【机器学习】和【人工智能】对未来绘图计算的影响

import tensorflow as tf
from tensorflow.keras import layers

# 构建生成器
def build_generator():
    model = tf.keras.Sequential()
    model.add(layers.Dense(256, input_dim=100))
    model.add(layers.LeakyReLU(alpha=0.2))
    model.add(layers.Dense(512))
    model.add(layers.LeakyReLU(alpha=0.2))
    model.add(layers.Dense(1024))
    model.add(layers.LeakyReLU(alpha=0.2))
    model.add(layers.Dense(28 * 28 * 1, activation='tanh'))
    model.add(layers.Reshape((28, 28, 1)))
    return model

# 构建判别器
def build_discriminator():
    model = tf.keras.Sequential()
    model.add(layers.Flatten(input_shape=(28, 28, 1)))
    model.add(layers.Dense(512))
    model.add(layers.LeakyReLU(alpha=0.2))
    model.add(layers.Dense(256))
    model.add(layers.LeakyReLU(alpha=0.2))
    model.add(layers.Dense(1, activation='sigmoid'))
    return model

# 训练GAN模型
def train_gan(generator, discriminator, epochs=10000, batch_size=128):
    (x_train, _), (_, _) = tf.keras.datasets.mnist.load_data()
    x_train = x_train / 127.5 - 1.0
    x_train = np.expand_dims(x_train, axis=-1)
    
    valid = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))
    
    for epoch in range(epochs):
        idx = np.random.randint(0, x_train.shape[0], batch_size)
        imgs = x_train[idx]
        
        noise = np.random.normal(0, 1, (batch_size, 100))
        gen_imgs = generator.predict(noise)
        
        d_loss_real = discriminator.train_on_batch(imgs, valid)
        d_loss_fake = discriminator.train_on_batch(gen_imgs, fake)
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
        
        noise = np.random.normal(0, 1, (batch_size, 100))
        g_loss = gan.train_on_batch(noise, valid)
        
        if epoch % 1000 == 0:
            print(f"{epoch} [D loss: {d_loss[0]}, acc.: {100*d_loss[1]}] [G loss: {g_loss}]")

# 构建和编译GAN模型
generator = build_generator()
discriminator = build_discriminator()
discriminator.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

z = layers.Input(shape=(100,))
img = generator(z)
discriminator.trainable = False
valid = discriminator(img)

gan = tf.keras.Model(z, valid)
gan.compile(loss='binary_crossentropy', optimizer='adam')

train_gan(generator, discriminator)

import cv2
import numpy as np

# 加载图像
image = cv2.imread('blueprint.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 边缘检测
edges = cv2.Canny(gray, 50, 150, apertureSize=3)

# 霍夫变换检测直线
lines = cv2.HoughLines(edges, 1, np.pi / 180, 200)
for rho, theta in lines[:, 0]:
    a = np.cos(theta)
    b = np.sin(theta)
    x0 = a * rho
    y0 = b * rho
    x1 = int(x0 + 1000 * (-b))
    y1 = int(y0 + 1000 * (a))
    x2 = int(x0 - 1000 * (-b))
    y2 = int(y0 - 1000 * (a))
    cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)

# 显示图像
cv2.imshow('Detected Lines', image)
cv2.waitKey(0)
cv2.destroyAllWindows()