對象分割是在圖像中找到目標對象的邊界的過程。單目標分割的重點是自動勾勒出圖像中一個目標對象的邊界。對象邊界通常由二進制掩碼定義。
通過二進制掩碼，可以在圖像上覆蓋輪廓以勾勒出對象邊界。例如以下圖片描繪了胎兒的超聲圖像、胎兒頭部的二進制掩碼以及覆蓋在超聲圖像上的胎兒頭部的圖像分割：
目錄
準備數據集
創建自定義數據集
劃分數據集
創建數據加載器
搭建模型
定義損失函數
定義優化器
訓練和評估模型
準備數據集
使用胎兒頭圍數據集Automated measurement of fetal head circumference，在懷孕期間，超聲成像用于測量胎兒頭圍，監測胎兒的生長。數據集包含標準平面的二維（2D）超聲圖像。Automated measurement of fetal head circumferenceFor more information about this dataset go to:?https://hc18.grand-challenge.org/
https://zenodo.org/record/1322001#.XcX1jk9KhhE
import os
path2train="./data/training_set/"imgsList=[pp for pp in os.listdir(path2train) if "Annotation" not in pp]
anntsList=[pp for pp in os.listdir(path2train) if "Annotation" in pp]
print("number of images:", len(imgsList))
print("number of annotations:", len(anntsList))import numpy as np
np.random.seed(2024)
rndImgs=np.random.choice(imgsList,4)
rndImgsimport matplotlib.pylab as plt
from PIL import Image
from scipy import ndimage as ndi
from skimage.segmentation import mark_boundaries
from torchvision.transforms.functional import to_tensor, to_pil_image
import torchdef show_img_mask(img, mask):if torch.is_tensor(img):img=to_pil_image(img)mask=to_pil_image(mask)img_mask=mark_boundaries(np.array(img), np.array(mask),outline_color=(0,1,0),color=(0,1,0))plt.imshow(img_mask)for fn in rndImgs:path2img = os.path.join(path2train, fn)path2annt= path2img.replace(".png", "_Annotation.png")img = Image.open(path2img)annt_edges = Image.open(path2annt)mask = ndi.binary_fill_holes(annt_edges) plt.figure()plt.subplot(1, 3, 1) plt.imshow(img, cmap="gray")plt.subplot(1, 3, 2) plt.imshow(mask, cmap="gray")plt.subplot(1, 3, 3) show_img_mask(img, mask)
plt.figure()
plt.subplot(1, 3, 1)
plt.imshow(img, cmap="gray")
plt.axis('off')plt.subplot(1, 3, 2)
plt.imshow(mask, cmap="gray")
plt.axis('off') plt.subplot(1, 3, 3)
show_img_mask(img, mask)
plt.axis('off')
# conda install conda-forge/label/cf202003::albumentations
from albumentations import (HorizontalFlip,VerticalFlip, Compose,Resize,
)h,w=128,192
transform_train = Compose([ Resize(h,w), HorizontalFlip(p=0.5), VerticalFlip(p=0.5), ])transform_val = Resize(h,w)創建自定義數據集
from torch.utils.data import Dataset
from PIL import Image
from torchvision.transforms.functional import to_tensor, to_pil_imageclass fetal_dataset(Dataset):def __init__(self, path2data, transform=None): imgsList=[pp for pp in os.listdir(path2data) if "Annotation" not in pp]anntsList=[pp for pp in os.listdir(path2train) if "Annotation" in pp]self.path2imgs = [os.path.join(path2data, fn) for fn in imgsList] self.path2annts= [p2i.replace(".png", "_Annotation.png") for p2i in self.path2imgs]self.transform = transformdef __len__(self):return len(self.path2imgs)def __getitem__(self, idx):path2img = self.path2imgs[idx]image = Image.open(path2img)path2annt = self.path2annts[idx]annt_edges = Image.open(path2annt)mask = ndi.binary_fill_holes(annt_edges) image= np.array(image)mask=mask.astype("uint8") if self.transform:augmented = self.transform(image=image, mask=mask)image = augmented['image']mask = augmented['mask'] image= to_tensor(image) mask=255*to_tensor(mask) return image, mask
fetal_ds1=fetal_dataset(path2train, transform=transform_train)
fetal_ds2=fetal_dataset(path2train, transform=transform_val)
img,mask=fetal_ds1[0]
print(img.shape, img.type(),torch.max(img))
print(mask.shape, mask.type(),torch.max(mask))show_img_mask(img, mask)
劃分數據集
按照8：2的比例劃分訓練數據集和驗證數據集
from sklearn.model_selection import ShuffleSplitsss = ShuffleSplit(n_splits=1, test_size=0.2, random_state=0)
indices=range(len(fetal_ds1))
for train_index, val_index in sss.split(indices):print(len(train_index))print("-"*10)print(len(val_index))
from torch.utils.data import Subsettrain_ds=Subset(fetal_ds1,train_index)
print(len(train_ds))
val_ds=Subset(fetal_ds2,val_index)
print(len(val_ds))
展示訓練數據集示例圖像?
plt.figure(figsize=(5,5))
for img,mask in train_ds:show_img_mask(img,mask)break
展示驗證數據集示例圖像?
plt.figure(figsize=(5,5))
for img,mask in val_ds:show_img_mask(img,mask)break
創建數據加載器
from torch.utils.data import DataLoader
train_dl = DataLoader(train_ds, batch_size=8, shuffle=True)
val_dl = DataLoader(val_ds, batch_size=16, shuffle=False) for img_b, mask_b in train_dl:print(img_b.shape,img_b.dtype)print(mask_b.shape, mask_b.dtype)breakfor img_b, mask_b in val_dl:print(img_b.shape,img_b.dtype)print(mask_b.shape, mask_b.dtype)breaktorch.max(img_b)
搭建模型
基于編碼器-解碼器模型encoder–decoder model搭建分割任務模型
import torch.nn as nn
import torch.nn.functional as Fclass SegNet(nn.Module):def __init__(self, params):super(SegNet, self).__init__()C_in, H_in, W_in=params["input_shape"]init_f=params["initial_filters"] num_outputs=params["num_outputs"] self.conv1 = nn.Conv2d(C_in, init_f, kernel_size=3,stride=1,padding=1)self.conv2 = nn.Conv2d(init_f, 2*init_f, kernel_size=3,stride=1,padding=1)self.conv3 = nn.Conv2d(2*init_f, 4*init_f, kernel_size=3,padding=1)self.conv4 = nn.Conv2d(4*init_f, 8*init_f, kernel_size=3,padding=1)self.conv5 = nn.Conv2d(8*init_f, 16*init_f, kernel_size=3,padding=1)self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)self.conv_up1 = nn.Conv2d(16*init_f, 8*init_f, kernel_size=3,padding=1)self.conv_up2 = nn.Conv2d(8*init_f, 4*init_f, kernel_size=3,padding=1)self.conv_up3 = nn.Conv2d(4*init_f, 2*init_f, kernel_size=3,padding=1)self.conv_up4 = nn.Conv2d(2*init_f, init_f, kernel_size=3,padding=1)self.conv_out = nn.Conv2d(init_f, num_outputs , kernel_size=3,padding=1) def forward(self, x):x = F.relu(self.conv1(x))x = F.max_pool2d(x, 2, 2)x = F.relu(self.conv2(x))x = F.max_pool2d(x, 2, 2)x = F.relu(self.conv3(x))x = F.max_pool2d(x, 2, 2)x = F.relu(self.conv4(x))x = F.max_pool2d(x, 2, 2)x = F.relu(self.conv5(x))x=self.upsample(x)x = F.relu(self.conv_up1(x))x=self.upsample(x)x = F.relu(self.conv_up2(x))x=self.upsample(x)x = F.relu(self.conv_up3(x))x=self.upsample(x)x = F.relu(self.conv_up4(x))x = self.conv_out(x)return x params_model={"input_shape": (1,h,w),"initial_filters": 16, "num_outputs": 1,}model = SegNet(params_model)import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model=model.to(device)打印模型結構
print(model)
獲取模型摘要?
from torchsummary import summary
summary(model, input_size=(1, h, w))
定義損失函數
def dice_loss(pred, target, smooth = 1e-5):intersection = (pred * target).sum(dim=(2,3))union= pred.sum(dim=(2,3)) + target.sum(dim=(2,3)) dice= 2.0 * (intersection + smooth) / (union+ smooth) loss = 1.0 - dicereturn loss.sum(), dice.sum()import torch.nn.functional as Fdef loss_func(pred, target):bce = F.binary_cross_entropy_with_logits(pred, target, reduction='sum')pred= torch.sigmoid(pred)dlv, _ = dice_loss(pred, target)loss = bce + dlvreturn lossfor img_v,mask_v in val_dl:mask_v= mask_v[8:]breakfor img_t,mask_t in train_dl:breakprint(dice_loss(mask_v,mask_v))
loss_func(mask_v,torch.zeros_like(mask_v))
import torchvisiondef metrics_batch(pred, target):pred= torch.sigmoid(pred)_, metric=dice_loss(pred, target)return metricdef loss_batch(loss_func, output, target, opt=None): loss = loss_func(output, target)with torch.no_grad():pred= torch.sigmoid(output)_, metric_b=dice_loss(pred, target)if opt is not None:opt.zero_grad()loss.backward()opt.step()return loss.item(), metric_b定義優化器
from torch import optim
opt = optim.Adam(model.parameters(), lr=3e-4)from torch.optim.lr_scheduler import ReduceLROnPlateau
lr_scheduler = ReduceLROnPlateau(opt, mode='min',factor=0.5, patience=20,verbose=1)def get_lr(opt):for param_group in opt.param_groups:return param_group['lr']current_lr=get_lr(opt)
print('current lr={}'.format(current_lr))
訓練和評估模型
def loss_epoch(model,loss_func,dataset_dl,sanity_check=False,opt=None):running_loss=0.0running_metric=0.0len_data=len(dataset_dl.dataset)for xb, yb in dataset_dl:xb=xb.to(device)yb=yb.to(device)output=model(xb)loss_b, metric_b=loss_batch(loss_func, output, yb, opt)running_loss += loss_bif metric_b is not None:running_metric+=metric_bif sanity_check is True:breakloss=running_loss/float(len_data)metric=running_metric/float(len_data)return loss, metricimport copy
def train_val(model, params):num_epochs=params["num_epochs"]loss_func=params["loss_func"]opt=params["optimizer"]train_dl=params["train_dl"]val_dl=params["val_dl"]sanity_check=params["sanity_check"]lr_scheduler=params["lr_scheduler"]path2weights=params["path2weights"]loss_history={"train": [],"val": []}metric_history={"train": [],"val": []} best_model_wts = copy.deepcopy(model.state_dict())best_loss=float('inf') for epoch in range(num_epochs):current_lr=get_lr(opt)print('Epoch {}/{}, current lr={}'.format(epoch, num_epochs - 1, current_lr)) model.train()train_loss, train_metric=loss_epoch(model,loss_func,train_dl,sanity_check,opt)loss_history["train"].append(train_loss)metric_history["train"].append(train_metric)model.eval()with torch.no_grad():val_loss, val_metric=loss_epoch(model,loss_func,val_dl,sanity_check)loss_history["val"].append(val_loss)metric_history["val"].append(val_metric) if val_loss < best_loss:best_loss = val_lossbest_model_wts = copy.deepcopy(model.state_dict())torch.save(model.state_dict(), path2weights)print("Copied best model weights!")lr_scheduler.step(val_loss)if current_lr != get_lr(opt):print("Loading best model weights!")model.load_state_dict(best_model_wts) print("train loss: %.6f, dice: %.2f" %(train_loss,100*train_metric))print("val loss: %.6f, dice: %.2f" %(val_loss,100*val_metric))print("-"*10) model.load_state_dict(best_model_wts)return model, loss_history, metric_history
opt = optim.Adam(model.parameters(), lr=3e-4)# 定義學習率調度器，當驗證集上的損失不再下降時，將學習率降低為原來的0.5倍，等待20個epoch后再次降低學習率
lr_scheduler = ReduceLROnPlateau(opt, mode='min',factor=0.5, patience=20,verbose=1)path2models= "./models/"# 判斷path2models路徑是否存在，如果不存在則創建該路徑
if not os.path.exists(path2models):os.mkdir(path2models)params_train={"num_epochs": 100,"optimizer": opt,"loss_func": loss_func,"train_dl": train_dl,"val_dl": val_dl,"sanity_check": False,"lr_scheduler": lr_scheduler,"path2weights": path2models+"weights.pt",
}model,loss_hist,metric_hist=train_val(model,params_train)
打印訓練驗證損失
num_epochs=params_train["num_epochs"]plt.title("Train-Val Loss")
plt.plot(range(1,num_epochs+1),loss_hist["train"],label="train")
plt.plot(range(1,num_epochs+1),loss_hist["val"],label="val")
plt.ylabel("Loss")
plt.xlabel("Training Epochs")
plt.legend()
plt.show()?
打印訓練驗證精度
# plot accuracy progress
plt.title("Train-Val Accuracy")
plt.plot(range(1,num_epochs+1),metric_hist["train"],label="train")
plt.plot(range(1,num_epochs+1),metric_hist["val"],label="val")
plt.ylabel("Accuracy")
plt.xlabel("Training Epochs")
plt.legend()
plt.show()
 D-F 題解)
