import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate

# 定义 U-Net 模型
def unet(input_size=(256, 256, 3)):
    inputs = Input(input_size)
    
    # 编码器部分
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    
    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
    
    # 中间层
    conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
    conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
    drop5 = Dropout(0.5)(conv5)
    
    # 解码器部分
    up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
    merge6 = concatenate([drop4, up6], axis=3)
    conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
    conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
    
    up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv6))
    merge7 = concatenate([conv3, up7], axis=3)
    conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
    conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
    
    up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv7))
    merge8 = concatenate([conv2, up8], axis=3)
    conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
    conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
    
    up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv8))
    merge9 = concatenate([conv1, up9], axis=3)
    conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
    conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
    
    outputs = Conv2D(1, 1, activation='sigmoid')(conv9)
    
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    
    return model

# 创建 U-Net 模型
model = unet()
model.summary()

一个简单的 U-Net 模型的 TensorFlow 2.x 代码示例。U-Net 模型通常用于图像分割任务。

import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import train_test_split

# 定义 U-Net 模型
def unet(input_size=(256, 256, 3)):
    inputs = Input(input_size)
    # 编码器部分...
    # 解码器部分...
    # 输出层...
    #请看上述代码
    model = Model(inputs=inputs, outputs=outputs)
    return model

# 准备训练数据和标签
# 这里假设你已经有了医学图像数据集，以及相应的分割标签
# 请根据你的数据集的格式来加载和预处理数据
# 假设训练数据和标签分别存储在 train_images 和 train_masks 中

# 划分训练集和验证集
X_train, X_val, y_train, y_val = train_test_split(train_images, train_masks, test_size=0.2, random_state=42)

# 创建 U-Net 模型
model = unet()

# 编译模型
model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, batch_size=4, epochs=10, validation_data=(X_val, y_val))

# 保存模型
model.save("unet_medical_segmentation.h5")

在加载和预处理医学图像数据时，可以使用 Python 中的图像处理库如 OpenCV 或 Pillow。调整大小和预处理医学图像的示例代码

import cv2
import numpy as np
def load_and_preprocess_image(image_path, target_size=(256, 256)):
    # 读取图像
    image = cv2.imread(image_path)
    # 如果需要，调整图像大小
    if target_size is not None:
        image = cv2.resize(image, target_size)
    # 预处理图像（根据你的需求进行进一步的预处理，如归一化）
    image = image / 255.0  # 归一化到 [0, 1] 范围
    return image
# 示例用法：
image_path = "your_image.jpg"  # 替换成你的图像文件路径
target_size = (256, 256)  # 替换成你想要的目标大小
# 加载和预处理图像
preprocessed_image = load_and_preprocess_image(image_path, target_size)
# 打印图像的形状（用于验证）
print("Image shape:", preprocessed_image.shape)

它遍历目录中的图像文件，加载并预处理这些图像，然后将它们转换为 TensorFlow 张量

import tensorflow as tf
import os
import cv2
import numpy as np

def load_and_preprocess_image(image_path, target_size=(256, 256)):
    # 读取图像
    image = cv2.imread(image_path)
    
    # 如果需要，调整图像大小
    if target_size is not None:
        image = cv2.resize(image, target_size)
    
    # 预处理图像（根据你的需求进行进一步的预处理，如归一化）
    image = image / 255.0  # 归一化到 [0, 1] 范围
    
    return image

def load_and_preprocess_images_in_directory(directory, target_size=(256, 256)):
    images = []
    
    # 遍历目录中的图像文件
    for filename in os.listdir(directory):
        if filename.endswith(".jpg") or filename.endswith(".png"):
            image_path = os.path.join(directory, filename)
            preprocessed_image = load_and_preprocess_image(image_path, target_size)
            images.append(preprocessed_image)
    
    # 将图像列表转换为 TensorFlow 张量
    images = np.array(images)
    images = tf.convert_to_tensor(images, dtype=tf.float32)
    
    return images

# 示例用法：
image_directory = "your_image_directory"  # 替换成包含图像的目录路径
target_size = (256, 256)  # 替换成你想要的目标大小

# 加载和预处理图像
image_tensor = load_and_preprocess_images_in_directory(image_directory, target_size)

# 打印图像张量的形状（用于验证）
print("Image tensor shape:", image_tensor.shape)