File size: 6,890 Bytes
e64d6ac
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import numpy as np
import cv2, os, sys, torch
from tqdm import tqdm
from PIL import Image 

# 3dmm extraction
import safetensors
import safetensors.torch 
from src.face3d.util.preprocess import align_img
from src.face3d.util.load_mats import load_lm3d
from src.face3d.models import networks

from scipy.io import loadmat, savemat
from src.utils.croper import Preprocesser


import warnings

from src.utils.safetensor_helper import load_x_from_safetensor 
warnings.filterwarnings("ignore")

def split_coeff(coeffs):
        """
        Return:
            coeffs_dict     -- a dict of torch.tensors

        Parameters:
            coeffs          -- torch.tensor, size (B, 256)
        """
        id_coeffs = coeffs[:, :80]
        exp_coeffs = coeffs[:, 80: 144]
        tex_coeffs = coeffs[:, 144: 224]
        angles = coeffs[:, 224: 227]
        gammas = coeffs[:, 227: 254]
        translations = coeffs[:, 254:]
        return {
            'id': id_coeffs,
            'exp': exp_coeffs,
            'tex': tex_coeffs,
            'angle': angles,
            'gamma': gammas,
            'trans': translations
        }


class CropAndExtract():
    def __init__(self, sadtalker_path, device):

        self.propress = Preprocesser(device)
        self.net_recon = networks.define_net_recon(net_recon='resnet50', use_last_fc=False, init_path='').to(device)
        
        if sadtalker_path['use_safetensor']:
            checkpoint = safetensors.torch.load_file(sadtalker_path['checkpoint'])    
            self.net_recon.load_state_dict(load_x_from_safetensor(checkpoint, 'face_3drecon'))
        else:
            checkpoint = torch.load(sadtalker_path['path_of_net_recon_model'], map_location=torch.device(device))    
            self.net_recon.load_state_dict(checkpoint['net_recon'])

        self.net_recon.eval()
        self.lm3d_std = load_lm3d(sadtalker_path['dir_of_BFM_fitting'])
        self.device = device
    
    def generate(self, input_path, save_dir, crop_or_resize='crop', source_image_flag=False, pic_size=256):

        pic_name = os.path.splitext(os.path.split(input_path)[-1])[0]  

        landmarks_path =  os.path.join(save_dir, pic_name+'_landmarks.txt') 
        coeff_path =  os.path.join(save_dir, pic_name+'.mat')  
        png_path =  os.path.join(save_dir, pic_name+'.png')  

        #load input
        if not os.path.isfile(input_path):
            raise ValueError('input_path must be a valid path to video/image file')
        elif input_path.split('.')[-1] in ['jpg', 'png', 'jpeg']:
            # loader for first frame
            full_frames = [cv2.imread(input_path)]
            fps = 25
        else:
            # loader for videos
            video_stream = cv2.VideoCapture(input_path)
            fps = video_stream.get(cv2.CAP_PROP_FPS)
            full_frames = [] 
            while 1:
                still_reading, frame = video_stream.read()
                if not still_reading:
                    video_stream.release()
                    break 
                full_frames.append(frame) 
                if source_image_flag:
                    break

        x_full_frames= [cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  for frame in full_frames] 

        #### crop images as the 
        if 'crop' in crop_or_resize.lower(): # default crop
            x_full_frames, crop, quad = self.propress.crop(x_full_frames, still=True if 'ext' in crop_or_resize.lower() else False, xsize=512)
            clx, cly, crx, cry = crop
            lx, ly, rx, ry = quad
            lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
            oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
            crop_info = ((ox2 - ox1, oy2 - oy1), crop, quad)
        elif 'full' in crop_or_resize.lower():
            x_full_frames, crop, quad = self.propress.crop(x_full_frames, still=True if 'ext' in crop_or_resize.lower() else False, xsize=512)
            clx, cly, crx, cry = crop
            lx, ly, rx, ry = quad
            lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
            oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
            crop_info = ((ox2 - ox1, oy2 - oy1), crop, quad)
        else: # resize mode
            oy1, oy2, ox1, ox2 = 0, x_full_frames[0].shape[0], 0, x_full_frames[0].shape[1] 
            crop_info = ((ox2 - ox1, oy2 - oy1), None, None)

        frames_pil = [Image.fromarray(cv2.resize(frame,(pic_size, pic_size))) for frame in x_full_frames]
        if len(frames_pil) == 0:
            print('No face is detected in the input file')
            return None, None

        # save crop info
        for frame in frames_pil:
            cv2.imwrite(png_path, cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR))

        # 2. get the landmark according to the detected face. 
        if not os.path.isfile(landmarks_path): 
            lm = self.propress.predictor.extract_keypoint(frames_pil, landmarks_path)
        else:
            print(' Using saved landmarks.')
            lm = np.loadtxt(landmarks_path).astype(np.float32)
            lm = lm.reshape([len(x_full_frames), -1, 2])

        if not os.path.isfile(coeff_path):
            # load 3dmm paramter generator from Deep3DFaceRecon_pytorch 
            video_coeffs, full_coeffs = [],  []
            for idx in tqdm(range(len(frames_pil)), desc='3DMM Extraction In Video:'):
                frame = frames_pil[idx]
                W,H = frame.size
                lm1 = lm[idx].reshape([-1, 2])
            
                if np.mean(lm1) == -1:
                    lm1 = (self.lm3d_std[:, :2]+1)/2.
                    lm1 = np.concatenate(
                        [lm1[:, :1]*W, lm1[:, 1:2]*H], 1
                    )
                else:
                    lm1[:, -1] = H - 1 - lm1[:, -1]

                trans_params, im1, lm1, _ = align_img(frame, lm1, self.lm3d_std)
 
                trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32)
                im_t = torch.tensor(np.array(im1)/255., dtype=torch.float32).permute(2, 0, 1).to(self.device).unsqueeze(0)
                
                with torch.no_grad():
                    full_coeff = self.net_recon(im_t)
                    coeffs = split_coeff(full_coeff)

                pred_coeff = {key:coeffs[key].cpu().numpy() for key in coeffs}
 
                pred_coeff = np.concatenate([
                    pred_coeff['exp'], 
                    pred_coeff['angle'],
                    pred_coeff['trans'],
                    trans_params[2:][None],
                    ], 1)
                video_coeffs.append(pred_coeff)
                full_coeffs.append(full_coeff.cpu().numpy())

            semantic_npy = np.array(video_coeffs)[:,0] 

            savemat(coeff_path, {'coeff_3dmm': semantic_npy, 'full_3dmm': np.array(full_coeffs)[0]})

        return coeff_path, png_path, crop_info