MeshroomCollab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "meshroom_test.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RD3eZSm6d6NZ",
        "colab_type": "text"
      },
      "source": [
        "#Setup\n",
        "Download Meshroom and Data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQgXdvk-qUvi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/alicevision/dataset_buddha\n",
        "!wget -N https://github.com/alicevision/meshroom/releases/download/v2019.1.0/Meshroom-2019.1.0-linux.tar.gz\n",
        "!mkdir meshroom\n",
        "!tar xzf Meshroom-2019.1.0-linux.tar.gz -C ./meshroom"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zTI6AlUXd-_M",
        "colab_type": "text"
      },
      "source": [
        "#Upload data\n",
        "Optional upload of pipline file and image source files\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_BfARNUdd_a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "# optional upload for the meshfile\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "    print('User uploaded file \"{name}\" with length {length} bytes'.format( name=fn, length=len(uploaded[fn])))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xA_XKpKqdoqb",
        "colab_type": "text"
      },
      "source": [
        "#Meshing\n",
        "use the --pipeline argument to provide a path to the meshfile you created/uploaded"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZWyk461sxGI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir ./buddha_out\n",
        "!./meshroom/Meshroom-2019.1.0/meshroom_photogrammetry --input ./dataset_buddha/buddha --output ./buddha_out\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wwo3nPyd0-w",
        "colab_type": "text"
      },
      "source": [
        "#Download\n",
        "Use the prefered download format\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPEdzNMo5fV6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!tar -czvf meshbuddha.tar.gz ./buddha_out\n",
        "from google.colab import files\n",
        "\n",
        "\n",
        "files.download('meshbuddha.tar.gz')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAXWX6SX8J_q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "168b3acd-bd30-47c7-ca77-b6870306dd78"
      },
      "source": [
        "!zip -r meshbuddha.zip ./buddha_out\n",
        "files.download('meshbuddha.zip')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  adding: buddha_out/ (stored 0%)\n",
            "  adding: buddha_out/texture_0.png (deflated 0%)\n",
            "  adding: buddha_out/texturedMesh.obj (deflated 72%)\n",
            "  adding: buddha_out/texturedMesh.mtl (deflated 28%)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}