forked from BUPTLdy/Land_Use_CNN
-
Notifications
You must be signed in to change notification settings - Fork 0
/
svm_predict.py
170 lines (137 loc) · 5.91 KB
/
svm_predict.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
# -*- coding: utf-8 -*-
"""
Created on Mon May 16 18:54:06 2016
@author: ldy
"""
# coding: utf-8
import numpy as np
from sklearn.externals import joblib
NUM_STYLE_LABELS=21
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import sys
caffe_root='/home/ldy/workspace/caffe/' #设置你caffe的安装目录
image_root='/home/ldy/workspace/caffe/data/UCMerced_LandUse/Images/'
sys.path.insert(0,caffe_root+'python')
import caffe #导入caffe
import time
caffe.set_mode_gpu()
print 'load the structure of the model...'
model_def = caffe_root + 'models/finetune_UCMerced_LandUse/deploy1.prototxt'
print 'load the weights of the model...'
model_weights = caffe_root + 'models/finetune_UCMerced_LandUse/weights.pretrained.caffemodel'
print 'build the trained net...'
net = caffe.Net(model_def, # defines the structure of the model
model_weights, # contains the trained weights
caffe.TEST) # use test mode (e.g., don't perform dropout)
# load the mean ImageNet image (as distributed with Caffe) for subtraction
mu = np.load('/home/ldy/workspace/caffe/examples/finetune_UCMerced_LandUse/mean.npy')
mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values
#print 'mean-subtracted values:', zip('BGR', mu),mu
# create trasformer for the input called 'data'
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1)) # move image channels to outermost dimension
transformer.set_mean('data', mu) # subtract the dataset-mean value in each channel
transformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]
transformer.set_channel_swap('data', (2,1,0)) # swap channels from RGB to BGR
def show_labes(image,probs,lables,true_label):
gs = gridspec.GridSpec(1, 2,width_ratios=[1,1],height_ratios=[1,1])
ax1 = plt.subplot(gs[0])
x = list(reversed(lables))
y = list(reversed(probs))
colors=['#edf8fb','#b2e2e2','#66c2a4','#2ca25f','#006d2c']
#colors = ['#624ea7', 'g', 'yellow', 'k', 'maroon']
#colors=list(reversed(colors))
width = 0.4 # the width of the bars
ind = np.arange(len(y)) # the x locations for the groups
ax1.barh(ind, y, width, align='center', color=colors)
ax1.set_yticks(ind+width/2)
ax1.set_yticklabels(x, minor=False)
for i, v in enumerate(y):
ax1.text(v, i, '%5.2f%%' %v,fontsize=14)
plt.title('Probability Output',fontsize=20)
ax2 = plt.subplot(gs[1])
ax2.axis('off')
ax2.imshow(image)
# fig = plt.gcf()
# fig.set_size_inches(8, 6)
plt.title(true_label,fontsize=20)
plt.show()
style_label_file = caffe_root + 'examples/finetune_UCMerced_LandUse/style_names.txt'
style_labels = list(np.loadtxt(style_label_file, str, delimiter='\n'))
if NUM_STYLE_LABELS > 0:
style_labels = style_labels[:NUM_STYLE_LABELS]
print style_labels,type(style_labels)
clf = joblib.load('svm_model/svm.pkl')
def return_maxkoflist(arr,k):
arr=np.array(arr[0])
# print arr.size,arr
index=arr.argsort()[-k:][::-1]
# print index.size
proba=arr[index]
proba=proba*100
labels=np.array(style_labels)
index=labels[index]
#print index
return proba,index
def disp_style_preds(feat):
proba=clf.predict_proba(feat)
probas,labels=return_maxkoflist(proba,5)
return probas,labels
def show_predict():
images='/home/ldy/workspace/caffe/data/UCMerced_LandUse/test.txt'
images = list(np.loadtxt(images, str, delimiter='\n'))
for image in images:
true_label=image.split('/')[-2]
image = caffe.io.load_image(image.split(' ')[-2])
t0=time.time()
transformed_image = transformer.preprocess('data', image)
net.blobs['data'].data[0, ...] = transformed_image
net.forward(start='conv1')
feat = net.blobs['fc7'].data.copy()
probas,labels=disp_style_preds(feat)
t1=time.time()
show_labes(image,probas,labels,true_label)
print '每张图片预测时间：%.3f s'%(t1-t0)
def show_acc_preclass():
images='/home/ldy/workspace/caffe/data/UCMerced_LandUse/test.txt'
images = list(np.loadtxt(images, str, delimiter='\n'))
preclass_num={}
precalss_corrct_num={}
for label in style_labels:
preclass_num[label]=0
precalss_corrct_num[label]=0
for i,image in enumerate(images):
true_label=image.split('/')[-2]
preclass_num[true_label]=preclass_num[true_label]+1
image = caffe.io.load_image(image.split(' ')[-2])
transformed_image = transformer.preprocess('data', image)
net.blobs['data'].data[0, ...] = transformed_image
net.forward(start='conv1')
feat = net.blobs['fc7'].data.copy()
probas,lables=disp_style_preds(feat)
if true_label==lables[0]:
precalss_corrct_num[true_label]+=1
#print i,true_label,preclass_num[true_label],precalss_corrct_num[true_label]
preclass_acc={}
for label in style_labels:
preclass_acc[label]=float(precalss_corrct_num[label])/float(preclass_num[label])
#print preclass_acc
k=1
plt.figure(figsize=(8,7))
#plt.tight_layout()
ind = np.arange(0, k*len(preclass_acc), k)
colors=['#edf8fb','#ccece6','#99d8c9','#66c2a4','#41ae76','#238b45','#005824','#f1eef6','#d4b9da','#c994c7','#df65b0','#e7298a','#ce1256','#91003f','#ffffb2','#fed976','#feb24c','#fd8d3c','#fc4e2a','#e31a1c','#b10026']
rects=plt.bar(ind, preclass_acc.values(),width=0.7,color=colors)
plt.xticks(ind+0.35, preclass_acc.keys(),rotation='vertical')
for rect in rects:
height = rect.get_height()
plt.text(rect.get_x() + rect.get_width()/2., 1.01*height,
'%.2f' %float(height),
ha='center', va='bottom')
plt.xlim([0,ind.size])
plt.tight_layout()
plt.savefig('acc2.eps')
plt.show()
#show_acc_preclass()
show_predict()