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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) |