From 9e62018acd6f9c3be20a546aec5b1230d7a87144 Mon Sep 17 00:00:00 2001
From: fima <fima@dtu.dk>
Date: Mon, 17 Jun 2024 13:06:00 +0200
Subject: [PATCH] Import speed refactoring
---
docs/releases.md | 4 +
qim3d/__init__.py | 10 +-
qim3d/gui/annotation_tool.py | 6 +-
qim3d/io/loading.py | 13 +-
qim3d/io/saving.py | 94 ++++++++------
qim3d/models/unet.py | 180 ++++++++++++++++----------
qim3d/processing/cc.py | 1 -
qim3d/processing/detection.py | 3 +-
qim3d/processing/local_thickness_.py | 14 +-
qim3d/processing/operations.py | 6 +-
qim3d/processing/structure_tensor_.py | 2 +-
qim3d/tests/viz/test_img.py | 2 +-
qim3d/utils/augmentations.py | 6 +-
qim3d/utils/cli.py | 28 ++--
qim3d/utils/data.py | 5 +-
qim3d/viz/colormaps.py | 63 ++++-----
qim3d/viz/k3d.py | 3 +-
requirements.txt | 44 +++----
setup.py | 2 +-
19 files changed, 263 insertions(+), 223 deletions(-)
diff --git a/docs/releases.md b/docs/releases.md
index b6e15863..257c7d49 100644
--- a/docs/releases.md
+++ b/docs/releases.md
@@ -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
diff --git a/qim3d/__init__.py b/qim3d/__init__.py
index c89b6fd0..ebb4e7b2 100644
--- a/qim3d/__init__.py
+++ b/qim3d/__init__.py
@@ -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()
diff --git a/qim3d/gui/annotation_tool.py b/qim3d/gui/annotation_tool.py
index da664528..40b3fda8 100644
--- a/qim3d/gui/annotation_tool.py
+++ b/qim3d/gui/annotation_tool.py
@@ -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"
diff --git a/qim3d/io/loading.py b/qim3d/io/loading.py
index e45eb121..28f5abe4 100644
--- a/qim3d/io/loading.py
+++ b/qim3d/io/loading.py
@@ -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
diff --git a/qim3d/io/saving.py b/qim3d/io/saving.py
index 64a6a1ca..675fa20e 100644
--- a/qim3d/io/saving.py
+++ b/qim3d/io/saving.py
@@ -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
@@ -116,24 +110,30 @@ 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.
+ """Save data to a NIfTI file to the given path.
Args:
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)
# Create NIfTI image object
img = nib.Nifti1Image(data, np.eye(4), header)
-
+
# nib does automatically compress if filetype ends with .gz
if self.compression and not path.endswith(".gz"):
path += ".gz"
@@ -141,13 +141,15 @@ 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)
def save_vol(self, path, data):
- """ Save data to a VOL file to the given path.
+ """Save data to a VOL file to the given path.
Args:
path (str): The path to save file to
@@ -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 += "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 += "{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 += "Datatype = {}\n".format(str(data.dtype)) # Get datatype as string
+ metadata += "Name = {}.vol\n".format(
+ path.rsplit("/", 1)[-1][:-4]
+ ) # Get filename without extension
# Save metadata
with open(path[:-4] + ".vgi", "w") as f:
@@ -173,39 +181,45 @@ class DataSaver:
data.tofile(path[:-4] + ".vol")
def save_h5(self, path, data):
- """ Save data to a HDF5 file to the given path.
+ """Save data to a HDF5 file to the given path.
Args:
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.
+ """Save data to a DICOM file to the given path.
Args:
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"
- ds.StudyInstanceUID = "1.2.3.4.5"
+ ds.StudyInstanceUID = "1.2.3.4.5"
ds.SamplesPerPixel = 1
ds.PixelRepresentation = 0
ds.BitsStored = 16
@@ -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
@@ -234,10 +248,9 @@ class DataSaver:
ds.PixelData = data_bytes
ds.save_as(path)
-
-
+
def save_PIL(self, path, data):
- """ Save data to a PIL file to the given path.
+ """Save data to a PIL file to the given path.
Args:
path (str): The path to save file to
@@ -246,7 +259,7 @@ class DataSaver:
# No support for compression yet
if self.compression and path.endswith(".png"):
raise NotImplementedError("png does not support compression")
- elif not self.compression and path.endswith((".jpeg",".jpg")):
+ elif not self.compression and path.endswith((".jpeg", ".jpg")):
raise NotImplementedError("jpeg does not support no compression")
# Convert to PIL image
@@ -255,7 +268,6 @@ class DataSaver:
# Save image
img.save(path)
-
def save(self, path, data):
"""Save data to the given path.
@@ -320,17 +332,19 @@ class DataSaver:
if path.endswith((".tif", ".tiff")):
return self.save_tiff(path, data)
- elif path.endswith((".nii","nii.gz")):
+ 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")
+ elif path.endswith(("TXRM", "XRM", "TXM")):
+ raise NotImplementedError(
+ "Saving TXRM files is not yet supported"
+ )
elif path.endswith((".h5")):
return self.save_h5(path, data)
- elif path.endswith((".vol",".vgi")):
+ elif path.endswith((".vol", ".vgi")):
return self.save_vol(path, data)
- elif path.endswith((".dcm",".DCM")):
+ elif path.endswith((".dcm", ".DCM")):
return self.save_dicom(path, data)
- elif path.endswith((".jpeg",".jpg", ".png")):
+ elif path.endswith((".jpeg", ".jpg", ".png")):
return self.save_PIL(path, data)
else:
raise ValueError("Unsupported file format")
diff --git a/qim3d/models/unet.py b/qim3d/models/unet.py
index c85648fd..6ca19bcb 100644
--- a/qim3d/models/unet.py
+++ b/qim3d/models/unet.py
@@ -1,14 +1,10 @@
"""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.
@@ -32,20 +28,23 @@ class UNet(nn.Module):
model = UNet(size='large')
```
"""
- def __init__(self, size = 'medium',
- dropout = 0,
- kernel_size = 3,
- up_kernel_size = 3,
- activation = 'PReLU',
- bias = True,
- adn_order = 'NDA'
- ):
+
+ def __init__(
+ self,
+ size="medium",
+ dropout=0,
+ kernel_size=3,
+ up_kernel_size=3,
+ activation="PReLU",
+ bias=True,
+ 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'."
)
-
+
self.size = size
self.dropout = dropout
self.kernel_size = kernel_size
@@ -53,21 +52,22 @@ class UNet(nn.Module):
self.activation = activation
self.bias = bias
self.adn_order = adn_order
-
+
self.model = self._model_choice()
-
+
def _model_choice(self):
-
- if self.size == 'small':
+ from monai.networks.nets import UNet as monai_UNet
+
+ 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(
spatial_dims=2,
- in_channels=1, #TODO: check if image has 1 or multiple input channels
+ in_channels=1, # TODO: check if image has 1 or multiple input channels
out_channels=1,
channels=self.channels,
strides=(2,) * (len(self.channels) - 1),
@@ -76,12 +76,11 @@ 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):
+
+ def forward(self, x):
x = self.model(x)
return x
@@ -114,29 +113,37 @@ class Hyperparameters:
n_epochs = params_dict['n_epochs']
```
"""
- def __init__(self,
- model,
- n_epochs = 10,
- learning_rate = 1e-3,
- optimizer = 'Adam',
- momentum = 0,
- weight_decay = 0,
- loss_function = 'Focal'):
+ def __init__(
+ self,
+ model,
+ n_epochs=10,
+ learning_rate=1e-3,
+ optimizer="Adam",
+ momentum=0,
+ weight_decay=0,
+ 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 (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 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."
+ )
+
self.model = model
self.n_epochs = n_epochs
self.learning_rate = learning_rate
@@ -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')
- return criterion
\ No newline at end of file
+ def _loss_functions(self, loss_function):
+ 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
diff --git a/qim3d/processing/cc.py b/qim3d/processing/cc.py
index d6ec0e85..48cc004a 100644
--- a/qim3d/processing/cc.py
+++ b/qim3d/processing/cc.py
@@ -1,7 +1,6 @@
import numpy as np
import torch
from scipy.ndimage import find_objects, label
-
from qim3d.io.logger import log
diff --git a/qim3d/processing/detection.py b/qim3d/processing/detection.py
index c5743a15..b967e844 100644
--- a/qim3d/processing/detection.py
+++ b/qim3d/processing/detection.py
@@ -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(
```
"""
+ from skimage.feature import blob_dog
if background == "bright":
log.info("Bright background selected, volume will be inverted.")
diff --git a/qim3d/processing/local_thickness_.py b/qim3d/processing/local_thickness_.py
index f0de7e99..7a9e30aa 100644
--- a/qim3d/processing/local_thickness_.py
+++ b/qim3d/processing/local_thickness_.py
@@ -1,13 +1,11 @@
"""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,
@@ -17,10 +15,10 @@ def local_thickness(
) -> np.ndarray:
"""Wrapper for the local thickness function from the [local thickness package](https://github.com/vedranaa/local-thickness)
- The "Fast Local Thickness" by Vedrana Andersen Dahl and Anders Bjorholm Dahl from the Technical University of Denmark is a efficient algorithm for computing local thickness in 2D and 3D images.
- Their method significantly reduces computation time compared to traditional algorithms by utilizing iterative dilation with small structuring elements, rather than the large ones typically used.
- This approach allows the local thickness to be determined much faster, making it feasible for high-resolution volumetric data that are common in contemporary 3D microscopy.
-
+ The "Fast Local Thickness" by Vedrana Andersen Dahl and Anders Bjorholm Dahl from the Technical University of Denmark is a efficient algorithm for computing local thickness in 2D and 3D images.
+ Their method significantly reduces computation time compared to traditional algorithms by utilizing iterative dilation with small structuring elements, rather than the large ones typically used.
+ This approach allows the local thickness to be determined much faster, making it feasible for high-resolution volumetric data that are common in contemporary 3D microscopy.
+
Testing against conventional methods and other Python-based tools like PoreSpy shows that the new algorithm is both accurate and faster, offering significant improvements in processing time for large datasets.
@@ -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:
diff --git a/qim3d/processing/operations.py b/qim3d/processing/operations.py
index dfaeb42e..1efaf9c9 100644
--- a/qim3d/processing/operations.py
+++ b/qim3d/processing/operations.py
@@ -1,7 +1,4 @@
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(
"""
+ import skimage
+ import scipy
+
# Compute distance transform of binary volume
distance= scipy.ndimage.distance_transform_edt(bin_vol)
diff --git a/qim3d/processing/structure_tensor_.py b/qim3d/processing/structure_tensor_.py
index a02e376b..1a1bad3f 100644
--- a/qim3d/processing/structure_tensor_.py
+++ b/qim3d/processing/structure_tensor_.py
@@ -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")
diff --git a/qim3d/tests/viz/test_img.py b/qim3d/tests/viz/test_img.py
index 1179a15f..84dd4ee5 100644
--- a/qim3d/tests/viz/test_img.py
+++ b/qim3d/tests/viz/test_img.py
@@ -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():
diff --git a/qim3d/utils/augmentations.py b/qim3d/utils/augmentations.py
index 19154b70..ea81e53a 100644
--- a/qim3d/utils/augmentations.py
+++ b/qim3d/utils/augmentations.py
@@ -1,6 +1,4 @@
"""Class for choosing the level of data augmentations with albumentations"""
-import albumentations as A
-from albumentations.pytorch import ToTensorV2
class Augmentation:
"""
@@ -54,7 +52,9 @@ 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']:
raise ValueError(f"Invalid transformation level: {level}. Please choose one of the following levels: None, 'light', 'moderate', 'heavy'.")
diff --git a/qim3d/utils/cli.py b/qim3d/utils/cli.py
index 92f01e33..e4a64b9a 100644
--- a/qim3d/utils/cli.py
+++ b/qim3d/utils/cli.py
@@ -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")
diff --git a/qim3d/utils/data.py b/qim3d/utils/data.py
index 332856e4..ae910179 100644
--- a/qim3d/utils/data.py
+++ b/qim3d/utils/data.py
@@ -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
@@ -187,7 +185,8 @@ 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)
test_loader = DataLoader(dataset=test_set, batch_size=batch_size, num_workers=num_workers, pin_memory=pin_memory)
diff --git a/qim3d/viz/colormaps.py b/qim3d/viz/colormaps.py
index 547e10fb..e99b0fd8 100644
--- a/qim3d/viz/colormaps.py
+++ b/qim3d/viz/colormaps.py
@@ -1,16 +1,16 @@
"""
This module provides a collection of colormaps useful for 3D visualization.
"""
-
+
import colorsys
from typing import Union, Tuple
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):
+
+def rearrange_colors(randRGBcolors_old, min_dist=0.5):
# Create new list for re-arranged colors
randRGBcolors_new = [randRGBcolors_old.pop(0)]
@@ -32,6 +32,7 @@ def rearrange_colors(randRGBcolors_old, min_dist = 0.5):
return randRGBcolors_new
+
def objects(
nlabels: int,
style: str = "bright",
@@ -66,12 +67,12 @@ def objects(
cmap_earth = qim3d.viz.colormaps.objects(nlabels=100, style = 'earth', first_color_background=True, background_color="black", min_dist=0.8)
cmap_ocean = qim3d.viz.colormaps.objects(nlabels=100, style = 'ocean', first_color_background=True, background_color="black", min_dist=0.9)
- display(cmap_bright)
+ display(cmap_bright)
display(cmap_soft)
display(cmap_earth)
display(cmap_ocean)
```
- 
+ 
```python
import qim3d
@@ -83,14 +84,16 @@ def objects(
cmap = qim3d.viz.colormaps.objects(num_labels, style = 'bright')
qim3d.viz.slicer(labeled_volume, axis = 1, cmap=cmap)
```
- 
-
+ 
+
Tip:
- The `min_dist` parameter can be used to control the distance between neighboring colors.
- 
-
+ The `min_dist` parameter can be used to control the distance between neighboring colors.
+ 
+
"""
+ from skimage import color
+
# Check style
if style not in ("bright", "soft", "earth", "ocean"):
raise ValueError(
@@ -148,9 +151,9 @@ def objects(
if style == "earth":
randLABColors = [
(
- rng.uniform(low=25, high=110),
- rng.uniform(low=-120, high=70),
- rng.uniform(low=-70, high=70),
+ rng.uniform(low=25, high=110),
+ rng.uniform(low=-120, high=70),
+ rng.uniform(low=-70, high=70),
)
for i in range(nlabels)
]
@@ -158,17 +161,15 @@ 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":
randLABColors = [
(
- rng.uniform(low=0, high=110),
- rng.uniform(low=-128, high=160),
- rng.uniform(low=-128, high=0),
+ rng.uniform(low=0, high=110),
+ rng.uniform(low=-128, high=160),
+ rng.uniform(low=-128, high=0),
)
for i in range(nlabels)
]
@@ -176,10 +177,8 @@ 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
- (1.0, 0.6, 0.0), #ff9900
- ])
+
+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'```
@@ -213,4 +214,4 @@ Example:
```
"""
-colormaps.register(qim)
\ No newline at end of file
+colormaps.register(qim)
diff --git a/qim3d/viz/k3d.py b/qim3d/viz/k3d.py
index e612568b..fc3a2cf4 100644
--- a/qim3d/viz/k3d.py
+++ b/qim3d/viz/k3d.py
@@ -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
diff --git a/requirements.txt b/requirements.txt
index c22d3ed7..0f8cd33d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,25 +1,25 @@
-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
diff --git a/setup.py b/setup.py
index 017d277a..5efe335a 100644
--- a/setup.py
+++ b/setup.py
@@ -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",
--
GitLab