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Commit 9e62018a authored by fima's avatar fima :beers:
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Import speed refactoring

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docs/releases.md
+ 4
− 0
View file @ 9e62018a
......@@ -9,6 +9,10 @@ As the library is still in its early development stages, **there may be breaking
And remember to keep your pip installation [up to date](/qim3d/#upgrade) so that you have the latest features!
### v0.3.7 (17/06/2024)
- Performance improvements when importing
- Refactoring for blob detection
### v0.3.6 (30/05/2024)
- Refactoring for performance improvement
- Welcome message for the CLI
......
qim3d/__init__.py
+ 2
− 8
View file @ 9e62018a
......@@ -8,20 +8,14 @@ Documentation available at https://platform.qim.dk/qim3d/
"""
__version__ = "0.3.6"
import logging
logging.basicConfig(level=logging.ERROR)
__version__ = "0.3.7"
from . import io
from . import gui
from . import viz
from . import utils
from . import processing
# Commenting out models because it takes too long to import
# from . import models
from . import models
examples = io.ImgExamples()
io.logger.set_level_info()
qim3d/gui/annotation_tool.py
+ 1
− 5
View file @ 9e62018a
......@@ -23,16 +23,11 @@ app = annotation_tool.launch(vol[0])
import getpass
import os
import tempfile
import time
import gradio as gr
import numpy as np
import tifffile
from PIL import Image
import qim3d.utils
from qim3d.io import load, save
from qim3d.io.logger import log
class Session:
......@@ -100,6 +95,7 @@ class Interface:
return gr.update(visible=True)
def create_interface(self, img=None):
from PIL import Image
if img is not None:
custom_css = "annotation-tool"
......
qim3d/io/loading.py
+ 9
− 4
View file @ 9e62018a
......@@ -17,11 +17,7 @@ from pathlib import Path
import dask
import dask.array as da
import h5py
import nibabel as nib
import numpy as np
import olefile
import pydicom
import tifffile
from dask import delayed
from PIL import Image, UnidentifiedImageError
......@@ -122,6 +118,7 @@ class DataLoader:
ValueError: If the dataset_name is not specified in case of multiple datasets in the HDF5 file
ValueError: If no datasets are found in the file.
"""
import h5py
# Read file
f = h5py.File(path, "r")
......@@ -256,6 +253,7 @@ class DataLoader:
Raises:
ValueError: If the dxchange library is not installed
"""
import olefile
try:
import dxchange
......@@ -323,6 +321,7 @@ class DataLoader:
If 'self.virtual_stack' is True, returns a nibabel.arrayproxy.ArrayProxy object
If 'self.return_metadata' is True, returns a tuple (volume, metadata).
"""
import nibabel as nib
data = nib.load(path)
......@@ -557,6 +556,8 @@ class DataLoader:
Args:
path (str): Path to file
"""
import pydicom
dcm_data = pydicom.dcmread(path)
if self.return_metadata:
......@@ -570,6 +571,8 @@ class DataLoader:
Args:
path (str): Directory path
"""
import pydicom
if not self.contains:
raise ValueError(
"Please specify a part of the name that is common for the DICOM file stack with the argument 'contains'"
......@@ -709,6 +712,8 @@ class DataLoader:
def _get_h5_dataset_keys(f):
import h5py
keys = []
f.visit(lambda key: keys.append(key) if isinstance(f[key], h5py.Dataset) else None)
return keys
......
qim3d/io/saving.py
+ 54
− 40
View file @ 9e62018a
......@@ -21,18 +21,12 @@ Example:
```
"""
import datetime
import os
import h5py
import nibabel as nib
import numpy as np
import PIL
import pydicom
import tifffile
from pydicom.dataset import FileDataset, FileMetaDataset
from pydicom.uid import UID
from qim3d.io.logger import log
from qim3d.utils.internal_tools import sizeof, stringify_path
......@@ -116,9 +110,13 @@ class DataSaver:
filepath = os.path.join(path, filename)
self.save_tiff(filepath, sliced)
pattern_string = filepath[:-(len(extension)+zfill_val)] + "-"*zfill_val + extension
pattern_string = (
filepath[: -(len(extension) + zfill_val)] + "-" * zfill_val + extension
)
log.info(f"Total of {no_slices} files saved following the pattern '{pattern_string}'")
log.info(
f"Total of {no_slices} files saved following the pattern '{pattern_string}'"
)
def save_nifti(self, path, data):
"""Save data to a NIfTI file to the given path.
......@@ -127,6 +125,8 @@ class DataSaver:
path (str): The path to save file to
data (numpy.ndarray): The data to be saved
"""
import nibabel as nib
# Create header
header = nib.Nifti1Header()
header.set_data_dtype(data.dtype)
......@@ -141,7 +141,9 @@ class DataSaver:
if not self.compression and path.endswith(".gz"):
path = path[:-3]
log.warning("File extension '.gz' is ignored since compression is disabled.")
log.warning(
"File extension '.gz' is ignored since compression is disabled."
)
# Save image
nib.save(img, path)
......@@ -155,15 +157,21 @@ class DataSaver:
"""
# No support for compression yet
if self.compression:
raise NotImplementedError("Saving compressed .vol files is not yet supported")
raise NotImplementedError(
"Saving compressed .vol files is not yet supported"
)
# Create custom .vgi metadata file
metadata = ""
metadata += "{volume1}\n" # .vgi organization
metadata += "[file1]\n" # .vgi organization
metadata += "Size = {} {} {}\n".format(data.shape[1], data.shape[2], data.shape[0]) # Swap axes to match .vol format
metadata += "Size = {} {} {}\n".format(
data.shape[1], data.shape[2], data.shape[0]
) # Swap axes to match .vol format
metadata += "Datatype = {}\n".format(str(data.dtype)) # Get datatype as string
metadata += "Name = {}.vol\n".format(path.rsplit('/', 1)[-1][:-4]) # Get filename without extension
metadata += "Name = {}.vol\n".format(
path.rsplit("/", 1)[-1][:-4]
) # Get filename without extension
# Save metadata
with open(path[:-4] + ".vgi", "w") as f:
......@@ -179,9 +187,12 @@ class DataSaver:
path (str): The path to save file to
data (numpy.ndarray): The data to be saved
"""
import h5py
with h5py.File(path, "w") as f:
f.create_dataset("dataset", data=data, compression="gzip" if self.compression else None)
f.create_dataset(
"dataset", data=data, compression="gzip" if self.compression else None
)
def save_dicom(self, path, data):
"""Save data to a DICOM file to the given path.
......@@ -190,18 +201,21 @@ class DataSaver:
path (str): The path to save file to
data (numpy.ndarray): The data to be saved
"""
import pydicom
from pydicom.dataset import FileDataset, FileMetaDataset
from pydicom.uid import UID
# based on https://pydicom.github.io/pydicom/stable/auto_examples/input_output/plot_write_dicom.html
# Populate required values for file meta information
file_meta = FileMetaDataset()
file_meta.MediaStorageSOPClassUID = UID('1.2.840.10008.5.1.4.1.1.2')
file_meta.MediaStorageSOPClassUID = UID("1.2.840.10008.5.1.4.1.1.2")
file_meta.MediaStorageSOPInstanceUID = UID("1.2.3")
file_meta.ImplementationClassUID = UID("1.2.3.4")
# Create the FileDataset instance (initially no data elements, but file_meta
# supplied)
ds = FileDataset(path, {},
file_meta=file_meta, preamble=b"\0" * 128)
ds = FileDataset(path, {}, file_meta=file_meta, preamble=b"\0" * 128)
ds.PatientName = "Test^Firstname"
ds.PatientID = "123456"
......@@ -220,8 +234,8 @@ class DataSaver:
# Set creation date/time
dt = datetime.datetime.now()
ds.ContentDate = dt.strftime('%Y%m%d')
timeStr = dt.strftime('%H%M%S.%f') # long format with micro seconds
ds.ContentDate = dt.strftime("%Y%m%d")
timeStr = dt.strftime("%H%M%S.%f") # long format with micro seconds
ds.ContentTime = timeStr
# Needs to be here because of bug in pydicom
ds.file_meta.TransferSyntaxUID = pydicom.uid.ImplicitVRLittleEndian
......@@ -235,7 +249,6 @@ class DataSaver:
ds.save_as(path)
def save_PIL(self, path, data):
"""Save data to a PIL file to the given path.
......@@ -255,7 +268,6 @@ class DataSaver:
# Save image
img.save(path)
def save(self, path, data):
"""Save data to the given path.
......@@ -323,7 +335,9 @@ class DataSaver:
elif path.endswith((".nii", "nii.gz")):
return self.save_nifti(path, data)
elif path.endswith(("TXRM", "XRM", "TXM")):
raise NotImplementedError("Saving TXRM files is not yet supported")
raise NotImplementedError(
"Saving TXRM files is not yet supported"
)
elif path.endswith((".h5")):
return self.save_h5(path, data)
elif path.endswith((".vol", ".vgi")):
......
qim3d/models/unet.py
+ 109
− 71
View file @ 9e62018a
"""UNet model and Hyperparameters class."""
from monai.networks.nets import UNet as monai_UNet
from monai.losses import FocalLoss, DiceLoss, DiceCELoss
import torch.nn as nn
from torch.nn import BCEWithLogitsLoss
from torch.optim import Adam, SGD, RMSprop
from qim3d.io.logger import log
class UNet(nn.Module):
"""
2D UNet model for QIM imaging.
......@@ -32,16 +28,19 @@ class UNet(nn.Module):
model = UNet(size='large')
```
"""
def __init__(self, size = 'medium',
def __init__(
self,
size="medium",
dropout=0,
kernel_size=3,
up_kernel_size=3,
activation = 'PReLU',
activation="PReLU",
bias=True,
adn_order = 'NDA'
adn_order="NDA",
):
super().__init__()
if size not in ['small','medium','large']:
if size not in ["small", "medium", "large"]:
raise ValueError(
f"Invalid model size: {size}. Size must be one of the following: 'small', 'medium', 'large'."
)
......@@ -57,12 +56,13 @@ class UNet(nn.Module):
self.model = self._model_choice()
def _model_choice(self):
from monai.networks.nets import UNet as monai_UNet
if self.size == 'small':
if self.size == "small":
self.channels = (64, 128, 256)
elif self.size == 'medium':
elif self.size == "medium":
self.channels = (64, 128, 256, 512, 1024)
elif self.size == 'large':
elif self.size == "large":
self.channels = (64, 128, 256, 512, 1024, 2048)
model = monai_UNet(
......@@ -76,11 +76,10 @@ class UNet(nn.Module):
act=self.activation,
dropout=self.dropout,
bias=self.bias,
adn_ordering=self.adn_order
adn_ordering=self.adn_order,
)
return model
def forward(self, x):
x = self.model(x)
return x
......@@ -114,28 +113,36 @@ class Hyperparameters:
n_epochs = params_dict['n_epochs']
```
"""
def __init__(self,
def __init__(
self,
model,
n_epochs=10,
learning_rate=1e-3,
optimizer = 'Adam',
optimizer="Adam",
momentum=0,
weight_decay=0,
loss_function = 'Focal'):
loss_function="Focal",
):
# TODO: implement custom loss_functions? then add a check to see if loss works for segmentation.
if loss_function not in ['BCE','Dice','Focal','DiceCE']:
raise ValueError(f"Invalid loss function: {loss_function}. Loss criterion must "
"be one of the following: 'BCE','Dice','Focal','DiceCE'.")
if loss_function not in ["BCE", "Dice", "Focal", "DiceCE"]:
raise ValueError(
f"Invalid loss function: {loss_function}. Loss criterion must "
"be one of the following: 'BCE','Dice','Focal','DiceCE'."
)
# TODO: implement custom optimizer? and add check to see if valid.
if optimizer not in ['Adam','SGD','RMSprop']:
raise ValueError(f"Invalid optimizer: {optimizer}. Optimizer must "
"be one of the following: 'Adam', 'SGD', 'RMSprop'.")
if optimizer not in ["Adam", "SGD", "RMSprop"]:
raise ValueError(
f"Invalid optimizer: {optimizer}. Optimizer must "
"be one of the following: 'Adam', 'SGD', 'RMSprop'."
)
if (momentum != 0) and optimizer == 'Adam':
log.info("Momentum isn't an input in the 'Adam' optimizer. "
"Change optimizer to 'SGD' or 'RMSprop' to use momentum.")
if (momentum != 0) and optimizer == "Adam":
log.info(
"Momentum isn't an input in the 'Adam' optimizer. "
"Change optimizer to 'SGD' or 'RMSprop' to use momentum."
)
self.model = model
self.n_epochs = n_epochs
......@@ -146,41 +153,72 @@ class Hyperparameters:
self.loss_function = loss_function
def __call__(self):
return self.model_params(self.model, self.n_epochs, self.optimizer, self.learning_rate,
self.weight_decay, self.momentum, self.loss_function)
return self.model_params(
self.model,
self.n_epochs,
self.optimizer,
self.learning_rate,
self.weight_decay,
self.momentum,
self.loss_function,
)
def model_params(self, model, n_epochs, optimizer, learning_rate, weight_decay, momentum, loss_function):
def model_params(
self,
model,
n_epochs,
optimizer,
learning_rate,
weight_decay,
momentum,
loss_function,
):
optim = self._optimizer(model, optimizer, learning_rate, weight_decay, momentum)
criterion = self._loss_functions(loss_function)
hyper_dict = {'optimizer': optim,
'criterion': criterion,
'n_epochs' : n_epochs,
hyper_dict = {
"optimizer": optim,
"criterion": criterion,
"n_epochs": n_epochs,
}
return hyper_dict
# selecting the optimizer
def _optimizer(self, model, optimizer, learning_rate, weight_decay, momentum):
if optimizer == 'Adam':
optim = Adam(model.parameters(), lr = learning_rate,
weight_decay = weight_decay)
elif optimizer == 'SGD':
optim = SGD(model.parameters(), lr = learning_rate,
momentum = momentum, weight_decay = weight_decay)
elif optimizer == 'RMSprop':
optim = RMSprop(model.parameters(),lr = learning_rate,
weight_decay = weight_decay, momentum = momentum)
from torch.optim import Adam, SGD, RMSprop
if optimizer == "Adam":
optim = Adam(
model.parameters(), lr=learning_rate, weight_decay=weight_decay
)
elif optimizer == "SGD":
optim = SGD(
model.parameters(),
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay,
)
elif optimizer == "RMSprop":
optim = RMSprop(
model.parameters(),
lr=learning_rate,
weight_decay=weight_decay,
momentum=momentum,
)
return optim
# selecting the loss function
def _loss_functions(self, loss_function):
if loss_function =='BCE':
criterion = BCEWithLogitsLoss(reduction='mean')
elif loss_function == 'Dice':
criterion = DiceLoss(sigmoid=True,reduction='mean')
elif loss_function == 'Focal':
criterion = FocalLoss(reduction='mean')
elif loss_function == 'DiceCE':
criterion = DiceCELoss(sigmoid=True,reduction='mean')
from monai.losses import FocalLoss, DiceLoss, DiceCELoss
from torch.nn import BCEWithLogitsLoss
if loss_function == "BCE":
criterion = BCEWithLogitsLoss(reduction="mean")
elif loss_function == "Dice":
criterion = DiceLoss(sigmoid=True, reduction="mean")
elif loss_function == "Focal":
criterion = FocalLoss(reduction="mean")
elif loss_function == "DiceCE":
criterion = DiceCELoss(sigmoid=True, reduction="mean")
return criterion
qim3d/processing/cc.py
+ 0
− 1
View file @ 9e62018a
import numpy as np
import torch
from scipy.ndimage import find_objects, label
from qim3d.io.logger import log
......
qim3d/processing/detection.py
+ 1
− 2
View file @ 9e62018a
......@@ -2,8 +2,6 @@
import numpy as np
from qim3d.io.logger import log
from skimage.feature import blob_dog
def blob_detection(
vol: np.ndarray,
......@@ -63,6 +61,7 @@ def blob_detection(
```
![blob detection](assets/screenshots/blob_get_mask.gif)
"""
from skimage.feature import blob_dog
if background == "bright":
log.info("Bright background selected, volume will be inverted.")
......
qim3d/processing/local_thickness_.py
+ 7
− 7
View file @ 9e62018a
"""Wrapper for the local thickness function from the localthickness package including visualization functions."""
import localthickness as lt
import numpy as np
from typing import Optional
from skimage.filters import threshold_otsu
from qim3d.io.logger import log
#from qim3d.viz import local_thickness as viz_local_thickness
import qim3d
def local_thickness(
image: np.ndarray,
scale: float = 1,
......@@ -79,6 +77,8 @@ def local_thickness(
"""
import localthickness as lt
from skimage.filters import threshold_otsu
# Check if input is binary
if np.unique(image).size > 2:
......
qim3d/processing/operations.py
+ 3
− 3
View file @ 9e62018a
import numpy as np
import scipy
import skimage
import qim3d.processing.filters as filters
from qim3d.io.logger import log
......@@ -86,6 +83,9 @@ def watershed(
![operations-watershed_after](assets/screenshots/operations-watershed_after.png)
"""
import skimage
import scipy
# Compute distance transform of binary volume
distance= scipy.ndimage.distance_transform_edt(bin_vol)
......
qim3d/processing/structure_tensor_.py
+ 1
− 1
View file @ 9e62018a
......@@ -2,7 +2,6 @@
from typing import Tuple
import numpy as np
import structure_tensor as st
from qim3d.viz.structure_tensor import vectors
......@@ -74,6 +73,7 @@ def structure_tensor(
```
"""
import structure_tensor as st
if vol.ndim != 3:
raise ValueError("The input volume must be 3D")
......
qim3d/tests/viz/test_img.py
+ 1
− 1
View file @ 9e62018a
......@@ -62,7 +62,7 @@ def test_slices_torch_tensor_input():
def test_slices_wrong_input_format():
input = 'not_a_volume'
with pytest.raises(ValueError, match = 'Input must be a numpy.ndarray or torch.Tensor'):
with pytest.raises(ValueError, match = 'Data type not supported'):
qim3d.viz.slices(input)
def test_slices_not_volume():
......
qim3d/utils/augmentations.py
+ 3
− 3
View file @ 9e62018a
"""Class for choosing the level of data augmentations with albumentations"""
import albumentations as A
from albumentations.pytorch import ToTensorV2
class Augmentation:
"""
......@@ -54,6 +52,8 @@ class Augmentation:
Raises:
ValueError: If `level` is neither None, light, moderate nor heavy.
"""
import albumentations as A
from albumentations.pytorch import ToTensorV2
# Check if one of standard augmentation levels
if level not in [None,'light','moderate','heavy']:
......
qim3d/utils/cli.py
+ 10
− 18
View file @ 9e62018a
......@@ -5,8 +5,15 @@ from qim3d.gui import annotation_tool, data_explorer, iso3d, local_thickness
from qim3d.io.loading import DataLoader
from qim3d.utils import image_preview
from qim3d import __version__ as version
import outputformat as ouf
import qim3d
QIM_TITLE = ouf.rainbow(
f"\n _ _____ __ \n ____ _(_)___ ___ |__ /____/ / \n / __ `/ / __ `__ \ /_ </ __ / \n/ /_/ / / / / / / /__/ / /_/ / \n\__, /_/_/ /_/ /_/____/\__,_/ \n /_/ v{version}\n\n",
return_str=True,
cmap="hot",
)
def main():
parser = argparse.ArgumentParser(description="Qim3d command-line interface.")
......@@ -139,29 +146,14 @@ def main():
)
elif args.subcommand is None:
print(QIM_TITLE)
welcome_text = (
"\n"
" _ _____ __ \n"
" ____ _(_)___ ___ |__ /____/ / \n"
" / __ `/ / __ `__ \ /_ </ __ / \n"
"/ /_/ / / / / / / /__/ / /_/ / \n"
"\__, /_/_/ /_/ /_/____/\__,_/ \n"
" /_/ \n"
"\n"
"--- Welcome to qim3d command-line interface ---\n"
"qim3d is a Python package for 3D image processing and visualization.\n"
"For more information, please visit: https://platform.qim.dk/qim3d/\n"
f"Current version of qim3d: {version}\n"
" \n"
"The qim3d command-line interface provides the following subcommands:\n"
"- gui: Graphical User Interfaces\n"
"- viz: Volumetric visualizations of volumes\n"
"- preview: Preview of an volume directly in the terminal\n"
"\nqim3d is a Python package for 3D image processing and visualization.\n"
f"For more information, please visit {ouf.c('https://platform.qim.dk/qim3d/', color='orange', return_str=True)}\n"
" \n"
"For more information on each subcommand, type 'qim3d <subcommand> --help'.\n"
)
print(welcome_text)
print("--- Help page for qim3d command-line interface shown below ---\n")
parser.print_help()
print("\n")
......
qim3d/utils/data.py
+ 2
− 3
View file @ 9e62018a
......@@ -2,8 +2,6 @@
from pathlib import Path
from PIL import Image
from qim3d.io.logger import log
from torch.utils.data import DataLoader
import torch
import numpy as np
......@@ -187,6 +185,7 @@ def prepare_dataloaders(train_set, val_set, test_set, batch_size, shuffle_train
num_workers (int, optional): Defines how many processes should be run in parallel.
pin_memory (bool, optional): Loads the datasets as CUDA tensors.
"""
from torch.utils.data import DataLoader
train_loader = DataLoader(dataset=train_set, batch_size=batch_size, shuffle=shuffle_train, num_workers=num_workers, pin_memory=pin_memory)
val_loader = DataLoader(dataset=val_set, batch_size=batch_size, num_workers=num_workers, pin_memory=pin_memory)
......
qim3d/viz/colormaps.py
+ 32
− 31
View file @ 9e62018a
......@@ -8,7 +8,7 @@ import numpy as np
import math
from matplotlib.colors import LinearSegmentedColormap
from matplotlib import colormaps
from skimage import color
def rearrange_colors(randRGBcolors_old, min_dist=0.5):
# Create new list for re-arranged colors
......@@ -32,6 +32,7 @@ def rearrange_colors(randRGBcolors_old, min_dist = 0.5):
return randRGBcolors_new
def objects(
nlabels: int,
style: str = "bright",
......@@ -91,6 +92,8 @@ def objects(
"""
from skimage import color
# Check style
if style not in ("bright", "soft", "earth", "ocean"):
raise ValueError(
......@@ -158,9 +161,7 @@ def objects(
# Convert LAB list to RGB
randRGBcolors = []
for LabColor in randLABColors:
randRGBcolors.append(
color.lab2rgb([[LabColor]])[0][0].tolist()
)
randRGBcolors.append(color.lab2rgb([[LabColor]])[0][0].tolist())
# Generate color map for ocean colors, based on LAB
if style == "ocean":
......@@ -176,9 +177,7 @@ def objects(
# Convert LAB list to RGB
randRGBcolors = []
for LabColor in randLABColors:
randRGBcolors.append(
color.lab2rgb([[LabColor]])[0][0].tolist()
)
randRGBcolors.append(color.lab2rgb([[LabColor]])[0][0].tolist())
# Re-arrange colors to have a minimum distance between neighboring colors
randRGBcolors = rearrange_colors(randRGBcolors, min_dist)
......@@ -191,16 +190,18 @@ def objects(
randRGBcolors[-1] = background_color
# Create colormap
objects = LinearSegmentedColormap.from_list(
"objects", randRGBcolors, N=nlabels
)
objects = LinearSegmentedColormap.from_list("objects", randRGBcolors, N=nlabels)
return objects
qim = LinearSegmentedColormap.from_list('qim',
[(0.6, 0.0, 0.0), #990000
qim = LinearSegmentedColormap.from_list(
"qim",
[
(0.6, 0.0, 0.0), # 990000
(1.0, 0.6, 0.0), # ff9900
])
],
)
"""
Defines colormap in QIM logo colors. Can be accessed as module attribute or easily by ```cmap = 'qim'```
......
qim3d/viz/k3d.py
+ 1
− 2
View file @ 9e62018a
......@@ -7,9 +7,7 @@ Volumetric visualization using K3D
"""
import k3d
import numpy as np
from qim3d.io.logger import log
from qim3d.utils.internal_tools import downscale_img, scale_to_float16
......@@ -73,6 +71,7 @@ def vol(
```
"""
import k3d
pixel_count = img.shape[0] * img.shape[1] * img.shape[2]
# target is 60fps on m1 macbook pro, using test volume: https://data.qim.dk/pages/foam.html
......
requirements.txt
+ 22
− 22
View file @ 9e62018a
albumentations>=1.3.1,
gradio>=4.27.0,
h5py>=3.9.0,
localthickness>=0.1.2,
matplotlib>=3.8.0,
monai>=1.2.0,
numpy>=1.26.0,
outputformat>=0.1.3,
Pillow>=10.0.1,
plotly>=5.14.1,
scipy>=1.11.2,
seaborn>=0.12.2,
pydicom>=2.4.4,
setuptools>=68.0.0,
tifffile>=2023.4.12,
torch>=2.0.1,
torchvision>=0.15.2,
torchinfo>=1.8.0,
tqdm>=4.65.0,
nibabel>=5.2.0,
ipywidgets>=8.1.2,
dask>=2023.6.0,
albumentations>=1.3.1
gradio>=4.27.0
h5py>=3.9.0
localthickness>=0.1.2
matplotlib>=3.8.0
monai>=1.2.0
numpy>=1.26.0
outputformat>=0.1.3
Pillow>=10.0.1
plotly>=5.14.1
scipy>=1.11.2
seaborn>=0.12.2
pydicom>=2.4.4
setuptools>=68.0.0
tifffile>=2023.4.12
torch>=2.0.1
torchvision>=0.15.2
torchinfo>=1.8.0
tqdm>=4.65.0
nibabel>=5.2.0
ipywidgets>=8.1.2
dask>=2023.6.0
k3d>=2.16.1
olefile>=0.46
psutil>=5.9.0
......
setup.py
+ 1
− 1
View file @ 9e62018a
......@@ -9,7 +9,7 @@ with open("README.md", "r", encoding="utf-8") as f:
setup(
name="qim3d",
version="0.3.6",
version="0.3.7",
author="Felipe Delestro",
author_email="fima@dtu.dk",
description="QIM tools and user interfaces",
......
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