Interactive parameter tuning with napari and magicgui
Interactive parameter tuning with napari and magicgui#
A common task when processing image is to manually tune parameters until a segementation algorithm works fine on a give dataset. The napari viewer and magicgui offer the possibility to automatically build a user-interface where the user can tune parameters and see immediately how a corresponding segmentation will look like. This notebook demonstrates how this can be achieved from a simple python function that does the image processing.
# Inspired by # https://github.com/pr4deepr/pyclesperanto_prototype/blob/master/napari_clij_widget.py import napari import pyclesperanto_prototype as cle from magicgui import magicgui from napari.types import ImageData, LabelsData
Our starting point is a function that implements a certain image processing workflow. In this example, we blur an image using a Gaussian filter, we threshold it using Otsu’s method and we apply connected component labeling to differentiate objects and label them.
@magicgui(auto_call=True) def process_image(image_input: ImageData, sigma: float = 2) -> LabelsData: if input is not None: # process the image blurred = cle.gaussian_blur(image_input, sigma_x=sigma, sigma_y=sigma) binary = cle.threshold_otsu(blurred) labels = cle.connected_components_labeling_box(binary) return labels
We also need an example image to demonstrate the procedure.
# load data from skimage.io import imread image = imread('https://samples.fiji.sc/blobs.png')
Next, we start up the napari viewer, add the original image and append a automatically generated user interface, a dock widget, that allows us to configure the parameters of the function provided above. Furthermore, we take a screenshot of the viewer.
# start up napari viewer = napari.Viewer() viewer.add_image(image, name='blobs') # generate a Graphical User Interface from the function above magically viewer.window.add_dock_widget(process_image) # take a screenshot napari.utils.nbscreenshot(viewer)
Warning: Could not find scikit-tensor which is needed for separable approximations... If you want to compute separable approximations, please install it with pip install scikit-tensor-py3