MeshroomCollab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MR2.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZh8CZsjwfD1",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Setup\n",
        "\n",
        "Download Meshroom\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4HH_r8CwZXa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!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": "mmSZ5le1wl1r",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Upload data\n",
        "\n",
        "Optional upload of pipline file and image source files\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BP3p_lGEq69X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir input"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bnYMYBQlrL0u",
        "colab_type": "text"
      },
      "source": [
        "Change dir before upload"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9NW_koxq-wj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cd input"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqxXyUJYrdV5",
        "colab_type": "text"
      },
      "source": [
        "Test directory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KI9G_jvtrgOm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpXT0L6ywoSa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "# optional upload for the images\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])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pc_52Wh9ruQF",
        "colab_type": "text"
      },
      "source": [
        "List Input contents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcIHvaherrAb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMspCFLAs_K7",
        "colab_type": "text"
      },
      "source": [
        "Navigate back to content folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2TywJh4lsNDK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cd ../\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4E_kAx-2wq3O",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Meshing\n",
        "\n",
        "use the --pipeline argument to provide a path to the meshfile you created/uploaded\n",
        "Note: the node temp files are stored in the temp folder out is only for the final result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GimHqrGwsmu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir ./out\n",
        "!./meshroom/Meshroom-2019.1.0/meshroom_photogrammetry --input ./input --output ./out\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_EZJtblswuZy",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Download\n",
        "\n",
        "Use the prefered download format\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IirusdKJwz-6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!tar -czvf out.tar.gz ./out\n",
        "from google.colab import files\n",
        "\n",
        "\n",
        "files.download('out.tar.gz')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQ8F_rxPw4dK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!zip -r out.zip ./out\n",
        "files.download('out.zip')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NNzXTKPYwwl3",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}