-
Notifications
You must be signed in to change notification settings - Fork 72
/
vision_for_anchors.py
137 lines (117 loc) · 5.33 KB
/
vision_for_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
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
from itertools import product as product
from math import ceil
import matplotlib.pyplot as plt
import numpy as np
import torch
from utils.config import cfg_mnet
#-----------------------------#
# 中心解码，宽高解码
#-----------------------------#
def decode(loc, priors, variances):
boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
boxes[:, :2] -= boxes[:, 2:] / 2
boxes[:, 2:] += boxes[:, :2]
return boxes
#-----------------------------#
# 关键点解码
#-----------------------------#
def decode_landm(pre, priors, variances):
landms = torch.cat((priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
), dim=1)
return landms
class Anchors(object):
def __init__(self, cfg, image_size=None):
super(Anchors, self).__init__()
self.min_sizes = cfg['min_sizes']
self.steps = cfg['steps']
self.clip = cfg['clip']
#---------------------------#
# 图片的尺寸
#---------------------------#
self.image_size = image_size
#---------------------------#
# 三个有效特征层高和宽
#---------------------------#
self.feature_maps = [[ceil(self.image_size[0]/step), ceil(self.image_size[1]/step)] for step in self.steps]
def get_anchors(self):
anchors = []
for k, f in enumerate(self.feature_maps):
min_sizes = self.min_sizes[k]
#-----------------------------------------#
# 对特征层的高和宽进行循环迭代
#-----------------------------------------#
for i, j in product(range(f[0]), range(f[1])):
for min_size in min_sizes:
s_kx = min_size / self.image_size[1]
s_ky = min_size / self.image_size[0]
dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
for cy, cx in product(dense_cy, dense_cx):
anchors += [cx, cy, s_kx, s_ky]
output = torch.Tensor(anchors).view(-1, 4)
output = np.zeros_like(anchors[:, :4])
output[:,0] = anchors[:,0] - anchors[:,2] / 2
output[:,1] = anchors[:,1] - anchors[:,3] / 2
output[:,2] = anchors[:,0] + anchors[:,2] / 2
output[:,3] = anchors[:,1] + anchors[:,3] / 2
if self.clip:
output = np.clip(output, 0, 1)
return output
if __name__ == "__main__":
cfg_mnet['image_size'] = 640
#--------------------------------#
# 先验框的生成
#--------------------------------#
cfg = cfg_mnet
anchors = Anchors(cfg, image_size = (cfg_mnet['image_size'], cfg_mnet['image_size'])).get_anchors()
anchors = anchors[-800:] * cfg_mnet['image_size']
#--------------------------------#
# 先验框中心绘制
#--------------------------------#
center_x = (anchors[:, 0] + anchors[:, 2]) / 2
center_y = (anchors[:, 1] + anchors[:, 3]) / 2
fig = plt.figure()
ax = fig.add_subplot(121)
plt.ylim(-300,900)
plt.xlim(-300,900)
ax.invert_yaxis()
plt.scatter(center_x,center_y)
#--------------------------------#
# 先验框宽高绘制
#--------------------------------#
box_widths = anchors[0:2,2] - anchors[0:2,0]
box_heights = anchors[0:2,3] - anchors[0:2,1]
for i in [0,1]:
rect = plt.Rectangle([anchors[i, 0], anchors[i, 1]], box_widths[i], box_heights[i], color="r", fill=False)
ax.add_patch(rect)
#--------------------------------#
# 先验框中心绘制
#--------------------------------#
ax = fig.add_subplot(122)
plt.ylim(-300,900)
plt.xlim(-300,900)
ax.invert_yaxis() #y轴反向
plt.scatter(center_x,center_y)
#--------------------------------#
# 对先验框调整获得预测框
#--------------------------------#
mbox_loc = np.random.randn(800, 4)
mbox_ldm = np.random.randn(800, 10)
anchors[:, :2] = (anchors[:, :2] + anchors[:, 2:]) / 2
anchors[:, 2:] = (anchors[:, 2:] - anchors[:, :2]) * 2
mbox_loc = torch.Tensor(mbox_loc)
anchors = torch.Tensor(anchors)
cfg_mnet['variance'] = torch.Tensor(cfg_mnet['variance'])
decode_bbox = decode(mbox_loc, anchors, cfg_mnet['variance'])
box_widths = decode_bbox[0: 2, 2] - decode_bbox[0: 2, 0]
box_heights = decode_bbox[0: 2, 3] - decode_bbox[0: 2, 1]
for i in [0,1]:
rect = plt.Rectangle([decode_bbox[i, 0], decode_bbox[i, 1]], box_widths[i], box_heights[i], color="r", fill=False)
plt.scatter((decode_bbox[i, 2] + decode_bbox[i, 0]) / 2, (decode_bbox[i,3] + decode_bbox[i,1]) / 2, color="b")
ax.add_patch(rect)
plt.show()