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# coding: utf-8
"""
Pipeline of LivePortrait
"""
import torch
torch.backends.cudnn.benchmark = True # disable CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR warning
import cv2
import numpy as np
import pickle
import os.path as osp
from rich.progress import track
from .config.argument_config import ArgumentConfig
from .config.inference_config import InferenceConfig
from .config.crop_config import CropConfig
from .utils.cropper import Cropper
from .utils.camera import get_rotation_matrix
from .utils.video import images2video, concat_frames, get_fps
from .utils.crop import _transform_img, prepare_paste_back, paste_back
from .utils.retargeting_utils import calc_lip_close_ratio
from .utils.io import load_image_rgb, load_driving_info, resize_to_limit
from .utils.helper import mkdir, basename, dct2cuda, is_video, is_template
from .utils.rprint import rlog as log
from .live_portrait_wrapper import LivePortraitWrapper
def make_abs_path(fn):
return osp.join(osp.dirname(osp.realpath(__file__)), fn)
class LivePortraitPipeline(object):
def __init__(self, inference_cfg: InferenceConfig, crop_cfg: CropConfig):
self.live_portrait_wrapper: LivePortraitWrapper = LivePortraitWrapper(cfg=inference_cfg)
self.cropper = Cropper(crop_cfg=crop_cfg)
def execute(self, args: ArgumentConfig):
inference_cfg = self.live_portrait_wrapper.cfg # for convenience
######## process source portrait ########
img_rgb = load_image_rgb(args.source_image)
img_rgb = resize_to_limit(img_rgb, inference_cfg.ref_max_shape, inference_cfg.ref_shape_n)
log(f"Load source image from {args.source_image}")
crop_info = self.cropper.crop_single_image(img_rgb)
source_lmk = crop_info['lmk_crop']
img_crop, img_crop_256x256 = crop_info['img_crop'], crop_info['img_crop_256x256']
if inference_cfg.flag_do_crop:
I_s = self.live_portrait_wrapper.prepare_source(img_crop_256x256)
else:
I_s = self.live_portrait_wrapper.prepare_source(img_rgb)
x_s_info = self.live_portrait_wrapper.get_kp_info(I_s)
x_c_s = x_s_info['kp']
R_s = get_rotation_matrix(x_s_info['pitch'], x_s_info['yaw'], x_s_info['roll'])
f_s = self.live_portrait_wrapper.extract_feature_3d(I_s)
x_s = self.live_portrait_wrapper.transform_keypoint(x_s_info)
if inference_cfg.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.live_portrait_wrapper.calc_combined_lip_ratio(c_d_lip_before_animation, source_lmk)
if combined_lip_ratio_tensor_before_animation[0][0] < inference_cfg.lip_zero_threshold:
inference_cfg.flag_lip_zero = False
else:
lip_delta_before_animation = self.live_portrait_wrapper.retarget_lip(x_s, combined_lip_ratio_tensor_before_animation)
############################################
######## process driving info ########
output_fps = 30 # default fps
if is_video(args.driving_info):
log(f"Load from video file (mp4 mov avi etc...): {args.driving_info}")
output_fps = int(get_fps(args.driving_info))
log(f'The FPS of {args.driving_info} is: {output_fps}')
# TODO: 这里track一下驱动视频 -> 构建模板
driving_rgb_lst = load_driving_info(args.driving_info)
driving_rgb_lst_256 = [cv2.resize(_, (256, 256)) for _ in driving_rgb_lst]
I_d_lst = self.live_portrait_wrapper.prepare_driving_videos(driving_rgb_lst_256)
n_frames = I_d_lst.shape[0]
if inference_cfg.flag_eye_retargeting or inference_cfg.flag_lip_retargeting:
driving_lmk_lst = self.cropper.get_retargeting_lmk_info(driving_rgb_lst)
input_eye_ratio_lst, input_lip_ratio_lst = self.live_portrait_wrapper.calc_retargeting_ratio(source_lmk, driving_lmk_lst)
elif is_template(args.driving_info):
log(f"Load from video templates {args.driving_info}")
with open(args.driving_info, 'rb') as f:
template_lst, driving_lmk_lst = pickle.load(f)
n_frames = template_lst[0]['n_frames']
input_eye_ratio_lst, input_lip_ratio_lst = self.live_portrait_wrapper.calc_retargeting_ratio(source_lmk, driving_lmk_lst)
else:
raise Exception("Unsupported driving types!")
#########################################
######## prepare for pasteback ########
if inference_cfg.flag_pasteback:
mask_ori = prepare_paste_back(inference_cfg.mask_crop, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0]))
I_p_paste_lst = []
#########################################
I_p_lst = []
R_d_0, x_d_0_info = None, None
for i in track(range(n_frames), description='Animating...', total=n_frames):
if is_video(args.driving_info):
# extract kp info by M
I_d_i = I_d_lst[i]
x_d_i_info = self.live_portrait_wrapper.get_kp_info(I_d_i)
R_d_i = get_rotation_matrix(x_d_i_info['pitch'], x_d_i_info['yaw'], x_d_i_info['roll'])
else:
# from template
x_d_i_info = template_lst[i]
x_d_i_info = dct2cuda(x_d_i_info, inference_cfg.device_id)
R_d_i = x_d_i_info['R_d']
if i == 0:
R_d_0 = R_d_i
x_d_0_info = x_d_i_info
if inference_cfg.flag_relative:
R_new = (R_d_i @ R_d_0.permute(0, 2, 1)) @ R_s
delta_new = x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp'])
scale_new = x_s_info['scale'] * (x_d_i_info['scale'] / x_d_0_info['scale'])
t_new = x_s_info['t'] + (x_d_i_info['t'] - x_d_0_info['t'])
else:
R_new = R_d_i
delta_new = x_d_i_info['exp']
scale_new = x_s_info['scale']
t_new = x_d_i_info['t']
t_new[..., 2].fill_(0) # zero tz
x_d_i_new = scale_new * (x_c_s @ R_new + delta_new) + t_new
# Algorithm 1:
if not inference_cfg.flag_stitching and not inference_cfg.flag_eye_retargeting and not inference_cfg.flag_lip_retargeting:
# without stitching or retargeting
if inference_cfg.flag_lip_zero:
x_d_i_new += lip_delta_before_animation.reshape(-1, x_s.shape[1], 3)
else:
pass
elif inference_cfg.flag_stitching and not inference_cfg.flag_eye_retargeting and not inference_cfg.flag_lip_retargeting:
# with stitching and without retargeting
if inference_cfg.flag_lip_zero:
x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new) + lip_delta_before_animation.reshape(-1, x_s.shape[1], 3)
else:
x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new)
else:
eyes_delta, lip_delta = None, None
if inference_cfg.flag_eye_retargeting:
c_d_eyes_i = input_eye_ratio_lst[i]
combined_eye_ratio_tensor = self.live_portrait_wrapper.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.live_portrait_wrapper.retarget_eye(x_s, combined_eye_ratio_tensor)
if inference_cfg.flag_lip_retargeting:
c_d_lip_i = input_lip_ratio_lst[i]
combined_lip_ratio_tensor = self.live_portrait_wrapper.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.live_portrait_wrapper.retarget_lip(x_s, combined_lip_ratio_tensor)
if inference_cfg.flag_relative: # 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 inference_cfg.flag_stitching:
x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new)
out = self.live_portrait_wrapper.warp_decode(f_s, x_s, x_d_i_new)
I_p_i = self.live_portrait_wrapper.parse_output(out['out'])[0]
I_p_lst.append(I_p_i)
if inference_cfg.flag_pasteback:
I_p_i_to_ori_blend = paste_back(I_p_i, crop_info['M_c2o'], img_rgb, mask_ori)
I_p_paste_lst.append(I_p_i_to_ori_blend)
mkdir(args.output_dir)
wfp_concat = None
if is_video(args.driving_info):
frames_concatenated = concat_frames(I_p_lst, driving_rgb_lst, img_crop_256x256)
# save (driving frames, source image, drived frames) result
wfp_concat = osp.join(args.output_dir, f'{basename(args.source_image)}--{basename(args.driving_info)}_concat.mp4')
images2video(frames_concatenated, wfp=wfp_concat, fps=output_fps)
# save drived result
wfp = osp.join(args.output_dir, f'{basename(args.source_image)}--{basename(args.driving_info)}.mp4')
if inference_cfg.flag_pasteback:
images2video(I_p_paste_lst, wfp=wfp, fps=output_fps)
else:
images2video(I_p_lst, wfp=wfp, fps=output_fps)
return wfp, wfp_concat
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