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Add codes for real time inference
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czk32611
295
scripts/realtime_inference.py
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295
scripts/realtime_inference.py
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import argparse
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import os
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from omegaconf import OmegaConf
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import numpy as np
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import cv2
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import torch
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import glob
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import pickle
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import sys
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from tqdm import tqdm
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import copy
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import json
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from musetalk.utils.utils import get_file_type,get_video_fps,datagen
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from musetalk.utils.preprocessing import get_landmark_and_bbox,read_imgs,coord_placeholder
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from musetalk.utils.blending import get_image,get_image_prepare_material,get_image_blending
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from musetalk.utils.utils import load_all_model
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import shutil
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import threading
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import queue
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import time
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# load model weights
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audio_processor,vae,unet,pe = load_all_model()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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timesteps = torch.tensor([0], device=device)
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def video2imgs(vid_path, save_path, ext = '.png',cut_frame = 10000000):
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cap = cv2.VideoCapture(vid_path)
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count = 0
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while True:
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if count > cut_frame:
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break
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ret, frame = cap.read()
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if ret:
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cv2.imwrite(f"{save_path}/{count:08d}.png", frame)
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count += 1
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else:
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break
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def osmakedirs(path_list):
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for path in path_list:
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os.makedirs(path) if not os.path.exists(path) else None
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@torch.no_grad()
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class Avatar:
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def __init__(self, avatar_id, video_path, bbox_shift, batch_size, preparation):
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self.avatar_id = avatar_id
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self.video_path = video_path
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self.bbox_shift = bbox_shift
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self.avatar_path = f"./results/avatars/{avatar_id}"
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self.full_imgs_path = f"{self.avatar_path}/full_imgs"
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self.coords_path = f"{self.avatar_path}/coords.pkl"
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self.latents_out_path= f"{self.avatar_path}/latents.pt"
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self.video_out_path = f"{self.avatar_path}/vid_output/"
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self.mask_out_path =f"{self.avatar_path}/mask"
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self.mask_coords_path =f"{self.avatar_path}/mask_coords.pkl"
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self.avatar_info_path = f"{self.avatar_path}/avator_info.json"
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self.avatar_info = {
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"avatar_id":avatar_id,
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"video_path":video_path,
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"bbox_shift":bbox_shift
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}
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self.preparation = preparation
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self.batch_size = batch_size
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self.idx = 0
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self.init()
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def init(self):
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if self.preparation:
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if os.path.exists(self.avatar_path):
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response = input(f"{self.avatar_id} exists, Do you want to re-create it ? (y/n)")
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if response.lower() == "y":
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shutil.rmtree(self.avatar_path)
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print("*********************************")
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print(f" creating avator: {self.avatar_id}")
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print("*********************************")
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osmakedirs([self.avatar_path,self.full_imgs_path,self.video_out_path,self.mask_out_path])
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self.prepare_material()
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else:
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self.input_latent_list_cycle = torch.load(self.latents_out_path)
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with open(self.coords_path, 'rb') as f:
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self.coord_list_cycle = pickle.load(f)
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input_img_list = glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]'))
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input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
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self.frame_list_cycle = read_imgs(input_img_list)
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with open(self.mask_coords_path, 'rb') as f:
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self.mask_coords_list_cycle = pickle.load(f)
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input_mask_list = glob.glob(os.path.join(self.mask_out_path, '*.[jpJP][pnPN]*[gG]'))
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input_mask_list = sorted(input_mask_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
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self.mask_list_cycle = read_imgs(input_mask_list)
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else:
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print("*********************************")
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print(f" creating avator: {self.avatar_id}")
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print("*********************************")
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osmakedirs([self.avatar_path,self.full_imgs_path,self.video_out_path,self.mask_out_path])
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self.prepare_material()
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else:
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with open(self.avatar_info_path, "r") as f:
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avatar_info = json.load(f)
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if avatar_info['bbox_shift'] != self.avatar_info['bbox_shift']:
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response = input(f" 【bbox_shift】 is changed, you need to re-create it ! (c/continue)")
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if response.lower() == "c":
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shutil.rmtree(self.avatar_path)
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print("*********************************")
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print(f" creating avator: {self.avatar_id}")
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print("*********************************")
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osmakedirs([self.avatar_path,self.full_imgs_path,self.video_out_path,self.mask_out_path])
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self.prepare_material()
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else:
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sys.exit()
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else:
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self.input_latent_list_cycle = torch.load(self.latents_out_path)
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with open(self.coords_path, 'rb') as f:
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self.coord_list_cycle = pickle.load(f)
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input_img_list = glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]'))
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input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
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self.frame_list_cycle = read_imgs(input_img_list)
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with open(self.mask_coords_path, 'rb') as f:
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self.mask_coords_list_cycle = pickle.load(f)
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input_mask_list = glob.glob(os.path.join(self.mask_out_path, '*.[jpJP][pnPN]*[gG]'))
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input_mask_list = sorted(input_mask_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
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self.mask_list_cycle = read_imgs(input_mask_list)
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def prepare_material(self):
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print("preparing data materials ... ...")
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with open(self.avatar_info_path, "w") as f:
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json.dump(self.avatar_info, f)
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if os.path.isfile(self.video_path):
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video2imgs(self.video_path, self.full_imgs_path, ext = 'png')
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else:
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print(f"copy files in {self.video_path}")
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files = os.listdir(self.video_path)
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files.sort()
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files = [file for file in files if file.split(".")[-1]=="png"]
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for filename in files:
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shutil.copyfile(f"{self.video_path}/{filename}", f"{self.full_imgs_path}/{filename}")
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input_img_list = sorted(glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]')))
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print("extracting landmarks...")
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coord_list, frame_list = get_landmark_and_bbox(input_img_list, self.bbox_shift)
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input_latent_list = []
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idx = -1
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# maker if the bbox is not sufficient
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coord_placeholder = (0.0,0.0,0.0,0.0)
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for bbox, frame in zip(coord_list, frame_list):
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idx = idx + 1
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if bbox == coord_placeholder:
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continue
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x1, y1, x2, y2 = bbox
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crop_frame = frame[y1:y2, x1:x2]
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resized_crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4)
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latents = vae.get_latents_for_unet(resized_crop_frame)
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input_latent_list.append(latents)
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self.frame_list_cycle = frame_list + frame_list[::-1]
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self.coord_list_cycle = coord_list + coord_list[::-1]
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self.input_latent_list_cycle = input_latent_list + input_latent_list[::-1]
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self.mask_coords_list_cycle = []
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self.mask_list_cycle = []
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for i,frame in enumerate(tqdm(self.frame_list_cycle)):
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cv2.imwrite(f"{self.full_imgs_path}/{str(i).zfill(8)}.png",frame)
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face_box = self.coord_list_cycle[i]
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mask,crop_box = get_image_prepare_material(frame,face_box)
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cv2.imwrite(f"{self.mask_out_path}/{str(i).zfill(8)}.png",mask)
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self.mask_coords_list_cycle += [crop_box]
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self.mask_list_cycle.append(mask)
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with open(self.mask_coords_path, 'wb') as f:
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pickle.dump(self.mask_coords_list_cycle, f)
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with open(self.coords_path, 'wb') as f:
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pickle.dump(self.coord_list_cycle, f)
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torch.save(self.input_latent_list_cycle, os.path.join(self.latents_out_path))
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#
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def process_frames(self, res_frame_queue,video_len):
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print(video_len)
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while True:
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if self.idx>=video_len-1:
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break
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try:
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start = time.time()
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res_frame = res_frame_queue.get(block=True, timeout=1)
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except queue.Empty:
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continue
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bbox = self.coord_list_cycle[self.idx%(len(self.coord_list_cycle))]
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ori_frame = copy.deepcopy(self.frame_list_cycle[self.idx%(len(self.frame_list_cycle))])
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x1, y1, x2, y2 = bbox
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try:
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res_frame = cv2.resize(res_frame.astype(np.uint8),(x2-x1,y2-y1))
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except:
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continue
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mask = self.mask_list_cycle[self.idx%(len(self.mask_list_cycle))]
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mask_crop_box = self.mask_coords_list_cycle[self.idx%(len(self.mask_coords_list_cycle))]
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#combine_frame = get_image(ori_frame,res_frame,bbox)
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combine_frame = get_image_blending(ori_frame,res_frame,bbox,mask,mask_crop_box)
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fps = 1/(time.time()-start)
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print(f"Generating the {self.idx}-th frame with FPS: {fps:.2f}")
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cv2.imwrite(f"{self.avatar_path}/tmp/{str(self.idx).zfill(8)}.png",combine_frame)
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self.idx = self.idx + 1
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def inference(self, audio_path, out_vid_name, fps):
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os.makedirs(self.avatar_path+'/tmp',exist_ok =True)
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############################################## extract audio feature ##############################################
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whisper_feature = audio_processor.audio2feat(audio_path)
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whisper_chunks = audio_processor.feature2chunks(feature_array=whisper_feature,fps=fps)
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############################################## inference batch by batch ##############################################
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video_num = len(whisper_chunks)
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print("start inference")
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res_frame_queue = queue.Queue()
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self.idx = 0
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# # Create a sub-thread and start it
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process_thread = threading.Thread(target=self.process_frames, args=(res_frame_queue,video_num))
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process_thread.start()
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start_time = time.time()
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gen = datagen(whisper_chunks,self.input_latent_list_cycle, self.batch_size)
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print(f"processing audio:{audio_path} costs {(time.time() - start_time) * 1000}ms")
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start_time = time.time()
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res_frame_list = []
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for i, (whisper_batch,latent_batch) in enumerate(tqdm(gen,total=int(np.ceil(float(video_num)/self.batch_size)))):
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start_time = time.time()
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tensor_list = [torch.FloatTensor(arr) for arr in whisper_batch]
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audio_feature_batch = torch.stack(tensor_list).to(unet.device) # torch, B, 5*N,384
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audio_feature_batch = pe(audio_feature_batch)
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pred_latents = unet.model(latent_batch, timesteps, encoder_hidden_states=audio_feature_batch).sample
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recon = vae.decode_latents(pred_latents)
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for res_frame in recon:
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res_frame_queue.put(res_frame)
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# Close the queue and sub-thread after all tasks are completed
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process_thread.join()
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if out_vid_name is not None:
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# optional
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cmd_img2video = f"ffmpeg -y -v fatal -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"
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print(cmd_img2video)
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os.system(cmd_img2video)
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output_vid = os.path.join(self.video_out_path, out_vid_name+".mp4") # on
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cmd_combine_audio = f"ffmpeg -y -v fatal -i {audio_path} -i {self.avatar_path}/temp.mp4 {output_vid}"
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print(cmd_combine_audio)
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os.system(cmd_combine_audio)
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os.remove(f"{self.avatar_path}/temp.mp4")
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shutil.rmtree(f"{self.avatar_path}/tmp")
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print(f"result is save to {output_vid}")
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if __name__ == "__main__":
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'''
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This script is used to simulate online chatting and applies necessary pre-processing such as face detection and face parsing in advance. During online chatting, only UNet and the VAE decoder are involved, which makes MuseTalk real-time.
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'''
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parser = argparse.ArgumentParser()
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parser.add_argument("--inference_config", type=str, default="configs/inference/realtime.yaml")
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parser.add_argument("--fps", type=int, default=25)
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parser.add_argument("--batch_size", type=int, default=4)
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args = parser.parse_args()
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inference_config = OmegaConf.load(args.inference_config)
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print(inference_config)
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for avatar_id in inference_config:
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data_preparation = inference_config[avatar_id]["preparation"]
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video_path = inference_config[avatar_id]["video_path"]
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bbox_shift = inference_config[avatar_id]["bbox_shift"]
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avatar = Avatar(
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avatar_id = avatar_id,
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video_path = video_path,
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bbox_shift = bbox_shift,
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batch_size = args.batch_size,
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preparation= data_preparation)
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audio_clips = inference_config[avatar_id]["audio_clips"]
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for audio_num, audio_path in audio_clips.items():
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print("Inferring using:",audio_path)
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avatar.inference(audio_path, audio_num, args.fps)
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