Python中Unet语义分割模型的示例分析

小编给大家分享一下Python中Unet语义分割模型的示例分析，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！

一、什么是语义分割

语义分割任务，如下图所示：

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：

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：

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。

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 - > 112,112,32    conv1 = 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 -> 56,56,64    conv2 = 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 -> 56,56,128    conv3 = 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+128    up1 = Concatenate(axis=-1)([up1,conv2])    #  #112,112,192 -> 112,112,64    conv4  = 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 - >224,224,64    up2 = UpSampling2D(2)(conv4)    #224,224,64 -> 224,224,64+32    up2 = Concatenate(axis=-1)([up2,conv1])    # 224,224,96 -> 224,224,32    conv5 =  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=6)def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):       channel_axis = 1 if IMAGE_ORDERING == 'channels_first' else -1    filters = 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 -1    pointwise_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 == 0    assert input_shape[1] % 32 == 0    alpha = 1.0    depth_multiplier = 1    img_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.h6")    [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 = f4    o = 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 - 1    return image#---------------------------------------------------#   对输入图像进行resize#---------------------------------------------------def resize_image(image, size):    iw, ih  = image.size    w, h    = size    scale   = 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_lines        self.length             = len(self.annotation_lines)        self.input_shape        = input_shape        self.batch_size         = batch_size        self.num_classes        = num_classes        self.train              = train        self.dataset_path       = dataset_path    def __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batchsize        for i in range(index*self.batch_size,(index+1)*self.batch_size):            #判断 i 越界情况            i = i%self.length            name = 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, targets    def rand(self, a=0, b=1):        return np.random.rand() * (b - a) + a    def 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_shape        if not random:            iw, ih  = image.size            scale   = 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 image        rand_jit1 = self.rand(1-jitter,1+jitter)        rand_jit2 = self.rand(1-jitter,1+jitter)        new_ar = w/h * rand_jit1/rand_jit2        scale = 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()<.5        if flip:             image = image.transpose(Image.FLIP_LEFT_RIGHT)            label = label.transpose(Image.FLIP_LEFT_RIGHT)                # place image        dx = 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_image        label = new_label        # distort image        hue = 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*360        x[..., 0][x[..., 0]>1] -= 1        x[..., 0][x[..., 0]<0] += 1        x[..., 1] *= sat        x[..., 2] *= val        x[x[:,:, 0]>360, 0] = 360        x[:, :, 1:][x[:, :, 1:]>1] = 1        x[x<0] = 0        image_data = cv2.cvtColor(x, cv2.COLOR_HSV2RGB)*255        return image_data,label    def on_epoch_begin(self):        shuffle(self.annotation_lines)

训练过程代码：

import numpy as npfrom  tensorflow.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_weights        CE_loss = K.mean(K.sum(CE_loss, axis = -1))        # dice_loss = tf.Print(CE_loss, [CE_loss])        return CE_loss    return _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]) - tp        fn = backend.sum(y_true[...,:-1], axis=[0,1,2]) - tp        score = ((1 + beta ** 2) * tp + smooth) \                / ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + smooth)        return score    return _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 = 4    learn_rate  = 1e-4    start_epoch = 0    end_epoch = 100    num_workers = 4    dataset_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}.h6',                        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_size    cls_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:

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