.ndim == 2:
return data
elif data.ndim == 3:
return data[:, :, 0]
# TODO we can optionally return also the instances and scores:
# instances = (
# data[:, :, 1].astype(np.int16) + data[:, :, 2].astype(np.int16) * 256
# )
# scores = data[:, :, 3].astype(np.float32) / 256.0
else:
raise IndexError
else:
segmentation = None
return segmentation
def segmentation_ignore_values(self, image: str) -> List[int]:
"""List of label values to ignore.
Pixels with these label values will be masked out and won't be
processed when extracting and matching features.
"""
return self.config.get("segmentation_ignore_values", [])
def undistorted_segmentation_ignore_values(self, image: str) -> List[int]:
"""List of label values to ignore on undistorted images
Pixels with these label values will be masked out and won't be
processed when computing depthmaps.
"""
return self.config.get(
"undistorted_segmentation_ignore_values",
self.segmentation_ignore_values(image),
)
def _is_image_file(self, filename: str) -> bool:
extensions = {"jpg", "jpeg", "png", "tif", "tiff", "pgm", "pnm", "gif"}
return filename.split(".")[-1].lower() in extensions
def _set_image_path(self, path: str) -> None:
"""Set image path and find all images in there"""
self.image_list = []
self.image_files = {}
if self.io_handler.exists(path):
for name in self.io_handler.ls(path):
if self._is_image_file(name):
self.image_list.append(name)
self.image_files[name] = os.path.join(path, name)
def _set_image_list(self, image_list: List[str]) -> None:
self.image_list = []
self.image_files = {}
for line in image_list:
path = os.path.join(self.data_path, line)
name = os.path.basename(path)
self.image_list.append(name)
self.image_files[name] = path
def _set_mask_path(self, path: str) -> None:
"""Set mask path and find all masks in there"""
self.mask_files = {}
if self.io_handler.isdir(path):
files = set(self.io_handler.ls(path))
for image in self.images():
mask = image + ".png"
if mask in files:
self.mask_files[image] = os.path.join(path, mask)
def _set_mask_list(self, mask_list_lines: List[str]) -> None:
self.mask_files = {}
for line in mask_list_lines:
image, relpath = line.split(None, 1)
path = os.path.join(self.data_path, relpath.strip())
self.mask_files[image.strip()] = path
def _exif_path(self) -> str:
"""Return path of extracted exif directory"""
return os.path.join(self.data_path, "exif")
def _exif_file(self, image: str) -> str:
"""
Return path of exif information for given image
:param image: Image name, with extension (i.e. 123.jpg)
"""
return os.path.join(self._exif_path(), image + ".exif")
def load_exif(self, image: str) -> Dict[str, Any]:
"""Load pre-extracted image exif metadata."""
with self.io_handler.open_rt(self._exif_file(image)) as fin:
return json.load(fin)
def save_exif(self, image: str, data: Dict[str, Any]) -> None:
self.io_handler.mkdir_p(self._exif_path())
with self.io_handler.open_wt(self._exif_file(image)) as fout:
io.json_dump(data, fout)
def exif_exists(self, image: str) -> bool:
return self.io_handler.isfile(self._exif_file(image))
def feature_type(self) -> str:
"""Return the type of local features (e.g. AKAZE, SURF, SIFT)"""
feature_name = self.config["feature_type"].lower()
if self.config["feature_root"]:
feature_name = "root_" + feature_name
return feature_name
def _feature_path(self) -> str:
"""Return path of feature descriptors and FLANN indices directory"""
return os.path.join(self.data_path, "features")
def _feature_file(self, image: str) -> str:
"""
Return path of feature file for specified image
:param image: Image name, with extension (i.e. 123.jpg)
"""
return os.path.join(self._feature_path(), image + ".features.npz")
def _feature_file_legacy(self, image: str) -> str:
"""
Return path of a legacy feature file for specified image
:param image: Image name, with extension (i.e. 123.jpg)
"""
return os.path.join(self._feature_path(), image + ".npz")
def _save_features(
self, filepath: str, features_data: features.FeaturesData
) -> None:
self.io_handler.mkdir_p(self._feature_path())
with self.io_handler.open(filepath, "wb") as fwb:
features_data.save(fwb, self.config)
def features_exist(self, image: str) -> bool:
return self.io_handler.isfile(
self._feature_file(image)
) or self.io_handler.isfile(self._feature_file_legacy(image))
def load_features(self, image: str) -> Optional[features.FeaturesData]:
features_filepath = (
self._feature_file_legacy(image)
if self.io_handler.isfile(self._feature_file_legacy(image))
else self._feature_file(image)
)
with self.io_handler.open(features_filepath, "rb") as f:
return features.FeaturesData.from_file(f, self.config)
def save_features(self, image: str, features_data: features.FeaturesData) -> None:
self._save_features(self._feature_file(image), features_data)
def _words_file(self, image: str) -> str:
return os.path.join(self._feature_path(), image + ".words.npz")
def words_exist(self, image: str) -> bool:
return self.io_handler.isfile(self._words_file(image))
def load_words(self, image: str) -> np.ndarray:
with self.io_handler.open(self._words_file(image), "rb") as f:
s = np.load(f)
return s["words"].astype(np.int32)
def save_words(self, image: str, words: np.ndarray) -> None:
with self.io_handler.open(self._words_file(image), "wb") as f:
# pyre-fixme[6]: For 1st argument expected `Union[_SupportsWrite[bytes],
# PathLike[str], str]` but got `IO[typing.Any]`.
np.savez_compressed(f, words=words.astype(np.uint16))
def _matches_path(self) -> str:
"""Return path of matches directory"""
return os.path.join(self.data_path, "matches")
def _matches_file(self, image: str) -> str:
"""File for matches for an image"""
return os.path.join(self._matches_path(), "{}_matches.pkl.gz".format(image))
def matches_exists(self, image: str) -> bool:
return self.io_handler.isfile(self._matches_file(image))
def load_matches(self, image: str) -> Dict[str, np.ndarray]:
# Prevent pickling of anything except what we strictly need
# as 'pickle.load' is RCE-prone. Will raise on any class other
# than the numpy ones we allow.
class MatchingUnpickler(pickle.Unpickler):
modules_map = {
"numpy.core.multiarray._reconstruct": np.core.multiarray,
"numpy.core.multiarray.scalar": np.core.multiarray,
"numpy.ndarray": np,
"numpy.dtype": np,
}
def find_class(self, module, name):
classname = f"{module}.{name}"
allowed_module = classname in self.modules_map
if not allowed_module:
raise pickle.UnpicklingError(
"global '%s.%s' is forbidden" % (module, name)
)
return getattr(self.modules_map[classname], name)
with self.io_handler.open(self._matches_file(image), "rb") as fin:
matches = MatchingUnpickler(BytesIO(gzip.decompress(fin.read()))).load()
return matches
def save_matches(self, image: str, matches: Dict[str, np.ndarray]) -> None:
self.io_handler.mkdir_p(self._matches_path())
with BytesIO() as buffer:
with gzip.GzipFile(fileobj=buffer, mode="w") as fzip:
pickle.dump(matches, fzip)
with self.io_handler.open(self._matches_file(image), "wb") as fw:
fw.write(buffer.getvalue())
def find_matches(self, im1: str, im2: str) -> np.ndarray:
if self.matches_exists(im1):
im1_matches = self.load_matches(im1)
if im2 in im1_matches:
return im1_matches[im2]
if self.matches_exists(im2):
im2_matches = self.load_matches(im2)
if im1 in im2_matches:
if len(im2_matches[im1]):
return im2_matches[im1][:, [1, 0]]
return np.array([])
def _tracks_manager_file(self, filename: Optional[str] = None) -> str:
"""Return path of tracks file"""
return os.path.join(self.data_path, filename or "tracks.csv")
def load_tracks_manager(
self, filename: Optional[str] = None
) -> pymap.TracksManager:
"""Return the tracks manager"""
with self.io_handler.open(self._tracks_manager_file(filename), "r") as f:
return pymap.TracksManager.instanciate_from_string(f.read())
def tracks_exists(self, filename: Optional[str] = None) -> bool:
return self.io_handler.isfile(self._tracks_manager_file(filename))
def save_tracks_manager(
self, tracks_manager: pymap.TracksManager, filename: Optional[str] = None
) -> None:
with self.io_handler.open(self._tracks_manager_file(filename), "w") as fw:
fw.write(tracks_manager.as_string())
def _reconstruction_file(self, filename: Optional[str]) -> str:
"""Return path of reconstruction file"""
return os.path.join(self.data_path, filename or "reconstruction.json")
def reconstruction_exists(self, filename: Optional[str] = None) -> bool:
return self.io_handler.isfile(self._reconstruction_file(filename))
def load_reconstruction(
self, filename: Optional[str] = None
) -> List[types.Reconstruction]:
with self.io_handler.open_rt(self._reconstruction_file(filename)) as fin:
reconstructions = io.reconstructions_from_json(io.json_load(fin))
return reconstructions
def save_reconstruction(
self,
reconstruction: List[types.Reconstruction],
filename: Optional[str] = None,
minify=False,
) -> None:
with self.io_handler.open_wt(self._reconstruction_file(filename)) as fout:
io.json_dump(io.reconstructions_to_json(reconstruction), fout, minify)
def _reference_lla_path(self) -> str:
return os.path.join(self.data_path, "reference_lla.json")
def init_reference(self, images: Optional[List[str]] = None) -> None:
"""Initializes the dataset reference if not done already."""
if not self.reference_exists():
reference = invent_reference_from_gps_and_gcp(self, images)
self.save_reference(reference)
def save_reference(self, reference: geo.TopocentricConverter) -> None:
reference_lla = {
"latitude": reference.lat,
"longitude": reference.lon,
"altitude": reference.alt,
}
with self.io_handler.open_wt(self._reference_lla_path()) as fout:
io.json_dump(reference_lla, fout)
def load_reference(self) -> geo.TopocentricConverter:
"""Load reference as a topocentric converter."""
with self.io_handler.open_rt(self._reference_lla_path()) as fin:
lla = io.json_load(fin)
return geo.TopocentricConverter(
lla["latitude"], lla["longitude"], lla["altitude"]
)
def reference_exists(self) -> bool:
return self.io_handler.isfile(self._reference_lla_path())
def _camera_models_file(self) -> str:
"""Return path of camera model file"""
return os.path.join(self.data_path, "camera_models.json")
def load_camera_models(self) -> Dict[str, pygeometry.Camera]:
"""Return camera models data"""
with self.io_handler.open_rt(self._camera_models_file()) as fin:
obj = json.load(fin)
return io.cameras_from_json(obj)
def save_camera_models(self, camera_models: Dict[str, pygeometry.Camera]) -> None:
"""Save camera models data"""
with self.io_handler.open_wt(self._camera_models_file()) as fout:
obj = io.cameras_to_json(camera_models)
io.json_dump(obj, fout)
def _camera_models_overrides_file(self) -> str:
"""Path to the camera model overrides file."""
return os.path.join(self.data_path, "camera_models_overrides.json")
def camera_models_overrides_exists(self) -> bool:
"""Check if camera overrides file exists."""
return self.io_handler.isfile(self._camera_models_overrides_file())
def load_camera_models_overrides(self) -> Dict[str, pygeometry.Camera]:
"""Load camera models overrides data."""
with self.io_handler.open_rt(self._camera_models_overrides_file()) as fin:
obj = json.load(fin)
return io.cameras_from_json(obj)
def save_camera_models_overrides(
self, camera_models: Dict[str, pygeometry.Camera]
) -> None:
"""Save camera models overrides data"""
with self.io_handler.open_wt(self._camera_models_overrides_file()) as fout:
obj = io.cameras_to_json(camera_models)
io.json_dump(obj, fout)
def _exif_overrides_file(self) -> str:
"""Path to the EXIF overrides file."""
return os.path.join(self.data_path, "exif_overrides.json")
def exif_overrides_exists(self) -> bool:
"""Check if EXIF overrides file exists."""
return self.io_handler.isfile(self._exif_overrides_file())
def load_exif_overrides(self) -> Dict[str, Any]:
"""Load EXIF overrides data."""
with self.io_handler.open_rt(self._exif_overrides_file()) as fin:
return json.load(fin)
def save_exif_overrides(self, exif_overrides: Dict[str, Any]) -> None:
"""Load EXIF overrides data."""
with self.io_handler.open_wt(self._exif_overrides_file()) as fout:
io.json_dump(exif_overrides, fout)
def _rig_cameras_file(self) -> str:
"""Return path of rig models file"""
return os.path.join(self.data_path, "rig_cameras.json")
def load_rig_cameras(self) -> Dict[str, pymap.RigCamera]:
"""Return rig models data"""
all_rig_cameras = rig.default_rig_cameras(self.load_camera_models())
if not self.io_handler.exists(self._rig_cameras_file()):
return all_rig_cameras
with self.io_handler.open_rt(self._rig_cameras_file()) as fin:
rig_cameras = io.rig_cameras_from_json(json.load(fin))
for rig_camera_id, rig_camera in rig_cameras.items():
all_rig_cameras[rig_camera_id] = rig_camera
return all_rig_cameras
def save_rig_cameras(self, rig_cameras: Dict[str, pymap.RigCamera]) -> None:
"""Save rig models data"""
with self.io_handler.open_wt(self._rig_cameras_file()) as fout:
io.json_dump(io.rig_cameras_to_json(rig_cameras), fout)
def _rig_assignments_file(self) -> str:
"""Return path of rig assignments file"""
return os.path.join(self.data_path, "rig_assignments.json")
def load_rig_assignments(self) -> Dict[str, List[Tuple[str, str]]]:
"""Return rig assignments data"""
if not self.io_handler.exists(self._rig_assignments_file()):
return {}
with self.io_handler.open_rt(self._rig_assignments_file()) as fin:
assignments = json.load(fin)
# Backward compatibility.
# Older versions of the file were stored as a list of instances without id.
if isinstance(assignments, list):
assignments = {str(i): v for i, v in enumerate(assignments)}
return assignments
def save_rig_assignments(
self, rig_assignments: Dict[str, List[Tuple[str, str]]]
) -> None:
"""Save rig assignments data"""
with self.io_handler.open_wt(self._rig_assignments_file()) as fout:
io.json_dump(rig_assignments, fout)
def append_to_profile_log(self, content: str) -> None:
"""Append content to the profile.log file."""
path = os.path.join(self.data_path, "profile.log")
with self.io_handler.open(path, "a") as fp:
fp.write(content)
def _report_path(self) -> str:
return os.path.join(self.data_path, "reports")
def load_report(self, path: str) -> str:
"""Load a report file as a string."""
with self.io_handler.open_rt(os.path.join(self._report_path(), path)) as fin:
return fin.read()
def save_report(self, report_str: str, path: str) -> None:
"""Save report string to a file."""
filepath = os.path.join(self._report_path(), path)
self.io_handler.mkdir_p(os.path.dirname(filepath))
with self.io_handler.open_wt(filepath) as fout:
return fout.write(report_str)
def _ply_file(self, filename: Optional[str]) -> str:
return os.path.join(self.data_path, filename or "reconstruction.ply")
def save_ply(
self,
reconstruction: types.Reconstruction,
tracks_manager: pymap.TracksManager,
filename: Optional[str] = None,
no_cameras: bool = False,
no_points: bool = False,
point_num_views: bool = False,
) -> None:
"""Save a reconstruction in PLY format."""
ply = io.reconstruction_to_ply(
reconstruction, tracks_manager, no_cameras, no_points, point_num_views
)
with self.io_handler.open_wt(self._ply_file(filename)) as fout:
fout.write(ply)
def _ground_control_points_file(self) -> str:
return os.path.join(self.data_path, "ground_control_points.json")
def _gcp_list_file(self) -> str:
return os.path.join(self.data_path, "gcp_list.txt")
def load_ground_control_points(self) -> List[pymap.GroundControlPoint]:
"""Load ground control points."""
exif = {image: self.load_exif(image) for image in self.images()}
gcp = []
if self.io_handler.isfile(self._gcp_list_file()):
with self.io_handler.open_rt(self._gcp_list_file()) as fin:
gcp = io.read_gcp_list(fin, exif)
pcs = []
if self.io_handler.isfile(self._ground_control_points_file()):
with self.io_handler.open_rt(self._ground_control_points_file()) as fin:
pcs = io.read_ground_control_points(fin)
return gcp + pcs
def save_ground_control_points(
self,
points: List[pymap.GroundControlPoint],
) -> None:
with self.io_handler.open_wt(self._ground_control_points_file()) as fout:
io.write_ground_control_points(points, fout)
def image_as_array(self, image: str) -> np.ndarray:
logger.warning("image_as_array() is deprecated. Use load_image() instead.")
return self.load_image(image)
def mask_as_array(self, image: str) -> Optional[np.ndarray]:
logger.warning("mask_as_array() is deprecated. Use load_mask() instead.")
return self.load_mask(image)
def subset(self, name: str, images_subset: List[str]) -> "DataSet":
"""Create a subset of this dataset by symlinking input data."""
subset_dataset_path = os.path.join(self.data_path, name)
self.io_handler.mkdir_p(subset_dataset_path)
folders = ["images", "segmentations", "masks"]
for folder in folders:
self.io_handler.mkdir_p(os.path.join(subset_dataset_path, folder))
subset_dataset = DataSet(subset_dataset_path, self.io_handler)
files = []
for method in [
"_camera_models_file",
"_config_file",
"_camera_models_overrides_file",
"_exif_overrides_file",
]:
files.append(
(
getattr(self, method)(),
getattr(subset_dataset, method)(),
)
)
for image in images_subset:
files.append(
(
self._image_file(image),
os.path.join(subset_dataset_path, "images", image),
)
)
files.append(
(
self._segmentation_file(image),
os.path.join(subset_dataset_path, "segmentations", image + ".png"),
)
)
if image in self.mask_files:
files.append(
(
self.mask_files[image],
os.path.join(subset_dataset_path, "masks", image + ".png"),
)
)
for src, dst in files:
if not self.io_handler.exists(src):
continue
self.io_handler.rm_if_exist(dst)
self.io_handler.symlink(src, dst)
return DataSet(subset_dataset_path, self.io_handler)
def undistorted_dataset(self) -> "UndistortedDataSet":
return UndistortedDataSet(
self, os.path.join(self.data_path, "undistorted"), self.io_handler
)
class UndistortedDataSet:
"""Accessors to the undistorted data of a dataset.
Data include undistorted images, masks, and segmentation as well
the undistorted reconstruction, tracks graph and computed depth maps.
All data is stored inside the single folder ``undistorted_data_path``.
By default, this path is set to the ``undistorted`` subfolder.
"""
base: DataSetBase
config: Dict[str, Any] = {}
data_path: str
def __init__(
self,
base_dataset: DataSetBase,
undistorted_data_path: str,
io_handler=io.IoFilesystemDefault,
) -> None:
"""Init dataset associated to a folder."""
self.base = base_dataset
self.config = self.base.config
self.data_path = undistorted_data_path
self.io_handler = io_handler
def load_undistorted_shot_ids(self) -> Dict[str, List[str]]:
filename = os.path.join(self.data_path, "undistorted_shot_ids.json")
with self.io_handler.open_rt(filename) as fin:
return io.json_load(fin)
def save_undistorted_shot_ids(self, ushot_dict: Dict[str, List[str]]) -> None:
filename = os.path.join(self.data_path, "undistorted_shot_ids.json")
self.io_handler.mkdir_p(self.data_path)
with self.io_handler.open_wt(filename) as fout:
io.json_dump(ushot_dict, fout, minify=False)
def _undistorted_image_path(self) -> str:
return os.path.join(self.data_path, "images")
def _undistorted_image_file(self, image: str) -> str:
"""Path of undistorted version of an image."""
return os.path.join(self._undistorted_image_path(), image)
def load_undistorted_image(self, image: str) -> np.ndarray:
"""Load undistorted image pixels as a numpy array."""
return self.io_handler.imread(self._undistorted_image_file(image))
def save_undistorted_image(self, image: str, array: np.ndarray) -> None:
"""Save undistorted image pixels."""
self.io_handler.mkdir_p(self._undistorted_image_path())
self.io_handler.imwrite(self._undistorted_image_file(image), array)
def undistorted_image_size(self, image: str) -> Tuple[int, int]:
"""Height and width of the undistorted image."""
return self.io_handler.image_size(self._undistorted_image_file(image))
def _undistorted_mask_path(self) -> str:
return os.path.join(self.data_path, "masks")
def _undistorted_mask_file(self, image: str) -> str:
"""Path of undistorted version of a mask."""
return os.path.join(self._undistorted_mask_path(), image + ".png")
def undistorted_mask_exists(self, image: str) -> bool:
"""Check if the undistorted mask file exists."""
return self.io_handler.isfile(self._undistorted_mask_file(image))
def load_undistorted_mask(self, image: str) -> np.ndarray:
"""Load undistorted mask pixels as a numpy array."""
return self.io_handler.imread(
self._undistorted_mask_file(image), grayscale=True
)
def save_undistorted_mask(self, image: str, array: np.ndarray) -> None:
"""Save the undistorted image mask."""
self.io_handler.mkdir_p(self._undistorted_mask_path())
self.io_handler.imwrite(self._undistorted_mask_file(image), array)
def _undistorted_segmentation_path(self) -> str:
return os.path.join(self.data_path, "segmentations")
def _undistorted_segmentation_file(self, image: str) -> str:
"""Path of undistorted version of a segmentation."""
return os.path.join(self._undistorted_segmentation_path(), image + ".png")
def undistorted_segmentation_exists(self, image: str) -> bool:
"""Check if the undistorted segmentation file exists."""
return self.io_handler.isfile(self._undistorted_segmentation_file(image))
def load_undistorted_segmentation(self, image: str) -> np.ndarray:
"""Load an undistorted image segmentation."""
segmentation_file = self._undistorted_segmentation_file(image)
with self.io_handler.open(segmentation_file, "rb") as fp:
with PngImageFile(fp) as png_image:
# TODO: We do not write a header tag in the metadata. Might be good safety check.
data = np.array(png_image)
if data.ndim == 2:
return data
elif data.ndim == 3:
return data[:, :, 0]
# TODO we can optionally return also the instances and scores:
# instances = (
# data[:, :, 1].astype(np.int16) + data[:, :, 2].astype(np.int16) * 256
# )
# scores = data[:, :, 3].astype(np.float32) / 256.0
else:
raise IndexError
def save_undistorted_segmentation(self, image: str, array: np.ndarray) -> None:
"""Save the undistorted image segmentation."""
self.io_handler.mkdir_p(self._undistorted_segmentation_path())
self.io_handler.imwrite(self._undistorted_segmentation_file(image), array)
def load_undistorted_segmentation_mask(self, image: str) -> Optional[np.ndarray]:
"""Build a mask from the undistorted segmentation.
The mask is non-zero only for pixels with segmentation
labels not in undistorted_segmentation_ignore_values.
If there are no undistorted_segmentation_ignore_values in the config,
the segmentation_ignore_values are used instead.
"""
ignore_values = self.base.undistorted_segmentation_ignore_values(image)
if not ignore_values:
return None
segmentation = self.load_undistorted_segmentation(image)
if segmentation is None:
return None
return masking.mask_from_segmentation(segmentation, ignore_values)
def load_undistorted_combined_mask(self, image: str) -> Optional[np.ndarray]:
"""Combine undistorted binary mask with segmentation mask.
Return a mask that is non-zero only where the binary
mask and the segmentation mask are non-zero.
"""
mask = None
if self.undistorted_mask_exists(image):
mask = self.load_undistorted_mask(image)
smask = None
if self.undistorted_segmentation_exists(image):
smask = self.load_undistorted_segmentation_mask(image)
return masking.combine_masks(mask, smask)
def _depthmap_path(self) -> str:
return os.path.join(self.data_path, "depthmaps")
def depthmap_file(self, image: str, suffix: str) -> str:
"""Path to the depthmap file"""
return os.path.join(self._depthmap_path(), image + "." + suffix)
def point_cloud_file(self, filename: str = "merged.ply") -> str:
return os.path.join(self._depthmap_path(), filename)
def load_point_cloud(
self, filename: str = "merged.ply"
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
with self.io_handler.open(self.point_cloud_file(filename), "r") as fp:
return io.point_cloud_from_ply(fp)
def save_point_cloud(
self,
points: np.ndarray,
normals: np.ndarray,
colors: np.ndarray,
labels: np.ndarray,
filename: str = "merged.ply",
) -> None:
self.io_handler.mkdir_p(self._depthmap_path())
with self.io_handler.open(self.point_cloud_file(filename), "w") as fp:
io.point_cloud_to_ply(points, normals, colors, labels, fp)
def raw_depthmap_exists(self, image: str) -> bool:
return self.io_handler.isfile(self.depthmap_file(image, "raw.npz"))
def save_raw_depthmap(
self,
image: str,
depth: np.ndarray,
plane: np.ndarray,
score: np.ndarray,
nghbr: np.ndarray,
nghbrs: np.ndarray,
) -> None:
self.io_handler.mkdir_p(self._depthmap_path())
filepath = self.depthmap_file(image, "raw.npz")
with self.io_handler.open(filepath, "wb") as f:
np.savez_compressed(
f, depth=depth, plane=plane, score=score, nghbr=nghbr, nghbrs=nghbrs
)
def load_raw_depthmap(
self, image: str
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
with self.io_handler.open(self.depthmap_file(image, "raw.npz"), "rb") as f:
o = np.load(f)
return o["depth"], o["plane"], o["score"], o["nghbr"], o["nghbrs"]
def clean_depthmap_exists(self, image: str) -> bool:
return self.io_handler.isfile(self.depthmap_file(image, "clean.npz"))
def save_clean_depthmap(
self, image: str, depth: np.ndarray, plane: np.ndarray, score: np.ndarray
) -> None:
self.io_handler.mkdir_p(self._depthmap_path())
filepath = self.depthmap_file(image, "clean.npz")
with self.io_handler.open(filepath, "wb") as f:
np.savez_compressed(f, depth=depth, plane=plane, score=score)
def load_clean_depthmap(
self, image: str
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
with self.io_handler.open(self.depthmap_file(image, "clean.npz"), "rb") as f:
o = np.load(f)
return o["depth"], o["plane"], o["score"]
def pruned_depthmap_exists(self, image: str) -> bool:
return self.io_handler.isfile(self.depthmap_file(image, "pruned.npz"))
def save_pruned_depthmap(
self,
image: str,
points: np.ndarray,
normals: np.ndarray,
colors: np.ndarray,
labels: np.ndarray,
) -> None:
self.io_handler.mkdir_p(self._depthmap_path())
filepath = self.depthmap_file(image, "pruned.npz")
with self.io_handler.open(filepath, "wb") as f:
np.savez_compressed(
f,
points=points,
normals=normals,
colors=colors,
labels=labels,
)
def load_pruned_depthmap(
self, image: str
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
with self.io_handler.open(self.depthmap_file(image, "pruned.npz"), "rb") as f:
o = np.load(f)
return (
o["points"],
o["normals"],
o["colors"],
o["labels"],
)
def load_undistorted_tracks_manager(self) -> pymap.TracksManager:
filename = os.path.join(self.data_path, "tracks.csv")
with self.io_handler.open(filename, "r") as f:
return pymap.TracksManager.instanciate_from_string(f.read())
def save_undistorted_tracks_manager(
self, tracks_manager: pymap.TracksManager
) -> None:
filename = os.path.join(self.data_path, "tracks.csv")
with self.io_handler.open(filename, "w") as fw:
fw.write(tracks_manager.as_string())
def load_undistorted_reconstruction(self) -> List[types.Reconstruction]:
filename = os.path.join(self.data_path, "reconstruction.json")
with self.io_handler.open_rt(filename) as fin:
return io.reconstructions_from_json(io.json_load(fin))
def save_undistorted_reconstruction(
self, reconstruction: List[types.Reconstruction]
) -> None:
filename = os.path.join(self.data_path, "reconstruction.json")
self.io_handler.mkdir_p(self.data_path)
with self.io_handler.open_wt(filename) as fout:
io.json_dump(io.reconstructions_to_json(reconstruction), fout, minify=True)
def invent_reference_from_gps_and_gcp(
data: DataSetBase, images: Optional[List[str]] = None
) -> geo.TopocentricConverter:
""" Invent the reference from the weighted average of lat/lon measurements.
Most of the time the altitude provided in the metadata is inaccurate, thus
the reference altitude is set equal to 0 regardless of the altitude measurements.
"""
lat, lon = 0.0, 0.0
wlat, wlon = 0.0, 0.0
if images is None:
images = data.images()
for image in images:
d = data.load_exif(image)