forked from yfeng95/PRNet
-
Notifications
You must be signed in to change notification settings - Fork 0
/
demo.py
167 lines (143 loc) · 7.78 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
import numpy as np
import os
from glob import glob
import scipy.io as sio
from skimage.io import imread, imsave
from skimage.transform import rescale, resize
from time import time
import argparse
import ast
from api import PRN
from utils.estimate_pose import estimate_pose
from utils.rotate_vertices import frontalize
from utils.render_app import get_visibility, get_uv_mask, get_depth_image
from utils.write import write_obj_with_colors, write_obj_with_texture
def main(args):
if args.isShow or args.isTexture:
import cv2
from utils.cv_plot import plot_kpt, plot_vertices, plot_pose_box
# ---- init PRN
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # GPU number, -1 for CPU
prn = PRN(is_dlib = args.isDlib)
# ------------- load data
image_folder = args.inputDir
save_folder = args.outputDir
if not os.path.exists(save_folder):
os.mkdir(save_folder)
types = ('*.jpg', '*.png')
image_path_list= []
for files in types:
image_path_list.extend(glob(os.path.join(image_folder, files)))
total_num = len(image_path_list)
for i, image_path in enumerate(image_path_list):
name = image_path.strip().split('/')[-1][:-4]
# read image
image = imread(image_path)
[h, w, c] = image.shape
if c>3:
image = image[:,:,:3]
# the core: regress position map
if args.isDlib:
max_size = max(image.shape[0], image.shape[1])
if max_size> 1000:
image = rescale(image, 1000./max_size)
image = (image*255).astype(np.uint8)
pos = prn.process(image) # use dlib to detect face
else:
if image.shape[0] == image.shape[1]:
image = resize(image, (256,256))
pos = prn.net_forward(image/255.) # input image has been cropped to 256x256
else:
box = np.array([0, image.shape[1]-1, 0, image.shape[0]-1]) # cropped with bounding box
pos = prn.process(image, box)
image = image/255.
if pos is None:
continue
if args.is3d or args.isMat or args.isPose or args.isShow:
# 3D vertices
vertices = prn.get_vertices(pos)
if args.isFront:
save_vertices = frontalize(vertices)
else:
save_vertices = vertices.copy()
save_vertices[:,1] = h - 1 - save_vertices[:,1]
if args.isImage:
imsave(os.path.join(save_folder, name + '.jpg'), image)
if args.is3d:
# corresponding colors
colors = prn.get_colors(image, vertices)
if args.isTexture:
if args.texture_size != 256:
pos_interpolated = resize(pos, (args.texture_size, args.texture_size), preserve_range = True)
else:
pos_interpolated = pos.copy()
texture = cv2.remap(image, pos_interpolated[:,:,:2].astype(np.float32), None, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT,borderValue=(0))
if args.isMask:
vertices_vis = get_visibility(vertices, prn.triangles, h, w)
uv_mask = get_uv_mask(vertices_vis, prn.triangles, prn.uv_coords, h, w, prn.resolution_op)
uv_mask = resize(uv_mask, (args.texture_size, args.texture_size), preserve_range = True)
texture = texture*uv_mask[:,:,np.newaxis]
write_obj_with_texture(os.path.join(save_folder, name + '.obj'), save_vertices, prn.triangles, texture, prn.uv_coords/prn.resolution_op)#save 3d face with texture(can open with meshlab)
else:
write_obj_with_colors(os.path.join(save_folder, name + '.obj'), save_vertices, prn.triangles, colors) #save 3d face(can open with meshlab)
if args.isDepth:
depth_image = get_depth_image(vertices, prn.triangles, h, w, True)
depth = get_depth_image(vertices, prn.triangles, h, w)
imsave(os.path.join(save_folder, name + '_depth.jpg'), depth_image)
sio.savemat(os.path.join(save_folder, name + '_depth.mat'), {'depth':depth})
if args.isMat:
sio.savemat(os.path.join(save_folder, name + '_mesh.mat'), {'vertices': vertices, 'colors': colors, 'triangles': prn.triangles})
if args.isKpt or args.isShow:
# get landmarks
kpt = prn.get_landmarks(pos)
np.savetxt(os.path.join(save_folder, name + '_kpt.txt'), kpt)
if args.isPose or args.isShow:
# estimate pose
camera_matrix, pose = estimate_pose(vertices)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
np.savetxt(os.path.join(save_folder, name + '_camera_matrix.txt'), camera_matrix)
np.savetxt(os.path.join(save_folder, name + '_pose.txt'), pose)
if args.isShow:
# ---------- Plot
image_pose = plot_pose_box(image, camera_matrix, kpt)
cv2.imshow('sparse alignment', plot_kpt(image, kpt))
cv2.imshow('dense alignment', plot_vertices(image, vertices))
cv2.imshow('pose', plot_pose_box(image, camera_matrix, kpt))
cv2.waitKey(0)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network')
parser.add_argument('-i', '--inputDir', default='TestImages/', type=str,
help='path to the input directory, where input images are stored.')
parser.add_argument('-o', '--outputDir', default='TestImages/results', type=str,
help='path to the output directory, where results(obj,txt files) will be stored.')
parser.add_argument('--gpu', default='0', type=str,
help='set gpu id, -1 for CPU')
parser.add_argument('--isDlib', default=True, type=ast.literal_eval,
help='whether to use dlib for detecting face, default is True, if False, the input image should be cropped in advance')
parser.add_argument('--is3d', default=True, type=ast.literal_eval,
help='whether to output 3D face(.obj). default save colors.')
parser.add_argument('--isMat', default=False, type=ast.literal_eval,
help='whether to save vertices,color,triangles as mat for matlab showing')
parser.add_argument('--isKpt', default=False, type=ast.literal_eval,
help='whether to output key points(.txt)')
parser.add_argument('--isPose', default=False, type=ast.literal_eval,
help='whether to output estimated pose(.txt)')
parser.add_argument('--isShow', default=False, type=ast.literal_eval,
help='whether to show the results with opencv(need opencv)')
parser.add_argument('--isImage', default=False, type=ast.literal_eval,
help='whether to save input image')
# update in 2017/4/10
parser.add_argument('--isFront', default=False, type=ast.literal_eval,
help='whether to frontalize vertices(mesh)')
# update in 2017/4/25
parser.add_argument('--isDepth', default=False, type=ast.literal_eval,
help='whether to output depth image')
# update in 2017/4/27
parser.add_argument('--isTexture', default=False, type=ast.literal_eval,
help='whether to save texture in obj file')
parser.add_argument('--isMask', default=False, type=ast.literal_eval,
help='whether to set invisible pixels(due to self-occlusion) in texture as 0')
# update in 2017/7/19
parser.add_argument('--texture_size', default=256, type=int,
help='size of texture map, default is 256. need isTexture is True')
main(parser.parse_args())