如何使用Python对图片进行风格迁移

如何使用Python对图片进行风格迁移

引言：风格迁移是计算机视觉领域一项有趣而有挑战性的任务，它可以将一张图片的内容与另一张图片的风格进行合成，创造出独特的艺术效果，被广泛应用于图像处理、设计以及娱乐等领域。本文将介绍如何使用Python编程语言，结合深度学习算法，实现对图片的风格迁移。

步骤一：导入所需库首先，我们需要导入一些必要的Python库，包括TensorFlow、Keras、NumPy和Matplotlib。执行以下代码：

import tensorflow as tf from tensorflow import keras import numpy as np import matplotlib.pyplot as plt登录后复制

vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet') vgg.trainable = False登录后复制

content_layers = ['block5_conv2'] content_extractor = keras.Model(inputs=vgg.input, outputs=[vgg.get_layer(name).output for name in content_layers])登录后复制

style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1'] style_extractor = keras.Model(inputs=vgg.input, outputs=[vgg.get_layer(name).output for name in style_layers]) def gram_matrix(input_tensor): channels = int(input_tensor.shape[-1]) a = tf.reshape(input_tensor, [-1, channels]) n = tf.shape(a)[0] gram = tf.matmul(a, a, transpose_a=True) return gram / tf.cast(n, tf.float32)登录后复制

def total_variation_loss(image): x = tf.image.image_gradients(image) return tf.reduce_sum(tf.abs(x[0])) + tf.reduce_sum(tf.abs(x[1]))登录后复制

def compute_loss(image, content_features, style_features): content_output = content_extractor(image) style_output = style_extractor(image) content_loss = tf.reduce_mean(tf.square(content_output - content_features)) style_loss = tf.add_n([tf.reduce_mean(tf.square(style_output[i] - style_features[i])) for i in range(len(style_output))]) content_loss *= content_weight style_loss *= style_weight tv_loss = total_variation_loss(image) * total_variation_weight loss = content_loss + style_loss + tv_loss return loss @tf.function() def train_step(image, content_features, style_features, optimizer): with tf.GradientTape() as tape: loss = compute_loss(image, content_features, style_features) gradients = tape.gradient(loss, image) optimizer.apply_gradients([(gradients, image)]) image.assign(tf.clip_by_value(image, 0.0, 1.0))登录后复制

def style_transfer(content_path, style_path, num_iteration=1000, content_weight=1e3, style_weight=1e-2, total_variation_weight=30): content_image = load_image(content_path) style_image = load_image(style_path) content_features = content_extractor(content_image) style_features = style_extractor(style_image) opt = keras.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1) image = tf.Variable(content_image) start_time = time.time() for i in range(num_iteration): train_step(image, content_features, style_features, opt) if i % 100 == 0: elapsed_time = time.time() - start_time print('Iteration: %d, Time: %.2fs' % (i, elapsed_time)) plt.imshow(image.read_value()[0]) plt.axis('off') plt.show() image = image.read_value()[0] return image登录后复制

content_path = 'content.jpg' style_path = 'style.jpg' output_image = style_transfer(content_path, style_path) plt.imshow(output_image) plt.axis('off') plt.show()登录后复制

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