File size: 62,143 Bytes
7b7527a |
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 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import typing
try:
from collections.abc import Sequence
except Exception:
from collections import Sequence
import cv2
import copy
import math
import numpy as np
from .operators import register_op, BaseOperator, Resize
from .op_helper import jaccard_overlap, gaussian2D, gaussian_radius, draw_umich_gaussian
from .atss_assigner import ATSSAssigner
from scipy import ndimage
from ppdet.modeling import bbox_utils
from ppdet.utils.logger import setup_logger
from ppdet.modeling.keypoint_utils import get_affine_transform, affine_transform
logger = setup_logger(__name__)
__all__ = [
'PadBatch',
'BatchRandomResize',
'Gt2YoloTarget',
'Gt2FCOSTarget',
'Gt2TTFTarget',
'Gt2Solov2Target',
'Gt2SparseTarget',
'PadMaskBatch',
'Gt2GFLTarget',
'Gt2CenterNetTarget',
'Gt2CenterTrackTarget',
'PadGT',
'PadRGT',
]
@register_op
class PadBatch(BaseOperator):
"""
Pad a batch of samples so they can be divisible by a stride.
The layout of each image should be 'CHW'.
Args:
pad_to_stride (int): If `pad_to_stride > 0`, pad zeros to ensure
height and width is divisible by `pad_to_stride`.
"""
def __init__(self, pad_to_stride=0):
super(PadBatch, self).__init__()
self.pad_to_stride = pad_to_stride
def __call__(self, samples, context=None):
"""
Args:
samples (list): a batch of sample, each is dict.
"""
coarsest_stride = self.pad_to_stride
# multi scale input is nested list
if isinstance(samples,
typing.Sequence) and len(samples) > 0 and isinstance(
samples[0], typing.Sequence):
inner_samples = samples[0]
else:
inner_samples = samples
max_shape = np.array(
[data['image'].shape for data in inner_samples]).max(axis=0)
if coarsest_stride > 0:
max_shape[1] = int(
np.ceil(max_shape[1] / coarsest_stride) * coarsest_stride)
max_shape[2] = int(
np.ceil(max_shape[2] / coarsest_stride) * coarsest_stride)
for data in inner_samples:
im = data['image']
im_c, im_h, im_w = im.shape[:]
padding_im = np.zeros(
(im_c, max_shape[1], max_shape[2]), dtype=np.float32)
padding_im[:, :im_h, :im_w] = im
data['image'] = padding_im
if 'semantic' in data and data['semantic'] is not None:
semantic = data['semantic']
padding_sem = np.zeros(
(1, max_shape[1], max_shape[2]), dtype=np.float32)
padding_sem[:, :im_h, :im_w] = semantic
data['semantic'] = padding_sem
if 'gt_segm' in data and data['gt_segm'] is not None:
gt_segm = data['gt_segm']
padding_segm = np.zeros(
(gt_segm.shape[0], max_shape[1], max_shape[2]),
dtype=np.uint8)
padding_segm[:, :im_h, :im_w] = gt_segm
data['gt_segm'] = padding_segm
return samples
@register_op
class BatchRandomResize(BaseOperator):
"""
Resize image to target size randomly. random target_size and interpolation method
Args:
target_size (int, list, tuple): image target size, if random size is True, must be list or tuple
keep_ratio (bool): whether keep_raio or not, default true
interp (int): the interpolation method
random_size (bool): whether random select target size of image
random_interp (bool): whether random select interpolation method
"""
def __init__(self,
target_size,
keep_ratio,
interp=cv2.INTER_NEAREST,
random_size=True,
random_interp=False):
super(BatchRandomResize, self).__init__()
self.keep_ratio = keep_ratio
self.interps = [
cv2.INTER_NEAREST,
cv2.INTER_LINEAR,
cv2.INTER_AREA,
cv2.INTER_CUBIC,
cv2.INTER_LANCZOS4,
]
self.interp = interp
assert isinstance(target_size, (
int, Sequence)), "target_size must be int, list or tuple"
if random_size and not isinstance(target_size, list):
raise TypeError(
"Type of target_size is invalid when random_size is True. Must be List, now is {}".
format(type(target_size)))
self.target_size = target_size
self.random_size = random_size
self.random_interp = random_interp
def __call__(self, samples, context=None):
if self.random_size:
index = np.random.choice(len(self.target_size))
target_size = self.target_size[index]
else:
target_size = self.target_size
if self.random_interp:
interp = np.random.choice(self.interps)
else:
interp = self.interp
resizer = Resize(target_size, keep_ratio=self.keep_ratio, interp=interp)
return resizer(samples, context=context)
@register_op
class Gt2YoloTarget(BaseOperator):
__shared__ = ['num_classes']
"""
Generate YOLOv3 targets by groud truth data, this operator is only used in
fine grained YOLOv3 loss mode
"""
def __init__(self,
anchors,
anchor_masks,
downsample_ratios,
num_classes=80,
iou_thresh=1.):
super(Gt2YoloTarget, self).__init__()
self.anchors = anchors
self.anchor_masks = anchor_masks
self.downsample_ratios = downsample_ratios
self.num_classes = num_classes
self.iou_thresh = iou_thresh
def __call__(self, samples, context=None):
assert len(self.anchor_masks) == len(self.downsample_ratios), \
"anchor_masks', and 'downsample_ratios' should have same length."
h, w = samples[0]['image'].shape[1:3]
an_hw = np.array(self.anchors) / np.array([[w, h]])
for sample in samples:
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
if 'gt_score' not in sample:
sample['gt_score'] = np.ones(
(gt_bbox.shape[0], 1), dtype=np.float32)
gt_score = sample['gt_score']
for i, (
mask, downsample_ratio
) in enumerate(zip(self.anchor_masks, self.downsample_ratios)):
grid_h = int(h / downsample_ratio)
grid_w = int(w / downsample_ratio)
target = np.zeros(
(len(mask), 6 + self.num_classes, grid_h, grid_w),
dtype=np.float32)
for b in range(gt_bbox.shape[0]):
gx, gy, gw, gh = gt_bbox[b, :]
cls = gt_class[b]
score = gt_score[b]
if gw <= 0. or gh <= 0. or score <= 0.:
continue
# find best match anchor index
best_iou = 0.
best_idx = -1
for an_idx in range(an_hw.shape[0]):
iou = jaccard_overlap(
[0., 0., gw, gh],
[0., 0., an_hw[an_idx, 0], an_hw[an_idx, 1]])
if iou > best_iou:
best_iou = iou
best_idx = an_idx
gi = int(gx * grid_w)
gj = int(gy * grid_h)
# gtbox should be regresed in this layes if best match
# anchor index in anchor mask of this layer
if best_idx in mask:
best_n = mask.index(best_idx)
# x, y, w, h, scale
target[best_n, 0, gj, gi] = gx * grid_w - gi
target[best_n, 1, gj, gi] = gy * grid_h - gj
target[best_n, 2, gj, gi] = np.log(
gw * w / self.anchors[best_idx][0])
target[best_n, 3, gj, gi] = np.log(
gh * h / self.anchors[best_idx][1])
target[best_n, 4, gj, gi] = 2.0 - gw * gh
# objectness record gt_score
target[best_n, 5, gj, gi] = score
# classification
target[best_n, 6 + cls, gj, gi] = 1.
# For non-matched anchors, calculate the target if the iou
# between anchor and gt is larger than iou_thresh
if self.iou_thresh < 1:
for idx, mask_i in enumerate(mask):
if mask_i == best_idx: continue
iou = jaccard_overlap(
[0., 0., gw, gh],
[0., 0., an_hw[mask_i, 0], an_hw[mask_i, 1]])
if iou > self.iou_thresh and target[idx, 5, gj,
gi] == 0.:
# x, y, w, h, scale
target[idx, 0, gj, gi] = gx * grid_w - gi
target[idx, 1, gj, gi] = gy * grid_h - gj
target[idx, 2, gj, gi] = np.log(
gw * w / self.anchors[mask_i][0])
target[idx, 3, gj, gi] = np.log(
gh * h / self.anchors[mask_i][1])
target[idx, 4, gj, gi] = 2.0 - gw * gh
# objectness record gt_score
target[idx, 5, gj, gi] = score
# classification
target[idx, 6 + cls, gj, gi] = 1.
sample['target{}'.format(i)] = target
# remove useless gt_class and gt_score after target calculated
sample.pop('gt_class')
sample.pop('gt_score')
return samples
@register_op
class Gt2FCOSTarget(BaseOperator):
"""
Generate FCOS targets by groud truth data
"""
def __init__(self,
object_sizes_boundary,
center_sampling_radius,
downsample_ratios,
num_shift=0.5,
multiply_strides_reg_targets=False,
norm_reg_targets=True):
super(Gt2FCOSTarget, self).__init__()
self.center_sampling_radius = center_sampling_radius
self.downsample_ratios = downsample_ratios
self.INF = np.inf
self.object_sizes_boundary = [-1] + object_sizes_boundary + [self.INF]
object_sizes_of_interest = []
for i in range(len(self.object_sizes_boundary) - 1):
object_sizes_of_interest.append([
self.object_sizes_boundary[i], self.object_sizes_boundary[i + 1]
])
self.object_sizes_of_interest = object_sizes_of_interest
self.num_shift = num_shift
self.multiply_strides_reg_targets = multiply_strides_reg_targets
self.norm_reg_targets = norm_reg_targets
def _compute_points(self, w, h):
"""
compute the corresponding points in each feature map
:param h: image height
:param w: image width
:return: points from all feature map
"""
locations = []
for stride in self.downsample_ratios:
shift_x = np.arange(0, w, stride).astype(np.float32)
shift_y = np.arange(0, h, stride).astype(np.float32)
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shift_x = shift_x.flatten()
shift_y = shift_y.flatten()
location = np.stack(
[shift_x, shift_y], axis=1) + stride * self.num_shift
locations.append(location)
num_points_each_level = [len(location) for location in locations]
locations = np.concatenate(locations, axis=0)
return locations, num_points_each_level
def _convert_xywh2xyxy(self, gt_bbox, w, h):
"""
convert the bounding box from style xywh to xyxy
:param gt_bbox: bounding boxes normalized into [0, 1]
:param w: image width
:param h: image height
:return: bounding boxes in xyxy style
"""
bboxes = gt_bbox.copy()
bboxes[:, [0, 2]] = bboxes[:, [0, 2]] * w
bboxes[:, [1, 3]] = bboxes[:, [1, 3]] * h
bboxes[:, 2] = bboxes[:, 0] + bboxes[:, 2]
bboxes[:, 3] = bboxes[:, 1] + bboxes[:, 3]
return bboxes
def _check_inside_boxes_limited(self, gt_bbox, xs, ys,
num_points_each_level):
"""
check if points is within the clipped boxes
:param gt_bbox: bounding boxes
:param xs: horizontal coordinate of points
:param ys: vertical coordinate of points
:return: the mask of points is within gt_box or not
"""
bboxes = np.reshape(
gt_bbox, newshape=[1, gt_bbox.shape[0], gt_bbox.shape[1]])
bboxes = np.tile(bboxes, reps=[xs.shape[0], 1, 1])
ct_x = (bboxes[:, :, 0] + bboxes[:, :, 2]) / 2
ct_y = (bboxes[:, :, 1] + bboxes[:, :, 3]) / 2
beg = 0
clipped_box = bboxes.copy()
for lvl, stride in enumerate(self.downsample_ratios):
end = beg + num_points_each_level[lvl]
stride_exp = self.center_sampling_radius * stride
clipped_box[beg:end, :, 0] = np.maximum(
bboxes[beg:end, :, 0], ct_x[beg:end, :] - stride_exp)
clipped_box[beg:end, :, 1] = np.maximum(
bboxes[beg:end, :, 1], ct_y[beg:end, :] - stride_exp)
clipped_box[beg:end, :, 2] = np.minimum(
bboxes[beg:end, :, 2], ct_x[beg:end, :] + stride_exp)
clipped_box[beg:end, :, 3] = np.minimum(
bboxes[beg:end, :, 3], ct_y[beg:end, :] + stride_exp)
beg = end
l_res = xs - clipped_box[:, :, 0]
r_res = clipped_box[:, :, 2] - xs
t_res = ys - clipped_box[:, :, 1]
b_res = clipped_box[:, :, 3] - ys
clipped_box_reg_targets = np.stack([l_res, t_res, r_res, b_res], axis=2)
inside_gt_box = np.min(clipped_box_reg_targets, axis=2) > 0
return inside_gt_box
def __call__(self, samples, context=None):
assert len(self.object_sizes_of_interest) == len(self.downsample_ratios), \
"object_sizes_of_interest', and 'downsample_ratios' should have same length."
for sample in samples:
im = sample['image']
bboxes = sample['gt_bbox']
gt_class = sample['gt_class']
# calculate the locations
h, w = im.shape[1:3]
points, num_points_each_level = self._compute_points(w, h)
object_scale_exp = []
for i, num_pts in enumerate(num_points_each_level):
object_scale_exp.append(
np.tile(
np.array([self.object_sizes_of_interest[i]]),
reps=[num_pts, 1]))
object_scale_exp = np.concatenate(object_scale_exp, axis=0)
gt_area = (bboxes[:, 2] - bboxes[:, 0]) * (
bboxes[:, 3] - bboxes[:, 1])
xs, ys = points[:, 0], points[:, 1]
xs = np.reshape(xs, newshape=[xs.shape[0], 1])
xs = np.tile(xs, reps=[1, bboxes.shape[0]])
ys = np.reshape(ys, newshape=[ys.shape[0], 1])
ys = np.tile(ys, reps=[1, bboxes.shape[0]])
l_res = xs - bboxes[:, 0]
r_res = bboxes[:, 2] - xs
t_res = ys - bboxes[:, 1]
b_res = bboxes[:, 3] - ys
reg_targets = np.stack([l_res, t_res, r_res, b_res], axis=2)
if self.center_sampling_radius > 0:
is_inside_box = self._check_inside_boxes_limited(
bboxes, xs, ys, num_points_each_level)
else:
is_inside_box = np.min(reg_targets, axis=2) > 0
# check if the targets is inside the corresponding level
max_reg_targets = np.max(reg_targets, axis=2)
lower_bound = np.tile(
np.expand_dims(
object_scale_exp[:, 0], axis=1),
reps=[1, max_reg_targets.shape[1]])
high_bound = np.tile(
np.expand_dims(
object_scale_exp[:, 1], axis=1),
reps=[1, max_reg_targets.shape[1]])
is_match_current_level = \
(max_reg_targets > lower_bound) & \
(max_reg_targets < high_bound)
points2gtarea = np.tile(
np.expand_dims(
gt_area, axis=0), reps=[xs.shape[0], 1])
points2gtarea[is_inside_box == 0] = self.INF
points2gtarea[is_match_current_level == 0] = self.INF
points2min_area = points2gtarea.min(axis=1)
points2min_area_ind = points2gtarea.argmin(axis=1)
labels = gt_class[points2min_area_ind] + 1
labels[points2min_area == self.INF] = 0
reg_targets = reg_targets[range(xs.shape[0]), points2min_area_ind]
ctn_targets = np.sqrt((reg_targets[:, [0, 2]].min(axis=1) / \
reg_targets[:, [0, 2]].max(axis=1)) * \
(reg_targets[:, [1, 3]].min(axis=1) / \
reg_targets[:, [1, 3]].max(axis=1))).astype(np.float32)
ctn_targets = np.reshape(
ctn_targets, newshape=[ctn_targets.shape[0], 1])
ctn_targets[labels <= 0] = 0
pos_ind = np.nonzero(labels != 0)
reg_targets_pos = reg_targets[pos_ind[0], :]
split_sections = []
beg = 0
for lvl in range(len(num_points_each_level)):
end = beg + num_points_each_level[lvl]
split_sections.append(end)
beg = end
labels_by_level = np.split(labels, split_sections, axis=0)
reg_targets_by_level = np.split(reg_targets, split_sections, axis=0)
ctn_targets_by_level = np.split(ctn_targets, split_sections, axis=0)
for lvl in range(len(self.downsample_ratios)):
grid_w = int(np.ceil(w / self.downsample_ratios[lvl]))
grid_h = int(np.ceil(h / self.downsample_ratios[lvl]))
if self.norm_reg_targets:
if self.multiply_strides_reg_targets:
sample['reg_target{}'.format(lvl)] = np.reshape(
reg_targets_by_level[lvl],
newshape=[grid_h, grid_w, 4])
else:
sample['reg_target{}'.format(lvl)] = \
np.reshape(
reg_targets_by_level[lvl] / \
self.downsample_ratios[lvl],
newshape=[grid_h, grid_w, 4])
else:
sample['reg_target{}'.format(lvl)] = np.reshape(
reg_targets_by_level[lvl],
newshape=[grid_h, grid_w, 4])
sample['labels{}'.format(lvl)] = np.reshape(
labels_by_level[lvl], newshape=[grid_h, grid_w, 1])
sample['centerness{}'.format(lvl)] = np.reshape(
ctn_targets_by_level[lvl], newshape=[grid_h, grid_w, 1])
sample.pop('is_crowd', None)
sample.pop('difficult', None)
sample.pop('gt_class', None)
sample.pop('gt_bbox', None)
return samples
@register_op
class Gt2GFLTarget(BaseOperator):
__shared__ = ['num_classes']
"""
Generate GFocal loss targets by groud truth data
"""
def __init__(self,
num_classes=80,
downsample_ratios=[8, 16, 32, 64, 128],
grid_cell_scale=4,
cell_offset=0,
compute_vlr_region=False):
super(Gt2GFLTarget, self).__init__()
self.num_classes = num_classes
self.downsample_ratios = downsample_ratios
self.grid_cell_scale = grid_cell_scale
self.cell_offset = cell_offset
self.compute_vlr_region = compute_vlr_region
self.assigner = ATSSAssigner()
def get_grid_cells(self, featmap_size, scale, stride, offset=0):
"""
Generate grid cells of a feature map for target assignment.
Args:
featmap_size: Size of a single level feature map.
scale: Grid cell scale.
stride: Down sample stride of the feature map.
offset: Offset of grid cells.
return:
Grid_cells xyxy position. Size should be [feat_w * feat_h, 4]
"""
cell_size = stride * scale
h, w = featmap_size
x_range = (np.arange(w, dtype=np.float32) + offset) * stride
y_range = (np.arange(h, dtype=np.float32) + offset) * stride
x, y = np.meshgrid(x_range, y_range)
y = y.flatten()
x = x.flatten()
grid_cells = np.stack(
[
x - 0.5 * cell_size, y - 0.5 * cell_size, x + 0.5 * cell_size,
y + 0.5 * cell_size
],
axis=-1)
return grid_cells
def get_sample(self, assign_gt_inds, gt_bboxes):
pos_inds = np.unique(np.nonzero(assign_gt_inds > 0)[0])
neg_inds = np.unique(np.nonzero(assign_gt_inds == 0)[0])
pos_assigned_gt_inds = assign_gt_inds[pos_inds] - 1
if gt_bboxes.size == 0:
# hack for index error case
assert pos_assigned_gt_inds.size == 0
pos_gt_bboxes = np.empty_like(gt_bboxes).reshape(-1, 4)
else:
if len(gt_bboxes.shape) < 2:
gt_bboxes = gt_bboxes.resize(-1, 4)
pos_gt_bboxes = gt_bboxes[pos_assigned_gt_inds, :]
return pos_inds, neg_inds, pos_gt_bboxes, pos_assigned_gt_inds
def __call__(self, samples, context=None):
assert len(samples) > 0
batch_size = len(samples)
# get grid cells of image
h, w = samples[0]['image'].shape[1:3]
multi_level_grid_cells = []
for stride in self.downsample_ratios:
featmap_size = (int(math.ceil(h / stride)),
int(math.ceil(w / stride)))
multi_level_grid_cells.append(
self.get_grid_cells(featmap_size, self.grid_cell_scale, stride,
self.cell_offset))
mlvl_grid_cells_list = [
multi_level_grid_cells for i in range(batch_size)
]
# pixel cell number of multi-level feature maps
num_level_cells = [
grid_cells.shape[0] for grid_cells in mlvl_grid_cells_list[0]
]
num_level_cells_list = [num_level_cells] * batch_size
# concat all level cells and to a single array
for i in range(batch_size):
mlvl_grid_cells_list[i] = np.concatenate(mlvl_grid_cells_list[i])
# target assign on all images
for sample, grid_cells, num_level_cells in zip(
samples, mlvl_grid_cells_list, num_level_cells_list):
gt_bboxes = sample['gt_bbox']
gt_labels = sample['gt_class'].squeeze()
if gt_labels.size == 1:
gt_labels = np.array([gt_labels]).astype(np.int32)
gt_bboxes_ignore = None
assign_gt_inds, _ = self.assigner(grid_cells, num_level_cells,
gt_bboxes, gt_bboxes_ignore,
gt_labels)
if self.compute_vlr_region:
vlr_region = self.assigner.get_vlr_region(
grid_cells, num_level_cells, gt_bboxes, gt_bboxes_ignore,
gt_labels)
sample['vlr_regions'] = vlr_region
pos_inds, neg_inds, pos_gt_bboxes, pos_assigned_gt_inds = self.get_sample(
assign_gt_inds, gt_bboxes)
num_cells = grid_cells.shape[0]
bbox_targets = np.zeros_like(grid_cells)
bbox_weights = np.zeros_like(grid_cells)
labels = np.ones([num_cells], dtype=np.int64) * self.num_classes
label_weights = np.zeros([num_cells], dtype=np.float32)
if len(pos_inds) > 0:
pos_bbox_targets = pos_gt_bboxes
bbox_targets[pos_inds, :] = pos_bbox_targets
bbox_weights[pos_inds, :] = 1.0
if not np.any(gt_labels):
labels[pos_inds] = 0
else:
labels[pos_inds] = gt_labels[pos_assigned_gt_inds]
label_weights[pos_inds] = 1.0
if len(neg_inds) > 0:
label_weights[neg_inds] = 1.0
sample['grid_cells'] = grid_cells
sample['labels'] = labels
sample['label_weights'] = label_weights
sample['bbox_targets'] = bbox_targets
sample['pos_num'] = max(pos_inds.size, 1)
sample.pop('is_crowd', None)
sample.pop('difficult', None)
sample.pop('gt_class', None)
sample.pop('gt_bbox', None)
sample.pop('gt_score', None)
return samples
@register_op
class Gt2TTFTarget(BaseOperator):
__shared__ = ['num_classes']
"""
Gt2TTFTarget
Generate TTFNet targets by ground truth data
Args:
num_classes(int): the number of classes.
down_ratio(int): the down ratio from images to heatmap, 4 by default.
alpha(float): the alpha parameter to generate gaussian target.
0.54 by default.
"""
def __init__(self, num_classes=80, down_ratio=4, alpha=0.54):
super(Gt2TTFTarget, self).__init__()
self.down_ratio = down_ratio
self.num_classes = num_classes
self.alpha = alpha
def __call__(self, samples, context=None):
output_size = samples[0]['image'].shape[1]
feat_size = output_size // self.down_ratio
for sample in samples:
heatmap = np.zeros(
(self.num_classes, feat_size, feat_size), dtype='float32')
box_target = np.ones(
(4, feat_size, feat_size), dtype='float32') * -1
reg_weight = np.zeros((1, feat_size, feat_size), dtype='float32')
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
bbox_w = gt_bbox[:, 2] - gt_bbox[:, 0] + 1
bbox_h = gt_bbox[:, 3] - gt_bbox[:, 1] + 1
area = bbox_w * bbox_h
boxes_areas_log = np.log(area)
boxes_ind = np.argsort(boxes_areas_log, axis=0)[::-1]
boxes_area_topk_log = boxes_areas_log[boxes_ind]
gt_bbox = gt_bbox[boxes_ind]
gt_class = gt_class[boxes_ind]
feat_gt_bbox = gt_bbox / self.down_ratio
feat_gt_bbox = np.clip(feat_gt_bbox, 0, feat_size - 1)
feat_hs, feat_ws = (feat_gt_bbox[:, 3] - feat_gt_bbox[:, 1],
feat_gt_bbox[:, 2] - feat_gt_bbox[:, 0])
ct_inds = np.stack(
[(gt_bbox[:, 0] + gt_bbox[:, 2]) / 2,
(gt_bbox[:, 1] + gt_bbox[:, 3]) / 2],
axis=1) / self.down_ratio
h_radiuses_alpha = (feat_hs / 2. * self.alpha).astype('int32')
w_radiuses_alpha = (feat_ws / 2. * self.alpha).astype('int32')
for k in range(len(gt_bbox)):
cls_id = gt_class[k]
fake_heatmap = np.zeros((feat_size, feat_size), dtype='float32')
self.draw_truncate_gaussian(fake_heatmap, ct_inds[k],
h_radiuses_alpha[k],
w_radiuses_alpha[k])
heatmap[cls_id] = np.maximum(heatmap[cls_id], fake_heatmap)
box_target_inds = fake_heatmap > 0
box_target[:, box_target_inds] = gt_bbox[k][:, None]
local_heatmap = fake_heatmap[box_target_inds]
ct_div = np.sum(local_heatmap)
local_heatmap *= boxes_area_topk_log[k]
reg_weight[0, box_target_inds] = local_heatmap / ct_div
sample['ttf_heatmap'] = heatmap
sample['ttf_box_target'] = box_target
sample['ttf_reg_weight'] = reg_weight
sample.pop('is_crowd', None)
sample.pop('difficult', None)
sample.pop('gt_class', None)
sample.pop('gt_bbox', None)
sample.pop('gt_score', None)
return samples
def draw_truncate_gaussian(self, heatmap, center, h_radius, w_radius):
h, w = 2 * h_radius + 1, 2 * w_radius + 1
sigma_x = w / 6
sigma_y = h / 6
gaussian = gaussian2D((h, w), sigma_x, sigma_y)
x, y = int(center[0]), int(center[1])
height, width = heatmap.shape[0:2]
left, right = min(x, w_radius), min(width - x, w_radius + 1)
top, bottom = min(y, h_radius), min(height - y, h_radius + 1)
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_gaussian = gaussian[h_radius - top:h_radius + bottom, w_radius -
left:w_radius + right]
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:
heatmap[y - top:y + bottom, x - left:x + right] = np.maximum(
masked_heatmap, masked_gaussian)
return heatmap
@register_op
class Gt2Solov2Target(BaseOperator):
"""Assign mask target and labels in SOLOv2 network.
The code of this function is based on:
https://github.com/WXinlong/SOLO/blob/master/mmdet/models/anchor_heads/solov2_head.py#L271
Args:
num_grids (list): The list of feature map grids size.
scale_ranges (list): The list of mask boundary range.
coord_sigma (float): The coefficient of coordinate area length.
sampling_ratio (float): The ratio of down sampling.
"""
def __init__(self,
num_grids=[40, 36, 24, 16, 12],
scale_ranges=[[1, 96], [48, 192], [96, 384], [192, 768],
[384, 2048]],
coord_sigma=0.2,
sampling_ratio=4.0):
super(Gt2Solov2Target, self).__init__()
self.num_grids = num_grids
self.scale_ranges = scale_ranges
self.coord_sigma = coord_sigma
self.sampling_ratio = sampling_ratio
def _scale_size(self, im, scale):
h, w = im.shape[:2]
new_size = (int(w * float(scale) + 0.5), int(h * float(scale) + 0.5))
resized_img = cv2.resize(
im, None, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)
return resized_img
def __call__(self, samples, context=None):
sample_id = 0
max_ins_num = [0] * len(self.num_grids)
for sample in samples:
gt_bboxes_raw = sample['gt_bbox']
gt_labels_raw = sample['gt_class'] + 1
im_c, im_h, im_w = sample['image'].shape[:]
gt_masks_raw = sample['gt_segm'].astype(np.uint8)
mask_feat_size = [
int(im_h / self.sampling_ratio), int(im_w / self.sampling_ratio)
]
gt_areas = np.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) *
(gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1]))
ins_ind_label_list = []
idx = 0
for (lower_bound, upper_bound), num_grid \
in zip(self.scale_ranges, self.num_grids):
hit_indices = ((gt_areas >= lower_bound) &
(gt_areas <= upper_bound)).nonzero()[0]
num_ins = len(hit_indices)
ins_label = []
grid_order = []
cate_label = np.zeros([num_grid, num_grid], dtype=np.int64)
ins_ind_label = np.zeros([num_grid**2], dtype=np.bool_)
if num_ins == 0:
ins_label = np.zeros(
[1, mask_feat_size[0], mask_feat_size[1]],
dtype=np.uint8)
ins_ind_label_list.append(ins_ind_label)
sample['cate_label{}'.format(idx)] = cate_label.flatten()
sample['ins_label{}'.format(idx)] = ins_label
sample['grid_order{}'.format(idx)] = np.asarray(
[sample_id * num_grid * num_grid + 0], dtype=np.int32)
idx += 1
continue
gt_bboxes = gt_bboxes_raw[hit_indices]
gt_labels = gt_labels_raw[hit_indices]
gt_masks = gt_masks_raw[hit_indices, ...]
half_ws = 0.5 * (
gt_bboxes[:, 2] - gt_bboxes[:, 0]) * self.coord_sigma
half_hs = 0.5 * (
gt_bboxes[:, 3] - gt_bboxes[:, 1]) * self.coord_sigma
for seg_mask, gt_label, half_h, half_w in zip(
gt_masks, gt_labels, half_hs, half_ws):
if seg_mask.sum() == 0:
continue
# mass center
upsampled_size = (mask_feat_size[0] * 4,
mask_feat_size[1] * 4)
center_h, center_w = ndimage.measurements.center_of_mass(
seg_mask)
coord_w = int(
(center_w / upsampled_size[1]) // (1. / num_grid))
coord_h = int(
(center_h / upsampled_size[0]) // (1. / num_grid))
# left, top, right, down
top_box = max(0,
int(((center_h - half_h) / upsampled_size[0])
// (1. / num_grid)))
down_box = min(num_grid - 1,
int(((center_h + half_h) / upsampled_size[0])
// (1. / num_grid)))
left_box = max(0,
int(((center_w - half_w) / upsampled_size[1])
// (1. / num_grid)))
right_box = min(num_grid - 1,
int(((center_w + half_w) /
upsampled_size[1]) // (1. / num_grid)))
top = max(top_box, coord_h - 1)
down = min(down_box, coord_h + 1)
left = max(coord_w - 1, left_box)
right = min(right_box, coord_w + 1)
cate_label[top:(down + 1), left:(right + 1)] = gt_label
seg_mask = self._scale_size(
seg_mask, scale=1. / self.sampling_ratio)
for i in range(top, down + 1):
for j in range(left, right + 1):
label = int(i * num_grid + j)
cur_ins_label = np.zeros(
[mask_feat_size[0], mask_feat_size[1]],
dtype=np.uint8)
cur_ins_label[:seg_mask.shape[0], :seg_mask.shape[
1]] = seg_mask
ins_label.append(cur_ins_label)
ins_ind_label[label] = True
grid_order.append(sample_id * num_grid * num_grid +
label)
if ins_label == []:
ins_label = np.zeros(
[1, mask_feat_size[0], mask_feat_size[1]],
dtype=np.uint8)
ins_ind_label_list.append(ins_ind_label)
sample['cate_label{}'.format(idx)] = cate_label.flatten()
sample['ins_label{}'.format(idx)] = ins_label
sample['grid_order{}'.format(idx)] = np.asarray(
[sample_id * num_grid * num_grid + 0], dtype=np.int32)
else:
ins_label = np.stack(ins_label, axis=0)
ins_ind_label_list.append(ins_ind_label)
sample['cate_label{}'.format(idx)] = cate_label.flatten()
sample['ins_label{}'.format(idx)] = ins_label
sample['grid_order{}'.format(idx)] = np.asarray(
grid_order, dtype=np.int32)
assert len(grid_order) > 0
max_ins_num[idx] = max(
max_ins_num[idx],
sample['ins_label{}'.format(idx)].shape[0])
idx += 1
ins_ind_labels = np.concatenate([
ins_ind_labels_level_img
for ins_ind_labels_level_img in ins_ind_label_list
])
fg_num = np.sum(ins_ind_labels)
sample['fg_num'] = fg_num
sample_id += 1
sample.pop('is_crowd')
sample.pop('gt_class')
sample.pop('gt_bbox')
sample.pop('gt_poly')
sample.pop('gt_segm')
# padding batch
for data in samples:
for idx in range(len(self.num_grids)):
gt_ins_data = np.zeros(
[
max_ins_num[idx],
data['ins_label{}'.format(idx)].shape[1],
data['ins_label{}'.format(idx)].shape[2]
],
dtype=np.uint8)
gt_ins_data[0:data['ins_label{}'.format(idx)].shape[
0], :, :] = data['ins_label{}'.format(idx)]
gt_grid_order = np.zeros([max_ins_num[idx]], dtype=np.int32)
gt_grid_order[0:data['grid_order{}'.format(idx)].shape[
0]] = data['grid_order{}'.format(idx)]
data['ins_label{}'.format(idx)] = gt_ins_data
data['grid_order{}'.format(idx)] = gt_grid_order
return samples
@register_op
class Gt2SparseTarget(BaseOperator):
def __init__(self, use_padding_shape=False):
super(Gt2SparseTarget, self).__init__()
self.use_padding_shape = use_padding_shape
def __call__(self, samples, context=None):
for sample in samples:
ori_h, ori_w = sample['h'], sample['w']
if self.use_padding_shape:
h, w = sample["image"].shape[1:3]
if "scale_factor" in sample:
sf_w, sf_h = sample["scale_factor"][1], sample[
"scale_factor"][0]
sample["scale_factor_whwh"] = np.array(
[sf_w, sf_h, sf_w, sf_h], dtype=np.float32)
else:
sample["scale_factor_whwh"] = np.array(
[1.0, 1.0, 1.0, 1.0], dtype=np.float32)
else:
h, w = round(sample['im_shape'][0]), round(sample['im_shape'][
1])
sample["scale_factor_whwh"] = np.array(
[w / ori_w, h / ori_h, w / ori_w, h / ori_h],
dtype=np.float32)
sample["img_whwh"] = np.array([w, h, w, h], dtype=np.float32)
sample["ori_shape"] = np.array([ori_h, ori_w], dtype=np.int32)
return samples
@register_op
class PadMaskBatch(BaseOperator):
"""
Pad a batch of samples so they can be divisible by a stride.
The layout of each image should be 'CHW'.
Args:
pad_to_stride (int): If `pad_to_stride > 0`, pad zeros to ensure
height and width is divisible by `pad_to_stride`.
return_pad_mask (bool): If `return_pad_mask = True`, return
`pad_mask` for transformer.
"""
def __init__(self, pad_to_stride=0, return_pad_mask=False):
super(PadMaskBatch, self).__init__()
self.pad_to_stride = pad_to_stride
self.return_pad_mask = return_pad_mask
def __call__(self, samples, context=None):
"""
Args:
samples (list): a batch of sample, each is dict.
"""
coarsest_stride = self.pad_to_stride
max_shape = np.array([data['image'].shape for data in samples]).max(
axis=0)
if coarsest_stride > 0:
max_shape[1] = int(
np.ceil(max_shape[1] / coarsest_stride) * coarsest_stride)
max_shape[2] = int(
np.ceil(max_shape[2] / coarsest_stride) * coarsest_stride)
for data in samples:
im = data['image']
im_c, im_h, im_w = im.shape[:]
padding_im = np.zeros(
(im_c, max_shape[1], max_shape[2]), dtype=np.float32)
padding_im[:, :im_h, :im_w] = im
data['image'] = padding_im
if 'semantic' in data and data['semantic'] is not None:
semantic = data['semantic']
padding_sem = np.zeros(
(1, max_shape[1], max_shape[2]), dtype=np.float32)
padding_sem[:, :im_h, :im_w] = semantic
data['semantic'] = padding_sem
if 'gt_segm' in data and data['gt_segm'] is not None:
gt_segm = data['gt_segm']
padding_segm = np.zeros(
(gt_segm.shape[0], max_shape[1], max_shape[2]),
dtype=np.uint8)
padding_segm[:, :im_h, :im_w] = gt_segm
data['gt_segm'] = padding_segm
if self.return_pad_mask:
padding_mask = np.zeros(
(max_shape[1], max_shape[2]), dtype=np.float32)
padding_mask[:im_h, :im_w] = 1.
data['pad_mask'] = padding_mask
return samples
@register_op
class Gt2CenterNetTarget(BaseOperator):
__shared__ = ['num_classes']
"""Gt2CenterNetTarget
Genterate CenterNet targets by ground-truth
Args:
down_ratio (int): The down sample ratio between output feature and
input image.
num_classes (int): The number of classes, 80 by default.
max_objs (int): The maximum objects detected, 128 by default.
"""
def __init__(self, num_classes=80, down_ratio=4, max_objs=128):
super(Gt2CenterNetTarget, self).__init__()
self.nc = num_classes
self.down_ratio = down_ratio
self.max_objs = max_objs
def __call__(self, sample, context=None):
input_h, input_w = sample['image'].shape[1:]
output_h = input_h // self.down_ratio
output_w = input_w // self.down_ratio
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
hm = np.zeros((self.nc, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.int32)
cat_spec_wh = np.zeros((self.max_objs, self.nc * 2), dtype=np.float32)
cat_spec_mask = np.zeros((self.max_objs, self.nc * 2), dtype=np.int32)
trans_output = get_affine_transform(
center=sample['center'],
input_size=[sample['scale'], sample['scale']],
rot=0,
output_size=[output_w, output_h])
gt_det = []
for i, (bbox, cls) in enumerate(zip(gt_bbox, gt_class)):
cls = int(cls)
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox_amodal = copy.deepcopy(bbox)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)), 0.7)
radius = max(0, int(radius))
ct = np.array(
[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
# get hm,wh,reg,ind,ind_mask
draw_umich_gaussian(hm[cls], ct_int, radius)
wh[i] = 1. * w, 1. * h
reg[i] = ct - ct_int
ind[i] = ct_int[1] * output_w + ct_int[0]
reg_mask[i] = 1
cat_spec_wh[i, cls * 2:cls * 2 + 2] = wh[i]
cat_spec_mask[i, cls * 2:cls * 2 + 2] = 1
gt_det.append([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] + h / 2,
1, cls
])
sample.pop('gt_bbox', None)
sample.pop('gt_class', None)
sample.pop('center', None)
sample.pop('scale', None)
sample.pop('is_crowd', None)
sample.pop('difficult', None)
sample['index'] = ind
sample['index_mask'] = reg_mask
sample['heatmap'] = hm
sample['size'] = wh
sample['offset'] = reg
return sample
@register_op
class PadGT(BaseOperator):
"""
Pad 0 to `gt_class`, `gt_bbox`, `gt_score`...
The num_max_boxes is the largest for batch.
Args:
return_gt_mask (bool): If true, return `pad_gt_mask`,
1 means bbox, 0 means no bbox.
"""
def __init__(self, return_gt_mask=True, pad_img=False, minimum_gtnum=0):
super(PadGT, self).__init__()
self.return_gt_mask = return_gt_mask
self.pad_img = pad_img
self.minimum_gtnum = minimum_gtnum
def _impad(self, img: np.ndarray,
*,
shape = None,
padding = None,
pad_val = 0,
padding_mode = 'constant') -> np.ndarray:
"""Pad the given image to a certain shape or pad on all sides with
specified padding mode and padding value.
Args:
img (ndarray): Image to be padded.
shape (tuple[int]): Expected padding shape (h, w). Default: None.
padding (int or tuple[int]): Padding on each border. If a single int is
provided this is used to pad all borders. If tuple of length 2 is
provided this is the padding on left/right and top/bottom
respectively. If a tuple of length 4 is provided this is the
padding for the left, top, right and bottom borders respectively.
Default: None. Note that `shape` and `padding` can not be both
set.
pad_val (Number | Sequence[Number]): Values to be filled in padding
areas when padding_mode is 'constant'. Default: 0.
padding_mode (str): Type of padding. Should be: constant, edge,
reflect or symmetric. Default: constant.
- constant: pads with a constant value, this value is specified
with pad_val.
- edge: pads with the last value at the edge of the image.
- reflect: pads with reflection of image without repeating the last
value on the edge. For example, padding [1, 2, 3, 4] with 2
elements on both sides in reflect mode will result in
[3, 2, 1, 2, 3, 4, 3, 2].
- symmetric: pads with reflection of image repeating the last value
on the edge. For example, padding [1, 2, 3, 4] with 2 elements on
both sides in symmetric mode will result in
[2, 1, 1, 2, 3, 4, 4, 3]
Returns:
ndarray: The padded image.
"""
assert (shape is not None) ^ (padding is not None)
if shape is not None:
width = max(shape[1] - img.shape[1], 0)
height = max(shape[0] - img.shape[0], 0)
padding = (0, 0, int(width), int(height))
# check pad_val
import numbers
if isinstance(pad_val, tuple):
assert len(pad_val) == img.shape[-1]
elif not isinstance(pad_val, numbers.Number):
raise TypeError('pad_val must be a int or a tuple. '
f'But received {type(pad_val)}')
# check padding
if isinstance(padding, tuple) and len(padding) in [2, 4]:
if len(padding) == 2:
padding = (padding[0], padding[1], padding[0], padding[1])
elif isinstance(padding, numbers.Number):
padding = (padding, padding, padding, padding)
else:
raise ValueError('Padding must be a int or a 2, or 4 element tuple.'
f'But received {padding}')
# check padding mode
assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric']
border_type = {
'constant': cv2.BORDER_CONSTANT,
'edge': cv2.BORDER_REPLICATE,
'reflect': cv2.BORDER_REFLECT_101,
'symmetric': cv2.BORDER_REFLECT
}
img = cv2.copyMakeBorder(
img,
padding[1],
padding[3],
padding[0],
padding[2],
border_type[padding_mode],
value=pad_val)
return img
def checkmaxshape(self, samples):
maxh, maxw = 0, 0
for sample in samples:
h,w = sample['im_shape']
if h>maxh:
maxh = h
if w>maxw:
maxw = w
return (maxh, maxw)
def __call__(self, samples, context=None):
num_max_boxes = max([len(s['gt_bbox']) for s in samples])
num_max_boxes = max(self.minimum_gtnum, num_max_boxes)
if self.pad_img:
maxshape = self.checkmaxshape(samples)
for sample in samples:
if self.pad_img:
img = sample['image']
padimg = self._impad(img, shape=maxshape)
sample['image'] = padimg
if self.return_gt_mask:
sample['pad_gt_mask'] = np.zeros(
(num_max_boxes, 1), dtype=np.float32)
if num_max_boxes == 0:
continue
num_gt = len(sample['gt_bbox'])
pad_gt_class = np.zeros((num_max_boxes, 1), dtype=np.int32)
pad_gt_bbox = np.zeros((num_max_boxes, 4), dtype=np.float32)
if num_gt > 0:
pad_gt_class[:num_gt] = sample['gt_class']
pad_gt_bbox[:num_gt] = sample['gt_bbox']
sample['gt_class'] = pad_gt_class
sample['gt_bbox'] = pad_gt_bbox
# pad_gt_mask
if 'pad_gt_mask' in sample:
sample['pad_gt_mask'][:num_gt] = 1
# gt_score
if 'gt_score' in sample:
pad_gt_score = np.zeros((num_max_boxes, 1), dtype=np.float32)
if num_gt > 0:
pad_gt_score[:num_gt] = sample['gt_score']
sample['gt_score'] = pad_gt_score
if 'is_crowd' in sample:
pad_is_crowd = np.zeros((num_max_boxes, 1), dtype=np.int32)
if num_gt > 0:
pad_is_crowd[:num_gt] = sample['is_crowd']
sample['is_crowd'] = pad_is_crowd
if 'difficult' in sample:
pad_diff = np.zeros((num_max_boxes, 1), dtype=np.int32)
if num_gt > 0:
pad_diff[:num_gt] = sample['difficult']
sample['difficult'] = pad_diff
if 'gt_joints' in sample:
num_joints = sample['gt_joints'].shape[1]
pad_gt_joints = np.zeros((num_max_boxes, num_joints, 3), dtype=np.float32)
if num_gt > 0:
pad_gt_joints[:num_gt] = sample['gt_joints']
sample['gt_joints'] = pad_gt_joints
if 'gt_areas' in sample:
pad_gt_areas = np.zeros((num_max_boxes, 1), dtype=np.float32)
if num_gt > 0:
pad_gt_areas[:num_gt, 0] = sample['gt_areas']
sample['gt_areas'] = pad_gt_areas
return samples
@register_op
class PadRGT(BaseOperator):
"""
Pad 0 to `gt_class`, `gt_bbox`, `gt_score`...
The num_max_boxes is the largest for batch.
Args:
return_gt_mask (bool): If true, return `pad_gt_mask`,
1 means bbox, 0 means no bbox.
"""
def __init__(self, return_gt_mask=True):
super(PadRGT, self).__init__()
self.return_gt_mask = return_gt_mask
def pad_field(self, sample, field, num_gt):
name, shape, dtype = field
if name in sample:
pad_v = np.zeros(shape, dtype=dtype)
if num_gt > 0:
pad_v[:num_gt] = sample[name]
sample[name] = pad_v
def __call__(self, samples, context=None):
num_max_boxes = max([len(s['gt_bbox']) for s in samples])
for sample in samples:
if self.return_gt_mask:
sample['pad_gt_mask'] = np.zeros(
(num_max_boxes, 1), dtype=np.float32)
if num_max_boxes == 0:
continue
num_gt = len(sample['gt_bbox'])
pad_gt_class = np.zeros((num_max_boxes, 1), dtype=np.int32)
pad_gt_bbox = np.zeros((num_max_boxes, 4), dtype=np.float32)
if num_gt > 0:
pad_gt_class[:num_gt] = sample['gt_class']
pad_gt_bbox[:num_gt] = sample['gt_bbox']
sample['gt_class'] = pad_gt_class
sample['gt_bbox'] = pad_gt_bbox
# pad_gt_mask
if 'pad_gt_mask' in sample:
sample['pad_gt_mask'][:num_gt] = 1
# gt_score
names = ['gt_score', 'is_crowd', 'difficult', 'gt_poly', 'gt_rbox']
dims = [1, 1, 1, 8, 5]
dtypes = [np.float32, np.int32, np.int32, np.float32, np.float32]
for name, dim, dtype in zip(names, dims, dtypes):
self.pad_field(sample, [name, (num_max_boxes, dim), dtype],
num_gt)
return samples
@register_op
class Gt2CenterTrackTarget(BaseOperator):
__shared__ = ['num_classes']
"""Gt2CenterTrackTarget
Genterate CenterTrack targets by ground-truth
Args:
num_classes (int): The number of classes, 1 by default.
down_ratio (int): The down sample ratio between output feature and
input image.
max_objs (int): The maximum objects detected, 256 by default.
"""
def __init__(self,
num_classes=1,
down_ratio=4,
max_objs=256,
hm_disturb=0.05,
lost_disturb=0.4,
fp_disturb=0.1,
pre_hm=True,
add_tracking=True,
add_ltrb_amodal=True):
super(Gt2CenterTrackTarget, self).__init__()
self.nc = num_classes
self.down_ratio = down_ratio
self.max_objs = max_objs
self.hm_disturb = hm_disturb
self.lost_disturb = lost_disturb
self.fp_disturb = fp_disturb
self.pre_hm = pre_hm
self.add_tracking = add_tracking
self.add_ltrb_amodal = add_ltrb_amodal
def _get_pre_dets(self, input_h, input_w, trans_input_pre, gt_bbox_pre,
gt_class_pre, gt_track_id_pre):
hm_h, hm_w = input_h, input_w
reutrn_hm = self.pre_hm
pre_hm = np.zeros(
(1, hm_h, hm_w), dtype=np.float32) if reutrn_hm else None
pre_cts, track_ids = [], []
for i, (
bbox, cls, track_id
) in enumerate(zip(gt_bbox_pre, gt_class_pre, gt_track_id_pre)):
cls = int(cls)
bbox[:2] = affine_transform(bbox[:2], trans_input_pre)
bbox[2:] = affine_transform(bbox[2:], trans_input_pre)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, hm_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, hm_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
max_rad = 1
if (h > 0 and w > 0):
radius = gaussian_radius((math.ceil(h), math.ceil(w)), 0.7)
radius = max(0, int(radius))
max_rad = max(max_rad, radius)
ct = np.array(
[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct0 = ct.copy()
conf = 1
ct[0] = ct[0] + np.random.randn() * self.hm_disturb * w
ct[1] = ct[1] + np.random.randn() * self.hm_disturb * h
conf = 1 if np.random.rand() > self.lost_disturb else 0
ct_int = ct.astype(np.int32)
if conf == 0:
pre_cts.append(ct / self.down_ratio)
else:
pre_cts.append(ct0 / self.down_ratio)
track_ids.append(track_id)
if reutrn_hm:
draw_umich_gaussian(pre_hm[0], ct_int, radius, k=conf)
if np.random.rand() < self.fp_disturb and reutrn_hm:
ct2 = ct0.copy()
# Hard code heatmap disturb ratio, haven't tried other numbers.
ct2[0] = ct2[0] + np.random.randn() * 0.05 * w
ct2[1] = ct2[1] + np.random.randn() * 0.05 * h
ct2_int = ct2.astype(np.int32)
draw_umich_gaussian(pre_hm[0], ct2_int, radius, k=conf)
return pre_hm, pre_cts, track_ids
def __call__(self, sample, context=None):
input_h, input_w = sample['image'].shape[1:]
output_h = input_h // self.down_ratio
output_w = input_w // self.down_ratio
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
# init
hm = np.zeros((self.nc, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.int32)
if self.add_tracking:
tr = np.zeros((self.max_objs, 2), dtype=np.float32)
if self.add_ltrb_amodal:
ltrb_amodal = np.zeros((self.max_objs, 4), dtype=np.float32)
trans_output = get_affine_transform(
center=sample['center'],
input_size=[sample['scale'], sample['scale']],
rot=0,
output_size=[output_w, output_h])
pre_hm, pre_cts, track_ids = self._get_pre_dets(
input_h, input_w, sample['trans_input'], sample['pre_gt_bbox'],
sample['pre_gt_class'], sample['pre_gt_track_id'])
for i, (bbox, cls) in enumerate(zip(gt_bbox, gt_class)):
cls = int(cls)
rect = np.array(
[[bbox[0], bbox[1]], [bbox[0], bbox[3]], [bbox[2], bbox[3]],
[bbox[2], bbox[1]]],
dtype=np.float32)
for t in range(4):
rect[t] = affine_transform(rect[t], trans_output)
bbox[:2] = rect[:, 0].min(), rect[:, 1].min()
bbox[2:] = rect[:, 0].max(), rect[:, 1].max()
bbox_amodal = copy.deepcopy(bbox)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)), 0.7)
radius = max(0, int(radius))
ct = np.array(
[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
# get hm,wh,reg,ind,ind_mask
draw_umich_gaussian(hm[cls], ct_int, radius)
wh[i] = 1. * w, 1. * h
reg[i] = ct - ct_int
ind[i] = ct_int[1] * output_w + ct_int[0]
reg_mask[i] = 1
if self.add_tracking:
if sample['gt_track_id'][i] in track_ids:
pre_ct = pre_cts[track_ids.index(sample['gt_track_id'][
i])]
tr[i] = pre_ct - ct_int
if self.add_ltrb_amodal:
ltrb_amodal[i] = \
bbox_amodal[0] - ct_int[0], bbox_amodal[1] - ct_int[1], \
bbox_amodal[2] - ct_int[0], bbox_amodal[3] - ct_int[1]
new_sample = {'image': sample['image']}
new_sample['index'] = ind
new_sample['index_mask'] = reg_mask
new_sample['heatmap'] = hm
new_sample['size'] = wh
new_sample['offset'] = reg
if self.add_tracking:
new_sample['tracking'] = tr
if self.add_ltrb_amodal:
new_sample['ltrb_amodal'] = ltrb_amodal
new_sample['pre_image'] = sample['pre_image']
new_sample['pre_hm'] = pre_hm
del sample
return new_sample
|