Spaces:
Sleeping
Sleeping
File size: 32,295 Bytes
427d150 |
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 |
"""
Manually labeled dataset
TODO:
1. Merge with superpixel dataset
"""
import glob
import numpy as np
import dataloaders.augutils as myaug
import torch
import random
import os
import copy
import platform
import json
import re
import cv2
from dataloaders.common import BaseDataset, Subset, ValidationDataset
# from common import BaseDataset, Subset
from dataloaders.dataset_utils import*
from pdb import set_trace
from util.utils import CircularList
from util.consts import IMG_SIZE
MODE_DEFAULT = "default"
MODE_FULL_SCAN = "full_scan"
class ManualAnnoDataset(BaseDataset):
def __init__(self, which_dataset, base_dir, idx_split, mode, image_size, transforms, scan_per_load, min_fg = '', fix_length = None, tile_z_dim = 3, nsup = 1, exclude_list = [], extern_normalize_func = None, **kwargs):
"""
Manually labeled dataset
Args:
which_dataset: name of the dataset to use
base_dir: directory of dataset
idx_split: index of data split as we will do cross validation
mode: 'train', 'val'.
transforms: data transform (augmentation) function
min_fg: minimum number of positive pixels in a 2D slice, mainly for stablize training when trained on manually labeled dataset
scan_per_load: loading a portion of the entire dataset, in case that the dataset is too large to fit into the memory. Set to -1 if loading the entire dataset at one time
tile_z_dim: number of identical slices to tile along channel dimension, for fitting 2D single-channel medical images into off-the-shelf networks designed for RGB natural images
nsup: number of support scans
fix_length: fix the length of dataset
exclude_list: Labels to be excluded
extern_normalize_function: normalization function used for data pre-processing
"""
super(ManualAnnoDataset, self).__init__(base_dir)
self.img_modality = DATASET_INFO[which_dataset]['MODALITY']
self.sep = DATASET_INFO[which_dataset]['_SEP']
self.label_name = DATASET_INFO[which_dataset]['REAL_LABEL_NAME']
self.image_size = image_size
self.transforms = transforms
self.is_train = True if mode == 'train' else False
self.phase = mode
self.fix_length = fix_length
self.all_label_names = self.label_name
self.nclass = len(self.label_name)
self.tile_z_dim = tile_z_dim
self.base_dir = base_dir
self.nsup = nsup
self.img_pids = [ re.findall('\d+', fid)[-1] for fid in glob.glob(self.base_dir + "/image_*.nii") ]
self.img_pids = CircularList(sorted( self.img_pids, key = lambda x: int(x))) # make it circular for the ease of spliting folds
if 'use_clahe' not in kwargs:
self.use_clahe = False
else:
self.use_clahe = kwargs['use_clahe']
if self.use_clahe:
self.clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(7,7))
self.use_3_slices = kwargs["use_3_slices"] if 'use_3_slices' in kwargs else False
if self.use_3_slices:
self.tile_z_dim=1
self.get_item_mode = MODE_DEFAULT
if 'get_item_mode' in kwargs:
self.get_item_mode = kwargs['get_item_mode']
self.exclude_lbs = exclude_list
if len(exclude_list) > 0:
print(f'###### Dataset: the following classes has been excluded {exclude_list}######')
self.idx_split = idx_split
self.scan_ids = self.get_scanids(mode, idx_split) # patient ids of the entire fold
self.min_fg = min_fg if isinstance(min_fg, str) else str(min_fg)
self.scan_per_load = scan_per_load
self.info_by_scan = None
self.img_lb_fids = self.organize_sample_fids() # information of scans of the entire fold
if extern_normalize_func is not None: # helps to keep consistent between training and testing dataset.
self.norm_func = extern_normalize_func
print(f'###### Dataset: using external normalization statistics ######')
else:
self.norm_func = get_normalize_op(self.img_modality, [ fid_pair['img_fid'] for _, fid_pair in self.img_lb_fids.items()])
print(f'###### Dataset: using normalization statistics calculated from loaded data ######')
if self.is_train:
if scan_per_load > 0: # buffer needed
self.pid_curr_load = np.random.choice( self.scan_ids, replace = False, size = self.scan_per_load)
else: # load the entire set without a buffer
self.pid_curr_load = self.scan_ids
elif mode == 'val':
self.pid_curr_load = self.scan_ids
self.potential_support_sid = []
else:
raise Exception
self.actual_dataset = self.read_dataset()
self.size = len(self.actual_dataset)
self.overall_slice_by_cls = self.read_classfiles()
self.update_subclass_lookup()
def get_scanids(self, mode, idx_split):
val_ids = copy.deepcopy(self.img_pids[self.sep[idx_split]: self.sep[idx_split + 1] + self.nsup])
self.potential_support_sid = val_ids[-self.nsup:] # this is actual file scan id, not index
if mode == 'train':
return [ ii for ii in self.img_pids if ii not in val_ids ]
elif mode == 'val':
return val_ids
def reload_buffer(self):
"""
Reload a portion of the entire dataset, if the dataset is too large
1. delete original buffer
2. update self.ids_this_batch
3. update other internel variables like __len__
"""
if self.scan_per_load <= 0:
print("We are not using the reload buffer, doing notiong")
return -1
del self.actual_dataset
del self.info_by_scan
self.pid_curr_load = np.random.choice( self.scan_ids, size = self.scan_per_load, replace = False )
self.actual_dataset = self.read_dataset()
self.size = len(self.actual_dataset)
self.update_subclass_lookup()
print(f'Loader buffer reloaded with a new size of {self.size} slices')
def organize_sample_fids(self):
out_list = {}
for curr_id in self.scan_ids:
curr_dict = {}
_img_fid = os.path.join(self.base_dir, f'image_{curr_id}.nii.gz')
_lb_fid = os.path.join(self.base_dir, f'label_{curr_id}.nii.gz')
curr_dict["img_fid"] = _img_fid
curr_dict["lbs_fid"] = _lb_fid
out_list[str(curr_id)] = curr_dict
return out_list
def read_dataset(self):
"""
Build index pointers to individual slices
Also keep a look-up table from scan_id, slice to index
"""
out_list = []
self.scan_z_idx = {}
self.info_by_scan = {} # meta data of each scan
glb_idx = 0 # global index of a certain slice in a certain scan in entire dataset
for scan_id, itm in self.img_lb_fids.items():
if scan_id not in self.pid_curr_load:
continue
img, _info = read_nii_bysitk(itm["img_fid"], peel_info = True) # get the meta information out
img = img.transpose(1,2,0)
self.info_by_scan[scan_id] = _info
if self.use_clahe:
img = np.stack([self.clahe.apply(slice.astype(np.uint8)) for slice in img], axis=0)
img = np.float32(img)
img = self.norm_func(img)
self.scan_z_idx[scan_id] = [-1 for _ in range(img.shape[-1])]
lb = read_nii_bysitk(itm["lbs_fid"])
lb = lb.transpose(1,2,0)
lb = np.float32(lb)
img = cv2.resize(img, (self.image_size, self.image_size), interpolation=cv2.INTER_LINEAR)
lb = cv2.resize(lb, (self.image_size, self.image_size), interpolation=cv2.INTER_NEAREST)
assert img.shape[-1] == lb.shape[-1]
base_idx = img.shape[-1] // 2 # index of the middle slice
# write the beginning frame
out_list.append( {"img": img[..., 0: 1],
"lb":lb[..., 0: 0 + 1],
"is_start": True,
"is_end": False,
"nframe": img.shape[-1],
"scan_id": scan_id,
"z_id":0})
self.scan_z_idx[scan_id][0] = glb_idx
glb_idx += 1
for ii in range(1, img.shape[-1] - 1):
out_list.append( {"img": img[..., ii: ii + 1],
"lb":lb[..., ii: ii + 1],
"is_start": False,
"is_end": False,
"nframe": -1,
"scan_id": scan_id,
"z_id": ii
})
self.scan_z_idx[scan_id][ii] = glb_idx
glb_idx += 1
ii += 1 # last frame, note the is_end flag
out_list.append( {"img": img[..., ii: ii + 1],
"lb":lb[..., ii: ii+ 1],
"is_start": False,
"is_end": True,
"nframe": -1,
"scan_id": scan_id,
"z_id": ii
})
self.scan_z_idx[scan_id][ii] = glb_idx
glb_idx += 1
return out_list
def read_classfiles(self):
with open( os.path.join(self.base_dir, f'.classmap_{self.min_fg}.json') , 'r' ) as fopen:
cls_map = json.load( fopen)
fopen.close()
with open( os.path.join(self.base_dir, '.classmap_1.json') , 'r' ) as fopen:
self.tp1_cls_map = json.load( fopen)
fopen.close()
return cls_map
def __getitem__(self, index):
if self.get_item_mode == MODE_DEFAULT:
return self.__getitem_default__(index)
elif self.get_item_mode == MODE_FULL_SCAN:
return self.__get_ct_scan___(index)
else:
raise NotImplementedError("Unknown mode")
def __get_ct_scan___(self, index):
scan_n = index % len(self.scan_z_idx)
scan_id = list(self.scan_z_idx.keys())[scan_n]
scan_slices = self.scan_z_idx[scan_id]
scan_imgs = np.concatenate([self.actual_dataset[_idx]["img"] for _idx in scan_slices], axis = -1).transpose(2, 0, 1)
scan_lbs = np.concatenate([self.actual_dataset[_idx]["lb"] for _idx in scan_slices], axis = -1).transpose(2, 0, 1)
scan_imgs = np.float32(scan_imgs)
scan_lbs = np.float32(scan_lbs)
scan_imgs = torch.from_numpy(scan_imgs).unsqueeze(0)
scan_lbs = torch.from_numpy(scan_lbs)
if self.tile_z_dim:
scan_imgs = scan_imgs.repeat(self.tile_z_dim, 1, 1, 1)
assert scan_imgs.ndimension() == 4, f'actual dim {scan_imgs.ndimension()}'
# # reshape to C, D, H, W
# scan_imgs = scan_imgs.permute(1, 0, 2, 3)
# scan_lbs = scan_lbs.permute(1, 0, 2, 3)
sample = {"image": scan_imgs,
"label":scan_lbs,
"scan_id": scan_id,
}
return sample
def get_3_slice_adjacent_image(self, image_t, index):
curr_dict = self.actual_dataset[index]
prev_image = np.zeros_like(image_t)
if index > 1 and not curr_dict["is_start"]:
prev_dict = self.actual_dataset[index - 1]
prev_image = prev_dict["img"]
next_image = np.zeros_like(image_t)
if index < len(self.actual_dataset) - 1 and not curr_dict["is_end"]:
next_dict = self.actual_dataset[index + 1]
next_image = next_dict["img"]
image_t = np.concatenate([prev_image, image_t, next_image], axis=-1)
return image_t
def __getitem_default__(self, index):
index = index % len(self.actual_dataset)
curr_dict = self.actual_dataset[index]
if self.is_train:
if len(self.exclude_lbs) > 0:
for _ex_cls in self.exclude_lbs:
if curr_dict["z_id"] in self.tp1_cls_map[self.label_name[_ex_cls]][curr_dict["scan_id"]]: # this slice need to be excluded since it contains label which is supposed to be unseen
return self.__getitem__(index + torch.randint(low = 0, high = self.__len__() - 1, size = (1,)))
comp = np.concatenate( [curr_dict["img"], curr_dict["lb"]], axis = -1 )
if self.transforms is not None:
img, lb = self.transforms(comp, c_img = 1, c_label = 1, nclass = self.nclass, use_onehot = False)
else:
raise Exception("No transform function is provided")
else:
img = curr_dict['img']
lb = curr_dict['lb']
img = np.float32(img)
lb = np.float32(lb).squeeze(-1) # NOTE: to be suitable for the PANet structure
if self.use_3_slices:
img = self.get_3_slice_adjacent_image(img, index)
img = torch.from_numpy( np.transpose(img, (2, 0, 1)) )
lb = torch.from_numpy( lb)
if self.tile_z_dim:
img = img.repeat( [ self.tile_z_dim, 1, 1] )
assert img.ndimension() == 3, f'actual dim {img.ndimension()}'
is_start = curr_dict["is_start"]
is_end = curr_dict["is_end"]
nframe = np.int32(curr_dict["nframe"])
scan_id = curr_dict["scan_id"]
z_id = curr_dict["z_id"]
sample = {"image": img,
"label":lb,
"is_start": is_start,
"is_end": is_end,
"nframe": nframe,
"scan_id": scan_id,
"z_id": z_id
}
# Add auxiliary attributes
if self.aux_attrib is not None:
for key_prefix in self.aux_attrib:
# Process the data sample, create new attributes and save them in a dictionary
aux_attrib_val = self.aux_attrib[key_prefix](sample, **self.aux_attrib_args[key_prefix])
for key_suffix in aux_attrib_val:
# one function may create multiple attributes, so we need suffix to distinguish them
sample[key_prefix + '_' + key_suffix] = aux_attrib_val[key_suffix]
return sample
def __len__(self):
"""
copy-paste from basic naive dataset configuration
"""
if self.get_item_mode == MODE_FULL_SCAN:
return len(self.scan_z_idx)
if self.fix_length != None:
assert self.fix_length >= len(self.actual_dataset)
return self.fix_length
else:
return len(self.actual_dataset)
def update_subclass_lookup(self):
"""
Updating the class-slice indexing list
Args:
[internal] overall_slice_by_cls:
{
class1: {pid1: [slice1, slice2, ....],
pid2: [slice1, slice2]},
...}
class2:
...
}
out[internal]:
{
class1: [ idx1, idx2, ... ],
class2: [ idx1, idx2, ... ],
...
}
"""
# delete previous ones if any
assert self.overall_slice_by_cls is not None
if not hasattr(self, 'idx_by_class'):
self.idx_by_class = {}
# filter the new one given the actual list
for cls in self.label_name:
if cls not in self.idx_by_class.keys():
self.idx_by_class[cls] = []
else:
del self.idx_by_class[cls][:]
for cls, dict_by_pid in self.overall_slice_by_cls.items():
for pid, slice_list in dict_by_pid.items():
if pid not in self.pid_curr_load:
continue
self.idx_by_class[cls] += [ self.scan_z_idx[pid][_sli] for _sli in slice_list ]
print("###### index-by-class table has been reloaded ######")
def getMaskMedImg(self, label, class_id, class_ids):
"""
Generate FG/BG mask from the segmentation mask. Used when getting the support
"""
# Dense Mask
fg_mask = torch.where(label == class_id,
torch.ones_like(label), torch.zeros_like(label))
bg_mask = torch.where(label != class_id,
torch.ones_like(label), torch.zeros_like(label))
for class_id in class_ids:
bg_mask[label == class_id] = 0
return {'fg_mask': fg_mask,
'bg_mask': bg_mask}
def subsets(self, sub_args_lst=None):
"""
Override base-class subset method
Create subsets by scan_ids
output: list [[<fid in each class>] <class1>, <class2> ]
"""
if sub_args_lst is not None:
subsets = []
ii = 0
for cls_name, index_list in self.idx_by_class.items():
subsets.append( Subset(dataset = self, indices = index_list, sub_attrib_args = sub_args_lst[ii]) )
ii += 1
else:
subsets = [Subset(dataset=self, indices=index_list) for _, index_list in self.idx_by_class.items()]
return subsets
def get_support(self, curr_class: int, class_idx: list, scan_idx: list, npart: int):
"""
getting (probably multi-shot) support set for evaluation
sample from 50% (1shot) or 20 35 50 65 80 (5shot)
Args:
curr_cls: current class to segment, starts from 1
class_idx: a list of all foreground class in nways, starts from 1
npart: how may chunks used to split the support
scan_idx: a list, indicating the current **i_th** (note this is idx not pid) training scan
being served as support, in self.pid_curr_load
"""
assert npart % 2 == 1
assert curr_class != 0; assert 0 not in class_idx
# assert not self.is_train
self.potential_support_sid = [self.pid_curr_load[ii] for ii in scan_idx ]
# print(f'###### Using {len(scan_idx)} shot evaluation!')
if npart == 1:
pcts = [0.5]
else:
half_part = 1 / (npart * 2)
part_interval = (1.0 - 1.0 / npart) / (npart - 1)
pcts = [ half_part + part_interval * ii for ii in range(npart) ]
# print(f'###### Parts percentage: {pcts} ######')
# norm_func = get_normalize_op(modality='MR', fids=None)
out_buffer = [] # [{scanid, img, lb}]
for _part in range(npart):
concat_buffer = [] # for each fold do a concat in image and mask in batch dimension
for scan_order in scan_idx:
_scan_id = self.pid_curr_load[ scan_order ]
print(f'Using scan {_scan_id} as support!')
# for _pc in pcts:
_zlist = self.tp1_cls_map[self.label_name[curr_class]][_scan_id] # list of indices
_zid = _zlist[int(pcts[_part] * len(_zlist))]
_glb_idx = self.scan_z_idx[_scan_id][_zid]
# almost copy-paste __getitem__ but no augmentation
curr_dict = self.actual_dataset[_glb_idx]
img = curr_dict['img']
lb = curr_dict['lb']
if self.use_3_slices:
prev_image = np.zeros_like(img)
if _glb_idx > 1 and not curr_dict["is_start"]:
prev_dict = self.actual_dataset[_glb_idx - 1]
prev_image = prev_dict["img"]
next_image = np.zeros_like(img)
if _glb_idx < len(self.actual_dataset) - 1 and not curr_dict["is_end"]:
next_dict = self.actual_dataset[_glb_idx + 1]
next_image = next_dict["img"]
img = np.concatenate([prev_image, img, next_image], axis=-1)
img = np.float32(img)
lb = np.float32(lb).squeeze(-1) # NOTE: to be suitable for the PANet structure
img = torch.from_numpy( np.transpose(img, (2, 0, 1)) )
lb = torch.from_numpy( lb )
if self.tile_z_dim:
img = img.repeat( [ self.tile_z_dim, 1, 1] )
assert img.ndimension() == 3, f'actual dim {img.ndimension()}'
is_start = curr_dict["is_start"]
is_end = curr_dict["is_end"]
nframe = np.int32(curr_dict["nframe"])
scan_id = curr_dict["scan_id"]
z_id = curr_dict["z_id"]
sample = {"image": img,
"label":lb,
"is_start": is_start,
"inst": None,
"scribble": None,
"is_end": is_end,
"nframe": nframe,
"scan_id": scan_id,
"z_id": z_id
}
concat_buffer.append(sample)
out_buffer.append({
"image": torch.stack([itm["image"] for itm in concat_buffer], dim = 0),
"label": torch.stack([itm["label"] for itm in concat_buffer], dim = 0),
})
# do the concat, and add to output_buffer
# post-processing, including keeping the foreground and suppressing background.
support_images = []
support_mask = []
support_class = []
for itm in out_buffer:
support_images.append(itm["image"])
support_class.append(curr_class)
support_mask.append( self.getMaskMedImg( itm["label"], curr_class, class_idx ))
return {'class_ids': [support_class],
'support_images': [support_images], #
'support_mask': [support_mask],
}
def get_support_scan(self, curr_class: int, class_idx: list, scan_idx: list):
self.potential_support_sid = [self.pid_curr_load[ii] for ii in scan_idx ]
# print(f'###### Using {len(scan_idx)} shot evaluation!')
scan_slices = self.scan_z_idx[self.potential_support_sid[0]]
scan_imgs = np.concatenate([self.actual_dataset[_idx]["img"] for _idx in scan_slices], axis = -1).transpose(2, 0, 1)
scan_lbs = np.concatenate([self.actual_dataset[_idx]["lb"] for _idx in scan_slices], axis = -1).transpose(2, 0, 1)
# binarize the labels
scan_lbs[scan_lbs != curr_class] = 0
scan_lbs[scan_lbs == curr_class] = 1
scan_imgs = torch.from_numpy(np.float32(scan_imgs)).unsqueeze(0)
scan_lbs = torch.from_numpy(np.float32(scan_lbs))
if self.tile_z_dim:
scan_imgs = scan_imgs.repeat(self.tile_z_dim, 1, 1, 1)
assert scan_imgs.ndimension() == 4, f'actual dim {scan_imgs.ndimension()}'
# reshape to C, D, H, W
sample = {"scan": scan_imgs,
"labels":scan_lbs,
}
return sample
def get_support_multiple_classes(self, classes: list, scan_idx: list, npart: int, use_3_slices=False):
"""
getting (probably multi-shot) support set for evaluation
sample from 50% (1shot) or 20 35 50 65 80 (5shot)
Args:
curr_cls: current class to segment, starts from 1
class_idx: a list of all foreground class in nways, starts from 1
npart: how may chunks used to split the support
scan_idx: a list, indicating the current **i_th** (note this is idx not pid) training scan
being served as support, in self.pid_curr_load
"""
assert npart % 2 == 1
# assert curr_class != 0; assert 0 not in class_idx
# assert not self.is_train
self.potential_support_sid = [self.pid_curr_load[ii] for ii in scan_idx ]
# print(f'###### Using {len(scan_idx)} shot evaluation!')
if npart == 1:
pcts = [0.5]
else:
half_part = 1 / (npart * 2)
part_interval = (1.0 - 1.0 / npart) / (npart - 1)
pcts = [ half_part + part_interval * ii for ii in range(npart) ]
# print(f'###### Parts percentage: {pcts} ######')
out_buffer = [] # [{scanid, img, lb}]
for _part in range(npart):
concat_buffer = [] # for each fold do a concat in image and mask in batch dimension
for scan_order in scan_idx:
_scan_id = self.pid_curr_load[ scan_order ]
print(f'Using scan {_scan_id} as support!')
# for _pc in pcts:
zlist = []
for curr_class in classes:
zlist.append(self.tp1_cls_map[self.label_name[curr_class]][_scan_id]) # list of indices
# merge all the lists in zlist and keep only the unique elements
# _zlist = sorted(list(set([item for sublist in zlist for item in sublist])))
# take only the indices that appear in all of the sublist
_zlist = sorted(list(set.intersection(*map(set, zlist))))
_zid = _zlist[int(pcts[_part] * len(_zlist))]
_glb_idx = self.scan_z_idx[_scan_id][_zid]
# almost copy-paste __getitem__ but no augmentation
curr_dict = self.actual_dataset[_glb_idx]
img = curr_dict['img']
lb = curr_dict['lb']
if use_3_slices:
prev_image = np.zeros_like(img)
if _glb_idx > 1 and not curr_dict["is_start"]:
prev_dict = self.actual_dataset[_glb_idx - 1]
assert prev_dict["scan_id"] == curr_dict["scan_id"]
assert prev_dict["z_id"] == curr_dict["z_id"] - 1
prev_image = prev_dict["img"]
next_image = np.zeros_like(img)
if _glb_idx < len(self.actual_dataset) - 1 and not curr_dict["is_end"]:
next_dict = self.actual_dataset[_glb_idx + 1]
assert next_dict["scan_id"] == curr_dict["scan_id"]
assert next_dict["z_id"] == curr_dict["z_id"] + 1
next_image = next_dict["img"]
img = np.concatenate([prev_image, img, next_image], axis=-1)
img = np.float32(img)
lb = np.float32(lb).squeeze(-1) # NOTE: to be suitable for the PANet structure
# zero all labels that are not in the classes arg
mask = np.zeros_like(lb)
for cls in classes:
mask[lb == cls] = 1
lb[~mask.astype(np.bool)] = 0
img = torch.from_numpy( np.transpose(img, (2, 0, 1)) )
lb = torch.from_numpy( lb )
if self.tile_z_dim:
img = img.repeat( [ self.tile_z_dim, 1, 1] )
assert img.ndimension() == 3, f'actual dim {img.ndimension()}'
is_start = curr_dict["is_start"]
is_end = curr_dict["is_end"]
nframe = np.int32(curr_dict["nframe"])
scan_id = curr_dict["scan_id"]
z_id = curr_dict["z_id"]
sample = {"image": img,
"label":lb,
"is_start": is_start,
"inst": None,
"scribble": None,
"is_end": is_end,
"nframe": nframe,
"scan_id": scan_id,
"z_id": z_id
}
concat_buffer.append(sample)
out_buffer.append({
"image": torch.stack([itm["image"] for itm in concat_buffer], dim = 0),
"label": torch.stack([itm["label"] for itm in concat_buffer], dim = 0),
})
# do the concat, and add to output_buffer
# post-processing, including keeping the foreground and suppressing background.
support_images = []
support_mask = []
support_class = []
for itm in out_buffer:
support_images.append(itm["image"])
support_class.append(curr_class)
# support_mask.append( self.getMaskMedImg( itm["label"], curr_class, class_idx ))
support_mask.append(itm["label"])
return {'class_ids': [support_class],
'support_images': [support_images], #
'support_mask': [support_mask],
'scan_id': scan_id
}
def get_nii_dataset(config, image_size, **kwargs):
print(f"Check config: {config}")
organ_mapping = {
"sabs":{
"rk": 2,
"lk": 3,
"liver": 6,
"spleen": 1
},
"chaost2":{
"liver": 1,
"rk": 2,
"lk": 3,
"spleen": 4
}}
transforms = None
data_name = config['dataset']
if data_name == 'SABS_Superpix' or data_name == 'SABS_Superpix_448' or data_name == 'SABS_Superpix_672':
baseset_name = 'SABS'
max_label = 13
modality="CT"
elif data_name == 'C0_Superpix':
raise NotImplementedError
baseset_name = 'C0'
max_label = 3
elif data_name == 'CHAOST2_Superpix' or data_name == 'CHAOST2_Superpix_672':
baseset_name = 'CHAOST2'
max_label = 4
modality="MR"
elif 'lits' in data_name.lower():
baseset_name = 'LITS17'
max_label = 4
else:
raise ValueError(f'Dataset: {data_name} not found')
# norm_func = get_normalize_op(modality=modality, fids=None) # TODO add global statistics
# norm_func = None
test_label = organ_mapping[baseset_name.lower()][config["curr_cls"]]
base_dir = config['path'][data_name]['data_dir']
testdataset = ManualAnnoDataset(which_dataset=baseset_name,
base_dir=base_dir,
idx_split = config['eval_fold'],
mode = 'val',
scan_per_load = 1,
transforms=transforms,
min_fg=1,
nsup = config["task"]["n_shots"],
fix_length=None,
image_size=image_size,
# extern_normalize_func=norm_func
**kwargs)
testdataset = ValidationDataset(testdataset, test_classes = [test_label], npart = config["task"]["npart"])
testdataset.set_curr_cls(test_label)
traindataset = None # TODO make this the support set later
return traindataset, testdataset |