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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)
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