图像翻转、图像旋转、图像扭曲、图像仿射变换、图像 缩放、图像压缩、图像随机crop、图像随机padding、图像对比度调整、亮度 调整、色度调整、饱和度调整、色彩抖动、添加噪声、图像模糊、图像区域随 机擦除、风格转换、生成对抗网络生成的python代码

以下是部分代码示例：

1. 图像翻转

import cv2

img = cv2.imread('image.jpg')
flipped_img = cv2.flip(img, 1) # 1表示水平翻转，0表示垂直翻转，-1表示水平垂直翻转
cv2.imshow('Original Image', img)
cv2.imshow('Flipped Image', flipped_img)
cv2.waitKey(0)

2. 图像旋转

import cv2

img = cv2.imread('image.jpg')
(h, w) = img.shape[:2]
center = (w // 2, h // 2)
angle = 45 # 旋转角度
M = cv2.getRotationMatrix2D(center, angle, 1.0) # 获取旋转矩阵
rotated_img = cv2.warpAffine(img, M, (w, h)) # 进行旋转
cv2.imshow('Original Image', img)
cv2.imshow('Rotated Image', rotated_img)
cv2.waitKey(0)

3. 图像缩放

import cv2

img = cv2.imread('image.jpg')
resized_img = cv2.resize(img, (500, 500)) # 500x500大小
cv2.imshow('Original Image', img)
cv2.imshow('Resized Image', resized_img)
cv2.waitKey(0)

4. 图像对比度、亮度和饱和度调整

import cv2

img = cv2.imread('image.jpg')
alpha = 1.5 # 对比度调整系数
beta = 50 # 亮度调整系数
gamma = 1.0 # 饱和度调整系数
adjusted_img = cv2.convertScaleAbs(cv2.addWeighted(img, alpha, np.zeros(img.shape, img.dtype), 0, beta), gamma)
cv2.imshow('Original Image', img)
cv2.imshow('Adjusted Image', adjusted_img)
cv2.waitKey(0)

5. 添加噪声

import cv2
import numpy as np

img = cv2.imread('image.jpg')
noise_img = img.copy()
noise = np.random.randint(0, 50, img.shape)
noise_img = cv2.addWeighted(noise_img, 0.5, noise, 0.5, 0)
cv2.imshow('Original Image', img)
cv2.imshow('Noisy Image', noise_img)
cv2.waitKey(0)

6. 图像模糊

import cv2

img = cv2.imread('image.jpg')
blurred_img = cv2.GaussianBlur(img, (5, 5), 0) # 5x5大小的高斯核
cv2.imshow('Original Image', img)
cv2.imshow('Blurred Image', blurred_img)
cv2.waitKey(0)

7. 风格转换

import cv2

img = cv2.imread('image.jpg')
style = cv2.imread('style.jpg')
stylized_img = cv2.stylization(img, style, sigma_s=60, sigma_r=0.6) # sigma_s和sigma_r是参数，可以根据需要进行调整
cv2.imshow('Original Image', img)
cv2.imshow('Stylized Image', stylized_img)
cv2.waitKey(0)

8. 生成对抗网络生成的python代码

由于生成对抗网络的模型比较复杂，生成的代码也比较复杂，这里只提供一个简单的示例：

import tensorflow as tf
import numpy as np

def generator(z, is_training=True, reuse=False):
    with tf.variable_scope('generator', reuse=reuse):
        fc1 = tf.layers.dense(z, 128)
        fc1 = tf.layers.batch_normalization(fc1, training=is_training)
        fc1 = tf.nn.relu(fc1)
        fc2 = tf.layers.dense(fc1, 784)
        img = tf.nn.tanh(fc2)
        return img

def discriminator(img, is_training=True, reuse=False):
    with tf.variable_scope('discriminator', reuse=reuse):
        fc1 = tf.layers.dense(tf.reshape(img, [-1, 784]), 128)
        fc1 = tf.layers.batch_normalization(fc1, training=is_training)
        fc1 = tf.nn.leaky_relu(fc1, alpha=0.2)
        logits = tf.layers.dense(fc1, 1)
        return logits

z = tf.placeholder(tf.float32, [None, 100])
real_img = tf.placeholder(tf.float32, [None, 28, 28, 1])

G = generator(z)
D_real = discriminator(real_img)
D_fake = discriminator(G, reuse=True)

D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_real, labels=tf.ones_like(D_real)))
D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake, labels=tf.zeros_like(D_fake)))
D_loss = D_loss_real + D_loss_fake

G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake, labels=tf.ones_like(D_fake)))

D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')
G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')

D_opt = tf.train.AdamOptimizer(learning_rate=0.0002).minimize(D_loss, var_list=D_vars)
G_opt = tf.train.AdamOptimizer(learning_rate=0.0002).minimize(G_loss, var_list=G_vars)

sess = tf.Session()
sess.run(tf.global_variables_initializer())

for i in range(10000):
    batch_z = np.random.uniform(-1, 1, [batch_size, 100])
    batch_imgs = ...
    _, D_loss_curr = sess.run([D_opt, D_loss], feed_dict={z: batch_z, real_img: batch_imgs})
    _, G_loss_curr = sess.run([G_opt, G_loss], feed_dict={z: batch_z})

    if i % 1000 == 0:
        print('Step %d, D_loss=%.4f, G_loss=%.4f' % (i, D_loss_curr, G_loss_curr))