From 1a754339b5954856314c8e2c9bf2cfd159de4d72 Mon Sep 17 00:00:00 2001 From: unknown Date: Wed, 10 Jul 2024 12:48:10 +0900 Subject: [PATCH] Added new inference code for webcam --- assets/examples/source/MY_photo.jpg | Bin 0 -> 37234 bytes assets/examples/source/k1.png | Bin 0 -> 331563 bytes assets/examples/source/k2.png | Bin 0 -> 485955 bytes assets/examples/source/s10.jpg | Bin 537372 -> 0 bytes assets/examples/source/solo.png | Bin 0 -> 383387 bytes inference.py | 163 +++++++++++++++++++++++++-- src/live_portrait_pipeline.py | 168 +++++++--------------------- 7 files changed, 194 insertions(+), 137 deletions(-) create mode 100644 assets/examples/source/MY_photo.jpg create mode 100644 assets/examples/source/k1.png create mode 100644 assets/examples/source/k2.png delete mode 100644 assets/examples/source/s10.jpg create mode 100644 assets/examples/source/solo.png diff --git a/assets/examples/source/MY_photo.jpg b/assets/examples/source/MY_photo.jpg new file mode 100644 index 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zsC{IkFncB<68hJ|OX_T=)YUudX#j&t!|m1X?RY76ilyeuog2fEmq}c32W@+LPCADQ zJ9*)pHxmrFC9H4n{_(#X7{)+glYN}4stO8|M!nSkTrk7?La|vn2#gmAEJMLD8wXBx zeRBCXVcgq9>QREldM2IOwtXIL+CWkke`b+-I>Sz|p&HdWb;39CNzD1TJmH}^>q~D2 zo5*rUewC>EnNjn8h_}{pg720o{q_S6QS0LMpC(^j2Dm@nzBVQzO{m!YyRhWQZ}^9X zsK6QLrJ((QnrisN{{ zpc}ar1($%-ga{BEV(YD=S9SlI_gDN 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!") - ######################################### + return x_s, f_s, R_s, x_s_info, lip_delta_before_animation, crop_info, img_rgb + + def generate_frame(self, x_s, f_s, R_s, x_s_info, lip_delta_before_animation, crop_info, img_rgb, driving_info): + inference_cfg = self.live_portrait_wrapper.cfg # for convenience + + # Process driving info + driving_rgb = cv2.resize(driving_info, (256, 256)) + I_d_i = self.live_portrait_wrapper.prepare_driving_videos([driving_rgb])[0] + + + 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']) + + R_new = R_d_i @ R_s + delta_new = x_s_info['exp'] + (x_d_i_info['exp'] - x_s_info['exp']) + scale_new = x_s_info['scale'] * (x_d_i_info['scale'] / x_s_info['scale']) + t_new = x_s_info['t'] + (x_d_i_info['t'] - x_s_info['t']) + t_new[..., 2].fill_(0) # zero tz + + x_d_i_new = scale_new * (x_s @ R_new + delta_new) + t_new + if inference_cfg.flag_lip_zero and lip_delta_before_animation is not None: + x_d_i_new += lip_delta_before_animation.reshape(-1, x_s.shape[1], 3) + + 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] - ######## 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) + I_p_i_to_ori_blend = paste_back(I_p_i, crop_info['M_c2o'], img_rgb, mask_ori) + return I_p_i_to_ori_blend else: - images2video(I_p_lst, wfp=wfp) + return I_p_i - return wfp, wfp_concat

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