implemented widget for generateing a mask

brainglobe_template_builder/napari/__init__.py

@@ -7,13 +7,9 @@
     __version__ = "unknown"
 
 from brainglobe_template_builder.napari._reader import napari_get_reader
-from brainglobe_template_builder.napari._widget import (
-    ExampleQWidget,
-    gaussian_blur_widget,
-)
+from brainglobe_template_builder.napari._widget import mask_widget
 
 __all__ = (
     "napari_get_reader",
-    "ExampleQWidget",
-    "gaussian_blur_widget",
+    "mask_widget",
 )

brainglobe_template_builder/napari/_widget.py

@@ -6,88 +6,95 @@
 
 Replace code below according to your needs.
 """
-from typing import TYPE_CHECKING
+from typing import Literal, Union
 
+import numpy as np
 from magicgui import magic_factory
-from napari_plugin_engine import napari_hook_implementation
-from qtpy.QtWidgets import QHBoxLayout, QPushButton, QWidget
-
-if TYPE_CHECKING:
-    import napari
-
-from napari import Viewer
 from napari.layers import Image
 from napari.types import LayerDataTuple
-
-
-class ExampleQWidget(QWidget):
-    # your QWidget.__init__ can optionally request the napari viewer instance
-    # in one of two ways:
-    # 1. use a parameter called `napari_viewer`, as done here
-    # 2. use a type annotation of 'napari.viewer.Viewer' for any parameter
-    def __init__(self, napari_viewer):
-        super().__init__()
-        self.viewer = napari_viewer
-
-        btn = QPushButton("Click me!")
-        btn.clicked.connect(self._on_click)
-
-        self.setLayout(QHBoxLayout())
-        self.layout().addWidget(btn)
-
-    def _on_click(self):
-        print("napari has", len(self.viewer.layers), "layers")
+from napari_plugin_engine import napari_hook_implementation
 
 
 @magic_factory(
-    call_button="gaussian blur",
-    sigma={"widget_type": "FloatSlider", "max": 10, "min": 0, "step": 1},
-    tooltips={"sigma": "Standard deviation for Gaussian kernel (in pixels)"},
-    # do not show the viewer argument, as it is not a magicgui argument
+    call_button="generate mask",
+    gauss_sigma={"widget_type": "FloatSlider", "max": 10, "min": 0, "step": 1},
+    threshold_method={"choices": ["triangle", "otsu"]},
+    erosion_size={"widget_type": "Slider", "max": 10, "min": 0, "step": 1},
 )
-def gaussian_blur_widget(
-    viewer: Viewer, image: Image, sigma: float = 3
-) -> LayerDataTuple:
-    """Smooth image with a gaussian filter and add to napari viewer.
+def mask_widget(
+    image: Image,
+    gauss_sigma: float = 3,
+    threshold_method: Literal["triangle", "otsu"] = "triangle",
+    erosion_size: int = 5,
+) -> Union[LayerDataTuple, None]:
+    """Threshold image and create a mask for the largest object.
+
+    The mask is generated by applying a Gaussian filter to the image,
+    thresholding the smoothed image, keeping only the largest object, and
+    eroding the resulting mask.
 
     Parameters
     ----------
     image : Image
-        A napari image layer to smooth.
-    sigma : float
-        Standard deviation for Gaussian kernel (in pixels).
+        A napari image layer to threshold.
+    gauss_sigma : float
+        Standard deviation for Gaussian kernel (in pixels) to smooth image
+        before thresholding. Set to 0 to skip smoothing.
+    threshold_method : str
+        Thresholding method to use. Options are 'triangle' and 'otsu'.
+    erosion_size : int
+        Size of the erosion footprint (in pixels) to apply to the mask.
+        Set to 0 to skip erosion.
 
     Returns
     -------
-    image_smoothed : Image
-        A napari image layer with the smoothed image.
+    layers : list[LayerDataTuple]
+        A list of napari layers to add to the viewer.
+        The first layer is the mask, and the second layer is the smoothed
+        image (if smoothing was applied).
     """
 
     if image is not None:
         assert isinstance(image, Image), "image must be a napari Image layer"
     else:
         print("Please select an image layer")
-        return
+        return None
+
+    from skimage import filters, measure, morphology
 
-    from skimage import filters
+    # Apply gaussian filter to image
+    if gauss_sigma > 0:
+        data_smoothed = filters.gaussian(image.data, sigma=gauss_sigma)
+    else:
+        data_smoothed = image.data
+
+    # Threshold the (smoothed) image
+    if threshold_method == "triangle":
+        thresholded = filters.threshold_triangle(data_smoothed)
+    elif threshold_method == "otsu":
+        thresholded = filters.threshold_otsu(data_smoothed)
+    else:
+        raise ValueError(f"Unknown thresholding method {threshold_method}")
 
-    image_smoothed = filters.gaussian(image.data, sigma=sigma)
+    binary = data_smoothed > thresholded
 
-    # return the smoothed image layer
-    return (
-        image_smoothed,
-        {"name": f"{image.name}_smoothed"},
-        "image",
-    )
+    # Keep only the largest object
+    labeled_image = measure.label(binary)
+    regions = measure.regionprops(labeled_image)
+    largest_region = max(regions, key=lambda region: region.area)
+    # Create a binary mask for the largest object
+    mask = labeled_image == largest_region.label
 
+    # Erode the mask
+    if erosion_size > 0:
+        mask = morphology.binary_erosion(
+            mask, footprint=np.ones((erosion_size,) * image.ndim)
+        )
 
-# Uses the `autogenerate: true` flag in the plugin manifest
-# to indicate it should be wrapped as a magicgui to autogenerate
-# a widget.
-def example_function_widget(img_layer: "napari.layers.Image"):
-    print(f"you have selected {img_layer}")
+    # return the mask as a napari Labels layer
+    return (mask, {"name": f"{image.name}_mask", "opacity": 0.5}, "labels")
 
 
 @napari_hook_implementation
 def napari_experimental_provide_dock_widget():
-    return gaussian_blur_widget
+    return mask_widget

brainglobe_template_builder/napari/napari.yaml

@@ -5,26 +5,15 @@ contributions:
     - id: brainglobe-template-builder.get_reader
       python_name: brainglobe_template_builder.napari._reader:napari_get_reader
       title: Open data with brainglobe-template-builder
-    - id: brainglobe-template-builder.make_qwidget
-      python_name: brainglobe_template_builder.napari._widget:ExampleQWidget
-      title: Make example QWidget
-    - id: brainglobe-template-builder.make_blur_widget
-      python_name: brainglobe_template_builder.napari._widget:gaussian_blur_widget
-      title: Apply Gaussian blur to image
-    - id: brainglobe-template-builder.make_func_widget
-      python_name: brainglobe_template_builder.napari._widget:example_function_widget
-      title: Make example function widget
+    - id: brainglobe-template-builder.make_mask_widget
+      python_name: brainglobe_template_builder.napari._widget:mask_widget
+      title: Generate organ mask
   readers:
     - command: brainglobe-template-builder.get_reader
       accepts_directories: false
       filename_patterns:
       - '*.tif'
       - '*.tiff'
   widgets:
-    - command: brainglobe-template-builder.make_qwidget
-      display_name: Example QWidget
-    - command: brainglobe-template-builder.make_blur_widget
-      display_name: Gaussian Blur
-    - command: brainglobe-template-builder.make_func_widget
-      autogenerate: true
-      display_name: Example Function Widget
+    - command: brainglobe-template-builder.make_mask_widget
+      display_name: Generate Mask