#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ __ __ _ _ ___ __ __ \ \ / / (_) | | |__ \ \ \ / / \ \ / / _ __| | ___ ___ ) | \ V / \ \/ / | | / _` | / _ \ / _ \ / / > < \ / | | | (_| | | __/ | (_) | / /_ / . \ \/ |_| \__,_| \___| \___/ |____| /_/ \_\ Name: Video2X Controller Author: K4YT3X Date Created: Feb 24, 2018 Last Modified: March 24, 2019 Licensed under the GNU General Public License Version 3 (GNU GPL v3), available at: https://www.gnu.org/licenses/gpl-3.0.txt (C) 2018-2019 K4YT3X Video2X is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. Video2X is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Description: Video2X is an automation software based on waifu2x image enlarging engine. It extracts frames from a video, enlarge it by a number of times without losing any details or quality, keeping lines smooth and edges sharp. """ from avalon_framework import Avalon from upscaler import Upscaler import argparse import GPUtil import json import os import psutil import shutil import tempfile import time import traceback VERSION = '2.6.3' # each thread might take up to 2.5 GB during initialization. # (system memory, not to be confused with GPU memory) SYS_MEM_PER_THREAD = 2.5 GPU_MEM_PER_THREAD = 3.5 def process_arguments(): """Processes CLI arguments This function parses all arguments This allows users to customize options for the output video. """ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) # video options file_options = parser.add_argument_group('File Options') file_options.add_argument('-i', '--input', help='Source video file/directory', action='store', required=True) file_options.add_argument('-o', '--output', help='Output video file/directory', action='store', required=True) # upscaler options upscaler_options = parser.add_argument_group('Upscaler Options') upscaler_options.add_argument('-m', '--method', help='Upscaling method', action='store', default='gpu', choices=['cpu', 'gpu', 'cudnn'], required=True) upscaler_options.add_argument('-d', '--driver', help='Waifu2x driver', action='store', default='waifu2x_caffe', choices=['waifu2x_caffe', 'waifu2x_converter']) upscaler_options.add_argument('-y', '--model_dir', help='Folder containing model JSON files', action='store', default=None) upscaler_options.add_argument('-t', '--threads', help='Number of threads to use for upscaling', action='store', type=int, default=5) upscaler_options.add_argument('-c', '--config', help='Video2X config file location', action='store', default='{}\\video2x.json'.format(os.path.dirname(os.path.abspath(__file__)))) upscaler_options.add_argument('-b', '--batch', help='Enable batch mode (select all default values to questions)', action='store_true', default=False) # scaling options scaling_options = parser.add_argument_group('Scaling Options') scaling_options.add_argument('--width', help='Output video width', action='store', type=int, default=False) scaling_options.add_argument('--height', help='Output video height', action='store', type=int, default=False) scaling_options.add_argument('-r', '--ratio', help='Scaling ratio', action='store', type=int, default=False) # parse arguments return parser.parse_args() def print_logo(): print('__ __ _ _ ___ __ __') print('\\ \\ / / (_) | | |__ \\ \\ \\ / /') print(' \\ \\ / / _ __| | ___ ___ ) | \\ V /') print(' \\ \\/ / | | / _` | / _ \\ / _ \\ / / > <') print(' \\ / | | | (_| | | __/ | (_) | / /_ / . \\') print(' \\/ |_| \\__,_| \\___| \\___/ |____| /_/ \\_\\') print('\n Video2X Video Enlarger') spaces = ((44 - len("Version {}".format(VERSION))) // 2) * " " print('{}\n{} Version {}\n{}'.format(Avalon.FM.BD, spaces, VERSION, Avalon.FM.RST)) def check_memory(): """ Check usable system memory Warn the user if insufficient memory is available for the number of threads that the user have chosen. """ memory_status = [] # get system available memory system_memory_available = psutil.virtual_memory().available / (1024 ** 3) memory_status.append(('system', system_memory_available)) # check if Nvidia-smi is available # GPUtil requires nvidia-smi.exe to interact with GPU if args.method == 'gpu' or args.method == 'cudnn': if not os.path.isfile('C:\\Program Files\\NVIDIA Corporation\\NVSMI\\nvidia-smi.exe'): # Nvidia System Management Interface not available Avalon.warning('Nvidia-smi not available, skipping available memory check') Avalon.warning('If you experience error \"cudaSuccess out of memory\", try reducing number of threads you\'re using') else: try: # "0" is GPU ID. Both waifu2x drivers use the first GPU available, therefore only 0 makes sense gpu_memory_available = (GPUtil.getGPUs()[0].memoryTotal - GPUtil.getGPUs()[0].memoryUsed) / 1024 memory_status.append(('GPU', gpu_memory_available)) except ValueError: pass # go though each checkable memory type and check availability for memory_type, memory_available in memory_status: if memory_type == 'system': mem_per_thread = SYS_MEM_PER_THREAD else: mem_per_thread = GPU_MEM_PER_THREAD # if user doesn't even have enough memory to run even one thread if memory_available < mem_per_thread: Avalon.warning('You might have insufficient amount of {} memory available to run this program ({} GB)'.format(memory_type, memory_available)) Avalon.warning('Proceed with caution') if args.threads > 1: if Avalon.ask('Reduce number of threads to avoid crashing?', default=True, batch=args.batch): args.threads = 1 # if memory available is less than needed, warn the user elif memory_available < (mem_per_thread * args.threads): Avalon.warning('Each waifu2x-caffe thread will require up to 2.5 GB of system memory') Avalon.warning('You demanded {} threads to be created, but you only have {} GB {} memory available'.format(args.threads, round(memory_available, 4), memory_type)) Avalon.warning('{} GB of {} memory is recommended for {} threads'.format(mem_per_thread * args.threads, memory_type, args.threads)) Avalon.warning('With your current amount of {} memory available, {} threads is recommended'.format(memory_type, int(memory_available // mem_per_thread))) # ask the user if he / she wants to change to the recommended # number of threads if Avalon.ask('Change to the recommended value?', default=True, batch=args.batch): args.threads = int(memory_available // mem_per_thread) else: Avalon.warning('Proceed with caution') def read_config(config_file): """ Reads configuration file Returns a dictionary read by JSON. """ with open(config_file, 'r') as raw_config: config = json.load(raw_config) return config # /////////////////// Execution /////////////////// # # this is not a library if __name__ != '__main__': Avalon.error('This file cannot be imported') raise ImportError('{} cannot be imported'.format(__file__)) print_logo() # process CLI arguments args = process_arguments() # arguments sanity check if args.driver == 'waifu2x_converter' and args.width and args.height: Avalon.error('Waifu2x Converter CPP accepts only scaling ratio') exit(1) if (args.width or args.height) and args.ratio: Avalon.error('You can only specify either scaling ratio or output width and height') exit(1) if (args.width and not args.height) or (not args.width and args.height): Avalon.error('You must specify both width and height') exit(1) # check available memory check_memory() # read configurations from JSON config = read_config(args.config) # load waifu2x configuration if args.driver == 'waifu2x_caffe': waifu2x_settings = config['waifu2x_caffe'] if not os.path.isfile(waifu2x_settings['waifu2x_caffe_path']): Avalon.error('Specified waifu2x-caffe directory doesn\'t exist') Avalon.error('Please check the configuration file settings') raise FileNotFoundError(waifu2x_settings['waifu2x_caffe_path']) elif args.driver == 'waifu2x_converter': waifu2x_settings = config['waifu2x_converter'] if not os.path.isdir(waifu2x_settings['waifu2x_converter_path']): Avalon.error('Specified waifu2x-conver-cpp directory doesn\'t exist') Avalon.error('Please check the configuration file settings') raise FileNotFoundError(waifu2x_settings['waifu2x_converter_path']) # check if waifu2x path is valid # read FFMPEG configuration ffmpeg_settings = config['ffmpeg'] # load video2x settings video2x_cache_folder = config['video2x']['video2x_cache_folder'] preserve_frames = config['video2x']['preserve_frames'] # create temp directories if they don't exist if not video2x_cache_folder: video2x_cache_folder = '{}\\video2x'.format(tempfile.gettempdir()) if video2x_cache_folder and not os.path.isdir(video2x_cache_folder): if not os.path.isfile(video2x_cache_folder) and not os.path.islink(video2x_cache_folder): Avalon.warning('Specified cache folder/directory {} does not exist'.format(video2x_cache_folder)) if Avalon.ask('Create folder/directory?', default=True, batch=args.batch): if os.mkdir(video2x_cache_folder) is None: Avalon.info('{} created'.format(video2x_cache_folder)) else: Avalon.error('Unable to create {}'.format(video2x_cache_folder)) Avalon.error('Aborting...') exit(1) else: Avalon.error('Specified cache folder/directory is a file/link') Avalon.error('Unable to continue, exiting...') exit(1) # start execution try: # start timer begin_time = time.time() if os.path.isfile(args.input): """ Upscale single video file """ Avalon.info('Upscaling single video file: {}'.format(args.input)) upscaler = Upscaler(input_video=args.input, output_video=args.output, method=args.method, waifu2x_settings=waifu2x_settings, ffmpeg_settings=ffmpeg_settings, waifu2x_driver=args.driver, scale_width=args.width, scale_height=args.height, scale_ratio=args.ratio, model_dir=args.model_dir, threads=args.threads, video2x_cache_folder=video2x_cache_folder) upscaler.run() upscaler.cleanup() elif os.path.isdir(args.input): """ Upscale videos in a folder/directory """ Avalon.info('Upscaling videos in folder/directory: {}'.format(args.input)) for input_video in [f for f in os.listdir(args.input) if os.path.isfile(os.path.join(args.input, f))]: output_video = '{}\\{}'.format(args.output, input_video) upscaler = Upscaler(input_video=os.path.join(args.input, input_video), output_video=output_video, method=args.method, waifu2x_settings=waifu2x_settings, ffmpeg_settings=ffmpeg_settings, waifu2x_driver=args.driver, scale_width=args.width, scale_height=args.height, scale_ratio=args.ratio, model_dir=args.model_dir, threads=args.threads, video2x_cache_folder=video2x_cache_folder) upscaler.run() upscaler.cleanup() else: Avalon.error('Input path is neither a file nor a folder/directory') raise FileNotFoundError('{} is neither file nor folder/directory'.format(args.input)) Avalon.info('Program completed, taking {} seconds'.format(round((time.time() - begin_time), 5))) except Exception: Avalon.error('An exception has occurred') traceback.print_exc() Avalon.warning('If you experience error \"cudaSuccess out of memory\", try reducing number of threads you\'re using') finally: # remove Video2X Cache folder try: if not preserve_frames: shutil.rmtree(video2x_cache_folder) except FileNotFoundError: pass