vgg 图像风格迁移

### 如何使用 VGG 实现图像风格迁移 #### 准备工作 为了实现基于VGG网络的图像风格迁移，需先安装必要的库并加载预训练的VGG19模型。此过程依赖于Python编程语言以及TensorFlow框架来构建和运行模型[^1]。 ```bash pip install tensorflow numpy matplotlib scipy pillow h5py ``` 接着导入所需模块： ```python import numpy as np from keras.applications import vgg19 from keras.preprocessing.image import load_img, img_to_array from scipy.optimize import fmin_l_bfgs_b from PIL import Image import time ``` 定义一些辅助函数用于读取、显示和保存图片: ```python def preprocess_image(image_path): img = load_img(image_path, target_size=(img_nrows, img_ncols)) img = img_to_array(img) img = np.expand_dims(img, axis=0) img = vgg19.preprocess_input(img) return img def deprocess_image(x): x[:, :, 0] += 103.939 x[:, :, 1] += 116.779 x[:, :, 2] += 123.68 x = x[:, :, ::-1] x = np.clip(x, 0, 255).astype('uint8') return x ``` 设置输入参数，包括原始图片路径、样式图片路径以及其他配置项： ```python content_image_path = 'path/to/content/image.jpg' style_reference_image_path = 'path/to/style/reference/image.jpg' width, height = load_img(content_image_path).size img_nrows = 400 img_ncols = int(width * img_nrows / height) total_variation_weight = 1e-4 style_weight = 1. content_weight = 0.025 iterations = 10 ``` 创建Keras张量表示内容图片、样式图片及组合后的目标图片，并通过`vgg19.VGG19()`方法获取不带分类层(`include_top=False`)且已在ImageNet数据集上预先训练好的VGG19实例[^3]: ```python input_tensor = backend.concatenate([ backend.variable(preprocess_image(content_image_path)), backend.variable(preprocess_image(style_reference_image_path)), backend.placeholder((1, img_nrows, img_ncols, 3)) ], axis=0) model = vgg19.VGG19(input_tensor=input_tensor, weights='imagenet', include_top=False) print('Model loaded.') ``` 计算损失函数及其梯度，优化器将尝试最小化这个总损失值以获得最佳的艺术效果[^2]: ```python outputs_dict = dict([(layer.name, layer.output) for layer in model.layers]) content_layer = 'block5_conv2' style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1'] loss = backend.variable(0.) layer_features = outputs_dict[content_layer] target_image_features = layer_features[0, :, :, :] combination_features = layer_features[2, :, :, :] loss += content_weight * content_loss(target_image_features, combination_features) for layer_name in style_layers: layer_features = outputs_dict[layer_name] style_reference_features = layer_features[1, :, :, :] combination_features = layer_features[2, :, :, :] sl = style_loss(style_reference_features, combination_features) loss += (style_weight / len(style_layers)) * sl grads = backend.gradients(loss, input_tensor[-1]) fetch_loss_and_grads = backend.function([input_tensor], [loss]+grads) class Evaluator(object): def __init__(self): self.loss_value = None self.grad_values = None def loss(self, x): assert self.loss_value is None x = x.reshape((1, img_nrows, img_ncols, 3)) outs = fetch_loss_and_grads([x]) loss_value = outs[0] grad_values = outs[1].flatten().astype('float64') self.loss_value = loss_value self.grad_values = grad_values return self.loss_value def grads(self, x): assert self.loss_value is not None grad_values = np.copy(self.grad_values) self.loss_value = None self.grad_values = None return grad_values evaluator = Evaluator() ``` 最后一步是执行迭代更新操作直至达到预定次数或满足收敛条件为止，在每次循环结束时可以查看当前合成的结果以便及时调整参数设定[^4]: ```python x = preprocess_image(content_image_path) x = x.flatten() for i in range(iterations): print('Start of iteration', i+1) start_time = time.time() x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x, fprime=evaluator.grads, maxfun=20) print('Current loss value:', min_val) end_time = time.time() print('Iteration %d completed in %ds' % (i+1, end_time-start_time)) result_img = x.copy().reshape((img_nrows, img_ncols, 3)) result_img = deprocess_image(result_img) fname = result_prefix + '_at_iteration_%d.png' % (i+1) save_img(fname, result_img) display(Image.open(fname)) ``` 上述代码展示了完整的流程，从准备环境到最终生成带有指定艺术风格的新图像。值得注意的是，实际应用过程中可能还需要根据具体需求微调超参如权重比例等，从而得到更理想的效果。