-
Notifications
You must be signed in to change notification settings - Fork 0
/
step3_get_head_geometry.py
executable file
·147 lines (127 loc) · 6.5 KB
/
step3_get_head_geometry.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
import argparse
import os
import numpy as np
import cv2
import tensorflow as tf
from tqdm import tqdm
from utils.recon_depth import split_data, get_pixel_value, get_face_texture, uvd2xyz
from utils.create_renderer import create_renderer_graph
from scipy.io import savemat, loadmat
_FACE_V_NUM = 35709
_FACE_T_NUM = 70789
_HAIREAR_V_NUM = 28000
_HAIREAR_T_NUM = 54000
def load_depthrecon_graph(graph_filename, image_size=256):
with tf.gfile.GFile(graph_filename,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
inputs = tf.placeholder(name='inputs', shape=[
None, image_size, image_size, 5], dtype=tf.float32)
tf.import_graph_def(graph_def, name='resnet', input_map={
'inputs:0': inputs})
output = graph.get_tensor_by_name('resnet/depth_map:0')
return graph, inputs, output
def create_shaperecon_graph(image_size=256):
with tf.Graph().as_default() as graph:
imgs = tf.placeholder(dtype=tf.float32, shape=[1, image_size, image_size, 3])
hairear_uv = tf.placeholder(dtype=tf.float32, shape=[1, _HAIREAR_V_NUM, 2])
hairear_dmap = tf.placeholder(dtype=tf.float32, shape=[1, image_size, image_size, 1])
face3d_data = tf.placeholder(dtype=tf.float32, shape=[1, 396])
face_shape2d = tf.placeholder(dtype=tf.float32, shape=[1, _FACE_V_NUM, 2])
focal, center, _ = split_data(face3d_data)
face_texture = get_face_texture(imgs, face_shape2d)
hairear_d = get_pixel_value(hairear_dmap, hairear_uv)
hairear_uv_trans = hairear_uv + 0.5
hairear_uv_trans = tf.concat([hairear_uv_trans[:, :, 0:1],
256 - hairear_uv_trans[:, :, 1:]], axis=-1)
hairear_uvd = tf.concat([hairear_uv_trans, hairear_d], axis=-1)
hairear_xyz = uvd2xyz(hairear_uvd, focal, center)
hairear_texture = get_pixel_value(imgs, hairear_uv)
return graph, imgs, hairear_uv, hairear_dmap, face3d_data, face_shape2d, face_texture, hairear_xyz, hairear_texture
def depth_recon(data_path, save_path):
print(f'[INFO] [Step3] Read data from {data_path}')
# create face recon graph
depthrecon_graph, inputs, depth_map = load_depthrecon_graph('model/depth_net.pb')
depth_recon_sess = tf.Session(graph=depthrecon_graph)
# create shape recon graph
shaperecon_graph, input_imgs, input_uv, input_dmap, input_facedata, input_face2d, \
output_face_texture, output_hairear_xyz, output_hairear_texture = create_shaperecon_graph()
shape_recon_sess = tf.Session(graph=shaperecon_graph)
# create renderer graph
depth_render_graph, input_focal, input_center, input_depth, \
input_vertex, input_tri, output_depthmap = create_renderer_graph(v_num=_FACE_V_NUM + _HAIREAR_V_NUM, t_num=_FACE_T_NUM + _HAIREAR_T_NUM)
render_sess = tf.Session(graph=depth_render_graph)
names = [i for i in os.listdir(data_path) if i.endswith('mat')]
names = tqdm(names)
for i, name in enumerate(names):
# print(i, name.split('.')[0])
# read and prepare data
data_input = loadmat(os.path.join(data_path, name))
imgs_input = data_input['img'].astype(np.float32).reshape([1, 256, 256, 3])
face_d_input = data_input['face_depthmap'].astype(np.float32).reshape([1, 256, 256, 1])
face_xyz_input = np.expand_dims(data_input['face_projection'].astype(np.float32), 0)
facewoh_m_input = data_input['facemask_withouthair'].astype(np.float32).reshape([1, 256, 256, 1])
face3d_data_input = data_input['face3d'].astype(np.float32).reshape([1, 396])
xy = data_input['points_index']
uv = np.concatenate([xy[:, 1:], xy[:, :1]], axis=1)
hairear_uv_input = uv.astype(np.float32).reshape([1, _HAIREAR_V_NUM, 2])
hairear_m_input = data_input['input_mask'].astype(np.float32).reshape([1, 256, 256, 1])
depth_input = np.concatenate([imgs_input/255, (10 - face_d_input) * facewoh_m_input,
hairear_m_input], -1)
# recon hairear depth
depth_output = depth_recon_sess.run(depth_map, feed_dict={
inputs: depth_input
})
# recover head shape from hairear depth
h_xyz, h_texture, f_texture = shape_recon_sess.run([
output_hairear_xyz, output_hairear_texture, output_face_texture],
feed_dict={
input_imgs: imgs_input,
input_dmap: depth_output,
input_face2d: face_xyz_input,
input_uv: hairear_uv_input,
input_facedata: face3d_data_input
})
# render head depth
head_xyz = np.concatenate([
np.expand_dims(data_input['face_shape'], 0), h_xyz], axis=-2)
head_d = np.tile(10 - head_xyz[..., -1:], [1, 1, 3])
head_tri = np.concatenate([
data_input['face_tri'], data_input['points_tri'] + _FACE_V_NUM], axis=0)
head_dmap = render_sess.run(output_depthmap, feed_dict={
input_focal: data_input['face3d'][:, 0].reshape([1]),
input_center: data_input['face3d'][:, 1:3].reshape([1, 1, 2]),
input_depth: head_d,
input_vertex: head_xyz,
input_tri: np.expand_dims(head_tri, 0) - 1,
})
result = {
'hairear_shape': h_xyz.squeeze(0),
'hairear_texture': h_texture.squeeze(0),
'hairear_tri': data_input['points_tri'],
'face_shape': data_input['face_shape'],
'face_texture': f_texture.squeeze(0),
'face_tri': data_input['face_tri'],
'hairear_index': data_input['points_index'],
'facemask_withouthair': data_input['facemask_withouthair'],
'depth': head_dmap[..., 0].squeeze(0),
'mask': head_dmap[..., -1].squeeze(0)
}
savemat(os.path.join(save_path, name), result, do_compression=True)
depth_recon_sess.close()
shape_recon_sess.close()
render_sess.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--root_dir', default='.')
parser.add_argument('--data_path', default='output/step2')
parser.add_argument('--save_path', default='output/step3')
# prepare directory
args = parser.parse_args()
data_path = os.path.join(args.root_dir, args.data_path)
save_path = os.path.join(args.root_dir, args.save_path)
if not os.path.isdir(save_path):
os.makedirs(save_path)
# recon depth and recover the head geometry
depth_recon(data_path, save_path)