Deeper_3D_Ken_Burns_single_pic.ipynb

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    "colab": {
      "name": "Deeper-3D-Ken-Burns-single-pic.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Deeper_3D_Ken_Burns_single_pic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uCEixJujApc8",
        "colab_type": "text"
      },
      "source": [
        "# Deeper 3D Ken Burns\n",
        "A fork of the repo metioned below to obtain more \"exaggerated\" results as shown in this Twitter thread: https://twitter.com/alexeev_eu/status/1219655482501140480?s=20\n",
        "\n",
        "## 3D Ken Burns\n",
        "This is a reference implementation of 3D Ken Burns Effect from a Single Image [1] using PyTorch. Given a single input image, it animates this still image with a virtual camera scan and zoom subject to motion parallax. Should you be making use of our work, please cite our paper [1].\n",
        "\n",
        "<a href=\"https://arxiv.org/abs/1909.05483\" rel=\"Paper\"><img src=\"http://content.sniklaus.com/kenburns/paper.jpg\" alt=\"Paper\" width=\"100%\"></a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cJFpObViMXDH",
        "colab_type": "text"
      },
      "source": [
        "> Colab created by [Manuel Romero](https://twitter.com/mrm8488)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXK9VNecNQEF",
        "colab_type": "text"
      },
      "source": [
        "### 1. Clone the project"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbYRw_GlCL5w",
        "colab_type": "code",
        "outputId": "9fcd4daf-10c8-48a2-8abc-b7198ba1393b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "!git clone https://github.com/mrm8488/3d-ken-burns.git"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into '3d-ken-burns'...\n",
            "remote: Enumerating objects: 33, done.\u001b[K\n",
            "remote: Counting objects:   3% (1/33)\u001b[K\rremote: Counting objects:   6% (2/33)\u001b[K\rremote: Counting objects:   9% (3/33)\u001b[K\rremote: Counting objects:  12% (4/33)\u001b[K\rremote: Counting objects:  15% (5/33)\u001b[K\rremote: Counting objects:  18% (6/33)\u001b[K\rremote: Counting objects:  21% (7/33)\u001b[K\rremote: Counting objects:  24% (8/33)\u001b[K\rremote: Counting objects:  27% (9/33)\u001b[K\rremote: Counting objects:  30% (10/33)\u001b[K\rremote: Counting objects:  33% (11/33)\u001b[K\rremote: Counting objects:  36% (12/33)\u001b[K\rremote: Counting objects:  39% (13/33)\u001b[K\rremote: Counting objects:  42% (14/33)\u001b[K\rremote: Counting objects:  45% (15/33)\u001b[K\rremote: Counting objects:  48% (16/33)\u001b[K\rremote: Counting objects:  51% (17/33)\u001b[K\rremote: Counting objects:  54% (18/33)\u001b[K\rremote: Counting objects:  57% (19/33)\u001b[K\rremote: Counting objects:  60% (20/33)\u001b[K\rremote: Counting objects:  63% (21/33)\u001b[K\rremote: Counting objects:  66% (22/33)\u001b[K\rremote: Counting objects:  69% (23/33)\u001b[K\rremote: Counting objects:  72% (24/33)\u001b[K\rremote: Counting objects:  75% (25/33)\u001b[K\rremote: Counting objects:  78% (26/33)\u001b[K\rremote: Counting objects:  81% (27/33)\u001b[K\rremote: Counting objects:  84% (28/33)\u001b[K\rremote: Counting objects:  87% (29/33)\u001b[K\rremote: Counting objects:  90% (30/33)\u001b[K\rremote: Counting objects:  93% (31/33)\u001b[K\rremote: Counting objects:  96% (32/33)\u001b[K\rremote: Counting objects: 100% (33/33)\u001b[K\rremote: Counting objects: 100% (33/33), done.\u001b[K\n",
            "remote: Compressing objects: 100% (33/33), done.\u001b[K\n",
            "remote: Total 69 (delta 17), reused 1 (delta 0), pack-reused 36\u001b[K\n",
            "Unpacking objects: 100% (69/69), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "06tpWil4A6Ck",
        "colab_type": "text"
      },
      "source": [
        "### 2. Download pretrained models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jy-x7Cm7AymM",
        "colab_type": "code",
        "outputId": "5c5807fe-dafd-42fe-88e6-9ba41ff50e14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        }
      },
      "source": [
        "!wget -O ./3d-ken-burns/models/disparity-estimation.pytorch http://content.sniklaus.com/kenburns/network-disparity.pytorch\n",
        "!wget -O ./3d-ken-burns/models/disparity-refinement.pytorch http://content.sniklaus.com/kenburns/network-refinement.pytorch\n",
        "!wget -O ./3d-ken-burns/models/pointcloud-inpainting.pytorch http://content.sniklaus.com/kenburns/network-inpainting.pytorch"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-01-27 15:17:10--  http://content.sniklaus.com/kenburns/network-disparity.pytorch\n",
            "Resolving content.sniklaus.com (content.sniklaus.com)... 51.15.58.24\n",
            "Connecting to content.sniklaus.com (content.sniklaus.com)|51.15.58.24|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 357438463 (341M) [application/octet-stream]\n",
            "Saving to: ‘./3d-ken-burns/models/disparity-estimation.pytorch’\n",
            "\n",
            "./3d-ken-burns/mode 100%[===================>] 340.88M   109MB/s    in 3.1s    \n",
            "\n",
            "2020-01-27 15:17:13 (109 MB/s) - ‘./3d-ken-burns/models/disparity-estimation.pytorch’ saved [357438463/357438463]\n",
            "\n",
            "--2020-01-27 15:17:15--  http://content.sniklaus.com/kenburns/network-refinement.pytorch\n",
            "Resolving content.sniklaus.com (content.sniklaus.com)... 51.15.58.24\n",
            "Connecting to content.sniklaus.com (content.sniklaus.com)|51.15.58.24|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2438928 (2.3M) [application/octet-stream]\n",
            "Saving to: ‘./3d-ken-burns/models/disparity-refinement.pytorch’\n",
            "\n",
            "./3d-ken-burns/mode 100%[===================>]   2.33M  12.2MB/s    in 0.2s    \n",
            "\n",
            "2020-01-27 15:17:15 (12.2 MB/s) - ‘./3d-ken-burns/models/disparity-refinement.pytorch’ saved [2438928/2438928]\n",
            "\n",
            "--2020-01-27 15:17:17--  http://content.sniklaus.com/kenburns/network-inpainting.pytorch\n",
            "Resolving content.sniklaus.com (content.sniklaus.com)... 51.15.58.24\n",
            "Connecting to content.sniklaus.com (content.sniklaus.com)|51.15.58.24|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 33179067 (32M) [application/octet-stream]\n",
            "Saving to: ‘./3d-ken-burns/models/pointcloud-inpainting.pytorch’\n",
            "\n",
            "./3d-ken-burns/mode 100%[===================>]  31.64M  71.7MB/s    in 0.4s    \n",
            "\n",
            "2020-01-27 15:17:17 (71.7 MB/s) - ‘./3d-ken-burns/models/pointcloud-inpainting.pytorch’ saved [33179067/33179067]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5PBRLqUGYbk",
        "colab_type": "code",
        "outputId": "3e23d8b3-42cb-4068-dc6c-401c49ecbbc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "import chainer\n",
        "chainer.print_runtime_info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Platform: Linux-4.14.137+-x86_64-with-Ubuntu-18.04-bionic\n",
            "Chainer: 6.5.0\n",
            "ChainerX: Not Available\n",
            "NumPy: 1.17.5\n",
            "CuPy:\n",
            "  CuPy Version          : 6.5.0\n",
            "  CUDA Root             : /usr/local/cuda\n",
            "  CUDA Build Version    : 10010\n",
            "  CUDA Driver Version   : 10010\n",
            "  CUDA Runtime Version  : 10010\n",
            "  cuDNN Build Version   : 7603\n",
            "  cuDNN Version         : 7603\n",
            "  NCCL Build Version    : 2402\n",
            "  NCCL Runtime Version  : 2402\n",
            "iDeep: 2.0.0.post3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkrEO7vIIPym",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.environ['CUDA_HOME'] = \"/usr/local/cuda\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vtd0th84B0Zg",
        "colab_type": "code",
        "outputId": "0be443be-65e6-453f-aac6-405ab61b0d6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "!pip install moviepy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: moviepy in /usr/local/lib/python3.6/dist-packages (0.2.3.5)\n",
            "Requirement already satisfied: imageio<3.0,>=2.1.2 in /usr/local/lib/python3.6/dist-packages (from moviepy) (2.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from moviepy) (1.17.5)\n",
            "Requirement already satisfied: tqdm<5.0,>=4.11.2 in /usr/local/lib/python3.6/dist-packages (from moviepy) (4.28.1)\n",
            "Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.6/dist-packages (from moviepy) (4.4.1)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from imageio<3.0,>=2.1.2->moviepy) (6.2.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXFs3ii5Co82",
        "colab_type": "code",
        "outputId": "0f1286c0-694c-4d88-f119-4cb011ed3abf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "cd /content/3d-ken-burns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/3d-ken-burns\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2z5hGImGGOEj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm images/README.md\n",
        "!mkdir results"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndl8FGKxM_hk",
        "colab_type": "text"
      },
      "source": [
        "### 3. Upload one picture your filesystem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgRSDEfRJ6ha",
        "colab_type": "code",
        "outputId": "5d050c63-c04a-4a7c-9a8e-be932fcd451d",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7Ci8vIE1heCBhbW91bnQgb2YgdGltZSB0byBibG9jayB3YWl0aW5nIGZvciB0aGUgdXNlci4KY29uc3QgRklMRV9DSEFOR0VfVElNRU9VVF9NUyA9IDMwICogMTAwMDsKCmZ1bmN0aW9uIF91cGxvYWRGaWxlcyhpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IHN0ZXBzID0gdXBsb2FkRmlsZXNTdGVwKGlucHV0SWQsIG91dHB1dElkKTsKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIC8vIENhY2hlIHN0ZXBzIG9uIHRoZSBvdXRwdXRFbGVtZW50IHRvIG1ha2UgaXQgYXZhaWxhYmxlIGZvciB0aGUgbmV4dCBjYWxsCiAgLy8gdG8gdXBsb2FkRmlsZXNDb250aW51ZSBmcm9tIFB5dGhvbi4KICBvdXRwdXRFbGVtZW50LnN0ZXBzID0gc3RlcHM7CgogIHJldHVybiBfdXBsb2FkRmlsZXNDb250aW51ZShvdXRwdXRJZCk7Cn0KCi8vIFRoaXMgaXMgcm91Z2hseSBhbiBhc3luYyBnZW5lcmF0b3IgKG5vdCBzdXBwb3J0ZWQgaW4gdGhlIGJyb3dzZXIgeWV0KSwKLy8gd2hlcmUgdGhlcmUgYXJlIG11bHRpcGxlIGFzeW5jaHJvbm91cyBzdGVwcyBhbmQgdGhlIFB5dGhvbiBzaWRlIGlzIGdvaW5nCi8vIHRvIHBvbGwgZm9yIGNvbXBsZXRpb24gb2YgZWFjaCBzdGVwLgovLyBUaGlzIHVzZXMgYSBQcm9taXNlIHRvIGJsb2NrIHRoZSBweXRob24gc2lkZSBvbiBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcCwKLy8gdGhlbiBwYXNzZXMgdGhlIHJlc3VsdCBvZiB0aGUgcHJldmlvdXMgc3RlcCBhcyB0aGUgaW5wdXQgdG8gdGhlIG5leHQgc3RlcC4KZnVuY3Rpb24gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpIHsKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIGNvbnN0IHN0ZXBzID0gb3V0cHV0RWxlbWVudC5zdGVwczsKCiAgY29uc3QgbmV4dCA9IHN0ZXBzLm5leHQob3V0cHV0RWxlbWVudC5sYXN0UHJvbWlzZVZhbHVlKTsKICByZXR1cm4gUHJvbWlzZS5yZXNvbHZlKG5leHQudmFsdWUucHJvbWlzZSkudGhlbigodmFsdWUpID0+IHsKICAgIC8vIENhY2hlIHRoZSBsYXN0IHByb21pc2UgdmFsdWUgdG8gbWFrZSBpdCBhdmFpbGFibGUgdG8gdGhlIG5leHQKICAgIC8vIHN0ZXAgb2YgdGhlIGdlbmVyYXRvci4KICAgIG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSA9IHZhbHVlOwogICAgcmV0dXJuIG5leHQudmFsdWUucmVzcG9uc2U7CiAgfSk7Cn0KCi8qKgogKiBHZW5lcmF0b3IgZnVuY3Rpb24gd2hpY2ggaXMgY2FsbGVkIGJldHdlZW4gZWFjaCBhc3luYyBzdGVwIG9mIHRoZSB1cGxvYWQKICogcHJvY2Vzcy4KICogQHBhcmFtIHtzdHJpbmd9IGlucHV0SWQgRWxlbWVudCBJRCBvZiB0aGUgaW5wdXQgZmlsZSBwaWNrZXIgZWxlbWVudC4KICogQHBhcmFtIHtzdHJpbmd9IG91dHB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIG91dHB1dCBkaXNwbGF5LgogKiBAcmV0dXJuIHshSXRlcmFibGU8IU9iamVjdD59IEl0ZXJhYmxlIG9mIG5leHQgc3RlcHMuCiAqLwpmdW5jdGlvbiogdXBsb2FkRmlsZXNTdGVwKGlucHV0SWQsIG91dHB1dElkKSB7CiAgY29uc3QgaW5wdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQoaW5wdXRJZCk7CiAgaW5wdXRFbGVtZW50LmRpc2FibGVkID0gZmFsc2U7CgogIGNvbnN0IG91dHB1dEVsZW1lbnQgPSBkb2N1bWVudC5nZXRFbGVtZW50QnlJZChvdXRwdXRJZCk7CiAgb3V0cHV0RWxlbWVudC5pbm5lckhUTUwgPSAnJzsKCiAgY29uc3QgcGlja2VkUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICBpbnB1dEVsZW1lbnQuYWRkRXZlbnRMaXN0ZW5lcignY2hhbmdlJywgKGUpID0+IHsKICAgICAgcmVzb2x2ZShlLnRhcmdldC5maWxlcyk7CiAgICB9KTsKICB9KTsKCiAgY29uc3QgY2FuY2VsID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnYnV0dG9uJyk7CiAgaW5wdXRFbGVtZW50LnBhcmVudEVsZW1lbnQuYXBwZW5kQ2hpbGQoY2FuY2VsKTsKICBjYW5jZWwudGV4dENvbnRlbnQgPSAnQ2FuY2VsIHVwbG9hZCc7CiAgY29uc3QgY2FuY2VsUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICBjYW5jZWwub25jbGljayA9ICgpID0+IHsKICAgICAgcmVzb2x2ZShudWxsKTsKICAgIH07CiAgfSk7CgogIC8vIENhbmNlbCB1cGxvYWQgaWYgdXNlciBoYXNuJ3QgcGlja2VkIGFueXRoaW5nIGluIHRpbWVvdXQuCiAgY29uc3QgdGltZW91dFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgc2V0VGltZW91dCgoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9LCBGSUxFX0NIQU5HRV9USU1FT1VUX01TKTsKICB9KTsKCiAgLy8gV2FpdCBmb3IgdGhlIHVzZXIgdG8gcGljayB0aGUgZmlsZXMuCiAgY29uc3QgZmlsZXMgPSB5aWVsZCB7CiAgICBwcm9taXNlOiBQcm9taXNlLnJhY2UoW3BpY2tlZFByb21pc2UsIHRpbWVvdXRQcm9taXNlLCBjYW5jZWxQcm9taXNlXSksCiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdzdGFydGluZycsCiAgICB9CiAgfTsKCiAgaWYgKCFmaWxlcykgewogICAgcmV0dXJuIHsKICAgICAgcmVzcG9uc2U6IHsKICAgICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICAgIH0KICAgIH07CiAgfQoKICBjYW5jZWwucmVtb3ZlKCk7CgogIC8vIERpc2FibGUgdGhlIGlucHV0IGVsZW1lbnQgc2luY2UgZnVydGhlciBwaWNrcyBhcmUgbm90IGFsbG93ZWQuCiAgaW5wdXRFbGVtZW50LmRpc2FibGVkID0gdHJ1ZTsKCiAgZm9yIChjb25zdCBmaWxlIG9mIGZpbGVzKSB7CiAgICBjb25zdCBsaSA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2xpJyk7CiAgICBsaS5hcHBlbmQoc3BhbihmaWxlLm5hbWUsIHtmb250V2VpZ2h0OiAnYm9sZCd9KSk7CiAgICBsaS5hcHBlbmQoc3BhbigKICAgICAgICBgKCR7ZmlsZS50eXBlIHx8ICduL2EnfSkgLSAke2ZpbGUuc2l6ZX0gYnl0ZXMsIGAgKwogICAgICAgIGBsYXN0IG1vZGlmaWVkOiAkewogICAgICAgICAgICBmaWxlLmxhc3RNb2RpZmllZERhdGUgPyBmaWxlLmxhc3RNb2RpZmllZERhdGUudG9Mb2NhbGVEYXRlU3RyaW5nKCkgOgogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAnbi9hJ30gLSBgKSk7CiAgICBjb25zdCBwZXJjZW50ID0gc3BhbignMCUgZG9uZScpOwogICAgbGkuYXBwZW5kQ2hpbGQocGVyY2VudCk7CgogICAgb3V0cHV0RWxlbWVudC5hcHBlbmRDaGlsZChsaSk7CgogICAgY29uc3QgZmlsZURhdGFQcm9taXNlID0gbmV3IFByb21pc2UoKHJlc29sdmUpID0+IHsKICAgICAgY29uc3QgcmVhZGVyID0gbmV3IEZpbGVSZWFkZXIoKTsKICAgICAgcmVhZGVyLm9ubG9hZCA9IChlKSA9PiB7CiAgICAgICAgcmVzb2x2ZShlLnRhcmdldC5yZXN1bHQpOwogICAgICB9OwogICAgICByZWFkZXIucmVhZEFzQXJyYXlCdWZmZXIoZmlsZSk7CiAgICB9KTsKICAgIC8vIFdhaXQgZm9yIHRoZSBkYXRhIHRvIGJlIHJlYWR5LgogICAgbGV0IGZpbGVEYXRhID0geWllbGQgewogICAgICBwcm9taXNlOiBmaWxlRGF0YVByb21pc2UsCiAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgYWN0aW9uOiAnY29udGludWUnLAogICAgICB9CiAgICB9OwoKICAgIC8vIFVzZSBhIGNodW5rZWQgc2VuZGluZyB0byBhdm9pZCBtZXNzYWdlIHNpemUgbGltaXRzLiBTZWUgYi82MjExNTY2MC4KICAgIGxldCBwb3NpdGlvbiA9IDA7CiAgICB3aGlsZSAocG9zaXRpb24gPCBmaWxlRGF0YS5ieXRlTGVuZ3RoKSB7CiAgICAgIGNvbnN0IGxlbmd0aCA9IE1hdGgubWluKGZpbGVEYXRhLmJ5dGVMZW5ndGggLSBwb3NpdGlvbiwgTUFYX1BBWUxPQURfU0laRSk7CiAgICAgIGNvbnN0IGNodW5rID0gbmV3IFVpbnQ4QXJyYXkoZmlsZURhdGEsIHBvc2l0aW9uLCBsZW5ndGgpOwogICAgICBwb3NpdGlvbiArPSBsZW5ndGg7CgogICAgICBjb25zdCBiYXNlNjQgPSBidG9hKFN0cmluZy5mcm9tQ2hhckNvZGUuYXBwbHkobnVsbCwgY2h1bmspKTsKICAgICAgeWllbGQgewogICAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgICBhY3Rpb246ICdhcHBlbmQnLAogICAgICAgICAgZmlsZTogZmlsZS5uYW1lLAogICAgICAgICAgZGF0YTogYmFzZTY0LAogICAgICAgIH0sCiAgICAgIH07CiAgICAgIHBlcmNlbnQudGV4dENvbnRlbnQgPQogICAgICAgICAgYCR7TWF0aC5yb3VuZCgocG9zaXRpb24gLyBmaWxlRGF0YS5ieXRlTGVuZ3RoKSAqIDEwMCl9JSBkb25lYDsKICAgIH0KICB9CgogIC8vIEFsbCBkb25lLgogIHlpZWxkIHsKICAgIHJlc3BvbnNlOiB7CiAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgIH0KICB9Owp9CgpzY29wZS5nb29nbGUgPSBzY29wZS5nb29nbGUgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYiA9IHNjb3BlLmdvb2dsZS5jb2xhYiB8fCB7fTsKc2NvcGUuZ29vZ2xlLmNvbGFiLl9maWxlcyA9IHsKICBfdXBsb2FkRmlsZXMsCiAgX3VwbG9hZEZpbGVzQ29udGludWUsCn07Cn0pKHNlbGYpOwo=",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "pic_name = list(uploaded.keys())[0]\n",
        "!mv ./$pic_name ./images/$pic_name"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-82db8875-730b-4e8b-a93c-e7ada6f052e9\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-82db8875-730b-4e8b-a93c-e7ada6f052e9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving doublestrike.jpg to doublestrike.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G9vz3wSqmSNz",
        "colab_type": "text"
      },
      "source": [
        "Params set as [jonathanfly](https://twitter.com/jonathanfly) proposed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfONcdQblmny",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dblShift = 10\n",
        "dblZoom = 10000000000000000000000000000000000000000000000000000000\n",
        "#dblSteps = \"0.0 8.0 400\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Daw9Ptv5P_EG",
        "colab_type": "text"
      },
      "source": [
        "### 4. Apply 3D Ken Burns Effect\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNsCub5OdVTl",
        "colab_type": "code",
        "outputId": "adc56f2e-b8e3-485b-9efb-bb978d28ed9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "!python autozoom.py \\\n",
        "--dblShift $dblShift \\\n",
        "--dblZoom $dblZoom \\\n",
        "--dblSteps \"0.0 8.0 400\" \\\n",
        "--in /content/3d-ken-burns/images/$pic_name \\\n",
        "--out ./results/autozoom.mp4"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0\n",
            "[MoviePy] >>>> Building video ./results/autozoom.mp4\n",
            "[MoviePy] Writing video ./results/autozoom.mp4\n",
            "100% 799/799 [00:47<00:00, 21.08it/s]\n",
            "[MoviePy] Done.\n",
            "[MoviePy] >>>> Video ready: ./results/autozoom.mp4 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vkoT7P4biRJe",
        "colab_type": "text"
      },
      "source": [
        "### 5. Play the generated video"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zL107cWdhk0t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "\n",
        "mp4 = open(\"./results/autozoom.mp4\",'rb').read()\n",
        "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "HTML('<video width=500 controls loop> <source src=\"%s\" type=\"video/mp4\"></video>' % data_url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOyjsg55NeI0",
        "colab_type": "text"
      },
      "source": [
        "### 6. Download the generated video\n",
        "(It can take a while)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vYU0HNyP2Yu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "files.download(\"./results/autozoom.mp4\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "72XSBhKkPKMA",
        "colab_type": "text"
      },
      "source": [
        " Did you enjoy? Buy me a coffe :)\n",
        " \n",
        "[![ko-fi](https://www.ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/Y8Y3VYYE)"
      ]
    }
  ]
}