{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 4,

   "id": "f11c51b1",

   "metadata": {},

   "outputs": [],

   "source": [

    "import pymskt as mskt\n",

    "import SimpleITK as sitk\n",

    "import numpy as np\n",

    "import os\n",

    "\n",

    "from pymskt.mesh import Mesh"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 10,

   "id": "20bbd34d",

   "metadata": {},

   "outputs": [],

   "source": [

    "location_seg = '../../data/muscles/segmentation.nii.gz'\n",

    "location_save = os.path.expanduser('~/Downloads')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 5,

   "id": "c43ac4d4",

   "metadata": {},

   "outputs": [

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "Use of `point_arrays` is deprecated. Use `point_data` instead.\n",

      "Use of `cell_arrays` is deprecated. Use `cell_data` instead.\n"

     ]

    }

   ],

   "source": [

    "# Loading the image in first so we can see what the labels are. \n",

    "# This can be skipped and a path provided directly if we know\n",

    "# the labels of interest. \n",

    "seg_image = sitk.ReadImage(location_seg)\n",

    "array = sitk.GetArrayFromImage(seg_image)\n",

    "seg_labels = np.unique(array)\n",

    "\n",

    "mesh1 = Mesh(\n",

    "    seg_image=seg_image,\n",

    "    label_idx=seg_labels[1]\n",

    "    \n",

    ")\n",

    "\n",

    "# Below is the command to create the mesh\n",

    "# all of the defaults inputs parameters are being listed \n",

    "# so that the available options are shown. \n",

    "mesh1.create_mesh(\n",

    "    smooth_image=True,            # I suggest leaving this on to help create smooth surfaces.   \n",

    "    smooth_image_var=0.3125/2,    # this is the variance of a gaussian filter - can probably be bigger for muscles.\n",

    "    marching_cubes_threshold=0.5, # this probably shouldnt be changed. \n",

    "    label_idx=None,               # Can specify this here instead of above if you want. \n",

    "    min_n_pixels=None,            # if there is a minimum number of pixels you want for it to create a mesh. \n",

    ")\n",

    "\n",

    "mesh1.resample_surface(clusters=10000) # Resample surface to be a specified # of vertices (w/ in a small margin of error)\n",

    "\n"

   ]

  },

  {

   "cell_type": "markdown",

   "id": "a4c3a458",

   "metadata": {},

   "source": [

    "## Save the mesh"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 11,

   "id": "7bd7c341",

   "metadata": {},

   "outputs": [],

   "source": [

    "mesh1.save_mesh(os.path.join(location_save, 'mesh1.vtk')) # Save .vtk version of the mesh\n",

    "mesh1.save_mesh(os.path.join(location_save, 'mesh1.stl')) # Save .stl version of the mesh. "

   ]

  },

  {

   "cell_type": "markdown",

   "id": "394b11b0",

   "metadata": {},

   "source": [

    "# If you want to iterate over all of the labels and save meshes: "

   ]

  },

  {

   "cell_type": "markdown",

   "id": "c4c39deb",

   "metadata": {},

   "source": [

    "### First create a dictionary to store all of the meshes"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "id": "4368b846",

   "metadata": {},

   "outputs": [],

   "source": [

    "dict_meshes = {}\n",

    "for label_idx in seg_labels[1:]:\n",

    "    dict_meshes[label_idx] = mskt.mesh.Mesh(\n",

    "        seg_image=seg_image,\n",

    "        label_idx=label_idx\n",

    "\n",

    "    )\n",

    "    \n",

    "    dict_meshes[label_idx].create_mesh(smooth_image_var=3.0) # I ADDED MORE SMOOTHING FOR THESE\n",

    "    dict_meshes[label_idx].resample_surface(clusters=10000)"

   ]

  },

  {

   "cell_type": "markdown",

   "id": "1cc29d8d",

   "metadata": {},

   "source": [

    "### Save each mesh"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 12,

   "id": "f5d6738e",

   "metadata": {},

   "outputs": [],

   "source": [

    "for key, mesh in dict_meshes.items():\n",

    "    mesh.save_mesh(os.path.join(location_save, f'muscle_mesh{key}.vtk')) # Save vtk version\n",

    "    mesh.save_mesh(os.path.join(location_save, f'muscle_mesh{key}.stl')) # Save stl version"

   ]

  },

  {

   "cell_type": "markdown",

   "id": "b0a66862",

   "metadata": {},

   "source": [

    "# Below is the viewer that should work to see these in jupyter notebooks\n",

    "- This wasnt working on my computer for some reason. "

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 13,

   "id": "b9f350ba",

   "metadata": {},

   "outputs": [],

   "source": [

    "from itkwidgets import view"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 14,

   "id": "505700e8",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "array([ 0, 14, 16, 18, 20, 22, 24, 26], dtype=uint16)"

      ]

     },

     "execution_count": 14,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "seg_labels"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 15,

   "id": "52169ea2",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "application/vnd.jupyter.widget-view+json": {

       "model_id": "5f7605d7f4274c78bbcbcb884feb90f8",

       "version_major": 2,

       "version_minor": 0

      },

      "text/plain": [

       "Viewer(geometries=[{'vtkClass': 'vtkPolyData', 'points': {'vtkClass': 'vtkPoints', 'name': '_points', 'numberO…"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    }

   ],

   "source": [

    "# view expects geometries to be in a list: \n",

    "geometries = [\n",

    "    dict_meshes[14].mesh  # the Mesh object has the actual mesh inside of it at Mesh.mesh\n",

    "]\n",

    "view(geometries=geometries)\n"

   ]

  },

  {

   "cell_type": "markdown",

   "id": "9c6b9c5a",

   "metadata": {},

   "source": [

    "### Show multiple meshes at once: "

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 16,

   "id": "ddf2bed6",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "application/vnd.jupyter.widget-view+json": {

       "model_id": "b9275aa069ce432b9ab363874895c7d4",

       "version_major": 2,

       "version_minor": 0

      },

      "text/plain": [

       "Viewer(geometries=[{'vtkClass': 'vtkPolyData', 'points': {'vtkClass': 'vtkPoints', 'name': '_points', 'numberO…"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    }

   ],

   "source": [

    "# view expects geometries to be in a list: \n",

    "geometries = [\n",

    "    dict_meshes[14].mesh,  # the Mesh object has the actual mesh inside of it at Mesh.mesh\n",

    "    dict_meshes[16].mesh,\n",

    "    dict_meshes[18].mesh,\n",

    "    dict_meshes[20].mesh,\n",

    "    dict_meshes[22].mesh,\n",

    "    dict_meshes[24].mesh,\n",

    "    dict_meshes[26].mesh\n",

    "]\n",

    "view(geometries=geometries)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "397e00d4",

   "metadata": {},

   "outputs": [],

   "source": []

  }

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