improve.py

import time
from config import *
import cv2
import glob
import numpy as np
import os
from basicsr.utils import imwrite
from pathos.pools import ParallelPool
import subprocess
import platform
from mutagen.wave import WAVE
import tqdm
from p_tqdm import *
import torch
from PIL import Image
from RealESRGAN import RealESRGAN

def vid2frames(vidPath, framesOutPath):
    print(vidPath)
    print(framesOutPath)
    vidcap = cv2.VideoCapture(vidPath)
    success,image = vidcap.read()
    frame = 1
    while success:
      cv2.imwrite(os.path.join(framesOutPath, str(frame).zfill(5) + '.png'), image)
      success,image = vidcap.read()
      frame += 1

def restore_frames(audiofilePath, videoOutPath, improveOutputPath):
    no_of_frames = count_files(improveOutputPath)
    audio_duration = get_audio_duration(audiofilePath)
    framesPath = improveOutputPath + "/%5d.png"
    fps = no_of_frames/audio_duration
    command = f"ffmpeg -y -r {fps} -f image2 -i {framesPath} -i {audiofilePath} -vcodec mpeg4 -b:v 20000k {videoOutPath}"
    print(command)
    subprocess.call(command, shell=platform.system() != 'Windows')

def get_audio_duration(audioPath):
    audio = WAVE(audioPath)
    duration = audio.info.length
    return duration    

def count_files(directory):
    return len([name for name in os.listdir(directory) if os.path.isfile(os.path.join(directory, name))])

def improve(disassembledPath, improvedPath):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = RealESRGAN(device, scale=4)
    model.load_weights('weights/RealESRGAN_x4.pth', download=True)

    files = glob.glob(os.path.join(disassembledPath,"*.png"))
    
    # pool = ParallelPool(nodes=20)    
    # results = pool.amap(real_esrgan, files, [model]*len(files), [improvedPath] * len(files))
    results = t_map(real_esrgan, files, [model]*len(files), [improvedPath] * len(files))

def real_esrgan(img_path, model, improvedPath):
    image = Image.open(img_path).convert('RGB')
    sr_image = model.predict(image)
    img_name = os.path.basename(img_path)
    sr_image.save(os.path.join(improvedPath, img_name))