From 0b1da10076d438efe3736f104f61795596c1bd7c Mon Sep 17 00:00:00 2001
From: Tom Ren <jiejiren@sjtu.edu.cn>
Date: Wed, 8 Aug 2018 05:50:37 +0800
Subject: [PATCH] Add functions for data augmentation (#310)

* add functions for images augmetation in image-augmentation.md

* add function img_aug to generate augmetation images in gluonbook/utils.py

* change a wrong word in neural-style.md
---
 chapter_computer-vision/image-augmentation.md | 43 +++++++++++++++++++
 chapter_computer-vision/neural-style.md       |  2 +-
 gluonbook/utils.py                            | 33 ++++++++++++++
 3 files changed, 77 insertions(+), 1 deletion(-)

diff --git a/chapter_computer-vision/image-augmentation.md b/chapter_computer-vision/image-augmentation.md
index f0ed5c9..7bfeee6 100644
--- a/chapter_computer-vision/image-augmentation.md
+++ b/chapter_computer-vision/image-augmentation.md
@@ -106,6 +106,49 @@ augs = gdata.vision.transforms.Compose([
 apply(img, augs)
 ```
 
+#### 对数据集图片进行增广操作
+如果希望对自己的数据进行曾广扩充，可以利用img_aug()函数来直接对某个文件夹下的图片进行随机变换和增强
+
+```{.python .input}
+# 先定义一组增广操作，其中的参数可以根据上述讲解来调整
+augs = [gdata.vision.transforms.RandomFlipLeftRight(),
+       gdata.vision.transforms.RandomResizedCrop(
+       (200, 200), scale=(0.1, 1), ratio=(0.8, 1.25)),
+       gdata.vision.transforms.RandomBrightness(0.1),
+       gdata.vision.transforms.RandomHue(0.5),
+       gdata.vision.transforms.RandomColorJitter(
+       brightness=0.1, contrast=0.5, saturation=0.5, hue=0.5)
+      ]    """augmentation list example"""
+```
+
+```{.python .input}
+#将增广操作应用于图像上，其中num_aug为每种操作进行的次数
+def apply_aug(img, augs, path, name, num_aug=10):
+    """Apply augmetaion manipulation on images"""
+    for aug in augs:
+        Y = [aug(img) for _ in range(num_aug)]
+        for i in range(len(Y)):
+            augged = Y[i].asnumpy()
+            id_name = name.split('.')[0]
+            new_name = path+'aug/'+id_name+'0'+str(i)+'.jpg'
+            cv2.imwrite(new_name,augged)
+
+#定义文件夹增广函数，作用后可以在path/aug/下生成变化后的增广数据
+def img_aug(path):
+    """Generate augmentation images, your train img store in path/"""
+    if not os.path.exists(path+'aug/'):
+        os.mkdir(path+'aug/')              
+    names = os.listdir(path)
+    for name in names:
+        if name.endswith('.jpg'):
+            aug_img = image.imread(path+name)
+            apply_aug(aug_img, augs,path,name)
+```
+
+```{.python .input}
+img_aug('./../where/you/store/your/images/')
+```
+
 ## 使用图片增广来训练
 
 接下来我们来看一个将图片增广应用在实际训练中的例子，并比较其与不使用时的区别。这里我们使用CIFAR-10数据集，而不是之前我们一直使用的Fashion-MNIST。原因在于Fashion-MNIST中物体位置和尺寸都已经归一化了，而CIFAR-10中物体颜色和大小区别更加显著。下面我们展示CIFAR-10中的前32张训练图片。
diff --git a/chapter_computer-vision/neural-style.md b/chapter_computer-vision/neural-style.md
index 43658be..fce92af 100644
--- a/chapter_computer-vision/neural-style.md
+++ b/chapter_computer-vision/neural-style.md
@@ -93,7 +93,7 @@ def extract_features(x, content_layers, style_layers):
     return contents, styles
 ```
 
-最后我们定义函数分别对内容图片和样式图片抽取对应的特征。因为在训练时我们不修改网络的权重，所以我们可以在训练开始之前提取抽所要的特征。
+最后我们定义函数分别对内容图片和样式图片抽取对应的特征。因为在训练时我们不修改网络的权重，所以我们可以在训练开始之前提取出所要的特征。
 
 ```{.python .input}
 def get_contents(image_shape, ctx):
diff --git a/gluonbook/utils.py b/gluonbook/utils.py
index c8cdb94..bce477b 100644
--- a/gluonbook/utils.py
+++ b/gluonbook/utils.py
@@ -10,6 +10,7 @@ import mxnet as mx
 from mxnet import autograd, gluon, image, nd
 from mxnet.gluon import nn, data as gdata, loss as gloss, utils as gutils
 import numpy as np
+import cv2
 
 
 voc_classes = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',
@@ -565,4 +566,36 @@ class VOCSegDataset(gdata.Dataset):
 
     def __len__(self):
         return len(self.data)
+        
+
+def apply_aug(img, augs, path, name, num_aug=10):
+    """Apply augmetaion manipulation on images"""
+    for aug in augs:
+        Y = [aug(img) for _ in range(num_aug)]
+        for i in range(len(Y)):
+            augged = Y[i].asnumpy()
+            id_name = name.split('.')[0]
+            new_name = path+'aug/'+id_name+'0'+str(i)+'.jpg'
+            cv2.imwrite(new_name,augged)
+    
 
+def img_aug(path):
+    """Generate augmentation images, your train img store in path/"""
+    
+    augs = [gdata.vision.transforms.RandomFlipLeftRight(),
+           gdata.vision.transforms.RandomResizedCrop(
+           (200, 200), scale=(0.1, 1), ratio=(0.8, 1.25)),
+           gdata.vision.transforms.RandomBrightness(0.1),
+           gdata.vision.transforms.RandomHue(0.5),
+           gdata.vision.transforms.RandomColorJitter(
+           brightness=0.1, contrast=0.5, saturation=0.5, hue=0.5)
+          ]    """augmentation list example"""
+
+    if not os.path.exists(path+'aug/'):
+        os.mkdir(path+'aug/') 
+              
+    names = os.listdir(path)
+    for name in names:
+        if name.endswith('.jpg'):
+            aug_img = image.imread(path+name)
+            apply_aug(aug_img, augs,path,name)
-- 
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