videos_to_colmap.py 12.6 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
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

parser = ArgumentParser(description='Take all the drone videos of a folder and put the frame '
                                    'location in a COLMAP file for vizualisation',
                        formatter_class=ArgumentDefaultsHelpFormatter)

parser.add_argument('--video_folder', metavar='DIR',
                    help='path to videos', type=Path)
parser.add_argument('--system', default='epsg:2154')
parser.add_argument('--centroid_path', default=None)
parser.add_argument('--output_folder', metavar='DIR', type=Path)
parser.add_argument('--workspace', metavar='DIR', type=Path)
parser.add_argument('--image_path', metavar='DIR', type=Path)
parser.add_argument('--output_format', metavar='EXT', default="bin")
parser.add_argument('--vid_ext', nargs='+', default=[".mp4", ".MP4"])
parser.add_argument('--pic_ext', nargs='+', default=[".jpg", ".JPG", ".png", ".PNG"])
parser.add_argument('--nw', default='',
                    help="native-wrapper.sh file location")
parser.add_argument('--fps', default=1, type=int,
                    help="framerate at which videos will be scanned WITH reconstruction")
33
parser.add_argument('--total_frames', default=200, type=int)
Clement Pinard's avatar
Clement Pinard committed
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
parser.add_argument('--orientation_weight', default=1, type=float)
parser.add_argument('--resolution_weight', default=1, type=float)
parser.add_argument('--num_neighbours', default=10, type=int)
parser.add_argument('--save_space', action="store_true")


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
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
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]
            set_gps_location(frame,
                             lat=row["location_latitude"],
                             lng=row["location_longitude"],
                             altitude=row["location_altitude"])


def get_georef(metadata):
    relevant_data = metadata[["location_valid", "image_path", "x", "y", "z"]]
    path_list = []
    georef_list = []
    for _, (gps, path, x, y, alt) in relevant_data.iterrows():
        path_list.append(path)
        if gps == 1:
            georef_list.append("{} {} {} {}\n".format(path, x, y, alt))
    return georef_list, path_list


Clement Pinard's avatar
Clement Pinard committed
80
81
def optimal_sample(metadata, num_frames, orientation_weight, resolution_weight):
    metadata["sampled"] = False
nicolas's avatar
nicolas committed
82
    XYZ = metadata[["x", "y", "z"]].values
Clement Pinard's avatar
Clement Pinard committed
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
    axis_angle = metadata[["frame_quat_x", "frame_quat_y", "frame_quat_z"]].values
    if True in metadata["indoor"].unique():
        diameter = (XYZ.max(axis=0) - XYZ.min(axis=0))
        videos = metadata.loc[metadata["indoor"]]["video"].unique()
        new_centroids = 2 * diameter * np.linspace(0, 10, len(videos)).reshape(-1, 1)
        for centroid, v in zip(new_centroids, videos):
            video_index = (metadata["video"] == v).values
            XYZ[video_index] += centroid

    frame_size = metadata["video_quality"].values
    weighted_point_cloud = np.concatenate([XYZ, orientation_weight * axis_angle], axis=1)

    if resolution_weight == 0:
        weights = None
    else:
        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)
    metadata.at[closest, "sampled"] = True
    return metadata


def register_new_cameras(cameras_dataframe, database, camera_dict, model_name="PINHOLE"):
    camera_ids = []
    for _, (w, h, f, hfov, vfov) in cameras_dataframe.iterrows():
        fx = w / (2 * np.tan(hfov * np.pi/360))
        fy = h / (2 * np.tan(vfov * np.pi/360))
        params = np.array([fx, fy, w/2, h/2])
        model_id = rm.CAMERA_MODEL_NAMES[model_name].model_id
        db_id = database.add_camera(model_id, w, h, params, prior_focal_length=True)
        camera_ids.append(db_id)
        camera_dict[db_id] = rm.Camera(id=db_id,
                                       model=model_name,
                                       width=int(w),
                                       height=int(h),
                                       params=params)
    ids_series = pd.Series(camera_ids)
    return cameras_dataframe.set_index(ids_series)


def process_video_folder(videos_list, existing_pictures, output_video_folder, image_path, system, centroid,
124
                         thorough_db, workspace, fps=1, total_frames=500, orientation_weight=1, resolution_weight=1,
Clément Pinard's avatar
Clément Pinard committed
125
                         output_colmap_format="bin", save_space=False, max_sequence_length=1000, **env):
Clement Pinard's avatar
Clement Pinard committed
126
127
128
129
130
131
    proj = Proj(system)
    indoor_videos = []
    final_metadata = []
    video_output_folders = {}
    images = {}
    colmap_cameras = {}
132
    tempfile_database = Path(tempfile.NamedTemporaryFile().name)
Clement Pinard's avatar
Clement Pinard committed
133
    path_lists_output = {}
134
    database = db.COLMAPDatabase.connect(thorough_db)
Clement Pinard's avatar
Clement Pinard committed
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
    database.create_tables()
    to_extract = total_frames - len(existing_pictures)

    print("extracting metadata for {} videos...".format(len(videos_list)))
    for v in tqdm(videos_list):
        width, height, framerate = env["ffmpeg"].get_size_and_framerate(v)
        video_output_folder = output_video_folder / "{}x{}".format(width, height) / v.namebase
        video_output_folder.makedirs_p()
        video_output_folders[v] = video_output_folder

        metadata = am.extract_metadata(v.parent, v, env["pdraw"], proj,
                                       width, height, framerate, centroid)
        final_metadata.append(metadata)
        if metadata["indoor"].iloc[0]:
            indoor_videos.append(v)
    final_metadata = pd.concat(final_metadata, ignore_index=True)
    print("{} outdoor videos".format(len(videos_list) - len(indoor_videos)))
    print("{} indoor videos".format(len(indoor_videos)))

    print("{} frames in total".format(len(final_metadata)))

    cam_fields = ["width", "height", "framerate", "picture_hfov", "picture_vfov"]
    cameras_dataframe = final_metadata[cam_fields].drop_duplicates()
    cameras_dataframe = register_new_cameras(cameras_dataframe, database, colmap_cameras, "PINHOLE")
nicolas's avatar
nicolas committed
159
    print("Cameras : ")
Clement Pinard's avatar
Clement Pinard committed
160
161
162
163
164
165
166
167
    print(cameras_dataframe)
    final_metadata["camera_id"] = 0
    for cam_id, row in cameras_dataframe.iterrows():
        final_metadata.loc[(final_metadata[cam_fields] == row).all(axis=1), "camera_id"] = cam_id
    if any(final_metadata["camera_id"] == 0):
        print("Error")
        print((final_metadata["camera_id"] == 0))

Clément Pinard's avatar
Clément Pinard committed
168
169
170
    if to_extract <= 0:
        final_metadata["sampled"] = False
    elif to_extract < len(final_metadata):
Clement Pinard's avatar
Clement Pinard committed
171
172
173
174
175
176
        print("subsampling based on K-Means, to get {}"
              " frames from videos, for a total of {} frames".format(to_extract, total_frames))
        final_metadata = optimal_sample(final_metadata, total_frames - len(existing_pictures),
                                        orientation_weight,
                                        resolution_weight)
        print("Done.")
Clément Pinard's avatar
Clément Pinard committed
177
178
    else:
        final_metadata["sampled"] = True
Clement Pinard's avatar
Clement Pinard committed
179
180
181

    print("Constructing COLMAP model with {:,} frames".format(len(final_metadata[final_metadata["sampled"]])))

182
183
184
185
    database.commit()
    thorough_db.copy(tempfile_database)
    temp_database = db.COLMAPDatabase.connect(tempfile_database)

Clement Pinard's avatar
Clement Pinard committed
186
    final_metadata["image_path"] = ""
187
188
    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
189
190
191
192
193
        video = row["video"]
        frame = row["frame"]
        camera_id = row["camera_id"]
        current_image_path = video_output_folders[video].relpath(image_path) / video.namebase + "_{:05d}.jpg".format(frame)

194
195
196
        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
197
198
199
200
201
202

        if row["sampled"]:
            frame_qvec = row[["frame_quat_w",
                              "frame_quat_x",
                              "frame_quat_y",
                              "frame_quat_z"]].values
nicolas's avatar
nicolas committed
203
204
            x, y, z = row[["x", "y", "z"]]
            frame_tvec = np.array([x, y, z])
Clement Pinard's avatar
Clement Pinard committed
205
206
207
208
209
210
            if row["location_valid"]:
                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)
211
212
213
214
            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
215
216
217

    database.commit()
    database.close()
218
219
    temp_database.commit()
    temp_database.close()
Clement Pinard's avatar
Clement Pinard committed
220
221
222
    rm.write_model(colmap_cameras, images, {}, output_video_folder, "." + output_colmap_format)
    print("COLMAP model created")

nicolas's avatar
nicolas committed
223
224
225
226
    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
227
228
229
230
231

    print("Extracting frames from videos")

    for v in tqdm(videos_list):
        video_metadata = final_metadata[final_metadata["video"] == v]
nicolas's avatar
nicolas committed
232
        by_time = video_metadata.set_index(pd.to_datetime(video_metadata["time"], unit="us"))
Clement Pinard's avatar
Clement Pinard committed
233
234
        video_folder = video_output_folders[v]
        video_metadata.to_csv(video_folder/"metadata.csv")
nicolas's avatar
nicolas committed
235
236
237
238
239
        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
240
241
        num_chunks = len(video_metadata) // max_sequence_length + 1
        path_lists_output[v]["frames_full"] = [list(frames) for frames in np.array_split(video_metadata["image_path"], num_chunks)]
Clement Pinard's avatar
Clement Pinard committed
242
243
        if save_space:
            frame_ids = list(video_metadata[video_metadata["sampled"]]["frame"].values)
Clément Pinard's avatar
Clément Pinard committed
244
            if len(frame_ids) > 0:
nicolas's avatar
nicolas committed
245
                extracted_frames = env["ffmpeg"].extract_specific_frames(v, video_folder, frame_ids)
Clement Pinard's avatar
Clement Pinard committed
246
        else:
nicolas's avatar
nicolas committed
247
248
            extracted_frames = env["ffmpeg"].extract_images(v, video_folder)
        set_gps(extracted_frames, video_metadata, image_path)
Clement Pinard's avatar
Clement Pinard committed
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

    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), [])
    args.workspace.makedirs_p()
    output_video_folder = args.output_folder / "Videos"
    output_video_folder.makedirs_p()
    env["image_path"] = args.output_folder
    env["output_video_folder"] = output_video_folder
    env["existing_pictures"] = sum((list(args.output_folder.walkfiles('*{}'.format(ext))) for ext in args.pic_ext), [])
    env["pdraw"] = PDraw(args.nw, quiet=True)
    env["ffmpeg"] = FFMpeg(quiet=True)

    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:
        with open(args.output_folder/"video_frames_for_thorough_scan.txt", "w") as f:
            f.write("\n".join(lists["thorough"]))
        with open(args.output_folder/"georef.txt", "w") as f:
            f.write("\n".join(lists["georef"]))
        for v in env["videos_list"]:
            with open(extracted_video_folders[v] / "to_scan.txt", "w") as f:
                f.write("\n".join(lists[v]))