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DOC: Add notebook example for SegmentWithGeodesicActiveContourLevelSet
Also update the Python code style to be more Pythonic
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-145
lines changed

4 files changed

+263
-145
lines changed

‎src/Segmentation/LevelSets/SegmentWithGeodesicActiveContourLevelSet/CMakeLists.txt

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

‎src/Segmentation/LevelSets/SegmentWithGeodesicActiveContourLevelSet/Code.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "196c6071",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import os\n",
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"from urllib.request import urlretrieve\n",
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"\n",
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"import itk\n",
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"\n",
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"from itkwidgets import view"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d6f35e01",
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"metadata": {},
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"outputs": [],
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"source": [
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"input_filename = 'BrainProtonDensitySlice.png'\n",
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"if not os.path.exists(input_filename):\n",
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" url = 'https://data.kitware.com/api/v1/file/57b5d8028d777f10f2694bbf/download'\n",
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" urlretrieve(url, input_filename)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "52104b31",
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"metadata": {},
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"outputs": [],
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"source": [
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"InputPixelType = itk.ctype('float')\n",
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"\n",
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"input_image = itk.imread(input_filename, InputPixelType)\n",
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"\n",
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"view(input_image)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4297e2ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"smoothed = itk.curvature_anisotropic_diffusion_image_filter(input_image,\n",
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" time_step=0.125,\n",
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" number_of_iterations=5,\n",
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" conductance_parameter=9.0)\n",
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"view(smoothed)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "99186583",
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"metadata": {},
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"outputs": [],
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"source": [
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"sigma = 1.0\n",
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"\n",
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"gradient_magnitude = itk.gradient_magnitude_recursive_gaussian_image_filter(smoothed,\n",
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" sigma=sigma)\n",
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"view(gradient_magnitude)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8a219f34",
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"metadata": {},
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"outputs": [],
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"source": [
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"alpha = -0.5\n",
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"beta = 3.0\n",
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"\n",
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"sigmoid = itk.sigmoid_image_filter(gradient_magnitude,\n",
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" output_minimum=0.0,\n",
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" output_maximum=1.0,\n",
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" alpha=alpha,\n",
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" beta=beta)\n",
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"\n",
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"view(sigmoid)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "499443a8",
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"metadata": {},
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"outputs": [],
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"source": [
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"Dimension = input_image.GetImageDimension()\n",
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"seeds = itk.VectorContainer[itk.UI, itk.LevelSetNode[InputPixelType, Dimension]].New()\n",
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"seeds.Initialize()\n",
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"\n",
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"seed_position = itk.Index[Dimension]()\n",
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"seed_position[0] = 81\n",
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"seed_position[1] = 114\n",
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"node = itk.LevelSetNode[InputPixelType, Dimension]()\n",
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"node.SetValue(-5.0)\n",
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"node.SetIndex(seed_position)\n",
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"seeds.InsertElement(0, node)\n",
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"\n",
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"fast_marching = itk.fast_marching_image_filter(trial_points=seeds,\n",
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" speed_constant=1.0,\n",
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" output_size=input_image.GetBufferedRegion().GetSize())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d2c5e205",
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"metadata": {},
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"outputs": [],
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"source": [
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"propagation_scaling = 2.0\n",
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"number_of_iterations = 800\n",
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"\n",
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"geodesic_active_contour = \\\n",
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" itk.geodesic_active_contour_level_set_image_filter(fast_marching,\n",
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" propagation_scaling=propagation_scaling,\n",
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" curvature_scaling=1.0,\n",
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" advection_scaling=1.0,\n",
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" maximum_r_m_s_error=0.02,\n",
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" number_of_iterations=number_of_iterations,\n",
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" feature_image=sigmoid)\n",
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"\n",
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"view(geodesic_active_contour)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1269f1b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"OutputPixelType = itk.ctype('unsigned char')\n",
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"thresholded = itk.binary_threshold_image_filter(geodesic_active_contour,\n",
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" lower_threshold=-1000.0,\n",
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" upper_threshold=0.0,\n",
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" outside_value=itk.NumericTraits[OutputPixelType].min(),\n",
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" inside_value=itk.NumericTraits[OutputPixelType].max(),\n",
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" ttype=[type(geodesic_active_contour), itk.Image[OutputPixelType,Dimension]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "58bdf582",
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"metadata": {},
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"outputs": [],
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"source": [
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"view(thresholded)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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