obj-voxel-visualizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OBJ Mesh Voxel Visualization Tool\n",
    "*A utility to convert voxel data stored in a 3D NumPy array into a collection of OBJ mesh cubes for visualizing, sharing, and rendering.*\n",
    "- Configured to accept voxel data stored in a numpy array (.npy).\n",
    "- *Does not* create a single body mesh for 3D printing purposes\n",
    "- Made to create obj files that can be easily shared or included in visuals/renders.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "from obj_voxel_visualizer import * \n",
    "\n",
    "# For data collection, inline display, and plotting metrics\n",
    "import os\n",
    "import time\n",
    "import glob\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import trimesh\n",
    "from string import Template"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### Convert Numpy Voxel Array to OBJ Voxel Mesh Visual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Mesh Voxels Created: 19149\n",
      "Surface View Enabled: True\n",
      "Exported OBJ Filename: lobe_gear_example.obj\n"
     ]
    }
   ],
   "source": [
    "# Export voxel array to OBJ mesh using function that includes loading and voxel count limit warning.\n",
    "\n",
    "npy_filename = \"example_visualized_voxels/lobe_gear/Lobe_Gear.npy\"\n",
    "obj_filename = \"lobe_gear_example.obj\"\n",
    "\n",
    "# Function checks voxel count limit to confirm creating large files.\n",
    "# Surface View is on by default\n",
    "obj_voxel_visualizer( npy_filename, obj_filename )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export OBJ mesh from pre-loaded NumPy array data\n",
    "\n",
    "example_voxel_array = np.load(\"example_visualized_voxels/lobe_gear/Lobe_Gear.npy\")\n",
    "\n",
    "voxel_array2voxel_mesh_obj( obj_filename, example_voxel_array )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load exported OBJ mesh to view voxel model\n",
    "mesh = trimesh.load(obj_filename)\n",
    "mesh.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### Improvements Gained from Original Voxel Array to Voxel Mesh Data Function\n",
    "The original conversion function suffered from a slow runtime. Refactoring the function to use NumPy's optimized functions resulted in a significant time savings especially with a large voxel count. The improvements are mainly located in the surface voxel filtering function which uses stride tricks in place of python loops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "548078\n"
     ]
    }
   ],
   "source": [
    "# Load in NumPy array with large number of voxels\n",
    "\n",
    "voxel_benchmark_array = np.load(\"npy_voxel_arrays_nittany_lion/nittany-lion-0.5-voxel-array.npy\")\n",
    "print(np.count_nonzero(voxel_benchmark_array)) # Note: this counts all voxels in array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total voxels in mesh model: 83142\n",
      "Elapsed function runtime = 5.7 seconds\n"
     ]
    }
   ],
   "source": [
    "# Measure runtime of old voxel array to vertices and faces function\n",
    "start_old = time.time()\n",
    "old_verts, old_faces = old_voxel_array2mesh( voxel_benchmark_array, surface_view = True )\n",
    "end_old = time.time()\n",
    "\n",
    "print(\"Total voxels in mesh model: %d\"% (old_faces.shape[0]/12))\n",
    "print(\"Elapsed function runtime = %s seconds\"% round((end_old - start_old), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total voxels in mesh model: 83142\n",
      "Elapsed function runtime = 0.24 seconds\n"
     ]
    }
   ],
   "source": [
    "# Measure runtime of old voxel array to vertices and faces function\n",
    "start_new = time.time()\n",
    "new_verts, new_faces = voxel_array2mesh( voxel_benchmark_array, surface_view = True )\n",
    "end_new = time.time()\n",
    "\n",
    "print(\"Total voxels in mesh model: %d\"% (new_faces.shape[0]/12))\n",
    "print(\"Elapsed function runtime = %s seconds\"% round((end_new - start_new), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The new Surface Voxel function is x23.56 faster\n"
     ]
    }
   ],
   "source": [
    "# Calculated Efficiency Gained\n",
    "print(\"The new Surface Voxel function is x%s faster\"% \n",
    "      round((end_old - start_old)/(end_new - start_new), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### Measuring Impact of Number of Voxels on File Size\n",
    "A clear drawback of the voxel mesh files create is the size of the file. As the voxel resolution increases, the size of both the OBJ and NPY file can increase rapidly. Voxel count warnings are included to prevent creating GB size files, but it is important to illustrate the relationship between voxel count, voxel resolution, and file size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collect files from respective folders\n",
    "voxel_data_array_folder = \"./npy_voxel_arrays_nittany_lion/*.npy\"\n",
    "voxel_mesh_model_folder = \"./obj_voxel_meshes_nittany_lion/*.obj\"\n",
    "\n",
    "data_arrays = np.asarray(sorted(glob.glob(voxel_data_array_folder)))\n",
    "mesh_models = np.asarray(sorted(glob.glob(voxel_mesh_model_folder)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize a dataframe to store the voxel data\n",
    "\n",
    "mesh_model_files = np.char.strip(mesh_models, './obj_voxel_meshes_nittany_lion/nittany-lion-')\n",
    "edge_lengths = np.char.strip(mesh_model_files, '-voxel-mesh.obj').astype('float')\n",
    "\n",
    "voxel_file_df = pd.DataFrame({\n",
    "  \"NPY File\": data_arrays,\n",
    "  \"OBJ File\": mesh_models,\n",
    "  \"Voxel Edge Length\": edge_lengths,\n",
    "}).sort_values( by=['Voxel Edge Length'], ignore_index=True )\n",
    "# voxel_file_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collect file size of NPY array and OBJ mesh files\n",
    "npy_size = []\n",
    "obj_size = []\n",
    "npy_raw_size = []\n",
    "obj_raw_size = []\n",
    "\n",
    "for idx, row in voxel_file_df.iterrows():\n",
    "  npy_size_bytes = os.path.getsize(row['NPY File'])\n",
    "  obj_size_bytes = os.path.getsize(row['OBJ File'])\n",
    "  npy_raw_size.append( npy_size_bytes )\n",
    "  obj_raw_size.append( obj_size_bytes )\n",
    "  npy_size.append( str(convert_size(npy_size_bytes)) )\n",
    "  obj_size.append( str(convert_size(obj_size_bytes)) )\n",
    "\n",
    "size_df = pd.DataFrame({\n",
    "  'NPY Raw Size': npy_raw_size,\n",
    "  'OBJ Raw Size': obj_raw_size,\n",
    "  'NPY File Size': npy_size,\n",
    "  'OBJ File Surf-Vox Size': obj_size\n",
    "})\n",
    "# size_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count total number of voxels and number of surface voxels in each file (long runtime)\n",
    "vox_count = []\n",
    "surfvox_count = []\n",
    "surfvox_percent = []\n",
    "\n",
    "for idx, file in voxel_file_df['NPY File'].items():\n",
    "  voxarr = np.load(file)\n",
    "  num_vox = np.count_nonzero(voxarr)\n",
    "  num_surfvox = np.count_nonzero(get_surface_voxels(voxarr))\n",
    "  vox_count.append( num_vox )\n",
    "  surfvox_count.append( num_surfvox )\n",
    "  surfvox_percent.append(round(( num_surfvox/num_vox )*100, 2 ))\n",
    "\n",
    "count_df = pd.DataFrame({ \n",
    "  'Voxel Count': vox_count,\n",
    "  'Surface Voxel Count': surfvox_count,\n",
    "  'Surface Voxel Percentage': surfvox_percent\n",
    "})\n",
    "# count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Voxel Scale Ratio Calculation (Percentage of the bounding box unit magnitude)\n",
    "bounding_unit_magnitude = []\n",
    "vox_scale_ratio = []\n",
    "\n",
    "for idx, row in voxel_file_df.iterrows():\n",
    "  voxel_arr = np.load(row['NPY File'])\n",
    "  unit_magnitude = np.linalg.norm(voxel_arr.shape)\n",
    "  inverse_mag_percent = (1/unit_magnitude) * 100\n",
    "  bounding_unit_magnitude.append(unit_magnitude)\n",
    "  vox_scale_ratio.append( round(inverse_mag_percent, 4) )\n",
    "\n",
    "scale_df = pd.DataFrame({\n",
    "  'Bounding Volume Unit Magnitude': bounding_unit_magnitude,\n",
    "  'Voxel Scale Percentage': vox_scale_ratio\n",
    "})\n",
    "# scale_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NPY Raw Size</th>\n",
       "      <th>OBJ Raw Size</th>\n",
       "      <th>NPY File Size</th>\n",
       "      <th>OBJ File Surf-Vox Size</th>\n",
       "      <th>Bounding Volume Unit Magnitude</th>\n",
       "      <th>Voxel Scale Percentage</th>\n",
       "      <th>Voxel Count</th>\n",
       "      <th>Surface Voxel Count</th>\n",
       "      <th>Surface Voxel Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>264792080</td>\n",
       "      <td>1199093602</td>\n",
       "      <td>252.53 MB</td>\n",
       "      <td>1.12 GB</td>\n",
       "      <td>1257.815567</td>\n",
       "      <td>0.0795</td>\n",
       "      <td>67816181</td>\n",
       "      <td>2151889</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16946768</td>\n",
       "      <td>180017728</td>\n",
       "      <td>16.16 MB</td>\n",
       "      <td>171.68 MB</td>\n",
       "      <td>503.126227</td>\n",
       "      <td>0.1988</td>\n",
       "      <td>4356081</td>\n",
       "      <td>340106</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2118416</td>\n",
       "      <td>41736194</td>\n",
       "      <td>2.02 MB</td>\n",
       "      <td>39.8 MB</td>\n",
       "      <td>251.563113</td>\n",
       "      <td>0.3975</td>\n",
       "      <td>548078</td>\n",
       "      <td>83142</td>\n",
       "      <td>15.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>633976</td>\n",
       "      <td>17924710</td>\n",
       "      <td>619.12 KB</td>\n",
       "      <td>17.09 MB</td>\n",
       "      <td>167.982142</td>\n",
       "      <td>0.5953</td>\n",
       "      <td>164997</td>\n",
       "      <td>36269</td>\n",
       "      <td>21.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>264872</td>\n",
       "      <td>9551346</td>\n",
       "      <td>258.66 KB</td>\n",
       "      <td>9.11 MB</td>\n",
       "      <td>125.781557</td>\n",
       "      <td>0.7950</td>\n",
       "      <td>69327</td>\n",
       "      <td>19786</td>\n",
       "      <td>28.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>76988</td>\n",
       "      <td>3875204</td>\n",
       "      <td>75.18 KB</td>\n",
       "      <td>3.7 MB</td>\n",
       "      <td>83.582295</td>\n",
       "      <td>1.1964</td>\n",
       "      <td>20422</td>\n",
       "      <td>8316</td>\n",
       "      <td>40.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33740</td>\n",
       "      <td>2100012</td>\n",
       "      <td>32.95 KB</td>\n",
       "      <td>2.0 MB</td>\n",
       "      <td>63.126856</td>\n",
       "      <td>1.5841</td>\n",
       "      <td>9025</td>\n",
       "      <td>4555</td>\n",
       "      <td>50.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10280</td>\n",
       "      <td>826556</td>\n",
       "      <td>10.04 KB</td>\n",
       "      <td>807.18 KB</td>\n",
       "      <td>42.201896</td>\n",
       "      <td>2.3696</td>\n",
       "      <td>2780</td>\n",
       "      <td>1857</td>\n",
       "      <td>66.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NPY Raw Size  OBJ Raw Size NPY File Size OBJ File Surf-Vox Size  \\\n",
       "0     264792080    1199093602     252.53 MB                1.12 GB   \n",
       "1      16946768     180017728      16.16 MB              171.68 MB   \n",
       "2       2118416      41736194       2.02 MB                39.8 MB   \n",
       "3        633976      17924710     619.12 KB               17.09 MB   \n",
       "4        264872       9551346     258.66 KB                9.11 MB   \n",
       "5         76988       3875204      75.18 KB                 3.7 MB   \n",
       "6         33740       2100012      32.95 KB                 2.0 MB   \n",
       "7         10280        826556      10.04 KB              807.18 KB   \n",
       "\n",
       "   Bounding Volume Unit Magnitude  Voxel Scale Percentage  Voxel Count  \\\n",
       "0                     1257.815567                  0.0795     67816181   \n",
       "1                      503.126227                  0.1988      4356081   \n",
       "2                      251.563113                  0.3975       548078   \n",
       "3                      167.982142                  0.5953       164997   \n",
       "4                      125.781557                  0.7950        69327   \n",
       "5                       83.582295                  1.1964        20422   \n",
       "6                       63.126856                  1.5841         9025   \n",
       "7                       42.201896                  2.3696         2780   \n",
       "\n",
       "   Surface Voxel Count  Surface Voxel Percentage  \n",
       "0              2151889                      3.17  \n",
       "1               340106                      7.81  \n",
       "2                83142                     15.17  \n",
       "3                36269                     21.98  \n",
       "4                19786                     28.54  \n",
       "5                 8316                     40.72  \n",
       "6                 4555                     50.47  \n",
       "7                 1857                     66.80  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assemble Voxel Info DataFrame\n",
    "voxel_df = pd.concat( [ size_df, scale_df, count_df ], axis=1 )\n",
    "voxel_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dual Y axis plot of file size, voxel count vs voxel scale resolution\n",
    "\n",
    "fig, ax1 = plt.subplots( figsize=(10,6), dpi=100 )\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "y_lower, y_upper = 10**3, 10**10\n",
    "ax1.set_ylim( y_lower, y_upper )\n",
    "ax2.set_ylim( y_lower, y_upper )\n",
    "\n",
    "x_lower, x_upper = -0.1, 2.5\n",
    "ax1.set_xlim( x_lower, x_upper)\n",
    "\n",
    "ax1.plot( voxel_df['Voxel Scale Percentage'], voxel_df['OBJ Raw Size'], color=\"#e6550d\", marker=\"o\", label='OBJ File Size (Surface Voxels Only)')\n",
    "ax1.plot( voxel_df['Voxel Scale Percentage'], voxel_df['NPY Raw Size'], color=\"#fd8d3c\", marker=\"o\", label='NPY File Size')\n",
    "ax1.set_yscale('log')\n",
    "ax1.set_xlabel(\"File Voxel Scale ( % of Bounding Box Unit Magnitude )\", fontweight='semibold', fontsize=10)\n",
    "ax1.set_ylabel(\"File Size ( bytes )\", fontweight='semibold', fontsize=10)\n",
    "ax1.tick_params(axis ='y', labelcolor = 'red') \n",
    "\n",
    "ax2.plot( voxel_df['Voxel Scale Percentage'], voxel_df['Voxel Count'], color=\"#3182bd\", marker=\"s\", label='Total Voxel Count')\n",
    "ax2.plot( voxel_df['Voxel Scale Percentage'], voxel_df['Surface Voxel Count'], color=\"#6baed6\", marker=\"s\", label='Surface Voxel Count')\n",
    "ax2.set_yscale('log')\n",
    "ax2.set_ylabel(\"Voxel Count\", fontweight='semibold', fontsize=10)\n",
    "ax2.tick_params(axis ='y', labelcolor = 'blue')\n",
    "\n",
    "# Plot Email Filesize Limit\n",
    "# ax1.axhline( y=25000000, color='0.75', linestyle='--')\n",
    "# ax1.axvline( x=0.52, color='0.75', linestyle='--')\n",
    "\n",
    "# OBJ File Size Labels\n",
    "for index, data in voxel_df.iterrows():\n",
    "\tif index == 0:\n",
    "\t\tax1.annotate(data['OBJ File Surf-Vox Size'], (data['Voxel Scale Percentage'],data['OBJ Raw Size']),\n",
    "\t\t\t\txytext=(-25,5), textcoords='offset points',\n",
    "\t\t\t\tfamily='sans-serif', fontsize=7, color='black')\n",
    "\telif index == len(voxel_df)-1:\n",
    "\t\tax1.annotate(data['OBJ File Surf-Vox Size'], (data['Voxel Scale Percentage'],data['OBJ Raw Size']),\n",
    "\t\t\t\txytext=(-15,6), textcoords='offset points',\n",
    "\t\t\t\tfamily='sans-serif', fontsize=7, color='black')\n",
    "\telse:\n",
    "\t\tax1.annotate(data['OBJ File Surf-Vox Size'], (data['Voxel Scale Percentage'],data['OBJ Raw Size']),\n",
    "\t\t\t\txytext=(7,2), textcoords='offset points',\n",
    "\t\t\t\tfamily='sans-serif', fontsize=7, color='black')\n",
    "\n",
    "# NPY File Size Labels (Excludes First and Last)\n",
    "for index, data in voxel_df.iterrows():\n",
    "\tif index == 0:\n",
    "\t\tax1.annotate(data['NPY File Size'], (data['Voxel Scale Percentage'],data['NPY Raw Size']),\n",
    "\t\t\t\txytext=(-29,5), textcoords='offset points',\n",
    "\t\t\t\tfamily='sans-serif', fontsize=7, color='black')\n",
    "\telif index == len(voxel_df)-1:\n",
    "\t\tax1.annotate(data['NPY File Size'], (data['Voxel Scale Percentage'],data['NPY Raw Size']),\n",
    "\t\t\t\txytext=(-15,6), textcoords='offset points',\n",
    "\t\t\t\tfamily='sans-serif', fontsize=7, color='black')\n",
    "\telse:\n",
    "\t\tax1.annotate(data['NPY File Size'], (data['Voxel Scale Percentage'],data['NPY Raw Size']),\n",
    "\t\t\t\txytext=(7,2), textcoords='offset points',\n",
    "\t\t\t\tfamily='sans-serif', fontsize=7, color='black')\n",
    "\n",
    "ax1.set_title('Effect of Voxel Resolution on File Size', fontsize=14, fontweight='bold')\n",
    "ax1.grid(axis='y', color='0.85')\n",
    "ax1.legend(loc = (0.625, 0.875))\n",
    "ax2.legend(loc = (0.75, 0.76))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Surface Voxel Count vs OBJ Mesh File Size\n",
    "\n",
    "fig, ax = plt.subplots( figsize=(10,6), dpi=100 )\n",
    "\n",
    "ax.plot( voxel_df['Surface Voxel Count'], voxel_df['OBJ Raw Size'], color=\"#e6550d\", marker=\"o\", label='OBJ File Size (Surface Voxels Only)')\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"Number of Surface Voxels in OBJ File\", fontweight='semibold', fontsize=10)\n",
    "ax.set_ylabel(\"File Size ( bytes )\", fontweight='semibold', fontsize=10) \n",
    "\n",
    "# Plot key file size to voxel count values \n",
    "ax.axhline( y=25000000, color='0.75', linestyle='--')\n",
    "ax.text( 2000, 27000000, '25MB (Email Limit)' )\n",
    "ax.axhline( y=550000000, color='0.75', linestyle='--')\n",
    "ax.text( 2000, 580000000, '550MB' )\n",
    "ax.axvline( x=50000, color='0.75', linestyle='--')\n",
    "ax.text( 43000, 1100000, '50,000 Voxels', rotation=90 )\n",
    "ax.axvline( x=1000000, color='0.75', linestyle='--')\n",
    "ax.text( 870000, 1100000, '1,000,000 Voxels', rotation=90 )\n",
    "ax.axvline( x=2152000, color='0.75', linestyle='--')\n",
    "ax.text( 1850000, 1100000, '2,1500,000 Voxels', rotation=90 )\n",
    "\n",
    "# OBJ File Size Labels\n",
    "for index, data in voxel_df.iterrows():\n",
    "\tif index == 0:\n",
    "\t\tax.annotate(data['OBJ File Surf-Vox Size'], (data['Surface Voxel Count'],data['OBJ Raw Size']),\n",
    "\t\t\t\txytext=(-40,-4), textcoords='offset points',\n",
    "\t\t\t\tfamily='sans-serif', fontsize=7, color='black')\n",
    "\telif index == len(voxel_df)-1:\n",
    "\t\tax.annotate(data['OBJ File Surf-Vox Size'], (data['Surface Voxel Count'],data['OBJ Raw Size']),\n",
    "\t\t\t\txytext=(15,0), textcoords='offset points',\n",
    "\t\t\t\tfamily='sans-serif', fontsize=7, color='black')\n",
    "\telse:\n",
    "\t\tax.annotate(data['OBJ File Surf-Vox Size'], (data['Surface Voxel Count'],data['OBJ Raw Size']),\n",
    "\t\t\t\txytext=(10,-4), textcoords='offset points',\n",
    "\t\t\t\tfamily='sans-serif', fontsize=7, color='black')\n",
    "\n",
    "ax.set_title('Number of Voxel Plotted in OBJ Mesh vs File Size', fontsize=14, fontweight='bold')\n",
    "ax.grid(axis='y', color='0.85')\n",
    "ax.grid(axis='x', color='0.85')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### Influence of Model Geometry on Surface Voxel Count\n",
    "Removing all internal voxels leaves an identical external view of the voxel model. Only exporting voxels that define the surface of the model benefits the file size of the created OBJ mesh. The total savings is not consistent across models and is dependent on the external surface geometry. The example below demonstrates how three models with similar total voxel counts produce surface views with very different surface voxel counts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load models with similar total voxel counts\n",
    "fcc_shell = np.load(\"./example_visualized_voxels/surface_geometry_voxel_count_comparison/fcc_shell.npy\")\n",
    "fcc_center = np.load(\"./example_visualized_voxels/surface_geometry_voxel_count_comparison/fcc_center.npy\")\n",
    "lobe_gear = np.load(\"./example_visualized_voxels/surface_geometry_voxel_count_comparison/lobe_gear.npy\")\n",
    "\n",
    "comparison_models = [ \n",
    "    {'name': 'Unit Shell FCC', 'model': fcc_shell}, \n",
    "    {'name': 'Centered FCC', 'model': fcc_center}, \n",
    "    {'name': 'Lobe Gear', 'model': lobe_gear} ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collect voxel totals, shapes, and file size\n",
    "\n",
    "df_data = []\n",
    "output_path = \"./example_visualized_voxels/surface_geometry_voxel_count_comparison/obj_models/\"\n",
    "\n",
    "for voxel_model in comparison_models:\n",
    "    filenames = { \n",
    "        'full': Template('$name full.obj').substitute( name=voxel_model['name'].lower() ).replace(\" \", \"_\"),\n",
    "        'surf': Template('$name surface.obj').substitute( name=voxel_model['name'].lower() ).replace(\" \", \"_\")}\n",
    "\n",
    "    full_voxel_model = voxel_model['model']\n",
    "    surface_view_voxel_model = get_surface_voxels(full_voxel_model)\n",
    "\n",
    "    total_count = np.count_nonzero( full_voxel_model )\n",
    "    surface_count = np.count_nonzero( surface_view_voxel_model )\n",
    "\n",
    "    voxel_array2voxel_mesh_obj( os.path.join( output_path, filenames['full'] ), full_voxel_model, surface_view=False )\n",
    "    voxel_array2voxel_mesh_obj( os.path.join( output_path, filenames['surf'] ), surface_view_voxel_model, surface_view=False )\n",
    "\n",
    "    full_obj_filesize = os.path.getsize( os.path.join( output_path, filenames['full'] ) )\n",
    "    surface_obj_filesize = os.path.getsize( os.path.join( output_path, filenames['surf'] ) )\n",
    "\n",
    "    df_data.append([ \n",
    "        total_count,\n",
    "        surface_count,\n",
    "        round(( surface_count/total_count )*100, 2),\n",
    "        full_voxel_model.shape,\n",
    "        full_obj_filesize,\n",
    "        surface_obj_filesize,\n",
    "        round(( surface_obj_filesize/full_obj_filesize )*100, 2)\n",
    "    ])\n",
    "\n",
    "# df_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Full Voxel Count</th>\n",
       "      <th>Surface Voxel Count</th>\n",
       "      <th>Surface Voxel Count %</th>\n",
       "      <th>Voxel Grid Shape</th>\n",
       "      <th>Full Voxel Model OBJ Filesize</th>\n",
       "      <th>Surface Voxel Model OBJ Filesize</th>\n",
       "      <th>Surface Voxel OBJ Filesize %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bounded FCC\\n Lattice Unit Cell</th>\n",
       "      <td>63356</td>\n",
       "      <td>32704</td>\n",
       "      <td>51.62</td>\n",
       "      <td>(64, 64, 64)</td>\n",
       "      <td>31684906</td>\n",
       "      <td>16113690</td>\n",
       "      <td>50.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centered FCC\\n Lattice Unit Cell</th>\n",
       "      <td>63356</td>\n",
       "      <td>24095</td>\n",
       "      <td>38.03</td>\n",
       "      <td>(64, 64, 64)</td>\n",
       "      <td>31684906</td>\n",
       "      <td>11740318</td>\n",
       "      <td>37.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lobe Gear Model</th>\n",
       "      <td>63404</td>\n",
       "      <td>19149</td>\n",
       "      <td>30.20</td>\n",
       "      <td>(80, 80, 34)</td>\n",
       "      <td>31709290</td>\n",
       "      <td>9227750</td>\n",
       "      <td>29.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Full Voxel Count  Surface Voxel Count  \\\n",
       "Bounded FCC\\n Lattice Unit Cell              63356                32704   \n",
       "Centered FCC\\n Lattice Unit Cell             63356                24095   \n",
       "Lobe Gear Model                              63404                19149   \n",
       "\n",
       "                                  Surface Voxel Count % Voxel Grid Shape  \\\n",
       "Bounded FCC\\n Lattice Unit Cell                   51.62     (64, 64, 64)   \n",
       "Centered FCC\\n Lattice Unit Cell                  38.03     (64, 64, 64)   \n",
       "Lobe Gear Model                                   30.20     (80, 80, 34)   \n",
       "\n",
       "                                  Full Voxel Model OBJ Filesize  \\\n",
       "Bounded FCC\\n Lattice Unit Cell                        31684906   \n",
       "Centered FCC\\n Lattice Unit Cell                       31684906   \n",
       "Lobe Gear Model                                        31709290   \n",
       "\n",
       "                                  Surface Voxel Model OBJ Filesize  \\\n",
       "Bounded FCC\\n Lattice Unit Cell                           16113690   \n",
       "Centered FCC\\n Lattice Unit Cell                          11740318   \n",
       "Lobe Gear Model                                            9227750   \n",
       "\n",
       "                                  Surface Voxel OBJ Filesize %  \n",
       "Bounded FCC\\n Lattice Unit Cell                          50.86  \n",
       "Centered FCC\\n Lattice Unit Cell                         37.05  \n",
       "Lobe Gear Model                                          29.10  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build Dataframe\n",
    "\n",
    "filenames = [\n",
    "    'Bounded FCC\\n Lattice Unit Cell',\n",
    "    'Centered FCC\\n Lattice Unit Cell',\n",
    "    'Lobe Gear Model']\n",
    "\n",
    "geom_v_count_columns = [ \n",
    "    'Full Voxel Count', \n",
    "    'Surface Voxel Count', \n",
    "    'Surface Voxel Count %', \n",
    "    'Voxel Grid Shape', \n",
    "    'Full Voxel Model OBJ Filesize',\n",
    "    'Surface Voxel Model OBJ Filesize',\n",
    "    'Surface Voxel OBJ Filesize %',]\n",
    "\n",
    "geom_v_surf_count_df = pd.DataFrame(df_data, columns=geom_v_count_columns, index=filenames)\n",
    "\n",
    "geom_v_surf_count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the reduced surface voxel count for each model\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8), dpi=100)\n",
    "\n",
    "ylabel_loc = np.arange(len(geom_v_surf_count_df.index))\n",
    "bar_span = 0.3\n",
    "\n",
    "full_count = ax.barh(ylabel_loc-bar_span/2, geom_v_surf_count_df['Full Voxel Count'], bar_span, label='Full Voxel Count')\n",
    "surf_count = ax.barh(ylabel_loc+bar_span/2, geom_v_surf_count_df['Surface Voxel Count'], bar_span, label='Surface Voxel Count')\n",
    "\n",
    "ax.set_title('Influence of Geometry on Surface Voxel Count Reduction', fontdict={'size':16, 'weight':'bold'})\n",
    "ax.set_xlabel('Voxel Count', fontsize=13, fontweight='semibold')\n",
    "ax.set_yticks( ylabel_loc, labels=geom_v_surf_count_df.index, fontsize=10, fontweight='medium')\n",
    "ax.invert_yaxis()\n",
    "ax.legend()\n",
    "\n",
    "ax.bar_label(full_count, fontsize=14, padding=4)\n",
    "ax.bar_label(surf_count, fontsize=14, padding=4)\n",
    "ax.bar_label(full_count, labels=['100%' for p in np.arange(len(geom_v_surf_count_df.index))], label_type='center', fontsize=14)\n",
    "ax.bar_label(surf_count, labels=['%.2f%%' % p for p in geom_v_surf_count_df['Surface Voxel Count %']], label_type='center', fontsize=14, padding=4)\n",
    "ax.set_xlim(right=69000)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the reduced surface voxel OBJ file size for each model\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8), dpi=100)\n",
    "\n",
    "label_fmt = lambda x, pos: '{:.0f}MB'.format(x/1000000, pos)\n",
    "\n",
    "ylabel_loc = np.arange( len(geom_v_surf_count_df.index) )\n",
    "bar_span = 0.3\n",
    "\n",
    "full_count = ax.barh( ylabel_loc-bar_span/2, geom_v_surf_count_df['Full Voxel Model OBJ Filesize'], bar_span, label='Full Voxel OBJ File Size' )\n",
    "surf_count = ax.barh( ylabel_loc+bar_span/2, geom_v_surf_count_df['Surface Voxel Model OBJ Filesize'], bar_span, label='Surface Voxel OBJ File Size' )\n",
    "\n",
    "ax.set_title('Influence of Geometry on Surface Voxel OBJ File Size', fontdict={'size':16, 'weight':'bold'})\n",
    "ax.set_xlabel('File Size', fontsize=13, fontweight='semibold')\n",
    "ax.set_yticks( ylabel_loc, labels=geom_v_surf_count_df.index, fontsize=10, fontweight='medium')\n",
    "ax.invert_yaxis()\n",
    "ax.legend()\n",
    "\n",
    "ax.bar_label(full_count, labels=[ convert_size(s) for s in geom_v_surf_count_df['Full Voxel Model OBJ Filesize']], fontsize=14, padding=4 )\n",
    "ax.bar_label(surf_count, labels=[ convert_size(s) for s in geom_v_surf_count_df['Surface Voxel Model OBJ Filesize']], fontsize=14, padding=4 )\n",
    "ax.bar_label(full_count, labels=['100%' for p in np.arange(len(geom_v_surf_count_df.index))], label_type='center', fontsize=14 )\n",
    "ax.bar_label(surf_count, labels=['%.2f%%' % p for p in geom_v_surf_count_df['Surface Voxel OBJ Filesize %']], label_type='center', fontsize=14, padding=4 )\n",
    "ax.set_xlim(right=36000000)\n",
    "\n",
    "ax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(label_fmt))\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "9f5bc9b68ab5156f2af75e5f9ffdfb892c593ee250d5b4ae65a79c44daece197"
  },
  "kernelspec": {
   "display_name": "Python 3.10.5 ('venv': venv)",
   "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": "3.10.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}