from glob import glob import shutil 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 from src.utils.init_path import init_path 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 input_yaw_list = args.input_yaw input_pitch_list = args.input_pitch input_roll_list = args.input_roll ref_eyeblink = args.ref_eyeblink ref_pose = args.ref_pose current_root_path = os.path.split(sys.argv[0])[0] sadtalker_paths = init_path(args.checkpoint_dir, os.path.join(current_root_path, 'src/config'), args.size, args.old_version, args.preprocess) #init model preprocess_model = CropAndExtract(sadtalker_paths, device) audio_to_coeff = Audio2Coeff(sadtalker_paths, device) animate_from_coeff = AnimateFromCoeff(sadtalker_paths, 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) print('3DMM Extraction for source image') first_coeff_path, crop_pic_path, crop_info = preprocess_model.generate(pic_path, first_frame_dir, args.preprocess,\ source_image_flag=True, pic_size=args.size) if first_coeff_path is None: print("Can't get the coeffs of the input") return if ref_eyeblink is not None: ref_eyeblink_videoname = os.path.splitext(os.path.split(ref_eyeblink)[-1])[0] ref_eyeblink_frame_dir = os.path.join(save_dir, ref_eyeblink_videoname) os.makedirs(ref_eyeblink_frame_dir, exist_ok=True) print('3DMM Extraction for the reference video providing eye blinking') ref_eyeblink_coeff_path, _, _ = preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir, args.preprocess, source_image_flag=False) else: ref_eyeblink_coeff_path=None if ref_pose is not None: if ref_pose == ref_eyeblink: ref_pose_coeff_path = ref_eyeblink_coeff_path else: ref_pose_videoname = os.path.splitext(os.path.split(ref_pose)[-1])[0] ref_pose_frame_dir = os.path.join(save_dir, ref_pose_videoname) os.makedirs(ref_pose_frame_dir, exist_ok=True) print('3DMM Extraction for the reference video providing pose') ref_pose_coeff_path, _, _ = preprocess_model.generate(ref_pose, ref_pose_frame_dir, args.preprocess, source_image_flag=False) else: ref_pose_coeff_path=None #audio2ceoff batch = get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path, still=args.still) coeff_path = audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path) # 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, input_yaw_list, input_pitch_list, input_roll_list, expression_scale=args.expression_scale, still_mode=args.still, preprocess=args.preprocess, size=args.size) result = animate_from_coeff.generate(data, save_dir, pic_path, crop_info, \ enhancer=args.enhancer, background_enhancer=args.background_enhancer, preprocess=args.preprocess, img_size=args.size) shutil.move(result, save_dir+'.mp4') print('The generated video is named:', save_dir+'.mp4') if not args.verbose: shutil.rmtree(save_dir) if __name__ == '__main__': parser = ArgumentParser() parser.add_argument("--driven_audio", default='./examples/driven_audio/voice.wav', help="path to driven audio") parser.add_argument("--source_image", default='./examples/source_image/istockphoto-487804668-612x612.png', help="path to source image") parser.add_argument("--ref_eyeblink", default=None, help="path to reference video providing eye blinking") parser.add_argument("--ref_pose", default=None, help="path to reference video providing pose") 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("--size", type=int, default=256, help="the image size of the facerender") parser.add_argument("--expression_scale", type=float, default=1., help="the batch size of facerender") parser.add_argument('--input_yaw', nargs='+', type=int, default=None, help="the input yaw degree of the user ") parser.add_argument('--input_pitch', nargs='+', type=int, default=None, help="the input pitch degree of the user") parser.add_argument('--input_roll', nargs='+', type=int, default=None, help="the input roll degree of the user") parser.add_argument('--enhancer', type=str, default=None, help="Face enhancer, [gfpgan, RestoreFormer]") parser.add_argument('--background_enhancer', type=str, default=None, help="background enhancer, [realesrgan]") 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", help="can crop back to the original videos for the full body aniamtion") parser.add_argument("--preprocess", default='crop', choices=['crop', 'extcrop', 'resize', 'full', 'extfull'], help="how to preprocess the images" ) parser.add_argument("--verbose",action="store_true", help="saving the intermedia output or not" ) parser.add_argument("--old_version",action="store_true", help="use the pth other than safetensor version" ) # net structure and parameters parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='useless') parser.add_argument('--init_path', type=str, default=None, help='Useless') 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)