unet网络python代码详解_Keras:Unet网络实现多类语义分割方式

1 介绍

u-net最初是用来对医学图像的语义分割，后来也有人将其应用于其他领域。但大多还是用来进行二分类，即将原始图像分成两个灰度级或者色度，依次找到图像中感兴趣的目标部分。

本文主要利用u-net网络结构实现了多类的语义分割，并展示了部分测试效果，希望对你有用！

2 源代码

(1)训练模型

from __future__ import print_function

import os

import datetime

import numpy as np

from keras.models import model

from keras.layers import input, concatenate, conv2d, maxpooling2d, conv2dtranspose, averagepooling2d, dropout, \

batchnormalization

from keras.optimizers import adam

from keras.layers.convolutional import upsampling2d, conv2d

from keras.callbacks import modelcheckpoint

from keras import backend as k

from keras.layers.advanced_activations import leakyrelu, relu

import cv2

pixel = 512 #set your image size

batch_size = 5

lr = 0.001

epoch = 100

x_channel = 3 # training images channel

y_channel = 1 # label iamges channel

x_num = 422 # your traning data number

pathx = 'i:\\pascal voc dataset\\train1\\images\\' #change your file path

pathy = 'i:\\pascal voc dataset\\train1\\segmentationobject\\' #change your file path

#data processing

def generator(pathx, pathy,batch_size):

while 1:

x_train_files = os.listdir(pathx)

y_train_files = os.listdir(pathy)

a = (np.arange(1, x_num))

x = []

y = []

for i in range(batch_size):

index = np.random.choice(a)

# print(index)

img = cv2.imread(pathx + x_train_files[index], 1)

img = np.array(img).reshape(pixel, pixel, x_channel)

x.append(img)

img1 = cv2.imread(pathy + y_train_files[index], 1)

img1 = np.array(img1).reshape(pixel, pixel, y_channel)

y.append(img1)

x = np.array(x)

y = np.array(y)

yield x, y

#creat unet network

inputs = input((pixel, pixel, 3))

conv1 = conv2d(8, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)

pool1 = averagepooling2d(pool_size=(2, 2))(conv1) # 16

conv2 = batchnormalization(momentum=0.99)(pool1)

conv2 = conv2d(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)

conv2 = batchnormalization(momentum=0.99)(conv2)

conv2 = conv2d(64, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)

conv2 = dropout(0.02)(conv2)

pool2 = averagepooling2d(pool_size=(2, 2))(conv2) # 8

conv3 = batchnormalization(momentum=0.99)(pool2)

conv3 = conv2d(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)

conv3 = batchnormalization(momentum=0.99)(conv3)

conv3 = conv2d(128, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)

conv3 = dropout(0.02)(conv3)

pool3 = averagepooling2d(pool_size=(2, 2))(conv3) # 4

conv4 = batchnormalization(momentum=0.99)(pool3)

conv4 = conv2d(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)

conv4 = batchnormalization(momentum=0.99)(conv4)

conv4 = conv2d(256, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)

conv4 = dropout(0.02)(conv4)

pool4 = averagepooling2d(pool_size=(2, 2))(conv4)

conv5 = batchnormalization(momentum=0.99)(pool4)

conv5 = conv2d(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)

conv5 = batchnormalization(momentum=0.99)(conv5)

conv5 = conv2d(512, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)

conv5 = dropout(0.02)(conv5)

pool4 = averagepooling2d(pool_size=(2, 2))(conv4)

# conv5 = conv2d(35, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)

# drop4 = dropout(0.02)(conv5)

pool4 = averagepooling2d(pool_size=(2, 2))(pool3) # 2

pool5 = averagepooling2d(pool_size=(2, 2))(pool4) # 1

conv6 = batchnormalization(momentum=0.99)(pool5)

conv6 = conv2d(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)

conv7 = conv2d(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)

up7 = (upsampling2d(size=(2, 2))(conv7)) # 2

conv7 = conv2d(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up7)

merge7 = concatenate([pool4, conv7], axis=3)

conv8 = conv2d(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)

up8 = (upsampling2d(size=(2, 2))(conv8)) # 4

conv8 = conv2d(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up8)

merge8 = concatenate([pool3, conv8], axis=3)

conv9 = conv2d(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)

up9 = (upsampling2d(size=(2, 2))(conv9)) # 8

conv9 = conv2d(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up9)

merge9 = concatenate([pool2, conv9], axis=3)

conv10 = conv2d(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)

up10 = (upsampling2d(size=(2, 2))(conv10)) # 16

conv10 = conv2d(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up10)

conv11 = conv2d(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv10)

up11 = (upsampling2d(size=(2, 2))(conv11)) # 32

conv11 = conv2d(8, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up11)

# conv12 = conv2d(3, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv11)

conv12 = conv2d(3, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv11)

model = model(input=inputs, output=conv12)

print(model.summary())

model.compile(optimizer=adam(lr=1e-3), loss='mse', metrics=['accuracy'])

history = model.fit_generator(generator(pathx, pathy,batch_size),

steps_per_epoch=600, nb_epoch=epoch)

end_time = datetime.datetime.now().strftime('%y-%m-%d %h:%m:%s')

#save your training model

model.save(r'v1_828.h5')

#save your loss data

mse = np.array((history.history['loss']))

np.save(r'v1_828.npy', mse)

(2)测试模型

from keras.models import load_model

import numpy as np

import matplotlib.pyplot as plt

import os

import cv2

model = load_model('v1_828.h5')

test_images_path = 'i:\\pascal voc dataset\\test\\test_images\\'

test_gt_path = 'i:\\pascal voc dataset\\test\\segmentationobject\\'

pre_path = 'i:\\pascal voc dataset\\test\\pre\\'

x = []

for info in os.listdir(test_images_path):

a = cv2.imread(test_images_path + info)

x.append(a)

# i += 1

x = np.array(x)

print(x.shape)

y = model.predict(x)

groudtruth = []

for info in os.listdir(test_gt_path):

a = cv2.imread(test_gt_path + info)

groudtruth.append(a)

groudtruth = np.array(groudtruth)

i = 0

for info in os.listdir(test_images_path):

cv2.imwrite(pre_path + info,y[i])

i += 1

a = range(10)

n = np.random.choice(a)

cv2.imwrite('prediction.png',y[n])

cv2.imwrite('groudtruth.png',groudtruth[n])

fig, axs = plt.subplots(1, 3)

# cnt = 1

# for j in range(1):

axs[0].imshow(np.abs(x[n]))

axs[0].axis('off')

axs[1].imshow(np.abs(y[n]))

axs[1].axis('off')

axs[2].imshow(np.abs(groudtruth[n]))

axs[2].axis('off')

# cnt += 1

fig.savefig("imagestest.png")

plt.close()

3 效果展示

说明：从左到右依次是预测图像，真实图像，标注图像。可以看出，对于部分数据的分割效果还有待改进，主要原因还是数据集相对复杂，模型难于找到其中的规律。

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