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# import packages
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

datagen = ImageDataGenerator(
rotation_range=5,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.2,
##horizontal_flip=True,
##zca_whitening=1,
fill_mode='nearest')

img = load_img('E:/python go/3-code/test.jpg') # this is a PIL image, please replace to your own file path
x = img_to_array(img) # this is a Numpy array with shape (3, 150, 150)
x = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 3, 150, 150)

# the .flow() command below generates batches of randomly transformed images
# and saves the results to the `preview/` 

 

 

# 统一图片大小-视频方法
# image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H)
# 统一图片大小-我的方法
image = tf.image.resize_images(image, [image_H, image_W], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

 

 

# # data argumentation

# image = tf.random_crop(image, [24, 24, 3])# randomly crop the image size to 24 x 24
# image = tf.image.random_flip_left_right(image)
# image = tf.image.random_brightness(image, max_delta=63)
# image = tf.image.random_contrast(image,lower=0.2,upper=1.8)