File size: 15,818 Bytes
f90241e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# Script to pre-process the arkitscenes dataset.
# Usage:
# python3 datasets_preprocess/preprocess_arkitscenes.py --arkitscenes_dir /path/to/arkitscenes --precomputed_pairs /path/to/arkitscenes_pairs
# --------------------------------------------------------
import os
import json
import os.path as osp
import decimal
import argparse
import math
from bisect import bisect_left
from PIL import Image
import numpy as np
import quaternion
from scipy import interpolate
import cv2


def get_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument('--arkitscenes_dir', required=True)
    parser.add_argument('--precomputed_pairs', required=True)
    parser.add_argument('--output_dir', default='data/arkitscenes_processed')
    return parser


def value_to_decimal(value, decimal_places):
    decimal.getcontext().rounding = decimal.ROUND_HALF_UP  # define rounding method
    return decimal.Decimal(str(float(value))).quantize(decimal.Decimal('1e-{}'.format(decimal_places)))


def closest(value, sorted_list):
    index = bisect_left(sorted_list, value)
    if index == 0:
        return sorted_list[0]
    elif index == len(sorted_list):
        return sorted_list[-1]
    else:
        value_before = sorted_list[index - 1]
        value_after = sorted_list[index]
        if value_after - value < value - value_before:
            return value_after
        else:
            return value_before


def get_up_vectors(pose_device_to_world):
    return np.matmul(pose_device_to_world, np.array([[0.0], [-1.0], [0.0], [0.0]]))


def get_right_vectors(pose_device_to_world):
    return np.matmul(pose_device_to_world, np.array([[1.0], [0.0], [0.0], [0.0]]))


def read_traj(traj_path):
    quaternions = []
    poses = []
    timestamps = []
    poses_p_to_w = []
    with open(traj_path) as f:
        traj_lines = f.readlines()
        for line in traj_lines:
            tokens = line.split()
            assert len(tokens) == 7
            traj_timestamp = float(tokens[0])

            timestamps_decimal_value = value_to_decimal(traj_timestamp, 3)
            timestamps.append(float(timestamps_decimal_value))  # for spline interpolation

            angle_axis = [float(tokens[1]), float(tokens[2]), float(tokens[3])]
            r_w_to_p, _ = cv2.Rodrigues(np.asarray(angle_axis))
            t_w_to_p = np.asarray([float(tokens[4]), float(tokens[5]), float(tokens[6])])

            pose_w_to_p = np.eye(4)
            pose_w_to_p[:3, :3] = r_w_to_p
            pose_w_to_p[:3, 3] = t_w_to_p

            pose_p_to_w = np.linalg.inv(pose_w_to_p)

            r_p_to_w_as_quat = quaternion.from_rotation_matrix(pose_p_to_w[:3, :3])
            t_p_to_w = pose_p_to_w[:3, 3]
            poses_p_to_w.append(pose_p_to_w)
            poses.append(t_p_to_w)
            quaternions.append(r_p_to_w_as_quat)
    return timestamps, poses, quaternions, poses_p_to_w


def main(rootdir, pairsdir, outdir):
    os.makedirs(outdir, exist_ok=True)

    subdirs = ['Test', 'Training']
    for subdir in subdirs:
        if not osp.isdir(osp.join(rootdir, subdir)):
            continue
        # STEP 1: list all scenes
        outsubdir = osp.join(outdir, subdir)
        os.makedirs(outsubdir, exist_ok=True)
        listfile = osp.join(pairsdir, subdir, 'scene_list.json')
        with open(listfile, 'r') as f:
            scene_dirs = json.load(f)

        valid_scenes = []
        for scene_subdir in scene_dirs:
            out_scene_subdir = osp.join(outsubdir, scene_subdir)
            os.makedirs(out_scene_subdir, exist_ok=True)

            scene_dir = osp.join(rootdir, subdir, scene_subdir)
            depth_dir = osp.join(scene_dir, 'lowres_depth')
            rgb_dir = osp.join(scene_dir, 'vga_wide')
            intrinsics_dir = osp.join(scene_dir, 'vga_wide_intrinsics')
            traj_path = osp.join(scene_dir, 'lowres_wide.traj')

            # STEP 2: read selected_pairs.npz
            selected_pairs_path = osp.join(pairsdir, subdir, scene_subdir, 'selected_pairs.npz')
            selected_npz = np.load(selected_pairs_path)
            selection, pairs = selected_npz['selection'], selected_npz['pairs']
            selected_sky_direction_scene = str(selected_npz['sky_direction_scene'][0])
            if len(selection) == 0 or len(pairs) == 0:
                # not a valid scene
                continue
            valid_scenes.append(scene_subdir)

            # STEP 3: parse the scene and export the list of valid (K, pose, rgb, depth) and convert images
            scene_metadata_path = osp.join(out_scene_subdir, 'scene_metadata.npz')
            if osp.isfile(scene_metadata_path):
                continue
            else:
                print(f'parsing {scene_subdir}')
                # loads traj
                timestamps, poses, quaternions, poses_cam_to_world = read_traj(traj_path)

                poses = np.array(poses)
                quaternions = np.array(quaternions, dtype=np.quaternion)
                quaternions = quaternion.unflip_rotors(quaternions)
                timestamps = np.array(timestamps)

                selected_images = [(basename, basename.split(".png")[0].split("_")[1]) for basename in selection]
                timestamps_selected = [float(frame_id) for _, frame_id in selected_images]

                sky_direction_scene, trajectories, intrinsics, images = convert_scene_metadata(scene_subdir,
                                                                                               intrinsics_dir,
                                                                                               timestamps,
                                                                                               quaternions,
                                                                                               poses,
                                                                                               poses_cam_to_world,
                                                                                               selected_images,
                                                                                               timestamps_selected)
                assert selected_sky_direction_scene == sky_direction_scene

                os.makedirs(os.path.join(out_scene_subdir, 'vga_wide'), exist_ok=True)
                os.makedirs(os.path.join(out_scene_subdir, 'lowres_depth'), exist_ok=True)
                assert isinstance(sky_direction_scene, str)
                for basename in images:
                    img_out = os.path.join(out_scene_subdir, 'vga_wide', basename.replace('.png', '.jpg'))
                    depth_out = os.path.join(out_scene_subdir, 'lowres_depth', basename)
                    if osp.isfile(img_out) and osp.isfile(depth_out):
                        continue

                    vga_wide_path = osp.join(rgb_dir, basename)
                    depth_path = osp.join(depth_dir, basename)

                    img = Image.open(vga_wide_path)
                    depth = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED)

                    # rotate the image
                    if sky_direction_scene == 'RIGHT':
                        try:
                            img = img.transpose(Image.Transpose.ROTATE_90)
                        except Exception:
                            img = img.transpose(Image.ROTATE_90)
                        depth = cv2.rotate(depth, cv2.ROTATE_90_COUNTERCLOCKWISE)
                    elif sky_direction_scene == 'LEFT':
                        try:
                            img = img.transpose(Image.Transpose.ROTATE_270)
                        except Exception:
                            img = img.transpose(Image.ROTATE_270)
                        depth = cv2.rotate(depth, cv2.ROTATE_90_CLOCKWISE)
                    elif sky_direction_scene == 'DOWN':
                        try:
                            img = img.transpose(Image.Transpose.ROTATE_180)
                        except Exception:
                            img = img.transpose(Image.ROTATE_180)
                        depth = cv2.rotate(depth, cv2.ROTATE_180)

                    W, H = img.size
                    if not osp.isfile(img_out):
                        img.save(img_out)

                    depth = cv2.resize(depth, (W, H), interpolation=cv2.INTER_NEAREST_EXACT)
                    if not osp.isfile(depth_out):  # avoid destroying the base dataset when you mess up the paths
                        cv2.imwrite(depth_out, depth)

                # save at the end
                np.savez(scene_metadata_path,
                         trajectories=trajectories,
                         intrinsics=intrinsics,
                         images=images,
                         pairs=pairs)

        outlistfile = osp.join(outsubdir, 'scene_list.json')
        with open(outlistfile, 'w') as f:
            json.dump(valid_scenes, f)

        # STEP 5: concat all scene_metadata.npz into a single file
        scene_data = {}
        for scene_subdir in valid_scenes:
            scene_metadata_path = osp.join(outsubdir, scene_subdir, 'scene_metadata.npz')
            with np.load(scene_metadata_path) as data:
                trajectories = data['trajectories']
                intrinsics = data['intrinsics']
                images = data['images']
                pairs = data['pairs']
            scene_data[scene_subdir] = {'trajectories': trajectories,
                                        'intrinsics': intrinsics,
                                        'images': images,
                                        'pairs': pairs}
        offset = 0
        counts = []
        scenes = []
        sceneids = []
        images = []
        intrinsics = []
        trajectories = []
        pairs = []
        for scene_idx, (scene_subdir, data) in enumerate(scene_data.items()):
            num_imgs = data['images'].shape[0]
            img_pairs = data['pairs']

            scenes.append(scene_subdir)
            sceneids.extend([scene_idx] * num_imgs)

            images.append(data['images'])

            K = np.expand_dims(np.eye(3), 0).repeat(num_imgs, 0)
            K[:, 0, 0] = [fx for _, _, fx, _, _, _ in data['intrinsics']]
            K[:, 1, 1] = [fy for _, _, _, fy, _, _ in data['intrinsics']]
            K[:, 0, 2] = [hw for _, _, _, _, hw, _ in data['intrinsics']]
            K[:, 1, 2] = [hh for _, _, _, _, _, hh in data['intrinsics']]

            intrinsics.append(K)
            trajectories.append(data['trajectories'])

            # offset pairs
            img_pairs[:, 0:2] += offset
            pairs.append(img_pairs)
            counts.append(offset)

            offset += num_imgs

        images = np.concatenate(images, axis=0)
        intrinsics = np.concatenate(intrinsics, axis=0)
        trajectories = np.concatenate(trajectories, axis=0)
        pairs = np.concatenate(pairs, axis=0)
        np.savez(osp.join(outsubdir, 'all_metadata.npz'),
                 counts=counts,
                 scenes=scenes,
                 sceneids=sceneids,
                 images=images,
                 intrinsics=intrinsics,
                 trajectories=trajectories,
                 pairs=pairs)


def convert_scene_metadata(scene_subdir, intrinsics_dir,
                           timestamps, quaternions, poses, poses_cam_to_world,
                           selected_images, timestamps_selected):
    # find scene orientation
    sky_direction_scene, rotated_to_cam = find_scene_orientation(poses_cam_to_world)

    # find/compute pose for selected timestamps
    # most images have a valid timestamp / exact pose associated
    timestamps_selected = np.array(timestamps_selected)
    spline = interpolate.interp1d(timestamps, poses, kind='linear', axis=0)
    interpolated_rotations = quaternion.squad(quaternions, timestamps, timestamps_selected)
    interpolated_positions = spline(timestamps_selected)

    trajectories = []
    intrinsics = []
    images = []
    for i, (basename, frame_id) in enumerate(selected_images):
        intrinsic_fn = osp.join(intrinsics_dir, f"{scene_subdir}_{frame_id}.pincam")
        if not osp.exists(intrinsic_fn):
            intrinsic_fn = osp.join(intrinsics_dir, f"{scene_subdir}_{float(frame_id) - 0.001:.3f}.pincam")
        if not osp.exists(intrinsic_fn):
            intrinsic_fn = osp.join(intrinsics_dir, f"{scene_subdir}_{float(frame_id) + 0.001:.3f}.pincam")
        assert osp.exists(intrinsic_fn)
        w, h, fx, fy, hw, hh = np.loadtxt(intrinsic_fn)  # PINHOLE

        pose = np.eye(4)
        pose[:3, :3] = quaternion.as_rotation_matrix(interpolated_rotations[i])
        pose[:3, 3] = interpolated_positions[i]

        images.append(basename)
        if sky_direction_scene == 'RIGHT' or sky_direction_scene == 'LEFT':
            intrinsics.append([h, w, fy, fx, hh, hw])  # swapped intrinsics
        else:
            intrinsics.append([w, h, fx, fy, hw, hh])
        trajectories.append(pose  @ rotated_to_cam)  # pose_cam_to_world @ rotated_to_cam = rotated(cam) to world

    return sky_direction_scene, trajectories, intrinsics, images


def find_scene_orientation(poses_cam_to_world):
    if len(poses_cam_to_world) > 0:
        up_vector = sum(get_up_vectors(p) for p in poses_cam_to_world) / len(poses_cam_to_world)
        right_vector = sum(get_right_vectors(p) for p in poses_cam_to_world) / len(poses_cam_to_world)
        up_world = np.array([[0.0], [0.0], [1.0], [0.0]])
    else:
        up_vector = np.array([[0.0], [-1.0], [0.0], [0.0]])
        right_vector = np.array([[1.0], [0.0], [0.0], [0.0]])
        up_world = np.array([[0.0], [0.0], [1.0], [0.0]])

    # value between 0, 180
    device_up_to_world_up_angle = np.arccos(np.clip(np.dot(np.transpose(up_world),
                                                           up_vector), -1.0, 1.0)).item() * 180.0 / np.pi
    device_right_to_world_up_angle = np.arccos(np.clip(np.dot(np.transpose(up_world),
                                                              right_vector), -1.0, 1.0)).item() * 180.0 / np.pi

    up_closest_to_90 = abs(device_up_to_world_up_angle - 90.0) < abs(device_right_to_world_up_angle - 90.0)
    if up_closest_to_90:
        assert abs(device_up_to_world_up_angle - 90.0) < 45.0
        # LEFT
        if device_right_to_world_up_angle > 90.0:
            sky_direction_scene = 'LEFT'
            cam_to_rotated_q = quaternion.from_rotation_vector([0.0, 0.0, math.pi / 2.0])
        else:
            # note that in metadata.csv RIGHT does not exist, but again it's not accurate...
            # well, turns out there are scenes oriented like this
            # for example Training/41124801
            sky_direction_scene = 'RIGHT'
            cam_to_rotated_q = quaternion.from_rotation_vector([0.0, 0.0, -math.pi / 2.0])
    else:
        # right is close to 90
        assert abs(device_right_to_world_up_angle - 90.0) < 45.0
        if device_up_to_world_up_angle > 90.0:
            sky_direction_scene = 'DOWN'
            cam_to_rotated_q = quaternion.from_rotation_vector([0.0, 0.0, math.pi])
        else:
            sky_direction_scene = 'UP'
            cam_to_rotated_q = quaternion.quaternion(1, 0, 0, 0)
    cam_to_rotated = np.eye(4)
    cam_to_rotated[:3, :3] = quaternion.as_rotation_matrix(cam_to_rotated_q)
    rotated_to_cam = np.linalg.inv(cam_to_rotated)
    return sky_direction_scene, rotated_to_cam


if __name__ == '__main__':
    parser = get_parser()
    args = parser.parse_args()
    main(args.arkitscenes_dir, args.precomputed_pairs, args.output_dir)