videos_to_colmap.py 23.4 KB
Newer Older
Clement Pinard's avatar
Clement Pinard committed
1
2
3
4
5
6
from colmap_util import read_model as rm, database as db
import anafi_metadata as am
from wrappers import FFMpeg, PDraw
from sklearn.cluster import KMeans
from sklearn.metrics import pairwise_distances_argmin_min
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
nicolas's avatar
nicolas committed
7
from edit_exif import set_gps_location
Clement Pinard's avatar
Clement Pinard committed
8
9
10
11
12
from path import Path
import pandas as pd
import numpy as np
from pyproj import Proj
from tqdm import tqdm
13
import tempfile
Clement Pinard's avatar
Clement Pinard committed
14
15

parser = ArgumentParser(description='Take all the drone videos of a folder and put the frame '
Clément Pinard's avatar
Clément Pinard committed
16
                                    'location in a COLMAP file for visualization',
Clement Pinard's avatar
Clement Pinard committed
17
18
19
20
                        formatter_class=ArgumentDefaultsHelpFormatter)

parser.add_argument('--video_folder', metavar='DIR',
                    help='path to videos', type=Path)
Clément Pinard's avatar
Clément Pinard committed
21
22
23
24
25
26
parser.add_argument('--system', default='epsg:2154',
                    help='coordinates system used for GPS, should be the same as the LAS files used')
parser.add_argument('--centroid_path', default=None, help="path to centroid generated in las2ply.py")
parser.add_argument('--colmap_img_root', metavar='DIR', type=Path,
                    help="folder that will be used as \"image_path\" parameter when using COLMAP", required=True)
parser.add_argument('--output_format', metavar='EXT', default="bin", choices=["bin", "txt"],
Clément Pinard's avatar
Clément Pinard committed
27
                    help='format of the COLMAP file that will be outputed, used for visualization only')
Clément Pinard's avatar
Clément Pinard committed
28
29
30
31
parser.add_argument('--vid_ext', nargs='+', default=[".mp4", ".MP4"],
                    help="format of video files that will be scraped from input folder")
parser.add_argument('--pic_ext', nargs='+', default=[".jpg", ".JPG", ".png", ".PNG"],
                    help='format of images that will be scraped from already existing images in colmap image_path folder')
Clement Pinard's avatar
Clement Pinard committed
32
parser.add_argument('--nw', default='',
Clément Pinard's avatar
Clément Pinard committed
33
                    help="native-wrapper.sh file location (see Anafi SDK documentation)")
Clement Pinard's avatar
Clement Pinard committed
34
35
parser.add_argument('--fps', default=1, type=int,
                    help="framerate at which videos will be scanned WITH reconstruction")
Clément Pinard's avatar
Clément Pinard committed
36
37
38
39
40
41
42
parser.add_argument('--total_frames', default=200, type=int, help="number of frames used for thorough photogrammetry")
parser.add_argument('--max_sequence_length', default=1000, help='Number max of frames for a chunk. '
                    'This is for RAM purpose, as loading feature matches of thousands of frames can take up GBs of RAM')
parser.add_argument('--orientation_weight', default=1, type=float,
                    help="Weight applied to orientation during optimal sample. "
                    "Higher means two pictures with same location but different orientation will be considered farer apart")
parser.add_argument('--resolution_weight', default=1, type=float, help="same as orientation, but with image size")
Clément Pinard's avatar
Clément Pinard committed
43
44
parser.add_argument('--save_space', action="store_true",
                    help="if selected, will only extract from ffmpeg frames used for thorough photogrammetry")
Clément Pinard's avatar
Clément Pinard committed
45
parser.add_argument('--thorough_db', type=Path, help="output db file which will be used by COLMAP for photogrammetry")
Clément Pinard's avatar
Clément Pinard committed
46
47
48
parser.add_argument('--generic_model', default='OPENCV',
                    help='COLMAP model for generic videos. Same zoom level assumed throughout the whole video. '
                    'See https://colmap.github.io/cameras.html')
49
50
parser.add_argument('--include_lowfps_thorough', action='store_true',
                    help="if selected, will include videos frames at lowfps for thorough scan, even for generic or indoor videos")
Clément Pinard's avatar
Clément Pinard committed
51
parser.add_argument('-v', '--verbose', action="count", default=0)
Clement Pinard's avatar
Clement Pinard committed
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70


def world_coord_from_frame(frame_qvec, frame_tvec):
    '''
    frame_qvec is written in the NED system (north east down)
    frame_tvec is already is the world system (east norht up)
    '''
    world2NED = np.float32([[0, 1, 0],
                            [1, 0, 0],
                            [0, 0, -1]])
    NED2cam = np.float32([[0, 1, 0],
                          [0, 0, 1],
                          [1, 0, 0]])
    world2cam = NED2cam @ rm.qvec2rotmat(frame_qvec).T @ world2NED
    cam_tvec = - world2cam  @ frame_tvec
    cam_qvec = rm.rotmat2qvec(world2cam)
    return cam_qvec, cam_tvec


nicolas's avatar
nicolas committed
71
72
73
74
75
76
def set_gps(frames_list, metadata, image_path):
    for frame in frames_list:
        relative = str(frame.relpath(image_path))
        row = metadata[metadata["image_path"] == relative]
        if len(row) > 0:
            row = row.iloc[0]
77
            if row["location_valid"] and not row['indoor']:
78
79
80
81
                set_gps_location(frame,
                                 lat=row["location_latitude"],
                                 lng=row["location_longitude"],
                                 altitude=row["location_altitude"])
nicolas's avatar
nicolas committed
82
83
84
85
86
87


def get_georef(metadata):
    relevant_data = metadata[["location_valid", "image_path", "x", "y", "z"]]
    path_list = []
    georef_list = []
Clément Pinard's avatar
Clément Pinard committed
88
    for _, (loc_valid, path, x, y, alt) in relevant_data.iterrows():
nicolas's avatar
nicolas committed
89
        path_list.append(path)
Clément Pinard's avatar
Clément Pinard committed
90
        if loc_valid:
nicolas's avatar
nicolas committed
91
92
93
94
            georef_list.append("{} {} {} {}\n".format(path, x, y, alt))
    return georef_list, path_list


Clement Pinard's avatar
Clement Pinard committed
95
def optimal_sample(metadata, num_frames, orientation_weight, resolution_weight):
96
    # already sampled frames are discarded as we want to sample frames in addition to them
97
    valid_metadata = metadata[~metadata["sampled"]].dropna()
Clément Pinard's avatar
Clément Pinard committed
98
99
    if len(valid_metadata) == 0:
        return metadata
100
101
    XYZ = valid_metadata[["x", "y", "z"]].values
    axis_angle = valid_metadata[["frame_quat_x", "frame_quat_y", "frame_quat_z"]].values
102
    if "indoor" in valid_metadata.keys() and (True in valid_metadata["indoor"].unique()):
103
104
105
        # We have indoor videos, without absolute positions. We assume each video is very far
        # from the other ones. As such we will have an optimal subsampling of each video
        # It won't leverage video proximity from each other but it's better than nothing
Clement Pinard's avatar
Clement Pinard committed
106
        diameter = (XYZ.max(axis=0) - XYZ.min(axis=0))
107
108
109
110
        indoor_videos = valid_metadata.loc[valid_metadata["indoor"]]["video"].unique()
        new_centroids = 2 * diameter * np.linspace(0, 10, len(indoor_videos)).reshape(-1, 1)
        for centroid, v in zip(new_centroids, indoor_videos):
            video_index = (valid_metadata["video"] == v).values
Clement Pinard's avatar
Clement Pinard committed
111
112
113
114
115
116
117
            XYZ[video_index] += centroid

    weighted_point_cloud = np.concatenate([XYZ, orientation_weight * axis_angle], axis=1)

    if resolution_weight == 0:
        weights = None
    else:
118
        frame_size = valid_metadata["video_quality"].values
Clement Pinard's avatar
Clement Pinard committed
119
120
121
        weights = frame_size ** resolution_weight
    km = KMeans(n_clusters=num_frames).fit(weighted_point_cloud, sample_weight=weights)
    closest, _ = pairwise_distances_argmin_min(km.cluster_centers_, weighted_point_cloud)
122
    metadata.at[valid_metadata.index[closest], "sampled"] = True
Clement Pinard's avatar
Clement Pinard committed
123
124
125
    return metadata


126
def register_new_cameras(metadata, device, fields, database, camera_dict):
Clement Pinard's avatar
Clement Pinard committed
127
    camera_ids = []
128
    cameras_dataframe = metadata[metadata["device"] == device][["device"] + fields].drop_duplicates()
Clément Pinard's avatar
Clément Pinard committed
129
    for _, row in cameras_dataframe.iterrows():
130
        camera_model, w, h, params = row.reindex(["camera_model", "width", "height", "camera_params"])
131
132
        model_id = rm.CAMERA_MODEL_NAMES[camera_model].model_id
        num_params = rm.CAMERA_MODEL_NAMES[camera_model].num_params
133
134
135
136
137
138
139
140
141
142
143
        assert num_params >= len(params), "Got {} params for camera {}".format(len(params), camera_model)
        # Single focal models are SIMPLE_PINHOLE, SIMPLE_RADIAL, SIMPLE_RADIAL_FISHEYE, RADIAL and RADIAL_FISHEYE
        single_focal = ('SIMPLE' in camera_model) or ('RADIAL' in camera_model)
        num_focals = 1 if single_focal else 2
        params = np.array(list(params) + [0] * (num_params - len(params)))

        # prior_focal_length is whether or not COLMAP should rely on it.
        prior_focal_length = all(params[:num_focals] != 0)
        # For unknown focal_length, put a generic placeholder
        params[:num_focals][params[:num_focals] == 0] = w / 2
        # We can get less params than actual params if they are unknown. We then pad it with zeros
Clément Pinard's avatar
Clément Pinard committed
144
        db_id = database.add_camera(model_id, int(w), int(h), params, prior_focal_length=prior_focal_length)
Clement Pinard's avatar
Clement Pinard committed
145
146
        camera_ids.append(db_id)
        camera_dict[db_id] = rm.Camera(id=db_id,
147
                                       model=camera_model,
Clement Pinard's avatar
Clement Pinard committed
148
149
150
                                       width=int(w),
                                       height=int(h),
                                       params=params)
151
        metadata.loc[(metadata[["device"] + fields] == row).all(axis=1), "camera_id"] = db_id
Clement Pinard's avatar
Clement Pinard committed
152
153
154
155
    ids_series = pd.Series(camera_ids)
    return cameras_dataframe.set_index(ids_series)


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
def get_video_metadata(v, output_video_folder, system, generic_model='OPENCV', ** env):
    width, height, framerate, num_frames = env["ffmpeg"].get_size_and_framerate(v)
    video_output_folder = output_video_folder / "{}x{}".format(width, height) / v.stem

    def string_to_tuple(tuple_string):
        assert(tuple_string[0] == '(' and tuple_string[-1] == ')')
        return tuple([float(f) for f in tuple_string[1:-1].split(', ')])

    def generic_metadata():
        metadata = pd.DataFrame({"video": [v] * num_frames})
        metadata["height"] = height
        metadata["width"] = width
        metadata["framerate"] = framerate
        metadata["video_quality"] = height * width / framerate
        metadata['frame'] = metadata.index + 1
        # timestemp is in microseconds
        metadata['time'] = 1e6 * metadata.index / framerate
        metadata['indoor'] = True
        metadata['location_valid'] = False
        metadata["device"] = "generic"
        metadata["camera_model"] = generic_model
        metadata["frame_quat_w"] = np.NaN
        metadata["frame_quat_x"] = np.NaN
        metadata["frame_quat_y"] = np.NaN
        metadata["frame_quat_z"] = np.NaN
        metadata["x"] = np.NaN
        metadata["y"] = np.NaN
        metadata["z"] = np.NaN
        metadata["camera_params"] = [tuple()] * len(metadata)
        return metadata

    # First, try to open the CSV file {video name}_metadata.csv which should contain the metadata
    # If it fails, try to get metadata from MP4 by using PDraw
    # At last resort, simply assume generic parameters

    metadata_file_path = v.parent / "{}_metadata.csv".format(v.stem)
    if metadata_file_path.isfile():
        metadata = pd.read_csv(metadata_file_path)
        # check that the pandas dataframe is well formed
        keys_to_check = ["camera_model", "camera_params", "x", "y", "z",
                         "frame_quat_w", "frame_quat_x", "frame_quat_y", "frame_quat_z",
                         "location_valid", "time"]
        for k in keys_to_check:
            assert k in metadata.keys(), "Metadata file does not contain required field {}".format(k)
        metadata["camera_params"] = metadata["camera_params"].apply(string_to_tuple)
        if "frame" not in metadata.keys():
            metadata["frame"] = range(1, len(metadata) + 1)
        metadata['video'] = v
        if 'indoor' not in metadata.keys():
            metadata['indoor'] = len(metadata[metadata["location_valid"]]) > 0
        if 'video_quality' not in metadata.keys():
            metadata["video_quality"] = height * width / framerate
        device = "other"
    else:
        try:
            proj = Proj(system)
            metadata = am.extract_metadata(v.parent, v, env["pdraw"], proj,
                                           width, height, framerate)
            metadata["camera_model"] = "PINHOLE"
            device = "anafi"
        except Exception:
            # No metadata found, construct a simpler dataframe without location
            metadata = generic_metadata()
            device = "generic"
    metadata["num_frames"] = num_frames
    metadata["device"] = device
    return metadata, device, video_output_folder


def process_video_folder(videos_list, existing_pictures, output_video_folder, image_path, centroid,
Clément Pinard's avatar
Clément Pinard committed
226
                         thorough_db, fps=1, total_frames=500, orientation_weight=1, resolution_weight=1,
227
                         output_colmap_format="bin", save_space=False, include_lowfps_thorough=False,
Clément Pinard's avatar
Clément Pinard committed
228
                         max_sequence_length=1000, num_neighbours=10, existing_georef=False, **env):
229
    metadata_list = []
Clement Pinard's avatar
Clement Pinard committed
230
231
232
    video_output_folders = {}
    images = {}
    colmap_cameras = {}
233
    tempfile_database = Path(tempfile.NamedTemporaryFile().name)
Clément Pinard's avatar
Clément Pinard committed
234
235
    if thorough_db.isfile():
        thorough_db.copy(thorough_db.stripext() + "_backup.db")
Clement Pinard's avatar
Clement Pinard committed
236
    path_lists_output = {}
237
    database = db.COLMAPDatabase.connect(thorough_db)
Clement Pinard's avatar
Clement Pinard committed
238
239
240
    database.create_tables()

    print("extracting metadata for {} videos...".format(len(videos_list)))
241
242
243
    videos_summary = {"anafi": {"indoor": 0, "outdoor": 0},
                      "other": {"indoor": 0, "outdoor": 0},
                      "generic": 0}
Clement Pinard's avatar
Clement Pinard committed
244
    for v in tqdm(videos_list):
245
246
247
        metadata, device, output_folder = get_video_metadata(v, output_video_folder, **env)
        video_output_folders[v] = output_folder
        output_folder.makedirs_p()
Clement Pinard's avatar
Clement Pinard committed
248

249
250
251
252
253
254
255
256
257
        if include_lowfps_thorough:
            by_time = metadata.set_index(pd.to_datetime(metadata["time"], unit="us"))
            by_time_lowfps = by_time.resample("{:.3f}S".format(1/fps)).first()
            metadata["sampled"] = by_time["time"].isin(by_time_lowfps["time"]).values
        else:
            metadata["sampled"] = False
        if device == "generic":
            videos_summary["generic"] += 1
        else:
Clément Pinard's avatar
Clément Pinard committed
258
            raw_positions = metadata[["x", "y", "z"]]
259
            if metadata["indoor"].iloc[0]:
260
                videos_summary[device]["indoor"] += 1
261
            else:
262
                videos_summary[device]["outdoor"] += 1
263
            if sum(metadata["location_valid"]) > 0:
264
                if centroid is None:
265
                    '''No centroid (possibly because there was no georeferenced lidar pointcloud in the first place)
266
                    set it as the first valid GPS position of the first outdoor video'''
Clément Pinard's avatar
Clément Pinard committed
267
                    centroid = raw_positions[metadata["location_valid"]].iloc[0].values
268
                zero_centered_positions = raw_positions.values - centroid
Clément Pinard's avatar
bug fix    
Clément Pinard committed
269
270
271
272
                radius = np.max(np.abs(zero_centered_positions))
                if radius > 1000:
                    print("Warning, your positions coordinates are most likely too high, have you configured the right GPS system ?")
                    print("It should be the same as the one used for the Lidar point cloud")
273
                metadata["x"], metadata["y"], metadata["z"] = zero_centered_positions.transpose()
274
275
        metadata_list.append(metadata)
    final_metadata = pd.concat(metadata_list, ignore_index=True)
276
277
    print("{} outdoor anafi videos".format(videos_summary["anafi"]["outdoor"]))
    print("{} indoor anafi videos".format(videos_summary["anafi"]["indoor"]))
278
279
    print("{} indoor other videos".format(videos_summary["other"]["outdoor"]))
    print("{} indoor other videos".format(videos_summary["other"]["indoor"]))
280
    print("{} generic videos".format(videos_summary["generic"]))
Clement Pinard's avatar
Clement Pinard committed
281

282
    if((not existing_georef) and (sum(final_metadata["location_valid"]) == 0) and (videos_summary["anafi"]["indoor"] > 0)):
Clément Pinard's avatar
Clément Pinard committed
283
284
        # We have no GPS data but we have navdata, which will help rescale the colmap model
        # Take the longest video and do as if the GPS was valid
285
286
287
288
289
290
291
292
293
294
295
296
297
298
        indoor_video_diameters = {}
        for md in metadata_list:
            if (metadata["device"].iloc[0] != "anafi") or (not metadata["indoor"].iloc[0]):
                continue
            positions = md[["x", "y", "z"]].values
            video_displacement_diameter = np.linalg.norm(positions.max(axis=0) - positions.min(axis=0))
            if not np.isnan(video_displacement_diameter):
                indoor_video_diameters[video_displacement_diameter] = v

        if len(indoor_video_diameters) > 0:
            longest_video = indoor_video_diameters[max(indoor_video_diameters)]
            print("Only indoor videos used, will use {} for COLMAP rescaling".format(longest_video))
            video_index = final_metadata["video"] == longest_video
            final_metadata.loc[video_index, "location_valid"] = True
Clément Pinard's avatar
Clément Pinard committed
299

Clement Pinard's avatar
Clement Pinard committed
300
301
302
    print("{} frames in total".format(len(final_metadata)))

    final_metadata["camera_id"] = 0
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
    # Set up Anafi cameras, zoom included
    cam_fields = ["camera_model", "width", "height", "camera_params"]
    cam_dfs = []

    if any(final_metadata["device"] == "other"):
        cam_dfs.append(register_new_cameras(final_metadata, "other", cam_fields, database, colmap_cameras))
    if any(final_metadata["device"] == "anafi"):
        # For anafi we don't treat cameras the same if the framerate is different
        # because potentially different rectification algorithms are applied
        anafi_cam_fields = cam_fields + ["framerate"]
        cam_dfs.append(register_new_cameras(final_metadata, "anafi", anafi_cam_fields, database, colmap_cameras))
    if any(final_metadata["device"] == "generic"):
        print("Undefined remaining devices, assigning generic models to them")
        # Fix a single camera per video. This doesn't support different levels of zoom, but
        # COLMAP is not robust to too many different independant camera models
Clément Pinard's avatar
Clément Pinard committed
318
        generic_cam_fields = cam_fields + ["video"]
319
        cam_dfs.append(register_new_cameras(final_metadata, "generic", generic_cam_fields, database, colmap_cameras))
320
    print("Cameras : ")
321
    print(pd.concat(cam_dfs))
322
323

    to_extract = total_frames - len(existing_pictures) - sum(final_metadata["sampled"])
Clement Pinard's avatar
Clement Pinard committed
324

Clément Pinard's avatar
Clément Pinard committed
325
    if to_extract <= 0:
326
        pass
Clément Pinard's avatar
Clément Pinard committed
327
    elif to_extract < len(final_metadata):
Clement Pinard's avatar
Clement Pinard committed
328
329
        print("subsampling based on K-Means, to get {}"
              " frames from videos, for a total of {} frames".format(to_extract, total_frames))
330
        final_metadata = optimal_sample(final_metadata, to_extract,
Clement Pinard's avatar
Clement Pinard committed
331
332
333
                                        orientation_weight,
                                        resolution_weight)
        print("Done.")
Clément Pinard's avatar
Clément Pinard committed
334
335
    else:
        final_metadata["sampled"] = True
Clement Pinard's avatar
Clement Pinard committed
336

337
    print("Constructing COLMAP model with {:,} frames".format(sum(final_metadata["sampled"])))
Clement Pinard's avatar
Clement Pinard committed
338

339
340
341
342
    database.commit()
    thorough_db.copy(tempfile_database)
    temp_database = db.COLMAPDatabase.connect(tempfile_database)

Clement Pinard's avatar
Clement Pinard committed
343
    final_metadata["image_path"] = ""
344
345
    final_metadata["db_id"] = -1
    for current_id, row in tqdm(final_metadata.iterrows(), total=len(final_metadata)):
Clement Pinard's avatar
Clement Pinard committed
346
347
348
        video = row["video"]
        frame = row["frame"]
        camera_id = row["camera_id"]
Clément Pinard's avatar
Clément Pinard committed
349
        current_image_path = video_output_folders[video].relpath(image_path) / video.stem + "_{:05d}.jpg".format(frame)
Clement Pinard's avatar
Clement Pinard committed
350

351
352
353
        final_metadata.at[current_id, "image_path"] = current_image_path
        db_image_id = temp_database.add_image(current_image_path, int(camera_id))
        final_metadata.at[current_id, "db_id"] = db_image_id
Clement Pinard's avatar
Clement Pinard committed
354
355
356
357
358
359

        if row["sampled"]:
            frame_qvec = row[["frame_quat_w",
                              "frame_quat_x",
                              "frame_quat_y",
                              "frame_quat_z"]].values
360
361
            if True in pd.isnull(frame_qvec):
                frame_qvec = np.array([1, 0, 0, 0])
nicolas's avatar
nicolas committed
362
363
            x, y, z = row[["x", "y", "z"]]
            frame_tvec = np.array([x, y, z])
364
            if row["location_valid"] and not row['indoor']:
Clement Pinard's avatar
Clement Pinard committed
365
366
367
368
369
                frame_gps = row[["location_longitude", "location_latitude", "location_altitude"]]
            else:
                frame_gps = np.full(3, np.NaN)

            world_qvec, world_tvec = world_coord_from_frame(frame_qvec, frame_tvec)
370
371
372
373
            database.add_image(current_image_path, int(camera_id), prior_t=frame_gps, image_id=db_image_id)
            images[db_image_id] = rm.Image(id=db_image_id, qvec=world_qvec, tvec=world_tvec,
                                           camera_id=camera_id, name=current_image_path,
                                           xys=[], point3D_ids=[])
Clement Pinard's avatar
Clement Pinard committed
374
375
376

    database.commit()
    database.close()
377
378
    temp_database.commit()
    temp_database.close()
Clement Pinard's avatar
Clement Pinard committed
379
380
381
    rm.write_model(colmap_cameras, images, {}, output_video_folder, "." + output_colmap_format)
    print("COLMAP model created")

nicolas's avatar
nicolas committed
382
383
384
385
    thorough_georef, thorough_paths = get_georef(final_metadata[final_metadata["sampled"]])
    path_lists_output["thorough"] = {}
    path_lists_output["thorough"]["frames"] = thorough_paths
    path_lists_output["thorough"]["georef"] = thorough_georef
Clement Pinard's avatar
Clement Pinard committed
386
387
388
389
390

    print("Extracting frames from videos")

    for v in tqdm(videos_list):
        video_metadata = final_metadata[final_metadata["video"] == v]
nicolas's avatar
nicolas committed
391
        by_time = video_metadata.set_index(pd.to_datetime(video_metadata["time"], unit="us"))
Clement Pinard's avatar
Clement Pinard committed
392
393
        video_folder = video_output_folders[v]
        video_metadata.to_csv(video_folder/"metadata.csv")
nicolas's avatar
nicolas committed
394
395
396
397
398
        path_lists_output[v] = {}
        video_metadata_1fps = by_time.resample("{:.3f}S".format(1/fps)).first()
        georef, frame_paths = get_georef(video_metadata_1fps)
        path_lists_output[v]["frames_lowfps"] = frame_paths
        path_lists_output[v]["georef_lowfps"] = georef
Clément Pinard's avatar
Clément Pinard committed
399
        num_chunks = len(video_metadata) // max_sequence_length + 1
400
401
402
403
        chunks = [list(frames) for frames in np.array_split(video_metadata["image_path"],
                                                            num_chunks)]
        # Add some overlap between chunks, in order to ease the model merging afterwards
        for chunk, next_chunk in zip(chunks, chunks[1:]):
Clément Pinard's avatar
Clément Pinard committed
404
            chunk.extend(next_chunk[:num_neighbours])
405
406
        path_lists_output[v]["frames_full"] = chunks

Clement Pinard's avatar
Clement Pinard committed
407
        if save_space:
Clément Pinard's avatar
Clément Pinard committed
408
409
            frame_ids = set(video_metadata[video_metadata["sampled"]]["frame"].values) | \
                set(video_metadata_1fps["frame"].values)
410
            frame_ids = sorted(list(frame_ids))
Clément Pinard's avatar
Clément Pinard committed
411
            if len(frame_ids) > 0:
nicolas's avatar
nicolas committed
412
                extracted_frames = env["ffmpeg"].extract_specific_frames(v, video_folder, frame_ids)
Clement Pinard's avatar
Clement Pinard committed
413
        else:
nicolas's avatar
nicolas committed
414
415
            extracted_frames = env["ffmpeg"].extract_images(v, video_folder)
        set_gps(extracted_frames, video_metadata, image_path)
Clement Pinard's avatar
Clement Pinard committed
416
417
418
419
420
421
422
423

    return path_lists_output, video_output_folders


if __name__ == '__main__':
    args = parser.parse_args()
    env = vars(args)
    env["videos_list"] = sum((list(args.video_folder.walkfiles('*{}'.format(ext))) for ext in args.vid_ext), [])
Clément Pinard's avatar
Clément Pinard committed
424
    output_video_folder = args.colmap_img_root / "Videos"
Clement Pinard's avatar
Clement Pinard committed
425
    output_video_folder.makedirs_p()
Clément Pinard's avatar
Clément Pinard committed
426
    env["image_path"] = args.colmap_img_root
Clement Pinard's avatar
Clement Pinard committed
427
    env["output_video_folder"] = output_video_folder
Clément Pinard's avatar
Clément Pinard committed
428
    env["existing_pictures"] = sum((list(args.colmap_img_root.walkfiles('*{}'.format(ext))) for ext in args.pic_ext), [])
Clément Pinard's avatar
Clément Pinard committed
429
430
431
    env["pdraw"] = PDraw(args.nw, verbose=args.verbose)
    env["ffmpeg"] = FFMpeg(verbose=args.verbose)
    env["output_colmap_format"] = args.output_format
Clement Pinard's avatar
Clement Pinard committed
432
433
434
435
436
437
438
439
440

    if args.centroid_path is not None:
        centroid = np.loadtxt(args.centroid_path)
    else:
        centroid = np.zeros(3)
    env["centroid"] = centroid
    lists, extracted_video_folders = process_video_folder(**env)

    if lists is not None:
Clément Pinard's avatar
Clément Pinard committed
441
442
443
        with open(args.colmap_img_root/"video_frames_for_thorough_scan.txt", "w") as f:
            f.write("\n".join(lists["thorough"]["frames"]) + "\n")
        with open(args.colmap_img_root/"georef.txt", "w") as f:
Clément Pinard's avatar
Clément Pinard committed
444
            f.write("\n".join(lists["thorough"]["georef"]))
Clement Pinard's avatar
Clement Pinard committed
445
        for v in env["videos_list"]:
Clément Pinard's avatar
Clément Pinard committed
446
447
448
449
450
451
452
453
454
            video_folder = extracted_video_folders[v]
            with open(video_folder / "lowfps.txt", "w") as f:
                f.write("\n".join(lists[v]["frames_lowfps"]) + "\n")
            with open(video_folder / "georef.txt", "w") as f:
                f.write("\n".join(lists["thorough"]["georef"]) + "\n")
                f.write("\n".join(lists[v]["georef_lowfps"]) + "\n")
            for j, l in enumerate(lists[v]["frames_full"]):
                with open(video_folder / "full_chunk_{}.txt".format(j), "w") as f:
                    f.write("\n".join(l) + "\n")