""" # 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 cv2 import argparse from src.approaches.train_image_translation import Image_translation_block import torch import pickle import face_alignment from src.autovc.AutoVC_mel_Convertor_retrain_version import AutoVC_mel_Convertor import shutil import util.utils as util from scipy.signal import savgol_filter from src.approaches.train_audio2landmark import Audio2landmark_model default_head_name = 'dali' ADD_NAIVE_EYE = True CLOSE_INPUT_FACE_MOUTH = False parser = argparse.ArgumentParser() parser.add_argument('--jpg', type=str, default='{}.jpg'.format(default_head_name)) parser.add_argument('--close_input_face_mouth', default=CLOSE_INPUT_FACE_MOUTH, action='store_true') 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') 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_image2image.pth') #ckpt_i2i_finetune_150.pth') #c parser.add_argument('--amp_lip_x', type=float, default=2.) parser.add_argument('--amp_lip_y', type=float, default=2.) parser.add_argument('--amp_pos', type=float, default=.5) parser.add_argument('--reuse_train_emb_list', type=str, nargs='+', default=[]) # ['iWeklsXc0H8']) #['45hn7-LXDX8']) #['E_kmpT-EfOg']) #'iWeklsXc0H8', '29k8RtSUjE0', '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') 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=1, 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() ''' STEP 1: preprocess input single image ''' img =cv2.imread('MakeItTalk/examples/' + opt_parser.jpg) predictor = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device='cuda', flip_input=True) shapes = predictor.get_landmarks(img) if (not shapes or len(shapes) != 1): print('Cannot detect face landmarks. Exit.') exit(-1) shape_3d = shapes[0] if(opt_parser.close_input_face_mouth): util.close_input_face_mouth(shape_3d) ''' Additional manual adjustment to input face landmarks (slimmer lips and wider eyes) ''' # shape_3d[48:, 0] = (shape_3d[48:, 0] - np.mean(shape_3d[48:, 0])) * 0.95 + np.mean(shape_3d[48:, 0]) shape_3d[49:54, 1] += 1. shape_3d[55:60, 1] -= 1. shape_3d[[37,38,43,44], 1] -=2 shape_3d[[40,41,46,47], 1] +=2 ''' STEP 2: normalize face as input to audio branch ''' shape_3d, scale, shift = util.norm_input_face(shape_3d) ''' STEP 3: Generate audio data as input to audio branch ''' # audio real data au_data = [] au_emb = [] ains = glob.glob1('MakeItTalk/examples', '*.wav') ains = [item for item in ains if item != '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 if(os.path.isfile('MakeItTalk/examples/tmp.wav')): os.remove('MakeItTalk/examples/tmp.wav') # landmark fake placeholder 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''' 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) ''' STEP 5: de-normalize the output to the original image scale ''' fls = glob.glob1('MakeItTalk/examples', 'pred_fls_*.txt') #it looks like fls is the name of our desired output video but as a group of numpy arrays in a txt file fls.sort() for i in range(0,len(fls)): fl = np.loadtxt(os.path.join('MakeItTalk/examples', fls[i])).reshape((-1, 68,3)) #this is our desired image loaded into numpy ndarray. Data read from the text file. fl[:, :, 0:2] = -fl[:, :, 0:2] #i think this is adjusting the color fl[:, :, 0:2] = fl[:, :, 0:2] / scale - shift #an ndarray image array is (H, W, D) i.e. (height, width, depth), so we are adjusting depth here if (ADD_NAIVE_EYE): fl = util.add_naive_eye(fl) # additional smooth fl = fl.reshape((-1, 204)) fl[:, :48 * 3] = savgol_filter(fl[:, :48 * 3], 15, 3, axis=0) fl[:, 48*3:] = savgol_filter(fl[:, 48*3:], 5, 3, axis=0) fl = fl.reshape((-1, 68, 3)) ''' STEP 6: Imag2image translation ''' model = Image_translation_block(opt_parser, single_test=True) with torch.no_grad(): model.single_test(jpg=img, fls=fl, filename=fls[i], prefix=opt_parser.jpg.split('.')[0]) #fls is the video we want print('finish image2image gen') os.remove(os.path.join('MakeItTalk/examples', fls[i]))