Spaces:
Running
on
T4
Running
on
T4
File size: 10,449 Bytes
22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 075b64e 22257c4 |
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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 |
"""
# Copyright 2020 Adobe
# All Rights Reserved.
# NOTICE: Adobe permits you to use, modify, and distribute this file in
# accordance with the terms of the Adobe license agreement accompanying
# it.
"""
import sys
sys.path.append('thirdparty/AdaptiveWingLoss')
import os, glob
import numpy as np
import argparse
import pickle
from src.autovc.AutoVC_mel_Convertor_retrain_version import AutoVC_mel_Convertor
import shutil
ADD_NAIVE_EYE = False
GEN_AUDIO = True
GEN_FLS = True
DEMO_CH = 'wilk.png'
parser = argparse.ArgumentParser()
parser.add_argument('--jpg', type=str, required=True, help='Puppet image name to animate (with filename extension), e.g. wilk.png')
parser.add_argument('--jpg_bg', type=str, required=True, help='Puppet image background (with filename extension), e.g. wilk_bg.jpg')
parser.add_argument('--inner_lip', default=False, action='store_true', help='add this if the puppet is created with only inner lip landmarks')
parser.add_argument('--out', type=str, default='out.mp4')
parser.add_argument('--load_AUTOVC_name', type=str, default='MakeItTalk/examples/ckpt/ckpt_autovc.pth')
parser.add_argument('--load_a2l_G_name', type=str, default='MakeItTalk/examples/ckpt/ckpt_speaker_branch.pth') #ckpt_audio2landmark_g.pth') #
parser.add_argument('--load_a2l_C_name', type=str, default='MakeItTalk/examples/ckpt/ckpt_content_branch.pth') #ckpt_audio2landmark_c.pth')
parser.add_argument('--load_G_name', type=str, default='MakeItTalk/examples/ckpt/ckpt_116_i2i_comb.pth') #ckpt_i2i_finetune_150.pth') #ckpt_image2image.pth') #
parser.add_argument('--amp_lip_x', type=float, default=2.0)
parser.add_argument('--amp_lip_y', type=float, default=2.0)
parser.add_argument('--amp_pos', type=float, default=0.5)
parser.add_argument('--reuse_train_emb_list', type=str, nargs='+', default=[]) # ['E_kmpT-EfOg']) # ['E_kmpT-EfOg']) # ['45hn7-LXDX8'])
parser.add_argument('--add_audio_in', default=False, action='store_true')
parser.add_argument('--comb_fan_awing', default=False, action='store_true')
parser.add_argument('--output_folder', type=str, default='MakeItTalk/examples_cartoon')
#### NEW POSE MODEL
parser.add_argument('--test_end2end', default=True, action='store_true')
parser.add_argument('--dump_dir', type=str, default='', help='')
parser.add_argument('--pos_dim', default=7, type=int)
parser.add_argument('--use_prior_net', default=True, action='store_true')
parser.add_argument('--transformer_d_model', default=32, type=int)
parser.add_argument('--transformer_N', default=2, type=int)
parser.add_argument('--transformer_heads', default=2, type=int)
parser.add_argument('--spk_emb_enc_size', default=16, type=int)
parser.add_argument('--init_content_encoder', type=str, default='')
parser.add_argument('--lr', type=float, default=1e-3, help='learning rate')
parser.add_argument('--reg_lr', type=float, default=1e-6, help='weight decay')
parser.add_argument('--write', default=False, action='store_true')
parser.add_argument('--segment_batch_size', type=int, default=512, help='batch size')
parser.add_argument('--emb_coef', default=3.0, type=float)
parser.add_argument('--lambda_laplacian_smooth_loss', default=1.0, type=float)
parser.add_argument('--use_11spk_only', default=False, action='store_true')
opt_parser = parser.parse_args()
DEMO_CH = opt_parser.jpg.split('.')[0]
shape_3d = np.loadtxt('MakeItTalk/examples_cartoon/{}_face_close_mouth.txt'.format(DEMO_CH))
''' STEP 3: Generate audio data as input to audio branch '''
au_data = []
au_emb = []
ains = glob.glob1('MakeItTalk/examples', '*.wav')
ains = [item for item in ains if item is not 'tmp.wav']
ains.sort()
for ain in ains:
os.system('ffmpeg -y -loglevel error -i MakeItTalk/examples/{} -ar 16000 MakeItTalk/examples/tmp.wav'.format(ain))
shutil.copyfile('MakeItTalk/examples/tmp.wav', 'MakeItTalk/examples/{}'.format(ain))
# au embedding
from thirdparty.resemblyer_util.speaker_emb import get_spk_emb
me, ae = get_spk_emb('MakeItTalk/examples/{}'.format(ain))
au_emb.append(me.reshape(-1))
print('Processing audio file', ain)
c = AutoVC_mel_Convertor('MakeItTalk/examples')
au_data_i = c.convert_single_wav_to_autovc_input(audio_filename=os.path.join('MakeItTalk/examples', ain),
autovc_model_path=opt_parser.load_AUTOVC_name)
au_data += au_data_i
# os.remove(os.path.join('MakeItTalk/examples', 'tmp.wav'))
if(os.path.isfile('MakeItTalk/examples/tmp.wav')):
os.remove('MakeItTalk/examples/tmp.wav')
fl_data = []
rot_tran, rot_quat, anchor_t_shape = [], [], []
for au, info in au_data:
au_length = au.shape[0]
fl = np.zeros(shape=(au_length, 68 * 3))
fl_data.append((fl, info))
rot_tran.append(np.zeros(shape=(au_length, 3, 4)))
rot_quat.append(np.zeros(shape=(au_length, 4)))
anchor_t_shape.append(np.zeros(shape=(au_length, 68 * 3)))
if(os.path.exists(os.path.join('MakeItTalk/examples', 'dump', 'random_val_fl.pickle'))):
os.remove(os.path.join('MakeItTalk/examples', 'dump', 'random_val_fl.pickle'))
if(os.path.exists(os.path.join('MakeItTalk/examples', 'dump', 'random_val_fl_interp.pickle'))):
os.remove(os.path.join('MakeItTalk/examples', 'dump', 'random_val_fl_interp.pickle'))
if(os.path.exists(os.path.join('MakeItTalk/examples', 'dump', 'random_val_au.pickle'))):
os.remove(os.path.join('MakeItTalk/examples', 'dump', 'random_val_au.pickle'))
if (os.path.exists(os.path.join('MakeItTalk/examples', 'dump', 'random_val_gaze.pickle'))):
os.remove(os.path.join('MakeItTalk/examples', 'dump', 'random_val_gaze.pickle'))
with open(os.path.join('MakeItTalk/examples', 'dump', 'random_val_fl.pickle'), 'wb') as fp:
pickle.dump(fl_data, fp)
with open(os.path.join('MakeItTalk/examples', 'dump', 'random_val_au.pickle'), 'wb') as fp:
pickle.dump(au_data, fp)
with open(os.path.join('MakeItTalk/examples', 'dump', 'random_val_gaze.pickle'), 'wb') as fp:
gaze = {'rot_trans':rot_tran, 'rot_quat':rot_quat, 'anchor_t_shape':anchor_t_shape}
pickle.dump(gaze, fp)
''' STEP 4: RUN audio->landmark network'''
from src.approaches.train_audio2landmark import Audio2landmark_model
model = Audio2landmark_model(opt_parser, jpg_shape=shape_3d)
if(len(opt_parser.reuse_train_emb_list) == 0):
model.test(au_emb=au_emb)
else:
model.test(au_emb=None)
print('finish gen fls')
''' STEP 5: de-normalize the output to the original image scale '''
fls_names = glob.glob1('MakeItTalk/examples_cartoon', 'pred_fls_*.txt')
fls_names.sort()
for i in range(0,len(fls_names)):
ains = glob.glob1('MakeItTalk/examples', '*.wav')
ains.sort()
ain = ains[i]
fl = np.loadtxt(os.path.join('MakeItTalk/examples_cartoon', fls_names[i])).reshape((-1, 68,3))
output_dir = os.path.join('MakeItTalk/examples_cartoon', fls_names[i][:-4])
try:
os.makedirs(output_dir)
except:
pass
from util.utils import get_puppet_info
bound, scale, shift = get_puppet_info(DEMO_CH, ROOT_DIR='MakeItTalk/examples_cartoon')
fls = fl.reshape((-1, 68, 3))
fls[:, :, 0:2] = -fls[:, :, 0:2]
fls[:, :, 0:2] = (fls[:, :, 0:2] / scale)
fls[:, :, 0:2] -= shift.reshape(1, 2)
fls = fls.reshape(-1, 204)
# additional smooth
from scipy.signal import savgol_filter
fls[:, 0:48*3] = savgol_filter(fls[:, 0:48*3], 17, 3, axis=0)
fls[:, 48*3:] = savgol_filter(fls[:, 48*3:], 11, 3, axis=0)
fls = fls.reshape((-1, 68, 3))
# if (DEMO_CH in ['paint', 'mulaney', 'cartoonM', 'beer', 'color', 'JohnMulaney', 'vangogh', 'jm', 'roy', 'lineface']):
if(not opt_parser.inner_lip):
r = list(range(0, 68))
fls = fls[:, r, :]
fls = fls[:, :, 0:2].reshape(-1, 68 * 2)
fls = np.concatenate((fls, np.tile(bound, (fls.shape[0], 1))), axis=1)
fls = fls.reshape(-1, 160)
else:
r = list(range(0, 48)) + list(range(60, 68))
fls = fls[:, r, :]
fls = fls[:, :, 0:2].reshape(-1, 56 * 2)
fls = np.concatenate((fls, np.tile(bound, (fls.shape[0], 1))), axis=1)
fls = fls.reshape(-1, 112 + bound.shape[1])
np.savetxt(os.path.join(output_dir, 'warped_points.txt'), fls, fmt='%.2f')
# static_points.txt
static_frame = np.loadtxt(os.path.join('MakeItTalk/examples_cartoon', '{}_face_open_mouth.txt'.format(DEMO_CH)))
static_frame = static_frame[r, 0:2]
static_frame = np.concatenate((static_frame, bound.reshape(-1, 2)), axis=0)
np.savetxt(os.path.join(output_dir, 'reference_points.txt'), static_frame, fmt='%.2f')
# triangle_vtx_index.txt
shutil.copy(os.path.join('MakeItTalk/examples_cartoon', DEMO_CH + '_delauney_tri.txt'),
os.path.join(output_dir, 'triangulation.txt'))
os.remove(os.path.join('MakeItTalk/examples_cartoon', fls_names[i]))
# ==============================================
# Step 4 : Vector art morphing
# ==============================================
warp_exe = os.path.join(os.getcwd(), 'facewarp', 'facewarp.exe')
import os
if (os.path.exists(os.path.join(output_dir, 'output'))):
shutil.rmtree(os.path.join(output_dir, 'output'))
os.mkdir(os.path.join(output_dir, 'output'))
os.chdir('{}'.format(os.path.join(output_dir, 'output')))
cur_dir = os.getcwd()
print(cur_dir)
if(os.name == 'nt'):
''' windows '''
os.system('{} {} {} {} {} {}'.format(
warp_exe,
os.path.join(cur_dir, '..', '..', opt_parser.jpg),
os.path.join(cur_dir, '..', 'triangulation.txt'),
os.path.join(cur_dir, '..', 'reference_points.txt'),
os.path.join(cur_dir, '..', 'warped_points.txt'),
os.path.join(cur_dir, '..', '..', opt_parser.jpg_bg),
'-novsync -dump'))
else:
''' linux '''
os.system('wine {} {} {} {} {} {}'.format(
warp_exe,
os.path.join(cur_dir, '..', '..', opt_parser.jpg),
os.path.join(cur_dir, '..', 'triangulation.txt'),
os.path.join(cur_dir, '..', 'reference_points.txt'),
os.path.join(cur_dir, '..', 'warped_points.txt'),
os.path.join(cur_dir, '..', '..', opt_parser.jpg_bg),
'-novsync -dump'))
os.system('ffmpeg -y -r 62.5 -f image2 -i "%06d.tga" -i {} -pix_fmt yuv420p -vf "pad=ceil(iw/2)*2:ceil(ih/2)*2" -shortest -strict -2 {}'.format(
os.path.join(cur_dir, '..', '..', '..', 'MakeItTalk/examples', ain),
os.path.join(cur_dir, '..', 'out.mp4')
))
|