src/pipelines/faster_live_portrait_pipeline.py

# -*- coding: utf-8 -*-
# @Author  : wenshao
# @Email   : wenshaoguo0611@gmail.com
# @Project : FasterLivePortrait
# @FileName: faster_live_portrait_pipeline.py

import copy
import os.path
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 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 update_cfg(self, args_user):
        update_ret = False
        for key in args_user:
            if key in self.cfg.infer_params:
                if self.cfg.infer_params[key] != args_user[key]:
                    update_ret = True
                print("update infer cfg {} from {} to {}".format(key, self.cfg.infer_params[key], args_user[key]))
                self.cfg.infer_params[key] = args_user[key]
            elif key in self.cfg.crop_params:
                if self.cfg.crop_params[key] != args_user[key]:
                    update_ret = True
                print("update crop cfg {} from {} to {}".format(key, self.cfg.crop_params[key], args_user[key]))
                self.cfg.crop_params[key] = args_user[key]
            else:
                if key in self.cfg.infer_params and self.cfg.infer_params[key] != args_user[key]:
                    update_ret = True
                print("add {}:{} to infer cfg".format(key, args_user[key]))
                self.cfg.infer_params[key] = args_user[key]
        return update_ret

    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
            checkpoint_dir = None
            for model_name in self.cfg.animal_models:
                print(f"loading model: {model_name}")
                print(self.cfg.animal_models[model_name])
                if checkpoint_dir is None and isinstance(self.cfg.animal_models[model_name].model_path, str):
                    checkpoint_dir = os.path.dirname(self.cfg.animal_models[model_name].model_path)
                self.model_dict[model_name] = getattr(models, self.cfg.animal_models[model_name]["name"])(
                    **self.cfg.animal_models[model_name])

            xpose_config_file_path: str = make_abs_path("models/XPose/config_model/UniPose_SwinT.py")
            xpose_ckpt_path: str = os.path.join(checkpoint_dir, "xpose.pth")
            xpose_embedding_cache_path: str = os.path.join(checkpoint_dir, '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)

    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, 0.3)
        self.exp_smooth = utils.OneEuroFilter(4, 0.3)

        ## 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_video(source_path):
                self.is_source_video = True
            else:
                self.is_source_video = False

            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
                            # 似乎要调参？
                            c_d_lip_before_animation = [0.05]
                            combined_lip_ratio_tensor_before_animation = self.calc_combined_lip_ratio(
                                c_d_lip_before_animation, source_lmk.copy())
                            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, src_info, x_d_i_info, x_d_0_info, R_d_i, R_d_0, realtime, input_eye_ratio, input_lip_ratio,
             I_p_pstbk, **kwargs):
        out_crop, out_org = None, None
        eye_delta_before_animation = None
        for j in range(len(src_info)):
            if self.is_source_video:
                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]
                # let lip-open scalar to be 0 at first if the input is a video and flag_relative_motion
                if not (self.cfg.infer_params.flag_normalize_lip and self.cfg.infer_params.flag_relative_motion):
                    lip_delta_before_animation = None
                # let eye-open scalar to be the same as the first frame if the latter is eye-open state
                if self.cfg.infer_params.flag_source_video_eye_retargeting and source_lmk is not None:
                    combined_eye_ratio_tensor_frame_zero = utils.calc_eye_close_ratio(src_info[0][1])
                    c_d_eye_before_animation_frame_zero = [
                        [combined_eye_ratio_tensor_frame_zero[0][:2].mean()]]
                    if c_d_eye_before_animation_frame_zero[0][
                        0] < self.cfg.infer_params.source_video_eye_retargeting_threshold:
                        c_d_eye_before_animation_frame_zero = [[0.39]]
                    combined_eye_ratio_tensor_before_animation = self.calc_combined_eye_ratio(
                        c_d_eye_before_animation_frame_zero, source_lmk)
                    eye_delta_before_animation = self.retarget_eye(x_s, combined_eye_ratio_tensor_before_animation)

                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:
                    mask_ori_float = prepare_paste_back(self.mask_crop, M.cpu().numpy(),
                                                        dsize=(self.src_imgs[0].shape[1], self.src_imgs[0].shape[0]))
                    mask_ori_float = torch.from_numpy(mask_ori_float).to(self.device)
            else:
                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.cfg.infer_params.animation_region in ["all", "pose"]:
                    if self.is_source_video:
                        R_new = self.R_d_smooth.process(R_d_i)
                    else:
                        R_new = (R_d_i @ np.transpose(R_d_0, (0, 2, 1))) @ R_s
                else:
                    R_new = R_s

                delta_new = x_s_info['exp'].copy()
                x_d_exp_smooth = x_d_i_info['exp'].copy()
                if self.is_source_video:
                    x_d_exp_smooth = self.exp_smooth.process(x_d_exp_smooth)
                if self.cfg.infer_params.animation_region in ["all", "exp"]:
                    if self.is_source_video:
                        for idx in [1, 2, 6, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]:
                            delta_new[:, idx, :] = x_d_exp_smooth[:, idx, :]
                        delta_new[:, 3:5, 1] = x_d_exp_smooth[:, 3:5, 1]
                        delta_new[:, 5, 2] = x_d_exp_smooth[:, 5, 2]
                        delta_new[:, 8, 2] = x_d_exp_smooth[:, 8, 2]
                        delta_new[:, 9, 1:] = x_d_exp_smooth[:, 9, 1:]
                    else:
                        delta_new = x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp'])
                elif self.cfg.infer_params.animation_region in ["lip"]:
                    for lip_idx in [6, 12, 14, 17, 19, 20]:
                        if self.is_source_video:
                            delta_new[:, lip_idx, :] = x_d_exp_smooth[:, lip_idx, :]
                        else:
                            delta_new[:, lip_idx, :] = (x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp']))[:,
                                                       lip_idx, :]
                elif self.cfg.infer_params.animation_region in ["eyes"]:
                    for eyes_idx in [11, 13, 15, 16, 18]:
                        if self.is_source_video:
                            delta_new[:, eyes_idx, :] = x_d_exp_smooth[:, eyes_idx, :]
                        else:
                            delta_new[:, eyes_idx, :] = (x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp']))[:,
                                                        eyes_idx, :]
                if self.cfg.infer_params.animation_region in ["all"]:
                    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'])
                else:
                    scale_new = x_s_info['scale']
                if self.cfg.infer_params.animation_region in ["all"]:
                    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:
                    t_new = x_s_info['t']
            else:
                if self.cfg.infer_params.animation_region in ["all", "pose"]:
                    if self.is_source_video:
                        R_new = self.R_d_smooth.process(R_d_i)
                    else:
                        R_new = R_d_i
                else:
                    R_new = R_s

                delta_new = x_s_info['exp'].copy()
                x_d_exp_smooth = x_d_i_info['exp'].copy()
                if self.is_source_video:
                    x_d_exp_smooth = self.exp_smooth.process(x_d_exp_smooth)
                if self.cfg.infer_params.animation_region in ["all", "exp"]:
                    for idx in [1, 2, 6, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]:
                        delta_new[:, idx, :] = x_d_exp_smooth[:, idx, :] if self.is_source_video else x_d_i_info['exp'][
                                                                                                      :, idx, :]
                    delta_new[:, 3:5, 1] = x_d_exp_smooth[:, 3:5, 1] if self.is_source_video else x_d_i_info['exp'][:,
                                                                                                  3:5, 1]
                    delta_new[:, 5, 2] = x_d_exp_smooth[:, 5, 2] if self.is_source_video else x_d_i_info['exp'][:,
                                                                                              5, 2]
                    delta_new[:, 8, 2] = x_d_exp_smooth[:, 8, 2] if self.is_source_video else x_d_i_info['exp'][:,
                                                                                              8, 2]
                    delta_new[:, 9, 1:] = x_d_exp_smooth[:, 9, 1:] if self.is_source_video else x_d_i_info['exp'][:,
                                                                                                9, 1:]
                elif self.cfg.infer_params.animation_region in ["lip"]:
                    for lip_idx in [6, 12, 14, 17, 19, 20]:
                        delta_new[:, lip_idx, :] = x_d_exp_smooth[:, lip_idx, :] if self.is_source_video else \
                            x_d_i_info['exp'][:, lip_idx, :]
                elif self.cfg.infer_params.animation_region in ["eyes"]:
                    for eyes_idx in [11, 13, 15, 16, 18]:
                        delta_new[:, eyes_idx, :] = x_d_exp_smooth[:, eyes_idx, :] if self.is_source_video else \
                            x_d_i_info['exp'][:, eyes_idx, :]
                scale_new = x_s_info['scale'].copy()
                if self.cfg.infer_params.animation_region in ["all", "pose"]:
                    t_new = x_d_i_info['t'].copy()
                else:
                    t_new = x_s_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 and lip_delta_before_animation is not None:
                        x_d_i_new += lip_delta_before_animation.reshape(-1, x_s.shape[1], 3)
                    if self.cfg.infer_params.flag_source_video_eye_retargeting and eye_delta_before_animation is not None:
                        x_d_i_new += eye_delta_before_animation
                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 and lip_delta_before_animation is not None:
                        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)
                    if self.cfg.infer_params.flag_source_video_eye_retargeting and eye_delta_before_animation is not None:
                        x_d_i_new += eye_delta_before_animation
                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 out_crop.to(dtype=torch.uint8).cpu().numpy(), I_p_pstbk.to(dtype=torch.uint8).cpu().numpy()

    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:
                    return None, 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:
                    return None, 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)
        x_d_i_info["R"] = R_d_i
        x_d_i_info_copy = copy.deepcopy(x_d_i_info)
        for key in x_d_i_info_copy:
            x_d_i_info_copy[key] = x_d_i_info_copy[key].astype(np.float32)
        dri_motion_info = [x_d_i_info_copy, copy.deepcopy(input_eye_ratio.astype(np.float32)),
                           copy.deepcopy(input_lip_ratio.astype(np.float32))]
        if kwargs.get("first_frame", False) or self.R_d_0 is None:
            self.frame_id = 0
            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, 0.3)
            self.exp_smooth = utils.OneEuroFilter(4, 0.3)
        R_d_0 = self.R_d_0.copy()
        x_d_0_info = copy.deepcopy(self.x_d_0_info)
        out_crop, I_p_pstbk = self._run(src_info, x_d_i_info, x_d_0_info, R_d_i, R_d_0, realtime, input_eye_ratio,
                                        input_lip_ratio,
                                        I_p_pstbk, **kwargs)
        return img_crop, out_crop, I_p_pstbk, dri_motion_info

    def run_with_pkl(self, dri_motion_info, img_src, src_info, **kwargs):
        I_p_pstbk = torch.from_numpy(img_src).to(self.device).float()
        realtime = kwargs.get("realtime", False)

        input_eye_ratio = dri_motion_info[1]
        input_lip_ratio = dri_motion_info[2]
        x_d_i_info = dri_motion_info[0]
        R_d_i = x_d_i_info["R"] if "R" in x_d_i_info else x_d_i_info["R_d"]

        if kwargs.get("first_frame", False) or self.R_d_0 is None:
            self.frame_id = 0
            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, 0.3)
            self.exp_smooth = utils.OneEuroFilter(4, 0.3)
        R_d_0 = self.R_d_0.copy()
        x_d_0_info = copy.deepcopy(self.x_d_0_info)
        out_crop, I_p_pstbk = self._run(src_info, x_d_i_info, x_d_0_info, R_d_i, R_d_0, realtime, input_eye_ratio,
                                        input_lip_ratio, I_p_pstbk, **kwargs)
        return out_crop, I_p_pstbk

    def __del__(self):
        self.clean_models()