网站建设实习报告范文,郑州商务网站建设,网站设计需求表,手机浏览器直接输入网址基于 U-Net 的视网膜血管分割是一种应用深度学习的方法#xff0c;特别是 U-Net 结构#xff0c;用于从眼底图像中分割出视网膜血管。U-Net 是一种全卷积神经网络#xff08;FCN#xff09;#xff0c;通常用于图像分割任务。以下是基于 U-Net 的视网膜血管分割的内容特别是 U-Net 结构用于从眼底图像中分割出视网膜血管。U-Net 是一种全卷积神经网络FCN通常用于图像分割任务。以下是基于 U-Net 的视网膜血管分割的内容 框架结构 代码结构
U-Net分割代码
unet_model.py
import torch.nn.functional as F
from .unet_parts import *
class UNet(nn.Module):def __init__(self, n_channels, n_classes, bilinearTrue):super(UNet, self).__init__()self.n_channels n_channelsself.n_classes n_classesself.bilinear bilinearself.inc DoubleConv(n_channels, 64)self.down1 Down(64, 128)self.down2 Down(128, 256)self.down3 Down(256, 512)self.down4 Down(512, 512)self.up1 Up(1024, 256, bilinear)self.up2 Up(512, 128, bilinear)self.up3 Up(256, 64, bilinear)self.up4 Up(128, 64, bilinear)self.outc OutConv(64, n_classes)def forward(self, x):x1 self.inc(x)# 在编码器下采样过程加空间注意力# x2 self.down1(self.sp1(x1))# x3 self.down2(self.sp2(x2))# x4 self.down3(self.sp3(x3))# x5 self.down4(self.sp4(x4))x2 self.down1(x1)x3 self.down2(x2)x4 self.down3(x3)x5 self.down4(x4)x self.up1(x5, x4)x self.up2(x, x3)x self.up3(x, x2)x self.up4(x, x1)logits self.outc(x)return logitsif __name__ __main__:net UNet(n_channels3, n_classes1)print(net)unet_parts.py
import torch
import torch.nn as nn
import torch.nn.functional as Fclass DoubleConv(nn.Module):(convolution [BN] ReLU) * 2def __init__(self, in_channels, out_channels):super().__init__()self.double_conv nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size3, padding1),nn.BatchNorm2d(out_channels),nn.ReLU(inplaceTrue),nn.Conv2d(out_channels, out_channels, kernel_size3, padding1),nn.BatchNorm2d(out_channels),nn.ReLU(inplaceTrue))for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight)elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.constant_(m.bias, 0)def forward(self, x):return self.double_conv(x)class Down(nn.Module):Downscaling with maxpool then double convdef __init__(self, in_channels, out_channels):super().__init__()self.maxpool_conv nn.Sequential(nn.MaxPool2d(2),DoubleConv(in_channels, out_channels))def forward(self, x):return self.maxpool_conv(x)class Up(nn.Module):Upscaling then double convdef __init__(self, in_channels, out_channels, bilinearTrue):super().__init__()# if bilinear, use the normal convolutions to reduce the number of channelsif bilinear:self.up nn.Upsample(scale_factor2, modebilinear, align_cornersTrue)else:self.up nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size2, stride2)self.conv DoubleConv(in_channels, out_channels)def forward(self, x1, x2):x1 self.up(x1)# input is CHWdiffY torch.tensor([x2.size()[2] - x1.size()[2]])diffX torch.tensor([x2.size()[3] - x1.size()[3]])x1 F.pad(x1, [diffX // 2, diffX - diffX // 2,diffY // 2, diffY - diffY // 2])x torch.cat([x2, x1], dim1)return self.conv(x)class OutConv(nn.Module):def __init__(self, in_channels, out_channels):super(OutConv, self).__init__()self.conv nn.Conv2d(in_channels, out_channels, kernel_size1)for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight)elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.constant_(m.bias, 0)def forward(self, x):return self.conv(x)trainval.py
from model.unet_model import UNet
from utils.dataset import FundusSeg_Loaderfrom torch import optim
import torch.nn as nn
import torch
import sys
import matplotlib.pyplot as plt
from tqdm import tqdm
import timetrain_data_path DRIVE/drive_train/
valid_data_path DRIVE/drive_test/
# hyperparameter-settings
N_epochs 500
Init_lr 0.00001def train_net(net, device, epochsN_epochs, batch_size1, lrInit_lr):# 加载训练集train_dataset FundusSeg_Loader(train_data_path, 1)valid_dataset FundusSeg_Loader(valid_data_path, 0)train_loader torch.utils.data.DataLoader(datasettrain_dataset, batch_sizebatch_size, shuffleTrue)valid_loader torch.utils.data.DataLoader(datasetvalid_dataset, batch_sizebatch_size, shuffleFalse)print(Traing images: %s % len(train_loader))print(Valid images: %s % len(valid_loader))# 定义RMSprop算法optimizer optim.RMSprop(net.parameters(), lrlr, weight_decay1e-8, momentum0.9)# 定义Loss算法# BCEWithLogitsLoss会对predict进行sigmoid处理# criterion 常被用来定义损失函数方便调换损失函数criterion nn.BCEWithLogitsLoss()# 训练epochs次# 求最小值所以初始化为正无穷best_loss float(inf)train_loss_list []val_loss_list []for epoch in range(epochs):# 训练模式net.train()train_loss 0print(fEpoch {epoch 1}/{epochs})# SGD# train_loss_list []# val_loss_list []with tqdm(totaltrain_loader.__len__()) as pbar:for i, (image, label, filename) in enumerate(train_loader):optimizer.zero_grad()# 将数据拷贝到device中image image.to(devicedevice, dtypetorch.float32)label label.to(devicedevice, dtypetorch.float32)# 使用网络参数输出预测结果pred net(image)# print(pred)# 计算lossloss criterion(pred, label)# print(loss)train_loss train_loss loss.item()loss.backward()optimizer.step()pbar.set_postfix(lossfloat(loss.cpu()), epochepoch)pbar.update(1)train_loss_list.append(train_loss / i)print(Loss/train, train_loss / i)# Validationnet.eval()val_loss 0for i, (image, label, filename) in tqdm(enumerate(valid_loader), totallen(valid_loader)):image image.to(devicedevice, dtypetorch.float32)label label.to(devicedevice, dtypetorch.float32)pred net(image)loss criterion(pred, label)val_loss val_loss loss.item()# net.state_dict()就是用来保存模型参数的if val_loss best_loss:best_loss val_losstorch.save(net.state_dict(), best_model.pth)print(saving model............................................)val_loss_list.append(val_loss / i)print(Loss/valid, val_loss / i)sys.stdout.flush()return val_loss_list, train_loss_listif __name__ __main__:# 选择设备cudadevice torch.device(cuda)# 加载网络图片单通道1分类为1。net UNet(n_channels3, n_classes1)# 将网络拷贝到deivce中net.to(devicedevice)# 开始训练val_loss_list, train_loss_list train_net(net, device)# 保存loss值到txt文件fileObject1 open(train_loss.txt, w)for train_loss in train_loss_list:fileObject1.write(str(train_loss))fileObject1.write(\n)fileObject1.close()fileObject2 open(val_loss.txt, w)for val_loss in val_loss_list:fileObject2.write(str(val_loss))fileObject2.write(\n)fileObject2.close()# 我这里迭代了5次所以x的取值范围为(05)然后再将每次相对应的5损失率附在x上x range(0, N_epochs)y1 val_loss_listy2 train_loss_list# 两行一列第一个plt.subplot(1, 1, 1)plt.plot(x, y1, r.-, labeluval_loss)plt.plot(x, y2, g.-, label utrain_loss)plt.title(loss)plt.xlabel(epochs)plt.ylabel(loss)plt.savefig(accuracy_loss.jpg)plt.show()predict.py
import numpy as np
import torch
import cv2
from model.unet_model import UNetfrom utils.dataset import FundusSeg_Loader
import copy
from sklearn.metrics import roc_auc_scoremodel_path./best_model.pth
test_data_path DRIVE/drive_test/
save_path./results/if __name__ __main__:test_dataset FundusSeg_Loader(test_data_path,0)test_loader torch.utils.data.DataLoader(datasettest_dataset, batch_size1, shuffleFalse)print(Testing images: %s %len(test_loader))# 选择设备CUDAdevice torch.device(cuda)# 加载网络图片单通道分类为1。net UNet(n_channels3, n_classes1)# 将网络拷贝到deivce中net.to(devicedevice)# 加载模型参数print(fLoading model {model_path})net.load_state_dict(torch.load(model_path, map_locationdevice))# 测试模式net.eval()tp 0tn 0fp 0fn 0pred_list []label_list []for image, label, filename in test_loader:image image.to(devicedevice, dtypetorch.float32)pred net(image)# Normalize to [0, 1]pred torch.sigmoid(pred)pred np.array(pred.data.cpu()[0])[0]pred_list.append(pred)# ConfusionMAtrixpred_bin copy.deepcopy(pred)label np.array(label.data.cpu()[0])[0]label_list.append(label)pred_bin[pred_bin 0.5] 1pred_bin[pred_bin 0.5] 0tp ((pred_bin 1) (label 1)).sum()tn ((pred_bin 0) (label 0)).sum()fn ((pred_bin 0) (label 1)).sum()fp ((pred_bin 1) (label 0)).sum()# 保存图片pred pred * 255save_filename save_path filename[0] .pngcv2.imwrite(save_filename, pred)print(f{save_filename} done!)# Evaluaiton Indicatorsprecision tp / (tp fp) # 预测为真并且正确/预测正确样本总和sen tp / (tp fn) # 预测为真并且正确/正样本总和spe tn / (tn fp)acc (tp tn) / (tp tn fp fn)f1score 2 * precision * sen / (precision sen)# auc computingpred_auc np.stack(pred_list, axis0)label_auc np.stack(label_list, axis0)auc roc_auc_score(label_auc.reshape(-1), pred_auc.reshape(-1))print(fPrecision: {precision} Sen: {sen} Spe:{spe} F1-score: {f1score} Acc: {acc} AUC: {auc})dataset.py
import torch
import cv2
import os
import glob
from torch.utils.data import Dataset# import random
# from PIL import Image
# import numpy as npclass FundusSeg_Loader(Dataset):def __init__(self, data_path, is_train):# 初始化函数读取所有data_path下的图片self.data_path data_pathself.imgs_path glob.glob(os.path.join(data_path, image/*.tif))self.labels_path glob.glob(os.path.join(data_path, label/*.tif))self.is_train is_trainprint(self.imgs_path)print(self.labels_path)def __getitem__(self, index):# 根据index读取图片image_path self.imgs_path[index]if self.is_train 1:label_path image_path.replace(image, label)label_path label_path.replace(training, manual1)else:label_path image_path.replace(image, label)label_path label_path.replace(test.tif, manual1.tif)# 读取训练图片和标签图片image cv2.imread(image_path)label cv2.imread(label_path)# image np.array(image)# label np.array(label)# label cv2.imread(label_path)# image cv2.resize(image, (600,400))# label cv2.resize(label, (600,400))# 转为单通道的图片# image cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)label cv2.cvtColor(label, cv2.COLOR_BGR2GRAY)# label Image.fromarray(label)# label label.convert(1)# reshape()函数可以改变数组的形状并且原始数据不发生变化。image image.transpose(2, 0, 1)# image image.reshape(1, label.shape[0], label.shape[1])label label.reshape(1, label.shape[0], label.shape[1])# 处理标签将像素值为255的改为1if label.max() 1:label[label 1] 1return image, label, image_path[len(image_path)-12:len(image_path)-4]def __len__(self):# 返回训练集大小return len(self.imgs_path)visual.py
import numpy as np
import matplotlib.pyplot as plt
import pylab as plfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes
data1_loss np.loadtxt(E:\\code\\UNet_lr00001\\train_loss.txt,dtypestr )
data2_loss np.loadtxt(E:\\code\\UNet_lr00001\\val_loss.txt,dtypestr)
x range(0,10)
y data1_loss[:, 0]
x1 range(0,10)
y1 data2_loss[:, 0]
fig plt.figure(figsize (7,5)) #figsize是图片的大小
ax1 fig.add_subplot(1, 1, 1) # ax1是子图的名字
pl.plot(x,y,g-,labeluDense_Unet(block layer5))
# ‘g‘代表“green”,表示画出的曲线是绿色“-”代表画的曲线是实线可自行选择label代表的是图例的名称一般要在名称前面加一个u如果名称是中文会显示不出来目前还不知道怎么解决。
p2 pl.plot(x, y,r-, label utrain_loss)
pl.legend()
#显示图例
p3 pl.plot(x1,y1, b-, label uval_loss)
pl.legend()
pl.xlabel(uepoch)
pl.ylabel(uloss)
plt.title(Compare loss for different models in training)这种基于 U-Net 的方法已在医学图像分割领域取得了一些成功特别是在视网膜图像处理中。通过深度学习的方法这种技术能够更准确地提取视网膜血管为眼科医生提供辅助诊断和治疗的信息。 如有疑问请评论。
