keras ImageDataGenerator 数据增强的数据显示查看

import skimage.io as io
import os,sys
from skimage import data_dir
import numpy as np
import matplotlib.pyplot as plt
import cv2

from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

datagen = ImageDataGenerator(rotation_range=40, # 随机旋转角度的范围
                            width_shift_range=0.2, # 随机转换图片宽度的范围
                            height_shift_range=0.2, # 随机转换图片高度的范围
                            shear_range=0.2, # 随机剪切转换比例
                            zoom_range=0.2, # 随机放缩比例
                            horizontal_flip=True,# 开启水平翻转
                            fill_mode='nearest' # 填充策略
                            )

path="/data_2/everyday/0312/others/pic/";     
dirs=os.listdir(path)

#保存本地
# for file in dirs:
#     img = load_img(path+file)
#     x = img_to_array(img)
#     x = x.reshape((1,) + x.shape)  # 这是一个numpy数组，形状为 (1,150, 150,3)
#     i = 0
#     for batch in datagen.flow(x, batch_size=1,
#                           save_to_dir='/data_2/everyday/0312/others/pic-aug'):
#        i += 1
#        if i > 50:             # 数据扩充倍数，此处为数据扩充50倍
#         break             #  否则生成器会退出循环


for file in dirs:
    img = load_img(path + file)
    x = img_to_array(img)
    x = x.reshape((1,) + x.shape)  # 这是一个numpy数组，形状为 (1,150, 150,3)
    i = 0
    for batch in datagen.flow(x,batch_size=1):
        # cv2.imshow("src",x[0][:,:,::-1])
        # cv2.waitKey(0)
        plt.figure(i)
        imgplot = plt.imshow(array_to_img(batch[0]))
        plt.show()
        i += 1
        if 0 == i % 10:
            break

不知道为啥用cv显示原图全是白的。。。。这样可以src = cv2.cvtColor(np.asarray(array_to_img(x[0])), cv2.COLOR_RGB2BGR)

keras fit 中的 verbose

verbose：日志显示

verbose = 0 为不在标准输出流输出日志信息

verbose = 1 为输出进度条记录

verbose = 2 为每个epoch输出一行记录

注意： 默认为 1