使用nets.mobilenet_v1进行图像风格转换的代码示例

import tensorflow as tf

import tensorflow_hub as hub

import numpy as np

import PIL.Image as Image

# Load the pre-trained MobileNet V1 model

module = hub.Module("https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/feature_vector/1")

# Define the model for style transfer

def style_transfer(content_image_path, style_image_path, output_image_path):

    # Load and preprocess the content and style images

    content_image = Image.open(content_image_path).resize((224, 224))

    content_image = np.array(content_image) / 255.0

    content_image = np.expand_dims(content_image, axis=0)

    style_image = Image.open(style_image_path).resize((224, 224))

    style_image = np.array(style_image) / 255.0

    style_image = np.expand_dims(style_image, axis=0)

    # Convert the images to tensors

    content_image_tensor = tf.placeholder(tf.float32, shape=(None, 224, 224, 3))

    style_image_tensor = tf.placeholder(tf.float32, shape=(None, 224, 224, 3))

    # Generate the content and style feature vectors

    content_features = module(content_image_tensor)

    style_features = module(style_image_tensor)

    # Define the loss function

    style_loss = tf.reduce_mean(tf.square(content_features - style_features))

    # Define the optimizer

    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)

    train_op = optimizer.minimize(style_loss)

    # Initialize the TensorFlow session

    init = tf.global_variables_initializer()

    sess = tf.Session()

    sess.run(init)

    # Perform style transfer

    for i in range(1000):

        sess.run(train_op, feed_dict={content_image_tensor: content_image, style_image_tensor: style_image})

        if i % 100 == 0:

            loss = sess.run(style_loss, feed_dict={content_image_tensor: content_image, style_image_tensor: style_image})

            print("Loss at iteration {}: {}".format(i, loss))

    # Save the stylized image

    stylized_image = sess.run(content_image_tensor, feed_dict={content_image_tensor: content_image, style_image_tensor: style_image})

    stylized_image = np.squeeze(stylized_image)

    stylized_image = Image.fromarray(np.uint8(stylized_image * 255))

    stylized_image.save(output_image_path)

# Example usage

style_transfer("content.jpg", "style.jpg", "output.jpg")