# 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(zhí)行函數(shù)

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(chuàng)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(gè)參數(shù)是 image_folder_path 需要處理的文件夾 要求絕對(duì)路徑 可以傳入多個(gè)第二個(gè)參數(shù)是 root_folder 處理文件夾的父級(jí)目錄 程序會(huì)將該目錄同級(jí)路徑下創(chuàng)建 new_folder, 同時(shí)創(chuàng)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(gè)參數(shù)是 new_folder 見root_folder第四個(gè)參數(shù)是 max_workers 線程數(shù)量 0 為單線程'''

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