基于深度学习的实时背景虚化解决方案需要多个步骤。这里我们提供一个使用 TensorFlow 和 OpenCV 的完整示例。为了简化问题，我们将使用 U-Net 进行图像分割。这个示例分为以下几个部分：

数据预处理
构建和训练 U-Net 模型
应用模型进行实时背景虚化

### 第一部分：数据预处理

这个示例假设你已经有一个包含图像和对应前景（主体）分割掩码的数据集。你可以从现有的数据集开始，例如 COCO 数据集。以下代码将图像数据加载到内存中，并将其分为训练、验证和测试集：

import os
import numpy as np
import cv2
from sklearn.model_selection import train_test_split

def load_data(image_dir, mask_dir, image_size=(256, 256)):
    image_files = os.listdir(image_dir)
    mask_files = os.listdir(mask_dir)
    
    images = []
    masks = []
    
    for img_file, mask_file in zip(image_files, mask_files):
        img = cv2.imread(os.path.join(image_dir, img_file))
        mask = cv2.imread(os.path.join(mask_dir, mask_file), cv2.IMREAD_GRAYSCALE)
        
        img = cv2.resize(img, image_size)
        mask = cv2.resize(mask, image_size)
        
        images.append(img)
        masks.append(mask)
    
    images = np.array(images, dtype=np.float32) / 255.0
    masks = np.array(masks, dtype=np.float32) / 255.0
    masks = np.expand_dims(masks, axis=-1)
    
    return images, masks

images, masks = load_data('path/to/image/dir', 'path/to/mask/dir')

X_train, X_test, y_train, y_test = train_test_split(images, masks, test_size=0.2, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)

### 第二部分：构建和训练 U-Net 模型

使用 TensorFlow 构建 U-Net 模型，并在训练数据上进行训练：

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

def build_unet(input_shape=(256, 256, 3)):
    inputs = tf.keras.Input(input_shape)

    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)
    drop1 = Dropout(0.5)(pool1)

    # 添加更多卷积层和上采样层，组成完整的 U-Net 结构

    up_last = UpSampling2D(size=(2, 2))(drop_last)
    merge_last = concatenate([conv1, up_last], axis=3)
    conv_last = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge_last)
    conv_last = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv_last)
    output = Conv2D(1, 1, activation='sigmoid')(conv_last)

    model = tf.keras.Model(inputs=inputs, outputs=output)
    
    return model

unet = build_unet()

unet.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
             loss='binary_crossentropy',
             metrics=['accuracy'])

unet.fit(X_train, y_train, batch_size=16, epochs=50, validation_data=(X_val, y_val))

### 第三部分：实时背景虚化

现在，我们将训练好的 U-Net 模型应用于摄像头实时捕获的画面，实现背景虚化效果：

def blur_background(frame, model, blur_intensity=21):
    original_shape = frame.shape[:2]
    resized_frame = cv2.resize(frame, (256, 256))

    mask = model.predict(np.expand_dims(resized_frame, axis=0))[0]
    mask = (mask > 0.5).astype(np.uint8)
    mask = cv2.resize(mask, original_shape[::-1])

    blurred_frame = cv2.GaussianBlur(frame, (blur_intensity, blur_intensity), 0)
    result = np.where(mask[..., np.newaxis] == 1, frame, blurred_frame)

    return result

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    result_frame = blur_background(frame, unet)
    cv2.imshow('Real-time background blur', result_frame)

    key = cv2.waitKey(1)
    if key == 27:  # Press ESC to exit
        break

cap.release()
cv2.destroyAllWindows()

这个示例应该能够实现实时背景虚化效果。然而，为了提高性能，可能需要进行优化，比如使用较小的模型、使用硬件加速器进行推理等。