keyword stringclasses 7
values | repo_name stringlengths 8 98 | file_path stringlengths 4 244 | file_extension stringclasses 29
values | file_size int64 0 84.1M | line_count int64 0 1.6M | content stringlengths 1 84.1M ⌀ | language stringclasses 14
values |
|---|---|---|---|---|---|---|---|
3D | oopil/3D_medical_image_FSS | UNet_upperbound/models/encoder.py | .py | 849 | 29 | import pdb
import torch
import torch.nn as nn
import torchvision
from .vgg import Encoder_vgg
# from torchsummary import summary
if __name__ == '__main__':
from nnutils import conv_unit
else:
from .nnutils import conv_unit
class Encoder(nn.Module):
def __init__(self, pretrained_path, device):
super... | Python |
3D | oopil/3D_medical_image_FSS | UNet_upperbound/models/nnutils.py | .py | 1,670 | 51 | import torch
import torch.nn as nn
def conv_unit(in_ch, out_ch, kernel_size, stride = 1, padding = 0, activation = 'relu', batch_norm = True):
seq_list = []
seq_list.append(nn.Conv2d(in_channels = in_ch, out_channels = out_ch, kernel_size = kernel_size, stride = stride, padding = padding))
if batch_norm:
... | Python |
3D | oopil/3D_medical_image_FSS | UNet_upperbound/models/__init__.py | .py | 0 | 0 | null | Python |
3D | oopil/3D_medical_image_FSS | UNet_upperbound/models/vgg.py | .py | 4,435 | 119 | """
Encoder for few shot segmentation (VGG16)
"""
import torch
import torch.nn as nn
import pdb
class Encoder_vgg(nn.Module):
"""
Encoder for few shot segmentation
Args:
in_channels:
number of input channels
pretrained_path:
path of the model for initialization
... | Python |
3D | oopil/3D_medical_image_FSS | UNet_upperbound/dataloaders_medical/common.py | .py | 6,690 | 210 | """
Dataset classes for common uses
"""
import random
import SimpleITK as sitk
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
import torch
import torchvision.transforms.functional as tr_F
from skimage.exposure import equalize_hist
import pdb
def crop_resize(slice):
x_size, y_size = n... | Python |
3D | oopil/3D_medical_image_FSS | UNet_upperbound/dataloaders_medical/dataset.py | .py | 9,037 | 259 | import os
import re
import sys
import json
import math
import random
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
# sys.path.append("../dataloaders_medical")
from dataloaders_medical.common import *
# from common import *
import cv2
from cv2 import resize
def prostate_img_process(img_... | Python |
3D | oopil/3D_medical_image_FSS | UNet_upperbound/dataloaders_medical/__init__.py | .py | 0 | 0 | null | Python |
3D | oopil/3D_medical_image_FSS | UNet_upperbound/dataloaders_medical/dataset_CT_ORG.py | .py | 8,872 | 247 | import os
import re
import sys
import json
import math
import random
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
# sys.path.append("../dataloaders_medical")
from dataloaders_medical.common import *
# from common import *
import cv2
from cv2 import resize
def totensor(arr):
tensor... | Python |
3D | oopil/3D_medical_image_FSS | UNet_upperbound/dataloaders_medical/dataset_decathlon.py | .py | 13,370 | 432 | import os
import re
import sys
import json
import random
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
# sys.path.append("../dataloaders_medical")
from dataloaders_medical.common import *
# from common import *
import cv2
from cv2 import resize
def totensor(arr):
tensor = torch.fro... | Python |
3D | oopil/3D_medical_image_FSS | UNet_upperbound/dataloaders_medical/prostate.py | .py | 11,390 | 299 | import sys
import glob
import json
import re
from glob import glob
from util.utils import *
from dataloaders_medical.dataset import *
from dataloaders_medical.dataset_decathlon import *
from dataloaders_medical.dataset_CT_ORG import *
import numpy as np
class MetaSliceData_train():
def __init__(self, datasets, ite... | Python |
3D | oopil/3D_medical_image_FSS | process/cervix_2d_3way.py | .py | 8,805 | 219 | import os
import sys
from glob import glob
import json
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
sys.path.append("../dataloaders_medical")
from PIL import Image
import SimpleITK as sitk
import numpy as np
import random
import shutil
import cv2
from cv2 import resize
import json
impo... | Python |
3D | oopil/3D_medical_image_FSS | process/abdomen_2d_3way.py | .py | 9,308 | 236 | import os
import sys
from glob import glob
import json
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
sys.path.append("../dataloaders_medical")
from PIL import Image
import SimpleITK as sitk
import numpy as np
import random
import shutil
import cv2
from cv2 import resize
import json
impo... | Python |
3D | oopil/3D_medical_image_FSS | process/reg_train_v2.py | .py | 3,259 | 99 | import os
import sys
from glob import glob
import json
import numpy as np
from PIL import Image
import random
import shutil
import cv2
from cv2 import resize
import json
from sklearn.model_selection import train_test_split
import SimpleITK as sitk
def metadata():
info = {
"src_dir" : "/media/NAS/nas_187/soopil... | Python |
3D | oopil/3D_medical_image_FSS | process/reg_train.py | .py | 3,126 | 99 | import os
import sys
from glob import glob
import json
import numpy as np
from PIL import Image
import random
import shutil
import cv2
from cv2 import resize
import json
from sklearn.model_selection import train_test_split
import SimpleITK as sitk
def metadata():
info = {
"src_dir" : "/media/NAS/nas_187/soopil... | Python |
3D | oopil/3D_medical_image_FSS | process/__init__.py | .py | 0 | 0 | null | Python |
3D | oopil/3D_medical_image_FSS | process/reg_train_nocrop.py | .py | 3,125 | 99 | import os
import sys
from glob import glob
import json
import numpy as np
from PIL import Image
import random
import shutil
import cv2
from cv2 import resize
import json
from sklearn.model_selection import train_test_split
import SimpleITK as sitk
def metadata():
info = {
"src_dir" : "/media/NAS/nas_187/soopil... | Python |
3D | oopil/3D_medical_image_FSS | process/cervix_2d.py | .py | 6,127 | 170 | import os
import sys
from glob import glob
import json
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
sys.path.append("../dataloaders_medical")
from PIL import Image
import SimpleITK as sitk
import numpy as np
import random
import shutil
import cv2
from cv2 import resize
import json
from... | Python |
3D | oopil/3D_medical_image_FSS | process/af_reg.py | .py | 5,941 | 165 | import sys
import pdb
import os
import SimpleITK as sitk
import numpy as np
from skimage.io import imsave
from glob import glob
import psutil
p = psutil.Process(os.getpid())
# p.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS) # for Windows
p.nice(19) # for Linux
def make_dir(path):
if not os.path.exists(path):
o... | Python |
3D | oopil/3D_medical_image_FSS | process/abdomen_2d.py | .py | 7,123 | 186 | import os
import sys
from glob import glob
import json
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
sys.path.append("../dataloaders_medical")
from PIL import Image
import SimpleITK as sitk
import numpy as np
import random
import shutil
import cv2
from cv2 import resize
import json
from... | Python |
3D | oopil/3D_medical_image_FSS | process/separate_label.py | .py | 1,835 | 59 | import os
import sys
from glob import glob
import json
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
sys.path.append("../dataloaders_medical")
import SimpleITK as sitk
import numpy as np
from skimage.segmentation import slic
def read_sitk(path):
itk_img = sitk.ReadImage(path)
a... | Python |
3D | oopil/3D_medical_image_FSS | process/figure_kidney.py | .py | 1,802 | 64 | import os
import sys
from glob import glob
import json
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
sys.path.append("../dataloaders_medical")
import SimpleITK as sitk
import numpy as np
from skimage.segmentation import slic
def read_sitk(path):
itk_img = sitk.ReadImage(path)
a... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/train_multishot.py | .py | 6,585 | 168 | """Training Script"""
import os
import shutil
import numpy as np
import pdb
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import MultiStepLR
import torch.backends.cudnn as cudnn
from torchvision.... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/settings.py | .py | 747 | 29 | import ast
import configparser
from collections.abc import Mapping
class Settings(Mapping):
def __init__(self, setting_file='settings.ini'):
config = configparser.ConfigParser()
config.read(setting_file)
self.settings_dict = _parse_values(config)
def __getitem__(self, key):
re... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/test_multishot.py | .py | 7,443 | 199 | """Evaluation Script"""
import os
import shutil
import pdb
import tqdm
import numpy as np
import torch
import torch.optim
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
from torchvision.utils import make_grid
from util.utils import set_seed, CLASS_LABELS, get_bbox, d... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/train_background.sh | .sh | 1,270 | 27 | # this code require gpu_id when running
mkdir runs/log
declare -a gpu_list
gpu_list=(0 1 2 3)
#gpu_list=(4 5 6 7)
j=$2
idx=0
for organ in 1 3 6 14
do
echo ""
echo "=================================================================="
gpu=${gpu_list[$idx]}
#gpu=0
echo "python train_multishot.py with ... | Shell |
3D | oopil/3D_medical_image_FSS | FSS_SE/solver.py | .py | 13,862 | 320 | import glob
import os
import pdb
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import lr_scheduler
import utils.common_utils as common_utils
from nn_common_modules import losses
# from nn_additional_losses import losses
from utils.data_utils import split_batch
f... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/few_shot_segmentor.py | .py | 10,170 | 277 | """Few-Shot_learning Segmentation"""
import pdb
import numpy as np
import torch
import torch.nn as nn
from nn_common_modules import modules as sm
from utils.data_utils import split_batch
# import torch.nn.functional as F
from squeeze_and_excitation import squeeze_and_excitation as se
class SDnetConditioner(nn.Module)... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/Finetuning.ipynb | .ipynb | 5,772 | 165 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import utils.data_utils as du\n",
"from utils.evaluator import binarize_label\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"import torch.n... | Unknown |
3D | oopil/3D_medical_image_FSS | FSS_SE/config.py | .py | 4,196 | 154 | """Experiment Configuration"""
import os
import re
import glob
import itertools
import sacred
from sacred import Experiment
from sacred.observers import FileStorageObserver
from sacred.utils import apply_backspaces_and_linefeeds
sacred.SETTINGS['CONFIG']['READ_ONLY_CONFIG'] = False
sacred.SETTINGS.CAPTURE_MODE = 'no'... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/run.py | .py | 6,709 | 186 | import argparse
import os
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import MultiStepLR
import torch.backends.cudnn as cudnn
from dataloaders_medical.prostate import *
import few_shot_segmentor as fs
import utils.evaluator as eu
from settings import Setti... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/evaluator_hack.py | .py | 37,255 | 705 | import os
import nibabel as nib
import numpy as np
import torch
import utils.common_utils as common_utils
import utils.data_utils as du
import torch.nn.functional as F
import shot_batch_sampler as SB
def dice_score_binary(vol_output, ground_truth, no_samples=10, phase='train'):
ground_truth = ground_truth.type(... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/evaluator_multi_volume_support.py | .py | 31,169 | 629 | import os
import nibabel as nib
import numpy as np
import torch
import utils.common_utils as common_utils
import utils.data_utils as du
import torch.nn.functional as F
import matplotlib.pyplot as plt
import shot_batch_sampler as SB
def dice_score_binary(vol_output, ground_truth, no_samples=10, phase='train'):
g... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/convert_h5.py | .py | 7,067 | 121 | """
Convert to h5 utility.
Sample command to create new dataset - python utils/convert_h5.py -dd /home/masterthesis/shayan/nas_drive/Data_Neuro/OASISchallenge/FS -ld /home/masterthesis/shayan/nas_drive/Data_Neuro/OASISchallenge -trv datasets/train_volumes.txt -tev datasets/test_volumes.txt -rc Neo -o COR -df datasets/M... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/common_utils.py | .py | 96 | 7 | import os
def create_if_not(path):
if not os.path.exists(path):
os.makedirs(path)
| Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/preprocessor.py | .py | 3,898 | 104 | import numpy as np
ORIENTATION = {
'coronal': "COR",
'axial': "AXI",
'sagital': "SAG"
}
def rotate_orientation(volume_data, volume_label, orientation=ORIENTATION['coronal']):
if orientation == ORIENTATION['coronal']:
return volume_data.transpose((2, 0, 1)), volume_label.transpose((2, 0, 1))
... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/__init__.py | .py | 0 | 0 | null | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/log_utils.py | .py | 8,456 | 200 | import itertools
import os
import re
import shutil
from textwrap import wrap
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from tensorboardX import SummaryWriter
import torch
import utils.evaluator as eu
import logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(thre... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/evaluator_kshot.py | .py | 30,640 | 592 | import os
import nibabel as nib
import numpy as np
import torch
import utils.common_utils as common_utils
import utils.data_utils as du
import torch.nn.functional as F
import shot_batch_sampler as SB
def dice_score_binary(vol_output, ground_truth, no_samples=10, phase='train'):
ground_truth = ground_truth.type(... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/evaluator_slow.py | .py | 28,985 | 565 | import os
import nibabel as nib
import numpy as np
import torch
import utils.common_utils as common_utils
import utils.data_utils as du
import torch.nn.functional as F
import shot_batch_sampler as SB
def dice_score_binary(vol_output, ground_truth, no_samples=10, phase='train'):
ground_truth = ground_truth.type(... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/evaluator.py | .py | 38,938 | 735 | import os
import nibabel as nib
import numpy as np
import torch
import utils.common_utils as common_utils
import utils.data_utils as du
import torch.nn.functional as F
def dice_score_binary(vol_output, ground_truth, no_samples=10, phase='train'):
ground_truth = ground_truth.type(torch.FloatTensor)
vol_outpu... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/shot_batch_sampler.py | .py | 2,744 | 89 | import numpy as np
lab_list_fold = {"fold1": {"train": [2, 6, 7, 8, 9], "val": [1]},
"fold2": {"train": [1, 6, 7, 8, 9], "val": [2]},
"fold3": {"train": [1, 2, 8, 9], "val": [6, 7]},
"fold4": {"train": [1, 2, 6, 7], "val": [8, 9]}}
def get_lab_list(phase, fold):
... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/data_utils.py | .py | 7,914 | 195 | import os
import h5py
import numpy as np
import torch
import torch.utils.data as data
import scipy.io as sio
import utils.preprocessor as preprocessor
import nibabel as nb
import math
from torchvision import transforms
# import utils.preprocessor as preprocessor
# transform_train = transforms.Compose([
# trans... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/utils/evaluator_finetuning.py | .py | 38,979 | 735 | import os
import nibabel as nib
import numpy as np
import torch
import utils.common_utils as common_utils
import utils.data_utils as du
import torch.nn.functional as F
def dice_score_binary(vol_output, ground_truth, no_samples=10, phase='train'):
ground_truth = ground_truth.type(torch.FloatTensor)
vol_outpu... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/util/sbd_instance_process.py | .py | 1,242 | 39 | """
This snippet processes SBD instance segmentation data
and transform it from .mat to .png. Then transformed
images will be saved in VOC data folder. The name of
the new folder is "SegmentationObjectAug"
"""
import os
from scipy.io import loadmat
from PIL import Image
# set path
voc_dir = '../Pascal/VOCdevkit/VOC2... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/util/metric.py | .py | 6,195 | 152 | """
Metrics for computing evalutation results
"""
import numpy as np
class Metric(object):
"""
Compute evaluation result
Args:
max_label:
max label index in the data (0 denoting background)
n_runs:
number of test runs
"""
def __init__(self, max_label=20, n_... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/util/__init__.py | .py | 0 | 0 | null | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/util/scribbles.py | .py | 12,787 | 344 | from __future__ import absolute_import, division
import networkx as nx
import numpy as np
from scipy.ndimage import binary_dilation, binary_erosion
from scipy.special import comb
from skimage.filters import rank
from skimage.morphology import dilation, disk, erosion, medial_axis
from sklearn.neighbors import radius_ne... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/util/voc_classwise_filenames.py | .py | 2,540 | 72 | """
This snippet processes VOC segmentation data
and generates filename list according to the
class labels each image contains.
This snippet will create folders under
"ImageSets/Segmentaion/" with the same
names as the splits. Each folder has 20 txt files
each contains the filenames whose associated image
contains thi... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/util/utils.py | .py | 1,995 | 74 | """Util functions"""
import random
from datetime import datetime
import torch
import numpy as np
def try_mkdir(path):
try:
os.mkdir(path)
print(f"mkdir : {path}")
except:
print(f"failed to make a directory : {path}")
def date():
now = datetime.now()
string = now.year + now.month + now.day
string ... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/test/decathlon_5shot.sh | .sh | 2,316 | 33 | # this code require gpu_id when running
# 1 2 3 4 5 6 7 8 9 10 11 12 13
mkdir runs/log
mkdir runs/log/decathlon_1shot_last
gpu=$1
j=$2
#j=9
j=5
organ=1
for support in 0 5 10 15 20
do
#echo "python train.py with mode=train gpu_id=${gpu} target=${organ} board=ID${j}_${organ} record=False n_work=3 external_train=decathl... | Shell |
3D | oopil/3D_medical_image_FSS | FSS_SE/test/summarize_test_results.py | .py | 4,955 | 154 | import re
import glob
import numpy as np
def summarize(files):
if len(files) < 5:
print("There is no results for this set.")
return 0,0
dices = []
for file in files[:5]:
fd = open(file)
lines = fd.readlines()
# print(len(lines),file)
result_line = lines[-2]
... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/test/bcv_5shot.sh | .sh | 1,096 | 34 | # test code for external dataset ctorg with different support data
mkdir runs/log
mkdir runs/log/bcv_dice_1shot_low
declare -a gpu_list
declare -a ID_list
#gpu_list=(0 1 2 7)
gpu=$1
# BCV configuration
#ID_list=(1 2 3 4) #1shot
ID_list=(9 10 11 12) #1shot
idx=0
for organ in 1 3 6 14
do
j=${ID_list[$idx]}
e... | Shell |
3D | oopil/3D_medical_image_FSS | FSS_SE/test/bcv_5shot_save_sample.sh | .sh | 816 | 26 | # test code for external dataset ctorg with different support data
mkdir runs/log
mkdir runs/log/bcv_dice_ce
declare -a gpu_list
gpu_list=(0 1 2 7)
gpu=$1
j=$2
idx=0
# BCV configuration
# 1 shot - 21, 3 shot - 5, 5 shot - 17
j=5
support=0
for organ in 1 3 6 14
do
echo "python test.py with target=${organ} snaps... | Shell |
3D | oopil/3D_medical_image_FSS | FSS_SE/test/ctorg_5shot.sh | .sh | 1,715 | 27 | # this code require gpu_id when running
# 1 2 3 4 5 6 7 8 9 10 11 12 13
mkdir runs/log
mkdir runs/log/ctorg_1shot_last
gpu=$1
j=$2
#j=10
j=6
for organ in 3 6 14
do
for support in 0 5 10 15 20
do
#echo "python train.py with mode=train gpu_id=${gpu} target=${organ} board=ID${j}_${organ} record=False n_... | Shell |
3D | oopil/3D_medical_image_FSS | FSS_SE/test/bcv.sh | .sh | 2,828 | 76 | # test code for external dataset ctorg with different support data
mkdir runs/log
mkdir runs/log/bcv_dice_last
declare -a gpu_list
declare -a ID_list
#gpu_list=(0 1 2 7)
gpu=$1
# BCV configuration
#ID_list=(1 2 3 4) #1shot
ID_list=(5 6 7 8) #1shot
idx=0
for organ in 1 3 6 14
do
j=${ID_list[$idx]}
echo ""
... | Shell |
3D | oopil/3D_medical_image_FSS | FSS_SE/dataloaders_medical/decathlon.py | .py | 10,510 | 285 | import os
import re
import sys
import json
import math
import random
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
# sys.path.append("../dataloaders_medical")
from dataloaders_medical.common import *
# from common import *
import cv2
from cv2 import resize
def prostate_img_process(img_... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/dataloaders_medical/common.py | .py | 6,690 | 210 | """
Dataset classes for common uses
"""
import random
import SimpleITK as sitk
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
import torch
import torchvision.transforms.functional as tr_F
from skimage.exposure import equalize_hist
import pdb
def crop_resize(slice):
x_size, y_size = n... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/dataloaders_medical/__init__.py | .py | 0 | 0 | null | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/dataloaders_medical/dataset_CT_ORG.py | .py | 10,422 | 278 | import os
import re
import sys
import json
import math
import random
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
# sys.path.append("../dataloaders_medical")
from dataloaders_medical.common import *
# from common import *
import cv2
from cv2 import resize
def totensor(arr):
tensor... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/dataloaders_medical/dataset_decathlon.py | .py | 14,795 | 458 | import os
import re
import sys
import json
import random
import numpy as np
sys.path.append("/home/soopil/Desktop/github/python_utils")
# sys.path.append("../dataloaders_medical")
from dataloaders_medical.common import *
# from common import *
import cv2
from cv2 import resize
def totensor(arr):
tensor = torch.fro... | Python |
3D | oopil/3D_medical_image_FSS | FSS_SE/dataloaders_medical/prostate.py | .py | 8,823 | 226 | import sys
import glob
import json
import re
from glob import glob
from util.utils import *
from dataloaders_medical.decathlon import *
from dataloaders_medical.dataset_decathlon import *
from dataloaders_medical.dataset_CT_ORG import *
import numpy as np
class MetaSliceData_train():
def __init__(self, datasets, i... | Python |
3D | hku-mars/ImMesh | include/types.h | .h | 1,421 | 37 | #ifndef TYPES_H
#define TYPES_H
#include <Eigen/Eigen>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
typedef pcl::PointXYZINormal PointType;
typedef pcl::PointXYZRGB PointTypeRGB;
typedef pcl::PointCloud<PointType> PointCloudXYZI;
typedef std::vector<PointType, Eigen::aligned_allocator<PointType>> PointV... | Unknown |
3D | hku-mars/ImMesh | include/so3_math.h | .h | 2,739 | 106 | #ifndef SO3_MATH_H
#define SO3_MATH_H
#include <math.h>
#include <Eigen/Core>
// #include <common_lib.h>
#define SKEW_SYM_MATRX(v) 0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0
template<typename T>
Eigen::Matrix<T, 3, 3> Exp(const Eigen::Matrix<T, 3, 1> &&ang)
{
T ang_norm = ang.norm();
Eigen::Matrix<T, 3, 3... | Unknown |
3D | hku-mars/ImMesh | include/feature.h | .h | 2,224 | 66 | // This file is part of SVO - Semi-direct Visual Odometry.
//
// Copyright (C) 2014 Christian Forster <forster at ifi dot uzh dot ch>
// (Robotics and Perception Group, University of Zurich, Switzerland).
//
// SVO is free software: you can redistribute it and/or modify it under the
// terms of the GNU General Public L... | Unknown |
3D | hku-mars/ImMesh | include/common_lib.h | .h | 12,251 | 404 | #ifndef COMMON_LIB_H
#define COMMON_LIB_H
#pragma once
#include <Eigen/Eigen>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <so3_math.h>
#include <types.h>
// #include <fast_lio/States.h>
// #include <fast_lio/Pose6D.h>
#include <boost/shared_ptr.hpp>
#include <cv_bridge/cv_bridge.h>
#include <ei... | Unknown |
3D | hku-mars/ImMesh | include/ikd-Tree/ikd_Tree.h | .h | 10,683 | 321 | #pragma once
#include <stdio.h>
#include <queue>
#include <pthread.h>
#include <chrono>
#include <time.h>
#include <unistd.h>
#include <math.h>
#include <algorithm>
#include <memory>
#include <pcl/point_types.h>
#include "tools_eigen.hpp"
#define EPSS 1e-6
#define Minimal_Unbalanced_Tree_Size 10
#define Multi_Thread_Re... | Unknown |
3D | hku-mars/ImMesh | include/ikd-Tree/ikd_Tree.cpp | .cpp | 73,438 | 1,828 | #include "ikd_Tree.h"
/*
Description: ikd-Tree: an incremental k-d tree for robotic applications
Author: Yixi Cai
email: yixicai@connect.hku.hk
*/
template < typename PointType >
KD_TREE< PointType >::KD_TREE( float delete_param, float balance_param, float box_length )
{
delete_criterion_param = delete_param;
... | C++ |
3D | hku-mars/ImMesh | src/voxel_loc.hpp | .hpp | 6,640 | 178 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | Unknown |
3D | hku-mars/ImMesh | src/voxel_loc.cpp | .cpp | 15,967 | 368 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | C++ |
3D | hku-mars/ImMesh | src/ImMesh_node.cpp | .cpp | 21,817 | 537 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | C++ |
3D | hku-mars/ImMesh | src/voxel_mapping_common.cpp | .cpp | 31,511 | 727 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | C++ |
3D | hku-mars/ImMesh | src/voxel_mapping.cpp | .cpp | 96,235 | 2,051 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | C++ |
3D | hku-mars/ImMesh | src/ImMesh_mesh_reconstruction.cpp | .cpp | 19,762 | 445 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | C++ |
3D | hku-mars/ImMesh | src/preprocess.cpp | .cpp | 46,590 | 1,375 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | C++ |
3D | hku-mars/ImMesh | src/voxel_mapping.hpp | .hpp | 19,127 | 416 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | Unknown |
3D | hku-mars/ImMesh | src/IMU_Processing.cpp | .cpp | 43,055 | 1,009 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | C++ |
3D | hku-mars/ImMesh | src/preprocess.h | .h | 7,511 | 196 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | Unknown |
3D | hku-mars/ImMesh | src/IMU_Processing.h | .h | 6,126 | 153 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | Unknown |
3D | hku-mars/ImMesh | src/shader/stb_image.h | .h | 254,334 | 7,196 | /* stb_image - v2.14 - public domain image loader - http://nothings.org/stb_image.h
no warranty implied; use at your own risk
Do this:
#define STB_IMAGE_IMPLEMENTATION
before you include this file in *one* C or C++ file to create the implementation.
// i.e. it should look like this:
#include ...
#include ...
#include... | Unknown |
3D | hku-mars/ImMesh | src/shader/stb_image_aug.c | .c | 113,757 | 3,683 | /* stbi-1.16 - public domain JPEG/PNG reader - http://nothings.org/stb_image.c
when you control the images you're loading
QUICK NOTES:
Primarily of interest to game developers and other people who can
avoid problematic images and only need the trivial interface
JPEG base... | C |
3D | hku-mars/ImMesh | src/shader/tools_my_point_shader.h | .h | 12,653 | 311 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | Unknown |
3D | hku-mars/ImMesh | src/shader/stb_image_aug.h | .h | 16,591 | 355 | /* stbi-1.16 - public domain JPEG/PNG reader - http://nothings.org/stb_image.c
when you control the images you're loading
QUICK NOTES:
Primarily of interest to game developers and other people who can
avoid problematic images and only need the trivial interface
JPEG base... | Unknown |
3D | hku-mars/ImMesh | src/shader/tools_my_dbg_utility.h | .h | 9,600 | 244 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | Unknown |
3D | hku-mars/ImMesh | src/shader/tools_my_camera_pose_shader.h | .h | 12,108 | 264 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | Unknown |
3D | hku-mars/ImMesh | src/shader/tools_my_texture_triangle_shader.h | .h | 9,093 | 218 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | Unknown |
3D | hku-mars/ImMesh | src/shader/tools_my_shader_common.h | .h | 9,106 | 252 | /*
This code is the implementation of our paper "ImMesh: An Immediate LiDAR Localization and Meshing Framework".
The source code of this package is released under GPLv2 license. We only allow it free for personal and academic usage.
If you use any code of this repo in your academic research, please cite at least on... | Unknown |
3D | hku-mars/ImMesh | src/shader/shader_m.h | .h | 7,770 | 199 | #ifndef SHADER_H
#define SHADER_H
#include "glad.h"
#include <glm/glm.hpp>
#include <Eigen/Eigen>
#include <string>
#include <fstream>
#include <sstream>
#include <iostream>
class Shader
{
public:
unsigned int ID;
// constructor generates the shader on the fly
// -------------------------------------------... | Unknown |
3D | hku-mars/ImMesh | src/shader/shader_s.h | .h | 4,566 | 123 | #ifndef SHADER_H
#define SHADER_H
// #include <glad/glad.h>
#include "../r4live_dev/openGL/glad.h"
#include <string>
#include <fstream>
#include <sstream>
#include <iostream>
class Shader
{
public:
unsigned int ID;
// constructor generates the shader on the fly
// ----------------------------------------... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/mesh.h | .h | 5,038 | 147 | #ifndef MESH_H
#define MESH_H
#include <glad/glad.h> // holds all OpenGL type declarations
#include <glm/glm.hpp>
#include <glm/gtc/matrix_transform.hpp>
#include "./shader.h"
#include <string>
#include <vector>
using namespace std;
#define MAX_BONE_INFLUENCE 4
struct Vertex {
// position
glm::vec3 Positi... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/model_animation.h | .h | 9,140 | 283 | #ifndef MODEL_H
#define MODEL_H
#include <glad/glad.h>
#include <glm/glm.hpp>
#include <glm/gtc/matrix_transform.hpp>
#include <stb_image.h>
#include <assimp/Importer.hpp>
#include <assimp/scene.h>
#include <assimp/postprocess.h>
#include <learnopengl/mesh.h>
#include <learnopengl/shader.h>
#include <string>
#incl... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/camera.h | .h | 4,341 | 131 | #ifndef CAMERA_H
#define CAMERA_H
#include <glad/glad.h>
#include <glm/glm.hpp>
#include <glm/gtc/matrix_transform.hpp>
#include <vector>
// Defines several possible options for camera movement. Used as abstraction to stay away from window-system specific input methods
enum Camera_Movement {
FORWARD,
BACKWAR... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/filesystem.h | .h | 1,281 | 54 | #ifndef FILESYSTEM_H
#define FILESYSTEM_H
#include <string>
#include <cstdlib>
static const char * logl_root = "/home/ziv/opt/LearnOpenGL";
// #include "root_directory.h" // This is a configuration file generated by CMake.
class FileSystem
{
private:
typedef std::string (*Builder) (const std::string& path);
public... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/shader_c.h | .h | 5,827 | 151 | #ifndef COMPUTE_SHADER_H
#define COMPUTE_SHADER_H
#include <glad/glad.h>
#include <glm/glm.hpp>
#include <string>
#include <fstream>
#include <sstream>
#include <iostream>
class ComputeShader
{
public:
unsigned int ID;
// constructor generates the shader on the fly
// ------------------------------------... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/entity.h | .h | 14,955 | 486 | #ifndef ENTITY_H
#define ENTITY_H
#include <glm/glm.hpp> //glm::mat4
#include <list> //std::list
#include <array> //std::array
#include <memory> //std::unique_ptr
class Transform
{
protected:
//Local space information
glm::vec3 m_pos = { 0.0f, 0.0f, 0.0f };
glm::vec3 m_eulerRot = { 0.0f, 0.0f, 0.0f }; //In degrees... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/shader.h | .h | 7,660 | 192 | #ifndef SHADER_H
#define SHADER_H
#include <glad/glad.h>
#include <glm/glm.hpp>
#include <string>
#include <fstream>
#include <sstream>
#include <iostream>
class Shader
{
public:
unsigned int ID;
// constructor generates the shader on the fly
// -----------------------------------------------------------... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/animdata.h | .h | 207 | 15 | #pragma once
#include<glm/glm.hpp>
struct BoneInfo
{
/*id is index in finalBoneMatrices*/
int id;
/*offset matrix transforms vertex from model space to bone space*/
glm::mat4 offset;
};
#pragma once
| Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/animation.h | .h | 2,857 | 112 | #pragma once
#include <vector>
#include <map>
#include <glm/glm.hpp>
#include <assimp/scene.h>
#include <learnopengl/bone.h>
#include <functional>
#include <learnopengl/animdata.h>
#include <learnopengl/model_animation.h>
struct AssimpNodeData
{
glm::mat4 transformation;
std::string name;
int childrenCount;
std::... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/shader_m.h | .h | 6,523 | 166 | #ifndef SHADER_H
#define SHADER_H
#include <glad/glad.h>
#include <glm/glm.hpp>
#include <string>
#include <fstream>
#include <sstream>
#include <iostream>
class Shader
{
public:
unsigned int ID;
// constructor generates the shader on the fly
// -----------------------------------------------------------... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/shader_t.h | .h | 9,618 | 235 | #ifndef SHADER_H
#define SHADER_H
#include <glad/glad.h>
#include <glm/glm.hpp>
#include <string>
#include <fstream>
#include <sstream>
#include <iostream>
class Shader
{
public:
unsigned int ID;
// constructor generates the shader on the fly
// -----------------------------------------------------------... | Unknown |
3D | hku-mars/ImMesh | src/shader/learnopengl/model.h | .h | 9,928 | 246 | #ifndef MODEL_H
#define MODEL_H
#include <glad/glad.h>
#include <glm/glm.hpp>
#include <glm/gtc/matrix_transform.hpp>
#include <assimp/Importer.hpp>
#include <assimp/scene.h>
#include <assimp/postprocess.h>
#include "../stb_image.h"
#include "./mesh.h"
#include "./shader.h"
#include <string>
#include <fstream>
#in... | Unknown |
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