问如何使用TensorFlow扩充文件夹中的所有图像

EN

import datetime
import glob
from pathlib import Path
import random
from tkinter import *
from tkinter import filedialog, simpledialog

def generate_augmented_images(args):
    """
    Generates augmented versions of images in a particular folder and saves them to a new folder.
    Each augmented image has prefix from original PNG image name.
    A dialog selects the source folder and another asks augmentation factor.
    
    """

    start_timestr = time.strftime("%Y%m%d-%H%M")
    src_data_folder = SRC_DATA_FOLDER
    os.chdir(src_data_folder)
    root = Tk()
    root.withdraw()
    src_data_folder = filedialog.askdirectory()
    if len(src_data_folder) == 0:
        log.info('aborted')
        quit(1)

    train_datagen = ImageDataGenerator(  # 实例化
        rescale=1. / 256,  # todo check this
        rotation_range=15,  # 图片随机转动的角度
        width_shift_range=0.2,  # 图片水平偏移的幅度
        height_shift_range=0.2,  # don't shift too much vertically to avoid losing top of card
        fill_mode='constant',
        cval=0,  # fill edge pixels with black; default fills with long lines of color
        zoom_range=[.9, 1.25],  # NOTE zoom >1 minifies, don't zoom in (<1) too much in to avoid losing joker part of card
        # horizontal_flip=False,
    )  # 随机放大或缩小

    log.info(f'train_datagen: {train_datagen}')

    aug_factor = simpledialog.askinteger('Augmentation factor','Factor by which to augment?')
    if aug_factor is None:
        log.info('aborted')
        quit(0)

    base=os.path.split(src_data_folder)[-1]
    aug_folder = os.path.join(SRC_DATA_FOLDER,f'{base}-augmented-{start_timestr}')
    data_path = os.path.join(src_data_folder, '*.png')
    files = glob.glob(data_path) # get list of all PNGs

    nsrc=len(files)
    ntotal=int(aug_factor*nsrc)
    log.info(f'saving {aug_factor}X={ntotal} augmented images to {aug_folder} from {src_data_folder} with {nsrc} samples')
    try:
        Path(aug_folder).mkdir(parents=True)
    except:
        log.error(f'{aug_folder} already exists')
        quit(1)

    i = 0
    cv2.namedWindow('original', cv2.WINDOW_NORMAL)
    cv2.resizeWindow('original', 600, 600)
    cv2.namedWindow('augmented', cv2.WINDOW_NORMAL)
    cv2.resizeWindow('augmented', 600, 600)
    cv2.moveWindow('augmented',0,600)
    nsaved=0
    for f in tqdm(files):
        showit=nsaved%100==0
        img = cv2.imread(f)
        x = img_to_array(img)
        if showit:
            cv2.imshow('original', (1/256.)*x)
        x = x.reshape((1,) + x.shape)
        i=0
        base = os.path.splitext(os.path.split(f)[-1])[0] # e.g. /a/b/c/d/0900111.png gives 0900111
        for batch in train_datagen.flow(x, batch_size=1, save_to_dir=aug_folder, save_prefix=base, save_format='png'):
            i += 1
            nsaved+=1
            img_aug=batch[0]
            if showit:
                cv2.imshow('augmented', img_aug)
            k = cv2.waitKey(15) & 0xff
            if k == ord('x') or k == ord('q'):
                log.info(f'saved {nsaved} augmented images to {aug_folder} from {src_data_folder}')
                quit(0)
            if i == aug_factor:
                break
    log.info(f'saved {nsaved} augmented images to {aug_folder} from {src_data_folder}')