diff --git a/qim3d/__init__.py b/qim3d/__init__.py
index eca387a06b555c30c94a7ea7d7ee8a3cfbdff604..73714bf3730e46ae2be9e91c6062acb52a66de4f 100644
--- a/qim3d/__init__.py
+++ b/qim3d/__init__.py
@@ -1,12 +1,14 @@
-# import qim3d.io as io
-# import qim3d.gui as gui
-# import qim3d.viz as viz
-# import qim3d.utils as utils
-# import qim3d.models as models
-# import qim3d.processing as processing
-from . import io, gui, viz, utils, models, processing
import logging
-__version__ = '0.3.2'
+logging.basicConfig(level=logging.ERROR)
+from qim3d import io
+from qim3d import gui
+from qim3d import viz
+from qim3d import utils
+from qim3d import models
+from qim3d import processing
+
+__version__ = "0.3.2"
examples = io.ImgExamples()
+io.logger.set_level_info()
diff --git a/qim3d/io/loading.py b/qim3d/io/loading.py
index 046f65136416f2cb79f980bd3ca86c63d5a72725..2da14e4c37d1c9fe1b22d3a4942401e9ee7a10db 100644
--- a/qim3d/io/loading.py
+++ b/qim3d/io/loading.py
@@ -17,12 +17,22 @@ import struct
from pathlib import Path
import dask.array as da
+import dask_image.imread
import h5py
import nibabel as nib
import numpy as np
import olefile
import pydicom
import tifffile
+
+# Dask
+import dask_image
+import dask
+from dask import delayed
+import dask.array as da
+
+dask.config.set(scheduler="processes") # Dask parallel goes brrrrr
+
from PIL import Image, UnidentifiedImageError
import qim3d
@@ -77,6 +87,7 @@ class DataLoader:
self.contains = kwargs.get("contains", None)
self.force_load = kwargs.get("force_load", False)
self.dim_order = kwargs.get("dim_order", (2, 1, 0))
+ self.PIL_extensions = (".jp2", ".jpg", "jpeg", ".png", "gif", ".bmp", ".webp")
def load_tiff(self, path):
"""Load a TIFF file from the specified path.
@@ -348,6 +359,90 @@ class DataLoader:
"""
return np.array(Image.open(path))
+ def load_PIL_stack(self, path):
+ """Load a stack of PIL files from the specified path.
+
+ Args:
+ path (str): The path to the stack of PIL files.
+
+ Returns:
+ numpy.ndarray or numpy.memmap: The loaded volume.
+ If 'self.virtual_stack' is True, returns a numpy.memmap object.
+
+ Raises:
+ ValueError: If the 'contains' argument is not specified.
+ ValueError: If the 'contains' argument matches multiple PIL stacks in the directory
+ """
+ if not self.contains:
+ raise ValueError(
+ "Please specify a part of the name that is common for the file stack with the argument 'contains'"
+ )
+
+ # List comprehension to filter files
+ PIL_stack = [
+ file
+ for file in os.listdir(path)
+ if file.endswith(self.PIL_extensions) and self.contains in file
+ ]
+
+ PIL_stack.sort() # Ensure proper ordering
+
+ # Check that only one stack in the directory contains the provided string in its name
+ PIL_stack_only_letters = []
+ for filename in PIL_stack:
+ name = os.path.splitext(filename)[0] # Remove file extension
+ PIL_stack_only_letters.append(
+ "".join(filter(str.isalpha, name))
+ ) # Remove everything else than letters from the name
+
+ # Get unique elements
+ unique_names = list(set(PIL_stack_only_letters))
+ if len(unique_names) > 1:
+ raise ValueError(
+ f"The provided part of the filename for the stack matches multiple stacks: {unique_names}.\nPlease provide a string that is unique for the image stack that is intended to be loaded"
+ )
+
+ if self.virtual_stack:
+
+ full_paths = [os.path.join(path, file) for file in PIL_stack]
+
+ def lazy_loader(path):
+ with Image.open(path) as img:
+ return np.array(img)
+
+ # Use delayed to load each image with PIL
+ lazy_images = [delayed(lazy_loader)(path) for path in full_paths]
+ # Compute the shape of the first image to define the array dimensions
+ sample_image = np.array(Image.open(full_paths[0]))
+ image_shape = sample_image.shape
+ dtype = sample_image.dtype
+
+ # Stack the images into a single Dask array
+ dask_images = [
+ da.from_delayed(img, shape=image_shape, dtype=dtype) for img in lazy_images
+ ]
+ stacked = da.stack(dask_images, axis=0)
+
+ return stacked
+
+ else:
+ # Generate placeholder volume
+ first_image = self.load_pil(os.path.join(path, PIL_stack[0]))
+ vol = np.zeros((len(PIL_stack), *first_image.shape), dtype=first_image.dtype)
+
+ # Load file sequence
+ for idx, file_name in enumerate(PIL_stack):
+
+ vol[idx] = self.load_pil(os.path.join(path, file_name))
+ return vol
+
+
+
+ # log.info("Found %s file(s)", len(PIL_stack))
+ # log.info("Loaded shape: %s", vol.shape)
+
+
+
def _load_vgi_metadata(self, path):
"""Helper functions that loads metadata from a VGI file
@@ -599,6 +694,9 @@ class DataLoader:
[f.endswith(".tif") or f.endswith(".tiff") for f in os.listdir(path)]
):
return self.load_tiff_stack(path)
+
+ elif any([f.endswith(self.PIL_extensions) for f in os.listdir(path)]):
+ return self.load_PIL_stack(path)
else:
return self.load_dicom_dir(path)
@@ -758,6 +856,5 @@ class ImgExamples:
img_examples_path = Path(qim3d.__file__).parents[0] / "img_examples"
img_paths = list(img_examples_path.glob("*.tif"))
-
- update_dict = {path.stem : load(path) for path in img_paths}
+ update_dict = {path.stem: load(path) for path in img_paths}
self.__dict__.update(update_dict)
diff --git a/qim3d/io/logger.py b/qim3d/io/logger.py
index afe4393d1cae80d7c35d88c32a94e3677afdec73..dbc88180713a0c38a74754c7261591090bf4519f 100644
--- a/qim3d/io/logger.py
+++ b/qim3d/io/logger.py
@@ -36,7 +36,7 @@ def set_simple_output():
formatter = logging.Formatter("%(message)s")
handler = logging.StreamHandler()
handler.setFormatter(formatter)
- logger = logging.getLogger("qim3d")
+ logger = logging.getLogger()
logger.handlers = []
logger.addHandler(handler)
@@ -132,5 +132,5 @@ def level(log_level):
# create the logger
log = logging.getLogger("qim3d")
-# set_simple_output() #TODO: This used to work, but now it gives duplicated messages. Need to be investigated.
-set_level_warning()
+set_simple_output() #TODO: This used to work, but now it gives duplicated messages. Need to be investigated.
+#set_level_warning()
diff --git a/qim3d/viz/img.py b/qim3d/viz/img.py
index d06697be9e0772135159026d27d0fa5c1e6dc23f..769db8cdbc2832c6cd189b1b887d6a9489bf0b05 100644
--- a/qim3d/viz/img.py
+++ b/qim3d/viz/img.py
@@ -11,6 +11,7 @@ import numpy as np
import torch
from matplotlib import colormaps
from matplotlib.colors import LinearSegmentedColormap
+import dask.array as da
from qim3d.io.logger import log
@@ -271,14 +272,17 @@ def slices(
"""
# Numpy array or Torch tensor input
- if not isinstance(vol, (np.ndarray, torch.Tensor)):
- raise ValueError("Input must be a numpy.ndarray or torch.Tensor")
+ if not isinstance(vol, (np.ndarray, torch.Tensor, da.core.Array)):
+ raise ValueError("Data type not supported")
if vol.ndim < 3:
raise ValueError(
"The provided object is not a volume as it has less than 3 dimensions."
)
+ if isinstance(vol, da.core.Array):
+ vol = vol.compute()
+
# Ensure axis is a valid choice
if not (0 <= axis < vol.ndim):
raise ValueError(
@@ -324,9 +328,11 @@ def slices(
if nrows == 1:
axs = [axs] # Convert to a list for uniformity
- # Convert Torch tensor to NumPy array in order to use the numpy.take method
+ # Convert to NumPy array in order to use the numpy.take method
if isinstance(vol, torch.Tensor):
vol = vol.numpy()
+ elif isinstance(vol, da.core.Array):
+ vol = vol.compute()
# Run through each ax of the grid
for i, ax_row in enumerate(axs):
diff --git a/qim3d/viz/k3d.py b/qim3d/viz/k3d.py
index 837b2fc748198aecad16f94053301fbe2530fe61..9271c56717b6f1ca95f674d16001c07ac3569dab 100644
--- a/qim3d/viz/k3d.py
+++ b/qim3d/viz/k3d.py
@@ -16,6 +16,8 @@ from qim3d.utils.internal_tools import downscale_img, scale_to_float16
def vol(
img,
+ vmin=None,
+ vmax=None,
aspectmode="data",
show=True,
save=False,
@@ -80,7 +82,7 @@ def vol(
a = (y1 - y2) / (x1 - x2)
b = y1 - a * x1
- samples = int(min(max(a * pixel_count + b, 32), 512))
+ samples = int(min(max(a * pixel_count + b, 64), 512))
else:
samples = int(samples) # make sure it's an integer
@@ -97,15 +99,23 @@ def vol(
f"Downsampled image for visualization. From {original_shape} to {new_shape}"
)
+ # Set color ranges
+ color_range = [np.min(img), np.max(img)]
+ if vmin:
+ color_range[0] = vmin
+ if vmax:
+ color_range[1] = vmax
+
plt_volume = k3d.volume(
scale_to_float16(img),
bounds=(
- [0, img.shape[0], 0, img.shape[1], 0, img.shape[2]]
+ [0, img.shape[2], 0, img.shape[1], 0, img.shape[0]]
if aspectmode.lower() == "data"
else None
),
color_map=cmap,
samples=samples,
+ color_range=color_range,
)
plot = k3d.plot(grid_visible=grid_visible, **kwargs)
plot += plt_volume