【模型训练第13波】U-net Keras 2019-7-3

来自：https://blog.youkuaiyun.com/awyyauqpmy/article/details/79290710

修改之后：

1、建好文件夹：

deform:存放原始训练（image+label）数据集：下载images：https://github.com/decouples/Unet

• train：30张（.tif）
• label：30张（.tif）

my_train_test:程序文件夹（3个.py（将图中的test.py移至上层路径下））+生成模型(.hdf5)的文件夹：

npydata：存放生成数据（代码从此文件夹中读入image、label、test数据）

result：存放测试结果

test：存放原始测试数据集(30个.tif)

完整文件结构：将图中的test.py移至上层路径下

2、生成训练数据

在my_train_test下运行creat_imagelabel_npy.py：生成imgs_train.npy和imgs_mask_train.npy

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import numpy as np 
import os
import glob
import cv2
from libtiff import TIFF

class myAugmentation(object):
	
	"""
	一个用于图像增强的类：
	首先：分别读取训练的图片和标签，然后将图片和标签合并用于下一个阶段使用
	然后：使用Keras的预处理来增强图像
	最后：将增强后的图片分解开，分为训练图片和训练标签
	"""
	def __init__(self, train_path="train", label_path="label", merge_path="merge", aug_merge_path="aug_merge", aug_train_path="aug_train", aug_label_path="aug_label", img_type="tif"):
		"""
		使用glob从路径中得到所有的“.img_type”文件，初始化类：__init__()
		"""
		self.train_imgs = glob.glob(train_path+"/*."+img_type)
		self.label_imgs = glob.glob(label_path+"/*."+img_type)
		self.train_path = train_path
		self.label_path = label_path
		self.merge_path = merge_path
		self.img_type = img_type
		self.aug_merge_path = aug_merge_path
		self.aug_train_path = aug_train_path
		self.aug_label_path = aug_label_path
		self.slices = len(self.train_imgs)
		self.datagen = ImageDataGenerator(
							        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')

	def Augmentation(self):

		"""
		Start augmentation.....
		"""
		trains = self.train_imgs
		labels = self.label_imgs
		path_train = self.train_path
		path_label = self.label_path
		path_merge = self.merge_path
		imgtype = self.img_type
		path_aug_merge = self.aug_merge_path
		print(path_label)
		if len(trains) != len(labels) or len(trains) == 0 or len(trains) == 0:
			print ("trains can't match labels")
			return 0
		for i in range(len(trains)):
			img_t = load_img(path_train+"/"+str(i)+"."+imgtype)
			img_l = load_img(path_label+"/"+str(i)+"."+imgtype)
			x_t = img_to_array(img_t)
			x_l = img_to_array(img_l)
			x_t[:,:,2] = x_l[:,:,0]
			img_tmp = array_to_img(x_t)
			img_tmp.save(path_merge+"/"+str(i)+"."+imgtype)
			img = x_t
			img = img.reshape((1,) + img.shape)
			savedir = path_aug_merge + "/" + str(i)
			if not os.path.lexists(savedir):
				os.mkdir(savedir)
			self.doAugmentate(img, savedir, str(i))

	def doAugmentate(self, img, save_to_dir, save_prefix, batch_size=1, save_format='tif', imgnum=30):
        # 增强一张图片的方法
		"""
		augmentate one image
		"""
		datagen = self.datagen
		i = 0
		for batch in datagen.flow(img,
                          batch_size=batch_size,
                          save_to_dir=save_to_dir,
                          save_prefix=save_prefix,
                          save_format=save_format):
		    i += 1
		    if i > imgnum:
		        break

	def splitMerge(self):
        # 将合在一起的图片分开
		"""
		split merged image apart
		"""
		path_merge = self.aug_merge_path
		path_train = self.aug_train_path
		path_label = self.aug_label_path

		for i in range(self.slices):
			path = path_merge + "/" + str(i)
			train_imgs = glob.glob(path+"/*."+self.img_type)
			savedir = path_train + "/" + str(i)
			if not os.path.lexists(savedir):
				os.mkdir(savedir)
			savedir = path_label + "/" + str(i)

			if not os.path.lexists(savedir):
				os.mkdir(savedir)
			for imgname in train_imgs:
				midname = imgname[imgname.rindex("/")+1:imgname.rindex("."+self.img_type)]
				img = cv2.imread(imgname)
				img_train = img[:,:,2] #cv2 read image rgb->bgr
				img_label = img[:,:,0]
				cv2.imwrite(path_train+"/"+str(i)+"/"+midname+"_train"+"."+self.img_type,img_train)
				cv2.imwrite(path_label+"/"+str(i)+"/"+midname+"_label"+"."+self.img_type,img_label)

	def splitTransform(self):
        # 拆分透视变换后的图像
		"""
		split perspective transform images
		"""
		#path_merge = "transform"
		#path_train = "transform/data/"
		#path_label = "transform/label/"

		path_merge = "deform/deform_norm2"
		path_train = "deform/train/"
		path_label = "deform/label/"

		train_imgs = glob.glob(path_merge+"/*."+self.img_type)
		for imgname in train_imgs:
			midname = imgname[imgname.rindex("/")+1:imgname.rindex("."+self.img_type)]
			img = cv2.imread(imgname)
			img_train = img[:,:,2]#cv2 read image rgb->bgr
			img_label = img[:,:,0]
			cv2.imwrite(path_train+midname+"."+self.img_type,img_train)
			cv2.imwrite(path_label+midname+"."+self.img_type,img_label)

class dataProcess(object):
	def __init__(self, out_rows, out_cols, data_path = "../deform/train", label_path = "../deform/label", test_path = "../test", npy_path = "../npydata", img_type = "tif"):
        # 数据处理类，初始化
		self.out_rows = out_rows
		self.out_cols = out_cols
		self.data_path = data_path
		self.label_path = label_path
		self.img_type = img_type
		self.test_path = test_path
		self.npy_path = npy_path

# 创建训练数据
	def create_train_data(self):
		i = 0
		print('-'*30)
		print('Creating training images...')
		print('-'*30)
		imgs = glob.glob(self.data_path+"/*."+self.img_type)
		print(len(imgs))

		imgdatas = np.ndarray((len(imgs),self.out_rows,self.out_cols,1), dtype=np.uint8)
		imglabels = np.ndarray((len(imgs),self.out_rows,self.out_cols,1), dtype=np.uint8)
		for imgname in imgs:
			midname = imgname[imgname.rindex("/")+1:]
			midname=midname.replace('train\\','')
			print(midname)
			img = load_img(self.data_path + '/' + midname,grayscale = True)
			print(self.data_path + '/' + midname)   #img路径
			label = load_img(self.label_path + "/" + midname,grayscale = True)
			print(self.label_path + "/" + midname)   #label路径
			img = img_to_array(img)
			label = img_to_array(label)
			imgdatas[i] = img
			imglabels[i] = label
			if i % 100 == 0:
				print('Done: {0}/{1} images'.format(i, len(imgs)))
			i += 1
		print('loading done')
		np.save(self.npy_path + '/imgs_train.npy', imgdatas)
		np.save(self.npy_path + '/imgs_mask_train.npy', imglabels)
		print('Saving to .npy files done.')

# 加载训练图片与mask
	def load_train_data(self):
		print('-'*30)
		print('load train images...')
		print('-'*30)
		imgs_train = np.load(self.npy_path+"/imgs_train.npy")
		imgs_mask_train = np.load(self.npy_path+"/imgs_mask_train.npy")
		imgs_train = imgs_train.astype('float32')
		imgs_mask_train = imgs_mask_train.astype('float32')
		imgs_train /= 255
		mean = imgs_train.mean(axis = 0)
		imgs_train -= mean
		imgs_mask_train /= 255
        # 做一个阈值处理，输出的概率值大于0.5的就认为是对象，否则认为是背景
		imgs_mask_train[imgs_mask_train > 0.5] = 1
		imgs_mask_train[imgs_mask_train <= 0.5] = 0
		return imgs_train,imgs_mask_train



if __name__ == "__main__":

	mydata = dataProcess(512,512)
	mydata.create_train_data()


	imgs_train,imgs_mask_train = mydata.load_train_data()
	print (imgs_train.shape,imgs_mask_train.shape)

3、train

在my_train_test下运行train_unet.py:  (159行可改参数)  生成模型：my_unet.hdf5

import os 
#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import numpy as np
from keras.models import *
from keras.layers import Input, merge, Conv2D, MaxPooling2D, UpSampling2D, Dropout, Cropping2D, concatenate
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras
from creat_imagelabel_npy import *




class myUnet(object):
	def __init__(self, img_rows = 512, img_cols = 512):
		self.img_rows = img_rows
		self.img_cols = img_cols
# 参数初始化定义
	def load_data(self):
		mydata = dataProcess(self.img_rows, self.img_cols)
		imgs_train, imgs_mask_train = mydata.load_train_data()
		#imgs_test = mydata.load_test_data()
		return imgs_train, imgs_mask_train,
# 载入数据
	def get_unet(self):
		inputs = Input((self.img_rows, self.img_cols,1))
		# 网络结构定义
		'''
		#unet with crop(because padding = valid) 

		conv1 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(inputs)
		print "conv1 shape:",conv1.shape
		conv1 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv1)
		print "conv1 shape:",conv1.shape
		crop1 = Cropping2D(cropping=((90,90),(90,90)))(conv1)
		print "crop1 shape:",crop1.shape
		pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
		print "pool1 shape:",pool1.shape

		conv2 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool1)
		print "conv2 shape:",conv2.shape
		conv2 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv2)
		print "conv2 shape:",conv2.shape
		crop2 = Cropping2D(cropping=((41,41),(41,41)))(conv2)
		print "crop2 shape:",crop2.shape
		pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
		print "pool2 shape:",pool2.shape

		conv3 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool2)
		print "conv3 shape:",conv3.shape
		conv3 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv3)
		print "conv3 shape:",conv3.shape
		crop3 = Cropping2D(cropping=((16,17),(16,17)))(conv3)
		print "crop3 shape:",crop3.shape
		pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
		print "pool3 shape:",pool3.shape

		conv4 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool3)
		conv4 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv4)
		drop4 = Dropout(0.5)(conv4)
		crop4 = Cropping2D(cropping=((4,4),(4,4)))(drop4)
		pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

		conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool4)
		conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'valid', 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([crop4,up6], mode = 'concat', concat_axis = 3)
		conv6 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge6)
		conv6 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv6)

		up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
		merge7 = merge([crop3,up7], mode = 'concat', concat_axis = 3)
		conv7 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge7)
		conv7 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv7)

		up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
		merge8 = merge([crop2,up8], mode = 'concat', concat_axis = 3)
		conv8 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge8)
		conv8 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv8)

		up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
		merge9 = merge([crop1,up9], mode = 'concat', concat_axis = 3)
		conv9 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge9)
		conv9 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv9)
		conv9 = Conv2D(2, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv9)
		'''

		conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
		print ("conv1 shape:",conv1.shape)
		conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
		print ("conv1 shape:",conv1.shape)
		pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
		print ("pool1 shape:",pool1.shape)

		conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
		print ("conv2 shape:",conv2.shape)
		conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
		print ("conv2 shape:",conv2.shape)
		pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
		print ("pool2 shape:",pool2.shape)

		conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
		print ("conv3 shape:",conv3.shape)
		conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
		print ("conv3 shape:",conv3.shape)
		pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
		print ("pool3 shape:",pool3.shape)

		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)

		model = Model(input = inputs, output = conv10)
		model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
		return model

# 如果需要修改输入的格式，那么可以从以下开始修改，上面的结构部分不需要修改
	def train(self):
		print("loading data")
		imgs_train, imgs_mask_train= self.load_data()
		print("loading data done")
		model = self.get_unet()
		print("got unet")
		model_checkpoint = ModelCheckpoint('my_unet.hdf5', monitor='loss',verbose=1, save_best_only=True)
		print('Fitting model...')
		model.fit(imgs_train, imgs_mask_train, batch_size=2, nb_epoch=3, verbose=0,validation_split=0.2, shuffle=True, callbacks=[model_checkpoint])
		#print('predict test data')
		# imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
		# np.save('../results/imgs_mask_test.npy', imgs_mask_test)

	# def save_img(self):
		# print("array to image")
		# imgs = np.load('../results/imgs_mask_test.npy')
		# for i in range(imgs.shape[0]):
			# img = imgs[i]
			# img = array_to_img(img)
			# img.save("../results/%d.jpg"%(i))

if __name__ == '__main__':
	myunet = myUnet()
	myunet.train()
	#myunet.save_img()

4、生成测试数据

在my_train_test下运行create_test_npy.py：  生成imgs_test.npy

'''from ../test       create testdata(.npy),         save to ../npydata/imgs_test.npy'''

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import numpy as np 
import os
import glob
import cv2
from libtiff import TIFF


class dataProcess(object):
	def __init__(self, out_rows, out_cols, data_path = "../deform/train", label_path = "../deform/label", test_path = "../test", npy_path = "../npydata", img_type = "tif"):
        # 数据处理类，初始化
		self.out_rows = out_rows
		self.out_cols = out_cols
		self.data_path = data_path
		self.label_path = label_path
		self.img_type = img_type
		self.test_path = test_path
		self.npy_path = npy_path


# 创建测试数据
	def create_test_data(self):
		i = 0
		print('-'*30)
		print('Creating test images...')
		print('-'*30)
		imgs = glob.glob(self.test_path+"/*."+self.img_type)
		print(self.test_path)  #../test
		print(len(imgs))
		imgdatas = np.ndarray((len(imgs),self.out_rows,self.out_cols,1), dtype=np.uint8)
		for imgname in imgs:
			midname = imgname[imgname.rindex("/")+1:]
			midname=midname.replace('test\\','/')
			print(midname)
			img = load_img(self.test_path + "/" + midname,grayscale = True)
			img = img_to_array(img)
			#img = cv2.imread(self.test_path + "/" + midname,cv2.IMREAD_GRAYSCALE)
			#img = np.array([img])
			imgdatas[i] = img
			i += 1
		print('loading done')
		np.save(self.npy_path + '/imgs_test.npy', imgdatas)
		print('Saving to imgs_test.npy files done.')

if __name__ == "__main__":
	mydata = dataProcess(512,512)
	mydata.create_test_data()


	#imgs_train,imgs_mask_train = mydata.load_train_data()

6、test

在u-net下运行，test.py: 结果会保存在u-net下

'''read     ../npydata/imgs_test.npy       '''
 
 
 
import os 
#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import numpy as np
from keras.models import *
from keras.layers import Input, merge, Conv2D, MaxPooling2D, UpSampling2D, Dropout, Cropping2D, concatenate
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras
from keras.preprocessing.image import array_to_img
#from data import *



class dataProcess(object):
	def __init__(self, out_rows, out_cols, test_path = "../test", npy_path = "../npydata", img_type = "tif"):
        # 数据处理类，初始化
		self.out_rows = out_rows
		self.out_cols = out_cols
		self.img_type = img_type
		self.test_path = test_path
		self.npy_path = npy_path


# 加载测试图片
	def load_test_data(self):
		print('-'*30)
		print('load test images...')
		print('-'*30)
		#(self.npy_path+"/imgs_test.npy")
		imgs_test = np.load("npydata/imgs_test.npy")
		imgs_test = imgs_test.astype('float32')
		imgs_test /= 255
		mean = imgs_test.mean(axis = 0)
		imgs_test -= mean
		return imgs_test

class myUnet(object):
	def __init__(self, img_rows = 512, img_cols = 512):
		self.img_rows = img_rows
		self.img_cols = img_cols
# 参数初始化定义
	def load_data(self):
		mydata = dataProcess(self.img_rows, self.img_cols)
		#imgs_train, imgs_mask_train = mydata.load_train_data()
		imgs_test = mydata.load_test_data()
		return imgs_test
# 载入数据
	def get_unet(self):
		inputs = Input((self.img_rows, self.img_cols,1))
		# 网络结构定义
		'''
		#unet with crop(because padding = valid) 
		conv1 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(inputs)
		print "conv1 shape:",conv1.shape
		conv1 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv1)
		print "conv1 shape:",conv1.shape
		crop1 = Cropping2D(cropping=((90,90),(90,90)))(conv1)
		print "crop1 shape:",crop1.shape
		pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
		print "pool1 shape:",pool1.shape
		conv2 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool1)
		print "conv2 shape:",conv2.shape
		conv2 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv2)
		print "conv2 shape:",conv2.shape
		crop2 = Cropping2D(cropping=((41,41),(41,41)))(conv2)
		print "crop2 shape:",crop2.shape
		pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
		print "pool2 shape:",pool2.shape
		conv3 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool2)
		print "conv3 shape:",conv3.shape
		conv3 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv3)
		print "conv3 shape:",conv3.shape
		crop3 = Cropping2D(cropping=((16,17),(16,17)))(conv3)
		print "crop3 shape:",crop3.shape
		pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
		print "pool3 shape:",pool3.shape
		conv4 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool3)
		conv4 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv4)
		drop4 = Dropout(0.5)(conv4)
		crop4 = Cropping2D(cropping=((4,4),(4,4)))(drop4)
		pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
		conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool4)
		conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'valid', 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([crop4,up6], mode = 'concat', concat_axis = 3)
		conv6 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge6)
		conv6 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv6)
		up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
		merge7 = merge([crop3,up7], mode = 'concat', concat_axis = 3)
		conv7 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge7)
		conv7 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv7)
		up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
		merge8 = merge([crop2,up8], mode = 'concat', concat_axis = 3)
		conv8 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge8)
		conv8 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv8)
		up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
		merge9 = merge([crop1,up9], mode = 'concat', concat_axis = 3)
		conv9 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge9)
		conv9 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv9)
		conv9 = Conv2D(2, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv9)
		'''

		conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
		print ("conv1 shape:",conv1.shape)
		conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
		print ("conv1 shape:",conv1.shape)
		pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
		print ("pool1 shape:",pool1.shape)

		conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
		print ("conv2 shape:",conv2.shape)
		conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
		print ("conv2 shape:",conv2.shape)
		pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
		print ("pool2 shape:",pool2.shape)

		conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
		print ("conv3 shape:",conv3.shape)
		conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
		print ("conv3 shape:",conv3.shape)
		pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
		print ("pool3 shape:",pool3.shape)

		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)
		print("nn")

		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)
		print("nn")

		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)

		model = Model(input = inputs, output = conv10)
		model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
		return model

# 如果需要修改输入的格式，那么可以从以下开始修改，上面的结构部分不需要修改
	def train(self):
		print("loading data")
		imgs_test = self.load_data()
		print("loading data done")
		model = self.get_unet()
		print("got unet")
		model_checkpoint = ModelCheckpoint('my_unet.hdf5', monitor='loss',verbose=1, save_best_only=True)
		print('Fitting model...')
		#model.fit(imgs_train, imgs_mask_train, batch_size=2, nb_epoch=10, verbose=1,validation_split=0.2, shuffle=True, callbacks=[model_checkpoint])
		model = load_model("my_train_test/my_unet.hdf5")

		print('predict test data')
		imgs_mask_test = model.predict(imgs_test, batch_size=2, verbose=1)
		np.save('imgs_mask_test.npy', imgs_mask_test)


	def save_img(self):
		print("array to image")
		imgs = np.load('imgs_mask_test.npy')
		for i in range(imgs.shape[0]):
			img = imgs[i]
			img = array_to_img(img)
			img.save("%d.jpg"%(i))

if __name__ == '__main__':
	myunet = myUnet()
	myunet.train()
	myunet.save_img()

报错记录：

1、如果test.py在my_train_test下运行且

第184行：np.save('results/imgs_mask_test.npy', imgs_mask_test)

189行：imgs = np.load('results/imgs_mask_test.npy')

193行：img.save("../results/%d.jpg"%(i))

会报错：

改为

184行：np.save('imgs_mask_test.npy', imgs_mask_test)

189行：imgs = np.load(imgs_mask_test.npy')

后在u-net下生成：

但未生成测试图片报错：

再把193行改为：img.save("%d.jpg"%(i))，于是：

在u-net下生成（不是很懂生成的图片及其顺序？）：