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A10G
Running
on
A10G
| 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) | |