keras图像增强

使用keras中的方法对图像进行增强，包括旋转，裁剪，灰度化，平移，仿射变换

from PIL import Image
import random

from keras.preprocessing.image import (
    random_rotation, random_shift, random_shear, random_zoom,
    random_channel_shift, transform_matrix_offset_center, img_to_array)

img = Image.open(f'./train/xxxx.jpg')

img_arr = img_to_array(img)

imgs = [
    random_rotation(img_arr, 30, row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest') * 255 for _ in range(5)]

imgs = [
    random_shift(img_arr, wrg=0.1, hrg=0.3, row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest') * 255  for _ in range(5)]

imgs = [
    random_shear(img_arr, intensity=0.4, row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest') * 255 for _ in range(5)]

imgs = [
    random_zoom(img_arr, zoom_range=(1.5, 0.7), row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest') * 255  for _ in range(5)]



def random_greyscale(img, p):
    if random.random() < p:
        return np.dot(img[...,:3], [0.299, 0.587, 0.114])
    
    return img

imgs = [
    random_greyscale(img_arr, 0.5) * 255  for _ in range(5)]

def augmentation_pipeline(img_arr):
    img_arr = random_rotation(img_arr, 18, row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest')
    img_arr = random_shear(img_arr, intensity=0.4, row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest')
    img_arr = random_zoom(img_arr, zoom_range=(0.9, 2.0), row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest')
    img_arr = random_greyscale(img_arr, 0.4)

    return img_arr

imgs = [augmentation_pipeline(img_arr) * 255 for _ in range(5)]

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