DOC: Add notebook example for SegmentWithGeodesicActiveContourLevelSet

Also update the Python code style to be more Pythonic

src/Segmentation/LevelSets/SegmentWithGeodesicActiveContourLevelSet/CMakeLists.txt

@@ -13,7 +13,7 @@ install(TARGETS SegmentWithGeodesicActiveContourLevelSet
   COMPONENT Runtime
   )
 
-install(FILES Code.cxx CMakeLists.txt
+install(FILES Code.cxx CMakeLists.txt Code.py
   DESTINATION share/ITKSphinxExamples/Code/Segmentation/LevelSets/SegmentWithGeodesicActiveContourLevelSet
   COMPONENT Code
   )

src/Segmentation/LevelSets/SegmentWithGeodesicActiveContourLevelSet/Code.ipynb

@@ -0,0 +1,195 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7c8b935b",
+   "metadata": {},
+   "source": [
+    "# Segment with geodesic active contour level set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "196c6071",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "from urllib.request import urlretrieve\n",
+    "\n",
+    "import itk\n",
+    "\n",
+    "from itkwidgets import view"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6f35e01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_filename = 'BrainProtonDensitySlice.png'\n",
+    "if not os.path.exists(input_filename):\n",
+    "    url = 'https://data.kitware.com/api/v1/file/57b5d8028d777f10f2694bbf/download'\n",
+    "    urlretrieve(url, input_filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52104b31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "InputPixelType = itk.ctype('float')\n",
+    "\n",
+    "input_image = itk.imread(input_filename, InputPixelType)\n",
+    "\n",
+    "view(input_image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4297e2ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "smoothed = itk.curvature_anisotropic_diffusion_image_filter(input_image,\n",
+    "                                                            time_step=0.125,\n",
+    "                                                            number_of_iterations=5,\n",
+    "                                                            conductance_parameter=9.0)\n",
+    "view(smoothed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99186583",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigma = 1.0\n",
+    "\n",
+    "gradient_magnitude = itk.gradient_magnitude_recursive_gaussian_image_filter(smoothed,\n",
+    "                                                                            sigma=sigma)\n",
+    "view(gradient_magnitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a219f34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = -0.5\n",
+    "beta = 3.0\n",
+    "\n",
+    "sigmoid = itk.sigmoid_image_filter(gradient_magnitude,\n",
+    "                                   output_minimum=0.0,\n",
+    "                                   output_maximum=1.0,\n",
+    "                                   alpha=alpha,\n",
+    "                                   beta=beta)\n",
+    "\n",
+    "view(sigmoid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "499443a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Dimension = input_image.GetImageDimension()\n",
+    "seeds = itk.VectorContainer[itk.UI, itk.LevelSetNode[InputPixelType, Dimension]].New()\n",
+    "seeds.Initialize()\n",
+    "\n",
+    "seed_position = itk.Index[Dimension]()\n",
+    "seed_position[0] = 81\n",
+    "seed_position[1] = 114\n",
+    "node = itk.LevelSetNode[InputPixelType, Dimension]()\n",
+    "node.SetValue(-5.0)\n",
+    "node.SetIndex(seed_position)\n",
+    "seeds.InsertElement(0, node)\n",
+    "\n",
+    "fast_marching = itk.fast_marching_image_filter(trial_points=seeds,\n",
+    "                                               speed_constant=1.0,\n",
+    "                                               output_size=input_image.GetBufferedRegion().GetSize())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2c5e205",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "propagation_scaling = 2.0\n",
+    "number_of_iterations = 800\n",
+    "\n",
+    "geodesic_active_contour = \\\n",
+    "  itk.geodesic_active_contour_level_set_image_filter(fast_marching,\n",
+    "                                                     propagation_scaling=propagation_scaling,\n",
+    "                                                     curvature_scaling=1.0,\n",
+    "                                                     advection_scaling=1.0,\n",
+    "                                                     maximum_r_m_s_error=0.02,\n",
+    "                                                     number_of_iterations=number_of_iterations,\n",
+    "                                                     feature_image=sigmoid)\n",
+    "\n",
+    "view(geodesic_active_contour)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1269f1b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "OutputPixelType = itk.ctype('unsigned char')\n",
+    "thresholded = itk.binary_threshold_image_filter(geodesic_active_contour,\n",
+    "                                                lower_threshold=-1000.0,\n",
+    "                                                upper_threshold=0.0,\n",
+    "                                                outside_value=itk.NumericTraits[OutputPixelType].min(),\n",
+    "                                                inside_value=itk.NumericTraits[OutputPixelType].max(),\n",
+    "                                                ttype=[type(geodesic_active_contour), itk.Image[OutputPixelType,Dimension]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58bdf582",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "view(thresholded)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}