def resize_image(self, image, width, height):
    """调整图像大小"""
    return cv2.resize(image, (width, height))

def convert_to_grayscale(self, image):
    """转换为灰度图"""
    if len(image.shape) == 3:
        return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return image

def gaussian_blur(self, image, kernel_size=5, sigma=1.0):
    """高斯模糊去噪"""
    return cv2.GaussianBlur(image, (kernel_size, kernel_size), sigma)

def histogram_equalization(self, image):
    """直方图均衡化"""
    if len(image.shape) == 2:
        return cv2.equalizeHist(image)
    else:
        # 对彩色图像的各通道分别处理
        channels = cv2.split(image)
        eq_channels = [cv2.equalizeHist(ch) for ch in channels]
        return cv2.merge(eq_channels)

def normalize_image(self, image):
    """图像归一化"""
    return cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX)

def bilateral_filter(self, image, d=9, sigma_color=75, sigma_space=75):
    """双边滤波（保边去噪）"""
    return cv2.bilateralFilter(image, d, sigma_color, sigma_space)

preprocessor = ImagePreprocessor()

# 应用各种预处理技术
gray = preprocessor.convert_to_grayscale(image)
blurred = preprocessor.gaussian_blur(gray)
equalized = preprocessor.histogram_equalization(gray)
normalized = preprocessor.normalize_image(gray)

# 可视化结果
plt.figure(figsize=(15, 10))

plt.subplot(2, 3, 1)
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.title('原始图像')
plt.axis('off')

plt.subplot(2, 3, 2)
plt.imshow(gray, cmap='gray')
plt.title('灰度图像')
plt.axis('off')

plt.subplot(2, 3, 3)
plt.imshow(blurred, cmap='gray')
plt.title('高斯模糊')
plt.axis('off')

plt.subplot(2, 3, 4)
plt.imshow(equalized, cmap='gray')
plt.title('直方图均衡化')
plt.axis('off')

plt.subplot(2, 3, 5)
plt.imshow(normalized, cmap='gray')
plt.title('归一化')
plt.axis('off')

plt.tight_layout()
plt.show()

def sobel_edge_detection(self, image):
    """Sobel边缘检测"""
    # X方向梯度
    grad_x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3)
    # Y方向梯度
    grad_y = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=3)

    # 计算梯度幅值
    magnitude = np.sqrt(grad_x**2 + grad_y**2)
    # 计算梯度方向
    angle = np.arctan2(grad_y, grad_x)

    return magnitude.astype(np.uint8), angle

def canny_edge_detection(self, image, low_threshold=50, high_threshold=150):
    """Canny边缘检测"""
    return cv2.Canny(image, low_threshold, high_threshold)

def laplacian_edge_detection(self, image):
    """拉普拉斯边缘检测"""
    return cv2.Laplacian(image, cv2.CV_64F)

def roberts_edge_detection(self, image):
    """Roberts边缘检测"""
    # Roberts算子
    roberts_x = np.array([[1, 0], [0, -1]], dtype=np.float32)
    roberts_y = np.array([[0, 1], [-1, 0]], dtype=np.float32)

    # 应用卷积
    edge_x = cv2.filter2D(image, cv2.CV_64F, roberts_x)
    edge_y = cv2.filter2D(image, cv2.CV_64F, roberts_y)

    # 计算边缘强度
    magnitude = np.sqrt(edge_x**2 + edge_y**2)
    return magnitude.astype(np.uint8)

detector = EdgeDetector()

# 应用不同的边缘检测方法
sobel_magnitude, sobel_angle = detector.sobel_edge_detection(image)
canny_edges = detector.canny_edge_detection(image)
laplacian_edges = detector.laplacian_edge_detection(image)
roberts_edges = detector.roberts_edge_detection(image)

# 可视化结果
plt.figure(figsize=(15, 10))

plt.subplot(2, 3, 1)
plt.imshow(image, cmap='gray')
plt.title('原始图像')
plt.axis('off')

plt.subplot(2, 3, 2)
plt.imshow(sobel_magnitude, cmap='gray')
plt.title('Sobel边缘检测')
plt.axis('off')

plt.subplot(2, 3, 3)
plt.imshow(canny_edges, cmap='gray')
plt.title('Canny边缘检测')
plt.axis('off')

plt.subplot(2, 3, 4)
plt.imshow(np.abs(laplacian_edges), cmap='gray')
plt.title('拉普拉斯边缘检测')
plt.axis('off')

plt.subplot(2, 3, 5)
plt.imshow(roberts_edges, cmap='gray')
plt.title('Roberts边缘检测')
plt.axis('off')

plt.tight_layout()
plt.show()

def extract_features(self, image):
    """提取SIFT特征"""
    # 检测关键点和计算描述符
    keypoints, descriptors = self.sift.detectAndCompute(image, None)
    return keypoints, descriptors

def draw_keypoints(self, image, keypoints):
    """绘制关键点"""
    return cv2.drawKeypoints(image, keypoints, None, 
                           flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

def match_features(self, desc1, desc2, ratio_threshold=0.7):
    """特征匹配"""
    # 使用FLANN匹配器
    FLANN_INDEX_KDTREE = 1
    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
    search_params = dict(checks=50)

    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(desc1, desc2, k=2)

    # 应用Lowe's ratio test
    good_matches = []
    for match_pair in matches:
        if len(match_pair) == 2:
            m, n = match_pair
            if m.distance < ratio_threshold * n.distance:
                good_matches.append(m)

    return good_matches

def extract_features(self, image):
    """提取HOG特征"""
    from skimage.feature import hog

    features = hog(image, 
                  orientations=self.orientations,
                  pixels_per_cell=self.pixels_per_cell,
                  cells_per_block=self.cells_per_block,
                  visualize=False,
                  feature_vector=True)
    return features

def visualize_hog(self, image):
    """可视化HOG特征"""
    from skimage.feature import hog

    features, hog_image = hog(image,
                             orientations=self.orientations,
                             pixels_per_cell=self.pixels_per_cell,
                             cells_per_block=self.cells_per_block,
                             visualize=True,
                             feature_vector=True)
    return features, hog_image

# SIFT特征提取
sift_extractor = SIFTFeatureExtractor()
keypoints, descriptors = sift_extractor.extract_features(image)
image_with_keypoints = sift_extractor.draw_keypoints(image, keypoints)

# HOG特征提取
hog_extractor = HOGFeatureExtractor()
hog_features, hog_image = hog_extractor.visualize_hog(image)

# 可视化结果
plt.figure(figsize=(15, 5))

plt.subplot(1, 3, 1)
plt.imshow(image, cmap='gray')
plt.title('原始图像')
plt.axis('off')

plt.subplot(1, 3, 2)
plt.imshow(image_with_keypoints, cmap='gray')
plt.title(f'SIFT特征点 ({len(keypoints)}个)')
plt.axis('off')

plt.subplot(1, 3, 3)
plt.imshow(hog_image, cmap='gray')
plt.title('HOG特征可视化')
plt.axis('off')

plt.tight_layout()
plt.show()

print(f"SIFT描述符维度: {descriptors.shape if descriptors is not None else 'None'}")
print(f"HOG特征维度: {hog_features.shape}")

def create_basic_cnn(self, input_shape, num_classes):
    """创建基础CNN模型"""
    model = models.Sequential([
        # 第一个卷积块
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
        layers.MaxPooling2D((2, 2)),

        # 第二个卷积块
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),

        # 第三个卷积块
        layers.Conv2D(64, (3, 3), activation='relu'),

        # 全连接层
        layers.Flatten(),
        layers.Dense(64, activation='relu'),
        layers.Dropout(0.5),
        layers.Dense(num_classes, activation='softmax')
    ])

    return model

def create_resnet_block(self, x, filters, kernel_size=3, stride=1):
    """创建ResNet残差块"""
    # 主路径
    fx = layers.Conv2D(filters, kernel_size, strides=stride, padding='same')(x)
    fx = layers.BatchNormalization()(fx)
    fx = layers.Activation('relu')(fx)

    fx = layers.Conv2D(filters, kernel_size, strides=1, padding='same')(fx)
    fx = layers.BatchNormalization()(fx)

    # 快捷连接
    if stride != 1 or x.shape[-1] != filters:
        x = layers.Conv2D(filters, 1, strides=stride, padding='same')(x)
        x = layers.BatchNormalization()(x)

    # 残差连接
    out = layers.Add()([fx, x])
    out = layers.Activation('relu')(out)

    return out

def create_mini_resnet(self, input_shape, num_classes):
    """创建简化版ResNet"""
    inputs = layers.Input(shape=input_shape)

    # 初始卷积
    x = layers.Conv2D(32, 7, strides=2, padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.MaxPooling2D(3, strides=2, padding='same')(x)

    # 残差块
    x = self.create_resnet_block(x, 32)
    x = self.create_resnet_block(x, 32)

    x = self.create_resnet_block(x, 64, stride=2)
    x = self.create_resnet_block(x, 64)

    # 全局平均池化和分类层
    x = layers.GlobalAveragePooling2D()(x)
    x = layers.Dense(num_classes, activation='softmax')(x)

    model = models.Model(inputs, x)
    return model

# 生成随机图像数据
X = np.random.random((num_samples, img_height, img_width, channels))
y = tf.keras.utils.to_categorical(np.random.randint(0, num_classes, num_samples), num_classes)

# 分割数据
split_idx = int(0.8 * num_samples)
X_train, X_test = X[:split_idx], X[split_idx:]
y_train, y_test = y[:split_idx], y[split_idx:]

# 创建和训练模型
cnn_builder = CNNBuilder()

# 基础CNN
basic_model = cnn_builder.create_basic_cnn((img_height, img_width, channels), num_classes)
basic_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

print("基础CNN模型结构:")
basic_model.summary()

# 训练模型
history = basic_model.fit(X_train, y_train, 
                        epochs=5, 
                        batch_size=32, 
                        validation_data=(X_test, y_test),
                        verbose=0)

# 评估模型
test_loss, test_accuracy = basic_model.evaluate(X_test, y_test, verbose=0)
print(f"\n基础CNN测试准确率: {test_accuracy:.4f}")

# ResNet模型
resnet_model = cnn_builder.create_mini_resnet((img_height, img_width, channels), num_classes)
resnet_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

print("\n简化ResNet模型结构:")
print(f"参数数量: {resnet_model.count_params():,}")

return basic_model, resnet_model

def __init__(self, input_size=416, num_classes=20, num_anchors=5):
    self.input_size = input_size
    self.num_classes = num_classes
    self.num_anchors = num_anchors

def create_yolo_model(self):
    """创建简化的YOLO模型"""
    inputs = layers.Input(shape=(self.input_size, self.input_size, 3))

    # 特征提取backbone（简化版）
    x = layers.Conv2D(32, 3, padding='same', activation='relu')(inputs)
    x = layers.MaxPooling2D(2)(x)

    x = layers.Conv2D(64, 3, padding='same', activation='relu')(x)
    x = layers.MaxPooling2D(2)(x)

    x = layers.Conv2D(128, 3, padding='same', activation='relu')(x)
    x = layers.MaxPooling2D(2)(x)

    x = layers.Conv2D(256, 3, padding='same', activation='relu')(x)
    x = layers.MaxPooling2D(2)(x)

    x = layers.Conv2D(512, 3, padding='same', activation='relu')(x)
    x = layers.MaxPooling2D(2)(x)

    # YOLO检测头
    # 输出: (batch_size, grid_height, grid_width, num_anchors * (5 + num_classes))
    # 5 = x, y, w, h, confidence
    output_channels = self.num_anchors * (5 + self.num_classes)
    outputs = layers.Conv2D(output_channels, 1, activation='sigmoid')(x)

    model = models.Model(inputs, outputs)
    return model

def non_max_suppression(self, boxes, scores, iou_threshold=0.5, score_threshold=0.5):
    """非极大值抑制"""
    # 过滤低分数的框
    valid_indices = scores > score_threshold
    valid_boxes = boxes[valid_indices]
    valid_scores = scores[valid_indices]

    if len(valid_boxes) == 0:
        return []

    # 按分数排序
    sorted_indices = np.argsort(valid_scores)[::-1]

    keep = []
    while len(sorted_indices) > 0:
        # 选择分数最高的框
        current = sorted_indices[0]
        keep.append(current)

        if len(sorted_indices) == 1:
            break

        # 计算IoU
        current_box = valid_boxes[current]
        other_boxes = valid_boxes[sorted_indices[1:]]

        ious = self.calculate_iou(current_box, other_boxes)

        # 保留IoU小于阈值的框
        sorted_indices = sorted_indices[1:][ious < iou_threshold]

    return keep

def calculate_iou(self, box1, boxes):
    """计算IoU（交并比）"""
    # box格式: [x1, y1, x2, y2]
    x1 = np.maximum(box1[0], boxes[:, 0])
    y1 = np.maximum(box1[1], boxes[:, 1])
    x2 = np.minimum(box1[2], boxes[:, 2])
    y2 = np.minimum(box1[3], boxes[:, 3])

    # 计算交集面积
    intersection = np.maximum(0, x2 - x1) * np.maximum(0, y2 - y1)

    # 计算各框面积
    box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
    boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])

    # 计算并集面积
    union = box1_area + boxes_area - intersection

    # 计算IoU
    iou = intersection / (union + 1e-8)
    return iou

print("YOLO模型结构:")
model.summary()

# 模拟检测结果
boxes = np.array([
    [10, 10, 50, 50],   # [x1, y1, x2, y2]
    [12, 12, 52, 52],   # 重叠框
    [100, 100, 150, 150],
    [200, 200, 250, 250]
])

scores = np.array([0.9, 0.7, 0.8, 0.6])

# 应用NMS
keep_indices = detector.non_max_suppression(boxes, scores)

print(f"\nNMS前: {len(boxes)} 个框")
print(f"NMS后: {len(keep_indices)} 个框")
print(f"保留的框索引: {keep_indices}")

def __init__(self, input_shape, num_classes):
    self.input_shape = input_shape
    self.num_classes = num_classes

def conv_block(self, inputs, num_filters):
    """卷积块"""
    x = layers.Conv2D(num_filters, 3, padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)

    x = layers.Conv2D(num_filters, 3, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)

    return x

def encoder_block(self, inputs, num_filters):
    """编码器块"""
    x = self.conv_block(inputs, num_filters)
    p = layers.MaxPooling2D((2, 2))(x)
    return x, p

def decoder_block(self, inputs, skip_features, num_filters):
    """解码器块"""
    x = layers.Conv2DTranspose(num_filters, (2, 2), strides=2, padding='same')(inputs)
    x = layers.Concatenate()([x, skip_features])
    x = self.conv_block(x, num_filters)
    return x

def create_unet(self):
    """创建U-Net模型"""
    inputs = layers.Input(self.input_shape)

    # 编码器（下采样路径）
    s1, p1 = self.encoder_block(inputs, 64)
    s2, p2 = self.encoder_block(p1, 128)
    s3, p3 = self.encoder_block(p2, 256)
    s4, p4 = self.encoder_block(p3, 512)

    # 桥接层
    b1 = self.conv_block(p4, 1024)

    # 解码器（上采样路径）
    d1 = self.decoder_block(b1, s4, 512)
    d2 = self.decoder_block(d1, s3, 256)
    d3 = self.decoder_block(d2, s2, 128)
    d4 = self.decoder_block(d3, s1, 64)

    # 输出层
    outputs = layers.Conv2D(self.num_classes, 1, padding='same', activation='softmax')(d4)

    model = models.Model(inputs, outputs, name='U-Net')
    return model

def dice_coefficient(self, y_true, y_pred, smooth=1e-6):
    """Dice系数损失函数"""
    y_true_f = tf.keras.backend.flatten(y_true)
    y_pred_f = tf.keras.backend.flatten(y_pred)
    intersection = tf.keras.backend.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (tf.keras.backend.sum(y_true_f) + tf.keras.backend.sum(y_pred_f) + smooth)

def dice_loss(self, y_true, y_pred):
    """Dice损失"""
    return 1 - self.dice_coefficient(y_true, y_pred)

# 创建U-Net模型
unet = UNetSegmentation(input_shape, num_classes)
model = unet.create_unet()

# 编译模型
model.compile(
    optimizer='adam',
    loss=unet.dice_loss,
    metrics=[unet.dice_coefficient, 'accuracy']
)

print("U-Net模型结构:")
print(f"参数数量: {model.count_params():,}")

# 生成模拟数据
num_samples = 50
X = np.random.random((num_samples, *input_shape))
y = tf.keras.utils.to_categorical(
    np.random.randint(0, num_classes, (num_samples, input_shape[0], input_shape[1])), 
    num_classes
)

# 训练模型（简单演示）
history = model.fit(X, y, epochs=2, batch_size=4, verbose=1)

return model

def __init__(self, latent_dim=100, img_shape=(28, 28, 1)):
    self.latent_dim = latent_dim
    self.img_shape = img_shape
    self.img_rows, self.img_cols, self.channels = img_shape

def build_generator(self):
    """构建生成器"""
    model = models.Sequential([
        layers.Dense(256, input_dim=self.latent_dim),
        layers.LeakyReLU(alpha=0.2),
        layers.BatchNormalization(momentum=0.8),

        layers.Dense(512),
        layers.LeakyReLU(alpha=0.2),
        layers.BatchNormalization(momentum=0.8),

        layers.Dense(1024),
        layers.LeakyReLU(alpha=0.2),
        layers.BatchNormalization(momentum=0.8),

        layers.Dense(np.prod(self.img_shape), activation='tanh'),
        layers.Reshape(self.img_shape)
    ])

    return model

def build_discriminator(self):
    """构建判别器"""
    model = models.Sequential([
        layers.Flatten(input_shape=self.img_shape),

        layers.Dense(512),
        layers.LeakyReLU(alpha=0.2),
        layers.Dropout(0.3),

        layers.Dense(256),
        layers.LeakyReLU(alpha=0.2),
        layers.Dropout(0.3),

        layers.Dense(1, activation='sigmoid')
    ])

    return model

def build_gan(self):
    """构建完整的GAN"""
    # 构建判别器
    self.discriminator = self.build_discriminator()
    self.discriminator.compile(loss='binary_crossentropy',
                             optimizer='adam',
                             metrics=['accuracy'])

    # 构建生成器
    self.generator = self.build_generator()

    # 构建GAN（生成器 + 判别器）
    z = layers.Input(shape=(self.latent_dim,))
    img = self.generator(z)

    # 训练GAN时，判别器参数不更新
    self.discriminator.trainable = False
    validity = self.discriminator(img)

    self.combined = models.Model(z, validity)
    self.combined.compile(loss='binary_crossentropy', optimizer='adam')

    return self.generator, self.discriminator, self.combined

def train(self, X_train, epochs=10000, batch_size=32, save_interval=1000):
    """训练GAN"""
    # 标签
    valid = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))

    for epoch in range(epochs):
        # 训练判别器
        # 选择随机的真实图像
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        imgs = X_train[idx]

        # 生成假图像
        noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
        gen_imgs = self.generator.predict(noise, verbose=0)

        # 训练判别器
        d_loss_real = self.discriminator.train_on_batch(imgs, valid)
        d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

        # 训练生成器
        noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
        g_loss = self.combined.train_on_batch(noise, valid)

        # 打印进度
        if epoch % save_interval == 0:
            print(f"{epoch} [D loss: {d_loss[0]:.4f}, acc.: {100*d_loss[1]:.2f}%] [G loss: {g_loss:.4f}]")
            self.save_imgs(epoch)

def save_imgs(self, epoch):
    """保存生成的图像"""
    r, c = 5, 5
    noise = np.random.normal(0, 1, (r * c, self.latent_dim))
    gen_imgs = self.generator.predict(noise, verbose=0)

    # 重新缩放到 [0, 1]
    gen_imgs = 0.5 * gen_imgs + 0.5

    fig, axs = plt.subplots(r, c, figsize=(10, 10))
    cnt = 0
    for i in range(r):
        for j in range(c):
            if self.channels == 1:
                axs[i,j].imshow(gen_imgs[cnt, :, :, 0], cmap='gray')
            else:
                axs[i,j].imshow(gen_imgs[cnt])
            axs[i,j].axis('off')
            cnt += 1
    fig.suptitle(f"Epoch {epoch}")
    plt.show()

# 归一化到[-1, 1]
X_train = X_train / 127.5 - 1.
X_train = np.expand_dims(X_train, axis=3)

# 创建和训练GAN
gan = BasicGAN(latent_dim=100, img_shape=(28, 28, 1))
generator, discriminator, combined = gan.build_gan()

print("生成器结构:")
generator.summary()

print("\n判别器结构:")
discriminator.summary()

# 训练（这里只训练几个epoch作为演示）
print("\n开始训练GAN...")
gan.train(X_train[:1000], epochs=100, batch_size=32, save_interval=50)

def __init__(self, content_layers=['block5_conv2'],
             style_layers=['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']):
    self.content_layers = content_layers
    self.style_layers = style_layers
    self.num_content_layers = len(content_layers)
    self.num_style_layers = len(style_layers)

def vgg_layers(self, layer_names):
    """创建VGG模型并返回指定层的输出"""
    vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')
    vgg.trainable = False

    outputs = [vgg.get_layer(name).output for name in layer_names]
    model = tf.keras.Model([vgg.input], outputs)
    return model

def gram_matrix(self, input_tensor):
    """计算Gram矩阵"""
    result = tf.linalg.einsum('bijc,bijd->bcd', input_tensor, input_tensor)
    input_shape = tf.shape(input_tensor)
    num_locations = tf.cast(input_shape[1]*input_shape[2], tf.float32)
    return result/(num_locations)

def style_content_model(self):
    """创建风格和内容提取模型"""
    vgg = self.vgg_layers(self.style_layers + self.content_layers)

    def model_fn(inputs):
        # 预处理输入
        inputs = inputs * 255.0
        preprocessed_input = tf.keras.applications.vgg19.preprocess_input(inputs)
        outputs = vgg(preprocessed_input)

        style_outputs, content_outputs = (outputs[:self.num_style_layers],
                                        outputs[self.num_style_layers:])

        # 计算风格特征的Gram矩阵
        style_outputs = [self.gram_matrix(style_output) for style_output in style_outputs]

        content_dict = {content_name: value for content_name, value in 
                      zip(self.content_layers, content_outputs)}

        style_dict = {style_name: value for style_name, value in 
                    zip(self.style_layers, style_outputs)}

        return {'content': content_dict, 'style': style_dict}

    return model_fn

def clip_0_1(self, image):
    """将图像像素值限制在[0,1]范围内"""
    return tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)

def style_content_loss(self, outputs, style_targets, content_targets, 
                      style_weight=1e-2, content_weight=1e4):
    """计算风格和内容损失"""
    style_outputs = outputs['style']
    content_outputs = outputs['content']

    # 计算风格损失
    style_loss = tf.add_n([tf.reduce_mean((style_outputs[name]-style_targets[name])**2) 
                          for name in style_outputs.keys()])
    style_loss *= style_weight / self.num_style_layers

    # 计算内容损失
    content_loss = tf.add_n([tf.reduce_mean((content_outputs[name]-content_targets[name])**2) 
                           for name in content_outputs.keys()])
    content_loss *= content_weight / self.num_content_layers

    loss = style_loss + content_loss
    return loss

def load_img(self, path_to_img):
    """加载和预处理图像"""
    max_dim = 512
    img = tf.io.read_file(path_to_img)
    img = tf.image.decode_image(img, channels=3)
    img = tf.image.convert_image_dtype(img, tf.float32)

    shape = tf.cast(tf.shape(img)[:-1], tf.float32)
    long_dim = max(shape)
    scale = max_dim / long_dim

    new_shape = tf.cast(shape * scale, tf.int32)
    img = tf.image.resize(img, new_shape)
    img = img[tf.newaxis, :]
    return img

# 创建模拟的内容和风格图像
content_image = tf.random.normal((1, 256, 256, 3))
style_image = tf.random.normal((1, 256, 256, 3))

# 提取风格和内容特征
extractor = nst.style_content_model()

style_targets = extractor(style_image)['style']
content_targets = extractor(content_image)['content']

print("风格迁移模型组件:")
print(f"内容层数: {nst.num_content_layers}")
print(f"风格层数: {nst.num_style_layers}")
print(f"内容特征维度: {[v.shape for v in content_targets.values()]}")
print(f"风格特征维度: {[v.shape for v in style_targets.values()]}")

def __init__(self):
    self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    self.recognizer = cv2.face.LBPHFaceRecognizer_create()
    self.face_database = {}
    self.is_trained = False

def detect_faces(self, image):
    """检测人脸"""
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces = self.face_cascade.detectMultiScale(gray, 1.3, 5)
    return faces, gray

def extract_face_features(self, image, face_rect):
    """提取人脸特征"""
    x, y, w, h = face_rect
    face_roi = image[y:y+h, x:x+w]
    face_roi = cv2.resize(face_roi, (100, 100))
    return face_roi

def add_face_to_database(self, name, face_images):
    """将人脸添加到数据库"""
    self.face_database[name] = face_images
    print(f"已添加 {name} 的 {len(face_images)} 张人脸图像到数据库")

def train_recognizer(self):
    """训练人脸识别器"""
    faces = []
    labels = []
    label_dict = {}
    current_label = 0

    for name, face_images in self.face_database.items():
        label_dict[current_label] = name
        for face_image in face_images:
            faces.append(face_image)
            labels.append(current_label)
        current_label += 1

    if len(faces) > 0:
        self.recognizer.train(faces, np.array(labels))
        self.label_dict = label_dict
        self.is_trained = True
        print("人脸识别器训练完成")
    else:
        print("数据库中没有人脸数据")

def recognize_face(self, face_image, confidence_threshold=50):
    """识别人脸"""
    if not self.is_trained:
        return "未知", 0

    label, confidence = self.recognizer.predict(face_image)

    if confidence < confidence_threshold:
        name = self.label_dict.get(label, "未知")
        return name, confidence
    else:
        return "未知", confidence

def process_image(self, image_path):
    """处理单张图像"""
    image = cv2.imread(image_path)
    faces, gray = self.detect_faces(image)

    results = []
    for (x, y, w, h) in faces:
        face_roi = self.extract_face_features(gray, (x, y, w, h))
        name, confidence = self.recognize_face(face_roi)

        # 在图像上标注
        cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)
        cv2.putText(image, f"{name} ({confidence:.1f})", (x, y-10), 
                   cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)

        results.append({
            'name': name,
            'confidence': confidence,
            'bbox': (x, y, w, h)
        })

    return image, results

# 模拟人脸数据库（实际应用中应该从真实图像中提取）
print("人脸识别系统演示")
print("=" * 50)

# 生成模拟人脸数据
alice_faces = [np.random.randint(0, 255, (100, 100), dtype=np.uint8) for _ in range(5)]
bob_faces = [np.random.randint(0, 255, (100, 100), dtype=np.uint8) for _ in range(5)]

# 添加到数据库
face_system.add_face_to_database("Alice", alice_faces)
face_system.add_face_to_database("Bob", bob_faces)

# 训练识别器
face_system.train_recognizer()

# 测试识别
test_face = np.random.randint(0, 255, (100, 100), dtype=np.uint8)
name, confidence = face_system.recognize_face(test_face)

print(f"测试结果: {name}, 置信度: {confidence:.2f}")

def __init__(self):
    pass

def calculate_psnr(self, img1, img2):
    """计算峰值信噪比(PSNR)"""
    mse = np.mean((img1 - img2) ** 2)
    if mse == 0:
        return float('inf')

    max_pixel = 255.0
    psnr = 20 * np.log10(max_pixel / np.sqrt(mse))
    return psnr

def calculate_ssim(self, img1, img2):
    """计算结构相似性指数(SSIM)"""
    from skimage.metrics import structural_similarity as ssim

    if len(img1.shape) == 3:
        return ssim(img1, img2, multichannel=True)
    else:
        return ssim(img1, img2)

def calculate_mse(self, img1, img2):
    """计算均方误差(MSE)"""
    return np.mean((img1 - img2) ** 2)

def calculate_mae(self, img1, img2):
    """计算平均绝对误差(MAE)"""
    return np.mean(np.abs(img1 - img2))

def assess_blur(self, image):
    """评估图像模糊程度（拉普拉斯方差）"""
    if len(image.shape) == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    else:
        gray = image

    # 计算拉普拉斯算子的方差
    laplacian_var = cv2.Laplacian(gray, cv2.CV_64F).var()
    return laplacian_var

def assess_noise(self, image):
    """评估图像噪声水平"""
    if len(image.shape) == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    else:
        gray = image

    # 使用高斯滤波后的差异估计噪声
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    noise_estimate = np.std(gray.astype(np.float32) - blurred.astype(np.float32))
    return noise_estimate

def assess_contrast(self, image):
    """评估图像对比度"""
    if len(image.shape) == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    else:
        gray = image

    # RMS对比度
    contrast = np.std(gray)
    return contrast

def comprehensive_assessment(self, image, reference_image=None):
    """综合图像质量评估"""
    results = {}

    # 无参考评估
    results['blur_measure'] = self.assess_blur(image)
    results['noise_level'] = self.assess_noise(image)
    results['contrast'] = self.assess_contrast(image)

    # 如果有参考图像，计算有参考指标
    if reference_image is not None:
        results['psnr'] = self.calculate_psnr(reference_image, image)
        results['ssim'] = self.calculate_ssim(reference_image, image)
        results['mse'] = self.calculate_mse(reference_image, image)
        results['mae'] = self.calculate_mae(reference_image, image)

    return results

# 创建不同质量的图像
# 添加噪声
noisy = original.copy()
noise = np.random.normal(0, 25, original.shape).astype(np.uint8)
noisy = cv2.add(noisy, noise)

# 模糊图像
blurred = cv2.GaussianBlur(original, (15, 15), 0)

# 压缩图像（模拟JPEG压缩）
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 30]
_, compressed_img = cv2.imencode('.jpg', original, encode_param)
compressed = cv2.imdecode(compressed_img, cv2.IMREAD_COLOR)

# 评估图像质量
assessor = ImageQualityAssessment()

images = {
    'Original': original,
    'Noisy': noisy,
    'Blurred': blurred,
    'Compressed': compressed
}

print("图像质量评估结果:")
print("=" * 60)

for name, img in images.items():
    if name == 'Original':
        results = assessor.comprehensive_assessment(img)
    else:
        results = assessor.comprehensive_assessment(img, original)

    print(f"\n{name}:")
    for metric, value in results.items():
        print(f"  {metric}: {value:.4f}")

# 可视化
plt.figure(figsize=(15, 10))

for i, (name, img) in enumerate(images.items()):
    plt.subplot(2, 2, i+1)
    plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    plt.title(name)
    plt.axis('off')

plt.tight_layout()
plt.show()

def __init__(self):
    self.challenges = {
        "数据质量与标注": {
            "问题": [
                "高质量标注数据获取困难",
                "标注一致性和准确性问题",
                "长尾分布数据稀缺",
                "标注成本高昂"
            ],
            "解决方案": [
                "半监督和无监督学习",
                "主动学习和增量学习",
                "数据增强技术",
                "合成数据生成"
            ]
        },
        "模型泛化能力": {
            "问题": [
                "域适应问题",
                "分布偏移",
                "小样本学习困难",
                "跨模态迁移"
            ],
            "解决方案": [
                "域适应和域泛化",
                "元学习方法",
                "多任务学习",
                "知识蒸馏"
            ]
        },
        "计算资源限制": {
            "问题": [
                "模型参数量巨大",
                "推理速度慢",
                "内存占用高",
                "能耗问题"
            ],
            "解决方案": [
                "模型压缩和剪枝",
                "量化技术",
                "知识蒸馏",
                "轻量级网络设计"
            ]
        },
        "可解释性": {
            "问题": [
                "黑盒模型难以解释",
                "决策过程不透明",
                "医疗等关键应用的信任问题"
            ],
            "解决方案": [
                "注意力机制可视化",
                "梯度回传分析",
                "可解释AI方法",
                "因果推理"
            ]
        }
    }

def print_challenges(self):
    """打印挑战分析"""
    print("计算机视觉面临的主要挑战")
    print("=" * 60)

    for challenge, details in self.challenges.items():
        print(f"\n{challenge}:")
        print(f"  问题:")
        for problem in details["问题"]:
            print(f"    • {problem}")
        print(f"  解决方案:")
        for solution in details["解决方案"]:
            print(f"    ✓ {solution}")

def __init__(self):
    pass

def quantization_demo(self, model):
    """量化演示"""
    # 转换为TensorFlow Lite模型
    converter = tf.lite.TFLiteConverter.from_keras_model(model)

    # 开启量化
    converter.optimizations = [tf.lite.Optimize.DEFAULT]

    # 转换模型
    quantized_model = converter.convert()

    # 比较模型大小
    original_size = model.count_params() * 4  # 假设float32
    quantized_size = len(quantized_model)

    compression_ratio = original_size / quantized_size

    return {
        'original_size': original_size,
        'quantized_size': quantized_size,
        'compression_ratio': compression_ratio
    }

def pruning_demo(self, model, target_sparsity=0.5):
    """剪枝演示"""
    import tensorflow_model_optimization as tfmot

    # 定义剪枝参数
    pruning_params = {
        'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(
            initial_sparsity=0.0,
            final_sparsity=target_sparsity,
            begin_step=0,
            end_step=1000
        )
    }

    # 应用剪枝
    pruned_model = tfmot.sparsity.keras.prune_low_magnitude(
        model, **pruning_params
    )

    # 编译剪枝模型
    pruned_model.compile(
        optimizer='adam',
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )

    return pruned_model

def __init__(self):
    self.trends = {
        "技术架构演进": [
            "Vision Transformer (ViT) 的广泛应用",
            "多模态统一架构",
            "神经符号学习结合",
            "自监督学习方法",
            "连续学习和终身学习"
        ],
        "应用领域拓展": [
            "自动驾驶技术成熟",
            "医疗影像智能诊断",
            "增强现实(AR)和虚拟现实(VR)",
            "智能制造质检",
            "农业智能监测",
            "环境监控和保护"
        ],
        "边缘计算集成": [
            "移动设备AI芯片优化",
            "实时视觉处理",
            "隐私保护计算",
            "联邦学习部署",
            "IoT设备智能化"
        ],
        "人机交互革新": [
            "手势识别和控制",
            "情感识别技术",
            "生物特征识别",
            "自然交互界面",
            "无障碍技术支持"
        ]
    }

def demonstrate_vision_transformer(self):
    """Vision Transformer演示"""
    class SimpleViT:
        def __init__(self, image_size=224, patch_size=16, num_classes=1000, 
                    embed_dim=768, num_heads=12, num_layers=12):
            self.image_size = image_size
            self.patch_size = patch_size
            self.num_patches = (image_size // patch_size) ** 2
            self.embed_dim = embed_dim
            self.num_heads = num_heads
            self.num_layers = num_layers
            self.num_classes = num_classes

        def create_model(self):
            """创建ViT模型"""
            inputs = layers.Input(shape=(self.image_size, self.image_size, 3))

            # 图像分块和嵌入
            patches = self.extract_patches(inputs)
            patch_embeddings = layers.Dense(self.embed_dim)(patches)

            # 位置编码
            positions = tf.range(start=0, limit=self.num_patches, delta=1)
            position_embeddings = layers.Embedding(
                input_dim=self.num_patches, output_dim=self.embed_dim
            )(positions)

            # 添加位置编码
            encoded_patches = patch_embeddings + position_embeddings

            # Transformer编码器
            for _ in range(self.num_layers):
                # 多头注意力
                attention_output = layers.MultiHeadAttention(
                    num_heads=self.num_heads, key_dim=self.embed_dim
                )(encoded_patches, encoded_patches)

                # 残差连接和层归一化
                x1 = layers.Add()([attention_output, encoded_patches])
                x1 = layers.LayerNormalization(epsilon=1e-6)(x1)

                # 前馈网络
                x2 = layers.Dense(self.embed_dim * 4, activation='gelu')(x1)
                x2 = layers.Dense(self.embed_dim)(x2)

                # 残差连接和层归一化
                encoded_patches = layers.Add()([x2, x1])
                encoded_patches = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)

            # 分类头
            representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
            representation = layers.Lambda(lambda x: x[:, 0])(representation)  # CLS token
            outputs = layers.Dense(self.num_classes, activation='softmax')(representation)

            model = models.Model(inputs, outputs)
            return model

        def extract_patches(self, images):
            """提取图像块"""
            batch_size = tf.shape(images)[0]
            patches = tf.image.extract_patches(
                images=images,
                sizes=[1, self.patch_size, self.patch_size, 1],
                strides=[1, self.patch_size, self.patch_size, 1],
                rates=[1, 1, 1, 1],
                padding="VALID"
            )
            patch_dims = patches.shape[-1]
            patches = tf.reshape(patches, [batch_size, -1, patch_dims])
            return patches

    # 创建ViT模型
    vit = SimpleViT(image_size=224, patch_size=16, num_classes=10)
    model = vit.create_model()

    print("Vision Transformer 模型结构:")
    print(f"参数数量: {model.count_params():,}")
    print(f"图像尺寸: {vit.image_size}x{vit.image_size}")
    print(f"块大小: {vit.patch_size}x{vit.patch_size}")
    print(f"块数量: {vit.num_patches}")

    return model

def print_trends(self):
    """打印发展趋势"""
    print("计算机视觉未来发展趋势")
    print("=" * 60)

    for trend, items in self.trends.items():
        print(f"\n{trend}:")
        for item in items:
            print(f"  • {item}")

def __init__(self):
    self.project_phases = {
        "1. 问题定义和需求分析": [
            "明确业务目标和技术需求",
            "定义性能指标和评估标准",
            "分析数据可用性和质量",
            "评估技术可行性和资源需求"
        ],
        "2. 数据准备和处理": [
            "数据收集和标注",
            "数据质量检查和清洗",
            "数据增强和预处理",
            "训练/验证/测试集划分"
        ],
        "3. 模型设计和实验": [
            "基线模型建立",
            "架构选择和设计",
            "超参数调优",
            "模型ensemble和融合"
        ],
        "4. 模型训练和优化": [
            "训练策略制定",
            "损失函数设计",
            "正则化技术应用",
            "训练监控和调试"
        ],
        "5. 模型评估和验证": [
            "多指标综合评估",
            "跨数据集验证",
            "错误案例分析",
            "鲁棒性测试"
        ],
        "6. 模型部署和监控": [
            "生产环境部署",
            "性能监控",
            "A/B测试",
            "持续改进"
        ]
    }

def development_checklist(self):
    """开发检查清单"""
    checklist = {
        "数据质量保证": [
            "□ 数据标注一致性检查",
            "□ 类别分布平衡性分析",
            "□ 数据偏差识别和处理",
            "□ 隐私和伦理合规检查"
        ],
        "模型开发": [
            "□ 可重现的实验设计",
            "□ 版本控制和实验跟踪",
            "□ 代码质量和文档",
            "□ 单元测试和集成测试"
        ],
        "性能优化": [
            "□ 推理速度优化",
            "□ 内存使用优化",
            "□ 模型压缩评估",
            "□ 硬件适配测试"
        ],
        "部署准备": [
            "□ API接口设计",
            "□ 容错和异常处理",
            "□ 监控指标定义",
            "□ 回滚机制设计"
        ]
    }

    return checklist

def common_pitfalls(self):
    """常见陷阱和解决方案"""
    pitfalls = {
        "数据泄露": {
            "问题": "训练和测试数据重叠或信息泄露",
            "解决方案": "严格的数据划分，时间序列数据按时间分割"
        },
        "过拟合": {
            "问题": "模型在训练集上表现好但泛化能力差",
            "解决方案": "数据增强、正则化、early stopping、dropout"
        },
        "标注不一致": {
            "问题": "不同标注者之间的标准不统一",
            "解决方案": "详细的标注指南、多人交叉验证、质量控制"
        },
        "类别不平衡": {
            "问题": "某些类别样本过少",
            "解决方案": "重采样、权重调整、focal loss、生成合成数据"
        },
        "域偏移": {
            "问题": "训练和实际应用环境差异大",
            "解决方案": "域适应、数据增强模拟真实环境、持续学习"
        }
    }

    return pitfalls

print("计算机视觉项目开发流程:")
print("=" * 60)

for phase, tasks in template.project_phases.items():
    print(f"\n{phase}:")
    for task in tasks:
        print(f"  • {task}")

print("\n" + "="*60)
print("开发检查清单:")

checklist = template.development_checklist()
for category, items in checklist.items():
    print(f"\n{category}:")
    for item in items:
        print(f"  {item}")

print("\n" + "="*60)
print("常见陷阱和解决方案:")

pitfalls = template.common_pitfalls()
for pitfall, details in pitfalls.items():
    print(f"\n{pitfall}:")
    print(f"  问题: {details['问题']}")
    print(f"  解决方案: {details['解决方案']}")

def __init__(self):
    self.optimization_strategies = {
        "模型架构优化": [
            "使用深度可分离卷积",
            "采用轻量级网络设计(MobileNet, EfficientNet)",
            "网络剪枝和稀疏化",
            "知识蒸馏"
        ],
        "推理优化": [
            "模型量化(INT8, FP16)",
            "批处理优化",
            "GPU/TPU加速",
            "TensorRT、OpenVINO等推理引擎"
        ],
        "数据处理优化": [
            "图像预处理流水线优化",
            "数据加载并行化",
            "内存映射文件",
            "预计算特征缓存"
        ],
        "系统级优化": [
            "异步处理",
            "负载均衡",
            "缓存策略",
            "边缘计算部署"
        ]
    }

def benchmark_models(self, models, test_data, device='cpu'):
    """模型性能基准测试"""
    import time
    import psutil
    import torch

    results = {}

    for name, model in models.items():
        print(f"\n测试模型: {name}")

        # 移动到指定设备
        if device == 'cuda' and torch.cuda.is_available():
            model = model.cuda()
            test_data = test_data.cuda()

        # 预热
        for _ in range(10):
            with torch.no_grad():
                _ = model(test_data)

        # 内存使用测试
        if device == 'cuda':
            torch.cuda.reset_peak_memory_stats()

        process = psutil.Process()
        memory_before = process.memory_info().rss / 1024 / 1024  # MB

        # 推理速度测试
        start_time = time.time()

        for _ in range(100):
            with torch.no_grad():
                output = model(test_data)

        end_time = time.time()

        memory_after = process.memory_info().rss / 1024 / 1024  # MB

        # 计算指标
        avg_inference_time = (end_time - start_time) / 100
        memory_usage = memory_after - memory_before

        if device == 'cuda':
            gpu_memory = torch.cuda.max_memory_allocated() / 1024 / 1024  # MB
        else:
            gpu_memory = 0

        results[name] = {
            'inference_time': avg_inference_time,
            'cpu_memory': memory_usage,
            'gpu_memory': gpu_memory,
            'parameters': sum(p.numel() for p in model.parameters()),
            'model_size': sum(p.numel() * p.element_size() for p in model.parameters()) / 1024 / 1024  # MB
        }

        print(f"  推理时间: {avg_inference_time*1000:.2f} ms")
        print(f"  参数数量: {results[name]['parameters']:,}")
        print(f"  模型大小: {results[name]['model_size']:.2f} MB")

    return results

def optimization_demo(self):
    """优化技术演示"""

    # 模型量化示例
    def quantization_example():
        import torch.quantization as quantization

        print("模型量化演示:")
        print("-" * 30)

        # 创建示例模型
        model = torch.nn.Sequential(
            torch.nn.Conv2d(3, 64, 3, padding=1),
            torch.nn.ReLU(),
            torch.nn.AdaptiveAvgPool2d((1, 1)),
            torch.nn.Flatten(),
            torch.nn.Linear(64, 10)
        )

        # 量化感知训练设置
        model.qconfig = quantization.get_default_qat_qconfig('fbgemm')
        quantization.prepare_qat(model, inplace=True)

        # 模拟训练后的量化
        model.eval()
        quantized_model = quantization.convert(model, inplace=False)

        # 比较模型大小
        original_size = sum(p.numel() * 4 for p in model.parameters()) / 1024 / 1024  # MB (float32)

        print(f"原始模型大小: {original_size:.2f} MB")
        print(f"量化后预期压缩: ~4x")

    # 知识蒸馏示例
    def knowledge_distillation_example():
        print("\n知识蒸馏演示:")
        print("-" * 30)

        # 教师模型（大模型）
        teacher_model = torch.nn.Sequential(
            torch.nn.Conv2d(3, 128, 3, padding=1),
            torch.nn.ReLU(),
            torch.nn.Conv2d(128, 256, 3, padding=1),
            torch.nn.ReLU(),
            torch.nn.AdaptiveAvgPool2d((1, 1)),
            torch.nn.Flatten(),
            torch.nn.Linear(256, 10)
        )

        # 学生模型（小模型）
        student_model = torch.nn.Sequential(
            torch.nn.Conv2d(3, 32, 3, padding=1),
            torch.nn.ReLU(),
            torch.nn.AdaptiveAvgPool2d((1, 1)),
            torch.nn.Flatten(),
            torch.nn.Linear(32, 10)
        )

        teacher_params = sum(p.numel() for p in teacher_model.parameters())
        student_params = sum(p.numel() for p in student_model.parameters())

        print(f"教师模型参数: {teacher_params:,}")
        print(f"学生模型参数: {student_params:,}")
        print(f"参数压缩比: {teacher_params/student_params:.1f}x")

    quantization_example()
    knowledge_distillation_example()

print("计算机视觉性能优化策略:")
print("=" * 60)

for category, strategies in optimizer.optimization_strategies.items():
    print(f"\n{category}:")
    for strategy in strategies:
        print(f"  • {strategy}")

print("\n" + "="*60)
optimizer.optimization_demo()

def __init__(self):
    self.key_directions = {
        "技术突破方向": [
            "多模态融合和理解",
            "少样本和零样本学习",
            "因果推理和可解释AI",
            "神经符号学习结合",
            "生物启发的视觉算法"
        ],
        "应用领域拓展": [
            "自动驾驶全面商业化",
            "医疗AI辅助诊疗",
            "增强现实(AR)和虚拟现实(VR)",
            "智能制造和工业4.0",
            "环境监测和保护",
            "教育个性化学习"
        ],
        "技术民主化": [
            "AutoML和无代码AI平台",
            "预训练模型生态",
            "边缘设备AI普及",
            "开源工具链完善"
        ],
        "社会影响": [
            "就业结构变化",
            "隐私保护需求",
            "AI伦理和治理",
            "数字鸿沟问题"
        ]
    }

def print_outlook(self):
    """打印未来展望"""
    print("计算机视觉未来发展展望")
    print("=" * 60)

    for direction, items in self.key_directions.items():
        print(f"\n{direction}:")
        for item in items:
            print(f"  • {item}")

def technology_roadmap(self):
    """技术路线图"""
    roadmap = {
        "2024-2025 近期目标": [
            "Vision Transformer架构优化",
            "多模态大模型成熟",
            "边缘AI芯片性能提升",
            "自动驾驶L4级别普及"
        ],
        "2026-2028 中期目标": [
            "通用视觉智能突破",
            "实时三维场景理解",
            "视觉-语言-动作统一模型",
            "个人AI助手视觉能力"
        ],
        "2029-2030 长期愿景": [
            "接近人类视觉能力的AI",
            "视觉AI全面产业化",
            "新型人机交互模式",
            "AI驱动的创意产业革命"
        ]
    }

    print("\n技术发展路线图:")
    print("=" * 60)

    for period, goals in roadmap.items():
        print(f"\n{period}:")
        for goal in goals:
            print(f"  → {goal}")