import os
import cv2
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
import torchvision
from src.facerender.modules.keypoint_detector import HEEstimator, KPDetector
from src.facerender.modules.mapping import MappingNet
from src.facerender.modules.generator import OcclusionAwareGenerator, OcclusionAwareSPADEGenerator
from src.facerender.modules.make_animation import make_animation
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():
def __init__(self, sadtalker_path, device):
with open(sadtalker_path['facerender_yaml']) as f:
config = yaml.safe_load(f)
generator = OcclusionAwareSPADEGenerator(**config['model_params']['generator_params'],
**config['model_params']['common_params'])
kp_extractor = KPDetector(**config['model_params']['kp_detector_params'],
**config['model_params']['common_params'])
he_estimator = HEEstimator(**config['model_params']['he_estimator_params'],
**config['model_params']['common_params'])
mapping = MappingNet(**config['model_params']['mapping_params'])
generator.to(device)
kp_extractor.to(device)
he_estimator.to(device)
mapping.to(device)
for param in generator.parameters():
param.requires_grad = False
for param in kp_extractor.parameters():
param.requires_grad = False
for param in he_estimator.parameters():
param.requires_grad = False
for param in mapping.parameters():
param.requires_grad = False
if sadtalker_path is not None:
if 'checkpoint' in sadtalker_path: # use safe tensor
self.load_cpk_facevid2vid_safetensor(sadtalker_path['checkpoint'], kp_detector=kp_extractor, generator=generator, he_estimator=None)
else:
self.load_cpk_facevid2vid(sadtalker_path['free_view_checkpoint'], kp_detector=kp_extractor, generator=generator, he_estimator=he_estimator)
else:
raise AttributeError("Checkpoint should be specified for video head pose estimator.")
if sadtalker_path['mappingnet_checkpoint'] is not None:
self.load_cpk_mapping(sadtalker_path['mappingnet_checkpoint'], mapping=mapping)
else:
raise AttributeError("Checkpoint should be specified for video head pose estimator.")
self.kp_extractor = kp_extractor
self.generator = generator
self.he_estimator = he_estimator
self.mapping = mapping
self.kp_extractor.eval()
self.generator.eval()
self.he_estimator.eval()
self.mapping.eval()
self.device = device
def load_cpk_facevid2vid_safetensor(self, checkpoint_path, generator=None,
kp_detector=None, he_estimator=None,
device="cpu"):
checkpoint = safetensors.torch.load_file(checkpoint_path)
if generator is not None:
x_generator = {}
for k,v in checkpoint.items():
if 'generator' in k:
x_generator[k.replace('generator.', '')] = v
generator.load_state_dict(x_generator)
if kp_detector is not None:
x_generator = {}
for k,v in checkpoint.items():
if 'kp_extractor' in k:
x_generator[k.replace('kp_extractor.', '')] = v
kp_detector.load_state_dict(x_generator)
if he_estimator is not None:
x_generator = {}
for k,v in checkpoint.items():
if 'he_estimator' in k:
x_generator[k.replace('he_estimator.', '')] = v
he_estimator.load_state_dict(x_generator)
return None
def load_cpk_facevid2vid(self, checkpoint_path, generator=None, discriminator=None,
kp_detector=None, he_estimator=None, optimizer_generator=None,
optimizer_discriminator=None, optimizer_kp_detector=None,
optimizer_he_estimator=None, device="cpu"):
checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
if generator is not None:
generator.load_state_dict(checkpoint['generator'])
if kp_detector is not None:
kp_detector.load_state_dict(checkpoint['kp_detector'])
if he_estimator is not None:
he_estimator.load_state_dict(checkpoint['he_estimator'])
if discriminator is not None:
try:
discriminator.load_state_dict(checkpoint['discriminator'])
except:
print ('No discriminator in the state-dict. Dicriminator will be randomly initialized')
if optimizer_generator is not None:
optimizer_generator.load_state_dict(checkpoint['optimizer_generator'])
if optimizer_discriminator is not None:
try:
optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
except RuntimeError as e:
print ('No discriminator optimizer in the state-dict. Optimizer will be not initialized')
if optimizer_kp_detector is not None:
optimizer_kp_detector.load_state_dict(checkpoint['optimizer_kp_detector'])
if optimizer_he_estimator is not None:
optimizer_he_estimator.load_state_dict(checkpoint['optimizer_he_estimator'])
return checkpoint['epoch']
def load_cpk_mapping(self, checkpoint_path, mapping=None, discriminator=None,
optimizer_mapping=None, optimizer_discriminator=None, device='cpu'):
checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
if mapping is not None:
mapping.load_state_dict(checkpoint['mapping'])
if discriminator is not None:
discriminator.load_state_dict(checkpoint['discriminator'])
if optimizer_mapping is not None:
optimizer_mapping.load_state_dict(checkpoint['optimizer_mapping'])
if optimizer_discriminator is not None:
optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
return checkpoint['epoch']
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)
if 'yaw_c_seq' in x:
yaw_c_seq = x['yaw_c_seq'].type(torch.FloatTensor)
yaw_c_seq = x['yaw_c_seq'].to(self.device)
else:
yaw_c_seq = None
if 'pitch_c_seq' in x:
pitch_c_seq = x['pitch_c_seq'].type(torch.FloatTensor)
pitch_c_seq = x['pitch_c_seq'].to(self.device)
else:
pitch_c_seq = None
if 'roll_c_seq' in x:
roll_c_seq = x['roll_c_seq'].type(torch.FloatTensor)
roll_c_seq = x['roll_c_seq'].to(self.device)
else:
roll_c_seq = None
frame_num = x['frame_num']
predictions_video = make_animation(source_image, source_semantics, target_semantics,
self.generator, self.kp_extractor, self.he_estimator, self.mapping,
yaw_c_seq, pitch_c_seq, roll_c_seq, use_exp = True)
predictions_video = predictions_video.reshape((-1,)+predictions_video.shape[2:])
predictions_video = predictions_video[:frame_num]
video = []
for idx in range(predictions_video.shape[0]):
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