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Runtime error
| 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 | |