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