tensorflow调用VGG16图像预处理的部分

以下是使用TensorFlow调用VGG16进行图像预处理的示例代码：

import tensorflow as tf
from tensorflow.keras.applications.vgg16 import preprocess_input

# 加载图像
img = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(224, 224))

# 将图像转换为数组
img_array = tf.keras.preprocessing.image.img_to_array(img)

# 扩充数组的维度，以符合VGG16模型的输入要求
img_array_expanded = tf.expand_dims(img_array, axis=0)

# 进行VGG16的图像预处理
processed_img = preprocess_input(img_array_expanded)

# 打印处理后的图像数组
print(processed_img)

在这个示例中，我们首先使用`load_img`函数加载图像，并将其转换为数组。然后，我们使用`expand_dims`函数将数组维度扩充，以符合VGG16模型的输入要求。最后，我们使用`preprocess_input`函数对图像进行预处理，以使其符合VGG16模型的输入要求。预处理后的图像数组将被打印出来。

相关问题

vgg19图像风格迁移tensorflow

### 如何使用TensorFlow和VGG19实现图像风格迁移

#### 导入必要的库
为了构建一个能够执行风格迁移的任务，首先需要导入一些基本的库。

import tensorflow as tf
from tensorflow.keras.applications.vgg19 import VGG19, preprocess_input
from tensorflow.keras.models import Model
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

#### 定义辅助函数来处理图像
定义几个帮助函数用于加载并预处理输入图片以及显示最终的结果图。

def load_image(image_path, max_dim=512):
    img = Image.open(image_path)
    img.thumbnail((max_dim, max_dim))
    img = np.array(img)

    # Add batch dimension and apply preprocessing required by VGG19.
    img = preprocess_input(np.expand_dims(img, axis=0))

    return img


def imshow(image, title=None):
    if len(image.shape) > 3:
        image = tf.squeeze(image, axis=0).numpy()

    plt.imshow(image)
    if title is not None:
        plt.title(title)

    plt.axis('off')

#### 构建VGG19模型实例化对象
创建一个特定配置下的VGG19模型实例，该模型仅保留卷积基部分而不包含顶部全连接分类器，并设置`trainable=False`以冻结参数防止更新。

content_layers = ['block5_conv2'] 

style_layers = [
    'block1_conv1',
    'block2_conv1', 
    'block3_conv1', 
    'block4_conv1', 
    'block5_conv1'
]

num_content_layers = len(content_layers)
num_style_layers = len(style_layers)


def get_model():
    """Creates our model with access to intermediate layers."""
    
    # Load pre-trained VGG19 without classifier on top.
    vgg = VGG19(include_top=False, weights='imagenet')

    outputs = [vgg.get_layer(name).output for name in (style_layers + content_layers)]

    # Build the custom model that exposes these outputs.
    model = Model(inputs=[vgg.input], outputs=outputs)

    # Set trainable flag off since we don't want to train this network further.
    model.trainable = False
    
    return model

#### 计算内容损失(Content Loss)
通过比较目标图像与原始内容图像之间指定层激活值之间的均方误差(MSE)，可以量化两者间的相似度差异。

def get_content_loss(base_content, target):
    return tf.reduce_mean(tf.square(target - base_content))

#### 计算样式损失(Style Loss)
对于每一层而言，先计算Gram矩阵再求解两幅画作间对应位置处元素差平方之和除以其总数目得到平均欧式距离；最后累加各层次得分即得总分。

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


def get_style_loss(base_style, generated_style):
    height, width, channels = generated_style.get_shape().as_list()
    size = height * width * channels
    loss = tf.reduce_sum(tf.square(generated_style - base_style)) / (4.0 * (channels ** 2) * ((height * width) ** 2))
    return loss

#### 组合内容损失和样式损失
将上述两种类型的损失按照一定比例组合起来形成总的优化目标。

def compute_loss(combination_image, content_features, style_features, model):
    combination_outputs = model(combination_image)

    style_weight = 1e-2
    content_weight = 1e4

    total_variation_weight = 30.

    style_score = 0
    content_score = 0

    # Accumulate style losses from all layers
    weight_per_style_layer = 1.0 / float(num_style_layers)
    for i in range(len(style_features)):
        style_score += weight_per_style_layer * \
            get_style_loss(style_features[i],
                           combination_outputs[num_content_layers+i])

    # Accumulate content losses from all layers
    weight_per_content_layer = 1.0 / float(num_content_layers)
    for j in range(len(content_features)):
        content_score += weight_per_content_layer * \
            get_content_loss(
                content_features[j],
                combination_outputs[j]
            )

    variation_loss = tf.image.total_variation(combination_image)[0]

    loss = style_weight*style_score + content_weight*content_score + total_variation_weight*variation_loss
    return loss

#### 执行梯度下降算法最小化损失函数
初始化随机噪声作为初始猜测值并通过AdamOptimizer迭代调整直至收敛至局部最优解。

@tf.function()
def train_step(image, extractor, optimizer, content_targets, style_targets):

    with tf.GradientTape() as tape:
        outputs = extractor(image)
        
        loss = compute_loss(image,
                            content_targets,
                            style_targets,
                            extractor.model)

    grad = tape.gradient(loss, image)
    optimizer.apply_gradients([(grad, image)])
    image.assign(tf.clip_by_value(image, clip_value_min=-1., clip_value_max=1.))

#### 创建提取器类封装前面提到的功能模块
简化调用接口方便后续操作流程设计。

class StyleContentModel(Model):
    def __init__(self, style_layers, content_layers):
        super().__init__()
        self.vgg = get_model()
        self.style_layers = style_layers
        self.content_layers = content_layers
        self.num_style_layers = len(style_layers)
        self.vgg.trainable = False
        
    @property
    def model(self):
        return self.vgg

    def call(self, inputs):
        "Expects float input in [-1,1]"
        inputs = inputs*127.5+127.5
        preprocessed_input = preprocess_input(inputs)
        outputs = self.vgg(preprocessed_input)
        style_outputs, content_outputs = (
            outputs[:self.num_style_layers],
            outputs[self.num_style_layers

调用VGG预训练模型

要调用VGG预训练模型，你需要先安装Keras和TensorFlow，并下载VGG预训练模型权重文件。

以下是调用VGG16模型的示例代码：

from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np

# 加载预训练模型
model = VGG16(weights='imagenet', include_top=True)

# 加载要测试的图像
img_path = 'path/to/your/image.jpg'
img = image.load_img(img_path, target_size=(224, 224))

# 将图像转换为数组形式
x = image.img_to_array(img)

# 将数组扩展为4D张量
x = np.expand_dims(x, axis=0)

# 预处理图像
x = preprocess_input(x)

# 预测图像的类别
preds = model.predict(x)

# 打印预测结果
print('Predicted:', decode_predictions(preds, top=3)[0])

这个代码片段中，`VGG16()`函数会加载预训练模型，`image.load_img()`函数会加载要测试的图像，并且`preprocess_input()`函数会对图像进行预处理。最后，`model.predict()`函数会对图像进行预测并输出预测结果。