{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "663IVxfrpIAb" }, "source": [ "#◢ DeOldify - Colorize your own videos!\n", "\n", "##Use this Colab notebook to colorize black & white videos in four simple steps.\n", "1. Specify media URL - YouTube, Twitter, Imgur, etc.\n", "2. Select 'Render Factor'. Generally, older and lower quality videos will render bettter with lower render factors (14-21 range) while higher quality videos will do better on higher render factors.\n", "3. Run DeOldify to extract single images from your video or gif. Behind the scenes, the code does the following:\n", " * Extracts single images from the specified media file.\n", " * Processes the images with [DeOldify](https://github.com/jantic/DeOldify).\n", " * Rebuilds the video from **colorized** images.\n", "4. Download the video to your device to view! \n", "\n", "_FYI: This notebook is intended as a tool to colorize gifs and short videos, if you are trying to convert longer video you may hit the limit on processing space. Running the Jupyter notebook on your own machine is recommended (and faster) for larger video sizes._\n", "\n", "---\n", "\n", "####**Credits:**\n", "\n", "Big special thanks to:\n", "\n", "Robert Bell for all his work on the video Colab notebook, and paving the way to video in DeOldify!\n", "\n", "Dana Kelley for doing things, breaking stuff & having an opinion on everything." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ZjPqTBNoohK9" }, "source": [ "\n", "\n", "---\n", "\n", "\n", "#◢ Verify Correct Runtime Settings\n", "\n", "** IMPORTANT **\n", "\n", "In the \"Runtime\" menu for the notebook window, select \"Change runtime type.\" Ensure that the following are selected:\n", "* Runtime Type = Python 3\n", "* Hardware Accelerator = GPU \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "00_GcC_trpdE" }, "outputs": [], "source": [ "from os import path\n", "import torch" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gaEJBGDlptEo" }, "source": [ "#◢ Git clone and install DeOldify" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "-T-svuHytJ-8" }, "outputs": [], "source": [ "!git clone -b FastAIv1 --single-branch https://github.com/jantic/DeOldify.git DeOldify\n", "#!git clone https://github.com/jantic/DeOldify.git DeOldify" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cd DeOldify" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "BDFjbNxaadNJ" }, "source": [ "#◢ Setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "Lsx7xCXNSVt6" }, "outputs": [], "source": [ "!pip install PyDrive\n", "!pip install ffmpeg-python\n", "!pip install youtube-dl\n", "!pip install tensorboardX" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "MsJa69CMwj3l" }, "outputs": [], "source": [ "import fastai\n", "from fasterai.visualize import *\n", "from pathlib import Path\n", "torch.backends.cudnn.benchmark=True" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!mkdir 'models'\n", "!wget https://www.dropbox.com/s/336vn9y4qwyg9yz/ColorizeVideo_gen.pth?dl=0 -O ./models/ColorizeVideo_gen.pth" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "file_name = 'video.mp4'\n", "source_dir = './video/source/'\n", "source_path = source_dir + file_name\n", "dest_dir = './video/result/'\n", "dest_path = dest_dir + file_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!mkdir file_dir" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "tzHVnegp21hC" }, "outputs": [], "source": [ "colorizer = get_video_colorizer()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "sUQrbSYipiJn" }, "source": [ "#◢ Colorize!!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Instructions\n", "\n", "#### source_url\n", "YouTube, Imgur, Twitter, Reddit ... files of type .gif, .gifv and .mp4 work. NOTE: If you want to use your own video, upload it first to a site like YouTube. \n", "\n", "#### render_factor\n", "The default value of 21 has been carefully chosen and should work -ok- for most scenarios (but probably won't be the -best-). This determines resolution at which video is rendered. Lower resolution will render faster, and colors also tend to look more vibrant. Older and lower quality film in particular will generally benefit by lowering the render factor. Higher render factors are often better for higher quality videos and inconsistencies (flashy render) will generally be reduced, but the colors may get slightly washed out. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "source_url = '' #@param {type:\"string\"}\n", "render_factor = 21 #@param {type: \"slider\", min: 7, max: 46}\n", "\n", "if source_url is not None and source_url !='':\n", " colorizer.colorize_from_url(source_url, file_name, render_factor)\n", "else:\n", " print('Provide a video url and try again.')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "A5WMS_GgP4fm" }, "source": [ "#◢ Download\n", "\n", "* In the menu to the left, click **Files**\n", "* If you don't see the 'DeOldify' folder, click \"Refresh\"\n", "* By default, rendered video will be in /DeOldify/video/result/" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "X7Ycv_Y9xAHp" }, "source": [ "---\n", "#⚙ Recommended video and gif sources \n", "* [/r/Nickelodeons/](https://www.reddit.com/r/Nickelodeons/)\n", "* https://twitter.com/silentmoviegifs " ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "DeOldify-video.ipynb", "provenance": [], "toc_visible": true, "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 2 }