-
Notifications
You must be signed in to change notification settings - Fork 0
/
demo.py
149 lines (119 loc) · 6.91 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
import face_alignment
import jittor as jt
from modules.generator import OcclusionAwareGenerator
from modules.keypoint_detector import KPDetector
import os
import imageio
from animate import normalize_kp
import numpy as np
from tqdm import tqdm
from scipy.spatial import ConvexHull
from argparse import ArgumentParser
from skimage.transform import resize
from skimage import img_as_ubyte
import yaml
jt.flags.use_cuda = 1
def load_checkpoints(config_path, checkpoint_path, cpu=False):
with open(config_path) as f:
#config = yaml.load(f)
config = yaml.safe_load(f)
generator = OcclusionAwareGenerator(**config['model_params']['generator_params'],
**config['model_params']['common_params'])
kp_detector = KPDetector(**config['model_params']['kp_detector_params'],
**config['model_params']['common_params'])
#checkpoint = jt.load(checkpoint_path)
checkpoint = jt.load('./weights/jt-vox-adv-cpk.pkl')
generator.load_state_dict(checkpoint['generator'])
kp_detector.load_state_dict(checkpoint['kp_detector'])
generator.eval()
kp_detector.eval()
return generator, kp_detector
#generator, kp_detector = load_checkpoints(config_path='./config/vox-256.yaml', checkpoint_path="")
def make_animation(source_image, driving_video, generator, kp_detector, relative=True, adapt_movement_scale=True):
with jt.no_grad():
predictions = []
source_numpy = np.array(source_image[np.newaxis].astype(np.float32))
print(type(source_numpy))
source = jt.array(source_numpy).permute(0, 3, 1, 2)
driving = jt.array(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3)
kp_source = kp_detector(source)
kp_driving_initial = kp_detector(driving[:, :, 0])
print(driving.shape[2])
num = driving.shape[2]
for frame_idx in tqdm(range(num)):
#for frame_idx in range(num):
driving_frame = driving[:, :, frame_idx]
kp_driving = kp_detector(driving_frame)
kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving,
kp_driving_initial=kp_driving_initial, use_relative_movement=relative,
use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale)
out = generator(source, kp_source=kp_source, kp_driving=kp_norm)
#print(out)
#out_numpy = out['prediction'].detach().numpy()
predictions.append(np.transpose(out['prediction'].detach().numpy(), [0, 2, 3, 1])[0])
#predictions.append(np.transpose(out['deformed'].data.cpu().numpy(), [0, 2, 3, 1])[0])
return predictions
def find_best_frame(source, driving, cpu=False):
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)):
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
return frame_num
#python demo.py --source_image compare_exp/ex4/frame00043.jpg --driving_video compare_exp/ex4/ori.mp4 --result_video compare_exp/ex4/relative.mp4 --relative --find_best_frame
#python demo.py --source_image compare_exp/ex4/frame00043.jpg --driving_video compare_exp/ex4/ori.mp4 --result_video compare_exp/ex4/absolute.mp4 --find_best_frame
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--config", default='./config/vox-256.yaml', help="path to config")
parser.add_argument("--checkpoint", default='./weights/jt-vox-adv-cpk.pkl', help="path to checkpoint to restore")
parser.add_argument("--source_image", default='sup-mat/source.png', help="path to source image")
parser.add_argument("--driving_video", default='sup-mat/source.png', help="path to driving video")
parser.add_argument("--result_video", default='result.mp4', help="path to output")
parser.add_argument("--relative", dest="relative", action="store_true", help="use relative or absolute keypoint coordinates")
parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true", help="adapt movement scale based on convex hull of keypoints")
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("--best_frame", dest="best_frame", type=int, default=None,
help="Set frame to start from.")
parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
parser.set_defaults(relative=False)
parser.set_defaults(adapt_scale=False)
opt = parser.parse_args()
generator, kp_detector = load_checkpoints(config_path='./config/vox-256.yaml', checkpoint_path=opt.checkpoint)
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()
source_image = resize(source_image, (256, 256))[..., :3]
driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video]
if opt.find_best_frame or opt.best_frame is not None:
i = opt.best_frame if opt.best_frame is not None else find_best_frame(source_image, driving_video, cpu=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, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale)
predictions_backward = make_animation(source_image, driving_backward, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale)
predictions = predictions_backward[::-1] + predictions_forward[1:]
else:
predictions = make_animation(source_image, driving_video, generator, kp_detector, relative=False, adapt_movement_scale=False)
imageio.mimsave(opt.result_video, [img_as_ubyte(frame) for frame in predictions], fps=fps)