深度学习中的Data Augmentation方法（转）基于keras
在深度学习中，当数据量不够大时候，常常采用下面4中方法：1. 人工增加训练集的大小. 通过平移, 翻转, 加噪声等方法从已有数据中创造出一批"新"的数据.也就是Data Augmentation
2. Regularization. 数据量比较小会导致模型过拟合, 使得训练误差很小而测试误差特别大. 通过在Loss Function 后面加上正则项可以抑制过拟合的产生. 缺点是引入了一个需要手动调整的hyper-parameter. 详见 https://www.wikiwand.com/en/Regularization_(mathematics)
3. Dropout. 这也是一种正则化手段. 不过跟以上不同的是它通过随机将部分神经元的输出置零来实现. 详见 http://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
4. Unsupervised Pre-training. 用Auto-Encoder或者RBM的卷积形式一层一层地做无监督预训练, 最后加上分类层做有监督的Fine-Tuning. 参考 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.207.1102&rep=rep1&type=pdf
下面我们来讨论Data Augmentation：
不同的任务背景下, 我们可以通过图像的几何变换, 使用以下一种或多种组合数据增强变换来增加输入数据的量. 这里具体的方法都来自数字图像处理的内容, 相关的知识点介绍, 网上都有, 就不一一介绍了．
[*]旋转 | 反射变换(Rotation/reflection): 随机旋转图像一定角度; 改变图像内容的朝向;
[*]翻转变换(flip): 沿着水平或者垂直方向翻转图像;
[*]缩放变换(zoom): 按照一定的比例放大或者缩小图像;
[*]平移变换(shift): 在图像平面上对图像以一定方式进行平移;
可以采用随机或人为定义的方式指定平移范围和平移步长, 沿水平或竖直方向进行平移. 改变图像内容的位置;
[*]尺度变换(scale): 对图像按照指定的尺度因子, 进行放大或缩小; 或者参照SIFT特征提取思想, 利用指定的尺度因子对图像滤波构造尺度空间. 改变图像内容的大小或模糊程度;
[*]对比度变换(contrast): 在图像的HSV颜色空间，改变饱和度S和V亮度分量，保持色调H不变. 对每个像素的S和V分量进行指数运算(指数因子在0.25到4之间), 增加光照变化;
[*]噪声扰动(noise): 对图像的每个像素RGB进行随机扰动, 常用的噪声模式是椒盐噪声和高斯噪声;
[*]颜色变换(color): 在训练集像素值的RGB颜色空间进行PCA, 得到RGB空间的3个主方向向量,3个特征值, p1, p2, p3, λ1, λ2, λ3. 对每幅图像的每个像素Ixy=T进行加上如下的变化:
[α1λ1,α2λ2,α3λ3]T
其中:αi是满足均值为0,方差为0.1的随机变量.
代码实现
作为实现部分, 这里介绍一下在python 环境下, 利用已有的开源代码库Keras作为实践:
1 # -*- coding: utf-8 -*-
2 __author__ = 'Administrator'
3
4 # import packages
5 from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
6
7 datagen = ImageDataGenerator(
8 rotation_range=0.2,
9 width_shift_range=0.2,
10 height_shift_range=0.2,
11 shear_range=0.2,
12 zoom_range=0.2,
13 horizontal_flip=True,
14 fill_mode='nearest')
15
16 img = load_img('C:\Users\Administrator\Desktop\dataA\lena.jpg')# this is a PIL image, please replace to your own file path
17 x = img_to_array(img)# this is a Numpy array with shape (3, 150, 150)
18 x = x.reshape((1,) + x.shape)# this is a Numpy array with shape (1, 3, 150, 150)
19
20 # the .flow() command below generates batches of randomly transformed images
21 # and saves the results to the `preview/` directory
22
23 i = 0
24 for batch in datagen.flow(x,
25 batch_size=1,
26 save_to_dir='C:\Users\Administrator\Desktop\dataA\pre',#生成后的图像保存路径
27 save_prefix='lena',
28 save_format='jpg'):
29 i += 1
30 if i > 20: #这个20指出要扩增多少个数据
31 break# otherwise the generator would loop indefinitely
主要函数：ImageDataGenerator 实现了大多数上文中提到的图像几何变换方法．
[*]rotation_range: 旋转范围, 随机旋转(0-180)度;
[*]width_shift and height_shift: 随机沿着水平或者垂直方向，以图像的长宽小部分百分比为变化范围进行平移;
[*]rescale: 对图像按照指定的尺度因子, 进行放大或缩小, 设置值在0 - 1之间，通常为1 / 255;
[*]shear_range: 水平或垂直投影变换, 参考这里 https://keras.io/preprocessing/image/
[*]zoom_range: 按比例随机缩放图像尺寸;
[*]horizontal_flip: 水平翻转图像;
[*]fill_mode: 填充像素, 出现在旋转或平移之后．
效果如下图所示：
转载于：http://blog.csdn.net/mduanfire/article/details/51674098
1 # -*- coding: utf-8 -*-
2 __author__ = 'Administrator'
3
4 # import packages
5 from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
6
7 datagen = ImageDataGenerator(
8 rotation_range=0.2,
9 width_shift_range=0.2,
10 height_shift_range=0.2,
11 shear_range=0.2,
12 zoom_range=0.2,
13 horizontal_flip=True,
14 fill_mode='nearest')
15
16 for k in range(33):
17 numstr = "{0:d}".format(k);
18 filename='C:\\Users\\Administrator\\Desktop\\bad\\'+numstr+'.jpg';
19 ufilename = unicode(filename , "utf8")
20 img = load_img(ufilename)# this is a PIL image, please replace to your own file path
21 x = img_to_array(img)# this is a Numpy array with shape (3, 150, 150)
22 x = x.reshape((1,) + x.shape)# this is a Numpy array with shape (1, 3, 150, 150)
23
24 # the .flow() command below generates batches of randomly transformed images
25 # and saves the results to the `preview/` directory
26
27 i = 0
28
29 for batch in datagen.flow(x,
30 batch_size=1,
31 save_to_dir='C:\\Users\\Administrator\\Desktop\\dataA\\',#生成后的图像保存路径
32 save_prefix=numstr,
33 save_format='jpg'):
34 i += 1
35 if i > 20:
36 break# otherwise the generator would loop indefinitely
37 end
View Code
1 # -*- coding: utf-8 -*-
2 __author__ = 'Administrator'
3
4 # import packages
5 from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
6
7 datagen = ImageDataGenerator(
8 rotation_range=10,
9 width_shift_range=0.2,
10 height_shift_range=0.2,
11 rescale=1./255,
12 shear_range=0.2,
13 zoom_range=0.2,
14 horizontal_flip=True,
15 fill_mode='nearest')
16 import os
17
18 import sys
19 reload(sys)
20 sys.setdefaultencoding('utf8')
21
22 ufilename = unicode("C:\\Users\\Administrator\\Desktop\\测试" , "utf8")
23
24 for filename in os.listdir(ufilename): #listdir的参数是文件夹的路径
25 print ( filename) #此时的filename是文件夹中文件的名称
26 pathname='C:\\Users\\Administrator\\Desktop\\测试\\'+filename;
27 #ufilename = unicode(pathname , "utf8")
28 img = load_img(pathname)# this is a PIL image, please replace to your own file path
29 x = img_to_array(img)# this is a Numpy array with shape (3, 150, 150)
30 x = x.reshape((1,) + x.shape)# this is a Numpy array with shape (1, 3, 150, 150)
31 # the .flow() command below generates batches of randomly transformed images
32 # and saves the results to the `preview/` directory
33 i = 0
34 for batch in datagen.flow(x,
35 batch_size=1,
36 save_to_dir='C:\\Users\\Administrator\\Desktop\\result\\',#生成后的图像保存路径
37 save_prefix=filename,
38 save_format='jpg'):
39 i += 1
40 if i > 100:
41 break# otherwise the generator would loop indefinitely
42
43
44 # datagen = ImageDataGenerator(
45 # rotation_range=0.2,
46 # width_shift_range=0.2,
47 # height_shift_range=0.2,
48 # rescale=1./255,
49 # shear_range=0.1,
50 # zoom_range=0.4,
51 # horizontal_flip=True,
52 # fill_mode='nearest')
53 #
54 # ufilename = unicode("C:\\Users\\Administrator\\Desktop\\训练" , "utf8")
55 # for filename in os.listdir(ufilename): #listdir的参数是文件夹的路径
56 # print ( filename) #此时的filename是文件夹中文件的名称
57 # pathname='C:\\Users\\Administrator\\Desktop\\训练\\'+filename;
58 # # ufilename = unicode(pathname , "utf8")
59 # img = load_img(pathname)# this is a PIL image, please replace to your own file path
60 # x = img_to_array(img)# this is a Numpy array with shape (3, 150, 150)
61 # x = x.reshape((1,) + x.shape)# this is a Numpy array with shape (1, 3, 150, 150)
62 #
63 # # the .flow() command below generates batches of randomly transformed images
64 # # and saves the results to the `preview/` directory
65 #
66 # i = 0
67 #
68 # for batch in datagen.flow(x,
69 # batch_size=1,
70 # save_to_dir='C:\\Users\\Administrator\\Desktop\\result\\',#生成后的图像保存路径
71 # save_prefix=filename,
72 # save_format='jpg'):
73 # i += 1
74 # if i > 100:
75 # break# otherwise the generator would loop indefinitely
View Code
页:
[1]