Keras实现Unet结构
在之前发的博客“基于卷积神经网络特征图的二值图像分割”中（https://blog.csdn.net/shi2xian2wei2/article/details/84329511）也提到，Unet结构主要是通过多个多通道特征图最大化的利用输入图片的特征，使得网络在训练集较小的情况下也能够得到较好的目标分割结果。Unet论文见https://arxiv.org/abs/1505.04597，这里通过Keras框架对Unet结构进行搭建，并使用之前博客中的伪造数据集对网络进行训练以及测试。
Unet论文中所提出的网络结构如下图所示：
Unet大量使用了拼接结构，以实现对图像不同尺度信息的采集，这样做也是为了能尽可能利用图片中的信息，论文中卷积的padding采用的是vaild方式，因此在进行拼接时需要对输出进行裁剪来保证尺寸的一致性。个人感觉padding选用same方式不会对性能产生任何不好的影响，并且实现起来也更加方便。keras框架实现的网络结构如下：
inpt = Input(shape=(input_size_1, input_size_2, 3))
conv1 = Conv2d_BN(inpt, 8, (3, 3))
conv1 = Conv2d_BN(conv1, 8, (3, 3))
pool1 = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='same')(conv1)
conv2 = Conv2d_BN(pool1, 16, (3, 3))
conv2 = Conv2d_BN(conv2, 16, (3, 3))
pool2 = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='same')(conv2)
conv3 = Conv2d_BN(pool2, 32, (3, 3))
conv3 = Conv2d_BN(conv3, 32, (3, 3))
pool3 = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='same')(conv3)
conv4 = Conv2d_BN(pool3, 64, (3, 3))
conv4 = Conv2d_BN(conv4, 64, (3, 3))
pool4 = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='same')(conv4)
conv5 = Conv2d_BN(pool4, 128, (3, 3))
conv5 = Dropout(0.5)(conv5)
conv5 = Conv2d_BN(conv5, 128, (3, 3))
conv5 = Dropout(0.5)(conv5)
convt1 = Conv2dT_BN(conv5, 64, (3, 3))
concat1 = concatenate([conv4, convt1], axis=3)
concat1 = Dropout(0.5)(concat1)
conv6 = Conv2d_BN(concat1, 64, (3, 3))
conv6 = Conv2d_BN(conv6, 64, (3, 3))
convt2 = Conv2dT_BN(conv6, 32, (3, 3))
concat2 = concatenate([conv3, convt2], axis=3)
concat2 = Dropout(0.5)(concat2)
conv7 = Conv2d_BN(concat2, 32, (3, 3))
conv7 = Conv2d_BN(conv7, 32, (3, 3))
convt3 = Conv2dT_BN(conv7, 16, (3, 3))
concat3 = concatenate([conv2, convt3], axis=3)
concat3 = Dropout(0.5)(concat3)
conv8 = Conv2d_BN(concat3, 16, (3, 3))
conv8 = Conv2d_BN(conv8, 16, (3, 3))
convt4 = Conv2dT_BN(conv8, 8, (3, 3))
concat4 = concatenate([conv1, convt4], axis=3)
concat4 = Dropout(0.5)(concat4)
conv9 = Conv2d_BN(concat4, 8, (3, 3))
conv9 = Conv2d_BN(conv9, 8, (3, 3))
conv9 = Dropout(0.5)(conv9)
outpt = Conv2D(filters=3, kernel_size=(1,1), strides=(1,1), padding='same', activation='sigmoid')(conv9)
model = Model(inpt, outpt)
model.compile(loss='mean_squared_error', optimizer='Nadam', metrics=['accuracy'])
model.summary()构建的网络参数进行了大幅度减少，主要是我的电脑显卡不给力/(ㄒoㄒ)/~~……加入大量的Dropout层是为了防止网络过拟合，因为样本数量比较少。网络之所以使用最大池化层进行下采样我觉得主要是考虑到对边缘特征的最大化利用。在1000张图片的训练集上训练约22个Epoch后，结果如下：
原始图像 真实标签 检测标签
虽然在训练过程中，训练样本中并没有包含任何的纹理信息，网络输出的结果中可以看到部分物体的一些纹理。这也从一个方面反映了Unet特征提取能力的强大。
附上网络完整代码，数据还请自行替换：
import numpy as np
import random
import os
from keras.models import save_model, load_model, Model
from keras.layers import Input, Dropout, BatchNormalization, LeakyReLU, concatenate
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Conv2DTranspose
import matplotlib.pyplot as plt
from skimage import io
from skimage.transform import resize
input_name = os.listdir('train_data3/JPEGImages')
n = len(input_name)
batch_size = 8
input_size_1 = 256
input_size_2 = 256
def batch_data(input_name, n, batch_size = 8, input_size_1 = 256, input_size_2 = 256):
rand_num = random.randint(0, n-1)
img1 = io.imread('train_data3/JPEGImages/'+input_name[rand_num]).astype("float")
img2 = io.imread('train_data3/TargetImages/'+input_name[rand_num]).astype("float")
img1 = resize(img1, [input_size_1, input_size_2, 3])
img2 = resize(img2, [input_size_1, input_size_2, 3])
img1 = np.reshape(img1, (1, input_size_1, input_size_2, 3))
img2 = np.reshape(img2, (1, input_size_1, input_size_2, 3))
img1 /= 255
img2 /= 255
batch_input = img1
batch_output = img2
for batch_iter in range(1, batch_size):
rand_num = random.randint(0, n-1)
img1 = io.imread('train_data3/JPEGImages/'+input_name[rand_num]).astype("float")
img2 = io.imread('train_data3/TargetImages/'+input_name[rand_num]).astype("float")
img1 = resize(img1, [input_size_1, input_size_2, 3])
img2 = resize(img2, [input_size_1, input_size_2, 3])
img1 = np.reshape(img1, (1, input_size_1, input_size_2, 3))
img2 = np.reshape(img2, (1, input_size_1, input_size_2, 3))
img1 /= 255
img2 /= 255
batch_input = np.concatenate((batch_input, img1), axis = 0)
batch_output = np.concatenate((batch_output, img2), axis = 0)
return batch_input, batch_output
def Conv2d_BN(x, nb_filter, kernel_size, strides=(1,1), padding='same'):
x = Conv2D(nb_filter, kernel_size, strides=strides, padding=padding)(x)
x = BatchNormalization(axis=3)(x)
x = LeakyReLU(alpha=0.1)(x)
return x
def Conv2dT_BN(x, filters, kernel_size, strides=(2,2), padding='same'):
x = Conv2DTranspose(filters, kernel_size, strides=strides, padding=padding)(x)
x = BatchNormalization(axis=3)(x)
x = LeakyReLU(alpha=0.1)(x)
return x
inpt = Input(shape=(input_size_1, input_size_2, 3))
conv1 = Conv2d_BN(inpt, 8, (3, 3))
conv1 = Conv2d_BN(conv1, 8, (3, 3))
pool1 = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='same')(conv1)
conv2 = Conv2d_BN(pool1, 16, (3, 3))
conv2 = Conv2d_BN(conv2, 16, (3, 3))
pool2 = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='same')(conv2)
conv3 = Conv2d_BN(pool2, 32, (3, 3))
conv3 = Conv2d_BN(conv3, 32, (3, 3))
pool3 = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='same')(conv3)
conv4 = Conv2d_BN(pool3, 64, (3, 3))
conv4 = Conv2d_BN(conv4, 64, (3, 3))
pool4 = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='same')(conv4)
conv5 = Conv2d_BN(pool4, 128, (3, 3))
conv5 = Dropout(0.5)(conv5)
conv5 = Conv2d_BN(conv5, 128, (3, 3))
conv5 = Dropout(0.5)(conv5)
convt1 = Conv2dT_BN(conv5, 64, (3, 3))
concat1 = concatenate([conv4, convt1], axis=3)
concat1 = Dropout(0.5)(concat1)
conv6 = Conv2d_BN(concat1, 64, (3, 3))
conv6 = Conv2d_BN(conv6, 64, (3, 3))
convt2 = Conv2dT_BN(conv6, 32, (3, 3))
concat2 = concatenate([conv3, convt2], axis=3)
concat2 = Dropout(0.5)(concat2)
conv7 = Conv2d_BN(concat2, 32, (3, 3))
conv7 = Conv2d_BN(conv7, 32, (3, 3))
convt3 = Conv2dT_BN(conv7, 16, (3, 3))
concat3 = concatenate([conv2, convt3], axis=3)
concat3 = Dropout(0.5)(concat3)
conv8 = Conv2d_BN(concat3, 16, (3, 3))
conv8 = Conv2d_BN(conv8, 16, (3, 3))
convt4 = Conv2dT_BN(conv8, 8, (3, 3))
concat4 = concatenate([conv1, convt4], axis=3)
concat4 = Dropout(0.5)(concat4)
conv9 = Conv2d_BN(concat4, 8, (3, 3))
conv9 = Conv2d_BN(conv9, 8, (3, 3))
conv9 = Dropout(0.5)(conv9)
outpt = Conv2D(filters=3, kernel_size=(1,1), strides=(1,1), padding='same', activation='sigmoid')(conv9)
model = Model(inpt, outpt)
model.compile(loss='mean_squared_error', optimizer='Nadam', metrics=['accuracy'])
model.summary()
itr = 3000
S = []
for i in range(itr):
print("iteration = ", i+1)
if i < 500:
bs = 4
elif i < 2000:
bs = 8
elif i < 5000:
bs = 16
else:
bs = 32
train_X, train_Y = batch_data(input_name, n, batch_size = bs)
model.fit(train_X, train_Y, epochs=1, verbose=0)
if i % 100 == 99:
save_model(model, 'unet.h5')
model = load_model('unet.h5')
def batch_data_test(input_name, n, batch_size = 8, input_size_1 = 256, input_size_2 = 256):
rand_num = random.randint(0, n-1)
img1 = io.imread('test_data3/JPEGImages/'+input_name[rand_num]).astype("float")
img2 = io.imread('test_data3/TargetImages/'+input_name[rand_num]).astype("float")
img1 = resize(img1, [input_size_1, input_size_2, 3])
img2 = resize(img2, [input_size_1, input_size_2, 3])
img1 = np.reshape(img1, (1, input_size_1, input_size_2, 3))
img2 = np.reshape(img2, (1, input_size_1, input_size_2, 3))
img1 /= 255
img2 /= 255
batch_input = img1
batch_output = img2
for batch_iter in range(1, batch_size):
rand_num = random.randint(0, n-1)
img1 = io.imread('test_data3/JPEGImages/'+input_name[rand_num]).astype("float")
img2 = io.imread('test_data3/TargetImages/'+input_name[rand_num]).astype("float")
img1 = resize(img1, [input_size_1, input_size_2, 3])
img2 = resize(img2, [input_size_1, input_size_2, 3])
img1 = np.reshape(img1, (1, input_size_1, input_size_2, 3))
img2 = np.reshape(img2, (1, input_size_1, input_size_2, 3))
img1 /= 255
img2 /= 255
batch_input = np.concatenate((batch_input, img1), axis = 0)
batch_output = np.concatenate((batch_output, img2), axis = 0)
return batch_input, batch_output
test_name = os.listdir('test_data3/JPEGImages')
n_test = len(test_name)
test_X, test_Y = batch_data_test(test_name, n_test, batch_size = 1)
pred_Y = model.predict(test_X)
ii = 0
plt.figure()
plt.imshow(test_X[ii, :, :, :])
plt.axis('off')
plt.figure()
plt.imshow(test_Y[ii, :, :, :])
plt.axis('off')
plt.figure()
plt.imshow(pred_Y[ii, :, :, :])
plt.axis('off')