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# coding: utf-8
"""
Wrapper for LivePortrait core functions
"""
import os.path as osp
import numpy as np
import cv2
import torch
import yaml
from .utils.timer import Timer
from .utils.helper import load_model, concat_feat
from .utils.camera import headpose_pred_to_degree, get_rotation_matrix
from .utils.retargeting_utils import calc_eye_close_ratio, calc_lip_close_ratio
from .config.inference_config import InferenceConfig
from .utils.rprint import rlog as log
class LivePortraitWrapper(object):
def __init__(self, inference_cfg: InferenceConfig):
self.inference_cfg = inference_cfg
self.device_id = inference_cfg.device_id
if inference_cfg.flag_force_cpu:
self.device = 'cpu'
else:
self.device = 'cuda:' + str(self.device_id)
model_config = yaml.load(open(inference_cfg.models_config, 'r'), Loader=yaml.SafeLoader)
# init F
self.appearance_feature_extractor = load_model(inference_cfg.checkpoint_F, model_config, self.device, 'appearance_feature_extractor')
log(f'Load appearance_feature_extractor done.')
# init M
self.motion_extractor = load_model(inference_cfg.checkpoint_M, model_config, self.device, 'motion_extractor')
log(f'Load motion_extractor done.')
# init W
self.warping_module = load_model(inference_cfg.checkpoint_W, model_config, self.device, 'warping_module')
log(f'Load warping_module done.')
# init G
self.spade_generator = load_model(inference_cfg.checkpoint_G, model_config, self.device, 'spade_generator')
log(f'Load spade_generator done.')
# init S and R
if inference_cfg.checkpoint_S is not None and osp.exists(inference_cfg.checkpoint_S):
self.stitching_retargeting_module = load_model(inference_cfg.checkpoint_S, model_config, self.device, 'stitching_retargeting_module')
log(f'Load stitching_retargeting_module done.')
else:
self.stitching_retargeting_module = None
self.timer = Timer()
def update_config(self, user_args):
for k, v in user_args.items():
if hasattr(self.inference_cfg, k):
setattr(self.inference_cfg, k, v)
def prepare_source(self, img: np.ndarray) -> torch.Tensor:
""" construct the input as standard
img: HxWx3, uint8, 256x256
"""
h, w = img.shape[:2]
if h != self.inference_cfg.input_shape[0] or w != self.inference_cfg.input_shape[1]:
x = cv2.resize(img, (self.inference_cfg.input_shape[0], self.inference_cfg.input_shape[1]))
else:
x = img.copy()
if x.ndim == 3:
x = x[np.newaxis].astype(np.float32) / 255. # HxWx3 -> 1xHxWx3, normalized to 0~1
elif x.ndim == 4:
x = x.astype(np.float32) / 255. # BxHxWx3, normalized to 0~1
else:
raise ValueError(f'img ndim should be 3 or 4: {x.ndim}')
x = np.clip(x, 0, 1) # clip to 0~1
x = torch.from_numpy(x).permute(0, 3, 1, 2) # 1xHxWx3 -> 1x3xHxW
x = x.to(self.device)
return x
def prepare_driving_videos(self, imgs) -> torch.Tensor:
""" construct the input as standard
imgs: NxBxHxWx3, uint8
"""
if isinstance(imgs, list):
_imgs = np.array(imgs)[..., np.newaxis] # TxHxWx3x1
elif isinstance(imgs, np.ndarray):
_imgs = imgs
else:
raise ValueError(f'imgs type error: {type(imgs)}')
y = _imgs.astype(np.float32) / 255.
y = np.clip(y, 0, 1) # clip to 0~1
y = torch.from_numpy(y).permute(0, 4, 3, 1, 2) # TxHxWx3x1 -> Tx1x3xHxW
y = y.to(self.device)
return y
def extract_feature_3d(self, x: torch.Tensor) -> torch.Tensor:
""" get the appearance feature of the image by F
x: Bx3xHxW, normalized to 0~1
"""
with torch.no_grad():
with torch.autocast(device_type=self.device[:4], dtype=torch.float16, enabled=self.inference_cfg.flag_use_half_precision):
feature_3d = self.appearance_feature_extractor(x)
return feature_3d.float()
def get_kp_info(self, x: torch.Tensor, **kwargs) -> dict:
""" get the implicit keypoint information
x: Bx3xHxW, normalized to 0~1
flag_refine_info: whether to transform the pose to degrees and the dimention of the reshape
return: A dict contains keys: 'pitch', 'yaw', 'roll', 't', 'exp', 'scale', 'kp'
"""
with torch.no_grad():
with torch.autocast(device_type=self.device[:4], dtype=torch.float16, enabled=self.inference_cfg.flag_use_half_precision):
kp_info = self.motion_extractor(x)
if self.inference_cfg.flag_use_half_precision:
# float the dict
for k, v in kp_info.items():
if isinstance(v, torch.Tensor):
kp_info[k] = v.float()
flag_refine_info: bool = kwargs.get('flag_refine_info', True)
if flag_refine_info:
bs = kp_info['kp'].shape[0]
kp_info['pitch'] = headpose_pred_to_degree(kp_info['pitch'])[:, None] # Bx1
kp_info['yaw'] = headpose_pred_to_degree(kp_info['yaw'])[:, None] # Bx1
kp_info['roll'] = headpose_pred_to_degree(kp_info['roll'])[:, None] # Bx1
kp_info['kp'] = kp_info['kp'].reshape(bs, -1, 3) # BxNx3
kp_info['exp'] = kp_info['exp'].reshape(bs, -1, 3) # BxNx3
return kp_info
def get_pose_dct(self, kp_info: dict) -> dict:
pose_dct = dict(
pitch=headpose_pred_to_degree(kp_info['pitch']).item(),
yaw=headpose_pred_to_degree(kp_info['yaw']).item(),
roll=headpose_pred_to_degree(kp_info['roll']).item(),
)
return pose_dct
def get_fs_and_kp_info(self, source_prepared, driving_first_frame):
# get the canonical keypoints of source image by M
source_kp_info = self.get_kp_info(source_prepared, flag_refine_info=True)
source_rotation = get_rotation_matrix(source_kp_info['pitch'], source_kp_info['yaw'], source_kp_info['roll'])
# get the canonical keypoints of first driving frame by M
driving_first_frame_kp_info = self.get_kp_info(driving_first_frame, flag_refine_info=True)
driving_first_frame_rotation = get_rotation_matrix(
driving_first_frame_kp_info['pitch'],
driving_first_frame_kp_info['yaw'],
driving_first_frame_kp_info['roll']
)
# get feature volume by F
source_feature_3d = self.extract_feature_3d(source_prepared)
return source_kp_info, source_rotation, source_feature_3d, driving_first_frame_kp_info, driving_first_frame_rotation
def transform_keypoint(self, kp_info: dict):
"""
transform the implicit keypoints with the pose, shift, and expression deformation
kp: BxNx3
"""
kp = kp_info['kp'] # (bs, k, 3)
pitch, yaw, roll = kp_info['pitch'], kp_info['yaw'], kp_info['roll']
t, exp = kp_info['t'], kp_info['exp']
scale = kp_info['scale']
pitch = headpose_pred_to_degree(pitch)
yaw = headpose_pred_to_degree(yaw)
roll = headpose_pred_to_degree(roll)
bs = kp.shape[0]
if kp.ndim == 2:
num_kp = kp.shape[1] // 3 # Bx(num_kpx3)
else:
num_kp = kp.shape[1] # Bxnum_kpx3
rot_mat = get_rotation_matrix(pitch, yaw, roll) # (bs, 3, 3)
# Eqn.2: s * (R * x_c,s + exp) + t
kp_transformed = kp.view(bs, num_kp, 3) @ rot_mat + exp.view(bs, num_kp, 3)
kp_transformed *= scale[..., None] # (bs, k, 3) * (bs, 1, 1) = (bs, k, 3)
kp_transformed[:, :, 0:2] += t[:, None, 0:2] # remove z, only apply tx ty
return kp_transformed
def retarget_eye(self, kp_source: torch.Tensor, eye_close_ratio: torch.Tensor) -> torch.Tensor:
"""
kp_source: BxNx3
eye_close_ratio: Bx3
Return: Bx(3*num_kp+2)
"""
feat_eye = concat_feat(kp_source, eye_close_ratio)
with torch.no_grad():
delta = self.stitching_retargeting_module['eye'](feat_eye)
return delta
def retarget_lip(self, kp_source: torch.Tensor, lip_close_ratio: torch.Tensor) -> torch.Tensor:
"""
kp_source: BxNx3
lip_close_ratio: Bx2
"""
feat_lip = concat_feat(kp_source, lip_close_ratio)
with torch.no_grad():
delta = self.stitching_retargeting_module['lip'](feat_lip)
return delta
def stitch(self, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
"""
kp_source: BxNx3
kp_driving: BxNx3
Return: Bx(3*num_kp+2)
"""
feat_stiching = concat_feat(kp_source, kp_driving)
with torch.no_grad():
delta = self.stitching_retargeting_module['stitching'](feat_stiching)
return delta
def stitching(self, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
""" conduct the stitching
kp_source: Bxnum_kpx3
kp_driving: Bxnum_kpx3
"""
if self.stitching_retargeting_module is not None:
bs, num_kp = kp_source.shape[:2]
kp_driving_new = kp_driving.clone()
delta = self.stitch(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
return kp_driving
def warp_decode(self, feature_3d: torch.Tensor, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
""" get the image after the warping of the implicit keypoints
feature_3d: Bx32x16x64x64, feature volume
kp_source: BxNx3
kp_driving: BxNx3
"""
# The line 18 in Algorithm 1: D(W(f_s; x_s, x′_d,i)）
with torch.no_grad():
with torch.autocast(device_type=self.device[:4], dtype=torch.float16, enabled=self.inference_cfg.flag_use_half_precision):
# get decoder input
ret_dct = self.warping_module(feature_3d, kp_source=kp_source, kp_driving=kp_driving)
# decode
ret_dct['out'] = self.spade_generator(feature=ret_dct['out'])
# float the dict
if self.inference_cfg.flag_use_half_precision:
for k, v in ret_dct.items():
if isinstance(v, torch.Tensor):
ret_dct[k] = v.float()
return ret_dct
def parse_output(self, out: torch.Tensor) -> np.ndarray:
""" construct the output as standard
return: 1xHxWx3, uint8
"""
out = np.transpose(out.data.cpu().numpy(), [0, 2, 3, 1]) # 1x3xHxW -> 1xHxWx3
out = np.clip(out, 0, 1) # clip to 0~1
out = np.clip(out * 255, 0, 255).astype(np.uint8) # 0~1 -> 0~255
return out
def calc_driving_ratio(self, driving_lmk_lst):
input_eye_ratio_lst = []
input_lip_ratio_lst = []
for lmk in driving_lmk_lst:
# for eyes retargeting
input_eye_ratio_lst.append(calc_eye_close_ratio(lmk[None]))
# for lip retargeting
input_lip_ratio_lst.append(calc_lip_close_ratio(lmk[None]))
return input_eye_ratio_lst, input_lip_ratio_lst
def calc_combined_eye_ratio(self, c_d_eyes_i, source_lmk):
c_s_eyes = calc_eye_close_ratio(source_lmk[None])
c_s_eyes_tensor = torch.from_numpy(c_s_eyes).float().to(self.device)
c_d_eyes_i_tensor = torch.Tensor([c_d_eyes_i[0][0]]).reshape(1, 1).to(self.device)
# [c_s,eyes, c_d,eyes,i]
combined_eye_ratio_tensor = torch.cat([c_s_eyes_tensor, c_d_eyes_i_tensor], dim=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_s_lip_tensor = torch.from_numpy(c_s_lip).float().to(self.device)
c_d_lip_i_tensor = torch.Tensor([c_d_lip_i[0]]).to(self.device).reshape(1, 1) # 1x1
# [c_s,lip, c_d,lip,i]
combined_lip_ratio_tensor = torch.cat([c_s_lip_tensor, c_d_lip_i_tensor], dim=1) # 1x2
return combined_lip_ratio_tensor
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