Practical-RIFE/inference_video_enhance.py

import os
import cv2
import torch
import argparse
import numpy as np
from tqdm import tqdm
from torch.nn import functional as F
import warnings
import _thread
import skvideo.io
from queue import Queue, Empty
from model.pytorch_msssim import ssim_matlab

warnings.filterwarnings("ignore")

def transferAudio(sourceVideo, targetVideo):
    import shutil
    import moviepy.editor
    tempAudioFileName = "./temp/audio.mkv"

    # split audio from original video file and store in "temp" directory
    if True:

        # clear old "temp" directory if it exits
        if os.path.isdir("temp"):
            # remove temp directory
            shutil.rmtree("temp")
        # create new "temp" directory
        os.makedirs("temp")
        # extract audio from video
        os.system('ffmpeg -y -i "{}" -c:a copy -vn {}'.format(sourceVideo, tempAudioFileName))

    targetNoAudio = os.path.splitext(targetVideo)[0] + "_noaudio" + os.path.splitext(targetVideo)[1]
    os.rename(targetVideo, targetNoAudio)
    # combine audio file and new video file
    os.system('ffmpeg -y -i "{}" -i {} -c copy "{}"'.format(targetNoAudio, tempAudioFileName, targetVideo))

    if os.path.getsize(targetVideo) == 0: # if ffmpeg failed to merge the video and audio together try converting the audio to aac
        tempAudioFileName = "./temp/audio.m4a"
        os.system('ffmpeg -y -i "{}" -c:a aac -b:a 160k -vn {}'.format(sourceVideo, tempAudioFileName))
        os.system('ffmpeg -y -i "{}" -i {} -c copy "{}"'.format(targetNoAudio, tempAudioFileName, targetVideo))
        if (os.path.getsize(targetVideo) == 0): # if aac is not supported by selected format
            os.rename(targetNoAudio, targetVideo)
            print("Audio transfer failed. Interpolated video will have no audio")
        else:
            print("Lossless audio transfer failed. Audio was transcoded to AAC (M4A) instead.")

            # remove audio-less video
            os.remove(targetNoAudio)
    else:
        os.remove(targetNoAudio)

    # remove temp directory
    shutil.rmtree("temp")

parser = argparse.ArgumentParser(description='Video SR')
parser.add_argument('--video', dest='video', type=str, default=None)
parser.add_argument('--output', dest='output', type=str, default=None)
parser.add_argument('--img', dest='img', type=str, default=None)
parser.add_argument('--model', dest='modelDir', type=str, default='train_log_SAFA', help='directory with trained model files')
parser.add_argument('--fp16', dest='fp16', action='store_true', help='fp16 mode for faster and more lightweight inference on cards with Tensor Cores')
parser.add_argument('--png', dest='png', action='store_true', help='whether to vid_out png format vid_outs')
parser.add_argument('--ext', dest='ext', type=str, default='mp4', help='vid_out video extension')

args = parser.parse_args()
assert (not args.video is None or not args.img is None)
if not args.img is None:
    args.png = True

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_grad_enabled(False)
if torch.cuda.is_available():
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = True
    if(args.fp16):
        print('set fp16')
        torch.set_default_tensor_type(torch.cuda.HalfTensor)

try:
    from train_log_SAFA.model import Model
except:
    print("Please download our model from model list")
model = Model()
model.device()
model.load_model(args.modelDir)
print("Loaded SAFA model.")
model.eval()

if not args.video is None:
    videoCapture = cv2.VideoCapture(args.video)
    fps = videoCapture.get(cv2.CAP_PROP_FPS)
    tot_frame = videoCapture.get(cv2.CAP_PROP_FRAME_COUNT)
    videoCapture.release()
    fpsNotAssigned = True
    videogen = skvideo.io.vreader(args.video)
    lastframe = next(videogen)
    # videogen = imageio.get_reader(args.video)
    # lastframe = next(iter(videogen))
    fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
    video_path_wo_ext, ext = os.path.splitext(args.video)
    if args.png == False and fpsNotAssigned == True:
        print("The audio will be merged after interpolation process")
    else:
        print("Will not merge audio because using png or fps flag!")
else:
    videogen = []
    for f in os.listdir(args.img):
        if 'png' in f:
            videogen.append(f)
    tot_frame = len(videogen)
    videogen.sort(key= lambda x:int(x[:-4]))
    lastframe = cv2.imread(os.path.join(args.img, videogen[0]), cv2.IMREAD_UNCHANGED)[:, :, ::-1].copy()
    videogen = videogen[1:]

h, w, _ = lastframe.shape

vid_out_name = None
vid_out = None
if args.png:
    if not os.path.exists('vid_out'):
        os.mkdir('vid_out')
else:
    if args.output is not None:
        vid_out_name = args.output
    else:
        vid_out_name = '{}_2X{}'.format(video_path_wo_ext, ext)
    vid_out = cv2.VideoWriter(vid_out_name, fourcc, fps, (w, h))

def clear_write_buffer(user_args, write_buffer):
    cnt = 0
    while True:
        item = write_buffer.get()
        if item is None:
            break
        if user_args.png:
            cv2.imwrite('vid_out/{:0>7d}.png'.format(cnt), item[:, :, ::-1])
            cnt += 1
        else:
            vid_out.write(item[:, :, ::-1])

def build_read_buffer(user_args, read_buffer, videogen):
    for frame in videogen:
        if not user_args.img is None:
            frame = cv2.imread(os.path.join(user_args.img, frame), cv2.IMREAD_UNCHANGED)[:, :, ::-1].copy()
        # if user_args.montage:
        #    frame = frame[:, left: left + w]
        read_buffer.put(frame)
    read_buffer.put(None)

def pad_image(img):
    if(args.fp16):
        return F.pad(img, padding, mode='reflect').half()
    else:
        return F.pad(img, padding, mode='reflect')

tmp = 64
ph = ((h - 1) // tmp + 1) * tmp
pw = ((w - 1) // tmp + 1) * tmp
padding = (0, pw - w, 0, ph - h)
pbar = tqdm(total=tot_frame)
write_buffer = Queue(maxsize=500)
read_buffer = Queue(maxsize=500)
_thread.start_new_thread(build_read_buffer, (args, read_buffer, videogen))
_thread.start_new_thread(clear_write_buffer, (args, write_buffer))

while True:
    frame = read_buffer.get()
    if frame is None:
        break
    # lastframe_2x = cv2.resize(lastframe, (0, 0), fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
    # frame_2x = cv2.resize(frame, (0, 0), fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
    I0 = pad_image(torch.from_numpy(np.transpose(lastframe, (2,0,1))).to(device, non_blocking=True).unsqueeze(0).float() / 255.)
    I1 = pad_image(torch.from_numpy(np.transpose(frame, (2,0,1))).to(device, non_blocking=True).unsqueeze(0).float() / 255.)
    I0_small = F.interpolate(I0, (32, 32), mode='bilinear', align_corners=False)
    I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False)
    ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
    if ssim < 0.2:
        out = [model.inference(I0, I0, [0])[0], model.inference(I1, I1, [0])[0]]
    else:
        out = model.inference(I0, I1, [0, 1])
    assert(len(out) == 2)
    write_buffer.put((out[0][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
    write_buffer.put((out[1][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
    lastframe = read_buffer.get()
    if lastframe is None:
        break
    pbar.update(2)

import time
while(not write_buffer.empty()):
    time.sleep(0.1)
pbar.close()
if not vid_out is None:
    vid_out.release()

# move audio to new video file if appropriate
if args.png == False and fpsNotAssigned == True and not args.video is None:
    try:
        transferAudio(args.video, vid_out_name)
    except:
        print("Audio transfer failed. Interpolated video will have no audio")
        targetNoAudio = os.path.splitext(vid_out_name)[0] + "_noaudio" + os.path.splitext(vid_out_name)[1]
        os.rename(targetNoAudio, vid_out_name)