-
Notifications
You must be signed in to change notification settings - Fork 138
/
render.py
165 lines (136 loc) · 7.5 KB
/
render.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
'''
Max-Planck-Gesellschaft zur Foerderung der Wissenschaften e.V. (MPG) is holder of all proprietary rights on this
computer program.
You can only use this computer program if you have closed a license agreement with MPG or you get the right to use
the computer program from someone who is authorized to grant you that right.
Any use of the computer program without a valid license is prohibited and liable to prosecution.
Copyright 2019 Max-Planck-Gesellschaft zur Foerderung der Wissenschaften e.V. (MPG). acting on behalf of its
Max Planck Institute for Intelligent Systems and the Max Planck Institute for Biological Cybernetics.
All rights reserved.
More information about VOCA is available at http://voca.is.tue.mpg.de.
For comments or questions, please email us at [email protected]
'''
import os, shutil
import cv2
import scipy
import tempfile
import numpy as np
from subprocess import call
import argparse
os.environ['PYOPENGL_PLATFORM'] = 'osmesa' #egl
import pyrender
import trimesh
from psbody.mesh import Mesh
# The implementation of rendering is borrowed from VOCA: https://github.com/TimoBolkart/voca/blob/master/utils/rendering.py
def render_mesh_helper(args,mesh, t_center, rot=np.zeros(3), tex_img=None, z_offset=0):
if args.dataset == "BIWI":
camera_params = {'c': np.array([400, 400]),
'k': np.array([-0.19816071, 0.92822711, 0, 0, 0]),
'f': np.array([4754.97941935 / 8, 4754.97941935 / 8])}
elif args.dataset == "vocaset":
camera_params = {'c': np.array([400, 400]),
'k': np.array([-0.19816071, 0.92822711, 0, 0, 0]),
'f': np.array([4754.97941935 / 2, 4754.97941935 / 2])}
frustum = {'near': 0.01, 'far': 3.0, 'height': 800, 'width': 800}
mesh_copy = Mesh(mesh.v, mesh.f)
mesh_copy.v[:] = cv2.Rodrigues(rot)[0].dot((mesh_copy.v-t_center).T).T+t_center
intensity = 2.0
rgb_per_v = None
primitive_material = pyrender.material.MetallicRoughnessMaterial(
alphaMode='BLEND',
baseColorFactor=[0.3, 0.3, 0.3, 1.0],
metallicFactor=0.8,
roughnessFactor=0.8
)
tri_mesh = trimesh.Trimesh(vertices=mesh_copy.v, faces=mesh_copy.f, vertex_colors=rgb_per_v)
render_mesh = pyrender.Mesh.from_trimesh(tri_mesh, material=primitive_material,smooth=True)
if args.background_black:
scene = pyrender.Scene(ambient_light=[.2, .2, .2], bg_color=[0, 0, 0])#[0, 0, 0] black,[255, 255, 255] white
else:
scene = pyrender.Scene(ambient_light=[.2, .2, .2], bg_color=[255, 255, 255])#[0, 0, 0] black,[255, 255, 255] white
camera = pyrender.IntrinsicsCamera(fx=camera_params['f'][0],
fy=camera_params['f'][1],
cx=camera_params['c'][0],
cy=camera_params['c'][1],
znear=frustum['near'],
zfar=frustum['far'])
scene.add(render_mesh, pose=np.eye(4))
camera_pose = np.eye(4)
camera_pose[:3,3] = np.array([0, 0, 1.0-z_offset])
scene.add(camera, pose=[[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 1],
[0, 0, 0, 1]])
angle = np.pi / 6.0
pos = camera_pose[:3,3]
light_color = np.array([1., 1., 1.])
light = pyrender.DirectionalLight(color=light_color, intensity=intensity)
light_pose = np.eye(4)
light_pose[:3,3] = pos
scene.add(light, pose=light_pose.copy())
light_pose[:3,3] = cv2.Rodrigues(np.array([angle, 0, 0]))[0].dot(pos)
scene.add(light, pose=light_pose.copy())
light_pose[:3,3] = cv2.Rodrigues(np.array([-angle, 0, 0]))[0].dot(pos)
scene.add(light, pose=light_pose.copy())
light_pose[:3,3] = cv2.Rodrigues(np.array([0, -angle, 0]))[0].dot(pos)
scene.add(light, pose=light_pose.copy())
light_pose[:3,3] = cv2.Rodrigues(np.array([0, angle, 0]))[0].dot(pos)
scene.add(light, pose=light_pose.copy())
flags = pyrender.RenderFlags.SKIP_CULL_FACES
try:
r = pyrender.OffscreenRenderer(viewport_width=frustum['width'], viewport_height=frustum['height'])
color, _ = r.render(scene, flags=flags)
except:
print('pyrender: Failed rendering frame')
color = np.zeros((frustum['height'], frustum['width'], 3), dtype='uint8')
return color[..., ::-1]
def render_sequence_meshes(args,sequence_vertices, template, out_path,predicted_vertices_path,vt, ft ,tex_img):
num_frames = sequence_vertices.shape[0]
file_name_pred = predicted_vertices_path.split('/')[-1].split('.')[0]
tmp_video_file_pred = tempfile.NamedTemporaryFile('w', suffix='.mp4', dir=out_path)
writer_pred = cv2.VideoWriter(tmp_video_file_pred.name, cv2.VideoWriter_fourcc(*'mp4v'), args.fps, (800, 800), True)
center = np.mean(sequence_vertices[0], axis=0)
video_fname_pred = os.path.join(out_path, file_name_pred+'.mp4')
for i_frame in range(num_frames):
render_mesh = Mesh(sequence_vertices[i_frame], template.f)
if vt is not None and ft is not None:
render_mesh.vt, render_mesh.ft = vt, ft
pred_img = render_mesh_helper(args,render_mesh, center, tex_img=tex_img)
pred_img = pred_img.astype(np.uint8)
img = pred_img
writer_pred.write(img)
writer_pred.release()
cmd = ('ffmpeg' + ' -i {0} -pix_fmt yuv420p -qscale 0 {1}'.format(
tmp_video_file_pred.name, video_fname_pred)).split()
call(cmd)
def main():
parser = argparse.ArgumentParser(description='FaceFormer: Speech-Driven 3D Facial Animation with Transformers')
parser.add_argument("--dataset", type=str, default="vocaset", help='vocaset or BIWI')
parser.add_argument("--render_template_path", type=str, default="templates", help='path of the mesh in FLAME/BIWI topology')
parser.add_argument('--background_black', type=bool, default=True, help='whether to use black background')
parser.add_argument('--fps', type=int,default=30, help='frame rate - 30 for vocaset; 25 for BIWI')
parser.add_argument("--vertice_dim", type=int, default=5023*3, help='number of vertices - 5023*3 for vocaset; 23370*3 for BIWI')
parser.add_argument("--pred_path", type=str, default="result", help='path of the predictions')
parser.add_argument("--output", type=str, default="output", help='path of the rendered video sequences')
args = parser.parse_args()
pred_path = os.path.join(args.dataset,args.pred_path)
output_path = os.path.join(args.dataset,args.output)
if os.path.exists(output_path):
shutil.rmtree(output_path)
os.makedirs(output_path)
for file in os.listdir(pred_path):
if file.endswith("npy"):
predicted_vertices_path = os.path.join(pred_path,file)
if args.dataset == "BIWI":
template_file = os.path.join(args.dataset, args.render_template_path, "BIWI.ply")
elif args.dataset == "vocaset":
template_file = os.path.join(args.dataset, args.render_template_path, "FLAME_sample.ply")
print("rendering: ", file)
template = Mesh(filename=template_file)
vt, ft = None, None
tex_img = None
predicted_vertices = np.load(predicted_vertices_path)
predicted_vertices = np.reshape(predicted_vertices,(-1,args.vertice_dim//3,3))
render_sequence_meshes(args,predicted_vertices, template, output_path,predicted_vertices_path,vt, ft ,tex_img)
if __name__=="__main__":
main()