-
Notifications
You must be signed in to change notification settings - Fork 99
/
evaluate_48.py
189 lines (175 loc) · 6.78 KB
/
evaluate_48.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
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import sys
import os
from math import pow
import skimage.io
from skimage import transform as tf
caffe_root = 'D:/caffe/caffe_python/caffe-windows-master/'
sys.path.insert(0, caffe_root + 'python')
import caffe
from nms import nms_average,nms_max
#caffe.set_device(1)
#caffe.set_mode_gpu()
#============
#Model related:
model_path = 'D:/caffe/人脸检测，特征点标定caffe实现/DeepFace-master/FaceDetection/try1_4/'
model_define= model_path+'deploy.prototxt'
model_weight =model_path+'snapshot_iter_100000.caffemodel'
model_define_fc =model_path+'deploy_fc.prototxt'
model_weight_fc =model_path+'snapshot_iter_100000_fc.caffemodel'
channel = 3
raw_scale = 255.0
face_w = 48
stride = 16
cellSize = face_w
threshold = 0.95
factor = 0.793700526 # 缩小因子
map_idx = 0
params = ['deepid', 'fc7']
params_fc = ['deepid-conv', 'fc7-conv']
def generateBoundingBox(featureMap, scale):
'''
@brief: 生成窗口
@param: featureMap,特征图，scale：尺度
'''
boundingBox = []
for (x,y), prob in np.ndenumerate(featureMap):
if(prob >= threshold):
#映射到原始的图像中的大小
x=x-1
y=y-1
boundingBox.append([float(stride * y)/scale, float(stride *x )/scale,
float(stride * y + cellSize - 1)/scale, float(stride * x + cellSize - 1)/scale, prob])
#boundingBox.append([float(stride * y-cellSize/2.0)/scale, float(stride *x -cellSize/2.0)/scale,
# float(stride * y + cellSize/2.0 - 1)/scale, float(stride * x + cellSize/2.0 - 1)/scale, prob])
return boundingBox
def convert_full_conv(model_define,model_weight,model_define_fc,model_weight_fc):
'''
@breif : 将原始网络转换为全卷积模型
@param: model_define,二分类网络定义文件
@param: model_weight，二分类网络训练好的参数
@param: model_define_fc,生成的全卷积网络定义文件
@param: model_weight_fc，转化好的全卷积网络的参数
'''
net = caffe.Net(model_define, model_weight, caffe.TEST)
fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}
net_fc = caffe.Net(model_define_fc, model_weight, caffe.TEST)
conv_params = {pr: (net_fc.params[pr][0].data, net_fc.params[pr][1].data) for pr in params_fc}
for pr, pr_conv in zip(params, params_fc):
conv_params[pr_conv][0].flat = fc_params[pr][0].flat # flat unrolls the arrays
conv_params[pr_conv][1][...] = fc_params[pr][1]
net_fc.save(model_weight_fc)
print 'convert done!'
return net_fc
def re_verify(net_vf, img):
'''
@breif: 对检测到的目标框进行重新的验证
'''
img= tf.resize(img,(face_w,face_w))
transformer = caffe.io.Transformer({'data': net_vf.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
transformer.set_channel_swap('data', (2,1,0))
transformer.set_raw_scale('data', raw_scale)
out = net_vf.forward_all(data=np.asarray([transformer.preprocess('data', img)]))
#print out['prob']
if out['prob'][0,map_idx] > threshold:
return True
else:
return False
def face_detection_image(net,net_vf,image_name):
'''
@检测单张人脸图像
'''
scales = []
imgs = skimage.io.imread(image_name)
if imgs.ndim==3:
rows,cols,ch = imgs.shape
else:
rows,cols = imgs.shape
#计算需要的检测的尺度因子
min = rows if rows<=cols else cols
max = rows if rows>=cols else cols
# 放大的尺度
delim = 2500/max
while (delim >= 1):
scales.append(delim)
delim=delim-0.5
#缩小的尺度
min = min * factor
factor_count = 1
while(min >= face_w):
scale = pow(factor, factor_count)
scales.append(scale)
min = min * factor
factor_count += 1
#=========================
#scales.append(1)
total_boxes = []
###显示热图用
num_scale = len(scales)
s1=int(np.sqrt(num_scale))+1
tt=1
plt.subplot(s1, s1+1, tt)
plt.axis('off')
plt.title("Input Image")
im=caffe.io.load_image(image_name)
plt.imshow(im)
#============
for scale in scales:
w,h = int(rows* scale),int(cols* scale)
scale_img= tf.resize(imgs,(w,h))
#更改网络输入data图像的大小
net.blobs['data'].reshape(1,channel,w,h)
#转换结构
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
#transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))
transformer.set_transpose('data', (2,0,1))
transformer.set_channel_swap('data', (2,1,0))
transformer.set_raw_scale('data', raw_scale)
#前馈一次
out = net.forward_all(data=np.asarray([transformer.preprocess('data', scale_img)]))
###显示热图用
tt=tt+1
plt.subplot(s1, s1+1, tt)
plt.axis('off')
plt.title("sacle: "+ "%.2f" %scale)
plt.imshow(out['prob'][0,map_idx])
#===========
boxes = generateBoundingBox(out['prob'][0,map_idx], scale)
if(boxes):
total_boxes.extend(boxes)
#非极大值抑制
boxes_nms = np.array(total_boxes)
true_boxes1 = nms_max(boxes_nms, overlapThresh=0.3)
true_boxes = nms_average(np.array(true_boxes1), overlapThresh=0.07)
#===================
plt.savefig('heatmap/'+image_name.split('/')[-1])
#在图像中画出检测到的人脸框
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(imgs)
for box in true_boxes:
im_crop = im[box[0]:box[2],box[1]:box[3],:]
if im_crop.shape[0] == 0 or im_crop.shape[1] == 0:
continue
if re_verify(net_vf, im_crop) == True:
rect = mpatches.Rectangle((box[0], box[1]), box[2]-box[0], box[3]-box[1],
fill=False, edgecolor='red', linewidth=1)
ax.text(box[0], box[1]+20,"{0:.3f}".format(box[4]),color='white', fontsize=6)
ax.add_patch(rect)
plt.savefig('result/'+image_name.split('/')[-1])
plt.close()
return out['prob'][0,map_idx]
if __name__ == "__main__":
if not os.path.isfile(model_weight_fc):
net_fc = convert_full_conv(model_define,model_weight,model_define_fc,model_weight_fc)
else:
net_fc = caffe.Net(model_define_fc, model_weight_fc, caffe.TEST)
net_vf = caffe.Net(model_define, model_weight, caffe.TEST)
for i in range(210):
image_name = 'D:/caffe/人脸检测，特征点标定caffe实现/DeepFace-master/FaceDetection/baseline/'+str(i+1)+'.jpg'
print i
fm = face_detection_image(net_fc,net_vf,image_name)
plt.close('all')