# encoding=utf-8

import os

import imageio

import random

import numpy as np

import imgaug as ia

import concurrent.futures

from imgaug import augmenters as iaa

import matplotlib.pyplot as plt

from argparse import ArgumentParser

ia.seed(4)

# %matplotlib inline

# 參考 https://github.com/aleju/imgaug

func_norm = iaa.ContrastNormalization(alpha=(0.5, 1.5), per_channel=False)

func_add = iaa.Add(value=(-30, 40), per_channel=False)

func_hue_saturation = iaa.AddToHueAndSaturation(

value=(-35, 30), per_channel=False)

func_multiply = iaa.Multiply(mul=(0.8, 1.5), per_channel=False)

func_gamma = iaa.GammaContrast(gamma=(0.8, 1.5), per_channel=False)

func_log = iaa.LogContrast(gain=(0.7, 1.2), per_channel=False)

func_sigmoid = iaa.SigmoidContrast(gain=(2, 5), cutoff=0.5, per_channel=False)

func_coarse = iaa.CoarseDropout(

p=(0.1, 0.2), size_percent=0.1, per_channel=False)

switch_func = {

0: func_norm,

1: func_add,

2: func_hue_saturation,

3: func_multiply,

4: func_gamma,

5: func_log,

6: func_sigmoid,

7: func_coarse

}

def getFilePath(root_path, file_list):

dir_or_files = os.listdir(root_path)

for dir_file in dir_or_files:

dir_file_path = os.path.join(root_path, dir_file)

if os.path.isdir(dir_file_path):

getFilePath(dir_file_path, file_list)

else:

file_list.append(dir_file_path)

def getFilePath2(root_path, file_list, folder_list):

dir_or_files = os.listdir(root_path)

for dir_file in dir_or_files:

dir_file_path = os.path.join(root_path, dir_file)

if os.path.isdir(dir_file_path):

folder_list.append(dir_file_path)

getFilePath2(dir_file_path, file_list, folder_list)

else:

file_list.append(dir_file_path)

def getFilePathFilters(root_path, file_list, dir_filters):

dir_or_files = os.listdir(root_path)

for dir_file in dir_or_files:

dir_file_path = os.path.join(root_path, dir_file)

if os.path.isdir(dir_file_path):

if dir_file in dir_filters:

getFilePathFilters(dir_file_path, file_list, dir_filters)

else:

file_list.append(dir_file_path)

def makeDir(root_path):

if not os.path.exists(root_path):

os.makedirs(root_path)

def showImage(image):

image = np.array(image)

plt.imshow(image)

plt.show()

def saveImage(image, write_path):

imageio.imwrite(write_path, image)

def augmentFunc(image, index):

return switch_func[index].augment_image(image)

# 多線程執行函數

def augExecute(file_path):

new_file_path = file_path.replace(args.root_folder, args.new_folder)

print(new_file_path, end='\n')

image = imageio.imread(file_path)

index = random.randint(0, 7)

new_image = augmentFunc(image, index)

# showImage(new_image)

saveImage(new_image, new_file_path)

def colorImageAugment(augment_list, max_workers, dir_dict):

for augment_folder in augment_list:

file_list = []

folder_list = []

getFilePath2(augment_folder, file_list, folder_list)

# 創建新目錄文件夾及其子文件夾

for folder_path in folder_list:

new_folder_path = folder_path.replace(

args.root_folder, args.new_folder)

makeDir(new_folder_path)

if max_workers == 0:

# 單線程

for file_path in file_list:

augExecute(file_path)

else:

# 多線程

with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:

for _ in executor.map(augExecute, file_list):

pass

def main(args):

aug_folder_list = args.image_folder_path

dir_dict = {'root_dir': args.root_folder, 'trans_dir': args.new_folder}

max_workers = int(args.max_workers)

print("Start to augment image")

colorImageAugment(aug_folder_list, max_workers, dir_dict)

print("The end")

if __name__ == "__main__":

parser = ArgumentParser(description="Augment color image")

parser.add_argument(

"image_folder_path", nargs='+', help="Absolute path of image folder that going to augmentation")

parser.add_argument(

"root_folder", type=str, help="The folder and file under the root_folder is going to augmentation")

parser.add_argument("new_folder", type=str,

help="The folder that going to be build")

parser.add_argument("max_workers", type=int,

default=9, help="Multi thread num")

args = parser.parse_args()

main(args)

'''Usepython data_aug.py /your/path/to/augmentation/train/subdir1/ /your/path/to/augmentation/train/subdir2/ ... /your/path/to/augmentation/train/subdirn/ train train_aug第一個參數是 image_folder_path 需要處理的文件夾 要求絕對路徑 可以傳入多個第二個參數是 root_folder 處理文件夾的父級目錄 程序會將該目錄同級路徑下創建 new_folder, 同時創建相同的字目錄, 然后將 root_folder 下的圖像處理保存到 new_folder 下相同自路徑下第三個參數是 new_folder 見root_folder第四個參數是 max_workers 線程數量 0 為單線程'''

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