-
Notifications
You must be signed in to change notification settings - Fork 26
/
Depth_Renderer.py
executable file
·145 lines (121 loc) · 5.42 KB
/
Depth_Renderer.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
# Copyright (c) 2020. Hanchen Wang, [email protected]
# Ref: https://github.com/wentaoyuan/pcn/blob/master/render/render_depth.py
# Usage: blender -b -P Depth_Renderer.py [ShapeNet directory] [model list] [output directory] [num scans per model]
import os, sys, bpy, time, mathutils, numpy as np
def random_pose():
"""generate a random camera pose"""
angle_x = np.random.uniform() * 2 * np.pi
angle_y = np.random.uniform() * 2 * np.pi
angle_z = np.random.uniform() * 2 * np.pi
Rx = np.array([[1, 0, 0],
[0, np.cos(angle_x), -np.sin(angle_x)],
[0, np.sin(angle_x), np.cos(angle_x)]])
Ry = np.array([[np.cos(angle_y), 0, np.sin(angle_y)],
[0, 1, 0],
[-np.sin(angle_y), 0, np.cos(angle_y)]])
Rz = np.array([[np.cos(angle_z), -np.sin(angle_z), 0],
[np.sin(angle_z), np.cos(angle_z), 0],
[0, 0, 1]])
R = np.dot(Rz, np.dot(Ry, Rx))
# a rotation matrix with arbitrarily chosen yaw, pitch, roll
# Set camera pointing to the origin and 1 unit away from the origin
t = np.expand_dims(R[:, 2], 1) # select the third column, reshape into (3, 1)-vector
# pose -> 4 * 4
pose = np.concatenate([np.concatenate([R, t], 1), [[0, 0, 0, 1]]], 0)
return pose
def setup_blender(width, height, focal_length):
"""using blender to rendering a scene"""
# camera, a class in the bpy
camera = bpy.data.objects['Camera']
camera.data.angle = np.arctan(width / 2 / focal_length) * 2
# render layer
scene = bpy.context.scene
scene.render.filepath = 'buffer'
scene.render.image_settings.color_depth = '16'
scene.render.resolution_percentage = 100
scene.render.resolution_x = width
scene.render.resolution_y = height
# compositor nodes
scene.use_nodes = True
tree = scene.node_tree
rl = tree.nodes.new('CompositorNodeRLayers')
output = tree.nodes.new('CompositorNodeOutputFile')
output.base_path = ''
output.format.file_format = 'OPEN_EXR'
# tree.links.new(rl.outputs['Depth'], output.inputs[0])
tree.links.new(rl.outputs['Z'], output.inputs[0])
# ref: https://github.com/panmari/stanford-shapenet-renderer/issues/8
# remove default cube
bpy.data.objects['Cube'].select = True
bpy.ops.object.delete()
return scene, camera, output
if __name__ == '__main__':
# Usage: blender -b -P Depth_Renderer.py [ShapeNet directory] [model list] [output directory] [num scans per model]
model_dir = sys.argv[-4]
list_path = sys.argv[-3]
output_dir = sys.argv[-2]
num_scans = int(sys.argv[-1])
'''Generate Intrinsic Camera Matrix'''
# High Resolution: width = 1600,
# Middle Resolution: width = 1600//4,
# Coarse Resolution: width = 1600//10,
width = 1600//4
height = 1200//4
focal = 1000//4
scene, camera, output = setup_blender(width, height, focal)
# offset is the center of images, the unit of focal here is the pixels(on the image)
intrinsics = np.array([[focal, 0, width / 2], [0, focal, height / 2], [0, 0, 1]])
# os.system('rm -rf %s' % output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(list_path)) as file:
model_list = [line.strip() for line in file]
open(os.path.join(output_dir, 'blender.log'), 'w+').close()
np.savetxt(os.path.join(output_dir, 'intrinsics.txt'), intrinsics, '%f')
# camera-referenced system
num_total_f = len(model_list)
start = time.time()
'''rendering from the mesh to 2.5D depth images'''
for idx, model_id in enumerate(model_list):
# start = time.time()
exr_dir = os.path.join(output_dir, 'exr', model_id)
pose_dir = os.path.join(output_dir, 'pose', model_id)
os.makedirs(exr_dir)
os.makedirs(pose_dir)
# os.removedirs(exr_dir)
# os.removedirs(pose_dir)
# Redirect output to log file
old_os_out = os.dup(1)
os.close(1)
os.open(os.path.join(output_dir, 'blender.log'), os.O_WRONLY)
# Import mesh model
# model_path = os.path.join(model_dir, model_id, 'models/model_normalized.obj')
# bpy.ops.import_scene.obj(filepath=model_path)
model_path = os.path.join(model_dir, model_id + '.obj')
bpy.ops.import_scene.obj(filepath=model_path)
# Rotate model by 90 degrees around x-axis (z-up => y-up) to match ShapeNet's coordinates
bpy.ops.transform.rotate(value=-np.pi / 2, axis=(1, 0, 0))
# Render
for i in range(num_scans):
scene.frame_set(i)
pose = random_pose()
camera.matrix_world = mathutils.Matrix(pose)
# output.file_slots[0].path = os.path.join(exr_dir, '#.exr')
output.file_slots[0].path = exr_dir + '_#.exr'
bpy.ops.render.render(write_still=True)
# np.savetxt(os.path.join(pose_dir, '%d.txt' % i), pose, '%f')
np.savetxt(pose_dir + '_%d.txt' % i, pose, '%f')
# Clean up
bpy.ops.object.delete()
for m in bpy.data.meshes:
bpy.data.meshes.remove(m)
for m in bpy.data.materials:
m.user_clear()
bpy.data.materials.remove(m)
# Print used time
os.close(1)
os.dup(old_os_out)
os.close(old_os_out)
print('%d/%d: %s done, time=%.4f sec' % (idx + 1, num_total_f, model_id, time.time() - start))
os.removedirs(exr_dir)
os.removedirs(pose_dir)