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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 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"
):
#os.makedirs(root_path, exist_ok=True)
import shutil; shutil.copytree('/home/user/app/stf/works', '/tmp/works', dirs_exist_ok=True)
import zipfile
dir_zip='/tmp/works/preprocess/nasilhong_f_v1_front/crop_video_front_one_piece_dress_nodded_cut.zip'
dir_target='/tmp/works/preprocess/nasilhong_f_v1_front/'
zipfile.ZipFile(dir_zip, 'r').extractall(dir_target)
dir_zip='/tmp/works/preprocess/nasilhong_f_v1_front/front_one_piece_dress_nodded_cut.zip'
dir_target='/tmp/works/preprocess/nasilhong_f_v1_front/'
import zipfile; 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)
print('frame=', frame.shape, frame[:10])
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)
return save_path |