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import torch
import os
from concurrent.futures import ThreadPoolExecutor
from pydub import AudioSegment
import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
from pathlib import Path
import subprocess
from pathlib import Path
import av
import imageio
import numpy as np
from rich.progress import track
from tqdm import tqdm
import zipfile
import shutil
import stf_alternative
def exec_cmd(cmd):
subprocess.run(
cmd, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT
)
def images2video(images, wfp, **kwargs):
fps = kwargs.get("fps", 24)
video_format = kwargs.get("format", "mp4") # default is mp4 format
codec = kwargs.get("codec", "libx264") # default is libx264 encoding
quality = kwargs.get("quality") # video quality
pixelformat = kwargs.get("pixelformat", "yuv420p") # video pixel format
image_mode = kwargs.get("image_mode", "rgb")
macro_block_size = kwargs.get("macro_block_size", 2)
ffmpeg_params = ["-crf", str(kwargs.get("crf", 18))]
writer = imageio.get_writer(
wfp,
fps=fps,
format=video_format,
codec=codec,
quality=quality,
ffmpeg_params=ffmpeg_params,
pixelformat=pixelformat,
macro_block_size=macro_block_size,
)
n = len(images)
for i in track(range(n), description="writing", transient=True):
if image_mode.lower() == "bgr":
writer.append_data(images[i][..., ::-1])
else:
writer.append_data(images[i])
writer.close()
# print(f':smiley: Dump to {wfp}\n', style="bold green")
print(f"Dump to {wfp}\n")
def merge_audio_video(video_fp, audio_fp, wfp):
if osp.exists(video_fp) and osp.exists(audio_fp):
cmd = f"ffmpeg -i {video_fp} -i {audio_fp} -c:v copy -c:a aac {wfp} -y"
exec_cmd(cmd)
print(f"merge {video_fp} and {audio_fp} to {wfp}")
else:
print(f"video_fp: {video_fp} or audio_fp: {audio_fp} not exists!")
class STFPipeline:
def __init__(self,
stf_path: str = "/home/user/app/stf/",
device: str = "cuda:0",
template_video_path: str = "templates/front_one_piece_dress_nodded_cut.webm",
config_path: str = "front_config.json",
checkpoint_path: str = "089.pth",
#root_path: str = "works"
root_path: str = "/tmp/works",
female_video: bool=True
):
#os.makedirs(root_path, exist_ok=True)
shutil.copytree('/home/user/app/stf/works', '/tmp/works', dirs_exist_ok=True)
if female_video:
dir_zip= os.path.join(root_path, 'preprocess/nasilhong_f_v1_front/crop_video_front_one_piece_dress_nodded_cut.zip')
dir_target=os.path.join(root_path,'preprocess/nasilhong_f_v1_front/')
zipfile.ZipFile(dir_zip, 'r').extractall(dir_target)
dir_zip=os.path.join(root_path,'preprocess/nasilhong_f_v1_front/front_one_piece_dress_nodded_cut.zip')
dir_target=os.path.join(root_path,'preprocess/nasilhong_f_v1_front/')
zipfile.ZipFile(dir_zip, 'r').extractall(dir_target)
else:
dir_zip= os.path.join(root_path, 'preprocess/Ian_v3_front/crop_video_Cam2_2309071202_0012_Natural_Looped.zip')
dir_target=os.path.join(root_path,'preprocess/Ian_v3_front/')
zipfile.ZipFile(dir_zip, 'r').extractall(dir_target)
dir_zip=os.path.join(root_path,'preprocess/Ian_v3_front/Cam2_2309071202_0012_Natural_Looped.zip')
dir_target=os.path.join(root_path,'preprocess/Ian_v3_front/')
zipfile.ZipFile(dir_zip, 'r').extractall(dir_target)
self.config_path = os.path.join(stf_path, config_path)
self.checkpoint_path = os.path.join(stf_path, checkpoint_path)
#self.work_root_path = os.path.join(stf_path, root_path)
self.work_root_path = os.path.join(root_path)
self.device = device
self.template_video_path=os.path.join(stf_path, template_video_path)
# model = stf_alternative.create_model(
# config_path=config_path,
# checkpoint_path=checkpoint_path,
# work_root_path=work_root_path,
# device=device,
# wavlm_path="microsoft/wavlm-large",
# )
# self.template = stf_alternative.Template(
# model=model,
# config_path=config_path,
# template_video_path=template_video_path,
# )
print('STFPipeline init')
def execute(self, audio: str):
print('STFPipeline execute')
model = stf_alternative.create_model(
config_path=self.config_path,
checkpoint_path=self.checkpoint_path,
work_root_path=self.work_root_path,
device=self.device,
wavlm_path="microsoft/wavlm-large",
)
print('STFPipeline execute 1')
self.template = stf_alternative.Template(
model=model,
config_path=self.config_path,
template_video_path=self.template_video_path,
)
print('STFPipeline execute 2')
# Path("dubbing").mkdir(exist_ok=True)
# save_path = os.path.join("dubbing", Path(audio).stem+"--lip.mp4")
Path("/tmp/dubbing").mkdir(exist_ok=True)
save_path = os.path.join("/tmp/dubbing", Path(audio).stem+"--lip.mp4")
reader = iter(self.template._get_reader(num_skip_frames=0))
print('execute,reader====', reader)
audio_segment = AudioSegment.from_file(audio)
pivot = 0
results = []
# try:
# gen_infer = self.template.gen_infer(
# audio_segment,
# pivot,
# )
# for idx, (it, chunk) in enumerate(gen_infer, pivot):
# frame = next(reader)
# composed = self.template.compose(idx, frame, it)
# frame_name = f"{idx}".zfill(5)+".jpg"
# results.append(it['pred'])
# pivot = idx + 1
# except StopIteration as e:
# pass
with ThreadPoolExecutor(1) as p:
try:
gen_infer = self.template.gen_infer_concurrent(
p,
audio_segment,
pivot,
)
for idx, (it, chunk) in enumerate(gen_infer, pivot):
frame = next(reader)
composed = self.template.compose(idx, frame, it)
frame_name = f"{idx}".zfill(5)+".jpg"
results.append(it['pred'])
pivot = idx + 1
except StopIteration as e:
pass
print('STFPipeline execute 3')
images2video(results, save_path)
save_path_aud = save_path.replace('.mp4', '_aud.mp4')
merge_audio_video(save_path, audio, save_path_aud)
return save_path_aud #save_path |