Spaces:
Sleeping
Sleeping
File size: 6,688 Bytes
a22eb82 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
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)
|