mirror of
https://github.com/jantic/DeOldify.git
synced 2026-09-01 15:36:32 +08:00
Jason Antic
127 lines
3.5 KiB
Plaintext
127 lines
3.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ['CUDA_VISIBLE_DEVICES']='0' "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from fasterai.visualize import *\n",
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"plt.style.use('dark_background')\n",
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"torch.backends.cudnn.benchmark=True"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Adjust render_factor (int) if image doesn't look quite right (max 45 on 11GB GPU). \n",
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"#Lower render factors (as low as 12-15) tend to work well for old and low quality videos.\n",
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"#High render factors (25-45) tend to work well for higher quality and more recent videos\n",
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"\n",
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"#Not satisfied with color saturation? Lower the render factor. \n",
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"#Unacceptable object flicker? Increase the render factor.\n",
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"\n",
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"#It literally just is a number multiplied by 16 to get the square render resolution. \n",
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"#Note that this doesn't affect the resolution of the final output- the output is the same resolution as the input.\n",
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"#Example: render_factor=21 => color is rendered at 16x21 = 336x336 px. \n",
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"render_factor=21\n",
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"#Specify media_url. Many sources will work (YouTube, Imgur, Twitter, Reddit, etc). \n",
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"#Complete list here: https://rg3.github.io/youtube-dl/supportedsites.html. \n",
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"#NOTE: Make source_url None to just read from file at ./video/source/[file_name] directly without modification\n",
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"#source_url= 'https://twitter.com/silentmoviegifs/status/1112256563182489600'\n",
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"#source_url='https://archive.org/details/impact'\n",
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"source_url='https://twitter.com/silentmoviegifs/status/1116751583386034176'\n",
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"file_name = 'DogBath.mp4'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"colorizer = get_video_colorizer()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"if source_url is not None:\n",
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" video_path = colorizer.colorize_from_url(source_url, file_name, render_factor=render_factor)\n",
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" show_video_in_notebook(video_path)\n",
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"else:\n",
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" video_path = colorizer.colorize_from_file_name(file_name)\n",
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" show_video_in_notebook(video_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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},
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"toc": {
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"colors": {
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"hover_highlight": "#DAA520",
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"navigate_num": "#000000",
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"navigate_text": "#333333",
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"running_highlight": "#FF0000",
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"selected_highlight": "#FFD700",
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"sidebar_border": "#EEEEEE",
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"wrapper_background": "#FFFFFF"
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},
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"moveMenuLeft": true,
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"nav_menu": {
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"height": "67px",
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"width": "252px"
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},
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"navigate_menu": true,
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"number_sections": true,
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"sideBar": true,
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"threshold": 4,
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"toc_cell": false,
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"toc_section_display": "block",
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"toc_window_display": false,
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"widenNotebook": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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