第92步深度学习图像分割：SegNet建模

基于WIN10的64位系统演示

一、写在前面

本期，我们继续学习深度学习图像分割系列的另一个模型，SegNet。

二、SegNet简介

（1）基本架构

SegNet由一个编码器网络和一个解码器网络组成，这两个网络都是卷积网络。编码器网络通常从一个预训练的VGG16网络中提取，并将其全连接层移除。解码器网络用于对从编码器网络中得到的特征进行上采样，以生成与输入图像相同大小的输出。

（2）池化索引

在编码阶段，当执行最大池化时，SegNet会记住每个池化区域的最大值的索引。在解码阶段，这些索引被用于上采样，从而提供更精确的结构重建。

（3）参数数量

由于SegNet只在编码器中使用了参数，而在解码器中使用了池化索引进行非学习上采样，所以相比其他模型如FCN，其参数数量更少。

（4）应用领域

由于其能够进行像素级的分类，SegNet特别适合于场景理解任务，如道路分割、建筑物分割等。

（5）性能

SegNet在多个基准数据集上的性能都很出色，并且由于其较少的参数和计算效率，它特别适合于需要实时响应的应用。

（6）优点

参数少，计算效率高。由于使用池化索引，能够更好地保留边界信息。

（7）缺点

尽管SegNet在多个任务上都表现得很好，但对于一些复杂的场景，它可能不如其他更复杂的模型，如U-Net或DeepLab。

三、数据源

来源于公共数据，主要目的是使用SegNet分割出电子显微镜下的细胞边缘：

数据分为训练集（train）、训练集的细胞边缘数据（label）以及验证集（test），注意哈，没有提供验证集的细胞边缘数据。因此，后面是算不出验证集的性能参数的。

四、SegNet实战：

这里，由于Pytorch并没有SegNet预训练模型，因此只能从头搭建咯。

上代码：

（a）数据读取和数据增强

import os
import numpy as np
from skimage.io import imread
from torchvision import transforms
from torch.utils.data import DataLoader, Dataset
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc, accuracy_score, recall_score, precision_score, f1_score


# 设置文件路径
data_folder = 'U-net-master\data_set'
train_images_folder = os.path.join(data_folder, 'train')
label_images_folder = os.path.join(data_folder, 'label')
test_images_folder = os.path.join(data_folder, 'test')

train_images = sorted(os.listdir(train_images_folder))
label_images = sorted(os.listdir(label_images_folder))
test_images = sorted(os.listdir(test_images_folder))

# 定义数据集类
class CustomDataset(Dataset):
    def __init__(self, image_paths, mask_paths, transform=None):
        self.image_paths = image_paths
        self.mask_paths = mask_paths
        self.transform = transform

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        image = imread(self.image_paths[idx])
        mask = imread(self.mask_paths[idx])
        mask[mask == 255] = 1
        # Ensure the image has 3 channels
        if len(image.shape) == 2:
            image = np.stack((image,) * 3, axis=-1)        
        sample = {'image': image, 'mask': mask}
        if self.transform:
            sample = self.transform(sample)
        return sample

class ToTensorAndNormalize(object):
    def __init__(self, mean, std):
        self.mean = mean
        self.std = std
        
    def __call__(self, sample):
        image, mask = sample['image'], sample['mask']
        # Swap color axis
        # numpy image: H x W x C
        # torch image: C x H x W
        image = image.transpose((2, 0, 1))
        image = torch.from_numpy(image).float()
        mask = torch.from_numpy(mask).float()

        # Normalize the image
        for t, m, s in zip(image, self.mean, self.std):
            t.sub_(m).div_(s)

        return {'image': image, 'mask': mask}

# Use the new transform for normalization
transform = ToTensorAndNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

# 获取数据集
train_dataset = CustomDataset(
    image_paths=[os.path.join(train_images_folder, img) for img in train_images],
    mask_paths=[os.path.join(label_images_folder, img) for img in label_images],
    transform=transform
)
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)

解读：

其他没什么好说的，就是要注意：上述代码的数据需要人工的安排训练集和测试集。严格按照下面格式放置好各个文件，包括文件夹的命名也不要变动：

（b）SegNet建模

class SegNet(nn.Module):
    def __init__(self, input_channels, output_channels):
        super(SegNet, self).__init__()

        # Encoder (Downsampling)
        self.enc1 = self.conv_block(input_channels, 64)
        self.enc2 = self.conv_block(64, 128)
        self.enc3 = self.conv_block(128, 256)
        self.enc4 = self.conv_block(256, 512)

        # Decoder (Upsampling)
        self.dec4 = self.upconv_block(512, 256)
        self.dec3 = self.upconv_block(256, 128)
        self.dec2 = self.upconv_block(128, 64)
        self.final_dec = nn.ConvTranspose2d(64, output_channels, kernel_size=3, stride=2, padding=1, output_padding=1)
        
        # Add the upsampling layer
        self.final_upsample = nn.Upsample(size=(512, 512), mode='bilinear', align_corners=True)

    def forward(self, x):
        # Encoder
        x1, idx1 = self.encode_block(x, self.enc1)
        x2, idx2 = self.encode_block(x1, self.enc2)
        x3, idx3 = self.encode_block(x2, self.enc3)
        x4, idx4 = self.encode_block(x3, self.enc4)

        # Decoder with skip connections
        d4 = self.decode_block(x4, idx4, self.dec4)
        d3 = self.decode_block(d4, idx3, self.dec3)
        d2 = self.decode_block(d3, idx2, self.dec2)
        
        out = self.final_dec(F.max_unpool2d(d2, idx1, kernel_size=2, stride=2))
        
        # Apply the upsampling layer before returning
        return self.final_upsample(out)

    def encode_block(self, x, encoder):
        x = encoder(x)
        return F.max_pool2d(x, 2, return_indices=True)

    def decode_block(self, x, indices, decoder):
        x = F.max_unpool2d(x, indices, kernel_size=2, stride=2)
        return decoder(x)

    def conv_block(self, in_channels, out_channels):
        return nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def upconv_block(self, in_channels, out_channels):
        return nn.Sequential(
            nn.ConvTranspose2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(out_channels, out_channels, kernel_size=3, padding=1,),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )


# 获取SegNet模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = SegNet(input_channels=3, output_channels=2).to(device)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
criterion = torch.nn.CrossEntropyLoss()

def calc_iou(pred, target):
    # Convert prediction to boolean values and flatten
    pred = (pred > 0.5).view(-1)
    target = target.view(-1)
    
    # Calculate intersection and union
    intersection = torch.sum(pred & target)
    union = torch.sum(pred | target)
    
    # Avoid division by zero
    iou = (intersection + 1e-8) / (union + 1e-8)
    
    return iou.item()

# 初始化损失和IoU的历史记录列表
losses_history = []
ious_history = []

# 训练模型
epochs = 100
for epoch in range(epochs):
    model.train()
    running_loss = 0.0
    total_iou = 0.0
    
    for samples in train_loader:
        images = samples['image'].to(device)
        masks = samples['mask'].long().to(device)

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, masks)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        
        # Calculate IOU
        pred_masks = F.softmax(outputs, dim=1)[:, 1]
        total_iou += calc_iou(pred_masks, masks)

    avg_loss = running_loss / len(train_loader)
    avg_iou = total_iou / len(train_loader)
    print(f"Epoch {epoch+1}/{epochs}, Loss: {avg_loss}, IOU: {avg_iou}")
    
    # Append to history
    losses_history.append(avg_loss)
    ious_history.append(avg_iou)

2分钟不到：

（c）各种性能指标打印和可视化

###################################误差曲线#######################################

import matplotlib.pyplot as plt

# 设置matplotlib支持中文显示
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

# 绘制训练损失和IoU
plt.figure(figsize=(12, 5))

# 绘制损失
plt.subplot(1, 2, 1)
plt.plot(losses_history, label='训练损失')
plt.title('损失随迭代次数的变化')
plt.xlabel('迭代次数')
plt.ylabel('损失')
plt.legend()

# 绘制IoU
plt.subplot(1, 2, 2)
plt.plot(ious_history, label='训练IoU')
plt.title('IoU随迭代次数的变化')
plt.xlabel('迭代次数')
plt.ylabel('IoU')
plt.legend()

plt.tight_layout()
plt.show()

直接看结果：

误差和IOU曲线，看起来模型收敛的不错。


##############################评价指标，对于某一个样本#######################################
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc, accuracy_score, recall_score, precision_score, f1_score

def calc_iou(y_true, y_pred):
    intersection = np.logical_and(y_true, y_pred)
    union = np.logical_or(y_true, y_pred)
    return np.sum(intersection) / np.sum(union)

# 从数据集中获取一个样本
sample = train_dataset[0]
sample_img = sample['image'].unsqueeze(0).to(device)
sample_mask = sample['mask'].cpu().numpy()

# 使用模型进行预测
with torch.no_grad():
    model.eval()
    prediction = model(sample_img)['out']

# 取前景类并转为CPU
predicted_mask = prediction[0, 1].cpu().numpy()
predicted_mask = (predicted_mask > 0.5).astype(np.uint8)

# 计算ROC曲线
fpr_train, tpr_train, _ = roc_curve(sample_mask.ravel(), predicted_mask.ravel())

# 计算AUC
auc_train = auc(fpr_train, tpr_train)

# 计算其他评估指标
pixel_accuracy_train = accuracy_score(sample_mask.ravel(), predicted_mask.ravel())
iou_train = calc_iou(sample_mask, predicted_mask)
accuracy_train = accuracy_score(sample_mask.ravel(), predicted_mask.ravel())
recall_train = recall_score(sample_mask.ravel(), predicted_mask.ravel())
precision_train = precision_score(sample_mask.ravel(), predicted_mask.ravel())
f1_train = f1_score(sample_mask.ravel(), predicted_mask.ravel())

# 绘制ROC曲线
plt.figure()
plt.plot(fpr_train, tpr_train, color='blue', lw=2, label='Train ROC curve (area = %0.2f)' % auc_train)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc='lower right')
plt.show()

# 定义指标列表
metrics = [
    ("Pixel Accuracy", pixel_accuracy_train),
    ("IoU", iou_train),
    ("Accuracy", accuracy_train),
    ("Recall", recall_train),
    ("Precision", precision_train),
    ("F1 Score", f1_train)
]

# 打印表格的头部
print("+-----------------+------------+")
print("| Metric          | Value      |")
print("+-----------------+------------+")

# 打印每个指标的值
for metric_name, metric_value in metrics:
    print(f"| {metric_name:15} | {metric_value:.6f} |")
print("+-----------------+------------+")

注意哈，这个代码只是针对某一个样本的结果：

ROC曲线：这里存疑，感觉没啥意义，而且这个曲线看起来有问题，是一个三点折线。

一些性能指标，稍微解释，主要是前两个：

A）Pixel Accuracy:

定义：它是所有正确分类的像素总数与图像中所有像素的总数的比率。

计算：(正确预测的像素数量) / (所有像素数量)。

说明：这个指标评估了模型在每个像素级别上的准确性。但在某些场景中（尤其是当类别非常不平衡时），这个指标可能并不完全反映模型的表现。

B）IoU (Intersection over Union):

定义：对于每个类别，IoU 是该类别的预测结果（预测为该类别的像素）与真实标签之间的交集与并集的比率。

计算：(预测与真实标签的交集) / (预测与真实标签的并集)。

说明：它是一个很好的指标，因为它同时考虑了假阳性和假阴性，尤其在类别不平衡的情况下。

C）Accuracy:

定义：是所有正确分类的像素与所有像素的比例，通常与 Pixel Accuracy 相似。

计算：(正确预测的像素数量) / (所有像素数量)。

D）Recall (or Sensitivity or True Positive Rate):

定义：是真实正样本被正确预测的比例。

计算：(真阳性) / (真阳性 + 假阴性)。

说明：高召回率表示少数阳性样本不容易被漏掉。

E）Precision:

定义：是被预测为正的样本中实际为正的比例。

计算：(真阳性) / (真阳性 + 假阳性)。

说明：高精度表示假阳性的数量很少。

F）F1 Score:

定义：是精度和召回率的调和平均值。它考虑了假阳性和假阴性，并试图找到两者之间的平衡。

计算：2 × (精度 × 召回率) / (精度 + 召回率)。

说明：在不平衡类别的场景中，F1 Score 通常比单一的精度或召回率更有用。

##############################评价指标，对于全部训练集的样本#######################################
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc, accuracy_score, recall_score, precision_score, f1_score

# 初始化变量来存储评估指标的累积值和真实标签与预测值
total_pixel_accuracy = 0
total_iou = 0
total_accuracy = 0
total_recall = 0
total_precision = 0
total_f1 = 0
total_auc = 0
all_true_masks = []
all_predicted_masks = []

# 遍历整个训练集
for sample in train_dataset:
    sample_img = sample['image'].unsqueeze(0).to(device)
    sample_mask = sample['mask'].cpu().numpy()

    # 使用模型进行预测
    with torch.no_grad():
        model.eval()
        prediction = model(sample_img)['out']

    # 取前景类并转为CPU
    predicted_mask = prediction[0, 1].cpu().numpy()
    predicted_mask = (predicted_mask > 0.5).astype(np.uint8)

    # 收集真实标签和预测值
    all_true_masks.extend(sample_mask.ravel())
    all_predicted_masks.extend(predicted_mask.ravel())

# 计算ROC曲线和AUC
fpr_train, tpr_train, _ = roc_curve(all_true_masks, all_predicted_masks)
avg_auc = auc(fpr_train, tpr_train)

# 计算其他评估指标
avg_pixel_accuracy = accuracy_score(all_true_masks, all_predicted_masks)
avg_iou = calc_iou(np.array(all_true_masks), np.array(all_predicted_masks))
avg_accuracy = accuracy_score(all_true_masks, all_predicted_masks)
avg_recall = recall_score(all_true_masks, all_predicted_masks)
avg_precision = precision_score(all_true_masks, all_predicted_masks)
avg_f1 = f1_score(all_true_masks, all_predicted_masks)

# 绘制ROC曲线
plt.figure()
plt.plot(fpr_train, tpr_train, color='blue', lw=2, label='Train ROC curve (area = %0.2f)' % avg_auc)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc='lower right')
plt.show()

# 打印评估指标的平均值
metrics = [
    ("Pixel Accuracy", avg_pixel_accuracy),
    ("IoU", avg_iou),
    ("Accuracy", avg_accuracy),
    ("Recall", avg_recall),
    ("Precision", avg_precision),
    ("F1 Score", avg_f1),
    ("AUC", avg_auc)
]

print("+-----------------+------------+")
print("| Metric          | Value      |")
print("+-----------------+------------+")
for metric_name, metric_value in metrics:
    print(f"| {metric_name:15} | {metric_value:.6f} |")
    print("+-----------------+------------+")

这个结果是针对有所训练集的：

（d）查看验证集和验证集的具体分割情况

#######################看训练集图片的具体分割效果###########################################
import matplotlib.pyplot as plt
import numpy as np

# 选择一张训练集图片
img_index = 20
sample = train_dataset[img_index]
train_img = sample['image'].unsqueeze(0).to(device)  # 为batch_size添加一个维度

with torch.no_grad():
    model.eval()
    prediction = model(train_img)['out']

# 使用阈值处理预测掩码
mask_threshold = 0.5
pred_mask = prediction[0, 1].cpu().numpy()  # 选择前景类
pred_mask = (pred_mask > mask_threshold).astype(np.uint8)

# 使用matplotlib来展示原始图像、真实掩码和预测的分割图像
plt.figure(figsize=(15, 5))

plt.subplot(1, 3, 1)
plt.title("Original Image")
# Normalize the image to [0,1] range
denorm_img = train_img[0].permute(1, 2, 0).cpu().numpy()
denorm_img = denorm_img - denorm_img.min()
denorm_img = denorm_img / denorm_img.max()
plt.imshow(denorm_img.clip(0, 1))

plt.subplot(1, 3, 2)
plt.title("True Segmentation")
true_mask = sample['mask'].cpu().numpy()
if len(true_mask.shape) == 1:  # Ensure mask is 2D
    true_mask = true_mask.reshape(int(np.sqrt(true_mask.shape[0])), -1)
plt.imshow(true_mask, cmap='gray')

plt.subplot(1, 3, 3)
plt.title("Predicted Segmentation")
plt.imshow(pred_mask, cmap='gray')

plt.show()

#######################看测试集图片的具体分割效果###########################################
#看具体分割的效果
import matplotlib.pyplot as plt

test_dataset = CustomDataset(
    image_paths=[os.path.join(test_images_folder, img) for img in test_images],
    mask_paths=[os.path.join(label_images_folder, img) for img in label_images],  # 这里我假设您的测试集的标签也在label_images_folder中
    transform=transform
)

# 选择一张测试图片
img_index = 20
sample = test_dataset[img_index]
test_img = sample['image'].unsqueeze(0).to(device)

with torch.no_grad():
    model.eval()
    prediction = model(test_img)['out']

# 使用阈值处理预测掩码
mask_threshold = 0.5
pred_mask = prediction[0, 1].cpu().numpy()
pred_mask = (pred_mask > mask_threshold).astype(np.uint8)

# 使用matplotlib来展示原始图像、真实掩码和预测的分割图像
plt.figure(figsize=(15, 5))

plt.subplot(1, 3, 1)
plt.title("Original Image")
# Normalize the image to [0,1] range
denorm_img = test_img[0].permute(1, 2, 0).cpu().numpy()
denorm_img = denorm_img - denorm_img.min()
denorm_img = denorm_img / denorm_img.max()
plt.imshow(denorm_img.clip(0, 1))

plt.subplot(1, 3, 3)
plt.title("Predicted Segmentation")
plt.imshow(pred_mask, cmap='gray')

plt.show()

查看训练集分割效果：