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import os
import cv2
from tqdm import tqdm
import yaml
import numpy as np
import warnings
from skimage import img_as_ubyte
import safetensors
import safetensors.torch
warnings.filterwarnings('ignore')
import imageio
import torch
from src.facerender.pirender.config import Config
from src.facerender.pirender.face_model import FaceGenerator
from pydub import AudioSegment
from src.utils.face_enhancer import enhancer_generator_with_len, enhancer_list
from src.utils.paste_pic import paste_pic
from src.utils.videoio import save_video_with_watermark
try:
import webui # in webui
in_webui = True
except:
in_webui = False
class AnimateFromCoeff_PIRender():
def __init__(self, sadtalker_path, device):
opt = Config(sadtalker_path['pirender_yaml_path'], None, is_train=False)
opt.device = device
self.net_G_ema = FaceGenerator(**opt.gen.param).to(opt.device)
checkpoint_path = sadtalker_path['pirender_checkpoint']
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
self.net_G_ema.load_state_dict(checkpoint['net_G_ema'], strict=False)
print('load [net_G] and [net_G_ema] from {}'.format(checkpoint_path))
self.net_G = self.net_G_ema.eval()
self.device = device
def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, background_enhancer=None, preprocess='crop', img_size=256):
source_image=x['source_image'].type(torch.FloatTensor)
source_semantics=x['source_semantics'].type(torch.FloatTensor)
target_semantics=x['target_semantics_list'].type(torch.FloatTensor)
source_image=source_image.to(self.device)
source_semantics=source_semantics.to(self.device)
target_semantics=target_semantics.to(self.device)
frame_num = x['frame_num']
with torch.no_grad():
predictions_video = []
for i in tqdm(range(target_semantics.shape[1]), 'FaceRender:'):
predictions_video.append(self.net_G(source_image, target_semantics[:, i])['fake_image'])
predictions_video = torch.stack(predictions_video, dim=1)
predictions_video = predictions_video.reshape((-1,)+predictions_video.shape[2:])
video = []
for idx in range(len(predictions_video)):
image = predictions_video[idx]
image = np.transpose(image.data.cpu().numpy(), [1, 2, 0]).astype(np.float32)
video.append(image)
result = img_as_ubyte(video)
### the generated video is 256x256, so we keep the aspect ratio,
original_size = crop_info[0]
if original_size:
result = [ cv2.resize(result_i,(img_size, int(img_size * original_size[1]/original_size[0]) )) for result_i in result ]
video_name = x['video_name'] + '.mp4'
path = os.path.join(video_save_dir, 'temp_'+video_name)
imageio.mimsave(path, result, fps=float(25))
av_path = os.path.join(video_save_dir, video_name)
return_path = av_path
audio_path = x['audio_path']
audio_name = os.path.splitext(os.path.split(audio_path)[-1])[0]
new_audio_path = os.path.join(video_save_dir, audio_name+'.wav')
start_time = 0
# cog will not keep the .mp3 filename
sound = AudioSegment.from_file(audio_path)
frames = frame_num
end_time = start_time + frames*1/25*1000
word1=sound.set_frame_rate(16000)
word = word1[start_time:end_time]
word.export(new_audio_path, format="wav")
save_video_with_watermark(path, new_audio_path, av_path, watermark= False)
print(f'The generated video is named {video_save_dir}/{video_name}')
if 'full' in preprocess.lower():
# only add watermark to the full image.
video_name_full = x['video_name'] + '_full.mp4'
full_video_path = os.path.join(video_save_dir, video_name_full)
return_path = full_video_path
paste_pic(path, pic_path, crop_info, new_audio_path, full_video_path, extended_crop= True if 'ext' in preprocess.lower() else False)
print(f'The generated video is named {video_save_dir}/{video_name_full}')
else:
full_video_path = av_path
#### paste back then enhancers
if enhancer:
video_name_enhancer = x['video_name'] + '_enhanced.mp4'
enhanced_path = os.path.join(video_save_dir, 'temp_'+video_name_enhancer)
av_path_enhancer = os.path.join(video_save_dir, video_name_enhancer)
return_path = av_path_enhancer
try:
enhanced_images_gen_with_len = enhancer_generator_with_len(full_video_path, method=enhancer, bg_upsampler=background_enhancer)
imageio.mimsave(enhanced_path, enhanced_images_gen_with_len, fps=float(25))
except:
enhanced_images_gen_with_len = enhancer_list(full_video_path, method=enhancer, bg_upsampler=background_enhancer)
imageio.mimsave(enhanced_path, enhanced_images_gen_with_len, fps=float(25))
save_video_with_watermark(enhanced_path, new_audio_path, av_path_enhancer, watermark= False)
print(f'The generated video is named {video_save_dir}/{video_name_enhancer}')
os.remove(enhanced_path)
os.remove(path)
os.remove(new_audio_path)
return return_path
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