libigl.ipynb

libigl.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNQxMBVI2Fx/cBuq2za8hPN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/stwind/b0c63bda7db5ac31becbac64bfa27f19/libigl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKjPXBf3jTAd",
        "outputId": "3d62b705-234a-4e65-f517-c9a6c440ff55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.2/16.2 MB\u001b[0m \u001b[31m33.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -Uq libigl"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -qc --show-progress https://github.com/libigl/libigl-tutorial-data/raw/master/fandisk.off"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtBmpTgPjtEE",
        "outputId": "41c4b53e-6bed-4d01-f6d6-abd3507baa8d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rfandisk.off           0%[                    ]       0  --.-KB/s               \rfandisk.off         100%[===================>] 397.76K  --.-KB/s    in 0.03s   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import igl\n",
        "\n",
        "def tri_areas(tri):\n",
        "    pa, pb = tri[:,0] - tri[:,2], tri[:,1] - tri[:,2]\n",
        "    return np.linalg.norm(np.cross(pa, pb), axis=-1) * .5\n",
        "\n",
        "def normed(x):\n",
        "    return x / np.maximum(np.linalg.norm(x, axis=-1, keepdims=True), 1e-8)\n",
        "\n",
        "def get_angles(pi, pj, pk):\n",
        "    return np.arccos((normed(pj - pi) * normed(pk - pi)).sum(-1))"
      ],
      "metadata": {
        "id": "4uid_ltnj3Fj"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "v, f = igl.read_triangle_mesh(\"fandisk.off\")"
      ],
      "metadata": {
        "id": "adwzyfqTj9x6"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_face = igl.per_face_normals(v, f, v)\n",
        "n_vert_uniform = igl.per_vertex_normals(v, f, igl.PER_VERTEX_NORMALS_WEIGHTING_TYPE_UNIFORM)\n",
        "n_vert_area = igl.per_vertex_normals(v, f, igl.PER_VERTEX_NORMALS_WEIGHTING_TYPE_AREA)\n",
        "n_vert_angle = igl.per_vertex_normals(v, f, igl.PER_VERTEX_NORMALS_WEIGHTING_TYPE_ANGLE)"
      ],
      "metadata": {
        "id": "_dliYo2QkAjN"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.testing.assert_allclose(tri_areas(v[f]) * 2, igl.doublearea(v, f))\n",
        "np.testing.assert_allclose(\n",
        "    igl.internal_angles(v,f),\n",
        "    np.c_[get_angles(v[f[:,0]], v[f[:,1]], v[f[:,2]]),\n",
        "          get_angles(v[f[:,1]], v[f[:,2]], v[f[:,0]]),\n",
        "          get_angles(v[f[:,2]], v[f[:,0]], v[f[:,1]])])"
      ],
      "metadata": {
        "id": "LwTUgKbftYia"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.savez_compressed(\"fandisk\",\n",
        "                    v=v.astype(np.float32),\n",
        "                    f=f.astype(np.uint16),\n",
        "                    n_face=n_face.astype(np.float32),\n",
        "                    n_vert_uniform=n_vert_uniform.astype(np.float32),\n",
        "                    n_vert_area=n_vert_area.astype(np.float32),\n",
        "                    n_vert_angle=n_vert_angle.astype(np.float32),\n",
        "                    centroids=v[f].mean(1).astype(np.float32),\n",
        "                    doublearea=igl.doublearea(v, f).astype(np.float32),\n",
        "                    internal_angles=igl.internal_angles(v,f).astype(np.float32)\n",
        "                    )"
      ],
      "metadata": {
        "id": "LjXMugMbAHJJ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls -hal"
      ],
      "metadata": {
        "id": "LsTvbKDpF23N",
        "outputId": "4f48dff7-7f48-4192-8fc6-3464e68246a8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 1.2M\n",
            "drwxr-xr-x 1 root root 4.0K May 14 12:19 .\n",
            "drwxr-xr-x 1 root root 4.0K May 14 12:15 ..\n",
            "drwxr-xr-x 4 root root 4.0K May  9 13:24 .config\n",
            "-rw-r--r-- 1 root root 769K May 14 12:19 fandisk.npz\n",
            "-rw-r--r-- 1 root root 398K May 14 12:19 fandisk.off\n",
            "drwxr-xr-x 1 root root 4.0K May  9 13:24 sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gYdgBE_sIirw"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}