'''
Created by Ping Yuhan on Sep. 18, 2018

This is a test program for testing data augmentation.

Latest updated on Oct. 16, 2018 
'''

from keras.preprocessing.image import ImageDataGenerator

import numpy as np
import glob
import os

path = 'F:\\课件/HKU/Dissertation/Code/img/'
gen_path = './img/gen/'

namelist = glob.glob(path + '*/*')
print(namelist)

datagen = ImageDataGenerator(rotation_range = 30)
gen_data = datagen.flow_from_directory(path, batch_size = 2, shuffle = False, save_to_dir = gen_path, 
	save_prefix = 'gen', target_size = (200, 200))

for i in range(3):
	gen_data.next()
d4bdbe70df5363712003336c4295de1246961f1e
<mask token>

<mask token>
print(namelist)
<mask token>
for i in range(3):
    gen_data.next()

<mask token>
path = 'F:\\课件/HKU/Dissertation/Code/img/'
gen_path = './img/gen/'
namelist = glob.glob(path + '*/*')
print(namelist)
datagen = ImageDataGenerator(rotation_range=30)
gen_data = datagen.flow_from_directory(path, batch_size=2, shuffle=False,
    save_to_dir=gen_path, save_prefix='gen', target_size=(200, 200))
for i in range(3):
    gen_data.next()

<mask token>
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import glob
import os
path = 'F:\\课件/HKU/Dissertation/Code/img/'
gen_path = './img/gen/'
namelist = glob.glob(path + '*/*')
print(namelist)
datagen = ImageDataGenerator(rotation_range=30)
gen_data = datagen.flow_from_directory(path, batch_size=2, shuffle=False,
    save_to_dir=gen_path, save_prefix='gen', target_size=(200, 200))
for i in range(3):
    gen_data.next()

'''
Created by Ping Yuhan on Sep. 18, 2018

This is a test program for testing data augmentation.

Latest updated on Oct. 16, 2018 
'''

from keras.preprocessing.image import ImageDataGenerator

import numpy as np
import glob
import os

path = 'F:\\课件/HKU/Dissertation/Code/img/'
gen_path = './img/gen/'

namelist = glob.glob(path + '*/*')
print(namelist)

datagen = ImageDataGenerator(rotation_range = 30)
gen_data = datagen.flow_from_directory(path, batch_size = 2, shuffle = False, save_to_dir = gen_path, 
	save_prefix = 'gen', target_size = (200, 200))

for i in range(3):
	gen_data.next()