{
"cells": [
{
"cell_type": "markdown",
"id": "536e5e37",
"metadata": {},
"source": [
"## Interactive parameter tuning with napari and magicgui\n",
"A common task when processing image is to manually tune parameters until a segementation algorithm works fine on a give dataset. The [napari](https://napari.org) viewer and [magicgui](https://napari.org/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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "83eb9620",
"metadata": {
"lines_to_next_cell": 1
},
"outputs": [],
"source": [
"# Inspired by\n",
"# https://github.com/pr4deepr/pyclesperanto_prototype/blob/master/napari_clij_widget.py\n",
"import napari\n",
"import pyclesperanto_prototype as cle\n",
"from magicgui import magicgui\n",
"from napari.types import ImageData, LabelsData"
]
},
{
"cell_type": "markdown",
"id": "e6322806",
"metadata": {},
"source": [
"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](https://ieeexplore.ieee.org/document/4310076) and we apply connected component labeling to differentiate objects and label them."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bc1a3ecc",
"metadata": {
"lines_to_next_cell": 1
},
"outputs": [],
"source": [
"@magicgui(auto_call=True)\n",
"def process_image(image_input: ImageData, sigma: float = 2) -> LabelsData:\n",
" if input is not None:\n",
" # process the image\n",
" blurred = cle.gaussian_blur(image_input, sigma_x=sigma, sigma_y=sigma)\n",
" binary = cle.threshold_otsu(blurred)\n",
" labels = cle.connected_components_labeling_box(binary)\n",
"\n",
" return labels"
]
},
{
"cell_type": "markdown",
"id": "43973a94",
"metadata": {},
"source": [
"We also need an example image to demonstrate the procedure."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "011c8e68",
"metadata": {},
"outputs": [],
"source": [
"# load data\n",
"from skimage.io import imread\n",
"image = imread('https://samples.fiji.sc/blobs.png')"
]
},
{
"cell_type": "markdown",
"id": "19a69f50",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "38eda89d",
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: Could not find scikit-tensor which is needed for separable approximations...\n",
"If you want to compute separable approximations, please install it with\n",
"pip install scikit-tensor-py3\n"
]
},
{
"data": {
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\n",
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# start up napari\n",
"viewer = napari.Viewer()\n",
"viewer.add_image(image, name='blobs')\n",
"\n",
"# generate a Graphical User Interface from the function above magically\n",
"viewer.window.add_dock_widget(process_image)\n",
"\n",
"# take a screenshot\n",
"napari.utils.nbscreenshot(viewer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8feab7a3",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [],
"source": []
}
],
"metadata": {
"jupytext": {
"cell_metadata_filter": "-all",
"notebook_metadata_filter": "-all",
"text_representation": {
"extension": ".py",
"format_name": "light"
}
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}