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| """run bash scripts/download_models.sh first to prepare the weights file""" | |
| import os | |
| import shutil | |
| from argparse import Namespace | |
| 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 cog import BasePredictor, Input, Path | |
| checkpoints = "checkpoints" | |
| class Predictor(BasePredictor): | |
| def setup(self): | |
| """Load the model into memory to make running multiple predictions efficient""" | |
| device = "cuda" | |
| path_of_lm_croper = os.path.join( | |
| checkpoints, "shape_predictor_68_face_landmarks.dat" | |
| ) | |
| path_of_net_recon_model = os.path.join(checkpoints, "epoch_20.pth") | |
| dir_of_BFM_fitting = os.path.join(checkpoints, "BFM_Fitting") | |
| wav2lip_checkpoint = os.path.join(checkpoints, "wav2lip.pth") | |
| audio2pose_checkpoint = os.path.join(checkpoints, "auido2pose_00140-model.pth") | |
| audio2pose_yaml_path = os.path.join("src", "config", "auido2pose.yaml") | |
| audio2exp_checkpoint = os.path.join(checkpoints, "auido2exp_00300-model.pth") | |
| audio2exp_yaml_path = os.path.join("src", "config", "auido2exp.yaml") | |
| free_view_checkpoint = os.path.join( | |
| checkpoints, "facevid2vid_00189-model.pth.tar" | |
| ) | |
| # init model | |
| self.preprocess_model = CropAndExtract( | |
| path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting, device | |
| ) | |
| self.audio_to_coeff = Audio2Coeff( | |
| audio2pose_checkpoint, | |
| audio2pose_yaml_path, | |
| audio2exp_checkpoint, | |
| audio2exp_yaml_path, | |
| wav2lip_checkpoint, | |
| device, | |
| ) | |
| self.animate_from_coeff = { | |
| "full": AnimateFromCoeff( | |
| free_view_checkpoint, | |
| os.path.join(checkpoints, "mapping_00109-model.pth.tar"), | |
| os.path.join("src", "config", "facerender_still.yaml"), | |
| device, | |
| ), | |
| "others": AnimateFromCoeff( | |
| free_view_checkpoint, | |
| os.path.join(checkpoints, "mapping_00229-model.pth.tar"), | |
| os.path.join("src", "config", "facerender.yaml"), | |
| device, | |
| ), | |
| } | |
| def predict( | |
| self, | |
| source_image: Path = Input( | |
| description="Upload the source image, it can be video.mp4 or picture.png", | |
| ), | |
| driven_audio: Path = Input( | |
| description="Upload the driven audio, accepts .wav and .mp4 file", | |
| ), | |
| enhancer: str = Input( | |
| description="Choose a face enhancer", | |
| choices=["gfpgan", "RestoreFormer"], | |
| default="gfpgan", | |
| ), | |
| preprocess: str = Input( | |
| description="how to preprocess the images", | |
| choices=["crop", "resize", "full"], | |
| default="full", | |
| ), | |
| ref_eyeblink: Path = Input( | |
| description="path to reference video providing eye blinking", | |
| default=None, | |
| ), | |
| ref_pose: Path = Input( | |
| description="path to reference video providing pose", | |
| default=None, | |
| ), | |
| still: bool = Input( | |
| description="can crop back to the original videos for the full body aniamtion when preprocess is full", | |
| default=True, | |
| ), | |
| ) -> Path: | |
| """Run a single prediction on the model""" | |
| animate_from_coeff = ( | |
| self.animate_from_coeff["full"] | |
| if preprocess == "full" | |
| else self.animate_from_coeff["others"] | |
| ) | |
| args = load_default() | |
| args.pic_path = str(source_image) | |
| args.audio_path = str(driven_audio) | |
| device = "cuda" | |
| args.still = still | |
| args.ref_eyeblink = None if ref_eyeblink is None else str(ref_eyeblink) | |
| args.ref_pose = None if ref_pose is None else str(ref_pose) | |
| # crop image and extract 3dmm from image | |
| results_dir = "results" | |
| if os.path.exists(results_dir): | |
| shutil.rmtree(results_dir) | |
| os.makedirs(results_dir) | |
| first_frame_dir = os.path.join(results_dir, "first_frame_dir") | |
| os.makedirs(first_frame_dir) | |
| print("3DMM Extraction for source image") | |
| first_coeff_path, crop_pic_path, crop_info = self.preprocess_model.generate( | |
| args.pic_path, first_frame_dir, preprocess, source_image_flag=True | |
| ) | |
| 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(results_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, _, _ = self.preprocess_model.generate( | |
| ref_eyeblink, ref_eyeblink_frame_dir | |
| ) | |
| 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(results_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, _, _ = self.preprocess_model.generate( | |
| ref_pose, ref_pose_frame_dir | |
| ) | |
| else: | |
| ref_pose_coeff_path = None | |
| # audio2ceoff | |
| batch = get_data( | |
| first_coeff_path, | |
| args.audio_path, | |
| device, | |
| ref_eyeblink_coeff_path, | |
| still=still, | |
| ) | |
| coeff_path = self.audio_to_coeff.generate( | |
| batch, results_dir, args.pose_style, ref_pose_coeff_path | |
| ) | |
| # coeff2video | |
| print("coeff2video") | |
| data = get_facerender_data( | |
| coeff_path, | |
| crop_pic_path, | |
| first_coeff_path, | |
| args.audio_path, | |
| args.batch_size, | |
| args.input_yaw, | |
| args.input_pitch, | |
| args.input_roll, | |
| expression_scale=args.expression_scale, | |
| still_mode=still, | |
| preprocess=preprocess, | |
| ) | |
| animate_from_coeff.generate( | |
| data, results_dir, args.pic_path, crop_info, | |
| enhancer=enhancer, background_enhancer=args.background_enhancer, | |
| preprocess=preprocess) | |
| output = "/tmp/out.mp4" | |
| mp4_path = os.path.join(results_dir, [f for f in os.listdir(results_dir) if "enhanced.mp4" in f][0]) | |
| shutil.copy(mp4_path, output) | |
| return Path(output) | |
| def load_default(): | |
| return Namespace( | |
| pose_style=0, | |
| batch_size=2, | |
| expression_scale=1.0, | |
| input_yaw=None, | |
| input_pitch=None, | |
| input_roll=None, | |
| background_enhancer=None, | |
| face3dvis=False, | |
| net_recon="resnet50", | |
| init_path=None, | |
| use_last_fc=False, | |
| bfm_folder="./checkpoints/BFM_Fitting/", | |
| bfm_model="BFM_model_front.mat", | |
| focal=1015.0, | |
| center=112.0, | |
| camera_d=10.0, | |
| z_near=5.0, | |
| z_far=15.0, | |
| ) | |