ESRGAN_Old_Colab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ESRGAN Old Colab.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AhabbscienceStudioPak/ESRGAN-old-/blob/master/ESRGAN_Old_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6VnlRGZ9PiH",
        "colab_type": "text"
      },
      "source": [
        "# ESRGAN (old arch.) on Colab\n",
        "Official Github Repo: https://github.com/xinntao/ESRGAN <br>\n",
        "This notebook was curated by M. Ahabb (Ahabbscience Studio)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Os9gHotRC8y1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "112cec90-40b2-49da-bc9c-eec8389e7799"
      },
      "source": [
        "!nvidia-smi #recommended gpus are p100 and T4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tue Jun 30 16:56:07 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 450.36.06    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   46C    P8     9W /  70W |      0MiB / 15079MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bi7GziskW-p5",
        "colab_type": "text"
      },
      "source": [
        "Clone ESRGAN repo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-qvTH2nWSDN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "5ea24ed0-1a45-46d2-c584-83eeae30df47"
      },
      "source": [
        "!git clone https://github.com/AhabbscienceStudioPak/ESRGAN-old-"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'ESRGAN-old-'...\n",
            "remote: Enumerating objects: 39, done.\u001b[K\n",
            "remote: Counting objects: 100% (39/39), done.\u001b[K\n",
            "remote: Compressing objects: 100% (36/36), done.\u001b[K\n",
            "remote: Total 39 (delta 2), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (39/39), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVx7tHDE8HnH",
        "colab_type": "text"
      },
      "source": [
        "This is optional if you want to link your google drive to the notebook to add files or pretrained models of your choice from your google drive. A list of Pre-trained models can be found here: https://upscale.wiki/wiki/Model_Database\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mojoepFQ8Gaf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "a84aee8f-5388-4f7b-9665-517dc0927093"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lf5gDnAI8cdf",
        "colab_type": "text"
      },
      "source": [
        "(Optional) Add Files via youtube_dl"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZ2Sn97Y81Ww",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install youtube_dl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hiGc2_eL8nrP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir -p /content/ESRGAN-old-/video_input\n",
        "!youtube-dl -f 18 -o \"/content/ESRGAN/video_input/video.mp4\" https://www.youtube.com/watch?v=ljwTaMfORzs "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kiE9gKYvZMqN",
        "colab_type": "text"
      },
      "source": [
        "Run this cell to upload images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZuFBZncXRy1",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "731445ad-b435-4e49-9869-dca0b6ab9a31"
      },
      "source": [
        "!mkdir -p /content/ESRGAN-old-/LR\n",
        "%cd /content/ESRGAN-old-/LR\n",
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload() \n",
        "for filename in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with {length} bytes'.format(name=filename, length=len(uploaded[filename])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/ESRGAN-old-/LR\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-4f28cdfe-db91-460f-9440-ebd47e18d8c1\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-4f28cdfe-db91-460f-9440-ebd47e18d8c1\">\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 animetest.jpg to animetest.jpg\n",
            "User uploaded file \"animetest.jpg\" with 28193 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-pVBNJJ5g7_",
        "colab_type": "text"
      },
      "source": [
        "Run this cell if you want to upload your video. Make sure your video filename contains no spaces, e.g, \"my_video\" - not \"my video\". And upload one video per operation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fgBfLuwYd8F",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7CgpmdW5jdGlvbiBfdXBsb2FkRmlsZXMoaW5wdXRJZCwgb3V0cHV0SWQpIHsKICBjb25zdCBzdGVwcyA9IHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCk7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICAvLyBDYWNoZSBzdGVwcyBvbiB0aGUgb3V0cHV0RWxlbWVudCB0byBtYWtlIGl0IGF2YWlsYWJsZSBmb3IgdGhlIG5leHQgY2FsbAogIC8vIHRvIHVwbG9hZEZpbGVzQ29udGludWUgZnJvbSBQeXRob24uCiAgb3V0cHV0RWxlbWVudC5zdGVwcyA9IHN0ZXBzOwoKICByZXR1cm4gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpOwp9CgovLyBUaGlzIGlzIHJvdWdobHkgYW4gYXN5bmMgZ2VuZXJhdG9yIChub3Qgc3VwcG9ydGVkIGluIHRoZSBicm93c2VyIHlldCksCi8vIHdoZXJlIHRoZXJlIGFyZSBtdWx0aXBsZSBhc3luY2hyb25vdXMgc3RlcHMgYW5kIHRoZSBQeXRob24gc2lkZSBpcyBnb2luZwovLyB0byBwb2xsIGZvciBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcC4KLy8gVGhpcyB1c2VzIGEgUHJvbWlzZSB0byBibG9jayB0aGUgcHl0aG9uIHNpZGUgb24gY29tcGxldGlvbiBvZiBlYWNoIHN0ZXAsCi8vIHRoZW4gcGFzc2VzIHRoZSByZXN1bHQgb2YgdGhlIHByZXZpb3VzIHN0ZXAgYXMgdGhlIGlucHV0IHRvIHRoZSBuZXh0IHN0ZXAuCmZ1bmN0aW9uIF91cGxvYWRGaWxlc0NvbnRpbnVlKG91dHB1dElkKSB7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICBjb25zdCBzdGVwcyA9IG91dHB1dEVsZW1lbnQuc3RlcHM7CgogIGNvbnN0IG5leHQgPSBzdGVwcy5uZXh0KG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSk7CiAgcmV0dXJuIFByb21pc2UucmVzb2x2ZShuZXh0LnZhbHVlLnByb21pc2UpLnRoZW4oKHZhbHVlKSA9PiB7CiAgICAvLyBDYWNoZSB0aGUgbGFzdCBwcm9taXNlIHZhbHVlIHRvIG1ha2UgaXQgYXZhaWxhYmxlIHRvIHRoZSBuZXh0CiAgICAvLyBzdGVwIG9mIHRoZSBnZW5lcmF0b3IuCiAgICBvdXRwdXRFbGVtZW50Lmxhc3RQcm9taXNlVmFsdWUgPSB2YWx1ZTsKICAgIHJldHVybiBuZXh0LnZhbHVlLnJlc3BvbnNlOwogIH0pOwp9CgovKioKICogR2VuZXJhdG9yIGZ1bmN0aW9uIHdoaWNoIGlzIGNhbGxlZCBiZXR3ZWVuIGVhY2ggYXN5bmMgc3RlcCBvZiB0aGUgdXBsb2FkCiAqIHByb2Nlc3MuCiAqIEBwYXJhbSB7c3RyaW5nfSBpbnB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIGlucHV0IGZpbGUgcGlja2VyIGVsZW1lbnQuCiAqIEBwYXJhbSB7c3RyaW5nfSBvdXRwdXRJZCBFbGVtZW50IElEIG9mIHRoZSBvdXRwdXQgZGlzcGxheS4KICogQHJldHVybiB7IUl0ZXJhYmxlPCFPYmplY3Q+fSBJdGVyYWJsZSBvZiBuZXh0IHN0ZXBzLgogKi8KZnVuY3Rpb24qIHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IGlucHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKGlucHV0SWQpOwogIGlucHV0RWxlbWVudC5kaXNhYmxlZCA9IGZhbHNlOwoKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIG91dHB1dEVsZW1lbnQuaW5uZXJIVE1MID0gJyc7CgogIGNvbnN0IHBpY2tlZFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgaW5wdXRFbGVtZW50LmFkZEV2ZW50TGlzdGVuZXIoJ2NoYW5nZScsIChlKSA9PiB7CiAgICAgIHJlc29sdmUoZS50YXJnZXQuZmlsZXMpOwogICAgfSk7CiAgfSk7CgogIGNvbnN0IGNhbmNlbCA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2J1dHRvbicpOwogIGlucHV0RWxlbWVudC5wYXJlbnRFbGVtZW50LmFwcGVuZENoaWxkKGNhbmNlbCk7CiAgY2FuY2VsLnRleHRDb250ZW50ID0gJ0NhbmNlbCB1cGxvYWQnOwogIGNvbnN0IGNhbmNlbFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgY2FuY2VsLm9uY2xpY2sgPSAoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9OwogIH0pOwoKICAvLyBXYWl0IGZvciB0aGUgdXNlciB0byBwaWNrIHRoZSBmaWxlcy4KICBjb25zdCBmaWxlcyA9IHlpZWxkIHsKICAgIHByb21pc2U6IFByb21pc2UucmFjZShbcGlja2VkUHJvbWlzZSwgY2FuY2VsUHJvbWlzZV0pLAogICAgcmVzcG9uc2U6IHsKICAgICAgYWN0aW9uOiAnc3RhcnRpbmcnLAogICAgfQogIH07CgogIGNhbmNlbC5yZW1vdmUoKTsKCiAgLy8gRGlzYWJsZSB0aGUgaW5wdXQgZWxlbWVudCBzaW5jZSBmdXJ0aGVyIHBpY2tzIGFyZSBub3QgYWxsb3dlZC4KICBpbnB1dEVsZW1lbnQuZGlzYWJsZWQgPSB0cnVlOwoKICBpZiAoIWZpbGVzKSB7CiAgICByZXR1cm4gewogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgICAgfQogICAgfTsKICB9CgogIGZvciAoY29uc3QgZmlsZSBvZiBmaWxlcykgewogICAgY29uc3QgbGkgPSBkb2N1bWVudC5jcmVhdGVFbGVtZW50KCdsaScpOwogICAgbGkuYXBwZW5kKHNwYW4oZmlsZS5uYW1lLCB7Zm9udFdlaWdodDogJ2JvbGQnfSkpOwogICAgbGkuYXBwZW5kKHNwYW4oCiAgICAgICAgYCgke2ZpbGUudHlwZSB8fCAnbi9hJ30pIC0gJHtmaWxlLnNpemV9IGJ5dGVzLCBgICsKICAgICAgICBgbGFzdCBtb2RpZmllZDogJHsKICAgICAgICAgICAgZmlsZS5sYXN0TW9kaWZpZWREYXRlID8gZmlsZS5sYXN0TW9kaWZpZWREYXRlLnRvTG9jYWxlRGF0ZVN0cmluZygpIDoKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgJ24vYSd9IC0gYCkpOwogICAgY29uc3QgcGVyY2VudCA9IHNwYW4oJzAlIGRvbmUnKTsKICAgIGxpLmFwcGVuZENoaWxkKHBlcmNlbnQpOwoKICAgIG91dHB1dEVsZW1lbnQuYXBwZW5kQ2hpbGQobGkpOwoKICAgIGNvbnN0IGZpbGVEYXRhUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICAgIGNvbnN0IHJlYWRlciA9IG5ldyBGaWxlUmVhZGVyKCk7CiAgICAgIHJlYWRlci5vbmxvYWQgPSAoZSkgPT4gewogICAgICAgIHJlc29sdmUoZS50YXJnZXQucmVzdWx0KTsKICAgICAgfTsKICAgICAgcmVhZGVyLnJlYWRBc0FycmF5QnVmZmVyKGZpbGUpOwogICAgfSk7CiAgICAvLyBXYWl0IGZvciB0aGUgZGF0YSB0byBiZSByZWFkeS4KICAgIGxldCBmaWxlRGF0YSA9IHlpZWxkIHsKICAgICAgcHJvbWlzZTogZmlsZURhdGFQcm9taXNlLAogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbnRpbnVlJywKICAgICAgfQogICAgfTsKCiAgICAvLyBVc2UgYSBjaHVua2VkIHNlbmRpbmcgdG8gYXZvaWQgbWVzc2FnZSBzaXplIGxpbWl0cy4gU2VlIGIvNjIxMTU2NjAuCiAgICBsZXQgcG9zaXRpb24gPSAwOwogICAgd2hpbGUgKHBvc2l0aW9uIDwgZmlsZURhdGEuYnl0ZUxlbmd0aCkgewogICAgICBjb25zdCBsZW5ndGggPSBNYXRoLm1pbihmaWxlRGF0YS5ieXRlTGVuZ3RoIC0gcG9zaXRpb24sIE1BWF9QQVlMT0FEX1NJWkUpOwogICAgICBjb25zdCBjaHVuayA9IG5ldyBVaW50OEFycmF5KGZpbGVEYXRhLCBwb3NpdGlvbiwgbGVuZ3RoKTsKICAgICAgcG9zaXRpb24gKz0gbGVuZ3RoOwoKICAgICAgY29uc3QgYmFzZTY0ID0gYnRvYShTdHJpbmcuZnJvbUNoYXJDb2RlLmFwcGx5KG51bGwsIGNodW5rKSk7CiAgICAgIHlpZWxkIHsKICAgICAgICByZXNwb25zZTogewogICAgICAgICAgYWN0aW9uOiAnYXBwZW5kJywKICAgICAgICAgIGZpbGU6IGZpbGUubmFtZSwKICAgICAgICAgIGRhdGE6IGJhc2U2NCwKICAgICAgICB9LAogICAgICB9OwogICAgICBwZXJjZW50LnRleHRDb250ZW50ID0KICAgICAgICAgIGAke01hdGgucm91bmQoKHBvc2l0aW9uIC8gZmlsZURhdGEuYnl0ZUxlbmd0aCkgKiAxMDApfSUgZG9uZWA7CiAgICB9CiAgfQoKICAvLyBBbGwgZG9uZS4KICB5aWVsZCB7CiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICB9CiAgfTsKfQoKc2NvcGUuZ29vZ2xlID0gc2NvcGUuZ29vZ2xlIHx8IHt9OwpzY29wZS5nb29nbGUuY29sYWIgPSBzY29wZS5nb29nbGUuY29sYWIgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYi5fZmlsZXMgPSB7CiAgX3VwbG9hZEZpbGVzLAogIF91cGxvYWRGaWxlc0NvbnRpbnVlLAp9Owp9KShzZWxmKTsK",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "8edeeb9e-8071-42e9-d136-5476f5fdb00b"
      },
      "source": [
        "!mkdir -p /content/ESRGAN-old-/video_input\n",
        "!mkdir -p /content/ESRGAN-old-/LR\n",
        "%cd /content/ESRGAN-old-/video_input\n",
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload() \n",
        "for filename in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with {length} bytes'.format(name=filename, length=len(uploaded[filename])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/ESRGAN-old-/video_input\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7865e9cc-c4cb-4ef0-aba4-e0f402a9d0a2\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7865e9cc-c4cb-4ef0-aba4-e0f402a9d0a2\">\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 orange.mp4 to orange.mp4\n",
            "User uploaded file \"orange.mp4\" with 3687591 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmIXu-DC5RyF",
        "colab_type": "text"
      },
      "source": [
        "Run this cell to convert your video to individual frames and save them to 'LR' folder."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-i7budAKZLgw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3b749adc-56c3-483b-b1dd-30a9d3cccb47"
      },
      "source": [
        "%cd /content/ESRGAN-old-/LR\n",
        "!ffmpeg  -i /content/ESRGAN-old-/video_input/* %04d.png"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/ESRGAN-old-/LR\n",
            "ffmpeg version 3.4.6-0ubuntu0.18.04.1 Copyright (c) 2000-2019 the FFmpeg developers\n",
            "  built with gcc 7 (Ubuntu 7.3.0-16ubuntu3)\n",
            "  configuration: --prefix=/usr --extra-version=0ubuntu0.18.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
            "  libavutil      55. 78.100 / 55. 78.100\n",
            "  libavcodec     57.107.100 / 57.107.100\n",
            "  libavformat    57. 83.100 / 57. 83.100\n",
            "  libavdevice    57. 10.100 / 57. 10.100\n",
            "  libavfilter     6.107.100 /  6.107.100\n",
            "  libavresample   3.  7.  0 /  3.  7.  0\n",
            "  libswscale      4.  8.100 /  4.  8.100\n",
            "  libswresample   2.  9.100 /  2.  9.100\n",
            "  libpostproc    54.  7.100 / 54.  7.100\n",
            "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/content/ESRGAN-old-/video_input/orange.mp4':\n",
            "  Metadata:\n",
            "    major_brand     : isom\n",
            "    minor_version   : 512\n",
            "    compatible_brands: isomiso2avc1mp41\n",
            "    encoder         : Lavf58.12.100\n",
            "  Duration: 00:00:16.07, start: 0.000000, bitrate: 1835 kb/s\n",
            "    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 640x360 [SAR 1:1 DAR 16:9], 1576 kb/s, 25.06 fps, 25 tbr, 12800 tbn, 50 tbc (default)\n",
            "    Metadata:\n",
            "      handler_name    : VideoHandler\n",
            "    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 254 kb/s (default)\n",
            "    Metadata:\n",
            "      handler_name    : SoundHandler\n",
            "File '/content/ESRGAN-old-/video_input/sharif3.mp4' already exists. Overwrite ? [y/N] y\n",
            "Stream mapping:\n",
            "  Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))\n",
            "  Stream #0:1 -> #0:1 (aac (native) -> aac (native))\n",
            "  Stream #0:0 -> #1:0 (h264 (native) -> png (native))\n",
            "Press [q] to stop, [?] for help\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0musing SAR=1/1\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0musing cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mprofile High, level 3.0\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0m264 - core 152 r2854 e9a5903 - H.264/MPEG-4 AVC codec - Copyleft 2003-2017 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=3 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n",
            "Output #0, mp4, to '/content/ESRGAN-old-/video_input/sharif3.mp4':\n",
            "  Metadata:\n",
            "    major_brand     : isom\n",
            "    minor_version   : 512\n",
            "    compatible_brands: isomiso2avc1mp41\n",
            "    encoder         : Lavf57.83.100\n",
            "    Stream #0:0(und): Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 640x360 [SAR 1:1 DAR 16:9], q=-1--1, 25 fps, 12800 tbn, 25 tbc (default)\n",
            "    Metadata:\n",
            "      handler_name    : VideoHandler\n",
            "      encoder         : Lavc57.107.100 libx264\n",
            "    Side data:\n",
            "      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1\n",
            "    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)\n",
            "    Metadata:\n",
            "      handler_name    : SoundHandler\n",
            "      encoder         : Lavc57.107.100 aac\n",
            "Output #1, image2, to '%04d.png':\n",
            "  Metadata:\n",
            "    major_brand     : isom\n",
            "    minor_version   : 512\n",
            "    compatible_brands: isomiso2avc1mp41\n",
            "    encoder         : Lavf57.83.100\n",
            "    Stream #1:0(und): Video: png, rgb24, 640x360 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 25 fps, 25 tbn, 25 tbc (default)\n",
            "    Metadata:\n",
            "      handler_name    : VideoHandler\n",
            "      encoder         : Lavc57.107.100 png\n",
            "frame=  401 fps= 26 q=-1.0 Lq=-0.0 size=    1429kB time=00:00:16.04 bitrate= 729.4kbits/s speed=1.02x    \n",
            "video:132837kB audio:251kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mframe I:2     Avg QP:23.07  size: 19184\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mframe P:129   Avg QP:23.07  size:  6100\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mframe B:270   Avg QP:26.67  size:  1352\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mconsecutive B-frames:  3.5% 18.0%  6.7% 71.8%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mmb I  I16..4:  9.0% 52.3% 38.6%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mmb P  I16..4:  4.6% 10.8%  2.6%  P16..4: 35.1% 19.1%  9.1%  0.0%  0.0%    skip:18.7%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mmb B  I16..4:  0.6%  1.3%  0.3%  B16..8: 32.3%  5.8%  1.0%  direct: 2.1%  skip:56.6%  L0:36.5% L1:50.0% BI:13.5%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0m8x8 transform intra:59.2% inter:71.1%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mcoded y,uvDC,uvAC intra: 47.1% 67.4% 24.3% inter: 14.4% 16.8% 1.2%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mi16 v,h,dc,p: 27% 49% 14% 11%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mi8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 26% 30% 23%  2%  3%  3%  6%  2%  5%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mi4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 32% 31% 11%  3%  5%  5%  6%  3%  5%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mi8c dc,h,v,p: 40% 34% 22%  3%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mWeighted P-Frames: Y:28.7% UV:22.5%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mref P L0: 67.2% 17.3% 11.9%  3.3%  0.2%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mref B L0: 94.0%  5.0%  1.0%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mref B L1: 98.0%  2.0%\n",
            "\u001b[1;36m[libx264 @ 0x55b671162800] \u001b[0mkb/s:593.73\n",
            "\u001b[1;36m[aac @ 0x55b671163700] \u001b[0mQavg: 329.188\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8dvwYOp-ArK",
        "colab_type": "text"
      },
      "source": [
        "Download pretrained models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsO_9uQelXoG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "96d3edf1-c17c-4a30-beae-922b906b242f"
      },
      "source": [
        "\n",
        "import gdown\n",
        "print(\"Downloading pretrained models\")\n",
        "\n",
        "output1 = '/content/ESRGAN-old-/models/RRDB_ESRGAN_x4.pth'\n",
        "output2 = '/content/ESRGAN-old-/models/RRDB_PSNR_x4.pth'\n",
        "output3 = '/content/ESRGAN-old-/models/deviantPixelHD_250000.pth'\n",
        "output4 = '/content/ESRGAN-old-/models/SmoothRealism.pth'\n",
        "output5 = '/content/ESRGAN-old-/models/Manga109Attempt.pth'\n",
        "print ('Downloading model RRDB_ESRGAN_x4.pth')\n",
        "gdown.download('https://drive.google.com/uc?id=1MJFgqXJrMkPdKtiuy7C6xfsU1QIbXEb-', output1, quiet=True)\n",
        "print ('Downloading model RRDB_PSNR_x4.pth') \n",
        "gdown.download('https://drive.google.com/uc?id=1mSJ6Z40weL-dnPvi390xDd3uZBCFMeqr', output2, quiet=True)\n",
        "print ('Downloading model deviantPixelHD_250000.pth by Raulsangonzalo')\n",
        "gdown.download('https://drive.google.com/uc?id=114yFJKeYCcr6st7aNNo9FJ8wDbEzLpdz', output3, quiet=True)\n",
        "print ('Downloading model SmoothRealism.pth by Joey') \n",
        "gdown.download('https://drive.google.com/uc?id=1Uc9RUc2YpZKpPoGQeGxNUb3Ro-U720cH', output4, quiet=True)\n",
        "print ('Downloading model Manga109Attempt.pth by Kingdomakrillic') \n",
        "gdown.download('https://drive.google.com/uc?id=1avmbwa-5dejkbakWI2WtcpmiRabs-yem', output5, quiet=True) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading pretrained models\n",
            "Downloading model RRDB_ESRGAN_x4.pth\n",
            "Downloading model RRDB_PSNR_x4.pth\n",
            "Downloading model deviantPixelHD_250000.pth by Raulsangonzalo\n",
            "Downloading model SmoothRealism.pth by Joey\n",
            "Downloading model Manga109Attempt.pth by Kingdomakrillic\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'/content/ESRGAN-old-/models/Manga109Attempt.pth'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7X7Y5t0Qxug7",
        "colab_type": "text"
      },
      "source": [
        "Download more pre-trained models if you want"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lj3nqHcS6Q3O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "1f64979e-2dcb-4d33-e404-d355fedc057c"
      },
      "source": [
        "!pip install requests\n",
        "%cd /content/ESRGAN-old-/models\n",
        "import requests\n",
        "\n",
        "\n",
        "#print ('Downloading model Lady0101.pth by DinJerr') \n",
        "#url = 'https://1drv.ms/u/s!Aip-EMByJHY200rP1TW3aAdb2dkZ?e=enlvAO'\n",
        "#r = requests.get(url, allow_redirects=True)\n",
        "#open('Lady0101.pth', 'wb').write(r.content)\n",
        "\n",
        "print ('Downloading model Fatality.pth by Twittman') \n",
        "url1 = 'https://de-next.owncube.com/index.php/s/gPjswdm6gCegQdz/download'\n",
        "r1 = requests.get(url1, allow_redirects=True)\n",
        "open('Fatality.pth', 'wb').write(r1.content)\n",
        "\n",
        "print ('Downloading model FatalPixels.pth by Twittman')\n",
        "url2 = 'https://de-next.owncube.com/index.php/s/ECsEHxdoYCnFsZA/download'\n",
        "r2 = requests.get(url2, allow_redirects=True)\n",
        "open('FatalPixels.pth', 'wb').write(r2.content)\n",
        "\n",
        "print ('Downloading model Faces.pth by Twittman')\n",
        "url3 = 'https://de-next.owncube.com/index.php/s/YbAqNMMTtxrajF6/download'\n",
        "r3 = requests.get(url2, allow_redirects=True)\n",
        "open('Faces.pth', 'wb').write(r3.content)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests) (2.9)\n",
            "/content/ESRGAN-old-/models\n",
            "Downloading model Fatality.pth by Twittman\n",
            "Downloading model FatalPixels.pth by Twittman\n",
            "Downloading model Faces.pth by Twittman\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66958607"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8taWI7VI43A7",
        "colab_type": "text"
      },
      "source": [
        "Configure the code and run the cell to upscale images/video frames"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rqh162AbbsjB",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "87e42662-85de-41f7-ae9f-0f00070b6e9c"
      },
      "source": [
        "#@title Upscale images/video frames\n",
        "%cd /content/ESRGAN-old-\n",
        "\n",
        "import sys\n",
        "import os.path\n",
        "import glob\n",
        "import cv2\n",
        "import numpy as np\n",
        "import torch\n",
        "import architecture as arch\n",
        "import requests\n",
        "import imageio\n",
        "import requests\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from google.colab import files\n",
        "import sys\n",
        "import architecture as arch\n",
        "\n",
        "Choose_device = \"cuda\"  #@param [\"cuda\",\"cpu\"]\n",
        "\n",
        "model_path =  \"/content/ESRGAN-old-/models/RRDB_ESRGAN_x4.pth\" #@param {type:\"string\"} \n",
        "device = torch.device(Choose_device)  # if you want to run on CPU, change 'cuda' -> cpu\n",
        "\n",
        "\n",
        "test_img_folder = 'LR/*'\n",
        "\n",
        "model = arch.RRDB_Net(3, 3, 64, 23, gc=32, upscale=4, norm_type=None, act_type='leakyrelu', \\\n",
        "                        mode='CNA', res_scale=1, upsample_mode='upconv')\n",
        "model.load_state_dict(torch.load(model_path), strict=True)\n",
        "model.eval()\n",
        "for k, v in model.named_parameters():\n",
        "    v.requires_grad = False\n",
        "model = model.to(device)\n",
        "\n",
        "print('Model path {:s}. \\nTesting...'.format(model_path))\n",
        "\n",
        "idx = 0\n",
        "for path in glob.glob(test_img_folder):\n",
        "    idx += 1\n",
        "    base = os.path.splitext(os.path.basename(path))[0]\n",
        "    print(idx, base)\n",
        "    # read image\n",
        "    img = cv2.imread(path, cv2.IMREAD_COLOR)\n",
        "    img = img * 1.0 / 255\n",
        "    img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()\n",
        "    img_LR = img.unsqueeze(0)\n",
        "    img_LR = img_LR.to(device)\n",
        "\n",
        "    output = model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy()\n",
        "    output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))\n",
        "    output = (output * 255.0).round()\n",
        "    cv2.imwrite('results/{:s}.png'.format(base), output)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/ESRGAN-old-\n",
            "Model path /content/ESRGAN-old-/models/RRDB_ESRGAN_x4.pth. \n",
            "Testing...\n",
            "1 animetest (2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6MfrTPk68a4",
        "colab_type": "text"
      },
      "source": [
        "Run this cell to encode your results into a video."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2IZXfzLcYJI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "f9599c03-bc27-4803-afdb-00d7e42030a7"
      },
      "source": [
        "!ffmpeg -f image2 -framerate 25 -i /content/ESRGAN-old-/results/%04d.png -c:v h264_nvenc -preset slow -qp 18 -pix_fmt yuv420p output.mp4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ffmpeg version 3.4.6-0ubuntu0.18.04.1 Copyright (c) 2000-2019 the FFmpeg developers\n",
            "  built with gcc 7 (Ubuntu 7.3.0-16ubuntu3)\n",
            "  configuration: --prefix=/usr --extra-version=0ubuntu0.18.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
            "  libavutil      55. 78.100 / 55. 78.100\n",
            "  libavcodec     57.107.100 / 57.107.100\n",
            "  libavformat    57. 83.100 / 57. 83.100\n",
            "  libavdevice    57. 10.100 / 57. 10.100\n",
            "  libavfilter     6.107.100 /  6.107.100\n",
            "  libavresample   3.  7.  0 /  3.  7.  0\n",
            "  libswscale      4.  8.100 /  4.  8.100\n",
            "  libswresample   2.  9.100 /  2.  9.100\n",
            "  libpostproc    54.  7.100 / 54.  7.100\n",
            "Input #0, image2, from '/content/ESRGAN-old-/results/%04d.png':\n",
            "  Duration: 00:00:00.04, start: 0.000000, bitrate: N/A\n",
            "    Stream #0:0: Video: png, rgb24(pc), 2560x1440, 25 tbr, 25 tbn, 25 tbc\n",
            "Stream mapping:\n",
            "  Stream #0:0 -> #0:0 (png (native) -> h264 (h264_nvenc))\n",
            "Press [q] to stop, [?] for help\n",
            "Output #0, mp4, to 'output.mp4':\n",
            "  Metadata:\n",
            "    encoder         : Lavf57.83.100\n",
            "    Stream #0:0: Video: h264 (h264_nvenc) (Main) (avc1 / 0x31637661), yuv420p, 2560x1440, q=-1--1, 2000 kb/s, 25 fps, 12800 tbn, 25 tbc\n",
            "    Metadata:\n",
            "      encoder         : Lavc57.107.100 h264_nvenc\n",
            "    Side data:\n",
            "      cpb: bitrate max/min/avg: 0/0/2000000 buffer size: 4000000 vbv_delay: -1\n",
            "frame=    1 fps=0.0 q=13.0 Lsize=     558kB time=00:00:00.00 bitrate=58565641.0kbits/s speed=0.000276x    \n",
            "video:557kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.141352%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iM6hHGvf7jhY",
        "colab_type": "text"
      },
      "source": [
        "Make a zip file of your results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrkFxYPH59rl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "6a4ce8de-a3f9-440e-af75-bfa1c0797c9e"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "!zip -r results.zip /content/ESRGAN-old-/results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  adding: content/ESRGAN-old-/results/ (stored 0%)\n",
            "  adding: content/ESRGAN-old-/results/animetest.png (deflated 4%)\n",
            "  adding: content/ESRGAN-old-/results/baboon_ESRGAN.png (deflated 0%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wu7WBi2E7w_O",
        "colab_type": "text"
      },
      "source": [
        "Download Zip file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sz6lLjTV6ZaR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f894e735-5c26-4c71-b0d0-4de2b65aec86"
      },
      "source": [
        "files.download('results.zip')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_9bc3ff52-504d-4ad9-bf63-6bc994e6907f\", \"results.zip\", 1887061)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OqbcvpuOeaz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import shutil\n",
        "\n",
        "#move zip file to google drive\n",
        "shutil.move(\"./results.zip\", \"/content/gdrive/My Drive/results.zip\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}