File size: 18,320 Bytes
63f3cf2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File   pram -> basicdataset
@IDE    PyCharm
@Author fx221@cam.ac.uk
@Date   29/01/2024 14:27
=================================================='''
import torchvision.transforms.functional as tvf
import torchvision.transforms as tvt
import os.path as osp
import numpy as np
import cv2
from colmap_utils.read_write_model import qvec2rotmat, read_model
from dataset.utils import normalize_size


class BasicDataset:
    def __init__(self,
                 img_list_fn,
                 feature_dir,
                 sfm_path,
                 seg_fn,
                 dataset_path,
                 n_class,
                 dataset,
                 nfeatures=1024,
                 query_p3d_fn=None,
                 train=True,
                 with_aug=False,
                 min_inliers=0,
                 max_inliers=4096,
                 random_inliers=False,
                 jitter_params=None,
                 scale_params=None,
                 image_dim=1,
                 pre_load=False,
                 query_info_path=None,
                 sc_mean_scale_fn=None,
                 ):
        self.n_class = n_class
        self.train = train
        self.min_inliers = min_inliers
        self.max_inliers = max_inliers if max_inliers < nfeatures else nfeatures
        self.random_inliers = random_inliers
        self.dataset_path = dataset_path
        self.with_aug = with_aug
        self.dataset = dataset
        self.jitter_params = jitter_params
        self.scale_params = scale_params
        self.image_dim = image_dim
        self.image_prefix = ''

        train_transforms = []
        if self.with_aug:
            train_transforms.append(tvt.ColorJitter(
                brightness=jitter_params['brightness'],
                contrast=jitter_params['contrast'],
                saturation=jitter_params['saturation'],
                hue=jitter_params['hue']))
            if jitter_params['blur'] > 0:
                train_transforms.append(tvt.GaussianBlur(kernel_size=int(jitter_params['blur'])))
        self.train_transforms = tvt.Compose(train_transforms)

        # only for testing of query images
        if not self.train:
            data = np.load(query_p3d_fn, allow_pickle=True)[()]
            self.img_p3d = data
        else:
            self.img_p3d = {}

        self.img_fns = []
        with open(img_list_fn, 'r') as f:
            lines = f.readlines()
            for l in lines:
                l = l.strip()
                self.img_fns.append(l)
        print('Load {} images from {} for {}...'.format(len(self.img_fns), dataset, 'training' if train else 'eval'))
        self.feats = {}
        if train:
            self.cameras, self.images, point3Ds = read_model(path=sfm_path, ext='.bin')
            self.name_to_id = {image.name: i for i, image in self.images.items()}

        data = np.load(seg_fn, allow_pickle=True)[()]
        p3d_id = data['id']
        seg_id = data['label']
        self.p3d_seg = {p3d_id[i]: seg_id[i] for i in range(p3d_id.shape[0])}
        self.p3d_xyzs = {}

        for pid in self.p3d_seg.keys():
            p3d = point3Ds[pid]
            self.p3d_xyzs[pid] = p3d.xyz

        with open(sc_mean_scale_fn, 'r') as f:
            lines = f.readlines()
            for l in lines:
                l = l.strip().split()
                self.mean_xyz = np.array([float(v) for v in l[:3]])
                self.scale_xyz = np.array([float(v) for v in l[3:]])

        if not train:
            self.query_info = self.read_query_info(path=query_info_path)

        self.nfeatures = nfeatures
        self.feature_dir = feature_dir
        print('Pre loaded {} feats, mean xyz {}, scale xyz {}'.format(len(self.feats.keys()), self.mean_xyz,
                                                                      self.scale_xyz))

    def normalize_p3ds(self, p3ds):
        mean_p3ds = np.ceil(np.mean(p3ds, axis=0))
        p3ds_ = p3ds - mean_p3ds
        dx = np.max(abs(p3ds_[:, 0]))
        dy = np.max(abs(p3ds_[:, 1]))
        dz = np.max(abs(p3ds_[:, 2]))
        scale_p3ds = np.ceil(np.array([dx, dy, dz], dtype=float).reshape(3, ))
        scale_p3ds[scale_p3ds < 1] = 1
        scale_p3ds[scale_p3ds == 0] = 1
        return mean_p3ds, scale_p3ds

    def read_query_info(self, path):
        query_info = {}
        with open(path, 'r') as f:
            lines = f.readlines()
            for l in lines:
                l = l.strip().split()
                image_name = l[0]
                cam_model = l[1]
                h, w = int(l[2]), int(l[3])
                params = np.array([float(v) for v in l[4:]])
                query_info[image_name] = {
                    'width': w,
                    'height': h,
                    'model': cam_model,
                    'params': params,
                }
        return query_info

    def extract_intrinsic_extrinsic_params(self, image_id):
        cam = self.cameras[self.images[image_id].camera_id]
        params = cam.params
        model = cam.model
        if model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"):
            fx = fy = params[0]
            cx = params[1]
            cy = params[2]
        elif model in ("PINHOLE", "OPENCV", "OPENCV_FISHEYE", "FULL_OPENCV"):
            fx = params[0]
            fy = params[1]
            cx = params[2]
            cy = params[3]
        else:
            raise Exception("Camera model not supported")
        K = np.eye(3, dtype=float)
        K[0, 0] = fx
        K[1, 1] = fy
        K[0, 2] = cx
        K[1, 2] = cy

        qvec = self.images[image_id].qvec
        tvec = self.images[image_id].tvec
        R = qvec2rotmat(qvec=qvec)
        P = np.eye(4, dtype=float)
        P[:3, :3] = R
        P[:3, 3] = tvec.reshape(3, )

        return {'K': K, 'P': P}

    def get_item_train(self, idx):
        img_name = self.img_fns[idx]
        if img_name in self.feats.keys():
            feat_data = self.feats[img_name]
        else:
            feat_data = np.load(osp.join(self.feature_dir, img_name.replace('/', '+') + '.npy'), allow_pickle=True)[()]
        # descs = feat_data['descriptors']  # [N, D]
        scores = feat_data['scores']  # [N, 1]
        kpts = feat_data['keypoints']  # [N, 2]
        image_size = feat_data['image_size']

        nfeat = kpts.shape[0]

        # print(img_name, self.name_to_id[img_name])
        p3d_ids = self.images[self.name_to_id[img_name]].point3D_ids
        p3d_xyzs = np.zeros(shape=(nfeat, 3), dtype=float)

        seg_ids = np.zeros(shape=(nfeat,), dtype=int)  # + self.n_class - 1
        for i in range(nfeat):
            p3d = p3d_ids[i]
            if p3d in self.p3d_seg.keys():
                seg_ids[i] = self.p3d_seg[p3d] + 1  # 0 for invalid
                if seg_ids[i] == -1:
                    seg_ids[i] = 0

            if p3d in self.p3d_xyzs.keys():
                p3d_xyzs[i] = self.p3d_xyzs[p3d]

        seg_ids = np.array(seg_ids).reshape(-1, )

        n_inliers = np.sum(seg_ids > 0)
        n_outliers = np.sum(seg_ids == 0)
        inlier_ids = np.where(seg_ids > 0)[0]
        outlier_ids = np.where(seg_ids == 0)[0]

        if n_inliers <= self.min_inliers:
            sel_inliers = n_inliers
            sel_outliers = self.nfeatures - sel_inliers

            out_ids = np.arange(n_outliers)
            np.random.shuffle(out_ids)
            sel_ids = np.hstack([inlier_ids, outlier_ids[out_ids[:self.nfeatures - n_inliers]]])
        else:
            sel_inliers = np.random.randint(self.min_inliers, self.max_inliers)
            if sel_inliers > n_inliers:
                sel_inliers = n_inliers

            if sel_inliers + n_outliers < self.nfeatures:
                sel_inliers = self.nfeatures - n_outliers

            sel_outliers = self.nfeatures - sel_inliers

            in_ids = np.arange(n_inliers)
            np.random.shuffle(in_ids)
            sel_inlier_ids = inlier_ids[in_ids[:sel_inliers]]

            out_ids = np.arange(n_outliers)
            np.random.shuffle(out_ids)
            sel_outlier_ids = outlier_ids[out_ids[:sel_outliers]]

            sel_ids = np.hstack([sel_inlier_ids, sel_outlier_ids])

        # sel_descs = descs[sel_ids]
        sel_scores = scores[sel_ids]
        sel_kpts = kpts[sel_ids]
        sel_seg_ids = seg_ids[sel_ids]
        sel_xyzs = p3d_xyzs[sel_ids]

        shuffle_ids = np.arange(sel_ids.shape[0])
        np.random.shuffle(shuffle_ids)
        # sel_descs = sel_descs[shuffle_ids]
        sel_scores = sel_scores[shuffle_ids]
        sel_kpts = sel_kpts[shuffle_ids]
        sel_seg_ids = sel_seg_ids[shuffle_ids]
        sel_xyzs = sel_xyzs[shuffle_ids]

        if sel_kpts.shape[0] < self.nfeatures:
            # print(sel_descs.shape, sel_kpts.shape, sel_scores.shape, sel_seg_ids.shape, sel_xyzs.shape)
            valid_sel_ids = np.array([v for v in range(sel_kpts.shape[0]) if sel_seg_ids[v] > 0], dtype=int)
            # ref_sel_id = np.random.choice(valid_sel_ids, size=1)[0]
            if valid_sel_ids.shape[0] == 0:
                valid_sel_ids = np.array([v for v in range(sel_kpts.shape[0])], dtype=int)
            random_n = self.nfeatures - sel_kpts.shape[0]
            random_scores = np.random.random((random_n,))
            random_kpts, random_seg_ids, random_xyzs = self.random_points_from_reference(
                n=random_n,
                ref_kpts=sel_kpts[valid_sel_ids],
                ref_segs=sel_seg_ids[valid_sel_ids],
                ref_xyzs=sel_xyzs[valid_sel_ids],
                radius=5,
            )
            # sel_descs = np.vstack([sel_descs, random_descs])
            sel_scores = np.hstack([sel_scores, random_scores])
            sel_kpts = np.vstack([sel_kpts, random_kpts])
            sel_seg_ids = np.hstack([sel_seg_ids, random_seg_ids])
            sel_xyzs = np.vstack([sel_xyzs, random_xyzs])

        gt_n_seg = np.zeros(shape=(self.n_class,), dtype=int)
        gt_cls = np.zeros(shape=(self.n_class,), dtype=int)
        gt_cls_dist = np.zeros(shape=(self.n_class,), dtype=float)
        uids = np.unique(sel_seg_ids).tolist()
        for uid in uids:
            if uid == 0:
                continue
            gt_cls[uid] = 1
            gt_n_seg[uid] = np.sum(sel_seg_ids == uid)
            gt_cls_dist[uid] = np.sum(seg_ids == uid) / np.sum(seg_ids > 0)  # [valid_id / total_valid_id]

        param_out = self.extract_intrinsic_extrinsic_params(image_id=self.name_to_id[img_name])

        img = self.read_image(image_name=img_name)
        image_size = img.shape[:2]
        if self.image_dim == 1:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        else:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        if self.with_aug:
            nh = img.shape[0]
            nw = img.shape[1]
            if self.scale_params is not None:
                do_scale = np.random.random()
                if do_scale <= 0.25:
                    p = np.random.randint(0, 11)
                    s = self.scale_params[0] + (self.scale_params[1] - self.scale_params[0]) / 10 * p
                    nh = int(img.shape[0] * s)
                    nw = int(img.shape[1] * s)
                    sh = nh / img.shape[0]
                    sw = nw / img.shape[1]
                    sel_kpts[:, 0] = sel_kpts[:, 0] * sw
                    sel_kpts[:, 1] = sel_kpts[:, 1] * sh
                    img = cv2.resize(img, dsize=(nw, nh))

            brightness = np.random.uniform(-self.jitter_params['brightness'], self.jitter_params['brightness']) * 255
            contrast = 1 + np.random.uniform(-self.jitter_params['contrast'], self.jitter_params['contrast'])
            img = cv2.addWeighted(img, contrast, img, 0, brightness)
            img = np.clip(img, a_min=0, a_max=255)
            if self.image_dim == 1:
                img = img[..., None]
            img = img.astype(float) / 255.
            image_size = np.array([nh, nw], dtype=int)
        else:
            if self.image_dim == 1:
                img = img[..., None].astype(float) / 255.

        output = {
            # 'descriptors': sel_descs,  # may not be used
            'scores': sel_scores,
            'keypoints': sel_kpts,
            'norm_keypoints': normalize_size(x=sel_kpts, size=image_size),
            'image': [img],
            'gt_seg': sel_seg_ids,
            'gt_cls': gt_cls,
            'gt_cls_dist': gt_cls_dist,
            'gt_n_seg': gt_n_seg,
            'file_name': img_name,
            'prefix_name': self.image_prefix,
            # 'mean_xyz': self.mean_xyz,
            # 'scale_xyz': self.scale_xyz,
            # 'gt_sc': sel_xyzs,
            # 'gt_norm_sc': (sel_xyzs - self.mean_xyz) / self.scale_xyz,
            'K': param_out['K'],
            'gt_P': param_out['P']
        }
        return output

    def get_item_test(self, idx):

        # evaluation of recognition only
        img_name = self.img_fns[idx]
        feat_data = np.load(osp.join(self.feature_dir, img_name.replace('/', '+') + '.npy'), allow_pickle=True)[()]
        descs = feat_data['descriptors']  # [N, D]
        scores = feat_data['scores']  # [N, 1]
        kpts = feat_data['keypoints']  # [N, 2]
        image_size = feat_data['image_size']

        nfeat = descs.shape[0]

        if img_name in self.img_p3d.keys():
            p3d_ids = self.img_p3d[img_name]
        p3d_xyzs = np.zeros(shape=(nfeat, 3), dtype=float)
        seg_ids = np.zeros(shape=(nfeat,), dtype=int)  # attention! by default invalid!!!
        for i in range(nfeat):
            p3d = p3d_ids[i]
            if p3d in self.p3d_seg.keys():
                seg_ids[i] = self.p3d_seg[p3d] + 1
                if seg_ids[i] == -1:
                    seg_ids[i] = 0  # 0  for in valid

            if p3d in self.p3d_xyzs.keys():
                p3d_xyzs[i] = self.p3d_xyzs[p3d]

        seg_ids = np.array(seg_ids).reshape(-1, )

        if self.nfeatures > 0:
            sorted_ids = np.argsort(scores)[::-1][:self.nfeatures]  # large to small
            descs = descs[sorted_ids]
            scores = scores[sorted_ids]
            kpts = kpts[sorted_ids]
            p3d_xyzs = p3d_xyzs[sorted_ids]

            seg_ids = seg_ids[sorted_ids]

        gt_n_seg = np.zeros(shape=(self.n_class,), dtype=int)
        gt_cls = np.zeros(shape=(self.n_class,), dtype=int)
        gt_cls_dist = np.zeros(shape=(self.n_class,), dtype=float)
        uids = np.unique(seg_ids).tolist()
        for uid in uids:
            if uid == 0:
                continue
            gt_cls[uid] = 1
            gt_n_seg[uid] = np.sum(seg_ids == uid)
            gt_cls_dist[uid] = np.sum(seg_ids == uid) / np.sum(
                seg_ids < self.n_class - 1)  # [valid_id / total_valid_id]

        gt_cls[0] = 0

        img = self.read_image(image_name=img_name)
        if self.image_dim == 1:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            img = img[..., None].astype(float) / 255.
        else:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(float) / 255.
        return {
            'descriptors': descs,
            'scores': scores,
            'keypoints': kpts,
            'image_size': image_size,
            'norm_keypoints': normalize_size(x=kpts, size=image_size),
            'gt_seg': seg_ids,
            'gt_cls': gt_cls,
            'gt_cls_dist': gt_cls_dist,
            'gt_n_seg': gt_n_seg,
            'file_name': img_name,
            'prefix_name': self.image_prefix,
            'image': [img],

            'mean_xyz': self.mean_xyz,
            'scale_xyz': self.scale_xyz,
            'gt_sc': p3d_xyzs,
            'gt_norm_sc': (p3d_xyzs - self.mean_xyz) / self.scale_xyz
        }

    def __getitem__(self, idx):
        if self.train:
            return self.get_item_train(idx=idx)
        else:
            return self.get_item_test(idx=idx)

    def __len__(self):
        return len(self.img_fns)

    def read_image(self, image_name):
        return cv2.imread(osp.join(self.dataset_path, image_name))

    def jitter_augmentation(self, img, params):
        brightness, contrast, saturation, hue = params
        p = np.random.randint(0, 20) / 20
        b = brightness[0] + (brightness[1] - brightness[0]) / 20 * p
        img = tvf.adjust_brightness(img=img, brightness_factor=b)

        p = np.random.randint(0, 20) / 20
        c = contrast[0] + (contrast[1] - contrast[0]) / 20 * p
        img = tvf.adjust_contrast(img=img, contrast_factor=c)

        p = np.random.randint(0, 20) / 20
        s = saturation[0] + (saturation[1] - saturation[0]) / 20 * p
        img = tvf.adjust_saturation(img=img, saturation_factor=s)

        p = np.random.randint(0, 20) / 20
        h = hue[0] + (hue[1] - hue[0]) / 20 * p
        img = tvf.adjust_hue(img=img, hue_factor=h)

        return img

    def random_points(self, n, d, h, w):
        desc = np.random.random((n, d))
        desc = desc / np.linalg.norm(desc, ord=2, axis=1)[..., None]
        xs = np.random.randint(0, w - 1, size=(n, 1))
        ys = np.random.randint(0, h - 1, size=(n, 1))
        kpts = np.hstack([xs, ys])
        return desc, kpts

    def random_points_from_reference(self, n, ref_kpts, ref_segs, ref_xyzs, radius=5):
        n_ref = ref_kpts.shape[0]
        if n_ref < n:
            ref_ids = np.random.choice([i for i in range(n_ref)], size=n).tolist()
        else:
            ref_ids = [i for i in range(n)]

        new_xs = []
        new_ys = []
        # new_descs = []
        new_segs = []
        new_xyzs = []
        for i in ref_ids:
            nx = np.random.randint(-radius, radius) + ref_kpts[i, 0]
            ny = np.random.randint(-radius, radius) + ref_kpts[i, 1]

            new_xs.append(nx)
            new_ys.append(ny)
            # new_descs.append(ref_descs[i])
            new_segs.append(ref_segs[i])
            new_xyzs.append(ref_xyzs[i])

        new_xs = np.array(new_xs).reshape(n, 1)
        new_ys = np.array(new_ys).reshape(n, 1)
        new_segs = np.array(new_segs).reshape(n, )
        new_kpts = np.hstack([new_xs, new_ys])
        # new_descs = np.array(new_descs).reshape(n, -1)
        new_xyzs = np.array(new_xyzs)
        return new_kpts, new_segs, new_xyzs