Spaces:
Running
on
A10G
Running
on
A10G
File size: 1,719 Bytes
a22eb82 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
# check the sync of 3dmm feature and the audio
import cv2
import numpy as np
from src.face3d.models.bfm import ParametricFaceModel
from src.face3d.models.facerecon_model import FaceReconModel
import torch
import subprocess, platform
import scipy.io as scio
from tqdm import tqdm
# draft
def gen_composed_video(args, device, first_frame_coeff, coeff_path, audio_path, save_path, exp_dim=64):
coeff_first = scio.loadmat(first_frame_coeff)['full_3dmm']
coeff_pred = scio.loadmat(coeff_path)['coeff_3dmm']
coeff_full = np.repeat(coeff_first, coeff_pred.shape[0], axis=0) # 257
coeff_full[:, 80:144] = coeff_pred[:, 0:64]
coeff_full[:, 224:227] = coeff_pred[:, 64:67] # 3 dim translation
coeff_full[:, 254:] = coeff_pred[:, 67:] # 3 dim translation
tmp_video_path = '/tmp/face3dtmp.mp4'
facemodel = FaceReconModel(args)
video = cv2.VideoWriter(tmp_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 25, (224, 224))
for k in tqdm(range(coeff_pred.shape[0]), 'face3d rendering:'):
cur_coeff_full = torch.tensor(coeff_full[k:k+1], device=device)
facemodel.forward(cur_coeff_full, device)
predicted_landmark = facemodel.pred_lm # TODO.
predicted_landmark = predicted_landmark.cpu().numpy().squeeze()
rendered_img = facemodel.pred_face
rendered_img = 255. * rendered_img.cpu().numpy().squeeze().transpose(1,2,0)
out_img = rendered_img[:, :, :3].astype(np.uint8)
video.write(np.uint8(out_img[:,:,::-1]))
video.release()
command = 'ffmpeg -v quiet -y -i {} -i {} -strict -2 -q:v 1 {}'.format(audio_path, tmp_video_path, save_path)
subprocess.call(command, shell=platform.system() != 'Windows')
|