简单的数据增广（Data Augmentation）（附代码）
简单的数据增广（Data Augmentation）
博主最近做一个小样本的项目，当时时间紧迫，就找了几何变换的数据增广方法。数据增广的原理就不说了，Some Improvements on Deep Convolutional Neural Network Based Image Classification这篇文章里有对我用的方法和作用有简单的介绍。
废话少说，现在开始说重点吧。
方法
本文为为数据使用了翻转、裁剪和添加噪声的操作。
工具
本文中使用的是VOC格式数据的数据增广，因为COCO格式的annotation解析真的太麻烦了，如果你是COCO格式的数据。。。那可能这篇文章对你没啥帮助了，但是如果你是VOC格式的数据，或者你要用coco格式但是也有VOC格式的，可以先对VOC格式数据进行数据增广，然后用以下链接的方法转为COCO格式: VOC2COCO
本次使用到的工具有如下：
# python3
# cv2
# xml
# numpy
代码
在使用的时候直接修改代码前面的地址参数就可以了。
代码如下:
import cv2 as cv
import numpy as np
import os
import xml.etree.ElementTree as ET
import copy
import random
data_path = 'E:/Working/CornerNet-master/data/coco/images/cancer_image'
save_img_path = 'test_imgs' # 'data/coco/images'
save_anno_path = 'test_annos' # 'data/coco/annotations'
mask_path = 'new_Annotations'
suffix = '.jpg'
def get_anno(xml_path):
tree = ET.parse(xml_path)
root = tree.getroot()
if root.tag != 'annotation':
raise Exception("root should be annotation")
object_ = []
img_shape = None
for elem in root:
tag = elem.tag
if tag == 'size':
img_shape = [int(elem[0].text), int(elem[1].text)]
if tag == 'object':
anno_shape = [int(elem[2][0].text), int(elem[2][1].text),
int(elem[2][2].text), int(elem[2][3].text)]
object_.append(anno_shape)
return img_shape, object_
def flip(img, i_shape, anno_shape):
'''
:param img:
:param i_shape: 列表
:param d_shape: 包含各个anno列表的list
:return:
'''
d_shape = copy.deepcopy(anno_shape)
xImg = cv.flip(img, 1, dst=None)
ret_ishape = i_shape
arr_long = len(d_shape)
# print(i_shape,d_shape,arr_long)
for i in range(arr_long):
# xmin. ymin, xmax, ymax
# 注意该标注中以宽为X，高为Y
# print(i_shape[1], d_shape[i][2])
temp = d_shape[i][0]
d_shape[i][0] = i_shape[1] - d_shape[i][2]
d_shape[i][2] = i_shape[1] - temp
# print(d_shape[i][0])
return xImg, ret_ishape, d_shape
def crop(img, i_shape, annos_shape):
crop_max_long = 0.4
d_shape = copy.deepcopy(annos_shape)
# 判断对一边裁剪或者两边裁剪：0左上,1右下
x_crop_loc = random.randint(0, 2)
y_crop_loc = random.randint(0, 2)
if x_crop_loc is 0:
rand = random.random()
x_min = 0
x_max = i_shape[1]*(1-crop_max_long*rand)
elif x_crop_loc is 1:
rand = random.random()
x_min = i_shape[1]*(crop_max_long*rand)
x_max = i_shape[1]
else:
rand1 = random.random()
rand2 = random.random()
x_min = i_shape[1]*(crop_max_long*rand1/2)
x_max = i_shape[1] * (1 - crop_max_long * rand2 / 2)
if y_crop_loc is 0:
rand = random.random()
y_min = 0
y_max = i_shape[0] * (1 - crop_max_long * rand)
elif x_crop_loc is 1:
rand = random.random()
y_min = i_shape[0] * (crop_max_long * rand)
y_max = i_shape[0]
else:
rand1 = random.random()
rand2 = random.random()
y_min = i_shape[0] * (crop_max_long * rand1 / 2)
y_max = i_shape[0] * (1 - crop_max_long * rand2 / 2)
x_max = int(x_max)
y_max = int(y_max)
x_min = int(x_min)
y_min = int(y_min)
new_img_shape = [y_max-y_min, x_max-x_min]
a_l = len(d_shape)
for i in range(a_l):
if x_crop_loc == 1 or x_crop_loc == 2:
d_shape[i][0] -= x_min # xmin
d_shape[i][2] -= x_min # xmax
if d_shape[i][0] < 0:
d_shape[i][0] = 0
elif d_shape[i][0] > new_img_shape[1]:
d_shape[i][0] = new_img_shape[1] - 1 # 此处直接认为mask溢出则把边界认为是mask是错误的
if d_shape[i][2] > new_img_shape[1]: # 但是推测对图片的影响不大
d_shape[i][2] = new_img_shape[1] # 如果修改了这里需要对Data_Augmentation修改
elif d_shape[i][2] < 0: # 所以就先试一试 ！#
d_shape[i][2] = 1
if y_crop_loc == 1 or y_crop_loc == 2:
d_shape[i][1] -= y_min # ymin
d_shape[i][3] -= y_min # ymax
if d_shape[i][1] < 0:
d_shape[i][1] = 0
elif d_shape[i][1] > new_img_shape[0]:
d_shape[i][1] = new_img_shape[0] - 1
if d_shape[i][3] > new_img_shape[0]:
d_shape[i][3] = new_img_shape[0]
elif d_shape[i][3] < 0:
d_shape[i][3] = 1
return copy.deepcopy(img[y_min:y_max, x_min:x_max, :]), new_img_shape, d_shape
def noise(img, i_shape, d_shape):
# 添加正态分布的高斯噪声
image = np.array(img, dtype=float)
noise = np.random.normal(size=(i_shape[0], i_shape[1]))
# print(i_shape)
out = np.zeros_like(image)
for i in range(3):
out[:, :, i] = image[:, :, i]+noise
out[out > 255] = 255
out[out < 0] = 0
out = out.astype('uint8')
return out, i_shape, d_shape
# def save(name, img, img_shape, annos_shape):
# i_name = name+suffix
# l_name = name+'.xml'
# save_i_name = os.path.join(save_img_path, i_name)
# save_a_name = os.path.join(save_anno_path, l_name)
def augmentation(img_path, xml_path):
imgs = []
img_shape, annos_shape = get_anno(xml_path)
img = cv.imread(img_path)
imgs.append((img, img_shape, annos_shape))
# flip
img1, img_shape1, annos_shape1 = flip(img, img_shape, annos_shape)
imgs.append((img1, img_shape1, annos_shape1))
# crop
temp = []
for i in imgs:
i_i, i_i_s, i_a_s = i[0], i[1], i[2]
t_img, t_i_s, t_a_s = crop(i_i, i_i_s, i_a_s)
temp.append((t_img, t_i_s, t_a_s))
imgs.extend(temp)
# noise
temp = []
for i in imgs:
i_i, i_i_s, i_a_s = i[0], i[1], i[2]
t_img, t_i_s, t_a_s = noise(i_i, i_i_s, i_a_s)
temp.append((t_img, t_i_s, t_a_s))
imgs.extend(temp)
return imgs
if __name__ == "__main__":
img_dirs = os.listdir(data_path)
xml_dirs = os.listdir(mask_path)
i = 0 # i作为保存文件的名字
for xml_dir in xml_dirs:
print(i)
img_dir = os.path.join(data_path, xml_dir.split('.')[0] + suffix)
xml_dir = os.path.join(mask_path, xml_dir)
imgs = augmentation(img_dir, xml_dir)
# print(imgs[0][0])
for j in range(8):
image_data = imgs[j]
name = str(i)
i += 1
img_path = os.path.join(save_img_path, name + suffix)
# print(img_path)
cv.imwrite(img_path, image_data[0])
tree = ET.parse(xml_dir)
root = tree.getroot()
object_num = 0
for elem in root:
tag = elem.tag
if tag == 'size':
elem[0].text, elem[1].text = str(image_data[1][0]), str(image_data[1][1])
if tag == 'filename':
elem.text = name + '.xml'
if tag == 'object':
elem[2][0].text, elem[2][1].text, elem[2][2].text, elem[2][3].text = \
str(image_data[2][object_num][0]), str(image_data[2][object_num][1]), \
str(image_data[2][object_num][2]), str(image_data[2][object_num][3])
object_num += 1
write_path = os.path.join(save_anno_path, name + '.xml')
tree.write(write_path)