Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN56Gm8o8OOI9UgBeQjFutj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/suhani1209/Geeksman_AISquad_Project1_SUHANI-MITTAL-/blob/main/Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHRvB7QkWwij"
      },
      "source": [
        "### **In this notebook, prediction part of the project is covered.**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ualgpRHLsV7"
      },
      "source": [
        "***Firstly, we will start by setting up kaggle***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GaJFGijCLug0"
      },
      "source": [
        "***Step 1***\n",
        "\n",
        "Upload kaggle.json file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaVZ-urfl0Wo",
        "outputId": "8b39341f-dd7f-4b2e-d46e-ae2b08eb019f",
        "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": 73
        }
      },
      "source": [
        "from google.colab import files\n",
        "my_file=files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2ede2a61-67d3-48ad-a165-28e4840d4d4c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2ede2a61-67d3-48ad-a165-28e4840d4d4c\">\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 kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R0li-v8GLzA6"
      },
      "source": [
        "***Step 2***\n",
        "\n",
        "Setting up environment variables so that kaggle api can work properly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1aHcmLFl3EA"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "cred = pd.read_json(\"kaggle.json\",lines=True)\n",
        "os.environ['KAGGLE_USERNAME'] = cred.iloc[0][0]\n",
        "os.environ['KAGGLE_KEY'] = cred.iloc[0][1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ms7TFwzOL7yM"
      },
      "source": [
        "***Step 3***\n",
        "\n",
        "Downloading the required dataset, since we require it for prediction.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Ld5ZrRvl36o",
        "outputId": "8eb90240-7bd7-49e8-b43c-762b2c9ce6c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "!kaggle datasets download -d suhanimittal12/mobile-gallery-image-classification"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading mobile-gallery-image-classification.zip to /content\n",
            "100% 456M/456M [00:03<00:00, 125MB/s]\n",
            "100% 456M/456M [00:03<00:00, 125MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzuVbv6rMOx_"
      },
      "source": [
        "***Step 4***\n",
        "\n",
        "Unzip downloaded data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fk_9s_xUl7A7",
        "outputId": "21efa921-e203-4d2a-ae56-fb03a62eb22d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!unzip \"*.zip\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  mobile-gallery-image-classification.zip\n",
            "  inflating: test/cars_008.jpg       \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jUhuur-qLClp"
      },
      "source": [
        "***Mounting Google Drive to access the previously saved results..***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_t0afT5cLQ5D",
        "outputId": "7fd7cee3-6dea-4d14-9ba6-d0b54bea953e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "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&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&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzUFdmyBKF4d"
      },
      "source": [
        "***And here the concept of prediction starts ***\n",
        "\n",
        "***Two things are required to get our model to make predictions for us.***\n",
        "\n",
        "1. fastai library import\n",
        "2. Our saved model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGCxdeL-NFn2"
      },
      "source": [
        "Just importing required **libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmiunX8mKPOC"
      },
      "source": [
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline\n",
        "from fastai.vision import *\n",
        "from fastai.metrics import error_rate\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pj_J_q2RMjj8"
      },
      "source": [
        "***Saving our directory path to a variable so that we don\"t have to write so long everytime to refer to the directory***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2vGOSrlLLke"
      },
      "source": [
        "my_dir=\"/content/drive/My Drive/dl_projects/mobile_gallery_img_classification\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPXRtBywlcuh"
      },
      "source": [
        "predictor = load_learner(my_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7Tn3xYUJu17"
      },
      "source": [
        "**Making prediction function which accepts a test_image address as input and will return result for us.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_c7UYjUUlgz3"
      },
      "source": [
        "def my_predictor(im):\n",
        "  test_img =open_image(im)\n",
        "  pred_class,pred_idx,outputs=predictor.predict(test_img)\n",
        "  return pred_class"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0y8pkC3EJuFA"
      },
      "source": [
        "***Predicting for a single image***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrlVihfTlnbO"
      },
      "source": [
        "all_test_images=os.listdir(\"test\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eN0_4aJfmRr2",
        "outputId": "4cc5159c-20ca-4f1f-b72e-e3a4e8298f7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        }
      },
      "source": [
        "idx=0\n",
        "print(my_predictor(\"test/\"+all_test_images[idx]))\n",
        "img = open_image(\"test/\"+all_test_images[idx])\n",
        "img.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selfies\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nN8-vdoTIwLL"
      },
      "source": [
        "## **Making** **Video** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNPe60Xtm_X_"
      },
      "source": [
        "import cv2\n",
        "test_files=os.listdir(\"test\")\n",
        "os.makedirs(\"test_result\",exist_ok=True)\n",
        "i=0\n",
        "for test_file in test_files:\n",
        "  img=cv2.imread(\"test/\"+test_file)\n",
        "  cv2.imwrite(\"temp.jpg\",img)\n",
        "  ans_text=my_predictor(\"temp.jpg\")\n",
        "  font = cv2.FONT_HERSHEY_SIMPLEX \n",
        "  i+=1\n",
        "  org = (0, 50) \n",
        "  fontScale = 2\n",
        "  color = (255, 0, 0) \n",
        "  thickness = 4\n",
        "  img = cv2.putText(img, str(ans_text), org, font,fontScale, color, thickness, cv2.LINE_AA) \n",
        "  cv2.imwrite(\"test_result/\"+str(i)+\".jpg\", img)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNX1kpHzmZ1a",
        "outputId": "feb24302-3f36-41e6-f2ee-bdd336c945c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "%cd \"test_result\"\n",
        "!ffmpeg -framerate 1 -pattern_type glob -i '*.jpg' \\-c:v libx264 -r 1 -pix_fmt yuv420p ../final_output_video.mp4\n",
        "%cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/test_result\n",
            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
            "  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --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 '*.jpg':\n",
            "  Duration: 00:02:07.00, start: 0.000000, bitrate: N/A\n",
            "    Stream #0:0: Video: mjpeg, yuvj420p(pc, bt470bg/unknown/unknown), 992x558 [SAR 1:1 DAR 16:9], 1 fps, 1 tbr, 1 tbn, 1 tbc\n",
            "Stream mapping:\n",
            "  Stream #0:0 -> #0:0 (mjpeg (native) -> h264 (libx264))\n",
            "Press [q] to stop, [?] for help\n",
            "\u001b[1;34m[swscaler @ 0x55af4ca44000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "\u001b[0m\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0musing SAR=1/1\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0musing cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mprofile High, level 3.1\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \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=1 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 '../final_output_video.mp4':\n",
            "  Metadata:\n",
            "    encoder         : Lavf57.83.100\n",
            "    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 992x558 [SAR 1:1 DAR 16:9], q=-1--1, 1 fps, 16384 tbn, 1 tbc\n",
            "    Metadata:\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",
            "\u001b[1;34m[swscaler @ 0x55af4ca44000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 29 times\n",
            "\u001b[1;34m[swscaler @ 0x55af4ca44000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 5 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 11 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 4 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 3 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 3 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 4 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 4 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 3 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 4 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 4 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 4 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 4 times\n",
            "\u001b[1;34m[swscaler @ 0x55af511f0000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "\u001b[0m\u001b[1;34m[swscaler @ 0x55af4c8e2000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 3 times\n",
            "\u001b[1;34m[swscaler @ 0x55af4c8e2000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 3 times\n",
            "\u001b[1;34m[swscaler @ 0x55af4c8e2000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 4 times\n",
            "\u001b[1;34m[swscaler @ 0x55af4c8e2000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 3 times\n",
            "\u001b[1;34m[swscaler @ 0x55af4c8e2000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 3 times\n",
            "\u001b[1;34m[swscaler @ 0x55af4c8e2000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 3 times\n",
            "\u001b[1;34m[swscaler @ 0x55af4ca44000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
            "    Last message repeated 3 times\n",
            "frame=  127 fps=8.0 q=-1.0 Lsize=   11655kB time=00:02:04.00 bitrate= 770.0kbits/s speed=7.79x    \n",
            "video:11654kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.012880%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mframe I:2     Avg QP:12.57  size: 65278\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mframe P:125   Avg QP:16.81  size: 94417\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mmb I  I16..4: 16.3% 42.4% 41.3%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mmb P  I16..4:  7.4% 54.2% 36.3%  P16..4:  1.2%  0.3%  0.3%  0.0%  0.0%    skip: 0.4%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0m8x8 transform intra:55.1% inter:40.1%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mcoded y,uvDC,uvAC intra: 84.8% 78.0% 68.6% inter: 21.1% 23.5% 14.5%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mi16 v,h,dc,p: 47% 37%  4% 13%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mi8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 16% 29% 12%  5%  6%  5%  9%  6% 12%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mi4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 17% 33%  8%  5%  7%  5%  9%  5% 10%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mi8c dc,h,v,p: 43% 34% 13%  9%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mWeighted P-Frames: Y:8.8% UV:8.8%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mref P L0:  9.7%  1.4% 48.6% 39.7%  0.5%\n",
            "\u001b[1;36m[libx264 @ 0x55af4c73fe00] \u001b[0mkb/s:751.67\n",
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1w0eCJ96nH2i",
        "outputId": "ee8ce5ec-d48a-46b3-c134-e08778353bf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "!pip install -U kora\n",
        "from kora.drive import upload_public\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting kora\n",
            "  Downloading https://files.pythonhosted.org/packages/4d/f1/09dcba79d143c9645251c2f2b86c0123e05af28d3a382cbe247093e51525/kora-0.5.5-py3-none-any.whl\n",
            "Installing collected packages: kora\n",
            "Successfully installed kora-0.5.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LP_GjTGlnPeM"
      },
      "source": [
        "url = upload_public('final_output_video.mp4')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYkBdMaNobnQ",
        "outputId": "cf46f8f6-5e30-48dc-d501-176e40fdf777",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "\n",
        "from IPython.display import HTML\n",
        "HTML(f\"\"\"<video src={url} width=500 controls/>\"\"\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<video src=https://drive.google.com/uc?id=1tDM45TC1qbWYBHoi5Ou7WAjis8OJLVYH width=500 controls/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XP9Cz5npCsL",
        "outputId": "cee62be0-d33c-488f-90cb-e656f75070cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "url"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://drive.google.com/uc?id=1tDM45TC1qbWYBHoi5Ou7WAjis8OJLVYH'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    }
  ]
}