keras孪生网络的图片相似度怎么计算？-创新互联
不懂keras孪生网络的图片相似度怎么计算？？其实想解决这个问题也不难，下面让小编带着大家一起学习怎么去解决，希望大家阅读完这篇文章后大所收获。
我就废话不多说了，大家还是直接看代码吧！
import keras
from keras.layers import Input,Dense,Conv2D
from keras.layers import MaxPooling2D,Flatten,Convolution2D
from keras.models import Model
import os
import numpy as np
from PIL import Image
from keras.optimizers import SGD
from scipy import misc
root_path = os.getcwd()
train_names = ['bear','blackswan','bus','camel','car','cows','dance','dog','hike','hoc','kite','lucia','mallerd','pigs','soapbox','stro','surf','swing','train','walking']
test_names = ['boat','dance-jump','drift-turn','elephant','libby']
def load_data(seq_names,data_number,seq_len):
#生成图片对
print('loading data.....')
frame_num = 51
train_data1 = []
train_data2 = []
train_lab = []
count = 0
while count < data_number:
count = count + 1
pos_neg = np.random.randint(0,2)
if pos_neg==0:
seed1 = np.random.randint(0,seq_len)
seed2 = np.random.randint(0,seq_len)
while seed1 == seed2:
seed1 = np.random.randint(0,seq_len)
seed2 = np.random.randint(0,seq_len)
frame1 = np.random.randint(1,frame_num)
frame2 = np.random.randint(1,frame_num)
path2 = os.path.join(root_path,'data','simility_data',seq_names[seed1],str(frame1)+'.jpg')
path3 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed2], str(frame2) + '.jpg')
image1 = np.array(misc.imresize(Image.open(path2),[224,224]))
image2 = np.array(misc.imresize(Image.open(path3),[224,224]))
train_data1.append(image1)
train_data2.append(image2)
train_lab.append(np.array(0))
else:
seed = np.random.randint(0,seq_len)
frame1 = np.random.randint(1, frame_num)
frame2 = np.random.randint(1, frame_num)
path2 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed], str(frame1) + '.jpg')
path3 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed], str(frame2) + '.jpg')
image1 = np.array(misc.imresize(Image.open(path2),[224,224]))
image2 = np.array(misc.imresize(Image.open(path3),[224,224]))
train_data1.append(image1)
train_data2.append(image2)
train_lab.append(np.array(1))
return np.array(train_data1),np.array(train_data2),np.array(train_lab)
def vgg_16_base(input_tensor):
net = Conv2D(64(3,3),activation='relu',padding='same',input_shape=(224,224,3))(input_tensor)
net = Convolution2D(64,(3,3),activation='relu',padding='same')(net)
net = MaxPooling2D((2,2),strides=(2,2))(net)
net = Convolution2D(128,(3,3),activation='relu',padding='same')(net)
net = Convolution2D(128,(3,3),activation='relu',padding='same')(net)
net= MaxPooling2D((2,2),strides=(2,2))(net)
net = Convolution2D(256,(3,3),activation='relu',padding='same')(net)
net = Convolution2D(256,(3,3),activation='relu',padding='same')(net)
net = Convolution2D(256,(3,3),activation='relu',padding='same')(net)
net = MaxPooling2D((2,2),strides=(2,2))(net)
net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
net = MaxPooling2D((2,2),strides=(2,2))(net)
net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
net = Convolution2D(512,(3,3),activation='relu',padding='same')(net)
net = MaxPooling2D((2,2),strides=(2,2))(net)
net = Flatten()(net)
return net
def siamese(vgg_path=None,siamese_path=None):
input_tensor = Input(shape=(224,224,3))
vgg_model = Model(input_tensor,vgg_16_base(input_tensor))
if vgg_path:
vgg_model.load_weights(vgg_path)
input_im1 = Input(shape=(224,224,3))
input_im2 = Input(shape=(224,224,3))
out_im1 = vgg_model(input_im1)
out_im2 = vgg_model(input_im2)
diff = keras.layers.substract([out_im1,out_im2])
out = Dense(500,activation='relu')(diff)
out = Dense(1,activation='sigmoid')(out)
model = Model([input_im1,input_im2],out)
if siamese_path:
model.load_weights(siamese_path)
return model
train = True
if train:
model = siamese(siamese_path='model/simility/vgg.h6')
sgd = SGD(lr=1e-6,momentum=0.9,decay=1e-6,nesterov=True)
model.compile(optimizer=sgd,loss='mse',metrics=['accuracy'])
tensorboard = keras.callbacks.TensorBoard(histogram_freq=5,log_dir='log/simility',write_grads=True,write_images=True)
ckpt = keras.callbacks.ModelCheckpoint(os.path.join(root_path,'model','simility','vgg.h6'),
verbose=1,period=5)
train_data1,train_data2,train_lab = load_data(train_names,4000,20)
model.fit([train_data1,train_data2],train_lab,callbacks=[tensorboard,ckpt],batch_size=64,epochs=50)
else:
model = siamese(siamese_path='model/simility/vgg.h6')
test_im1,test_im2,test_labe = load_data(test_names,1000,5)
TP = 0
for i in range(1000):
im1 = np.expand_dims(test_im1[i],axis=0)
im2 = np.expand_dims(test_im2[i],axis=0)
lab = test_labe[i]
pre = model.predict([im1,im2])
if pre>0.9 and lab==1:
TP = TP + 1
if pre<0.9 and lab==0:
TP = TP + 1
print(float(TP)/1000) 当前名称：keras孪生网络的图片相似度怎么计算？-创新互联
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