import os import sys sys.path.append('./Musetalk') import os import time import re from huggingface_hub import snapshot_download import requests import numpy as np import cv2 import torch import glob import pickle from tqdm import tqdm import copy from argparse import Namespace import gdown import imageio import json import shutil import threading import queue from moviepy.editor import * from musetalk.utils.utils import get_file_type,get_video_fps,datagen from musetalk.utils.preprocessing import get_landmark_and_bbox,read_imgs,coord_placeholder,get_bbox_range from musetalk.utils.blending import get_image,get_image_prepare_material,get_image_blending from musetalk.utils.utils import load_all_model import gradio as gr # ProjectDir = os.path.abspath(os.path.dirname(__file__)) CheckpointsDir = "Musetalk/Musetalk/models" def download_model(): if not os.path.exists(CheckpointsDir): os.makedirs(CheckpointsDir) print("Checkpoint Not Downloaded, start downloading...") tic = time.time() snapshot_download( repo_id="TMElyralab/MuseTalk", local_dir=CheckpointsDir, max_workers=8, local_dir_use_symlinks=True, force_download=True, resume_download=False ) # weight os.makedirs(f"{CheckpointsDir}/sd-vae-ft-mse/") snapshot_download( repo_id="stabilityai/sd-vae-ft-mse", local_dir=CheckpointsDir+'/sd-vae-ft-mse', max_workers=8, local_dir_use_symlinks=True, force_download=True, resume_download=False ) #dwpose os.makedirs(f"{CheckpointsDir}/dwpose/") snapshot_download( repo_id="yzd-v/DWPose", local_dir=CheckpointsDir+'/dwpose', max_workers=8, local_dir_use_symlinks=True, force_download=True, resume_download=False ) #vae url = "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt" response = requests.get(url) # 确保请求成功 if response.status_code == 200: # 指定文件保存的位置 file_path = f"{CheckpointsDir}/whisper/tiny.pt" os.makedirs(f"{CheckpointsDir}/whisper/") # 将文件内容写入指定位置 with open(file_path, "wb") as f: f.write(response.content) else: print(f"请求失败，状态码：{response.status_code}") #gdown face parse url = "https://drive.google.com/uc?id=154JgKpzCPW82qINcVieuPH3fZ2e0P812" os.makedirs(f"{CheckpointsDir}/face-parse-bisent/") file_path = f"{CheckpointsDir}/face-parse-bisent/79999_iter.pth" gdown.download(url, file_path, quiet=False) #resnet url = "https://download.pytorch.org/models/resnet18-5c106cde.pth" response = requests.get(url) # 确保请求成功 if response.status_code == 200: # 指定文件保存的位置 file_path = f"{CheckpointsDir}/face-parse-bisent/resnet18-5c106cde.pth" # 将文件内容写入指定位置 with open(file_path, "wb") as f: f.write(response.content) else: print(f"请求失败，状态码：{response.status_code}") toc = time.time() print(f"download cost {toc-tic} seconds") print_directory_contents(CheckpointsDir) else: print("Already download the model.") # download_model() # for huggingface deployment. def video2imgs(vid_path, save_path, ext = '.png',cut_frame = 10000000): cap = cv2.VideoCapture(vid_path) count = 0 while True: if count > cut_frame: break ret, frame = cap.read() if ret: cv2.imwrite(f"{save_path}/{count:08d}.png", frame) count += 1 else: break def osmakedirs(path_list): for path in path_list: os.makedirs(path) if not os.path.exists(path) else None class MuseTalk_RealTime: def __init__(self): if torch.cuda.is_available(): device = "cuda" elif torch.backends.mps.is_available(): device = "mps" else: device = "cpu" self.device = device self.load = False # self.avatar_info = { # "avatar_id":avatar_id, # "video_path":video_path, # "bbox_shift":bbox_shift # } self.skip_save_images = False self.avatar_id = None self.avatar_path = None self.full_imgs_path = None self.coords_path = None self.latents_out_path = None self.video_out_path = None self.mask_out_path = None self.mask_coords_path = None self.avatar_info_path = None self.input_latent_list_cycle = None self.mask_coords_list_cycle = None self.mask_list_cycle = None self.frame_list_cycle = None def init_model(self): # load model weights self.audio_processor, self.vae, self.unet, self.pe = load_all_model() self.timesteps = torch.tensor([0], device=self.device) self.pe = self.pe.half() self.vae.vae = self.vae.vae.half() self.unet.model = self.unet.model.half() self.load = True def process_frames(self, res_frame_queue, video_len): print(video_len) while True: if self.idx>=video_len-1: break try: start = time.time() res_frame = res_frame_queue.get(block=True, timeout=1) except queue.Empty: continue bbox = self.coord_list_cycle[self.idx%(len(self.coord_list_cycle))] ori_frame = copy.deepcopy(self.frame_list_cycle[self.idx%(len(self.frame_list_cycle))]) x1, y1, x2, y2 = bbox try: res_frame = cv2.resize(res_frame.astype(np.uint8),(x2-x1,y2-y1)) except: continue mask = self.mask_list_cycle[self.idx%(len(self.mask_list_cycle))] mask_crop_box = self.mask_coords_list_cycle[self.idx%(len(self.mask_coords_list_cycle))] #combine_frame = get_image(ori_frame,res_frame,bbox) combine_frame = get_image_blending(ori_frame,res_frame,bbox,mask,mask_crop_box) if self.skip_save_images is False: cv2.imwrite(f"{self.avatar_path}/tmp/{str(self.idx).zfill(8)}.png",combine_frame) self.idx = self.idx + 1 def prepare_material(self, video_path, bbox_shift, progress=gr.Progress(track_tqdm=True)): self.video_path = video_path self.bbox_shift = bbox_shift self.avatar_id = os.path.basename(video_path).split(".")[0] self.avatar_path = f"./results/avatars/{self.avatar_id}" self.full_imgs_path = f"{self.avatar_path}/full_imgs" self.coords_path = f"{self.avatar_path}/coords.pkl" self.latents_out_path= f"{self.avatar_path}/latents.pt" self.video_out_path = f"{self.avatar_path}/vid_output/" self.mask_out_path =f"{self.avatar_path}/mask" self.mask_coords_path =f"{self.avatar_path}/mask_coords.pkl" self.avatar_info_path = f"{self.avatar_path}/avator_info.json" # 若存在先删除 if os.path.exists(self.full_imgs_path): shutil.rmtree(self.full_imgs_path) shutil.rmtree(self.mask_out_path) shutil.rmtree(self.video_out_path) osmakedirs([self.avatar_path,self.full_imgs_path,self.video_out_path,self.mask_out_path]) print("preparing data materials ... ...") progress(0, desc = "preparing data materials ...") if os.path.isfile(video_path): video2imgs(video_path, self.full_imgs_path, ext = 'png') else: print(f"copy files in {video_path}") files = os.listdir(video_path) files.sort() files = [file for file in files if file.split(".")[-1]=="png"] for filename in files: shutil.copyfile(f"{video_path}/{filename}", f"{self.full_imgs_path}/{filename}") input_img_list = sorted(glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]'))) # bbox_shift_text = get_bbox_range(input_img_list, self.bbox_shift) progress(0, desc = "extracting landmarks...") print("extracting landmarks ...") coord_list, frame_list, bbox_shift_text = get_landmark_and_bbox(input_img_list, bbox_shift) input_latent_list = [] idx = -1 # maker if the bbox is not sufficient coord_placeholder = (0.0,0.0,0.0,0.0) for bbox, frame in zip(coord_list, frame_list): idx = idx + 1 if bbox == coord_placeholder: continue x1, y1, x2, y2 = bbox crop_frame = frame[y1:y2, x1:x2] resized_crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4) latents = self.vae.get_latents_for_unet(resized_crop_frame) input_latent_list.append(latents) self.frame_list_cycle = frame_list + frame_list[::-1] self.coord_list_cycle = coord_list + coord_list[::-1] self.input_latent_list_cycle = input_latent_list + input_latent_list[::-1] self.mask_coords_list_cycle = [] self.mask_list_cycle = [] progress(0, desc = "saving masks...") for i,frame in enumerate(tqdm(self.frame_list_cycle)): cv2.imwrite(f"{self.full_imgs_path}/{str(i).zfill(8)}.png",frame) face_box = self.coord_list_cycle[i] mask,crop_box = get_image_prepare_material(frame,face_box) cv2.imwrite(f"{self.mask_out_path}/{str(i).zfill(8)}.png",mask) self.mask_coords_list_cycle += [crop_box] self.mask_list_cycle.append(mask) with open(self.mask_coords_path, 'wb') as f: pickle.dump(self.mask_coords_list_cycle, f) with open(self.coords_path, 'wb') as f: pickle.dump(self.coord_list_cycle, f) torch.save(self.input_latent_list_cycle, os.path.join(self.latents_out_path)) return video_path, bbox_shift_text def prepare_material_(self): print("preparing data materials ... ...") # with open(self.avatar_info_path, "w") as f: # json.dump(self.avatar_info, f) if os.path.isfile(self.video_path): video2imgs(self.video_path, self.full_imgs_path, ext = 'png') else: print(f"copy files in {self.video_path}") files = os.listdir(self.video_path) files.sort() files = [file for file in files if file.split(".")[-1]=="png"] for filename in files: shutil.copyfile(f"{self.video_path}/{filename}", f"{self.full_imgs_path}/{filename}") input_img_list = sorted(glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]'))) bbox_shift_text = get_bbox_range(input_img_list, self.bbox_shift) print("extracting landmarks...") coord_list, frame_list = get_landmark_and_bbox(input_img_list, self.bbox_shift) input_latent_list = [] idx = -1 # maker if the bbox is not sufficient coord_placeholder = (0.0,0.0,0.0,0.0) for bbox, frame in zip(coord_list, frame_list): idx = idx + 1 if bbox == coord_placeholder: continue x1, y1, x2, y2 = bbox crop_frame = frame[y1:y2, x1:x2] resized_crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4) latents = self.vae.get_latents_for_unet(resized_crop_frame) input_latent_list.append(latents) self.frame_list_cycle = frame_list + frame_list[::-1] self.coord_list_cycle = coord_list + coord_list[::-1] self.input_latent_list_cycle = input_latent_list + input_latent_list[::-1] self.mask_coords_list_cycle = [] self.mask_list_cycle = [] for i,frame in enumerate(tqdm(self.frame_list_cycle)): cv2.imwrite(f"{self.full_imgs_path}/{str(i).zfill(8)}.png",frame) face_box = self.coord_list_cycle[i] mask,crop_box = get_image_prepare_material(frame,face_box) cv2.imwrite(f"{self.mask_out_path}/{str(i).zfill(8)}.png",mask) self.mask_coords_list_cycle += [crop_box] self.mask_list_cycle.append(mask) with open(self.mask_coords_path, 'wb') as f: pickle.dump(self.mask_coords_list_cycle, f) with open(self.coords_path, 'wb') as f: pickle.dump(self.coord_list_cycle, f) torch.save(self.input_latent_list_cycle, os.path.join(self.latents_out_path)) return bbox_shift_text def inference_noprepare(self, audio_path, source_video, bbox_shift, batch_size = 4, fps = 25, progress = gr.Progress(track_tqdm=True)): out_vid_name = "res" os.makedirs(self.avatar_path+'/tmp',exist_ok =True) print("start inference") ############################################## extract audio feature ############################################## start_time = time.time() whisper_feature = self.audio_processor.audio2feat(audio_path) whisper_chunks = self.audio_processor.feature2chunks(feature_array=whisper_feature,fps=fps) print(f"processing audio:{audio_path} costs {(time.time() - start_time) * 1000}ms") ############################################## inference batch by batch ############################################## video_num = len(whisper_chunks) res_frame_queue = queue.Queue() self.idx = 0 # # Create a sub-thread and start it process_thread = threading.Thread(target=self.process_frames, args=(res_frame_queue, video_num)) process_thread.start() gen = datagen(whisper_chunks, self.input_latent_list_cycle, batch_size) start_time = time.time() res_frame_list = [] for i, (whisper_batch,latent_batch) in enumerate(tqdm(gen,total=int(np.ceil(float(video_num)/batch_size)))): audio_feature_batch = torch.from_numpy(whisper_batch) audio_feature_batch = audio_feature_batch.to(device=self.unet.device, dtype=self.unet.model.dtype) audio_feature_batch = self.pe(audio_feature_batch) latent_batch = latent_batch.to(dtype=self.unet.model.dtype) 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_queue.put(res_frame) # Close the queue and sub-thread after all tasks are completed process_thread.join() if self.skip_save_images is True: print('Total process time of {} frames without saving images = {}s'.format( video_num, time.time()-start_time)) else: print('Total process time of {} frames including saving images = {}s'.format( video_num, time.time()-start_time)) if out_vid_name is not None and self.skip_save_images is False: # optional cmd_img2video = f"ffmpeg -y -v warning -r {fps} -f image2 -i {self.avatar_path}/tmp/%08d.png -vcodec libx264 -vf format=rgb24,scale=out_color_matrix=bt709,format=yuv420p -crf 18 {self.avatar_path}/temp.mp4" print(cmd_img2video) os.system(cmd_img2video) output_vid = os.path.join(self.video_out_path, out_vid_name+".mp4") # on cmd_combine_audio = f"ffmpeg -y -v warning -i {audio_path} -i {self.avatar_path}/temp.mp4 {output_vid}" print(cmd_combine_audio) os.system(cmd_combine_audio) os.remove(f"{self.avatar_path}/temp.mp4") shutil.rmtree(f"{self.avatar_path}/tmp") print(f"result is save to {output_vid}") print("\n") return output_vid def inference(self, audio_path, source_video, bbox_shift, batch_size = 4, fps = 25, progress = gr.Progress(track_tqdm=True)): self.video_path = source_video self.bbox_shift = bbox_shift self.avatar_id = os.path.basename(source_video).split(".")[0] self.avatar_path = f"./results/avatars/{self.avatar_id}" self.full_imgs_path = f"{self.avatar_path}/full_imgs" self.coords_path = f"{self.avatar_path}/coords.pkl" self.latents_out_path= f"{self.avatar_path}/latents.pt" self.video_out_path = f"{self.avatar_path}/vid_output/" self.mask_out_path =f"{self.avatar_path}/mask" self.mask_coords_path =f"{self.avatar_path}/mask_coords.pkl" self.avatar_info_path = f"{self.avatar_path}/avator_info.json" osmakedirs([self.avatar_path,self.full_imgs_path,self.video_out_path,self.mask_out_path]) bbox_shift_text = None if os.path.exists(self.avatar_path): response = input(f"{self.avatar_id} exists, Do you want to re-create it ? (y/n)") if response.lower() == "y": shutil.rmtree(self.avatar_path) print("*********************************") print(f" creating avator: {self.avatar_id}") print("*********************************") osmakedirs([self.avatar_path,self.full_imgs_path,self.video_out_path,self.mask_out_path]) bbox_shift_text = self.prepare_material_() else: self.input_latent_list_cycle = torch.load(self.latents_out_path) with open(self.coords_path, 'rb') as f: self.coord_list_cycle = pickle.load(f) input_img_list = glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]')) input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0])) self.frame_list_cycle = read_imgs(input_img_list) with open(self.mask_coords_path, 'rb') as f: self.mask_coords_list_cycle = pickle.load(f) input_mask_list = glob.glob(os.path.join(self.mask_out_path, '*.[jpJP][pnPN]*[gG]')) input_mask_list = sorted(input_mask_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0])) self.mask_list_cycle = read_imgs(input_mask_list) else: print("*********************************") print(f" creating avator: {self.avatar_id}") print("*********************************") osmakedirs([self.avatar_path,self.full_imgs_path,self.video_out_path,self.mask_out_path]) bbox_shift_text = self.prepare_material_() if self.input_latent_list_cycle is None: self.input_latent_list_cycle = torch.load(self.latents_out_path) if self.mask_list_cycle is None: with open(self.coords_path, 'rb') as f: self.coord_list_cycle = pickle.load(f) if self.frame_list_cycle is None: input_img_list = glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]')) input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0])) self.frame_list_cycle = read_imgs(input_img_list) if self.mask_coords_list_cycle is None: with open(self.mask_coords_path, 'rb') as f: self.mask_coords_list_cycle = pickle.load(f) if self.mask_list_cycle is None: input_mask_list = glob.glob(os.path.join(self.mask_out_path, '*.[jpJP][pnPN]*[gG]')) input_mask_list = sorted(input_mask_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0])) self.mask_list_cycle = read_imgs(input_mask_list) with open(self.coords_path, 'rb') as f: self.coord_list_cycle = pickle.load(f) if bbox_shift_text is None: bbox_shift_text = get_bbox_range(input_img_list, bbox_shift) out_vid_name = "res" os.makedirs(self.avatar_path+'/tmp',exist_ok =True) print("start inference") ############################################## extract audio feature ############################################## start_time = time.time() whisper_feature = self.audio_processor.audio2feat(audio_path) whisper_chunks = self.audio_processor.feature2chunks(feature_array=whisper_feature,fps=fps) print(f"processing audio:{audio_path} costs {(time.time() - start_time) * 1000}ms") ############################################## inference batch by batch ############################################## video_num = len(whisper_chunks) res_frame_queue = queue.Queue() self.idx = 0 # # Create a sub-thread and start it process_thread = threading.Thread(target=self.process_frames, args=(res_frame_queue, video_num)) process_thread.start() gen = datagen(whisper_chunks, self.input_latent_list_cycle, batch_size) start_time = time.time() res_frame_list = [] for i, (whisper_batch,latent_batch) in enumerate(tqdm(gen,total=int(np.ceil(float(video_num)/batch_size)))): audio_feature_batch = torch.from_numpy(whisper_batch) audio_feature_batch = audio_feature_batch.to(device=self.unet.device, dtype=self.unet.model.dtype) audio_feature_batch = self.pe(audio_feature_batch) latent_batch = latent_batch.to(dtype=self.unet.model.dtype) 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_queue.put(res_frame) # Close the queue and sub-thread after all tasks are completed process_thread.join() if self.skip_save_images is True: print('Total process time of {} frames without saving images = {}s'.format( video_num, time.time()-start_time)) else: print('Total process time of {} frames including saving images = {}s'.format( video_num, time.time()-start_time)) if out_vid_name is not None and self.skip_save_images is False: # optional cmd_img2video = f"ffmpeg -y -v warning -r {fps} -f image2 -i {self.avatar_path}/tmp/%08d.png -vcodec libx264 -vf format=rgb24,scale=out_color_matrix=bt709,format=yuv420p -crf 18 {self.avatar_path}/temp.mp4" print(cmd_img2video) os.system(cmd_img2video) output_vid = os.path.join(self.video_out_path, out_vid_name+".mp4") # on cmd_combine_audio = f"ffmpeg -y -v warning -i {audio_path} -i {self.avatar_path}/temp.mp4 {output_vid}" print(cmd_combine_audio) os.system(cmd_combine_audio) os.remove(f"{self.avatar_path}/temp.mp4") shutil.rmtree(f"{self.avatar_path}/tmp") print(f"result is save to {output_vid}") print("\n") return output_vid, bbox_shift_text class MuseTalk: def __init__(self): # load model weights self.audio_processor, self.vae, self.unet, self.pe = load_all_model() import platform if torch.cuda.is_available(): device = "cuda" elif platform.system() == 'Darwin': # macos device = "mps" else: device = "cpu" self.timesteps = torch.tensor([0], device=device) @torch.no_grad() def inference(self, audio_path, video_path, bbox_shift): args_dict={"result_dir":'./results/output', "fps":25, "batch_size":8, "output_vid_name":'', "use_saved_coord":False}#same with inferenece script args = Namespace(**args_dict) print(args) 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(args.result_dir, output_basename) # related to video & audio inputs crop_coord_save_path = os.path.join(result_img_save_path, input_basename+".pkl") # only related to video input os.makedirs(result_img_save_path,exist_ok =True) if args.output_vid_name=="": output_vid_name = os.path.join(args.result_dir, output_basename+".mp4") else: output_vid_name = os.path.join(args.result_dir, args.output_vid_name) ############################################## extract frames from source video ############################################## if get_file_type(video_path)=="video": save_dir_full = os.path.join(args.result_dir, input_basename) os.makedirs(save_dir_full,exist_ok = True) # cmd = f"ffmpeg -v fatal -i {video_path} -start_number 0 {save_dir_full}/%08d.png" # os.system(cmd) # 读取视频 reader = imageio.get_reader(video_path) # 保存图片 for i, im in enumerate(reader): imageio.imwrite(f"{save_dir_full}/{i:08d}.png", im) 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 = args.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 args.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(input_img_list, bbox_shift) with open(crop_coord_save_path, 'wb') as f: pickle.dump(coord_list, f) bbox_shift_text=get_bbox_range(input_img_list, bbox_shift) 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 = args.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(ori_frame,res_frame,bbox) cv2.imwrite(f"{result_img_save_path}/{str(i).zfill(8)}.png",combine_frame) # cmd_img2video = f"ffmpeg -y -v fatal -r {fps} -f image2 -i {result_img_save_path}/%08d.png -vcodec libx264 -vf format=rgb24,scale=out_color_matrix=bt709,format=yuv420p temp.mp4" # print(cmd_img2video) # os.system(cmd_img2video) # 帧率 fps = 25 # 图片路径 # 输出视频路径 output_video = 'temp.mp4' # 读取图片 def is_valid_image(file): pattern = re.compile(r'\d{8}\.png') return pattern.match(file) images = [] files = [file for file in os.listdir(result_img_save_path) if is_valid_image(file)] files.sort(key=lambda x: int(x.split('.')[0])) for file in files: filename = os.path.join(result_img_save_path, file) images.append(imageio.imread(filename)) # 保存视频 imageio.mimwrite(output_video, images, 'FFMPEG', fps=fps, codec='libx264', pixelformat='yuv420p') # 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) input_video = './temp.mp4' # Check if the input_video and audio_path exist if not os.path.exists(input_video): raise FileNotFoundError(f"Input video file not found: {input_video}") if not os.path.exists(audio_path): raise FileNotFoundError(f"Audio file not found: {audio_path}") # 读取视频 reader = imageio.get_reader(input_video) fps = reader.get_meta_data()['fps'] # 获取原视频的帧率 # 将帧存储在列表中 frames = images # 保存视频并添加音频 # imageio.mimwrite(output_vid_name, frames, 'FFMPEG', fps=fps, codec='libx264', audio_codec='aac', input_params=['-i', audio_path]) # input_video = ffmpeg.input(input_video) # input_audio = ffmpeg.input(audio_path) print(len(frames)) # imageio.mimwrite( # output_video, # frames, # 'FFMPEG', # fps=25, # codec='libx264', # audio_codec='aac', # input_params=['-i', audio_path], # output_params=['-y'], # Add the '-y' flag to overwrite the output file if it exists # ) # writer = imageio.get_writer(output_vid_name, fps = 25, codec='libx264', quality=10, pixelformat='yuvj444p') # for im in frames: # writer.append_data(im) # writer.close() # Load the video video_clip = VideoFileClip(input_video) # Load the audio audio_clip = AudioFileClip(audio_path) # Set the audio to the video video_clip = video_clip.set_audio(audio_clip) # Write the output video video_clip.write_videofile(output_vid_name, codec='libx264', audio_codec='aac',fps=25) os.remove("temp.mp4") #shutil.rmtree(result_img_save_path) print(f"result is save to {output_vid_name}", bbox_shift_text) return output_vid_name, bbox_shift_text def check_video(self, video): if not isinstance(video, str): return video # in case of none type # Define the output video file name dir_path, file_name = os.path.split(video) if file_name.startswith("outputxxx_"): return video # Add the output prefix to the file name output_file_name = "outputxxx_" + file_name os.makedirs('./results',exist_ok=True) os.makedirs('./results/output',exist_ok=True) os.makedirs('./results/input',exist_ok=True) # Combine the directory path and the new file name output_video = os.path.join('./results/input', output_file_name) # # Run the ffmpeg command to change the frame rate to 25fps # command = f"ffmpeg -i {video} -r 25 -vcodec libx264 -vtag hvc1 -pix_fmt yuv420p crf 18 {output_video} -y" # read video reader = imageio.get_reader(video) fps = reader.get_meta_data()['fps'] # get fps from original video # conver fps to 25 frames = [im for im in reader] target_fps = 25 L = len(frames) L_target = int(L / fps * target_fps) original_t = [x / fps for x in range(1, L+1)] t_idx = 0 target_frames = [] for target_t in tqdm(range(1, L_target+1)): while target_t / target_fps > original_t[t_idx]: t_idx += 1 # find the first t_idx so that target_t / target_fps <= original_t[t_idx] if t_idx >= L: break target_frames.append(frames[t_idx]) # save video imageio.mimwrite(output_video, target_frames, 'FFMPEG', fps=25, codec='libx264', quality=9, pixelformat='yuv420p') return output_video if __name__ == "__main__": # musetalk = MuseTalk() musetalk = MuseTalk_RealTime() audio_path = "Musetalk/data/audio/sun.wav" video_path = "Musetalk/data/video/yongen_musev.mp4" bbox_shift = 5 video_path, bbox_shift_text = musetalk.prepare_material(video_path, bbox_shift) # print(video_path, bbox_shift_text) print("Inference Params:", audio_path, video_path, bbox_shift) res_video = musetalk.inference_noprepare(audio_path, video_path, bbox_shift) # output_video = musetalk.check_video(video_path) # print("output_video:", output_video) # res_video, bbox_shift_scale = musetalk.inference(audio_path, video_path, bbox_shift) # print(bbox_shift_scale)