""" # 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') ))