add support for bia-bob

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = napari-simpleitk-image-processing
-version = 0.4.8
+version = 0.4.9
 author = Robert Haase
 author_email = robert.haase@tu-dresden.de
 url = https://github.com/haesleinhuepf/napari-simpleitk-image-processing
@@ -51,3 +51,5 @@ where = src
 [options.entry_points] 
 napari.plugin = 
     napari-simpleitk-image-processing = napari_simpleitk_image_processing
+bia_bob_plugins =
+    nsitk_plugin = napari_simpleitk_image_processing._bia_bob_plugins:list_bia_bob_plugins

src/napari_simpleitk_image_processing/__init__.py

@@ -1,5 +1,5 @@
 
-__version__ = "0.4.8"
+__version__ = "0.4.9"
 __common_alias__ = "nsitk"
 
 

src/napari_simpleitk_image_processing/_bia_bob_plugins.py

@@ -0,0 +1,93 @@
+def list_bia_bob_plugins():
+    """List of function hints for bia_bob"""
+    good_alternative_installed = False
+    try:
+        import napari_segment_blobs_and_things_with_membranes
+        good_alternative_installed = True
+    except:
+        pass
+    try:
+        import pyclesperanto
+        good_alternative_installed = True
+    except:
+        pass
+    try:
+        import pyclesperanto_prototype
+        good_alternative_installed = True
+    except:
+        pass
+
+    basic_hints = ""
+    if not good_alternative_installed:
+        basic_hints = """
+
+    - Apply a median filter to an image to remove noise while preserving edges.
+      nsitk.median_filter(image, radius=5)
+
+    - Apply a Gaussian blur to smooth the image.
+      nsitk.gaussian_blur(image, sigma=1.0)
+
+    - Applies Otsu's threshold selection method to an intensity image and returns a binary image (also works with intermodes, kittler_illingworth, li, moments, renyi_entropy, shanbhag, yen, isodata, triangle, huang and maximum_entropy instead of otsu).
+      nsitk.threshold_otsu(image)
+
+    - Computes the gradient magnitude of an image.
+      nsitk.gradient_magnitude(image)
+
+    - Identifies the regional maxima of an image.
+      nsitk.regional_maxima(image)
+
+    - Rescales the intensity of an input image to a specified range.
+      nsitk.rescale_intensity(image, output_min=0, output_max=255)
+
+    - Applies the Sobel operator to an image to find edges.
+      nsitk.sobel(image)
+
+    - Applies a standard deviation filter to an image.
+      nsitk.standard_deviation_filter(image)
+
+    - Labels the connected components in a binary image.
+      nsitk.connected_component_labeling(binary_image)
+
+    - Applies the Laplacian of Gaussian filter to find edges in an image.
+      nsitk.laplacian_of_gaussian_filter(image, sigma=1.0)
+
+    - Identifies h-maxima of an image, suppressing maxima smaller than h.
+      nsitk.h_maxima(image, h=10)
+
+    - Removes background in an image using the Top-Hat filter.
+      nsitk.white_top_hat(image, radius=5)
+        """
+
+    advanced_hints = """
+
+    - Computes the signed Maurer distance map of the input image.
+      nsitk.signed_maurer_distance_map(binary_image)
+
+    - Detects edges in the image using Canny edge detection.
+      nsitk.canny_edge_detection(image, variance=1.0)
+
+    - Enhances the contrast of an image using adaptive histogram equalization.
+      nsitk.adaptive_histogram_equalization(image)
+
+    - Labels objects in a binary image and can split object that are touching..
+      nsitk.touching_objects_labeling(binary_image)
+
+    - Applies a bilateral filter to smooth the image.
+      nsitk.bilateral_filter(image, domainSigma=2.0, rangeSigma=50.0)
+
+    - Computes basic statistics for labeled object regions in an image.
+      nsitk.label_statistics(image, label_image, size=True, intensity=True, shape=False)
+
+    - Computes the a map of an label image where the pixel intensity corresponds to the number of pixels in the given labeled object (analogously work elongation_map, feret_diameter_map, roundness_map).
+      nsitk.pixel_count_map(label_image)
+        """
+
+    return f"""
+    ## napari-simpleitk-image-processing (nsitk)
+    nsitk is a Python library that processes images using SimpleITK.
+    When you use it, you always start by importing the library: `import napari_simpleitk_image_processing as nsitk`.
+    When asked for how to use nsitk, you can adapt one of the following code snippets:
+
+    {basic_hints}
+    {advanced_hints}
+    """