mirror of
https://github.com/k4yt3x/video2x.git
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2.2.0 main file rewritten, various of enhancements
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K4YT3X
71
bin/upscaler.py
Normal file → Executable file
71
bin/upscaler.py
Normal file → Executable file
@@ -4,7 +4,7 @@
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Name: Video2X Upscaler
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Author: K4YT3X
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Date Created: December 10, 2018
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Last Modified: December 10, 2018
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Last Modified: December 19, 2018
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Licensed under the GNU General Public License Version 3 (GNU GPL v3),
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available at: https://www.gnu.org/licenses/gpl-3.0.txt
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@@ -13,24 +13,19 @@ Licensed under the GNU General Public License Version 3 (GNU GPL v3),
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"""
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from avalon_framework import Avalon
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from exceptions import *
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from ffmpeg import Ffmpeg
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from fractions import Fraction
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from waifu2x import Waifu2x
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import json
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import os
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import psutil
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import shutil
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import subprocess
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import tempfile
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import threading
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# Each thread might take up to 2.5 GB during initialization.
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MEM_PER_THREAD = 2.5
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class ArgumentError(Exception):
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def __init__(self, message):
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super().__init__(message)
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MODELS_AVAILABLE = ['upconv_7_anime_style_art_rgb', 'upconv_7_photo',
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'anime_style_art_rgb', 'photo', 'anime_style_art_y']
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class Upscaler:
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@@ -66,14 +61,8 @@ class Upscaler:
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def _check_model_type(self, args):
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""" Validate upscaling model
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"""
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models_available = ['upconv_7_anime_style_art_rgb', 'upconv_7_photo',
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'anime_style_art_rgb', 'photo', 'anime_style_art_y']
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if self.model_type not in models_available:
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Avalon.error('Specified model type not found!')
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Avalon.info('Available models:')
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for model in models_available:
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print(model)
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exit(1)
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if self.model_type not in MODELS_AVAILABLE:
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raise InvalidModelType('Specified model type not available')
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def _check_arguments(self):
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# Check if arguments are valid / all necessary argument
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@@ -82,36 +71,11 @@ class Upscaler:
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raise ArgumentError('You need to specify the video to process')
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elif (not self.output_width or not self.output_height) and not self.factor:
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raise ArgumentError('You must specify output video width and height or upscale factor')
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elif not self.video_output:
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elif not self.output_video:
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raise ArgumentError('You need to specify the output video name')
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elif not self.method:
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raise ArgumentError('You need to specify the enlarging processing unit')
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def _check_memory(self):
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""" Check usable memory
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Warn the user if insufficient memory is available for
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the number of threads that the user have chosen.
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"""
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memory_available = psutil.virtual_memory().available / (1024 ** 3)
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# If user doesn't even have enough memory to run even one thread
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if memory_available < MEM_PER_THREAD:
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Avalon.warning('You might have an insufficient amount of memory available to run this program ({} GB)'.format(memory_available))
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Avalon.warning('Proceed with caution')
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# If memory available is less than needed, warn the user
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elif memory_available < (MEM_PER_THREAD * self.threads):
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Avalon.warning('Each waifu2x-caffe thread will require up to 2.5 GB during initialization')
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Avalon.warning('You demanded {} threads to be created, but you only have {} GB memory available'.format(self.threads, round(memory_available, 4)))
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Avalon.warning('{} GB of memory is recommended for {} threads'.format(MEM_PER_THREAD * self.threads, self.threads))
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Avalon.warning('With your current amount of memory available, {} threads is recommended'.format(int(memory_available // MEM_PER_THREAD)))
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# Ask the user if he / she wants to change to the recommended
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# number of threads
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if Avalon.ask('Change to the recommended value?', True):
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self.threads = int(memory_available // MEM_PER_THREAD)
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else:
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Avalon.warning('Proceed with caution')
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def _upscale_frames(self, w2):
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""" Upscale video frames with waifu2x-caffe
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@@ -159,7 +123,7 @@ class Upscaler:
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# Create threads and start them
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for thread_info in thread_pool:
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# Create thread
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thread = threading.Thread(target=w2.upscale, args=(thread_info[0], self.upscaled_frames, self.width, self.height))
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thread = threading.Thread(target=w2.upscale, args=(thread_info[0], self.upscaled_frames, self.output_width, self.output_height))
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thread.name = thread_info[1]
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# Add threads into the pool
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@@ -184,7 +148,6 @@ class Upscaler:
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# Check argument sanity
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self._check_model_type(self.model_type)
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self._check_arguments()
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self._check_memory()
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# Convert paths to absolute paths
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self.input_video = os.path.abspath(self.input_video)
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@@ -202,7 +165,7 @@ class Upscaler:
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raise FileNotFoundError(self.waifu2x_path)
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# Initialize objects for ffmpeg and waifu2x-caffe
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fm = Ffmpeg(self.ffmpeg_path, self.video_output, self.ffmpeg_arguments)
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fm = Ffmpeg(self.ffmpeg_path, self.output_video, self.ffmpeg_arguments)
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w2 = Waifu2x(self.waifu2x_path, self.method, self.model_type)
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# Extract frames from video
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@@ -228,6 +191,13 @@ class Upscaler:
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framerate = float(Fraction(info['streams'][video_stream_index]['avg_frame_rate']))
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Avalon.info('Framerate: {}'.format(framerate))
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# Width/height will be coded width/height x upscale factor
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if self.factor:
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coded_width = info['streams'][video_stream_index]['coded_width']
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coded_height = info['streams'][video_stream_index]['coded_height']
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self.output_width = self.factor * coded_width
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self.output_height = self.factor * coded_height
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# Upscale images one by one using waifu2x
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Avalon.info('Starting to upscale extracted images')
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self._upscale_frames(w2)
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@@ -236,15 +206,8 @@ class Upscaler:
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# Frames to Video
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Avalon.info('Converting extracted frames into video')
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# Width/height will be coded width/height x upscale factor
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if self.factor:
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coded_width = info['streams'][video_stream_index]['coded_width']
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coded_height = info['streams'][video_stream_index]['coded_height']
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fm.convert_video(framerate, '{}x{}'.format(self.factor * coded_width, self.factor * coded_height), self.upscaled_frames)
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# Use user defined output size
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else:
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fm.convert_video(framerate, '{}x{}'.format(self.width, self.height), self.upscaled_frames)
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fm.convert_video(framerate, '{}x{}'.format(self.output_width, self.output_height), self.upscaled_frames)
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Avalon.info('Conversion completed')
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# Extract and press audio in
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