# coding: utf-8 """ Pipeline of LivePortrait """ # TODO: # 1. 当前假定所有的模板都是已经裁好的，需要修改下 # 2. pick样例图 source + driving 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 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 reference 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 ######## if is_video(args.driving_info): log(f"Load from video file (mp4 mov avi etc...): {args.driving_info}") # 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) # 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) else: images2video(I_p_lst, wfp=wfp) return wfp, wfp_concat