keras实现基于孪生网络的图片相似度计算方式

keras实现基于孪生网络的图片相似度计算方式实现基于孪生网络的图片相似度计算方式

主要介绍了keras实现基于孪生网络的图片相似度计算方式，具有很好的参考价值，希望对大家有所帮助。一起

跟随小编过来看看吧

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

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 = []

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)

path1 = os.path.join(root_path,'data','simility_data',seq_names[seed1],str(frame1)+'.jpg')

path2 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed2], str(frame2) + '.jpg')

image1 = np.array(misc.imresize(Image.open(path1),[224,224]))

image2 = np.array(misc.imresize(Image.open(path2),[224,224]))

frame2 = np.random.randint(1, frame_num)

path1 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed], str(frame1) + '.jpg')

path2 = os.path.join(root_path, 'data', 'simility_data', seq_names[seed], str(frame2) + '.jpg')

image1 = np.array(misc.imresize(Image.open(path1),[224,224]))

image2 = np.array(misc.imresize(Image.open(path2),[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)

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)