mirror of
https://github.com/KwaiVGI/LivePortrait.git
synced 2025-03-14 21:22:43 +00:00
414 lines
23 KiB
Python
414 lines
23 KiB
Python
# coding: utf-8
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"""
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Pipeline of LivePortrait
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"""
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import torch
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torch.backends.cudnn.benchmark = True # disable CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR warning
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import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
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import numpy as np
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import os
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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, get_fps, add_audio_to_video, has_audio_stream
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from .utils.crop import prepare_paste_back, paste_back
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from .utils.io import load_image_rgb, load_video, resize_to_limit, dump, load
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from .utils.helper import mkdir, basename, dct2device, is_video, is_template, remove_suffix, is_image, is_square_video
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from .utils.filter import smooth
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from .utils.rprint import rlog as log
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# from .utils.viz import viz_lmk
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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(inference_cfg=inference_cfg)
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self.cropper: Cropper = Cropper(crop_cfg=crop_cfg)
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def make_motion_template(self, I_lst, c_eyes_lst, c_lip_lst, **kwargs):
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n_frames = I_lst.shape[0]
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template_dct = {
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'n_frames': n_frames,
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'output_fps': kwargs.get('output_fps', 25),
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'motion': [],
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'c_eyes_lst': [],
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'c_lip_lst': [],
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'x_i_info_lst': [],
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}
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for i in track(range(n_frames), description='Making motion templates...', total=n_frames):
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# collect s, R, δ and t for inference
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I_i = I_lst[i]
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x_i_info = self.live_portrait_wrapper.get_kp_info(I_i)
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R_i = get_rotation_matrix(x_i_info['pitch'], x_i_info['yaw'], x_i_info['roll'])
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item_dct = {
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'scale': x_i_info['scale'].cpu().numpy().astype(np.float32),
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'R': R_i.cpu().numpy().astype(np.float32),
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'exp': x_i_info['exp'].cpu().numpy().astype(np.float32),
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't': x_i_info['t'].cpu().numpy().astype(np.float32),
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}
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template_dct['motion'].append(item_dct)
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c_eyes = c_eyes_lst[i].astype(np.float32)
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template_dct['c_eyes_lst'].append(c_eyes)
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c_lip = c_lip_lst[i].astype(np.float32)
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template_dct['c_lip_lst'].append(c_lip)
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template_dct['x_i_info_lst'].append(x_i_info)
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return template_dct
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def execute(self, args: ArgumentConfig):
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# for convenience
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inf_cfg = self.live_portrait_wrapper.inference_cfg
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device = self.live_portrait_wrapper.device
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crop_cfg = self.cropper.crop_cfg
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######## load source input ########
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flag_is_source_video = False
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source_fps = None
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if is_image(args.source):
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flag_is_source_video = False
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img_rgb = load_image_rgb(args.source)
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img_rgb = resize_to_limit(img_rgb, inf_cfg.source_max_dim, inf_cfg.source_division)
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log(f"Load source image from {args.source}")
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source_rgb_lst = [img_rgb]
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elif is_video(args.source):
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flag_is_source_video = True
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source_rgb_lst = load_video(args.source)
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source_rgb_lst = [resize_to_limit(img, inf_cfg.source_max_dim, inf_cfg.source_division) for img in source_rgb_lst]
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source_fps = int(get_fps(args.source))
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log(f"Load source video from {args.source}, FPS is {source_fps}")
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else: # source input is an unknown format
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raise Exception(f"Unknown source format: {args.source}")
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######## process driving info ########
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flag_load_from_template = is_template(args.driving)
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driving_rgb_crop_256x256_lst = None
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wfp_template = None
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if flag_load_from_template:
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# NOTE: load from template, it is fast, but the cropping video is None
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log(f"Load from template: {args.driving}, NOT the video, so the cropping video and audio are both NULL.", style='bold green')
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driving_template_dct = load(args.driving)
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c_d_eyes_lst = driving_template_dct['c_eyes_lst'] if 'c_eyes_lst' in driving_template_dct.keys() else driving_template_dct['c_d_eyes_lst'] # compatible with previous keys
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c_d_lip_lst = driving_template_dct['c_lip_lst'] if 'c_lip_lst' in driving_template_dct.keys() else driving_template_dct['c_d_lip_lst']
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driving_n_frames = driving_template_dct['n_frames']
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if flag_is_source_video:
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n_frames = min(len(source_rgb_lst), driving_n_frames) # minimum number as the number of the animated frames
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else:
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n_frames = driving_n_frames
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# set output_fps
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output_fps = driving_template_dct.get('output_fps', inf_cfg.output_fps)
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log(f'The FPS of template: {output_fps}')
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if args.flag_crop_driving_video:
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log("Warning: flag_crop_driving_video is True, but the driving info is a template, so it is ignored.")
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elif osp.exists(args.driving) and is_video(args.driving):
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# load from video file, AND make motion template
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output_fps = int(get_fps(args.driving))
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log(f"Load driving video from: {args.driving}, FPS is {output_fps}")
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driving_rgb_lst = load_video(args.driving)
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driving_n_frames = len(driving_rgb_lst)
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######## make motion template ########
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log("Start making driving motion template...")
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if flag_is_source_video:
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n_frames = min(len(source_rgb_lst), driving_n_frames) # minimum number as the number of the animated frames
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driving_rgb_lst = driving_rgb_lst[:n_frames]
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else:
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n_frames = driving_n_frames
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if inf_cfg.flag_crop_driving_video or (not is_square_video(args.driving)):
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ret_d = self.cropper.crop_driving_video(driving_rgb_lst)
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log(f'Driving video is cropped, {len(ret_d["frame_crop_lst"])} frames are processed.')
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if len(ret_d["frame_crop_lst"]) is not n_frames:
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n_frames = min(n_frames, len(ret_d["frame_crop_lst"]))
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driving_rgb_crop_lst, driving_lmk_crop_lst = ret_d['frame_crop_lst'], ret_d['lmk_crop_lst']
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driving_rgb_crop_256x256_lst = [cv2.resize(_, (256, 256)) for _ in driving_rgb_crop_lst]
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else:
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driving_lmk_crop_lst = self.cropper.calc_lmks_from_cropped_video(driving_rgb_lst)
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driving_rgb_crop_256x256_lst = [cv2.resize(_, (256, 256)) for _ in driving_rgb_lst] # force to resize to 256x256
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#######################################
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c_d_eyes_lst, c_d_lip_lst = self.live_portrait_wrapper.calc_ratio(driving_lmk_crop_lst)
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# save the motion template
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I_d_lst = self.live_portrait_wrapper.prepare_videos(driving_rgb_crop_256x256_lst)
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driving_template_dct = self.make_motion_template(I_d_lst, c_d_eyes_lst, c_d_lip_lst, output_fps=output_fps)
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wfp_template = remove_suffix(args.driving) + '.pkl'
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dump(wfp_template, driving_template_dct)
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log(f"Dump motion template to {wfp_template}")
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else:
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raise Exception(f"{args.driving} not exists or unsupported driving info types!")
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######## prepare for pasteback ########
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I_p_pstbk_lst = None
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if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching:
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I_p_pstbk_lst = []
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log("Prepared pasteback mask done.")
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I_p_lst = []
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R_d_0, x_d_0_info = None, None
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flag_normalize_lip = inf_cfg.flag_normalize_lip # not overwrite
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flag_source_video_eye_retargeting = inf_cfg.flag_source_video_eye_retargeting # not overwrite
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lip_delta_before_animation, eye_delta_before_animation = None, None
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######## process source info ########
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if flag_is_source_video:
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log(f"Start making source motion template...")
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source_rgb_lst = source_rgb_lst[:n_frames]
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if inf_cfg.flag_do_crop:
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ret_s = self.cropper.crop_source_video(source_rgb_lst, crop_cfg)
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log(f'Source video is cropped, {len(ret_s["frame_crop_lst"])} frames are processed.')
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if len(ret_s["frame_crop_lst"]) is not n_frames:
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n_frames = min(n_frames, len(ret_s["frame_crop_lst"]))
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img_crop_256x256_lst, source_lmk_crop_lst, source_M_c2o_lst = ret_s['frame_crop_lst'], ret_s['lmk_crop_lst'], ret_s['M_c2o_lst']
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else:
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source_lmk_crop_lst = self.cropper.calc_lmks_from_cropped_video(source_rgb_lst)
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img_crop_256x256_lst = [cv2.resize(_, (256, 256)) for _ in source_rgb_lst] # force to resize to 256x256
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c_s_eyes_lst, c_s_lip_lst = self.live_portrait_wrapper.calc_ratio(source_lmk_crop_lst)
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# save the motion template
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I_s_lst = self.live_portrait_wrapper.prepare_videos(img_crop_256x256_lst)
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source_template_dct = self.make_motion_template(I_s_lst, c_s_eyes_lst, c_s_lip_lst, output_fps=source_fps)
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key_r = 'R' if 'R' in driving_template_dct['motion'][0].keys() else 'R_d' # compatible with previous keys
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if inf_cfg.flag_relative_motion:
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x_d_exp_lst = [source_template_dct['motion'][i]['exp'] + driving_template_dct['motion'][i]['exp'] - driving_template_dct['motion'][0]['exp'] for i in range(n_frames)]
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x_d_exp_lst_smooth = smooth(x_d_exp_lst, source_template_dct['motion'][0]['exp'].shape, device, inf_cfg.driving_smooth_observation_variance)
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if inf_cfg.flag_video_editing_head_rotation:
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x_d_r_lst = [(np.dot(driving_template_dct['motion'][i][key_r], driving_template_dct['motion'][0][key_r].transpose(0, 2, 1))) @ source_template_dct['motion'][i]['R'] for i in range(n_frames)]
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x_d_r_lst_smooth = smooth(x_d_r_lst, source_template_dct['motion'][0]['R'].shape, device, inf_cfg.driving_smooth_observation_variance)
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else:
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x_d_exp_lst = [driving_template_dct['motion'][i]['exp'] for i in range(n_frames)]
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x_d_exp_lst_smooth = smooth(x_d_exp_lst, source_template_dct['motion'][0]['exp'].shape, device, inf_cfg.driving_smooth_observation_variance)
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if inf_cfg.flag_video_editing_head_rotation:
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x_d_r_lst = [driving_template_dct['motion'][i][key_r] for i in range(n_frames)]
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x_d_r_lst_smooth = smooth(x_d_r_lst, source_template_dct['motion'][0]['R'].shape, device, inf_cfg.driving_smooth_observation_variance)
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else: # if the input is a source image, process it only once
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if inf_cfg.flag_do_crop:
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crop_info = self.cropper.crop_source_image(source_rgb_lst[0], crop_cfg)
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if crop_info is None:
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raise Exception("No face detected in the source image!")
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source_lmk = crop_info['lmk_crop']
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img_crop_256x256 = crop_info['img_crop_256x256']
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else:
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source_lmk = self.cropper.calc_lmk_from_cropped_image(source_rgb_lst[0])
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img_crop_256x256 = cv2.resize(source_rgb_lst[0], (256, 256)) # force to resize to 256x256
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I_s = self.live_portrait_wrapper.prepare_source(img_crop_256x256)
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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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# let lip-open scalar to be 0 at first
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if flag_normalize_lip and inf_cfg.flag_relative_motion and source_lmk is not None:
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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] >= inf_cfg.lip_normalize_threshold:
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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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if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching:
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mask_ori_float = prepare_paste_back(inf_cfg.mask_crop, crop_info['M_c2o'], dsize=(source_rgb_lst[0].shape[1], source_rgb_lst[0].shape[0]))
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######## animate ########
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log(f"The animated video consists of {n_frames} frames.")
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for i in track(range(n_frames), description='🚀Animating...', total=n_frames):
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if flag_is_source_video: # source video
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x_s_info_tiny = source_template_dct['motion'][i]
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x_s_info_tiny = dct2device(x_s_info_tiny, device)
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source_lmk = source_lmk_crop_lst[i]
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img_crop_256x256 = img_crop_256x256_lst[i]
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I_s = I_s_lst[i]
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x_s_info = source_template_dct['x_i_info_lst'][i]
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x_c_s = x_s_info['kp']
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R_s = x_s_info_tiny['R']
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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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# let lip-open scalar to be 0 at first if the input is a video
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if flag_normalize_lip and inf_cfg.flag_relative_motion and source_lmk is not None:
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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] >= inf_cfg.lip_normalize_threshold:
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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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else:
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lip_delta_before_animation = None
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# let eye-open scalar to be the same as the first frame if the latter is eye-open state
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if flag_source_video_eye_retargeting and source_lmk is not None:
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if i == 0:
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combined_eye_ratio_tensor_frame_zero = c_s_eyes_lst[0]
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c_d_eye_before_animation_frame_zero = [[combined_eye_ratio_tensor_frame_zero[0][:2].mean()]]
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if c_d_eye_before_animation_frame_zero[0][0] < inf_cfg.source_video_eye_retargeting_threshold:
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c_d_eye_before_animation_frame_zero = [[0.39]]
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combined_eye_ratio_tensor_before_animation = self.live_portrait_wrapper.calc_combined_eye_ratio(c_d_eye_before_animation_frame_zero, source_lmk)
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eye_delta_before_animation = self.live_portrait_wrapper.retarget_eye(x_s, combined_eye_ratio_tensor_before_animation)
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if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching: # prepare for paste back
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mask_ori_float = prepare_paste_back(inf_cfg.mask_crop, source_M_c2o_lst[i], dsize=(source_rgb_lst[i].shape[1], source_rgb_lst[i].shape[0]))
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x_d_i_info = driving_template_dct['motion'][i]
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x_d_i_info = dct2device(x_d_i_info, device)
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R_d_i = x_d_i_info['R'] if 'R' in x_d_i_info.keys() else x_d_i_info['R_d'] # compatible with previous keys
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if i == 0: # cache the first frame
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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 inf_cfg.flag_relative_motion:
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if flag_is_source_video:
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if inf_cfg.flag_video_editing_head_rotation:
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R_new = x_d_r_lst_smooth[i]
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else:
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R_new = R_s
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else:
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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_d_exp_lst_smooth[i] if flag_is_source_video else x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp'])
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scale_new = x_s_info['scale'] if flag_is_source_video else x_s_info['scale'] * (x_d_i_info['scale'] / x_d_0_info['scale'])
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t_new = x_s_info['t'] if flag_is_source_video else x_s_info['t'] + (x_d_i_info['t'] - x_d_0_info['t'])
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else:
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if flag_is_source_video:
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if inf_cfg.flag_video_editing_head_rotation:
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R_new = x_d_r_lst_smooth[i]
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else:
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R_new = R_s
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else:
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R_new = R_d_i
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delta_new = x_d_exp_lst_smooth[i] if flag_is_source_video else 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 inf_cfg.flag_stitching and not inf_cfg.flag_eye_retargeting and not inf_cfg.flag_lip_retargeting:
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# without stitching or retargeting
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if flag_normalize_lip and lip_delta_before_animation is not None:
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x_d_i_new += lip_delta_before_animation
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if flag_source_video_eye_retargeting and eye_delta_before_animation is not None:
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x_d_i_new += eye_delta_before_animation
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else:
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pass
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elif inf_cfg.flag_stitching and not inf_cfg.flag_eye_retargeting and not inf_cfg.flag_lip_retargeting:
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# with stitching and without retargeting
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if flag_normalize_lip and lip_delta_before_animation is not None:
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x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new) + lip_delta_before_animation
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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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if flag_source_video_eye_retargeting and eye_delta_before_animation is not None:
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x_d_i_new += eye_delta_before_animation
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else:
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eyes_delta, lip_delta = None, None
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if inf_cfg.flag_eye_retargeting and source_lmk is not None:
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c_d_eyes_i = c_d_eyes_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 inf_cfg.flag_lip_retargeting and source_lmk is not None:
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c_d_lip_i = c_d_lip_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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|
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if inf_cfg.flag_relative_motion: # use x_s
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x_d_i_new = x_s + \
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(eyes_delta if eyes_delta is not None else 0) + \
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(lip_delta 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 if eyes_delta is not None else 0) + \
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(lip_delta if lip_delta is not None else 0)
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|
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if inf_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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|
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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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|
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if inf_cfg.flag_pasteback and inf_cfg.flag_do_crop and inf_cfg.flag_stitching:
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|
# TODO: the paste back procedure is slow, considering optimize it using multi-threading or GPU
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|
if flag_is_source_video:
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I_p_pstbk = paste_back(I_p_i, source_M_c2o_lst[i], source_rgb_lst[i], mask_ori_float)
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else:
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I_p_pstbk = paste_back(I_p_i, crop_info['M_c2o'], source_rgb_lst[0], mask_ori_float)
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|
I_p_pstbk_lst.append(I_p_pstbk)
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|
|
|
mkdir(args.output_dir)
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|
wfp_concat = None
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|
flag_source_has_audio = flag_is_source_video and has_audio_stream(args.source)
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|
flag_driving_has_audio = (not flag_load_from_template) and has_audio_stream(args.driving)
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|
|
|
######### build the final concatenation result #########
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|
# driving frame | source frame | generation, or source frame | generation
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|
if flag_is_source_video:
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|
frames_concatenated = concat_frames(driving_rgb_crop_256x256_lst, img_crop_256x256_lst, I_p_lst)
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|
else:
|
|
frames_concatenated = concat_frames(driving_rgb_crop_256x256_lst, [img_crop_256x256], I_p_lst)
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|
wfp_concat = osp.join(args.output_dir, f'{basename(args.source)}--{basename(args.driving)}_concat.mp4')
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|
|
|
# NOTE: update output fps
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|
output_fps = source_fps if flag_is_source_video else output_fps
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|
images2video(frames_concatenated, wfp=wfp_concat, fps=output_fps)
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|
|
|
if flag_source_has_audio or flag_driving_has_audio:
|
|
# final result with concatenation
|
|
wfp_concat_with_audio = osp.join(args.output_dir, f'{basename(args.source)}--{basename(args.driving)}_concat_with_audio.mp4')
|
|
audio_from_which_video = args.driving if ((flag_driving_has_audio and args.audio_priority == 'driving') or (not flag_source_has_audio)) else args.source
|
|
log(f"Audio is selected from {audio_from_which_video}, concat mode")
|
|
add_audio_to_video(wfp_concat, audio_from_which_video, wfp_concat_with_audio)
|
|
os.replace(wfp_concat_with_audio, wfp_concat)
|
|
log(f"Replace {wfp_concat} with {wfp_concat_with_audio}")
|
|
|
|
# save the animated result
|
|
wfp = osp.join(args.output_dir, f'{basename(args.source)}--{basename(args.driving)}.mp4')
|
|
if I_p_pstbk_lst is not None and len(I_p_pstbk_lst) > 0:
|
|
images2video(I_p_pstbk_lst, wfp=wfp, fps=output_fps)
|
|
else:
|
|
images2video(I_p_lst, wfp=wfp, fps=output_fps)
|
|
|
|
######### build the final result #########
|
|
if flag_source_has_audio or flag_driving_has_audio:
|
|
wfp_with_audio = osp.join(args.output_dir, f'{basename(args.source)}--{basename(args.driving)}_with_audio.mp4')
|
|
audio_from_which_video = args.driving if ((flag_driving_has_audio and args.audio_priority == 'driving') or (not flag_source_has_audio)) else args.source
|
|
log(f"Audio is selected from {audio_from_which_video}")
|
|
add_audio_to_video(wfp, audio_from_which_video, wfp_with_audio)
|
|
os.replace(wfp_with_audio, wfp)
|
|
log(f"Replace {wfp} with {wfp_with_audio}")
|
|
|
|
# final log
|
|
if wfp_template not in (None, ''):
|
|
log(f'Animated template: {wfp_template}, you can specify `-d` argument with this template path next time to avoid cropping video, motion making and protecting privacy.', style='bold green')
|
|
log(f'Animated video: {wfp}')
|
|
log(f'Animated video with concat: {wfp_concat}')
|
|
|
|
return wfp, wfp_concat
|