ai绘画源码

以下是一个使用Python的AI绘画源码示例，可以通过深度学习模型生成艺术作品：

import numpy as np
from keras.preprocessing.image import load_img, img_to_array
from keras.applications import vgg19
from keras import backend as K
from scipy.optimize import fmin_l_bfgs_b
import time

# 定义图像尺寸
height = 512
width = 512
channels = 3

# 加载图像
content_image = load_img('content.jpg', target_size=(height, width))
style_image = load_img('style.jpg', target_size=(height, width))

# 将图像转换为数组
content_array = img_to_array(content_image)
content_array = np.expand_dims(content_array, axis=0)
style_array = img_to_array(style_image)
style_array = np.expand_dims(style_array, axis=0)

# 预处理图像
content_array = vgg19.preprocess_input(content_array)
style_array = vgg19.preprocess_input(style_array)

# 将图像转换为Keras张量
content_image = K.variable(content_array)
style_image = K.variable(style_array)
combination_image = K.placeholder((1, height, width, channels))

# 合并图像为一个批次
input_tensor = K.concatenate([content_image, style_image, combination_image], axis=0)

# 加载预训练的VGG19模型
model = vgg19.VGG19(input_tensor=input_tensor, weights='imagenet', include_top=False)

# 定义内容损失函数
def content_loss(content, combination):
    return K.sum(K.square(combination - content))

# 定义风格损失函数
def gram_matrix(x):
    features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
    gram = K.dot(features, K.transpose(features))
    return gram

def style_loss(style, combination):
    S = gram_matrix(style)
    C = gram_matrix(combination)
    channels = 3
    size = height * width
    return K.sum(K.square(S - C)) / (4.0 * (channels ** 2) * (size ** 2))

# 定义总变差损失函数
def total_variation_loss(x):
    a = K.square(x[:, :height-1, :width-1, :] - x[:, 1:, :width-1, :])
    b = K.square(x[:, :height-1, :width-1, :] - x[:, :height-1, 1:, :])
    return K.sum(K.pow(a + b, 1.25))

# 定义损失函数
content_weight = 0.025
style_weight = 1.0
total_variation_weight = 1.0

loss = K.variable(0.0)
loss = loss + content_weight * content_loss(content_image, combination_image)
loss = loss + style_weight * style_loss(style_image, combination_image)
loss = loss + total_variation_weight * total_variation_loss(combination_image)

# 计算梯度和损失
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)

# 定义评估函数
def evaluate_loss_and_gradients(x):
    x = x.reshape((1, height, width, channels))
    outs = f_outputs([x])
    loss_value = outs[0]
    grad_values = outs[1].flatten().astype('float64')
    return loss_value, grad_values

# 定义回调函数
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 = evaluate_loss_and_gradients(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()

# 运行L-BFGS算法
x = np.random.uniform(0, 255, (1, height, width, 3)) - 128.0
iterations = 10

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)
    end_time = time.time()
    print('Iteration %d completed in %ds' % (i, end_time - start_time))

# 保存生成的图像
from scipy.misc import imsave
x = x.reshape((height, width, channels))
x = x[:, :, ::-1]
x[:, :, 0] += 103.939
x[:, :, 1] += 116.779
x[:, :, 2] += 123.68
x = np.clip(x, 0, 255).astype('uint8')
imsave('result.jpg', x)

请注意，此代码依赖于Keras和TensorFlow，你需要先安装它们才能运行此代码。此外，这只是一个示例，你可以根据需要调整代码以生成不同类型的艺术作品。