Delete animation/SadTalker directory

animation/SadTalker/src/face3d/visualize.py

@@ -1,48 +0,0 @@
-# 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')
-