TensorFlow基础笔记 图像风格化实验
Posted Image Process
tags:
篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了TensorFlow基础笔记 图像风格化实验相关的知识，希望对你有一定的参考价值。
neural_style.py
# Copyright (c) 2015-2016 Anish Athalye. Released under GPLv3. import os import numpy as np import scipy.misc from stylize import stylize import math from argparse import ArgumentParser #python neural_style.py --content gril.jpg --styles 3dtest.jpg --out output.jpg # default arguments CONTENT_WEIGHT = 5e0 STYLE_WEIGHT = 1e2 TV_WEIGHT = 1e2 LEARNING_RATE = 1e2 STYLE_SCALE = 1.0 ITERATIONS = 1000 VGG_PATH = ‘./imagenet-vgg-verydeep-19.mat‘ content = ‘./image/content.jpg‘ styles = ‘./image/styles.jpg‘ out = ‘./image/output.jpg‘ def build_parser(): parser = ArgumentParser() parser.add_argument(‘--content‘, dest=‘content‘, help=‘content image‘, metavar=‘CONTENT‘, default=content) parser.add_argument(‘--styles‘, dest=‘styles‘, nargs=‘+‘, help=‘one or more style images‘, metavar=‘STYLE‘, default=styles) parser.add_argument(‘--output‘, dest=‘output‘, help=‘output path‘, metavar=‘OUTPUT‘, default=out) parser.add_argument(‘--checkpoint-output‘, dest=‘checkpoint_output‘, help=‘checkpoint output format‘, metavar=‘OUTPUT‘) parser.add_argument(‘--iterations‘, type=int, dest=‘iterations‘, help=‘iterations (default %(default)s)‘, metavar=‘ITERATIONS‘, default=ITERATIONS) parser.add_argument(‘--width‘, type=int, dest=‘width‘, help=‘output width‘, metavar=‘WIDTH‘) parser.add_argument(‘--style-scales‘, type=float, dest=‘style_scales‘, nargs=‘+‘, help=‘one or more style scales‘, metavar=‘STYLE_SCALE‘) parser.add_argument(‘--network‘, dest=‘network‘, help=‘path to network parameters (default %(default)s)‘, metavar=‘VGG_PATH‘, default=VGG_PATH) parser.add_argument(‘--content-weight‘, type=float, dest=‘content_weight‘, help=‘content weight (default %(default)s)‘, metavar=‘CONTENT_WEIGHT‘, default=CONTENT_WEIGHT) parser.add_argument(‘--style-weight‘, type=float, dest=‘style_weight‘, help=‘style weight (default %(default)s)‘, metavar=‘STYLE_WEIGHT‘, default=STYLE_WEIGHT) parser.add_argument(‘--style-blend-weights‘, type=float, dest=‘style_blend_weights‘, help=‘style blending weights‘, nargs=‘+‘, metavar=‘STYLE_BLEND_WEIGHT‘) parser.add_argument(‘--tv-weight‘, type=float, dest=‘tv_weight‘, help=‘total variation regularization weight (default %(default)s)‘, metavar=‘TV_WEIGHT‘, default=TV_WEIGHT) parser.add_argument(‘--learning-rate‘, type=float, dest=‘learning_rate‘, help=‘learning rate (default %(default)s)‘, metavar=‘LEARNING_RATE‘, default=LEARNING_RATE) parser.add_argument(‘--initial‘, dest=‘initial‘, help=‘initial image‘, metavar=‘INITIAL‘) parser.add_argument(‘--print-iterations‘, type=int, dest=‘print_iterations‘, help=‘statistics printing frequency‘, metavar=‘PRINT_ITERATIONS‘) parser.add_argument(‘--checkpoint-iterations‘, type=int, dest=‘checkpoint_iterations‘, help=‘checkpoint frequency‘, metavar=‘CHECKPOINT_ITERATIONS‘) return parser def build_parser_src(): parser = ArgumentParser() parser.add_argument(‘--content‘, dest=‘content‘, help=‘content image‘, metavar=‘CONTENT‘, required=True) parser.add_argument(‘--styles‘, dest=‘styles‘, nargs=‘+‘, help=‘one or more style images‘, metavar=‘STYLE‘, required=True) parser.add_argument(‘--output‘, dest=‘output‘, help=‘output path‘, metavar=‘OUTPUT‘, required=True) parser.add_argument(‘--checkpoint-output‘, dest=‘checkpoint_output‘, help=‘checkpoint output format‘, metavar=‘OUTPUT‘) parser.add_argument(‘--iterations‘, type=int, dest=‘iterations‘, help=‘iterations (default %(default)s)‘, metavar=‘ITERATIONS‘, default=ITERATIONS) parser.add_argument(‘--width‘, type=int, dest=‘width‘, help=‘output width‘, metavar=‘WIDTH‘) parser.add_argument(‘--style-scales‘, type=float, dest=‘style_scales‘, nargs=‘+‘, help=‘one or more style scales‘, metavar=‘STYLE_SCALE‘) parser.add_argument(‘--network‘, dest=‘network‘, help=‘path to network parameters (default %(default)s)‘, metavar=‘VGG_PATH‘, default=VGG_PATH) parser.add_argument(‘--content-weight‘, type=float, dest=‘content_weight‘, help=‘content weight (default %(default)s)‘, metavar=‘CONTENT_WEIGHT‘, default=CONTENT_WEIGHT) parser.add_argument(‘--style-weight‘, type=float, dest=‘style_weight‘, help=‘style weight (default %(default)s)‘, metavar=‘STYLE_WEIGHT‘, default=STYLE_WEIGHT) parser.add_argument(‘--style-blend-weights‘, type=float, dest=‘style_blend_weights‘, help=‘style blending weights‘, nargs=‘+‘, metavar=‘STYLE_BLEND_WEIGHT‘) parser.add_argument(‘--tv-weight‘, type=float, dest=‘tv_weight‘, help=‘total variation regularization weight (default %(default)s)‘, metavar=‘TV_WEIGHT‘, default=TV_WEIGHT) parser.add_argument(‘--learning-rate‘, type=float, dest=‘learning_rate‘, help=‘learning rate (default %(default)s)‘, metavar=‘LEARNING_RATE‘, default=LEARNING_RATE) parser.add_argument(‘--initial‘, dest=‘initial‘, help=‘initial image‘, metavar=‘INITIAL‘) parser.add_argument(‘--print-iterations‘, type=int, dest=‘print_iterations‘, help=‘statistics printing frequency‘, metavar=‘PRINT_ITERATIONS‘) parser.add_argument(‘--checkpoint-iterations‘, type=int, dest=‘checkpoint_iterations‘, help=‘checkpoint frequency‘, metavar=‘CHECKPOINT_ITERATIONS‘) return parser def main(): parser = build_parser() options = parser.parse_args() if not os.path.isfile(options.network): parser.error("Network %s does not exist. (Did you forget to download it?)" % options.network) content_image = imread(options.content) #for style in options.styles: # print(style) #style_images = [imread(style) for style in options.styles] style_images = [imread(options.styles)] width = options.width if width is not None: new_shape = (int(math.floor(float(content_image.shape[0]) / content_image.shape[1] * width)), width) content_image = scipy.misc.imresize(content_image, new_shape) target_shape = content_image.shape for i in range(len(style_images)): style_scale = STYLE_SCALE if options.style_scales is not None: style_scale = options.style_scales[i] style_images[i] = scipy.misc.imresize(style_images[i], style_scale * target_shape[1] / style_images[i].shape[1]) style_blend_weights = options.style_blend_weights if style_blend_weights is None: # default is equal weights style_blend_weights = [1.0/len(style_images) for _ in style_images] else: total_blend_weight = sum(style_blend_weights) style_blend_weights = [weight/total_blend_weight for weight in style_blend_weights] initial = options.initial if initial is not None: initial = scipy.misc.imresize(imread(initial), content_image.shape[:2]) if options.checkpoint_output and "%s" not in options.checkpoint_output: parser.error("To save intermediate images, the checkpoint output " "parameter must contain `%s` (e.g. `foo%s.jpg`)") for iteration, image in stylize( network=options.network, initial=initial, content=content_image, styles=style_images, iterations=options.iterations, content_weight=options.content_weight, style_weight=options.style_weight, style_blend_weights=style_blend_weights, tv_weight=options.tv_weight, learning_rate=options.learning_rate, print_iterations=options.print_iterations, checkpoint_iterations=options.checkpoint_iterations ): output_file = None if iteration is not None: if options.checkpoint_output: output_file = options.checkpoint_output % iteration else: output_file = options.output if output_file: imsave(output_file, image) def imread(path): return scipy.misc.imread(path).astype(np.float) def imsave(path, img): img = np.clip(img, 0, 255).astype(np.uint8) scipy.misc.imsave(path, img) if __name__ == ‘__main__‘: main()
stylize.py
# Copyright (c) 2015-2016 Anish Athalye. Released under GPLv3. import vgg import tensorflow as tf import numpy as np from sys import stderr CONTENT_LAYER = ‘relu4_2‘ STYLE_LAYERS = (‘relu1_1‘, ‘relu2_1‘, ‘relu3_1‘, ‘relu4_1‘, ‘relu5_1‘) try: reduce except NameError: from functools import reduce def stylize(network, initial, content, styles, iterations, content_weight, style_weight, style_blend_weights, tv_weight, learning_rate, print_iterations=None, checkpoint_iterations=None): """ Stylize images. This function yields tuples (iteration, image); `iteration` is None if this is the final image (the last iteration). Other tuples are yielded every `checkpoint_iterations` iterations. :rtype: iterator[tuple[int|None,image]] """ shape = (1,) + content.shape style_shapes = [(1,) + style.shape for style in styles] content_features = {} style_features = [{} for _ in styles] # compute content features in feedforward mode g = tf.Graph() with g.as_default(), g.device(‘/cpu:0‘), tf.Session() as sess: image = tf.placeholder(‘float‘, shape=shape) net, mean_pixel = vgg.net(network, image) content_pre = np.array([vgg.preprocess(content, mean_pixel)]) content_features[CONTENT_LAYER] = net[CONTENT_LAYER].eval( feed_dict={image: content_pre}) # compute style features in feedforward mode for i in range(len(styles)): g = tf.Graph() with g.as_default(), g.device(‘/cpu:0‘), tf.Session() as sess: image = tf.placeholder(‘float‘, shape=style_shapes[i]) net, _ = vgg.net(network, image) style_pre = np.array([vgg.preprocess(styles[i], mean_pixel)]) for layer in STYLE_LAYERS: features = net[layer].eval(feed_dict={image: style_pre}) features = np.reshape(features, (-1, features.shape[3])) gram = np.matmul(features.T, features) / features.size style_features[i][layer] = gram # make stylized image using backpropogation with tf.Graph().as_default(): if initial is None: noise = np.random.normal(size=shape, scale=np.std(content) * 0.1) initial = tf.random_normal(shape) * 0.256 else: initial = np.array([vgg.preprocess(initial, mean_pixel)]) initial = initial.astype(‘float32‘) image = tf.Variable(initial) net, _ = vgg.net(network, image) # content loss content_loss = content_weight * (2 * tf.nn.l2_loss( net[CONTENT_LAYER] - content_features[CONTENT_LAYER]) / content_features[CONTENT_LAYER].size) # style loss style_loss = 0 for i in range(len(styles)): style_losses = [] for style_layer in STYLE_LAYERS: layer = net[style_layer] _, height, width, number = map(lambda i: i.value, layer.get_shape()) size = height * width * number feats = tf.reshape(layer, (-1, number)) gram = tf.matmul(tf.transpose(feats), feats) / size style_gram = style_features[i][style_layer] style_losses.append(2 * tf.nn.l2_loss(gram - style_gram) / style_gram.size) style_loss += style_weight * style_blend_weights[i] * reduce(tf.add, style_losses) # total variation denoising tv_y_size = _tensor_size(image[:,1:,:,:]) tv_x_size = _tensor_size(image[:,:,1:,:]) tv_loss = tv_weight * 2 * ( (tf.nn.l2_loss(image[:,1:,:,:] - image[:,:shape[1]-1,:,:]) / tv_y_size) + (tf.nn.l2_loss(image[:,:,1:,:] - image[:,:,:shape[2]-1,:]) / tv_x_size)) # overall loss loss = content_loss + style_loss + tv_loss # optimizer setup train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss) def print_progress(i, last=False): stderr.write(‘Iteration %d/%d\n‘ % (i + 1, iterations)) if last or (print_iterations and i % print_iterations == 0): stderr.write(‘ content loss: %g\n‘ % content_loss.eval()) stderr.write(‘ style loss: %g\n‘ % style_loss.eval()) stderr.write(‘ tv loss: %g\n‘ % tv_loss.eval()) stderr.write(‘ total loss: %g\n‘ % loss.eval()) # optimization best_loss = float(‘inf‘) best = None with tf.Session() as sess: sess.run(tf.initialize_all_variables()) for i in range(iterations): last_step = (i == iterations - 1) print_progress(i, last=last_step) train_step.run() if (checkpoint_iterations and i % checkpoint_iterations == 0) or last_step: this_loss = loss.eval() if this_loss < best_loss: best_loss = this_loss best = image.eval() yield ( (None if last_step else i), vgg.unprocess(best.reshape(shape[1:]), mean_pixel) ) def _tensor_size(tensor): from operator import mul return reduce(mul, (d.value for d in tensor.get_shape()), 1)
vgg.py
# Copyright (c) 2015-2016 Anish Athalye. Released under GPLv3. import tensorflow as tf import numpy as np import scipy.io def net(data_path, input_image): layers = ( ‘conv1_1‘, ‘relu1_1‘, ‘conv1_2‘, ‘relu1_2‘, ‘pool1‘, ‘conv2_1‘, ‘relu2_1‘, ‘conv2_2‘, ‘relu2_2‘, ‘pool2‘, ‘conv3_1‘, ‘relu3_1‘, ‘conv3_2‘, ‘relu3_2‘, ‘conv3_3‘, ‘relu3_3‘, ‘conv3_4‘, ‘relu3_4‘, ‘pool3‘, ‘conv4_1‘, ‘relu4_1‘, ‘conv4_2‘, ‘relu4_2‘, ‘conv4_3‘, ‘relu4_3‘, ‘conv4_4‘, ‘relu4_4‘, ‘pool4‘, ‘conv5_1‘, ‘relu5_1‘, ‘conv5_2‘, ‘relu5_2‘, ‘conv5_3‘, ‘relu5_3‘, ‘conv5_4‘, ‘relu5_4‘ ) data = scipy.io.loadmat(data_path) mean = data[‘normalization‘][0][0][0] mean_pixel = np.mean(mean, axis=(0, 1)) weights = data[‘layers‘][0] net = {} current = input_image for i, name in enumerate(layers): kind = name[:4] if kind == ‘conv‘: kernels, bias = weights[i][0][0][0][0] # matconvnet: weights are [width, height, in_channels, out_channels] # tensorflow: weights are [height, width, in_channels, out_channels] kernels = np.transpose(kernels, (1, 0, 2, 3)) bias = bias.reshape(-1) current = _conv_layer(current, kernels, bias) elif kind == ‘relu‘: current = tf.nn.relu(current) elif kind == ‘pool‘: current = _pool_layer(current) net[name] = current assert len(net) == len(layers) return net, mean_pixel def _conv_layer(input, weights, bias): conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1), padding=‘SAME‘) return tf.nn.bias_add(conv, bias) def _pool_layer(input): return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding=‘SAME‘) def preprocess(image, mean_pixel): return image - mean_pixel def unprocess(image, mean_pixel): return image + mean_pixel
以上是关于TensorFlow基础笔记 图像风格化实验的主要内容，如果未能解决你的问题，请参考以下文章
TensorFlow实战——CNN（VGGNet19）——图像风格转化
[图像处理] 实验笔记 - 直线检测（line detection）之霍夫变换
URL fetch failure on https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz: None(代码片