Tensorflow的几种数据增强的方式：缩放、扩大、翻转、调整亮度、调整对比度等

好久之前的代码，有些乱。

import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
from numpy import uint8

with tf.Session() as sess:
# 读取原图像并显示
    original_image = Image.open("C:/Users/ccc/test/3.jpg")
    plt.title('original_image')
    plt.imshow(original_image)
    plt.show()

    # 将图像缩小至224X224
    reduced_image = tf.image.resize_images(original_image, [10, 10])
    # 将图像扩大至1024X768
    expanded_image = tf.image.resize_images(original_image, [1024, 768])
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    
    # 将tensor转换为numpy数组
    reduced_image = sess.run(reduced_image)
    # 将numpy数组转换为图像
    reduced_image=Image.fromarray(uint8(reduced_image))
    # 显示图像
    plt.title('reduced_image')
    plt.imshow(reduced_image)
    plt.show()
    # 保存图像
    #reduced_image.save('D:/date_1/haha/0_Label_0-reduce.jpg')
 
    expanded_image = sess.run(expanded_image)
    expanded_image=Image.fromarray(uint8(expanded_image))
    plt.title('expanded_image')
    plt.imshow(expanded_image)
    plt.show()
    #expanded_image.save('D:/date_1/haha/0_Label_0-expand.jpg')
    
    
# 随机裁剪图像64x64
    crop_image = tf.random_crop(original_image,[20,20,3])
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    # 将tensor转换为numpy数组
    crop_image = sess.run(crop_image)
    # 将numpy数组转换为图像
    crop_image=Image.fromarray(uint8(crop_image))
    # 显示图像
    plt.title('crop_image')
    plt.imshow(crop_image)
    plt.show()
    # 保存图像
    #crop_image.save('D:/date_1/haha/0_Label_0-cai.jpg') 
    
#水平上下翻转    
    # 对图像进行左右翻转
    horizontal_image = tf.image.flip_left_right(original_image)
    # 对图像进行上下翻转
    vertical_image = tf.image.flip_up_down(original_image)
    horizontal_image = sess.run(horizontal_image)
    horizontal_image=Image.fromarray(uint8(horizontal_image))
    plt.title('horizontal_image')
    plt.imshow(horizontal_image)
    plt.show()
    #horizontal_image.save('D:/date_1/haha/0_Label_0-shui.jpg') 

    vertical_image = sess.run(vertical_image)
    vertical_image=Image.fromarray(uint8(vertical_image))
    plt.title('vertical_image')
    plt.imshow(vertical_image)
    plt.show()
    #vertical_image.save('D:/date_1/haha/0_Label_0-fan.jpg')
    
#颜色变换
     # 通过随机因子调整图像的亮度
    random_brightness_image = tf.image.random_brightness(original_image, 0.5)
    # 通过随机因子调整图像的对比度
    random_contrast_image = tf.image.random_contrast(original_image, 0.1, 0.5 )
    # 通过随机因子调整RGB图像的色调
    random_hue_image = tf.image.random_hue(original_image, 0.5)
    # 通过随机因子调整RGB图像的饱和度
    random_saturation_image = tf.image.random_saturation(original_image, 0.3, 0.5)
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())

    random_brightness_image = sess.run(random_brightness_image)
    random_brightness_image=Image.fromarray(uint8(random_brightness_image))
    plt.title('random_brightness_image')
    plt.imshow(random_brightness_image)
    plt.show()
    #random_brightness_image.save('D:/date_1/haha/0_Label_0-brighness.jpg') 

    random_contrast_image = sess.run(random_contrast_image)
    random_contrast_image=Image.fromarray(uint8(random_contrast_image))
    plt.title('random_contrast_image')
    plt.imshow(random_contrast_image)
    plt.show()
    #random_contrast_image.save('D:/date_1/haha/0_Label_0-contrast.jpg')

    random_hue_image = sess.run(random_hue_image)
    random_hue_image=Image.fromarray(uint8(random_hue_image))
    plt.title('random_hue_image')
    plt.imshow(random_hue_image)
    plt.show()
    #random_hue_image.save('D:/date_1/haha/0_Label_0-hue.jpg')

    random_saturation_image = sess.run(random_saturation_image)
    random_saturation_image=Image.fromarray(uint8(random_saturation_image))
    plt.title('random_saturation_image')
    plt.imshow(random_saturation_image)
    plt.show()
    #random_saturation_image.save('D:/date_1/haha/0_Label_0-saturation.jpg')

最后

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