用Python做个海量小姐姐素描图("用Python生成海量小姐姐素描图教程")

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ithorizon 6个月前 (10-21) 阅读数 78 #后端开发

用Python生成海量小姐姐素描图教程

一、前言

在如今这个数字时代，人工智能已经可以完成许多令人惊叹的任务，其中包括生成艺术作品。本文将为您介绍怎样使用Python生成海量小姐姐素描图。我们将使用深度学习技术，结合生成对抗网络（GAN）来实现这一目标。

二、所需环境

在进行本项目之前，请确保您的Python环境已经安装以下库：

TensorFlow 2.x

Keras

NumPy

Matplotlib

OpenCV

三、项目步骤

以下是生成海量小姐姐素描图的详细步骤：

1. 准备数据集

首先，我们需要准备一个包含小姐姐素描图的数据集。这里我们使用一个开源的数据集，例如Face Dataset。将数据集下载到本地后，解压并整理成以下目录结构：

data/

train/

girl1.jpg

girl2.jpg

...

val/

girl1.jpg

girl2.jpg

...

2. 数据预处理

在训练之前，我们需要对数据进行预处理。以下是一个数据预处理的示例代码：


import os
import numpy as np
from tensorflow.keras.preprocessing.image import ImageDataGenerator
def load_data(data_dir, img_size=(128, 128)):
    train_datagen = ImageDataGenerator(rescale=1./255)
    val_datagen = ImageDataGenerator(rescale=1./255)
    train_generator = train_datagen.flow_from_directory(
        os.path.join(data_dir, 'train'),
        target_size=img_size,
        batch_size=32,
        class_mode='binary'
    )
    val_generator = val_datagen.flow_from_directory(
        os.path.join(data_dir, 'val'),
        target_size=img_size,
        batch_size=32,
        class_mode='binary'
    )
    return train_generator, val_generator

3. 构建生成对抗网络（GAN）

接下来，我们将构建一个生成对抗网络。以下是一个单纯的GAN结构示例代码：


import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Flatten, Reshape
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, BatchNormalization, LeakyReLU
from tensorflow.keras.models import Model
def build_generator(z_dim):
    inputs = Input(shape=(z_dim,))
    x = Dense(128 * 7 * 7)(inputs)
    x = Reshape((7, 7, 128))(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2DTranspose(128, kernel_size=4, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2DTranspose(128, kernel_size=4, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(3, kernel_size=3, padding='same', activation='tanh')(x)
    generator = Model(inputs, x)
    return generator
def build_discriminator(img_shape):
    inputs = Input(shape=img_shape)
    x = Conv2D(64, kernel_size=3, strides=2, padding='same')(inputs)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(128, kernel_size=3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(128, kernel_size=3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Flatten()(x)
    x = Dense(1, activation='sigmoid')(x)
    discriminator = Model(inputs, x)
    return discriminator
z_dim = 100
img_shape = (128, 128, 3)
generator = build_generator(z_dim)
discriminator = build_discriminator(img_shape)

4. 训练生成对抗网络（GAN）

现在我们已经构建了生成器和判别器，接下来就是训练GAN了。以下是一个训练GAN的示例代码：


import matplotlib.pyplot as plt
def train_gan(generator, discriminator, train_generator, val_generator, epochs, batch_size):
    generator.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001))
    discriminator.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001))
    for epoch in range(epochs):
        for batch_idx, (real_images, _) in enumerate(train_generator):
            batch_size = real_images.shape[0]
            # 生成随机噪声
            z = np.random.normal(0, 1, (batch_size, z_dim))
            # 生成假图像
            generated_images = generator.predict(z)
            # 训练判别器
            real_images = real_images / 255.0
            generated_images = generated_images / 255.0
            d_loss_real = discriminator.train_on_batch(real_images, np.ones((batch_size, 1)))
            d_loss_fake = discriminator.train_on_batch(generated_images, np.zeros((batch_size, 1)))
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
            # 生成随机噪声
            z = np.random.normal(0, 1, (batch_size, z_dim))
            # 训练生成器
            g_loss = generator.train_on_batch(z, np.ones((batch_size, 1)))
            # 打印进度
            if batch_idx % 100 == 0:
                print(f"Epoch {epoch + 1}/{epochs}, Batch {batch_idx}/{train_generator.samples // batch_size}, "
                      f"Discriminator loss: {d_loss}, Generator loss: {g_loss}")
        # 生成一些图像以进行可视化
        if (epoch + 1) % 10 == 0:
            z = np.random.normal(0, 1, (10, z_dim))
            generated_images = generator.predict(z)
            generated_images = generated_images / 255.0
            plt.figure(figsize=(10, 10))
            for i in range(10):
                plt.subplot(1, 10, i + 1)
                plt.imshow(generated_images[i])
                plt.axis('off')
            plt.show()
train_gan(generator, discriminator, train_generator, val_generator, epochs=100, batch_size=32)