对小样本数据进行数据增强
一、前情介绍
在之前对yolov3的学习中，有时候发现小样本数据集容易出现过拟合或者泛化能力不强的问题，在对这一问题提出的不同解决方法进行了摸索和尝试，发现提高数据集样本容量是一个比较直接和简单粗暴的方法，以下纪录这一实验方法。
二、环境
直接交代环境，都是相对较简单，在这里博主没遇到过坑
- os
- numpy
- PIL
- imgaug
三、代码
import xml.etree.ElementTree as ET
import os
import numpy as np
from PIL import Image
import shutil
import imgaug as ia
from imgaug import augmenters as iaa
ia.seed(1)
def read_xml_annotation(root, image_id):
in_file = open(os.path.join(root, image_id))
tree = ET.parse(in_file)
root = tree.getroot()
bndboxlist = []
for object in root.findall('object'): # 找到root节点下的所有country节点
bndbox = object.find('bndbox') # 子节点下节点rank的值
xmin = int(bndbox.find('xmin').text)
xmax = int(bndbox.find('xmax').text)
ymin = int(bndbox.find('ymin').text)
ymax = int(bndbox.find('ymax').text)
# print(xmin,ymin,xmax,ymax)
bndboxlist.append([xmin, ymin, xmax, ymax])
# print(bndboxlist)
bndbox = root.find('object').find('bndbox')
return bndboxlist
# (506.0000, 330.0000, 528.0000, 348.0000) -> (520.4747, 381.5080, 540.5596, 398.6603)
def change_xml_annotation(root, image_id, new_target):
new_xmin = new_target[0]
new_ymin = new_target[1]
new_xmax = new_target[2]
new_ymax = new_target[3]
in_file = open(os.path.join(root, str(image_id) + '.xml')) # 这里root分别由两个意思
tree = ET.parse(in_file)
xmlroot = tree.getroot()
object = xmlroot.find('object')
bndbox = object.find('bndbox')
xmin = bndbox.find('xmin')
xmin.text = str(new_xmin)
ymin = bndbox.find('ymin')
ymin.text = str(new_ymin)
xmax = bndbox.find('xmax')
xmax.text = str(new_xmax)
ymax = bndbox.find('ymax')
ymax.text = str(new_ymax)
tree.write(os.path.join(root, str("%06d" % (str(id) + '.xml'))))
def change_xml_list_annotation(root, image_id, new_target, saveroot, id):
in_file = open(os.path.join(root, str(image_id) + '.xml')) # 这里root分别由两个意思
tree = ET.parse(in_file)
elem = tree.find('filename')
elem.text = (str("%06d" % int(id)) + '.jpg')
xmlroot = tree.getroot()
index = 0
for object in xmlroot.findall('object'): # 找到root节点下的所有country节点
bndbox = object.find('bndbox') # 子节点下节点rank的值
# xmin = int(bndbox.find('xmin').text)
# xmax = int(bndbox.find('xmax').text)
# ymin = int(bndbox.find('ymin').text)
# ymax = int(bndbox.find('ymax').text)
new_xmin = new_target[index][0]
new_ymin = new_target[index][1]
new_xmax = new_target[index][2]
new_ymax = new_target[index][3]
xmin = bndbox.find('xmin')
xmin.text = str(new_xmin)
ymin = bndbox.find('ymin')
ymin.text = str(new_ymin)
xmax = bndbox.find('xmax')
xmax.text = str(new_xmax)
ymax = bndbox.find('ymax')
ymax.text = str(new_ymax)
index = index + 1
tree.write(os.path.join(saveroot, str("%06d" % int(id)) + '.xml'))
def mkdir(path):
# 去除首位空格
path = path.strip()
# 去除尾部 \ 符号
path = path.rstrip("/")
# 判断路径是否存在
# 存在 True
# 不存在 False
isExists = os.path.exists()
# 判断结果
if not isExists:
# 如果不存在则创建目录
# 创建目录操作函数
os.makedirs(path)
print(path + ' 创建成功')
return True
else:
# 如果目录存在则不创建，并提示目录已存在
print(path + ' 目录已存在')
return False
if __name__ == "__main__":
IMG_DIR = "自己的文件路径/image"
XML_DIR = "自己的文件路径/Anotations"
AUG_XML_DIR = "F:/crop/Annotations" # 存储增强后的XML文件夹路径
try:
shutil.rmtree(AUG_XML_DIR)
except FileNotFoundError as e:
a = 1
mkdir(AUG_XML_DIR)
AUG_IMG_DIR = "F:/crop/images" # 存储增强后的影像文件夹路径
try:
shutil.rmtree(AUG_IMG_DIR)
except FileNotFoundError as e:
a = 1
mkdir(AUG_IMG_DIR)
AUGLOOP = 10 # 每张影像增强的数量
boxes_img_aug_list = []
new_bndbox = []
new_bndbox_list = []
# 影像增强
seq = iaa.Sequential([
iaa.Flipud(0.5), # vertically flip 20% of all images
iaa.Fliplr(0.5), # 镜像
iaa.Multiply((1.2, 1.5)), # change brightness, doesn't affect BBs
iaa.GaussianBlur(sigma=(0, 2.0)), # iaa.GaussianBlur(0.5),
iaa.Affine(
translate_px={"x": 15, "y": 15},
scale=(0.8, 0.95),
rotate=(-30, 30)
) # translate by 40/60px on x/y axis, and scale to 50-70%, affects BBs
])
for root, sub_folders, files in os.walk(XML_DIR):
for name in files:
bndbox = read_xml_annotation(XML_DIR, name)
shutil.copy(os.path.join(XML_DIR, name), AUG_XML_DIR)
shutil.copy(os.path.join(IMG_DIR, name[:-4] + '.jpg'), AUG_IMG_DIR)
for epoch in range(AUGLOOP):
seq_det = seq.to_deterministic() # 保持坐标和图像同步改变，而不是随机
# 读取图片
img = Image.open(os.path.join(IMG_DIR, name[:-4] + '.jpg'))
# sp = img.size
img = np.asarray(img)
# bndbox 坐标增强
for i in range(len(bndbox)):
bbs = ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=bndbox[i][0], y1=bndbox[i][1], x2=bndbox[i][2], y2=bndbox[i][3]),
], shape=img.shape)
bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]
boxes_img_aug_list.append(bbs_aug)
# new_bndbox_list:[[x1,y1,x2,y2],...[],[]]
n_x1 = int(max(1, min(img.shape[1], bbs_aug.bounding_boxes[0].x1)))
n_y1 = int(max(1, min(img.shape[0], bbs_aug.bounding_boxes[0].y1)))
n_x2 = int(max(1, min(img.shape[1], bbs_aug.bounding_boxes[0].x2)))
n_y2 = int(max(1, min(img.shape[0], bbs_aug.bounding_boxes[0].y2)))
if n_x1 == 1 and n_x1 == n_x2:
n_x2 += 1
if n_y1 == 1 and n_y2 == n_y1:
n_y2 += 1
if n_x1 >= n_x2 or n_y1 >= n_y2:
print('error', name)
new_bndbox_list.append([n_x1, n_y1, n_x2, n_y2])
# 存储变化后的图片
image_aug = seq_det.augment_images([img])[0]
path = os.path.join(AUG_IMG_DIR,
str("%06d" % (len(files) + int(name[:-4]) + epoch * 250)) + '.jpg')
image_auged = bbs.draw_on_image(image_aug, thickness=0)
Image.fromarray(image_auged).save(path)
# 存储变化后的XML
change_xml_list_annotation(XML_DIR, name[:-4], new_bndbox_list, AUG_XML_DIR,
len(files) + int(name[:-4]) + epoch * 250)
print(str("%06d" % (len(files) + int(name[:-4]) + epoch * 250)) + '.jpg')
new_bndbox_list = []
四、结果对比
原数据集，大概只有27张图片
这是增强了11倍的结果：
对增强后的数据集进行训练，没有任何问题：