🚸 Handle Multidimensional Features in WSIReader

test.py

@@ -0,0 +1,32 @@
+"""Import modules required to run the Jupyter notebook."""
+# Clear logger to use tiatoolbox.logger
+import logging
+
+if logging.getLogger().hasHandlers():
+    logging.getLogger().handlers.clear()
+
+import numpy as np
+
+from tiatoolbox.tools import patchextraction
+
+input_img = np.zeros((1000, 1000, 6))
+
+patch_extractor = patchextraction.get_patch_extractor(
+    input_img=input_img,  # input image path, numpy array, or WSI object
+    # path to list of points (csv, json), numpy list, panda DF
+    method_name="slidingwindow",  # also supports "slidingwindow"
+    patch_size=(
+        32,
+        32,
+    ),  # size of the patch to extract around the centroids from centroids_list
+    resolution=0,
+    units="level",
+)
+
+i = 1
+# show only first 16 patches
+num_patches_to_show = 16
+
+for patch in patch_extractor:
+    print(i)
+    i += 1

tiatoolbox/wsicore/wsireader.py

@@ -2781,8 +2781,14 @@ class VirtualWSIReader(WSIReader):
     :func:`~tiatoolbox.utils.image.sub_pixel_read`.
 
     Attributes:
-        img (:class:`numpy.ndarray`)
-        mode (str)
+        img (:class:`numpy.ndarray`):
+            Input image as :class:`numpy.ndarray`.
+        mode (str):
+            Mode of the input image. Default is 'rgb'. Allowed values
+            are: rgb, bool, feature. "rgb" mode supports bright-field color images.
+            "bool" mode supports binary masks,
+            interpolation in this case will be "nearest" instead of "bicubic".
+            "feature" mode allows multichannel features.
 
     Args:
         input_img (str, :obj:`Path`, :class:`numpy.ndarray`):
@@ -2791,7 +2797,10 @@ class VirtualWSIReader(WSIReader):
             Metadata for the virtual wsi.
         mode (str):
             Mode of the input image. Default is 'rgb'. Allowed values
-            are: rgb, bool.
+            are: rgb, bool, feature. "rgb" mode supports bright-field color images.
+            "bool" mode supports binary masks,
+            interpolation in this case will be "nearest" instead of "bicubic".
+            "feature" mode allows multichannel features.
 
     """
 
@@ -2809,15 +2818,24 @@ def __init__(
             mpp=mpp,
             power=power,
         )
-        if mode.lower() not in ["rgb", "bool"]:
+        if mode.lower() not in ["rgb", "bool", "feature"]:
             msg = "Invalid mode."
             raise ValueError(msg)
-        self.mode = mode.lower()
+
         if isinstance(input_img, np.ndarray):
             self.img = input_img
         else:
             self.img = utils.imread(self.input_path)
 
+        if mode != "bool":
+            if self.img.ndim == 2:  # noqa: PLR2004, SIM114
+                mode = "feature"
+
+            elif self.img.shape[2] not in [3, 4]:
+                mode = "feature"
+
+        self.mode = mode.lower()
+
         if info is not None:
             self._m_info = info
 
@@ -3264,6 +3282,9 @@ class docstrings for more information.
         )
         bounds_at_read = utils.transforms.locsize2bounds(location_at_read, size_at_read)
 
+        if self.mode == "bool":
+            interpolation = "nearest"
+
         if interpolation in [None, "none"]:
             interpolation = None