Python+AI实现给老照片上色

Python+AI实现给老照片上色攻略

给老照片上色是一个比较有趣的任务，我们可以使用Python和AI技术来完成这一任务。在此提供一个完整攻略，包括数据准备，模型训练和照片上色三个部分。

数据准备

在开始训练之前，我们需要准备数据集。可以从互联网上找到带颜色的图片作为我们的ground truth，然后将其转换为黑白照片。我们可以使用pillow库中的Image模块来实现这一步骤。例如：

from PIL import Image

# open the colored image 
color_image = Image.open('color_image.png')

# convert to grayscale
gray_image = color_image.convert('L')
gray_image.save('gray_image.jpg')

此时，我们已经得到了一张黑白照片。我们需要将其分成许多小块，然后在训练过程中将这些小块送入模型中。可以使用scikit-image库中的view_as_blocks函数对照片进行切分，例如：

from skimage.util.shape import view_as_blocks

# open the grayscale image
gray_image = Image.open('gray_image.jpg')

# convert to numpy array
gray_np = np.array(gray_image)

# divide the grayscale image into small blocks
block_shape = (32, 32) # block size
blocks = view_as_blocks(gray_np, block_shape)

现在我们已经得到了许多小的32x32的小块，可以用它们进行后续的模型训练。

模型训练

接下来需要建立一个深度学习模型，用来将黑白照片上色。我们采用深度生成对抗网络（GAN）来实现这一任务。使用Keras库建立模型。

模型的核心部分是判别器和生成器。判别器的作用是区分输入图像是彩色还是黑白的，生成器用于从黑白图像生成彩色图像。

from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Input, LeakyReLU
from tensorflow.keras.models import Model

# define the generator
def build_generator():

    # define the input layer
    input = Input(shape=(32, 32, 1))

    # encode the input image
    x1 = Conv2D(64, (3,3), strides=2, padding='same')(input)
    x1 = LeakyReLU(alpha=0.2)(x1)

    x2 = Conv2D(64, (3,3), strides=2, padding='same')(x1)
    x2 = LeakyReLU(alpha=0.2)(x2)

    # generate the output image
    x3 = Conv2DTranspose(64, (3,3), strides=2, padding='same')(x2)
    x3 = LeakyReLU(alpha=0.2)(x3)

    output = Conv2DTranspose(3, (3,3), strides=2, activation='sigmoid', padding='same')(x3)

    # define the generator model
    model = Model(inputs=input, outputs=output)

    return model

我们还需要定义优化器、损失函数和训练过程。

from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.optimizers import Adam

# compile the generator model
generator = build_generator()
generator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))

# define the adversarial model
real_input = Input(shape=(32, 32, 3))
generator_output = generator(Input(shape=(32, 32, 1)))
discriminator_output = discriminator(generator_output)
adversarial_model = Model(inputs=real_input, outputs=[discriminator_output, generator_output])
adversarial_model.compile(loss=['binary_crossentropy', 'mse'], optimizer=Adam(lr=0.0002, beta_1=0.5))

# train the model
for epoch in range(NB_EPOCHS):

    for i in range(len(x_train)):

        # create real and fake images
        real_image = x_train[i]
        fake_image = generator.predict(x_train_gray[i:i+1])[0]

        # train the discriminator
        loss_real = discriminator.train_on_batch(real_image, np.ones((1, 1)))
        loss_fake = discriminator.train_on_batch(fake_image, np.zeros((1, 1)))
        discriminator_loss = 0.5 * np.add(loss_real, loss_fake)

        # train the generator
        generator_loss = adversarial_model.train_on_batch(x_train_gray[i:i+1], [np.ones((1, 1)), x_train[i:i+1]])

照片上色

一旦我们训练了深度学习模型，就可以使用这个模型来实现照片上色了。我们只需要将黑白照片分成许多小块，然后送入训练好的生成器中，得到相应的彩色图块，最后将这些小块合并，就可以得到一张完整的上色图片。可以使用PIL库来进行这一步骤，例如：

# open the grayscale image
gray_image = Image.open('gray_image.jpg')

# divide the grayscale image into small blocks
block_shape = (32, 32) # block size
blocks = view_as_blocks(np.array(gray_image), block_shape)

# build the generator model
generator = build_generator()
generator.load_weights('generator_weights.h5')

# create a new image
new_image = Image.new('RGB', (gray_image.width, gray_image.height))

# fill the image with color
for i in range(blocks.shape[0]):
    for j in range(blocks.shape[1]):
        gray_block = blocks[i,j]
        color_block = generator.predict(gray_block.reshape((1, 32, 32, 1)))[0]
        color_block = np.clip(color_block * 255, 0, 255)
        color_block = color_block.astype('uint8')
        x1, y1 = j * 32, i * 32
        x2, y2 = x1 + 32, y1 + 32
        new_image.paste(Image.fromarray(color_block), (x1, y1, x2, y2))

# show the result image
new_image.show()

以上就是Python+AI实现给老照片上色的完整攻略。