-
Notifications
You must be signed in to change notification settings - Fork 556
/
demo.py
179 lines (145 loc) · 8.05 KB
/
demo.py
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
import matplotlib
matplotlib.use('Agg')
import sys
import yaml
from argparse import ArgumentParser
from tqdm import tqdm
from scipy.spatial import ConvexHull
import numpy as np
import imageio
from skimage.transform import resize
from skimage import img_as_ubyte
import torch
from modules.inpainting_network import InpaintingNetwork
from modules.keypoint_detector import KPDetector
from modules.dense_motion import DenseMotionNetwork
from modules.avd_network import AVDNetwork
if sys.version_info[0] < 3:
raise Exception("You must use Python 3 or higher. Recommended version is Python 3.9")
def relative_kp(kp_source, kp_driving, kp_driving_initial):
source_area = ConvexHull(kp_source['fg_kp'][0].data.cpu().numpy()).volume
driving_area = ConvexHull(kp_driving_initial['fg_kp'][0].data.cpu().numpy()).volume
adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
kp_new = {k: v for k, v in kp_driving.items()}
kp_value_diff = (kp_driving['fg_kp'] - kp_driving_initial['fg_kp'])
kp_value_diff *= adapt_movement_scale
kp_new['fg_kp'] = kp_value_diff + kp_source['fg_kp']
return kp_new
def load_checkpoints(config_path, checkpoint_path, device):
with open(config_path) as f:
config = yaml.full_load(f)
inpainting = InpaintingNetwork(**config['model_params']['generator_params'],
**config['model_params']['common_params'])
kp_detector = KPDetector(**config['model_params']['common_params'])
dense_motion_network = DenseMotionNetwork(**config['model_params']['common_params'],
**config['model_params']['dense_motion_params'])
avd_network = AVDNetwork(num_tps=config['model_params']['common_params']['num_tps'],
**config['model_params']['avd_network_params'])
kp_detector.to(device)
dense_motion_network.to(device)
inpainting.to(device)
avd_network.to(device)
checkpoint = torch.load(checkpoint_path, map_location=device)
inpainting.load_state_dict(checkpoint['inpainting_network'])
kp_detector.load_state_dict(checkpoint['kp_detector'])
dense_motion_network.load_state_dict(checkpoint['dense_motion_network'])
if 'avd_network' in checkpoint:
avd_network.load_state_dict(checkpoint['avd_network'])
inpainting.eval()
kp_detector.eval()
dense_motion_network.eval()
avd_network.eval()
return inpainting, kp_detector, dense_motion_network, avd_network
def make_animation(source_image, driving_video, inpainting_network, kp_detector, dense_motion_network, avd_network, device, mode = 'relative'):
assert mode in ['standard', 'relative', 'avd']
with torch.no_grad():
predictions = []
source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
source = source.to(device)
driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3).to(device)
kp_source = kp_detector(source)
kp_driving_initial = kp_detector(driving[:, :, 0])
for frame_idx in tqdm(range(driving.shape[2])):
driving_frame = driving[:, :, frame_idx]
driving_frame = driving_frame.to(device)
kp_driving = kp_detector(driving_frame)
if mode == 'standard':
kp_norm = kp_driving
elif mode=='relative':
kp_norm = relative_kp(kp_source=kp_source, kp_driving=kp_driving,
kp_driving_initial=kp_driving_initial)
elif mode == 'avd':
kp_norm = avd_network(kp_source, kp_driving)
dense_motion = dense_motion_network(source_image=source, kp_driving=kp_norm,
kp_source=kp_source, bg_param = None,
dropout_flag = False)
out = inpainting_network(source, dense_motion)
predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
return predictions
def find_best_frame(source, driving, cpu):
import face_alignment
def normalize_kp(kp):
kp = kp - kp.mean(axis=0, keepdims=True)
area = ConvexHull(kp[:, :2]).volume
area = np.sqrt(area)
kp[:, :2] = kp[:, :2] / area
return kp
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True,
device= 'cpu' if cpu else 'cuda')
kp_source = fa.get_landmarks(255 * source)[0]
kp_source = normalize_kp(kp_source)
norm = float('inf')
frame_num = 0
for i, image in tqdm(enumerate(driving)):
try:
kp_driving = fa.get_landmarks(255 * image)[0]
kp_driving = normalize_kp(kp_driving)
new_norm = (np.abs(kp_source - kp_driving) ** 2).sum()
if new_norm < norm:
norm = new_norm
frame_num = i
except:
pass
return frame_num
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--config", required=True, help="path to config")
parser.add_argument("--checkpoint", default='checkpoints/vox.pth.tar', help="path to checkpoint to restore")
parser.add_argument("--source_image", default='./assets/source.png', help="path to source image")
parser.add_argument("--driving_video", default='./assets/driving.mp4', help="path to driving video")
parser.add_argument("--result_video", default='./result.mp4', help="path to output")
parser.add_argument("--img_shape", default="256,256", type=lambda x: list(map(int, x.split(','))),
help='Shape of image, that the model was trained on.')
parser.add_argument("--mode", default='relative', choices=['standard', 'relative', 'avd'], help="Animate mode: ['standard', 'relative', 'avd'], when use the relative mode to animate a face, use '--find_best_frame' can get better quality result")
parser.add_argument("--find_best_frame", dest="find_best_frame", action="store_true",
help="Generate from the frame that is the most alligned with source. (Only for faces, requires face_aligment lib)")
parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
opt = parser.parse_args()
source_image = imageio.imread(opt.source_image)
reader = imageio.get_reader(opt.driving_video)
fps = reader.get_meta_data()['fps']
driving_video = []
try:
for im in reader:
driving_video.append(im)
except RuntimeError:
pass
reader.close()
if opt.cpu:
device = torch.device('cpu')
else:
device = torch.device('cuda')
source_image = resize(source_image, opt.img_shape)[..., :3]
driving_video = [resize(frame, opt.img_shape)[..., :3] for frame in driving_video]
inpainting, kp_detector, dense_motion_network, avd_network = load_checkpoints(config_path = opt.config, checkpoint_path = opt.checkpoint, device = device)
if opt.find_best_frame:
i = find_best_frame(source_image, driving_video, opt.cpu)
print ("Best frame: " + str(i))
driving_forward = driving_video[i:]
driving_backward = driving_video[:(i+1)][::-1]
predictions_forward = make_animation(source_image, driving_forward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
predictions_backward = make_animation(source_image, driving_backward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
predictions = predictions_backward[::-1] + predictions_forward[1:]
else:
predictions = make_animation(source_image, driving_video, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
imageio.mimsave(opt.result_video, [img_as_ubyte(frame) for frame in predictions], fps=fps)