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| import numpy as np | |
| import cv2 | |
| from data_util.face3d_helper import Face3DHelper | |
| from utils.visualization.ffmpeg_utils import imgs_to_video | |
| import os | |
| face3d_helper = Face3DHelper('deep_3drecon/BFM', keypoint_mode='mediapipe') | |
| # lrs3_stats = np.load('data/binary/lrs3/stats.npy',allow_pickle=True).tolist() | |
| # lrs3_idexp_mean = lrs3_stats['idexp_lm3d_mean'].reshape([1,204]) | |
| # lrs3_idexp_std = lrs3_stats['idexp_lm3d_std'].reshape([1,204]) | |
| def render_idexp_npy_to_lm_video(npy_name, out_video_name, audio_name=None): | |
| try: | |
| idexp_lm3d = np.load(npy_name) | |
| except: | |
| coeff = np.load(npy_name, allow_pickle=True).tolist() | |
| t = coeff['exp'].shape[0] | |
| # print(coeff['id'][0]-coeff['id'][1]) | |
| if len(coeff['id']) == 1: | |
| coeff['id'] = np.repeat(coeff['id'], t, axis=0) | |
| idexp_lm3d = face3d_helper.reconstruct_idexp_lm3d_np(coeff['id'], coeff['exp']).reshape([t, -1]) | |
| lm3d = idexp_lm3d / 10 + face3d_helper.key_mean_shape.squeeze().reshape([1, -1]).cpu().numpy() | |
| lm3d = lm3d.reshape([t, -1, 3]) | |
| # lm3d[..., 0] = 0.5 # lm3d[:,:1, 0].repeat(lm3d.shape[1], axis=1) | |
| tmp_img_dir = os.path.join(os.path.dirname(out_video_name), "tmp_lm3d_imgs") | |
| os.makedirs(tmp_img_dir, exist_ok=True) | |
| WH = 512 | |
| lm3d = (lm3d * WH/2 + WH/2).astype(int) | |
| # eye_idx = list(range(36,48)) | |
| # mouth_idx = list(range(48,68)) | |
| for i_img in range(len(lm3d)): | |
| lm2d = lm3d[i_img ,:, :2] # [68, 2] | |
| img = np.ones([WH, WH, 3], dtype=np.uint8) * 255 | |
| for i in range(len(lm2d)): | |
| x, y = lm2d[i] | |
| color = (255,0,0) | |
| img = cv2.circle(img, center=(x,y), radius=3, color=color, thickness=-1) | |
| font = cv2.FONT_HERSHEY_SIMPLEX | |
| img = cv2.flip(img, 0) | |
| for i in range(len(lm2d)): | |
| x, y = lm2d[i] | |
| y = WH - y | |
| img = cv2.putText(img, f"{i}", org=(x,y), fontFace=font, fontScale=0.3, color=(255,0,0)) | |
| out_name = os.path.join(tmp_img_dir, f'{format(i_img, "05d")}.png') | |
| cv2.imwrite(out_name, img) | |
| imgs_to_video(tmp_img_dir, out_video_name, audio_name) | |
| os.system(f"rm -r {tmp_img_dir}") | |
| print(f"landmark video saved at {out_video_name}") | |
| if __name__ == '__main__': | |
| import argparse | |
| argparser = argparse.ArgumentParser() | |
| argparser.add_argument('--npy_name', type=str, default="infer_out/May/pred_lm3d/zozo.npy", help='the path of landmark .npy') | |
| argparser.add_argument('--audio_name', type=str, default="data/raw/val_wavs/zozo.wav", help='the path of audio file') | |
| argparser.add_argument('--out_path', type=str, default="infer_out/May/visualized_lm3d/zozo.mp4", help='the path to save visualization results') | |
| args = argparser.parse_args() | |
| render_idexp_npy_to_lm_video(args.npy_name, args.out_path, audio_name=args.audio_name) |