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import os | |
import cv2 | |
import yaml | |
import numpy as np | |
import warnings | |
from skimage import img_as_ubyte | |
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 as face_enhancer | |
from src.utils.paste_pic import paste_pic | |
from src.utils.videoio import save_video_with_watermark | |
class AnimateFromCoeff(): | |
def __init__(self, free_view_checkpoint, mapping_checkpoint, | |
config_path, device): | |
with open(config_path) 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 free_view_checkpoint is not None: | |
self.load_cpk_facevid2vid(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 mapping_checkpoint is not None: | |
self.load_cpk_mapping(mapping_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(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'): | |
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,(256, int(256.0 * 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') | |
print('new_audio_path',new_audio_path) | |
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") | |
base64_video,temp_file_path = save_video_with_watermark(path, new_audio_path, av_path, watermark= False) | |
print(f'The generated video is named {video_name} in {video_save_dir}') | |
if preprocess.lower() == 'full': | |
# 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 | |
base64_video,temp_file_path = paste_pic(path, pic_path, crop_info, new_audio_path, full_video_path) | |
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 | |
enhanced_images = face_enhancer(temp_file_path, method=enhancer, bg_upsampler=background_enhancer) | |
imageio.mimsave(enhanced_path, enhanced_images, fps=float(25)) | |
base64_video,temp_file_path = 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,base64_video,temp_file_path, new_audio_path | |