import torch from time import strftime import os, sys, time from argparse import ArgumentParser from src.utils.preprocess import CropAndExtract from src.test_audio2coeff import Audio2Coeff from src.facerender.animate import AnimateFromCoeff from src.generate_batch import get_data from src.generate_facerender_batch import get_facerender_data def main(args): #torch.backends.cudnn.enabled = False pic_path = args.source_image audio_path = args.driven_audio save_dir = os.path.join(args.result_dir, strftime("%Y_%m_%d_%H.%M.%S")) os.makedirs(save_dir, exist_ok=True) pose_style = args.pose_style device = args.device batch_size = args.batch_size camera_yaw_list = args.camera_yaw camera_pitch_list = args.camera_pitch camera_roll_list = args.camera_roll current_code_path = sys.argv[0] current_root_path = os.path.split(current_code_path)[0] os.environ['TORCH_HOME']=os.path.join(current_root_path, args.checkpoint_dir) path_of_lm_croper = os.path.join(current_root_path, args.checkpoint_dir, 'shape_predictor_68_face_landmarks.dat') path_of_net_recon_model = os.path.join(current_root_path, args.checkpoint_dir, 'epoch_20.pth') dir_of_BFM_fitting = os.path.join(current_root_path, args.checkpoint_dir, 'BFM_Fitting') wav2lip_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'wav2lip.pth') audio2pose_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'auido2pose_00140-model.pth') audio2pose_yaml_path = os.path.join(current_root_path, 'src', 'config', 'auido2pose.yaml') audio2exp_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'auido2exp_00300-model.pth') audio2exp_yaml_path = os.path.join(current_root_path, 'src', 'config', 'auido2exp.yaml') free_view_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'facevid2vid_00189-model.pth.tar') mapping_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'mapping_00229-model.pth.tar') facerender_yaml_path = os.path.join(current_root_path, 'src', 'config', 'facerender.yaml') #init model print(path_of_net_recon_model) preprocess_model = CropAndExtract(path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting, device) print(audio2pose_checkpoint) print(audio2exp_checkpoint) audio_to_coeff = Audio2Coeff(audio2pose_checkpoint, audio2pose_yaml_path, audio2exp_checkpoint, audio2exp_yaml_path, wav2lip_checkpoint, device) print(free_view_checkpoint) print(mapping_checkpoint) animate_from_coeff = AnimateFromCoeff(free_view_checkpoint, mapping_checkpoint, facerender_yaml_path, device) #crop image and extract 3dmm from image first_frame_dir = os.path.join(save_dir, 'first_frame_dir') os.makedirs(first_frame_dir, exist_ok=True) first_coeff_path, crop_pic_path = preprocess_model.generate(pic_path, first_frame_dir) if first_coeff_path is None: print("Can't get the coeffs of the input") return #audio2ceoff batch = get_data(first_coeff_path, audio_path, device) coeff_path = audio_to_coeff.generate(batch, save_dir, pose_style) # 3dface render if args.face3dvis: from src.face3d.visualize import gen_composed_video gen_composed_video(args, device, first_coeff_path, coeff_path, audio_path, os.path.join(save_dir, '3dface.mp4')) #coeff2video data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, batch_size, camera_yaw_list, camera_pitch_list, camera_roll_list, expression_scale=args.expression_scale, still_mode=args.still) animate_from_coeff.generate(data, save_dir, enhancer=args.enhancer) video_name = data['video_name'] if args.enhancer is not None: print(f'The generated video is named {video_name}_enhanced in {save_dir}') else: print(f'The generated video is named {video_name} in {save_dir}') return os.path.join(save_dir, video_name+'.mp4'), os.path.join(save_dir, video_name+'.mp4') if __name__ == '__main__': parser = ArgumentParser() parser.add_argument("--driven_audio", default='./examples/driven_audio/japanese.wav', help="path to driven audio") parser.add_argument("--source_image", default='./examples/source_image/art_0.png', help="path to source image") parser.add_argument("--checkpoint_dir", default='./checkpoints', help="path to output") parser.add_argument("--result_dir", default='./results', help="path to output") parser.add_argument("--pose_style", type=int, default=0, help="input pose style from [0, 46)") parser.add_argument("--batch_size", type=int, default=2, help="the batch size of facerender") parser.add_argument("--expression_scale", type=float, default=1., help="the batch size of facerender") parser.add_argument('--camera_yaw', nargs='+', type=int, default=[0], help="the camera yaw degree") parser.add_argument('--camera_pitch', nargs='+', type=int, default=[0], help="the camera pitch degree") parser.add_argument('--camera_roll', nargs='+', type=int, default=[0], help="the camera roll degree") parser.add_argument('--enhancer', type=str, default=None, help="Face enhancer, [GFPGAN]") parser.add_argument("--cpu", dest="cpu", action="store_true") parser.add_argument("--face3dvis", action="store_true", help="generate 3d face and 3d landmarks") parser.add_argument("--still", action="store_true") # net structure and parameters parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='not use') parser.add_argument('--init_path', type=str, default=None, help='not Use') parser.add_argument('--use_last_fc',default=False, help='zero initialize the last fc') parser.add_argument('--bfm_folder', type=str, default='./checkpoints/BFM_Fitting/') parser.add_argument('--bfm_model', type=str, default='BFM_model_front.mat', help='bfm model') # default renderer parameters parser.add_argument('--focal', type=float, default=1015.) parser.add_argument('--center', type=float, default=112.) parser.add_argument('--camera_d', type=float, default=10.) parser.add_argument('--z_near', type=float, default=5.) parser.add_argument('--z_far', type=float, default=15.) args = parser.parse_args() if torch.cuda.is_available() and not args.cpu: args.device = "cuda" else: args.device = "cpu" main(args)