import os import cv2 import glob,copy import shutil from tqdm import tqdm import torch import pickle import numpy as np from cuda_malloc import cuda_malloc_supported from typing import Any import folder_paths from .musetalk.utils.face_parsing import FaceParsing from mmpose.apis import init_model from .musetalk.utils.blending import get_image from .musetalk.utils.utils import load_all_model,get_file_type,get_video_fps,datagen from .musetalk.utils.preprocessing import get_landmark_and_bbox,read_imgs,coord_placeholder parent_directory = os.path.dirname(os.path.abspath(__file__)) input_path = folder_paths.get_input_directory() out_path = folder_paths.get_output_directory() class MuseTalk_INFER: def __init__(self,bbox_shift=0,fps=25, batch_size=8,batch_size_fa=2, use_saved_coord=False) -> None: self.fps = fps self.bbox_shift = bbox_shift self.batch_size = batch_size self.batch_size_fa = batch_size_fa self.use_saved_coord = use_saved_coord self.device = torch.device("cuda" if cuda_malloc_supported() else "cpu") config_file = os.path.join(parent_directory,"musetalk","utils","dwpose","rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py") checkpoint_file = os.path.join(parent_directory,'models','dwpose','dw-ll_ucoco_384.pth') resnet_path = os.path.join(parent_directory,'models','face-parse-bisent','resnet18-5c106cde.pth') face_model_pth = os.path.join(parent_directory,"models','face-parse-bisent','79999_iter.pth") self.fp_model = FaceParsing(resnet_path,face_model_pth) self.dwpose_model = init_model(config_file, checkpoint_file, device=self.device) self.audio_processor,self.vae,self.unet,self.pe = load_all_model(os.path.join(parent_directory,"models")) self.timesteps = torch.tensor([0], device=self.device) def __call__(self, video_path,audio_path,*args: Any, **kwds: Any) -> Any: input_basename = os.path.basename(video_path).split('.')[0] audio_basename = os.path.basename(audio_path).split('.')[0] output_basename = f"{input_basename}_{audio_basename}" result_img_save_path = os.path.join(out_path,"musetalk_result", output_basename) # related to video & audio inputs os.makedirs(result_img_save_path, exist_ok=True) crop_coord_save_path = os.path.join(result_img_save_path, input_basename+".pkl") # only related to video input output_vid_name = os.path.join(out_path, output_basename+".mp4") ############################################## extract frames from source video ############################################## if get_file_type(video_path)=="video": save_dir_full = os.path.join(out_path,"musetalk_result",input_basename) os.makedirs(save_dir_full,exist_ok = True) png_path = os.path.join(save_dir_full,"%08d.png") cmd = f"ffmpeg -v fatal -i {video_path} -start_number 0 {png_path}" os.system(cmd) input_img_list = sorted(glob.glob(os.path.join(save_dir_full, '*.[jpJP][pnPN]*[gG]'))) fps = get_video_fps(video_path) else: # input img folder input_img_list = glob.glob(os.path.join(video_path, '*.[jpJP][pnPN]*[gG]')) input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0])) fps = self.fps #print(input_img_list) ############################################## extract audio feature ############################################## whisper_feature = self.audio_processor.audio2feat(audio_path) whisper_chunks = self.audio_processor.feature2chunks(feature_array=whisper_feature,fps=fps) ############################################## preprocess input image ############################################## if os.path.exists(crop_coord_save_path) and self.use_saved_coord: print("using extracted coordinates") with open(crop_coord_save_path,'rb') as f: coord_list = pickle.load(f) frame_list = read_imgs(input_img_list) else: print("extracting landmarks...time consuming") coord_list, frame_list = get_landmark_and_bbox(self.dwpose_model,input_img_list, self.batch_size_fa,self.bbox_shift) with open(crop_coord_save_path, 'wb') as f: pickle.dump(coord_list, f) i = 0 input_latent_list = [] for bbox, frame in zip(coord_list, frame_list): if bbox == coord_placeholder: continue x1, y1, x2, y2 = bbox crop_frame = frame[y1:y2, x1:x2] crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4) latents = self.vae.get_latents_for_unet(crop_frame) input_latent_list.append(latents) # to smooth the first and the last frame frame_list_cycle = frame_list + frame_list[::-1] coord_list_cycle = coord_list + coord_list[::-1] input_latent_list_cycle = input_latent_list + input_latent_list[::-1] ############################################## inference batch by batch ############################################## print("start inference") video_num = len(whisper_chunks) batch_size = self.batch_size gen = datagen(whisper_chunks,input_latent_list_cycle,batch_size) res_frame_list = [] for i, (whisper_batch,latent_batch) in enumerate(tqdm(gen,total=int(np.ceil(float(video_num)/batch_size)))): tensor_list = [torch.FloatTensor(arr) for arr in whisper_batch] audio_feature_batch = torch.stack(tensor_list).to(self.unet.device) # torch, B, 5*N,384 audio_feature_batch = self.pe(audio_feature_batch) pred_latents = self.unet.model(latent_batch, self.timesteps, encoder_hidden_states=audio_feature_batch).sample recon = self.vae.decode_latents(pred_latents) for res_frame in recon: res_frame_list.append(res_frame) ############################################## pad to full image ############################################## print("pad talking image to original video") for i, res_frame in enumerate(tqdm(res_frame_list)): bbox = coord_list_cycle[i%(len(coord_list_cycle))] ori_frame = copy.deepcopy(frame_list_cycle[i%(len(frame_list_cycle))]) x1, y1, x2, y2 = bbox try: res_frame = cv2.resize(res_frame.astype(np.uint8),(x2-x1,y2-y1)) except: # print(bbox) continue combine_frame = get_image(self.fp_model, ori_frame,res_frame,bbox) cv2.imwrite(os.path.join(result_img_save_path,f"{str(i).zfill(8)}.png"),combine_frame) res_tmp_path = os.path.join(result_img_save_path,"%08d.png") cmd_img2video = f"ffmpeg -y -v fatal -r {fps} -f image2 -i {res_tmp_path} -vcodec libx264 -vf format=rgb24,scale=out_color_matrix=bt709,format=yuv420p -crf 18 {output_vid_name}" print(cmd_img2video) os.system(cmd_img2video) ''' cmd_combine_audio = f"ffmpeg -y -v fatal -i {audio_path} -i temp.mp4 {output_vid_name}" print(cmd_combine_audio) os.system(cmd_combine_audio) ''' del self.fp_model,self.dwpose_model,self.audio_processor,self.vae,self.unet,self.pe torch.cuda.empty_cache() # os.remove("temp.mp4") shutil.rmtree(result_img_save_path) print(f"result is save to {output_vid_name}") return output_vid_name