深度学习之unet深度网络用于煤岩裂缝提取
本文为github上开源的unet神经网络，用于复习和记录
1.环境配置情况：
h5py 2.10.0
html5lib 0.9999999
Keras 2.0.6
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.2
matplotlib 3.3.2
numpy 1.19.2
opencv-contrib-python 4.4.0.44
opencv-python 4.4.0.44
pandas 1.1.3
requests 2.25.1
scikit-image 0.17.2
scipy 1.5.2
tensorflow-gpu 1.3.0
tensorflow-tensorboard 0.1.8
2.训练集数据处理：
from __future__ import print_function
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import glob
import skimage.io as io
import skimage.transform as trans
Sky = [128,128,128]
Building = [128,0,0]
Pole = [192,192,128]
Road = [128,64,128]
Pavement = [60,40,222]
Tree = [128,128,0]
SignSymbol = [192,128,128]
Fence = [64,64,128]
Car = [64,0,128]
Pedestrian = [64,64,0]
Bicyclist = [0,128,192]
Unlabelled = [0,0,0]
COLOR_DICT = np.array([Sky, Building, Pole, Road, Pavement,
Tree, SignSymbol, Fence, Car, Pedestrian, Bicyclist, Unlabelled])
def adjustData(img,mask,flag_multi_class,num_class):
if(flag_multi_class):#此程序中不是多类情况，所以不考虑这个
img = img / 255
mask = mask[:,:,:,0] if(len(mask.shape) == 4) else mask[:,:,0]
new_mask = np.zeros(mask.shape + (num_class,))
for i in range(num_class):
#for one pixel in the image, find the class in mask and convert it into one-hot vector
#index = np.where(mask == i)
#index_mask = (index[0],index[1],index[2],np.zeros(len(index[0]),dtype = np.int64) + i) if (len(mask.shape) == 4) else (index[0],index[1],np.zeros(len(index[0]),dtype = np.int64) + i)
#new_mask[index_mask] = 1
new_mask[mask == i,i] = 1
new_mask = np.reshape(new_mask,(new_mask.shape[0],new_mask.shape[1]*new_mask.shape[2],new_mask.shape[3])) if flag_multi_class else np.reshape(new_mask,(new_mask.shape[0]*new_mask.shape[1],new_mask.shape[2]))
mask = new_mask
elif(np.max(img) > 1):
img = img / 255
mask = mask /255
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0
return (img,mask)
#上面这个函数主要是对训练集的数据和标签的像素值进行归一化
def trainGenerator(batch_size,train_path,image_folder,mask_folder,aug_dict,image_color_mode = "grayscale",
mask_color_mode = "grayscale",image_save_prefix = "image",mask_save_prefix = "mask",
flag_multi_class = False,num_class = 2,save_to_dir = None,target_size = (512,512),seed = 1):
'''
can generate image and mask at the same time
use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the same
if you want to visualize the results of generator, set save_to_dir = "your path"
'''
image_datagen = ImageDataGenerator(**aug_dict)
mask_datagen = ImageDataGenerator(**aug_dict)
image_generator = image_datagen.flow_from_directory(
train_path,#训练数据文件夹路径
classes = [image_folder],#类别文件夹,对哪一个类进行增强
class_mode = None,#不返回标签
color_mode = image_color_mode,#灰度，单通道模式
target_size = target_size,#转换后的目标图片大小
batch_size = batch_size,#每次产生的（进行转换的）图片张数
save_to_dir = save_to_dir,#保存的图片路径
save_prefix = image_save_prefix,#生成图片的前缀，仅当提供save_to_dir时有效
seed = seed)
mask_generator = mask_datagen.flow_from_directory(
train_path,
classes = [mask_folder],
class_mode = None,
color_mode = mask_color_mode,
target_size = target_size,
batch_size = batch_size,
save_to_dir = save_to_dir,
save_prefix = mask_save_prefix,
seed = seed)
train_generator = zip(image_generator, mask_generator)
for (img,mask) in train_generator:
# 由于batch是2，所以一次返回两张，即img是一个2张灰度图片的数组，[2,256,256]
img,mask = adjustData(img,mask,flag_multi_class,num_class)
yield (img,mask)
#每次分别产出两张图片和标签
#上面这个函数主要是产生一个数据增强的图片生成器，方便后面使用这个生成器不断生成图片
def testGenerator(test_path,num_image = 30,target_size = (512,512),flag_multi_class = False,as_gray = True):
for i in range(num_image):
img = io.imread(os.path.join(test_path,"%d.png"%i),as_gray = as_gray)
img = img / 255
img = trans.resize(img,target_size)
img = np.reshape(img,img.shape+(1,)) if (not flag_multi_class) else img
img = np.reshape(img,(1,)+img.shape)
# 将测试图片扩展一个维度，与训练时的输入[2,256,256]保持一致
yield img
#上面这个函数主要是对测试图片进行规范，使其尺寸和维度上和训练图片保持一致
def geneTrainNpy(image_path,mask_path,flag_multi_class = False,num_class = 2,image_prefix = "image",mask_prefix = "mask",image_as_gray = True,mask_as_gray = True):
image_name_arr = glob.glob(os.path.join(image_path,"%s*.png"%image_prefix))
# 相当于文件搜索，搜索某路径下与字符匹配的文件
image_arr = []
mask_arr = []
for index,item in enumerate(image_name_arr):#enumerate是枚举，输出[(0,item0),(1,item1),(2,item2)]
img = io.imread(item,as_gray = image_as_gray)
img = np.reshape(img,img.shape + (1,)) if image_as_gray else img
mask = io.imread(item.replace(image_path,mask_path).replace(image_prefix,mask_prefix),as_gray = mask_as_gray)
# 重新在mask_path文件夹下搜索带有mask字符的图片（标签图片）
mask = np.reshape(mask,mask.shape + (1,)) if mask_as_gray else mask
img,mask = adjustData(img,mask,flag_multi_class,num_class)
image_arr.append(img)
mask_arr.append(mask)
image_arr = np.array(image_arr)
mask_arr = np.array(mask_arr)#转换成array
return image_arr,mask_arr
#该函数主要是分别在训练集文件夹下和标签文件夹下搜索图片，然后扩展一个维度后以array的形式返回，是为了在没用数据增强时的读取文件夹内自带的数据
def labelVisualize(num_class,color_dict,img):
img = img[:,:,0] if len(img.shape) == 3 else img
img_out = np.zeros(img.shape + (3,))
# 变成RGB空间，因为其他颜色只能再RGB空间才会显示
for i in range(num_class):
img_out[img == i,:] = color_dict[i]
# 为不同类别涂上不同的颜色，color_dict[i]是与类别数有关的颜色，img_out[img == i,:]是img_out在img中等于i类的位置上的点
return img_out / 255
#上面函数是给出测试后的输出之后，为输出涂上不同的颜色，多类情况下才起作用，两类的话无用
def saveResult(save_path,npyfile,flag_multi_class = False,num_class = 2):
for i,item in enumerate(npyfile):
img = labelVisualize(num_class,COLOR_DICT,item) if flag_multi_class else item[:,:,0]
# 多类的话就图成彩色，非多类（两类）的话就是黑白色
io.imsave(os.path.join(save_path,"%d_predict.png"%i),img)
3.unet模型建立：
import numpy as np
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras
def unet(pretrained_weights = None,input_size = (512,512,1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
# merge6 = merge([drop4,up6], mode = 'concat', concat_axis = 3)
merge6 = concatenate([drop4, up6], axis=3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
# merge7 = merge([conv3,up7], mode = 'concat', concat_axis = 3)
merge7 = concatenate([conv3, up7], axis=3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
# merge8 = merge([conv2,up8], mode = 'concat', concat_axis = 3)
merge8 = concatenate([conv2, up8], axis=3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
# merge9 = merge([conv1,up9], mode = 'concat', concat_axis = 3)
merge9 = concatenate([conv1, up9], axis=3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)#我怀疑这个sigmoid激活函数是多余的，因为在后面的loss中用到的就是二进制交叉熵，包含了sigmoid
# model = Model(input = inputs, output = conv10)
model = Model(inputs=inputs, outputs=conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])#模型执行之前必须要编译
# 利用二进制交叉熵，也就是sigmoid交叉熵，metrics一般选用准确率，它会使准确率往高处发展
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
3.main主函数：
from model import *
from data import *
#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
data_gen_args = dict(rotation_range=0.2,
width_shift_range=0.05,
height_shift_range=0.05,
shear_range=0.05,
zoom_range=0.05,
horizontal_flip=True,
fill_mode='nearest')#数据增强时的变换方式的字典
myGene = trainGenerator(2,'/home/lab/lfl/u-net/data/membrane/train','image1','label1',data_gen_args,save_to_dir = None)
#得到一个生成器，以batch=2的速率无限生成增强后的数据
model = unet()
model_checkpoint = ModelCheckpoint('unet_membrane.hdf6', monitor='loss',verbose=1, save_best_only=True)
#回调函数，第一个是保存模型路径，第二个是检测的值，检测Loss是使它最小，第三个是只保存在验证集上性能最好的模型
model.fit_generator(myGene,steps_per_epoch=1000,epochs=3,callbacks=[model_checkpoint])
#steps_per_epoch指的是每个epoch有多少个batch_size，也就是训练集总样本数除以batch_size的值
#上面一行是利用生成器进行batch_size数量的训练，样本和标签通过myGene传入
testGene = testGenerator("/home/lab/lfl/u-net/data/membrane/test1")
results = model.predict_generator(testGene,30,verbose=1)
#30是step,steps: 在停止之前，来自 generator 的总步数 (样本批次)。 可选参数 Sequence：如果未指定，将使用len(generator) 作为步数。
#上面的返回值是：预测值的 Numpy 数组。
saveResult("/home/lab/lfl/u-net/data/membrane/test1",results)#保存结果