把上面的步骤生成示例代码

以下是一个Python示例代码，用于按照上述步骤从一个数据集中提取图像特征：

import numpy as np
import tensorflow as tf
from tensorflow.keras.applications import VGG16
from PIL import Image

# Load pre-trained VGG16 model
vgg16 = VGG16(weights='imagenet', include_top=False)

# Define a function to extract features from an image
def extract_features(img_path):
    # Load the image and resize it to fit the input size of VGG16
    img = Image.open(img_path)
    img = img.resize((224, 224))
    
    # Convert the image to an array of pixels
    img_array = np.array(img)
    img_array = img_array.reshape((1, 224, 224, 3))
    img_array = img_array.astype('float32')
    
    # Preprocess the image data by subtracting the mean pixel values of ImageNet
    img_array = tf.keras.applications.vgg16.preprocess_input(img_array)
    
    # Use VGG16 to extract features from the image
    features = vgg16.predict(img_array)
    
    # Flatten the feature map to a 1D array
    features = features.reshape((-1,))
    
    return features

# Load a list of image paths
img_paths = ['image1.jpg', 'image2.jpg', 'image3.jpg']

# Extract features from each image and store them in a list
features_list = []
for img_path in img_paths:
    features = extract_features(img_path)
    features_list.append(features)
    
# Convert the list of features to a NumPy array
features_array = np.array(features_list)

# Save the features array to a file for later use
np.save('features.npy', features_array)

在这个示例代码中，我们首先加载了预训练的VGG16模型，并定义了一个函数来从图像中提取特征。然后，我们加载一个包含多个图像路径的列表，并对每个图像调用函数来提取特征。最后，我们将所有的特征存储在一个NumPy数组中，并将它保存到一个文件中以备后续使用。