keras-siamese用自己的数据集实现详解

Siamese网络不做过多介绍，思想并不难，输入两个图像，输出这两张图像的相似度，两个输入的网络结构是相同的，参数共享。

主要发现很多代码都是基于mnist数据集的，下面说一下怎么用自己的数据集实现siamese网络。

首先，先整理数据集，相同的类放到同一个文件夹下，如下图所示：

接下来，将pairs及对应的label写到csv中，代码如下：

import os
import random
import csv
#图片所在的路径
path = '/Users/mac/Desktop/wxd/flag/category/'
#files列表保存所有类别的路径
files=[]
same_pairs=[]
different_pairs=[]
for file in os.listdir(path):
  if file[0]=='.':
    continue
  file_path = os.path.join(path,file)
  files.append(file_path)
#该地址为csv要保存到的路径，a表示追加写入
with open('/Users/mac/Desktop/wxd/flag/data.csv','a') as f:
  #保存相同对
  writer = csv.writer(f)
  for file in files:
    imgs = os.listdir(file)
    for i in range(0,len(imgs)-1):
      for j in range(i+1,len(imgs)):
        pairs = []
        name = file.split(sep='/')[-1]
        pairs.append(path+name+'/'+imgs[i])
        pairs.append(path+name+'/'+imgs[j])
        pairs.append(1)
        writer.writerow(pairs)
  #保存不同对
  for i in range(0,len(files)-1):
    for j in range(i+1,len(files)):
      filea = files[i]
      fileb = files[j]
      imga_li = os.listdir(filea)
      imgb_li = os.listdir(fileb)
      random.shuffle(imga_li)
      random.shuffle(imgb_li)
      a_li = imga_li[:]
      b_li = imgb_li[:]
      for p in range(len(a_li)):
        for q in range(len(b_li)):
          pairs = []
          name1 = filea.split(sep='/')[-1]
          name2 = fileb.split(sep='/')[-1]
          pairs.append(path+name1+'/'+a_li[p])
          pairs.append(path+name2+'/'+b_li[q])
          pairs.append(0)
          writer.writerow(pairs)

相当于csv每一行都包含一对结果，每一行有三列，第一列第一张图片路径，第二列第二张图片路径，第三列是不是相同的label，属于同一个类的label为1，不同类的为0，可参考下图：

然后，由于keras的fit函数需要将训练数据都塞入内存，而大部分训练数据都较大，因此才用fit_generator生成器的方法，便可以训练大数据，代码如下：

from __future__ import absolute_import
from __future__ import print_function
import numpy as np
from keras.models import Model
from keras.layers import Input, Dense, Dropout, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D, concatenate, \
  Activation, ZeroPadding2D
from keras.layers import add, Flatten
from keras.utils import plot_model
from keras.metrics import top_k_categorical_accuracy
from keras.preprocessing.image import ImageDataGenerator
from keras.models import load_model
import tensorflow as tf
import random
import os
import cv2
import csv
import numpy as np
from keras.models import Model
from keras.layers import Input, Flatten, Dense, Dropout, Lambda
from keras.optimizers import RMSprop
from keras import backend as K
from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import img_to_array

"""
自定义的参数
"""
im_width = 224
im_height = 224
epochs = 100
batch_size = 64
iterations = 1000
csv_path = ''
model_result = ''

# 计算欧式距离
def euclidean_distance(vects):
  x, y = vects
  sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)
  return K.sqrt(K.maximum(sum_square, K.epsilon()))

def eucl_dist_output_shape(shapes):
  shape1, shape2 = shapes
  return (shape1[0], 1)

# 计算loss
def contrastive_loss(y_true, y_pred):
  '''Contrastive loss from Hadsell-et-al.'06
  http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
  '''
  margin = 1
  square_pred = K.square(y_pred)
  margin_square = K.square(K.maximum(margin - y_pred, 0))
  return K.mean(y_true * square_pred + (1 - y_true) * margin_square)

def compute_accuracy(y_true, y_pred):
  '''计算准确率
  '''
  pred = y_pred.ravel() < 0.5
  print('pred:', pred)
  return np.mean(pred == y_true)

def accuracy(y_true, y_pred):
  '''Compute classification accuracy with a fixed threshold on distances.
  '''
  return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))

def processImg(filename):
  """
  :param filename: 图像的路径
  :return: 返回的是归一化矩阵
  """
  img = cv2.imread(filename)
  img = cv2.resize(img, (im_width, im_height))
  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
  img = img_to_array(img)
  img /= 255
  return img

def Conv2d_BN(x, nb_filter, kernel_size, strides=(1, 1), padding='same', name=None):
  if name is not None:
    bn_name = name + '_bn'
    conv_name = name + '_conv'
  else:
    bn_name = None
    conv_name = None

  x = Conv2D(nb_filter, kernel_size, padding=padding, strides=strides, activation='relu', name=conv_name)(x)
  x = BatchNormalization(axis=3, name=bn_name)(x)
  return x

def bottleneck_Block(inpt, nb_filters, strides=(1, 1), with_conv_shortcut=False):
  k1, k2, k3 = nb_filters
  x = Conv2d_BN(inpt, nb_filter=k1, kernel_size=1, strides=strides, padding='same')
  x = Conv2d_BN(x, nb_filter=k2, kernel_size=3, padding='same')
  x = Conv2d_BN(x, nb_filter=k3, kernel_size=1, padding='same')
  if with_conv_shortcut:
    shortcut = Conv2d_BN(inpt, nb_filter=k3, strides=strides, kernel_size=1)
    x = add([x, shortcut])
    return x
  else:
    x = add([x, inpt])
    return x

def resnet_50():
  width = im_width
  height = im_height
  channel = 3
  inpt = Input(shape=(width, height, channel))
  x = ZeroPadding2D((3, 3))(inpt)
  x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='valid')
  x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

  # conv2_x
  x = bottleneck_Block(x, nb_filters=[64, 64, 256], strides=(1, 1), with_conv_shortcut=True)
  x = bottleneck_Block(x, nb_filters=[64, 64, 256])
  x = bottleneck_Block(x, nb_filters=[64, 64, 256])

  # conv3_x
  x = bottleneck_Block(x, nb_filters=[128, 128, 512], strides=(2, 2), with_conv_shortcut=True)
  x = bottleneck_Block(x, nb_filters=[128, 128, 512])
  x = bottleneck_Block(x, nb_filters=[128, 128, 512])
  x = bottleneck_Block(x, nb_filters=[128, 128, 512])

  # conv4_x
  x = bottleneck_Block(x, nb_filters=[256, 256, 1024], strides=(2, 2), with_conv_shortcut=True)
  x = bottleneck_Block(x, nb_filters=[256, 256, 1024])
  x = bottleneck_Block(x, nb_filters=[256, 256, 1024])
  x = bottleneck_Block(x, nb_filters=[256, 256, 1024])
  x = bottleneck_Block(x, nb_filters=[256, 256, 1024])
  x = bottleneck_Block(x, nb_filters=[256, 256, 1024])

  # conv5_x
  x = bottleneck_Block(x, nb_filters=[512, 512, 2048], strides=(2, 2), with_conv_shortcut=True)
  x = bottleneck_Block(x, nb_filters=[512, 512, 2048])
  x = bottleneck_Block(x, nb_filters=[512, 512, 2048])

  x = AveragePooling2D(pool_size=(7, 7))(x)
  x = Flatten()(x)
  x = Dense(128, activation='relu')(x)
  return Model(inpt, x)

def generator(imgs, batch_size):
  """
  自定义迭代器
  :param imgs: 列表，每个包含一对矩阵以及label
  :param batch_size:
  :return:
  """
  while 1:
    random.shuffle(imgs)
    li = imgs[:batch_size]
    pairs = []
    labels = []
    for i in li:
      img1 = i[0]
      img2 = i[1]
      im1 = cv2.imread(img1)
      im2 = cv2.imread(img2)
      if im1 is None or im2 is None:
        continue
      label = int(i[2])
      img1 = processImg(img1)
      img2 = processImg(img2)
      pairs.append([img1, img2])
      labels.append(label)
    pairs = np.array(pairs)
    labels = np.array(labels)
    yield [pairs[:, 0], pairs[:, 1]], labels

input_shape = (im_width, im_height, 3)
base_network = resnet_50()

input_a = Input(shape=input_shape)
input_b = Input(shape=input_shape)

# because we re-use the same instance `base_network`,
# the weights of the network
# will be shared across the two branches
processed_a = base_network(input_a)
processed_b = base_network(input_b)

distance = Lambda(euclidean_distance,
         output_shape=eucl_dist_output_shape)([processed_a, processed_b])
with tf.device("/gpu:0"):
  model = Model([input_a, input_b], distance)
  # train
  rms = RMSprop()
  rows = csv.reader(open(csv_path, 'r'), delimiter=',')
  imgs = list(rows)
  checkpoint = ModelCheckpoint(filepath=model_result+'flag_{epoch:03d}.h5', verbose=1)
  model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy])
  model.fit_generator(generator(imgs, batch_size), epochs=epochs, steps_per_epoch=iterations, callbacks=[checkpoint])

用了回调函数保存了每一个epoch后的模型，也可以保存最好的，之后需要对模型进行测试。

测试时直接用load_model会报错，而应该变成如下形式调用：

model = load_model(model_path,custom_objects={'contrastive_loss': contrastive_loss }) #选取自己的.h模型名称

emmm，到这里，就成功训练测试完了～～～写的比较粗，因为这个代码在官方给的mnist上的改动不大，只是方便大家用自己的数据集，大家如果有更好的方法可以提出意见～～～

以上这篇keras-siamese用自己的数据集实现详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持我们。