File size: 5,242 Bytes
9a70c5d |
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 |
"""
这是带注释的，我用中文写了
"""
#%% 导入必要的包
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import roc_curve
from scipy.interpolate import interp1d
from scipy.optimize import brentq
import matplotlib.pyplot as plt
from scipy.io.wavfile import read
from sklearn.preprocessing import normalize
from generate_array_feature import mald_feature, get_filelist
import time
#%% 定义分类器model
# 这一个代码块是用来定义model的。
# 定义model的batch_size, feature长度之类的
batch_size = 10
feature_len = 110
loss_function = binary_crossentropy
no_epochs = 150
optimizer = Adam()
verbosity = 1
model = Sequential()
model.add(Dense(64, input_dim=feature_len, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(16, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss=loss_function, optimizer=optimizer, metrics=['accuracy'])
# 至此，分类器模型的基本参数已经设置完毕，接下来可以从hdf5文件中导入预先训练好的model
model.load_weights(r"/home/fazhong/Github/czx/model.hdf5")
# 从train2.hdf5导入model。
# train2.hdf5 是从 data2.npy训练来的。
# 这样与 data1.npy数据不会有重叠
#%% 导入音频
path_wave = r"/home/fazhong/Github/czx/voice"
print("Loading data ...")
name_all = get_filelist(path_wave)
voice = []
# voice 是从 一堆 wav 音频文件中提取的波形
X = [] # X is the feature ~ data[0]
y = [] # y is the normal (1) or attack (0) ~ data[1]
for file_path in name_all:
file_name = file_path.split("\\")[-1]
# define the normal or attack in variable cur_y
if 'normal' in file_name:
cur_y = 1 # normal case
elif 'attack' in file_name:
cur_y = 0
# split the file name
# read the data
rate, data = read(file_path)
voice += [list(data)]
X += [list(mald_feature(rate, data))]
print(list(mald_feature(rate, data)))
# 从wav 文件提取特征的函数是 generate_array_feature.py
# X 是特征，特征的维度是110维
y += [cur_y]
# y是标签，1代表正常样本，0代表攻击样本
# normalization
norm_X = normalize(X, axis=0, norm='max')
# X_y = [(norm_X[i], y[i]) for i in range(len(norm_X))]
# # print(len(X_y))
# # for i in X_y: print(i[1])
# X_y = np.asarray(X_y)
X = np.asarray(norm_X)
y = np.asarray(y)
# X = np.asarray([x[0] for x in X_y])
# y = np.asarray([x[1] for x in X_y])
#%% 画出特征来
index1 = [5] # 选第2121个元素
x1 = X[index1]
y1 = y[index1] # 1，代表normal
plt.plot(x1.T, label='normal')
index2 = [1] # 选择第10个元素
x2 = X[index2]
y2 = y[index2] # 0, 代表attack
plt.plot(x2.T, label='attack')
plt.legend()
plt.show()
# 可以明显看出 normal 与 attack 的区别，这也是我们分类的基础
#%% 开始预测
scores = model.evaluate(X, y) # 这是一个总体的预测
y_pred = np.round(model.predict(X)) # 这里会给出一个预测的结论
index1 = 8 # 8 是一个正常样本
index3 = [1, 3, 5, 7, 9] # 选一些样本，等wav 文件到了，输入就直接是wav
for i in index3:
print('Starting detection:')
plt.plot(voice[i], label='Voice Signal')
plt.show()
time.sleep(2)
if y[i] == 1: # 正常情况
print('the ' + str(i) + ' sample is normal')
title = 'the ' + str(i) + ' sample is normal'
plt.subplot(1, 2, 1)
plt.plot(X[index1])
plt.subplot(1, 2, 2)
plt.plot(X[i], label='New')
plt.title(title)
plt.show()
time.sleep(1)
if y_pred[i] == y[i]:
print("Successfully Detect") # 成功预测
print("Run the car")
title = "Successfully Detect, " + "Run the car"
plt.title(title)
plt.show()
else:
print("Detection is false.") # 失败预测
print("Don't run the car")
title = "Detection is false, " + "Don't run the car"
plt.title(title)
plt.show()
else: # 异常情况，决策是相反的
print('the ' + str(i) + ' sample is attack')
title = 'the ' + str(i) + ' sample is attack'
plt.subplot(1, 2, 1)
plt.plot(X[index1], label='Normal')
plt.subplot(1, 2, 2)
plt.plot(X[i], label='New')
plt.title(title)
plt.show()
time.sleep(1)
if y_pred[i] == y[i]:
print("Successfully Detect") # 成功预测
print("Don't run the car")
title = "Successfully Detect, " + "Don't run the car"
plt.title(title)
plt.show()
else:
print("Detection is false.") # 失败预测
print("Run the car")
title = "Detection is false, " + "Run the car"
plt.title(title)
plt.show()
print("-------------------------")
|