Unet代码实战_unet模型实战

一.简介

unet是一个语义分割模型，执行过程大体上也是先进行下采样再进行上采样，首先利用卷积进行下采样，提取多个不同下采样阶段的特征，然后再进行上采样，不同阶段的上采样与同一大小的特征图结合，最后得出种类图像。

模型的输入 shape:572,572,3

模型的输出 shape:388,388,n_classes

二.编码器Encoder

Encoder部分用于特征提取，一般对特征进行四次压缩，每次压缩后特征图的大小都减小一半。Encoder的主干网络使用MobileNet。

2.基于MobilenetV1的Encoder

深度可分离卷积在tensorflow2中有两种实现方法，(DepthwiseConv2D + Conv1x1 ) 实现与(SeparableConv2D)实现

(1).DepthwiseConv2D + Conv1x1

from tensorflow.keras.layers import *

def conv_block(inputs, filters, kernel, strides):
    x = ZeroPadding2D(1)(inputs)
    x = Conv2D(filters, kernel, strides, padding='valid', use_bias=False)(x)
    x = BatchNormalization()(x)
    x = ReLU(max_value=6)(x)
    return x

def dw_pw_block(inputs, dw_strides, pw_filters, name):
    x = ZeroPadding2D(1)(inputs)
    # dw
    x = DepthwiseConv2D((3, 3), dw_strides, padding='valid', use_bias=False, name=name)(x)
    x = BatchNormalization()(x)
    x = ReLU(max_value=6)(x)
    # pw
    x = Conv2D(pw_filters, (1, 1), 1, padding='valid', use_bias=False)(x)
    x = BatchNormalization()(x)
    x = ReLU(max_value=6)(x)
    return x

# 基于 Mobilenet 的 segnet 编码器(DepthwiseConv2D + Conv1x1 实现)
def segnet_encoder_MobilenetV1_1(height=416, width=416):
    img_input = Input(shape=(height, width, 3))

    # block1:con1 + dw_pw_1
    # 416,416,3 -- 208,208,32 -- 208,208,64
    x = conv_block(img_input, 32, (3, 3), (2, 2))
    x = dw_pw_block(x, 1, 64, 'dw_pw_1')

    # block2:dw_pw_2
    # 208,208,64 -- 104,104,128
    x = dw_pw_block(x, 2, 128, 'dw_pw_2_1')
    x = dw_pw_block(x, 1, 128, 'dw_pw_2_2')

    # block3:dw_pw_3
    # 104,104,128 -- 52,52,256
    x = dw_pw_block(x, 2, 256, 'dw_pw_3_1')
    x = dw_pw_block(x, 1, 256, 'dw_pw_3_2')

    # block4:dw_pw_4
    # 52,52,256 -- 26,26,512
    x = dw_pw_block(x, 2, 512, 'dw_pw_4_1')
    for i in range(5):
        x = dw_pw_block(x, 1, 512, 'dw_pw_4_' + str(i + 2))
    out4 = x

    # block5:dw_pw_5
    # 26,26,512 -- 13,13,1024
    x = dw_pw_block(x, 2, 1024, 'dw_pw_5_1')
    x = dw_pw_block(x, 1, 1024, 'dw_pw_5_2')

    return img_input, out4
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(2).SeparableConv2D

from tensorflow.keras.layers import *

def sp_block(x, dw_strides, pw_filters, name):
    x = ZeroPadding2D(1)(x)
    x = SeparableConv2D(pw_filters, (3, 3), dw_strides, use_bias=False, name=name)(x)
    x = BatchNormalization()(x)
    x = ReLU(max_value=6)(x)
    return x

# 基于 Mobilenet 的 segnet 编码器(SeparableConv2D实现)
def segnet_encoder_MobilenetV1_2(height=416, width=416):
    img_input = Input(shape=(height, width, 3))
    # block1:con1 + dw_pw_1
    # 416,416,3 -- 208,208,32 -- 208,208,64
    x = conv_block(img_input, 32, (3, 3), (2, 2))
    x = sp_block(x, 1, 64, 'dw_pw_1')

    # block2:dw_pw_2
    # 208,208,64 -- 104,104,128
    x = sp_block(x, 2, 128, 'dw_pw_2_1')
    x = sp_block(x, 1, 128, 'dw_pw_2_2')

    # block3:dw_pw_3
    # 104,104,128 -- 52,52,256
    x = sp_block(x, 2, 256, 'dw_pw_3_1')
    x = sp_block(x, 1, 256, 'dw_pw_3_2')

    # block4:dw_pw_4
    # 52,52,256 -- 26,26,512
    x = sp_block(x, 2, 512, 'dw_pw_4_1')
    for i in range(5):
        x = sp_block(x, 1, 512, 'dw_pw_4_' + str(i + 2))
    out4 = x

    # block5:dw_pw_5
    # 26,26,512 -- 13,13,1024
    x = sp_block(x, 2, 1024, 'dw_pw_5_1')
    x = sp_block(x, 1, 1024, 'dw_pw_5_2')

    return img_input, out4
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三.解码并创建模型

from tensorflow.keras.layers import *
from encoders import encoder_MobilenetV1_1,encoder_MobilenetV1_2
from tensorflow.keras.models import Model

def zero_conv_bn(input,filters):
    x = ZeroPadding2D(1)(input)
    x = Conv2D(filters, 3)(x)
    x = BatchNormalization()(x)
    return x

def build_unet(n_classes,input_height=416,input_width=416,encoder_type='MobilenetV1_1'):
    # 1.获取encoder的输出 (416,416,3--26,26,512)
    if encoder_type == 'MobilenetV1_1':
        img_input, [out1,out2,out3,out4,out5] = encoder_MobilenetV1_1(input_height, input_width)
    elif encoder_type == 'MobilenetV1_2':
        img_input, [out1,out2,out3,out4,out5] = encoder_MobilenetV1_2(input_height, input_width)
    else:
        raise RuntimeError('unet encoder name is error')

    # 26,26,512 -- 26,26,512
    x = zero_conv_bn(out4, 512)
    # 26,26,512 -- 52,52,512
    x = UpSampling2D((2,2))(x)
    # 52,52,512 + 52,52,256 -- 52,52,768
    x = Concatenate()([x,out3])

    # 52,52,768 -- 52,52,256
    x = zero_conv_bn(x, 256)
    # 52,52,256 -- 104,104,256
    x = UpSampling2D((2, 2))(x)
    # 104,104,256 + 104,104,128 -- 104,104,384
    x = Concatenate()([x, out2])

    # 104,104,384 -- 104,104,128
    x = zero_conv_bn(x, 128)
    # 104,104,128 -- 208,208,128
    x = UpSampling2D((2, 2))(x)
    # 208,208,128 + 208,208,64 -- 208,208,192
    x = Concatenate()([x, out1])

    # 208,208,192 -- 208,208,64
    x = zero_conv_bn(x, 64)

    # 208,208,64 -- 208,208,n_classes
    x = Conv2D(n_classes,3,padding='same')(x)

    out = Reshape((int(input_height/2)*int(input_width/2),-1))(x)
    out = Softmax()(out)

    model = Model(img_input,out)

    return model
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五.Unet训练斑马线语义分割

from unet import build_unet
from tensorflow.keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,EarlyStopping
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy
import numpy as np
from PIL import Image
import os
import argparse

def parse_opt():
    parse = argparse.ArgumentParser()

    parse.add_argument('--datasets_path',type=str,default='../../datasets/banmaxian',help='数据集路径')
    parse.add_argument('--n_classes',type=int,default=2,help='标签种类（含背景）')
    parse.add_argument('--height',type=int,default=416,help='图片高度')
    parse.add_argument('--width',type=int,default=416,help='图片宽度')
    parse.add_argument('--batch_size',type=int,default=2)
    parse.add_argument('--lr',type=float,default=0.0001)
    parse.add_argument('--epochs',type=int,default=50)
    parse.add_argument('--encoder_type',type=str,default='MobilenetV1_2',help='unet模型编码器的类型[MobilenetV1_1,MobilenetV1_2]')
    opt = parse.parse_args()
    return opt

def get_data_from_file(opt):
    datasets_path,height,width,n_classes = opt.datasets_path,opt.height,opt.width,opt.n_classes
    with open(os.path.join(datasets_path,'train.txt')) as f:
        lines = f.readlines()
        lines = [line.replace('\n','') for line in lines]
    X = []
    Y = []
    for i in range(len(lines)):
        names = lines[i].split(';')
        real_name = names[0]    # xx.jpg
        label_name = names[1]   # xx.png
        # 读取真实图像
        real_img = Image.open(os.path.join(datasets_path,'jpg',real_name))
        real_img = real_img.resize((height,width))
        real_img = np.array(real_img)/255   # (416,416,3) [0,1]
        X.append(real_img)
        # 读取标签图像，3通道，每个通道的数据都一样，每个像素点就是对应的类别，0表示背景
        label_img = Image.open(os.path.join(datasets_path, 'png', label_name))
        label_img = label_img.resize((int(height/2), int(width/2)))
        label_img = np.array(label_img) # (208,208,3) [0,1]
        # 根据标签图像来创建训练标签数据，n类对应的 seg_labels 就有n个通道
        # 此时 seg_labels 每个通道的都值为 0
        seg_labels = np.zeros((int(height/2), int(width/2),n_classes))  # (208,208,2)
        # 第0通道表示第0类
        # 第1通道表示第1类
        # .....
        # 第n_classes通道表示第n_classes类
        for c in range(n_classes):
            seg_labels[:,:,c] = (label_img[:,:,0]==c).astype(int)
        # 此时 seg_labels 每个通道的值为0或1, 1 表示该像素点是该类，0 则不是

        seg_labels = np.reshape(seg_labels,(-1,n_classes))  # (208*208,2)
        Y.append(seg_labels)

    return np.array(X),np.array(Y)


if __name__ == '__main__':
    # 1.参数初始化
    opt = parse_opt()
    # 2.获取数据集
    X,Y = get_data_from_file(opt)

    # 3.创建模型
    # 每5个epoch保存一次
    weight_path = 'weights/unet_' + opt.encoder_type+'_weight/'
    model = build_unet(opt.n_classes,opt.height,opt.width,opt.encoder_type,)
    os.makedirs(weight_path,exist_ok=True)
    checkpoint = ModelCheckpoint(
        filepath=weight_path+'acc{accuracy:.4f}-ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
        monitor='val_loss',
        verbose=1,save_best_only=True,save_weights_only=True,period=5
    )
    lr_sh = ReduceLROnPlateau(monitor='val_loss',factor=0.5,patience=5,verbose=1)
    es = EarlyStopping(monitor='val_loss',patience=10,verbose=1)
    model.compile(loss=CategoricalCrossentropy(),optimizer=Adam(opt.lr),metrics='accuracy')
    # 4.模型训练
    model.fit(
        x=X,y=Y,
        batch_size=opt.batch_size,
        epochs=opt.epochs,
        callbacks=[checkpoint,lr_sh,es],
        verbose=1,
        validation_split=0.1,
        shuffle=True,
    )
    # 5.模型保存
    model.save_weights(weight_path+'/last.h5')
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六.测试

from unet import build_unet
from PIL import Image
import numpy as np
import copy
import os
import argparse


def parse_opt():
    parse = argparse.ArgumentParser()

    parse.add_argument('--test_imgs', type=str, default='test_imgs', help='测试数据集')
    parse.add_argument('--test_out', type=str, default='test_res', help='测试数据集')
    parse.add_argument('--n_classes', type=int, default=2, help='标签种类（含背景）')
    parse.add_argument('--height', type=int, default=416, help='输入模型的图片高度')
    parse.add_argument('--width', type=int, default=416, help='输入模型的图片宽度')
    parse.add_argument('--encoder_type', type=str, default='MobilenetV1_1', help='unet模型编码器的类型[MobilenetV1_1,MobilenetV1_2]')
    opt = parse.parse_args()
    return opt

def resize_img(path,real_width,real_height):
    img_names = os.listdir(path)
    for img_name in img_names:
        img = Image.open(os.path.join(path, img_name))
        img = img.resize((real_width,real_height))
        img.save(os.path.join(path, img_name))

if __name__ == '__main__':
    # 1.参数初始化
    opt = parse_opt()
    # class_colors 要根据图像的语义标签来设定;n_classes 行 3 列;
    # 3列为RGB的值
    class_colors = [[0, 0, 0],
                    [0, 255, 0]]
    imgs_path = os.listdir(opt.test_imgs)
    imgs_test = []
    imgs_init = []
    jpg_names = []
    real_width,real_height = 1280,720
    resize_img(opt.test_imgs, real_width,real_height)
    # 2.获取测试图片
    for i,jpg_name in enumerate(imgs_path):
        img_init = Image.open(os.path.join(opt.test_imgs, jpg_name))
        img = copy.deepcopy(img_init)
        img = img.resize((opt.width,opt.height))
        img = np.array(img) / 255  # (416,416,3) [0,1]
        imgs_test.append(img)
        imgs_init.append(img_init)
        jpg_names.append(jpg_name)

    imgs_test = np.array(imgs_test)  # (-1,416,416,3)
    # 3.模型创建
    weight_path = 'weights/unet_' + opt.encoder_type + '_weight/'
    model = build_unet(opt.n_classes,opt.height,opt.width, opt.encoder_type)
    model.load_weights(os.path.join(weight_path, 'last.h5'))
    # 4.模型预测语义分类结果
    prs = model.predict(imgs_test)  # (-1, 43264, 2)
    # 结果 reshape
    prs = prs.reshape(-1, int(opt.height / 2), int(opt.width / 2), opt.n_classes)  # (-1, 208, 208, 2)
    # 找到结果每个像素点所属类别的索引 两类就是 0 或 1
    prs = prs.argmax(axis=-1)   # (-1, 208, 208)
    # 此时 prs 就是预测出来的类别，argmax 求得是最大值所在的索引，这个索引和类别值相同
    # 所以 prs 每个像素点就是对应的类别
    # 5.创建语义图像
    # 和训练集中的语义标签图像不同，这里要显示图像，所以固定3通道
    imgs_seg = np.zeros((len(prs), int(opt.height / 2), int(opt.width / 2), 3)) # (-1,208,208,3)
    for c in range(opt.n_classes):
        # 每个通道都要判断是否属于第0,1,2... n-1 类，是的话就乘以对应的颜色，每个类别都要判断一次
        # 因为是RGB三个通道，所以3个通道分别乘以class_colors的每个通道颜色值
        imgs_seg[:,:,:,0] += ((prs[:,:,:]==c)*(class_colors[c][0])).astype(int)
        imgs_seg[:,:,:,1] += ((prs[:,:,:]==c)*(class_colors[c][1])).astype(int)
        imgs_seg[:,:,:,2] += ((prs[:,:,:]==c)*(class_colors[c][2])).astype(int)
    # 6.保存结果
    save_path = opt.test_out+'/'+opt.encoder_type
    os.makedirs(save_path,exist_ok=True)
    for img_init,img_seg,img_name in zip(imgs_init,imgs_seg,jpg_names):
        img_seg = Image.fromarray(np.uint8(img_seg)).resize((real_width,real_height))
        images = Image.blend(img_init,img_seg,0.3)
        images.save(os.path.join(opt.test_out+'/'+opt.encoder_type,img_name))
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