关于python做过的比较好玩的事

这段时间学了python，对于python这种优雅简洁的语言深深吸引，在网上看到一个github上的开源项目，觉得比较有意思，就自己研究一番，现将结果记录下来，以志自己这一路学习走来的历程。

因为python初学者对于python的环境安装的学习比较陡峭，所以安装了Anaconda3 这一集成环境软件。

这个项目能够通过算法将一些世界名画的风格应用到自己的照片。

第一步

安装环境依赖  keras   h5py   tensorflow

第二步

配置运行环境

下载VGG16模型 放入如下目录当中

C:\Users\Administrator\.keras\models  如果没有可以创建

第三步

# -*- coding: utf-8 -*-

""" Spyder Editor

This is a temporary script file.

"""

from __future__ import print_function

from keras.preprocessing.image import load_img, img_to_array from scipy.misc import imsave import numpy as np from scipy.optimize import fmin_l_bfgs_b import time import argparse

from keras.applications import vgg16

from keras import backend as K

parser = argparse.ArgumentParser(description='Neural style transfer with Keras.') parser.add_argument('base_image_path', metavar='base', type=str,                     help='Path to the image to transform.') parser.add_argument('style_reference_image_path', metavar='ref', type=str,                     help='Path to the style reference image.') parser.add_argument('result_prefix', metavar='res_prefix', type=str,                     help='Prefix for the saved results.') parser.add_argument('--iter', type=int, default=20, required=False,      # 此处为设置迭代次数                     help='Number of iterations to run.') parser.add_argument('--content_weight', type=float, default=0.025, required=False,                     help='Content weight.') parser.add_argument('--style_weight', type=float, default=1.0, required=False,                     help='Style weight.') parser.add_argument('--tv_weight', type=float, default=1.0, required=False,                     help='Total Variation weight.')

args = parser.parse_args()

base_image_path = args.base_image_path

print('base_image_path',base_image_path)

style_reference_image_path = args.style_reference_image_path

print('style_reference_image_path',style_reference_image_path)

result_prefix = args.result_prefix

print('result_prefix',result_prefix)

iterations = args.iter

print('iter',iterations)

# these are the weights of the different loss components

total_variation_weight = args.tv_weight

print('tv_weight',total_variation_weight)

style_weight = args.style_weight

print('style_weight',style_weight)

content_weight = args.content_weight

print('content_weight',content_weight)

# dimensions of the generated picture.

width, height = load_img(base_image_path).size

print('width : %s,height : %s' %(width,height) )

img_nrows = 400

print('img_nrows',img_nrows)

img_ncols = int(width * img_nrows / height)

print('img_ncols',img_ncols)

# util function to open, resize and format pictures into appropriate tensors

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 = vgg16.preprocess_input(img)     return img

# util function to convert a tensor into a valid image

def deprocess_image(x):     if K.image_data_format() == 'channels_first':         x = x.reshape((3, img_nrows, img_ncols))         x = x.transpose((1, 2, 0))     else:         x = x.reshape((img_nrows, img_ncols, 3))     # Remove zero-center by mean pixel     x[:, :, 0] += 103.939     x[:, :, 1] += 116.779     x[:, :, 2] += 123.68     # 'BGR'->'RGB'     x = x[:, :, ::-1]     x = np.clip(x, 0, 255).astype('uint8')     return x

# get tensor representations of our images

base_image = K.variable(preprocess_image(base_image_path)) style_reference_image = K.variable(preprocess_image(style_reference_image_path))

# this will contain our generated image

if K.image_data_format() == 'channels_first':     combination_image = K.placeholder((1, 3, img_nrows, img_ncols)) else:     combination_image = K.placeholder((1, img_nrows, img_ncols, 3))

# combine the 3 images into a single Keras tensor

input_tensor = K.concatenate([base_image,                               style_reference_image,                               combination_image], axis=0)

# build the VGG16 network with our 3 images as input

# the model will be loaded with pre-trained ImageNet weights model = vgg16.VGG16(input_tensor=input_tensor,                     weights='imagenet', include_top=False) print('Model loaded.')

# get the symbolic outputs of each "key" layer (we gave them unique names).

outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])

# compute the neural style loss

# first we need to define 4 util functions

# the gram matrix of an image tensor (feature-wise outer product)

def gram_matrix(x):     assert K.ndim(x) == 3     if K.image_data_format() == 'channels_first':         features = K.batch_flatten(x)     else:         features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))     gram = K.dot(features, K.transpose(features))     return gram

# the "style loss" is designed to maintain

# the style of the reference image in the generated image. # It is based on the gram matrices (which capture style) of # feature maps from the style reference image # and from the generated image

def style_loss(style, combination):     assert K.ndim(style) == 3     assert K.ndim(combination) == 3     S = gram_matrix(style)     C = gram_matrix(combination)     channels = 3     size = img_nrows * img_ncols     return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))

# an auxiliary loss function

# designed to maintain the "content" of the # base image in the generated image

def content_loss(base, combination):     return K.sum(K.square(combination - base))

# the 3rd loss function, total variation loss,

# designed to keep the generated image locally coherent

def total_variation_loss(x):     assert K.ndim(x) == 4     if K.image_data_format() == 'channels_first':         a = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, 1:, :img_ncols - 1])         b = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, :img_nrows - 1, 1:])     else:         a = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, 1:, :img_ncols - 1, :])         b = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, :img_nrows - 1, 1:, :])     return K.sum(K.pow(a + b, 1.25))

# combine these loss functions into a single scalar

loss = K.variable(0.) layer_features = outputs_dict['block4_conv2'] base_image_features = layer_features[0, :, :, :] combination_features = layer_features[2, :, :, :] loss += content_weight * content_loss(base_image_features,                                       combination_features)

feature_layers = ['block1_conv1', 'block2_conv1',

                  'block3_conv1', 'block4_conv1',                   'block5_conv1'] for layer_name in feature_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(feature_layers)) * sl loss += total_variation_weight * total_variation_loss(combination_image)

# get the gradients of the generated image wrt the loss

grads = K.gradients(loss, combination_image)

outputs = [loss]

if isinstance(grads, (list, tuple)):     outputs += grads else:     outputs.append(grads)

f_outputs = K.function([combination_image], outputs)

def eval_loss_and_grads(x):     if K.image_data_format() == 'channels_first':         x = x.reshape((1, 3, img_nrows, img_ncols))     else:         x = x.reshape((1, img_nrows, img_ncols, 3))     outs = f_outputs([x])     loss_value = outs[0]     if len(outs[1:]) == 1:         grad_values = outs[1].flatten().astype('float64')     else:         grad_values = np.array(outs[1:]).flatten().astype('float64')     return loss_value, grad_values

# this Evaluator class makes it possible

# to compute loss and gradients in one pass # while retrieving them via two separate functions, # "loss" and "grads". This is done because scipy.optimize # requires separate functions for loss and gradients, # but computing them separately would be inefficient.

class Evaluator(object):

    def __init__(self):

        self.loss_value = None         self.grads_values = None

    def loss(self, x):

        assert self.loss_value is None         loss_value, grad_values = eval_loss_and_grads(x)         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()

# run scipy-based optimization (L-BFGS) over the pixels of the generated image

# so as to minimize the neural style loss if K.image_data_format() == 'channels_first':     x = np.random.uniform(0, 255, (1, 3, img_nrows, img_ncols)) - 128. else:     x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.

for i in range(iterations):

    print('Start of iteration', i)     start_time = time.time()     x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),                                      fprime=evaluator.grads, maxfun=20)     print('Current loss value:', min_val)     # save current generated image     img = deprocess_image(x.copy())     fname = result_prefix + '_at_iteration_%d.png' % i     imsave(fname, img)     end_time = time.time()     print('Image saved as', fname) print('Iteration %d completed in %ds' % (i, end_time - start_time))

第四步

将该py文件放到一个文件夹中，也将模板图片放到该文件夹中 ，将需要转换的照片也放到该文件夹中，

启动 Anaconda  Prompt 进入到该文件夹中，执行 

python   py文件名.py      需要转换的照片      模板图片    生成的图片(生成的图片不需要加后缀)

python    style.py     ./yxc.jpg      ./style.jpg       ./yxc_style   

 

模板图片