Python深度学习之Unet|Python深度学习之Unet 语义分割模型(Keras) Python深度学习之Unet语义分割模

前言
一、什么是语义分割
二、Unet

1.基本原理
2.mini_unet
3. Mobilenet_unet
4.数据加载部分

参考

前言最近由于在寻找方向上迷失自我，准备了解更多的计算机视觉任务重的模型。看到语义分割任务重Unet一个有意思的模型，我准备来复现一下它。

一、什么是语义分割语义分割任务，如下图所示：

Python深度学习之Unet|Python深度学习之Unet 语义分割模型(Keras)

文章图片

简而言之，语义分割任务就是将图片中的不同类别，用不同的颜色标记出来，每一个类别使用一种颜色。常用于医学图像，卫星图像任务。
那如何做到将像素点上色呢？
其实语义分割的输出和图像分类网络类似，图像分类类别数是一个一维的one hot 矩阵。例如：三分类的[0,1,0]。
语义分割任务最后的输出特征图是一个三维结构，大小与原图类似，通道数就是类别数。如下图（图片来源于知乎）所示：

文章图片

其中通道数是类别数，每个通道所标记的像素点，是该类别在图像中的位置，最后通过argmax 取每个通道有用像素合成一张图像，用不同颜色表示其类别位置。语义分割任务其实也是分类任务中的一种，他不过是对每一个像素点进行细分，找到每一个像素点所述的类别。这就是语义分割任务啦~
下面我们来复现 unet 模型

二、Unet
1.基本原理
什么是Unet，它的网络结构如下图所示：

文章图片

整个网络是一个“U” 的形状，Unet 网络可以分成两部分，上图红色方框中是特征提取部分，和其他卷积神经网络一样，都是通过堆叠卷积提取图像特征，通过池化来压缩特征图。蓝色方框中为图像还原部分（这样称它可能不太专业，大家理解就好），通过上采样和卷积来来将压缩的图像进行还原。特征提取部分可以使用优秀的网络，例如：Resnet50，VGG等。
注意：由于 Resnet50和VGG 网络太大。本文将使用Mobilenet 作为主干特征提取网络。为了方便理解Unet，本文将使用自己搭建的一个mini_unet 去帮祝大家理解。为了方便计算，复现过程会把压缩后的特征图上采样和输入的特征图一样大小。
【Python深度学习之Unet|Python深度学习之Unet 语义分割模型(Keras)】代码github地址: 一直上不去
先上传到码云： https://gitee.com/Boss-Jian/unet

2.mini_unet
mini_unet 是搭建来帮助大家理解语义分割的网络流程，并不能作为一个优秀的模型完成语义分割任务，来看一下代码的实现：

from keras.layers import Input,Conv2D,Dropout,MaxPooling2D,Concatenate,UpSampling2Dfrom numpy import padfrom keras.models import Modeldef unet_mini(n_classes=21,input_shape=(224,224,3)):img_input = Input(shape=input_shape)#------------------------------------------------------# #encoder 部分#224,224,3 - > 112,112,32conv1 = Conv2D(32,(3,3),activation='relu',padding='same')(img_input)conv1 = Dropout(0.2)(conv1)conv1 = Conv2D(32,(3,3),activation='relu',padding='same')(conv1)pool1 = MaxPooling2D((2,2),strides=2)(conv1)#112,112,32 -> 56,56,64conv2 = Conv2D(64,(3,3),activation='relu',padding='same')(pool1)conv2 = Dropout(0.2)(conv2)conv2 = Conv2D(64,(3,3),activation='relu',padding='same')(conv2)pool2 = MaxPooling2D((2,2),strides=2)(conv2)#56,56,64 -> 56,56,128conv3 = Conv2D(128,(3,3),activation='relu',padding='same')(pool2)conv3 = Dropout(0.2)(conv3)conv3 = Conv2D(128,(3,3),activation='relu',padding='same')(conv3)#-------------------------------------------------# decoder 部分#56,56,128 -> 112,112,64 up1 = UpSampling2D(2)(conv3)#112,112,64 -> 112,112,64+128up1 = Concatenate(axis=-1)([up1,conv2])##112,112,192 -> 112,112,64conv4= Conv2D(64,(3,3),activation='relu',padding='same')(up1)conv4= Dropout(0.2)(conv4)conv4= Conv2D(64,(3,3),activation='relu',padding='same')(conv4)#112,112,64 - >224,224,64up2 = UpSampling2D(2)(conv4)#224,224,64 -> 224,224,64+32up2 = Concatenate(axis=-1)([up2,conv1])# 224,224,96 -> 224,224,32conv5 =Conv2D(32,(3,3),activation='relu',padding='same')(up2)conv5= Dropout(0.2)(conv5)conv5= Conv2D(32,(3,3),activation='relu',padding='same')(conv5)o = Conv2D(n_classes,1,padding='same')(conv5)return Model(img_input,o,name="unet_mini")if __name__=="__main__":model = unet_mini()model.summary()

mini_unet 通过encoder 部分将 224x224x3的图像变成 112x112x64 的特征图，再通过上采样方法将特征图放大到 224x224x32。最后通过卷积：

o = Conv2D(n_classes,1,padding='same')(conv5)

将特征图的通道数调节成和类别数一样。

3. Mobilenet_unet
Mobilenet_unet 是使用Mobinet 作为主干特征提取网络，并且加载预训练权重来提升特征提取的能力。decoder 的还原部分和上面一致，下面是Mobilenet_unet 的网络结构：

from keras.models import *from keras.layers import *import keras.backend as Kimport kerasfrom tensorflow.python.keras.backend import shapeIMAGE_ORDERING ="channels_last"# channel lastdef relu6(x):return K.relu(x, max_value=https://www.it610.com/article/6)def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):channel_axis = 1 if IMAGE_ORDERING =='channels_first' else -1filters = int(filters * alpha)x = ZeroPadding2D(padding=(1, 1), name='conv1_pad',data_format=IMAGE_ORDERING)(inputs)x = Conv2D(filters, kernel, data_format=IMAGE_ORDERING,padding='valid',use_bias=False,strides=strides,name='conv1')(x)x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x)return Activation(relu6, name='conv1_relu')(x)def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha,depth_multiplier=1, strides=(1, 1), block_id=1):channel_axis = 1 if IMAGE_ORDERING == 'channels_first' else -1pointwise_conv_filters = int(pointwise_conv_filters * alpha)x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING,name='conv_pad_%d' % block_id)(inputs)x = DepthwiseConv2D((3, 3), data_format=IMAGE_ORDERING,padding='valid',depth_multiplier=depth_multiplier,strides=strides,use_bias=False,name='conv_dw_%d' % block_id)(x)x = BatchNormalization(axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x)x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x)x = Conv2D(pointwise_conv_filters, (1, 1), data_format=IMAGE_ORDERING,padding='same',use_bias=False,strides=(1, 1),name='conv_pw_%d' % block_id)(x)x = BatchNormalization(axis=channel_axis,name='conv_pw_%d_bn' % block_id)(x)return Activation(relu6, name='conv_pw_%d_relu' % block_id)(x)def get_mobilnet_eocoder(input_shape=(224,224,3),weights_path=""):# 必须是32 的倍数assert input_shape[0] % 32 == 0assert input_shape[1] % 32 == 0alpha = 1.0depth_multiplier = 1img_input = Input(shape=input_shape)#(None, 224, 224, 3) ->(None, 112, 112, 64)x = _conv_block(img_input, 32, alpha, strides=(2, 2))x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1)f1 = x #(None, 112, 112, 64) -> (None, 56, 56, 128)x = _depthwise_conv_block(x, 128, alpha, depth_multiplier,strides=(2, 2), block_id=2)x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3)f2 = x#(None, 56, 56, 128) -> (None, 28, 28, 256)x = _depthwise_conv_block(x, 256, alpha, depth_multiplier,strides=(2, 2), block_id=4)x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5)f3 = x# (None, 28, 28, 256) ->(None, 14, 14, 512)x = _depthwise_conv_block(x, 512, alpha, depth_multiplier,strides=(2, 2), block_id=6)x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7)x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8)x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9)x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10)x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11)f4 = x# (None, 14, 14, 512) -> (None, 7, 7, 1024)x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier,strides=(2, 2), block_id=12)x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13)f5 = x# 加载预训练模型if weights_path!="":Model(img_input, x).load_weights(weights_path, by_name=True, skip_mismatch=True)# f1: (None, 112, 112, 64)# f2: (None, 56, 56, 128)# f3: (None, 28, 28, 256)# f4: (None, 14, 14, 512)# f5: (None, 7, 7, 1024)return img_input, [f1, f2, f3, f4, f5]def mobilenet_unet(num_classes=2,input_shape=(224,224,3)):#encoder img_input,levels = get_mobilnet_eocoder(input_shape=input_shape,weights_path="model_data\mobilenet_1_0_224_tf_no_top.h5")[f1, f2, f3, f4, f5] = levels# f1: (None, 112, 112, 64)# f2: (None, 56, 56, 128)# f3: (None, 28, 28, 256)# f4: (None, 14, 14, 512)# f5: (None, 7, 7, 1024)#decoder#(None, 14, 14, 512) - > (None, 14, 14, 512)o = f4o = ZeroPadding2D()(o)o = Conv2D(512, (3, 3), padding='valid' , activation='relu' , data_format=IMAGE_ORDERING)(o)o = BatchNormalization()(o)#(None, 14, 14, 512) ->(None,28,28,256)o = UpSampling2D(2)(o)o = Concatenate(axis=-1)([o,f3])o = ZeroPadding2D()(o)o = Conv2D(256, (3, 3), padding='valid' , activation='relu' , data_format=IMAGE_ORDERING)(o)o = BatchNormalization()(o)# None,28,28,256)->(None,56,56,128)o = UpSampling2D(2)(o)o = Concatenate(axis=-1)([o,f2])o = ZeroPadding2D()(o)o = Conv2D(128, (3, 3), padding='valid' , activation='relu' , data_format=IMAGE_ORDERING)(o)o = BatchNormalization()(o)#(None,56,56,128) ->(None,112,112,64)o = UpSampling2D(2)(o)o = Concatenate(axis=-1)([o,f1])o = ZeroPadding2D()(o)o = Conv2D(128, (3, 3), padding='valid' , activation='relu' , data_format=IMAGE_ORDERING)(o)o = BatchNormalization()(o)#(None,112,112,64) -> (None,112,112,num_classes)# 再上采样让输入和出处图片大小一致o = UpSampling2D(2)(o)o = ZeroPadding2D()(o)o = Conv2D(64, (3, 3), padding='valid' , activation='relu' , data_format=IMAGE_ORDERING)(o)o = BatchNormalization()(o)o = Conv2D(num_classes, (3, 3), padding='same',data_format=IMAGE_ORDERING)(o)return Model(img_input,o)if __name__=="__main__":mobilenet_unet(input_shape=(512,512,3)).summary()

特征图的大小变化，以及代码含义都已经注释在代码里了。大家仔细阅读吧

4.数据加载部分

import mathimport osfrom random import shuffleimport cv2import kerasimport numpy as npfrom PIL import Image#-------------------------------# 将图片转换为 rgb#------------------------------def cvtColor(image):if len(np.shape(image)) == 3 and np.shape(image)[2] == 3:return image else:image = image.convert('RGB')return image #-------------------------------# 图片归一化 0~1#------------------------------def preprocess_input(image):image = image / 127.5 - 1return image#---------------------------------------------------#对输入图像进行resize#---------------------------------------------------def resize_image(image, size):iw, ih= image.sizew, h= sizescale= min(w/iw, h/ih)nw= int(iw*scale)nh= int(ih*scale)image= image.resize((nw,nh), Image.BICUBIC)new_image = Image.new('RGB', size, (128,128,128))new_image.paste(image, ((w-nw)//2, (h-nh)//2))return new_image, nw, nhclass UnetDataset(keras.utils.Sequence):def __init__(self, annotation_lines, input_shape, batch_size, num_classes, train, dataset_path):self.annotation_lines= annotation_linesself.length= len(self.annotation_lines)self.input_shape= input_shapeself.batch_size= batch_sizeself.num_classes= num_classesself.train= trainself.dataset_path= dataset_pathdef __len__(self):return math.ceil(len(self.annotation_lines) / float(self.batch_size))def __getitem__(self, index):#图片和标签、images= []targets = []# 读取一个batchsizefor i in range(index*self.batch_size,(index+1)*self.batch_size):#判断 i 越界情况i = i%self.lengthname = self.annotation_lines[i].split()[0]# 从路径中读取图像 jpg 表示图片，png 表示标签jpg = Image.open(os.path.join(os.path.join(self.dataset_path,'Images'),name+'.png'))png = Image.open(os.path.join(os.path.join(self.dataset_path,'Labels'),name+'.png'))#-------------------# 数据增强和归一化#-------------------jpg,png = self.get_random_data(jpg,png,self.input_shape,random=self.train)jpg = preprocess_input(np.array(jpg,np.float64))png = np.array(png)#-----------------------------------# 医学图像中描绘出的是细胞边缘 #将小于 127.5的像素点作为目标像素点#------------------------------------seg_labels = np.zeros_like(png)seg_labels[png<=127.5] = 1#--------------------------------# 转化为 one hot 标签# -------------------------seg_labels= np.eye(self.num_classes + 1)[seg_labels.reshape([-1])]seg_labels= seg_labels.reshape((int(self.input_shape[0]), int(self.input_shape[1]), self.num_classes + 1))images.append(jpg)targets.append(seg_labels)images= np.array(images)targets = np.array(targets)return images, targetsdef rand(self, a=0, b=1):return np.random.rand() * (b - a) + adef get_random_data(self, image, label, input_shape, jitter=.3, hue=.1, sat=1.5, val=1.5, random=True):image = cvtColor(image)label = Image.fromarray(np.array(label))h, w = input_shapeif not random:iw, ih= image.sizescale= min(w/iw, h/ih)nw= int(iw*scale)nh= int(ih*scale)image= image.resize((nw,nh), Image.BICUBIC)new_image= Image.new('RGB', [w, h], (128,128,128))new_image.paste(image, ((w-nw)//2, (h-nh)//2))label= label.resize((nw,nh), Image.NEAREST)new_label= Image.new('L', [w, h], (0))new_label.paste(label, ((w-nw)//2, (h-nh)//2))return new_image, new_label# resize imagerand_jit1 = self.rand(1-jitter,1+jitter)rand_jit2 = self.rand(1-jitter,1+jitter)new_ar = w/h * rand_jit1/rand_jit2scale = self.rand(0.25, 2)if new_ar < 1:nh = int(scale*h)nw = int(nh*new_ar)else:nw = int(scale*w)nh = int(nw/new_ar)image = image.resize((nw,nh), Image.BICUBIC)label = label.resize((nw,nh), Image.NEAREST)flip = self.rand()<.5if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)label = label.transpose(Image.FLIP_LEFT_RIGHT)# place imagedx = int(self.rand(0, w-nw))dy = int(self.rand(0, h-nh))new_image = Image.new('RGB', (w,h), (128,128,128))new_label = Image.new('L', (w,h), (0))new_image.paste(image, (dx, dy))new_label.paste(label, (dx, dy))image = new_imagelabel = new_label# distort imagehue = self.rand(-hue, hue)sat = self.rand(1, sat) if self.rand()<.5 else 1/self.rand(1, sat)val = self.rand(1, val) if self.rand()<.5 else 1/self.rand(1, val)x = cv2.cvtColor(np.array(image,np.float32)/255, cv2.COLOR_RGB2HSV)x[..., 0] += hue*360x[..., 0][x[..., 0]>1] -= 1x[..., 0][x[..., 0]<0] += 1x[..., 1] *= satx[..., 2] *= valx[x[:,:, 0]>360, 0] = 360x[:, :, 1:][x[:, :, 1:]>1] = 1x[x<0] = 0image_data = https://www.it610.com/article/cv2.cvtColor(x, cv2.COLOR_HSV2RGB)*255return image_data,labeldef on_epoch_begin(self):shuffle(self.annotation_lines)

训练过程代码：

import numpy as npfromtensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoardfrom keras.optimizers import Adamimport osfrom unet_mini import unet_minifrom mobilnet_unet import mobilenet_unetfrom callbacks import ExponentDecayScheduler,LossHistoryfrom keras import backend as Kfrom keras import backend from data_loader import UnetDataset#--------------------------------------# 交叉熵损失函数 cls_weights 类别的权重#-------------------------------------def CE(cls_weights):cls_weights = np.reshape(cls_weights, [1, 1, 1, -1])def _CE(y_true, y_pred):y_pred = K.clip(y_pred, K.epsilon(), 1.0 - K.epsilon())CE_loss = - y_true[...,:-1] * K.log(y_pred) * cls_weightsCE_loss = K.mean(K.sum(CE_loss, axis = -1))# dice_loss = tf.Print(CE_loss, [CE_loss])return CE_lossreturn _CEdef f_score(beta=1, smooth = 1e-5, threhold = 0.5):def _f_score(y_true, y_pred):y_pred = backend.greater(y_pred, threhold)y_pred = backend.cast(y_pred, backend.floatx())tp = backend.sum(y_true[...,:-1] * y_pred, axis=[0,1,2])fp = backend.sum(y_pred, axis=[0,1,2]) - tpfn = backend.sum(y_true[...,:-1], axis=[0,1,2]) - tpscore = ((1 + beta ** 2) * tp + smooth) \/ ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + smooth)return scorereturn _f_scoredef train():#-------------------------# 细胞图像分为细胞壁和其他# 初始化参数#-------------------------num_classes= 2 input_shape = (512,512,3)# 从第几个epoch 继续训练batch_size = 4learn_rate= 1e-4start_epoch = 0end_epoch = 100num_workers = 4dataset_path = 'Medical_Datasets'model = mobilenet_unet(num_classes,input_shape=input_shape)model.summary()# 读取数据图片的路劲with open(os.path.join(dataset_path, "ImageSets/Segmentation/train.txt"),"r") as f:train_lines = f.readlines()logging= TensorBoard(log_dir = 'logs/')checkpoint= ModelCheckpoint('logs/ep{epoch:03d}-loss{loss:.3f}.h5',monitor = 'loss', save_weights_only = True, save_best_only = False, period = 1)reduce_lr= ExponentDecayScheduler(decay_rate = 0.96, verbose = 1)early_stopping= EarlyStopping(monitor='loss', min_delta=0, patience=10, verbose=1)loss_history= LossHistory('logs/', val_loss_flag = False)epoch_step= len(train_lines) // batch_sizecls_weights= np.ones([num_classes], np.float32)loss = CE(cls_weights)model.compile(loss = loss,optimizer = Adam(lr=learn_rate),metrics = [f_score()])train_dataloader= UnetDataset(train_lines, input_shape[:2], batch_size, num_classes, True, dataset_path)print('Train on {} samples, with batch size {}.'.format(len(train_lines), batch_size))model.fit_generator(generator= train_dataloader,steps_per_epoch= epoch_step,epochs= end_epoch,initial_epoch= start_epoch,# use_multiprocessing = True if num_workers > 1 else False,workers= num_workers,callbacks= [logging, checkpoint, early_stopping,reduce_lr,loss_history])if __name__=="__main__":train()

最后的预测结果:

文章图片

完整的代大家感兴趣可以去github下载下来再看，代码比较多，全部贴出来博客显得太长了。
这就是简单的语义分割任务啦。

参考 https://github.com/bubbliiiing/unet-keras
https://github.com/divamgupta/image-segmentation-keras
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