# -*- coding: utf-8 -*- # @Author : wenshao # @Email : wenshaoguo0611@gmail.com # @Project : FasterLivePortrait # @FileName: faster_live_portrait_pipeline.py import copy import pdb import time import traceback from PIL import Image import cv2 from tqdm import tqdm import numpy as np import torch from .. import models from ..utils.crop import crop_image, parse_bbox_from_landmark, crop_image_by_bbox, paste_back, paste_back_pytorch from ..utils.utils import resize_to_limit, prepare_paste_back, get_rotation_matrix, calc_lip_close_ratio, \ calc_eye_close_ratio, transform_keypoint, concat_feat from difpoint.src.utils import utils class FasterLivePortraitPipeline: def __init__(self, cfg, **kwargs): self.cfg = cfg self.init(**kwargs) def init(self, **kwargs): self.init_vars(**kwargs) self.init_models(**kwargs) def clean_models(self, **kwargs): """ clean model :param kwargs: :return: """ for key in list(self.model_dict.keys()): del self.model_dict[key] self.model_dict = {} def init_models(self, **kwargs): if not kwargs.get("is_animal", False): print("load Human Model >>>") self.is_animal = False self.model_dict = {} for model_name in self.cfg.models: print(f"loading model: {model_name}") print(self.cfg.models[model_name]) self.model_dict[model_name] = getattr(models, self.cfg.models[model_name]["name"])( **self.cfg.models[model_name]) else: print("load Animal Model >>>") self.is_animal = True self.model_dict = {} from src.utils.animal_landmark_runner import XPoseRunner from src.utils.utils import make_abs_path xpose_ckpt_path: str = make_abs_path("../difpoint/checkpoints/liveportrait_animal_onnx/xpose.pth") xpose_config_file_path: str = make_abs_path("models/XPose/config_model/UniPose_SwinT.py") xpose_embedding_cache_path: str = make_abs_path('../difpoint/checkpoints/liveportrait_animal_onnx/clip_embedding') self.model_dict["xpose"] = XPoseRunner(model_config_path=xpose_config_file_path, model_checkpoint_path=xpose_ckpt_path, embeddings_cache_path=xpose_embedding_cache_path, flag_use_half_precision=True) for model_name in self.cfg.animal_models: print(f"loading model: {model_name}") print(self.cfg.animal_models[model_name]) self.model_dict[model_name] = getattr(models, self.cfg.animal_models[model_name]["name"])( **self.cfg.animal_models[model_name]) def init_vars(self, **kwargs): self.mask_crop = cv2.imread(self.cfg.infer_params.mask_crop_path, cv2.IMREAD_COLOR) self.frame_id = 0 self.src_lmk_pre = None self.R_d_0 = None self.x_d_0_info = None self.R_d_smooth = utils.OneEuroFilter(4, 1) self.exp_smooth = utils.OneEuroFilter(4, 1) ## 记录source的信息 self.source_path = None self.src_infos = [] self.src_imgs = [] self.is_source_video = False self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") def calc_combined_eye_ratio(self, c_d_eyes_i, source_lmk): c_s_eyes = calc_eye_close_ratio(source_lmk[None]) c_d_eyes_i = np.array(c_d_eyes_i).reshape(1, 1) # [c_s,eyes, c_d,eyes,i] combined_eye_ratio_tensor = np.concatenate([c_s_eyes, c_d_eyes_i], axis=1) return combined_eye_ratio_tensor def calc_combined_lip_ratio(self, c_d_lip_i, source_lmk): c_s_lip = calc_lip_close_ratio(source_lmk[None]) c_d_lip_i = np.array(c_d_lip_i).reshape(1, 1) # 1x1 # [c_s,lip, c_d,lip,i] combined_lip_ratio_tensor = np.concatenate([c_s_lip, c_d_lip_i], axis=1) # 1x2 return combined_lip_ratio_tensor def prepare_source(self, source_path, **kwargs): print(f"process source:{source_path} >>>>>>>>") try: if utils.is_image(source_path): self.is_source_video = False elif utils.is_video(source_path): self.is_source_video = True else: # source input is an unknown format raise Exception(f"Unknown source format: {source_path}") if self.is_source_video: src_imgs_bgr = [] src_vcap = cv2.VideoCapture(source_path) while True: ret, frame = src_vcap.read() if not ret: break src_imgs_bgr.append(frame) src_vcap.release() else: img_bgr = cv2.imread(source_path, cv2.IMREAD_COLOR) src_imgs_bgr = [img_bgr] self.src_imgs = [] self.src_infos = [] self.source_path = source_path for ii, img_bgr in tqdm(enumerate(src_imgs_bgr), total=len(src_imgs_bgr)): img_bgr = resize_to_limit(img_bgr, self.cfg.infer_params.source_max_dim, self.cfg.infer_params.source_division) img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB) src_faces = [] if self.is_animal: with torch.no_grad(): img_rgb_pil = Image.fromarray(img_rgb) lmk = self.model_dict["xpose"].run( img_rgb_pil, 'face', 'animal_face', 0, 0 ) if lmk is None: continue self.src_imgs.append(img_rgb) src_faces.append(lmk) else: src_faces = self.model_dict["face_analysis"].predict(img_bgr) if len(src_faces) == 0: print("No face detected in the this image.") continue self.src_imgs.append(img_rgb) # 如果是实时，只关注最大的那张脸 if kwargs.get("realtime", False): src_faces = src_faces[:1] crop_infos = [] for i in range(len(src_faces)): # NOTE: temporarily only pick the first face, to support multiple face in the future lmk = src_faces[i] # crop the face ret_dct = crop_image( img_rgb, # ndarray lmk, # 106x2 or Nx2 dsize=self.cfg.crop_params.src_dsize, scale=self.cfg.crop_params.src_scale, vx_ratio=self.cfg.crop_params.src_vx_ratio, vy_ratio=self.cfg.crop_params.src_vy_ratio, ) if self.is_animal: ret_dct["lmk_crop"] = lmk else: lmk = self.model_dict["landmark"].predict(img_rgb, lmk) ret_dct["lmk_crop"] = lmk ret_dct["lmk_crop_256x256"] = ret_dct["lmk_crop"] * 256 / self.cfg.crop_params.src_dsize # update a 256x256 version for network input ret_dct["img_crop_256x256"] = cv2.resize( ret_dct["img_crop"], (256, 256), interpolation=cv2.INTER_AREA ) crop_infos.append(ret_dct) src_infos = [[] for _ in range(len(crop_infos))] for i, crop_info in enumerate(crop_infos): source_lmk = crop_info['lmk_crop'] img_crop, img_crop_256x256 = crop_info['img_crop'], crop_info['img_crop_256x256'] pitch, yaw, roll, t, exp, scale, kp = self.model_dict["motion_extractor"].predict( img_crop_256x256) x_s_info = { "pitch": pitch, "yaw": yaw, "roll": roll, "t": t, "exp": exp, "scale": scale, "kp": kp } src_infos[i].append(copy.deepcopy(x_s_info)) x_c_s = kp R_s = get_rotation_matrix(pitch, yaw, roll) f_s = self.model_dict["app_feat_extractor"].predict(img_crop_256x256) x_s = transform_keypoint(pitch, yaw, roll, t, exp, scale, kp) src_infos[i].extend([source_lmk.copy(), R_s.copy(), f_s.copy(), x_s.copy(), x_c_s.copy()]) if not self.is_animal: flag_lip_zero = self.cfg.infer_params.flag_normalize_lip # not overwrite if flag_lip_zero: # let lip-open scalar to be 0 at first c_d_lip_before_animation = [0.] combined_lip_ratio_tensor_before_animation = self.calc_combined_lip_ratio( c_d_lip_before_animation, source_lmk) if combined_lip_ratio_tensor_before_animation[0][ 0] < self.cfg.infer_params.lip_normalize_threshold: flag_lip_zero = False src_infos[i].append(None) src_infos[i].append(flag_lip_zero) else: lip_delta_before_animation = self.model_dict['stitching_lip_retarget'].predict( concat_feat(x_s, combined_lip_ratio_tensor_before_animation)) src_infos[i].append(lip_delta_before_animation.copy()) src_infos[i].append(flag_lip_zero) else: src_infos[i].append(None) src_infos[i].append(flag_lip_zero) else: src_infos[i].append(None) src_infos[i].append(False) ######## prepare for pasteback ######## if self.cfg.infer_params.flag_pasteback and self.cfg.infer_params.flag_do_crop and self.cfg.infer_params.flag_stitching: mask_ori_float = prepare_paste_back(self.mask_crop, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0])) mask_ori_float = torch.from_numpy(mask_ori_float).to(self.device) src_infos[i].append(mask_ori_float) else: src_infos[i].append(None) M = torch.from_numpy(crop_info['M_c2o']).to(self.device) src_infos[i].append(M) self.src_infos.append(src_infos[:]) print(f"finish process source:{source_path} >>>>>>>>") return len(self.src_infos) > 0 except Exception as e: traceback.print_exc() return False def retarget_eye(self, kp_source, eye_close_ratio): """ kp_source: BxNx3 eye_close_ratio: Bx3 Return: Bx(3*num_kp+2) """ feat_eye = concat_feat(kp_source, eye_close_ratio) delta = self.model_dict['stitching_eye_retarget'].predict(feat_eye) return delta def retarget_lip(self, kp_source, lip_close_ratio): """ kp_source: BxNx3 lip_close_ratio: Bx2 """ feat_lip = concat_feat(kp_source, lip_close_ratio) delta = self.model_dict['stitching_lip_retarget'].predict(feat_lip) return delta def stitching(self, kp_source, kp_driving): """ conduct the stitching kp_source: Bxnum_kpx3 kp_driving: Bxnum_kpx3 """ bs, num_kp = kp_source.shape[:2] kp_driving_new = kp_driving.copy() delta = self.model_dict['stitching'].predict(concat_feat(kp_source, kp_driving_new)) delta_exp = delta[..., :3 * num_kp].reshape(bs, num_kp, 3) # 1x20x3 delta_tx_ty = delta[..., 3 * num_kp:3 * num_kp + 2].reshape(bs, 1, 2) # 1x1x2 kp_driving_new += delta_exp kp_driving_new[..., :2] += delta_tx_ty return kp_driving_new def run(self, image, img_src, src_info, **kwargs): img_bgr = image img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB) I_p_pstbk = torch.from_numpy(img_src).to(self.device).float() realtime = kwargs.get("realtime", False) if self.cfg.infer_params.flag_crop_driving_video: if self.src_lmk_pre is None: src_face = self.model_dict["face_analysis"].predict(img_bgr) if len(src_face) == 0: self.src_lmk_pre = None return None, None, None lmk = src_face[0] lmk = self.model_dict["landmark"].predict(img_rgb, lmk) self.src_lmk_pre = lmk.copy() else: lmk = self.model_dict["landmark"].predict(img_rgb, self.src_lmk_pre) self.src_lmk_pre = lmk.copy() ret_bbox = parse_bbox_from_landmark( lmk, scale=self.cfg.crop_params.dri_scale, vx_ratio_crop_video=self.cfg.crop_params.dri_vx_ratio, vy_ratio=self.cfg.crop_params.dri_vy_ratio, )["bbox"] global_bbox = [ ret_bbox[0, 0], ret_bbox[0, 1], ret_bbox[2, 0], ret_bbox[2, 1], ] ret_dct = crop_image_by_bbox( img_rgb, global_bbox, lmk=lmk, dsize=kwargs.get("dsize", 512), flag_rot=False, borderValue=(0, 0, 0), ) lmk_crop = ret_dct["lmk_crop"] img_crop = ret_dct["img_crop"] img_crop = cv2.resize(img_crop, (256, 256)) else: if self.src_lmk_pre is None: src_face = self.model_dict["face_analysis"].predict(img_bgr) if len(src_face) == 0: self.src_lmk_pre = None return None, None, None lmk = src_face[0] lmk = self.model_dict["landmark"].predict(img_rgb, lmk) self.src_lmk_pre = lmk.copy() else: lmk = self.model_dict["landmark"].predict(img_rgb, self.src_lmk_pre) self.src_lmk_pre = lmk.copy() lmk_crop = lmk.copy() img_crop = cv2.resize(img_rgb, (256, 256)) input_eye_ratio = calc_eye_close_ratio(lmk_crop[None]) input_lip_ratio = calc_lip_close_ratio(lmk_crop[None]) pitch, yaw, roll, t, exp, scale, kp = self.model_dict["motion_extractor"].predict(img_crop) x_d_i_info = { "pitch": pitch, "yaw": yaw, "roll": roll, "t": t, "exp": exp, "scale": scale, "kp": kp } R_d_i = get_rotation_matrix(pitch, yaw, roll) if kwargs.get("first_frame", False) or self.R_d_0 is None: self.R_d_0 = R_d_i.copy() self.x_d_0_info = copy.deepcopy(x_d_i_info) # realtime smooth self.R_d_smooth = utils.OneEuroFilter(4, 1) self.exp_smooth = utils.OneEuroFilter(4, 1) R_d_0 = self.R_d_0.copy() x_d_0_info = copy.deepcopy(self.x_d_0_info) out_crop, out_org = None, None for j in range(len(src_info)): x_s_info, source_lmk, R_s, f_s, x_s, x_c_s, lip_delta_before_animation, flag_lip_zero, mask_ori_float, M = \ src_info[j] if self.cfg.infer_params.flag_relative_motion: if self.is_source_video: if self.cfg.infer_params.flag_video_editing_head_rotation: R_new = (R_d_i @ np.transpose(R_d_0, (0, 2, 1))) @ R_s R_new = self.R_d_smooth.process(R_new) else: R_new = R_s else: R_new = (R_d_i @ np.transpose(R_d_0, (0, 2, 1))) @ R_s delta_new = x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp']) if self.is_source_video: delta_new = self.exp_smooth.process(delta_new) scale_new = x_s_info['scale'] if self.is_source_video else x_s_info['scale'] * (x_d_i_info['scale'] / x_d_0_info['scale']) t_new = x_s_info['t'] if self.is_source_video else x_s_info['t'] + (x_d_i_info['t'] - x_d_0_info['t']) else: if self.is_source_video: if self.cfg.infer_params.flag_video_editing_head_rotation: R_new = R_d_i R_new = self.R_d_smooth.process(R_new) else: R_new = R_s else: R_new = R_d_i delta_new = x_d_i_info['exp'].copy() if self.is_source_video: delta_new = self.exp_smooth.process(delta_new) scale_new = x_s_info['scale'].copy() t_new = x_d_i_info['t'].copy() t_new[..., 2] = 0 # zero tz x_d_i_new = scale_new * (x_c_s @ R_new + delta_new) + t_new if not self.is_animal: # Algorithm 1: if not self.cfg.infer_params.flag_stitching and not self.cfg.infer_params.flag_eye_retargeting and not self.cfg.infer_params.flag_lip_retargeting: # without stitching or retargeting if flag_lip_zero: x_d_i_new += lip_delta_before_animation.reshape(-1, x_s.shape[1], 3) else: pass elif self.cfg.infer_params.flag_stitching and not self.cfg.infer_params.flag_eye_retargeting and not self.cfg.infer_params.flag_lip_retargeting: # with stitching and without retargeting if flag_lip_zero: x_d_i_new = self.stitching(x_s, x_d_i_new) + lip_delta_before_animation.reshape( -1, x_s.shape[1], 3) else: x_d_i_new = self.stitching(x_s, x_d_i_new) else: eyes_delta, lip_delta = None, None if self.cfg.infer_params.flag_eye_retargeting: c_d_eyes_i = input_eye_ratio combined_eye_ratio_tensor = self.calc_combined_eye_ratio(c_d_eyes_i, source_lmk) # ∆_eyes,i = R_eyes(x_s; c_s,eyes, c_d,eyes,i) eyes_delta = self.retarget_eye(x_s, combined_eye_ratio_tensor) if self.cfg.infer_params.flag_lip_retargeting: c_d_lip_i = input_lip_ratio combined_lip_ratio_tensor = self.calc_combined_lip_ratio(c_d_lip_i, source_lmk) # ∆_lip,i = R_lip(x_s; c_s,lip, c_d,lip,i) lip_delta = self.retarget_lip(x_s, combined_lip_ratio_tensor) if self.cfg.infer_params.flag_relative_motion: # use x_s x_d_i_new = x_s + \ (eyes_delta.reshape(-1, x_s.shape[1], 3) if eyes_delta is not None else 0) + \ (lip_delta.reshape(-1, x_s.shape[1], 3) if lip_delta is not None else 0) else: # use x_d,i x_d_i_new = x_d_i_new + \ (eyes_delta.reshape(-1, x_s.shape[1], 3) if eyes_delta is not None else 0) + \ (lip_delta.reshape(-1, x_s.shape[1], 3) if lip_delta is not None else 0) if self.cfg.infer_params.flag_stitching: x_d_i_new = self.stitching(x_s, x_d_i_new) else: if self.cfg.infer_params.flag_stitching: x_d_i_new = self.stitching(x_s, x_d_i_new) x_d_i_new = x_s + (x_d_i_new - x_s) * self.cfg.infer_params.driving_multiplier out_crop = self.model_dict["warping_spade"].predict(f_s, x_s, x_d_i_new) if not realtime and self.cfg.infer_params.flag_pasteback and self.cfg.infer_params.flag_do_crop and self.cfg.infer_params.flag_stitching: # TODO: pasteback is slow, considering optimize it using multi-threading or GPU # I_p_pstbk = paste_back(out_crop, crop_info['M_c2o'], I_p_pstbk, mask_ori_float) I_p_pstbk = paste_back_pytorch(out_crop, M, I_p_pstbk, mask_ori_float) return img_crop, out_crop.to(dtype=torch.uint8).cpu().numpy(), I_p_pstbk.to(dtype=torch.uint8).cpu().numpy() def __del__(self): self.clean_models()