#!usr/bin/python
# -*- coding: utf-8 -*-
import cv2
from imgaug import augmenters as iaa
import os
# Sometimes(0.5, ...) applies the given augmenter in 50% of all cases,
# e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image.
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
# 定义一组变换方法.
seq = iaa.Sequential([
# 选择0到5种方法做变换
iaa.SomeOf((0, 5),
[
iaa.Fliplr(0.5), # 对50%的图片进行水平镜像翻转
iaa.Flipud(0.5), # 对50%的图片进行垂直镜像翻转
# Convert some images into their superpixel representation,
# sample between 20 and 200 superpixels per image, but do
# not replace all superpixels with their average, only
# some of them (p_replace).
sometimes(
iaa.Superpixels(
p_replace=(0, 1.0),
n_segments=(20, 200)
)
),
# Blur each image with varying strength using
# gaussian blur (sigma between 0 and 3.0),
# average/uniform blur (kernel size between 2x2 and 7x7)
# median blur (kernel size between 3x3 and 11x11).
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)),
iaa.AverageBlur(k=(2, 7)),
iaa.MedianBlur(k=(3, 11)),
]),
# Sharpen each image, overlay the result with the original
# image using an alpha between 0 (no sharpening) and 1
# (full sharpening effect).
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
# Same as sharpen, but for an embossing effect.
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)),
# Add gaussian noise to some images.
# In 50% of these cases, the noise is randomly sampled per
# channel and pixel.
# In the other 50% of all cases it is sampled once per
# pixel (i.e. brightness change).
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05*255)
),
# Invert each image's chanell with 5% probability.
# This sets each pixel value v to 255-v.
iaa.Invert(0.05, per_channel=True), # invert color channels
# Add a value of -10 to 10 to each pixel.
iaa.Add((-10, 10), per_channel=0.5),
# Add random values between -40 and 40 to images, with each value being sampled per pixel:
iaa.AddElementwise((-40, 40)),
# Change brightness of images (50-150% of original value).
iaa.Multiply((0.5, 1.5)),
# Multiply each pixel with a random value between 0.5 and 1.5.
iaa.MultiplyElementwise((0.5, 1.5)),
# Improve or worsen the contrast of images.
iaa.ContrastNormalization((0.5, 2.0)),
],
# do all of the above augmentations in random order
random_order=True
)
],random_order=True) #apply augmenters in random order
# 图片文件相关路径
path = 'F:/input/'
savedpath = 'F:/output/'
imglist=[]
filelist = os.listdir(path)
# 遍历要增强的文件夹，把所有的图片保存在imglist中
for item in filelist:
img = cv2.imread(path + item)
#print('item is ',item)
#print('img is ',img)
#images = load_batch(batch_idx)
imglist.append(img)
#print('imglist is ' ,imglist)
print('all the picture have been appent to imglist')
#对文件夹中的图片进行增强操作，循环100次
for count in range(100):
images_aug = seq.augment_images(imglist)
for index in range(len(images_aug)):
filename = str(count) + str(index) +'.jpg'
#保存图片
cv2.imwrite(savedpath + filename,images_aug[index])
print('image of count%s index%s has been writen'%(count,index))
以上代码是图像批处理范例，输入图像放在一个文件夹中，输出图像都自动保存在指定文件夹中。转载自https://blog.csdn.net/zong596568821xp/article/details/83111124
以下代码是单张图片增强示例：
import cv2
from imgaug import augmenters as iaa
#imgaug test
seq = iaa.Sequential([
iaa.Crop(px=(0, 16)), # 从每侧裁剪图像0到16px（随机选择）
iaa.Fliplr(0.5), # 水平翻转图像
iaa.GaussianBlur(sigma=(0, 3.0)) # 使用0到3.0的sigma模糊图像
])
imglist=[]
img = cv2.imread('kobe.jpg')
imglist.append(img)
images_aug = seq.augment_images(imglist)
cv2.imwrite("imgaug.jpg",images_aug[0])
---------------------
作者：ZONG_XP
来源：
原文：https://blog.csdn.net/zong596568821xp/article/details/83105700
版权声明：本文为博主原创文章，转载请附上博文链接！
