mirror of
https://github.com/KwaiVGI/LivePortrait.git
synced 2024-12-23 05:02:38 +00:00
196 lines
9.9 KiB
Python
196 lines
9.9 KiB
Python
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# coding: utf-8
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"""
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Pipeline of LivePortrait
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"""
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# TODO:
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# 1. 当前假定所有的模板都是已经裁好的，需要修改下
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# 2. pick样例图 source + driving
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import cv2
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import numpy as np
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import pickle
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import os.path as osp
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from rich.progress import track
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from .config.argument_config import ArgumentConfig
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from .config.inference_config import InferenceConfig
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from .config.crop_config import CropConfig
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from .utils.cropper import Cropper
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from .utils.camera import get_rotation_matrix
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from .utils.video import images2video, concat_frames
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from .utils.crop import _transform_img
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from .utils.retargeting_utils import calc_lip_close_ratio
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from .utils.io import load_image_rgb, load_driving_info
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from .utils.helper import mkdir, basename, dct2cuda, is_video, is_template, resize_to_limit
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from .utils.rprint import rlog as log
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from .live_portrait_wrapper import LivePortraitWrapper
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def make_abs_path(fn):
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return osp.join(osp.dirname(osp.realpath(__file__)), fn)
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class LivePortraitPipeline(object):
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def __init__(self, inference_cfg: InferenceConfig, crop_cfg: CropConfig):
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self.live_portrait_wrapper: LivePortraitWrapper = LivePortraitWrapper(cfg=inference_cfg)
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self.cropper = Cropper(crop_cfg=crop_cfg)
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def execute(self, args: ArgumentConfig):
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inference_cfg = self.live_portrait_wrapper.cfg # for convenience
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######## process reference portrait ########
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img_rgb = load_image_rgb(args.source_image)
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img_rgb = resize_to_limit(img_rgb, inference_cfg.ref_max_shape, inference_cfg.ref_shape_n)
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log(f"Load source image from {args.source_image}")
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crop_info = self.cropper.crop_single_image(img_rgb)
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source_lmk = crop_info['lmk_crop']
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img_crop, img_crop_256x256 = crop_info['img_crop'], crop_info['img_crop_256x256']
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if inference_cfg.flag_do_crop:
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I_s = self.live_portrait_wrapper.prepare_source(img_crop_256x256)
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else:
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I_s = self.live_portrait_wrapper.prepare_source(img_rgb)
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x_s_info = self.live_portrait_wrapper.get_kp_info(I_s)
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x_c_s = x_s_info['kp']
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R_s = get_rotation_matrix(x_s_info['pitch'], x_s_info['yaw'], x_s_info['roll'])
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f_s = self.live_portrait_wrapper.extract_feature_3d(I_s)
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x_s = self.live_portrait_wrapper.transform_keypoint(x_s_info)
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if inference_cfg.flag_lip_zero:
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# let lip-open scalar to be 0 at first
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c_d_lip_before_animation = [0.]
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combined_lip_ratio_tensor_before_animation = self.live_portrait_wrapper.calc_combined_lip_ratio(c_d_lip_before_animation, source_lmk)
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if combined_lip_ratio_tensor_before_animation[0][0] < inference_cfg.lip_zero_threshold:
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inference_cfg.flag_lip_zero = False
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else:
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lip_delta_before_animation = self.live_portrait_wrapper.retarget_lip(x_s, combined_lip_ratio_tensor_before_animation)
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############################################
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######## process driving info ########
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if is_video(args.driving_info):
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log(f"Load from video file (mp4 mov avi etc...): {args.driving_info}")
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# TODO: 这里track一下驱动视频 -> 构建模板
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driving_rgb_lst = load_driving_info(args.driving_info)
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driving_rgb_lst_256 = [cv2.resize(_, (256, 256)) for _ in driving_rgb_lst]
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I_d_lst = self.live_portrait_wrapper.prepare_driving_videos(driving_rgb_lst_256)
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n_frames = I_d_lst.shape[0]
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if inference_cfg.flag_eye_retargeting or inference_cfg.flag_lip_retargeting:
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driving_lmk_lst = self.cropper.get_retargeting_lmk_info(driving_rgb_lst)
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input_eye_ratio_lst, input_lip_ratio_lst = self.live_portrait_wrapper.calc_retargeting_ratio(source_lmk, driving_lmk_lst)
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elif is_template(args.driving_info):
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log(f"Load from video templates {args.driving_info}")
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with open(args.driving_info, 'rb') as f:
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template_lst, driving_lmk_lst = pickle.load(f)
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n_frames = template_lst[0]['n_frames']
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input_eye_ratio_lst, input_lip_ratio_lst = self.live_portrait_wrapper.calc_retargeting_ratio(source_lmk, driving_lmk_lst)
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else:
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raise Exception("Unsupported driving types!")
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#########################################
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######## prepare for pasteback ########
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if inference_cfg.flag_pasteback:
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if inference_cfg.mask_crop is None:
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inference_cfg.mask_crop = cv2.imread(make_abs_path('./utils/resources/mask_template.png'), cv2.IMREAD_COLOR)
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mask_ori = _transform_img(inference_cfg.mask_crop, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0]))
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mask_ori = mask_ori.astype(np.float32) / 255.
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I_p_paste_lst = []
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#########################################
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I_p_lst = []
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R_d_0, x_d_0_info = None, None
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for i in track(range(n_frames), description='Animating...', total=n_frames):
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if is_video(args.driving_info):
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# extract kp info by M
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I_d_i = I_d_lst[i]
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x_d_i_info = self.live_portrait_wrapper.get_kp_info(I_d_i)
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R_d_i = get_rotation_matrix(x_d_i_info['pitch'], x_d_i_info['yaw'], x_d_i_info['roll'])
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else:
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# from template
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x_d_i_info = template_lst[i]
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x_d_i_info = dct2cuda(x_d_i_info, inference_cfg.device_id)
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R_d_i = x_d_i_info['R_d']
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if i == 0:
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R_d_0 = R_d_i
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x_d_0_info = x_d_i_info
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if inference_cfg.flag_relative:
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R_new = (R_d_i @ R_d_0.permute(0, 2, 1)) @ R_s
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delta_new = x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp'])
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scale_new = x_s_info['scale'] * (x_d_i_info['scale'] / x_d_0_info['scale'])
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t_new = x_s_info['t'] + (x_d_i_info['t'] - x_d_0_info['t'])
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else:
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R_new = R_d_i
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delta_new = x_d_i_info['exp']
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scale_new = x_s_info['scale']
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t_new = x_d_i_info['t']
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t_new[..., 2].fill_(0) # zero tz
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x_d_i_new = scale_new * (x_c_s @ R_new + delta_new) + t_new
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# Algorithm 1:
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if not inference_cfg.flag_stitching and not inference_cfg.flag_eye_retargeting and not inference_cfg.flag_lip_retargeting:
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# without stitching or retargeting
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if inference_cfg.flag_lip_zero:
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x_d_i_new += lip_delta_before_animation.reshape(-1, x_s.shape[1], 3)
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else:
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pass
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elif inference_cfg.flag_stitching and not inference_cfg.flag_eye_retargeting and not inference_cfg.flag_lip_retargeting:
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# with stitching and without retargeting
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if inference_cfg.flag_lip_zero:
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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)
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else:
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x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new)
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else:
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eyes_delta, lip_delta = None, None
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if inference_cfg.flag_eye_retargeting:
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c_d_eyes_i = input_eye_ratio_lst[i]
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combined_eye_ratio_tensor = self.live_portrait_wrapper.calc_combined_eye_ratio(c_d_eyes_i, source_lmk)
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# ∆_eyes,i = R_eyes(x_s; c_s,eyes, c_d,eyes,i)
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eyes_delta = self.live_portrait_wrapper.retarget_eye(x_s, combined_eye_ratio_tensor)
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if inference_cfg.flag_lip_retargeting:
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c_d_lip_i = input_lip_ratio_lst[i]
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combined_lip_ratio_tensor = self.live_portrait_wrapper.calc_combined_lip_ratio(c_d_lip_i, source_lmk)
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# ∆_lip,i = R_lip(x_s; c_s,lip, c_d,lip,i)
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lip_delta = self.live_portrait_wrapper.retarget_lip(x_s, combined_lip_ratio_tensor)
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if inference_cfg.flag_relative: # use x_s
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x_d_i_new = x_s + \
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(eyes_delta.reshape(-1, x_s.shape[1], 3) if eyes_delta is not None else 0) + \
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(lip_delta.reshape(-1, x_s.shape[1], 3) if lip_delta is not None else 0)
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else: # use x_d,i
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x_d_i_new = x_d_i_new + \
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(eyes_delta.reshape(-1, x_s.shape[1], 3) if eyes_delta is not None else 0) + \
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(lip_delta.reshape(-1, x_s.shape[1], 3) if lip_delta is not None else 0)
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if inference_cfg.flag_stitching:
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x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new)
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out = self.live_portrait_wrapper.warp_decode(f_s, x_s, x_d_i_new)
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I_p_i = self.live_portrait_wrapper.parse_output(out['out'])[0]
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I_p_lst.append(I_p_i)
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if inference_cfg.flag_pasteback:
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I_p_i_to_ori = _transform_img(I_p_i, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0]))
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I_p_i_to_ori_blend = np.clip(mask_ori * I_p_i_to_ori + (1 - mask_ori) * img_rgb, 0, 255).astype(np.uint8)
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out = np.hstack([I_p_i_to_ori, I_p_i_to_ori_blend])
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I_p_paste_lst.append(I_p_i_to_ori_blend)
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mkdir(args.output_dir)
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wfp_concat = None
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if is_video(args.driving_info):
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frames_concatenated = concat_frames(I_p_lst, driving_rgb_lst, img_crop_256x256)
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# save (driving frames, source image, drived frames) result
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wfp_concat = osp.join(args.output_dir, f'{basename(args.source_image)}--{basename(args.driving_info)}_concat.mp4')
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images2video(frames_concatenated, wfp=wfp_concat)
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# save drived result
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wfp = osp.join(args.output_dir, f'{basename(args.source_image)}--{basename(args.driving_info)}.mp4')
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if inference_cfg.flag_pasteback:
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images2video(I_p_paste_lst, wfp=wfp)
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else:
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images2video(I_p_lst, wfp=wfp)
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return wfp, wfp_concat
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