This repository has been archived by the owner on Jun 5, 2024. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 134
/
generate_anchors.py
executable file
·121 lines (107 loc) · 3.62 KB
/
generate_anchors.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
# https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/rpn/generate_anchors.py
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
from six.moves import range
import numpy as np
# Verify that we compute the same anchors as Shaoqing's matlab implementation:
#
# >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat
# >> anchors
#
# anchors =
#
# -83 -39 100 56
# -175 -87 192 104
# -359 -183 376 200
# -55 -55 72 72
# -119 -119 136 136
# -247 -247 264 264
# -35 -79 52 96
# -79 -167 96 184
# -167 -343 184 360
#array([[ -83., -39., 100., 56.],
# [-175., -87., 192., 104.],
# [-359., -183., 376., 200.],
# [ -55., -55., 72., 72.],
# [-119., -119., 136., 136.],
# [-247., -247., 264., 264.],
# [ -35., -79., 52., 96.],
# [ -79., -167., 96., 184.],
# [-167., -343., 184., 360.]])
# base_size -> anchor_stride=16,
# scales -> scales=np.array((32, 64, 128, 256, 512), dtype=np.float) / 16,
# generate anchor for one position
def generate_anchors(base_size=16, ratios=[0.5, 1, 2],
scales=2**np.arange(3, 6)):
"""
Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, 15, 15) window.
"""
# anchor box, 0-indexed, x1,y1,x2,y2
base_anchor = np.array([1, 1, base_size, base_size], dtype='float32') - 1
# with the same center, same size, -> [0.5,1.0,2.0] boxes
# [[0,0,15,15],[0,0,22,11.],..]
ratio_anchors = _ratio_enum(base_anchor, ratios)
# -> [[0,0,31,31],....]
anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales)
for i in range(ratio_anchors.shape[0])])
return anchors
def _whctrs(anchor): # x1,y1,x2,y2: (0,0,15,15) -> (16,16,8,8)
"""
Return width, height, x center, and y center for an anchor (window).
"""
w = anchor[2] - anchor[0] + 1
h = anchor[3] - anchor[1] + 1
x_ctr = anchor[0] + 0.5 * (w - 1)
y_ctr = anchor[1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def _mkanchors(ws, hs, x_ctr, y_ctr):
"""
Given a vector of widths (ws) and heights (hs) around a center
(x_ctr, y_ctr), output a set of anchors (windows).
"""
ws = ws[:, np.newaxis] # [k] -> [k,1]
hs = hs[:, np.newaxis]
anchors = np.hstack((x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1)))
return anchors
def _ratio_enum(anchor, ratios):
"""
Enumerate a set of anchors for each aspect ratio wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor) # 0,0,15,15 -> # 16,16, 8,8,
size = w * h # 16 * 16 = 256
# given the same size, get the box with different ratio
size_ratios = size / ratios # ratios: [0.5,1,2] -> [512,256,128]
ws = np.round(np.sqrt(size_ratios)) # np_round to a int, -> [sqrt(512),16,sqrt(128)]
hs = np.round(ws * ratios) # [sqrt(512)*0.5, 16 * 1, sqrt(128)*2]
# ws*hs == w*h
# get anchors with the same x,y,center
# a list of [x1,y1,x2,y2]
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
def _scale_enum(anchor, scales):
"""
Enumerate a set of anchors for each scale wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor)
ws = w * scales
hs = h * scales
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
if __name__ == '__main__':
#import time
#t = time.time()
#a = generate_anchors()
#print(time.time() - t)
#print(a)
#from IPython import embed; embed()
print(generate_anchors(
16, scales=np.asarray((2, 4, 8, 16, 32), 'float32'),
ratios=[0.5,1,2]))