研究背景
由于研究时常根据使用情况，制作符合要求的数据集，因而将需要的代码整理。
数据集结构
└── VOCdevkit #根目录
└── VOC2012 #不同年份的数据集，这里只下载了2012的，还有2007等其它年份的
├── Annotations #存放xml文件，与JPEGImages中的图片一一对应，解释图片的内容等等
├── ImageSets #该目录下存放的都是txt文件，txt文件中每一行包含一个图片的名称，末尾会加上±1表示正负样本
│ ├── Action
│ ├── Layout
│ ├── Main
│ └── Segmentation
├── JPEGImages #存放源图片
├── SegmentationClass #存放的是图片，分割后的效果，见下文的例子
└── SegmentationObject #存放的是图片，分割后的效果，见下文的例子
- Annotation文件夹存放的是xml文件，该文件是对图片的解释，每张图片都对于一个同名的xml文件。
- ImageSets文件夹存放的是txt文件，这些txt将数据集的图片分成了各种集合。如Main下的train.txt中记录的是用于训练的图片集合
- JPEGImages文件夹存放的是数据集的原图片
- SegmentationClass以及SegmentationObject文件夹存放的都是图片，且都是图像分割结果图
参考链接制作VOC格式数据集
参考链接修改xml文件的节点值方法详解
代码实现
- 图片重命名保存在JPEGImages，将原命名数字+12682，再补0到6位数字。
import os
path = "/home/henry/File/URPC2018/VOC/VOC2007/JPEG/YDXJ0013"
#path1 = "/home/henry/File/URPC2018/VOC/VOC2007/JPEG/1"
filelist = os.listdir(path) #该文件夹下所有的文件（包括文件夹）
for file in filelist: #遍历所有文件
Olddir=os.path.join(path,file) #原来的文件路径
if os.path.isdir(Olddir): #如果是文件夹则跳过
continue
filename=os.path.splitext(file)[0] #文件名
filetype=os.path.splitext(file)[1] #文件扩展名
Newdir=os.path.join(path,str(int(filename)+12682).zfill(6)+filetype) #用字符串函数zfill 以0补全所需位数
os.rename(Olddir,Newdir)#重命名
- VOC格式数据集从000000.jpg转换为从1.jpg开始的自然排列。
import os
path = "/home/henry/Files/URPC2018/UPRC2018UnderWaterDetection/cla6/JPEGImagesc"
path1 = "/home/henry/Files/URPC2018/UPRC2018UnderWaterDetection/cla6/1"
filelist = os.listdir(path) #该文件夹下所有的文件（包括文件夹）
for file in filelist: #遍历所有文件
Olddir=os.path.join(path,file) #原来的文件路径
if os.path.isdir(Olddir): #如果是文件夹则跳过
continue
filename=os.path.splitext(file)[0] #文件名
filetype=os.path.splitext(file)[1] #文件扩展名
Newdir=os.path.join(path1,str(int(filename)+1)+filetype)
os.rename(Olddir,Newdir)#重命名
- Layout和Main文件夹所需text文档。
制作VOC2007数据集中的trainval.txt， train.txt ， test.txt ， val.txt
trainval占总数据集的50%，test占总数据集的50%；train占trainval的50%，val占trainval的50%；
import os
import random
trainval_percent = 0.5
train_percent = 0.5
xmlfilepath = 'Anno/G0024173'
txtsavepath = 'test'
total_xml = os.listdir(xmlfilepath)
num=len(total_xml)
list=range(num)
tv=int(num*trainval_percent)
tr=int(tv*train_percent)
trainval= random.sample(list,tv)
train=random.sample(trainval,tr)
ftrainval = open('test\\trainval.txt', 'w')
ftest = open('test\\test.txt', 'w')
ftrain = open('test\\train.txt', 'w')
fval = open('test\\val.txt', 'w')
for i in list:
name=total_xml[i][:-4]+'\n'
if i in trainval:
ftrainval.write(name)
if i in train:
ftrain.write(name)
else:
fval.write(name)
else:
ftest.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest .close()
- 标签文件txt转xml格式代码。
python解析VOC的xml文件并转成自己需要的txt格式
# ! /usr/bin/python
# -*- coding:UTF-8 -*-
import os, sys
import glob
from PIL import Image
# VEDAI 图像存储位置
src_img_dir = os.path.abspath('.')+'/13'
# VEDAI 图像的 ground truth 的 xml 文件存放位置
src_xml_dir = '/home/henry/File/URPC2018/all_train_data_0829/111'
# 遍历目录读取图片
img_Lists = []
def get_img_list(dir_path):
if os.path.isdir(dir_path):
for x in os.listdir(dir_path):
get_img_list(os.path.join(dir_path, x))
elif os.path.isfile(dir_path) and dir_path.split('.')[-1] == 'jpg':
img_Lists.append(dir_path)
get_img_list(src_img_dir)
img_Lists.sort(key=lambda x:x[-10:])
# for i in img_Lists:
# print(i)
# 创建xml文件，存入图片信息
for img_item in img_Lists:
im = Image.open(img_item) #打开图片 为了记录图片的长宽数据
img = os.path.split(img_item)[1].split('.')[0]
width, height = im.size
# write in xml file
# os.mknod(src_xml_dir + '/' + img + '.xml')
xml_file = open((src_xml_dir + '/' + img + '.xml'), 'w')
xml_file.write('<annotation>\n')
xml_file.write(' <folder>VOC2007</folder>\n')
xml_file.write(' <filename>' + str(img) + '.jpg' + '</filename>\n')
xml_file.write(' <size>\n')
xml_file.write(' <width>' + str(width) + '</width>\n')
xml_file.write(' <height>' + str(height) + '</height>\n')
xml_file.write(' <depth>3</depth>\n')
xml_file.write(' </size>\n')
xml_file.close()
# 读取全部信息
txt_file = open('YDXJ0013.txt')
for line in txt_file.readlines():
gt = line.splitlines()
# print(gt)
# gt = txt_file.readline().splitlines()
# # gt = open(src_txt_dir + '/gt_' + img + '.txt').read().splitlines()
# write the region of image on xml file
for img_each_label in gt:
spt = img_each_label.split(' ') # 这里如果txt里面是以逗号‘，’隔开的，那么就改为spt = img_each_label.split(',')。
# 判断是否需要写入xml
if spt[6] == '0':
# print (gt)
# 打开相应xml文件
# print(spt[5].zfill(6))
xml_file = open((src_xml_dir + '/' + spt[5].zfill(6) + '.xml'), 'a')
xml_file.write(' <object>\n')
xml_file.write(' <name>' + str(spt[9]) + '</name>\n')
xml_file.write(' <pose>Unspecified</pose>\n')
xml_file.write(' <truncated>0</truncated>\n')
xml_file.write(' <difficult>0</difficult>\n')
xml_file.write(' <bndbox>\n')
xml_file.write(' <xmin>' + str(spt[1]) + '</xmin>\n')
xml_file.write(' <ymin>' + str(spt[2]) + '</ymin>\n')
xml_file.write(' <xmax>' + str(spt[3]) + '</xmax>\n')
xml_file.write(' <ymax>' + str(spt[4]) + '</ymax>\n')
xml_file.write(' </bndbox>\n')
xml_file.write(' </object>\n')
xml_file.close()
# 补上结尾
for i in range(4500):
xml_file = open((src_xml_dir + '/' + str(i).zfill(6) + '.xml'), 'a')
xml_file.write('</annotation>')
xml_file.close()
生产xml文档格式，以000017.xml为例
<annotation>
<folder>VOC2007</folder>
<filename>000017.jpg</filename>
<size> //图像尺寸（长宽以及通道数）
<width>720</width>
<height>405</height>
<depth>3</depth></size>
<object> //检测到的物体
<name>"scallop"</name> //物体类别
<pose>Unspecified</pose> //拍摄角度
<truncated>0</truncated> //是否被截断（0表示完整）
<difficult>0</difficult> //目标是否难以识别（0表示容易识别）
<bndbox> //bounding-box 目标框坐标
<xmin>690</xmin> 左上角x
<ymin>299</ymin> 左上角y
<xmax>718</xmax> 右下角x
<ymax>356</ymax> 右下角y
</bndbox>
</object> //检测到多个物体
<object>
<name>"scallop"</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>472</xmin>
<ymin>296</ymin>
<xmax>709</xmax>
<ymax>403</ymax>
</bndbox>
</object>
<object>
<name>"scallop"</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>674</xmin>
<ymin>89</ymin>
<xmax>717</xmax>
<ymax>155</ymax>
</bndbox>
</object>
<object>
<name>"seaurchin"</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>663</xmin>
<ymin>12</ymin>
<xmax>716</xmax>
<ymax>67</ymax>
</bndbox>
</object>
<object>
<name>"scallop"</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>507</xmin>
<ymin>110</ymin>
<xmax>647</xmax>
<ymax>210</ymax>
</bndbox></object>
<object>
<name>"seaurchin"</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>576</xmin>
<ymin>173</ymin>
<xmax>714</xmax>
<ymax>297</ymax>
</bndbox></object>
<object>
<name>"scallop"</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>90</xmin>
<ymin>122</ymin>
<xmax>199</xmax>
<ymax>187</ymax>
</bndbox>
</object>
<object>
<name>"scallop"</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>202</xmin>
<ymin>76</ymin>
<xmax>303</xmax>
<ymax>138</ymax>
</bndbox>
</object>
<object>
<name>"scallop"</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>201</xmin>
<ymin>170</ymin>
<xmax>366</xmax>
<ymax>294</ymax>
</bndbox>
</object>
<object>
<name>"seaurchin"</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>52</xmin>
<ymin>137</ymin>
<xmax>189</xmax>
<ymax>279</ymax>
</bndbox></object>
<object>
<name>"seacucumber"</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>36</xmin>
<ymin>234</ymin>
<xmax>137</xmax>
<ymax>373</ymax>
</bndbox>
</object>
</annotation>
- 修改xml文件，frame改filename。
from xml.etree import ElementTree
import os, sys
import glob
from PIL import Image
path = "/media/leequens/File/YOLO/test/xml/11"
filelist = os.listdir(path)
for file in filelist:
filename = os.path.splitext(file)[0] # 文件名
filetype = os.path.splitext(file)[1] # 文件扩展名
xmldoc = ElementTree.parse('/media/leequens/File/YOLO/test/xml/rebox/'+str(int(filename)).zfill(6)+'.xml')
root = xmldoc.getroot()
for child in root:
if child.tag == 'frame':
temp_node = 'filename'
child.tag = temp_node
break
xmldoc.write('/media/leequens/File/YOLO/test/xml/'+str(int(filename)).zfill(6)+'.xml')
- 根据xml框出图片物体
import os
import os.path
import numpy as np
import xml.etree.ElementTree as xmlET
from PIL import Image, ImageDraw
classes = ('__background__', # always index 0
'"seacucumber"', '"seaurchin"', '"scallop"', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
file_path_img = '/home/henry/File/URPC2018/all_train_data_0829/13'
file_path_xml = '/home/henry/File/URPC2018/all_train_data_0829/111'
save_file_path = '/home/henry/File/URPC2018/all_train_data_0829/test'
pathDir = os.listdir(file_path_xml)
for idx in range(len(pathDir)):
filename = pathDir[idx]
tree = xmlET.parse(os.path.join(file_path_xml, filename))
objs = tree.findall('object')
num_objs = len(objs)
boxes = np.zeros((num_objs, 5), dtype=np.uint16)
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cla = obj.find('name').text
label = classes.index(cla)
boxes[ix, 0:4] = [x1, y1, x2, y2]
boxes[ix, 4] = label
image_name = os.path.splitext(filename)[0]
img = Image.open(os.path.join(file_path_img, image_name + '.jpg'))
draw = ImageDraw.Draw(img)
for ix in range(len(boxes)):
xmin = int(boxes[ix, 0])
ymin = int(boxes[ix, 1])
xmax = int(boxes[ix, 2])
ymax = int(boxes[ix, 3])
draw.rectangle([xmin, ymin, xmax, ymax], outline=(255, 0, 0))
draw.text([xmin, ymin],classes[boxes[ix, 4]], (255, 0, 0))
img.save(os.path.join(save_file_path, image_name + '.jpg'))
- 生成/VOCdevkit/VOC2007/ImageSets/Layout/train.txt，含图片路径，目前图片保存在/home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173路径，在终端运行下面命令，txt文档生成到根目录～。其他txt文件类似处理。
ls -R /home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173/*.jpg >train.txt
/home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173/000000.jpg
/home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173/000001.jpg
/home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173/000002.jpg
/home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173/000003.jpg
/home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173/000004.jpg
/home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173/000005.jpg
/home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173/000006.jpg
/home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173/000007.jpg
/home/henry/File/URPC2018/VOC/VOC2007/JPEGImages/G0024173/000008.jpg
- 生成/VOCdevkit/VOC2007/test/train.txt，不含图片路径，代码应在VOC2007文件夹下运行，图片从000000.jpg到001800.jpg。
import os
import random
file = open('/test/train.txt', 'w')
for i in range(0, 1800):
file.write(str("%06d" % i) + '\n')
file.close()
- Linux Shell 批量重命名的方法总览
Linux Shell 批量重命名的方法总览
c=0;for i in *.jpg;do mv -f $i $((c+=1)).jpg;done
- ubuntu系统对txt文本文档的批量处理
Linux sed命令操作 删除文件每一行的前k个字符、在文本的行尾或行首添加字符
常用操作
在行尾添加.jpg ^代表行首，$代表行尾 如果要在原文件上修改，加上参数-i
tmp.txt原内容:
23456789
23456789
23456789
终端在tmp.txt路径下运行
sed -i 's/$/.jpg&/g' tmp.txt
23456789.jpg
23456789.jpg
23456789.jpg
删除共同后缀，可以用查找替换方法。
- 去掉文件名前导0方法
rename "s/^0{1,2}//g" *
Linux文件批量改名/排序总结（rename，sort等）
- 将比赛用的test_list.txt保存为字典，并将测试结果result.txt按照字典方式对应为。
test_list.txt内容
CHN083846_0000 1
CHN083846_0043 2
CHN083846_0076 3
CHN083846_0099 4
CHN083846_0124 5
CHN083846_0182 6
CHN083846_0237 7
CHN083846_0257 8
CHN083846_0262 9
CHN083846_0268 10
CHN083846_0276 11
CHN083846_0286 12
CHN083846_0290 13
CHN083846_0300 14
CHN083846_0308 15
CHN083846_0311 16
CHN083846_0321 17
CHN083846_0324 18
CHN083846_0326 19
CHN083846_0334 20
result.txt内容
CHN083846_0000.jpg 2 0.99687284 311 234 389 304
CHN083846_0000.jpg 2 0.9967654 379 105 464 184
CHN083846_0000.jpg 2 0.99383944 394 219 465 294
CHN083846_0000.jpg 2 0.993507 366 99 416 157
CHN083846_0000.jpg 2 0.98956084 498 164 571 238
CHN083846_0000.jpg 2 0.9875843 491 370 584 474
CHN083846_0000.jpg 2 0.98697644 373 178 430 232
CHN083846_0000.jpg 2 0.9807468 316 201 383 250
CHN083846_0043.jpg 2 0.99795675 325 120 404 198
CHN083846_0043.jpg 2 0.9977519 228 219 305 288
CHN083846_0043.jpg 2 0.9969998 309 111 359 156
CHN083846_0043.jpg 2 0.99581474 427 193 495 270
CHN083846_0043.jpg 2 0.9956038 304 229 374 302
CHN083846_0043.jpg 2 0.9946083 543 405 585 457
CHN083846_0043.jpg 2 0.9940837 372 394 466 479
CHN083846_0043.jpg 2 0.9934238 295 183 360 240
CHN083846_0043.jpg 2 0.99061626 233 188 307 249
CHN083846_0043.jpg 2 0.9718845 552 86 585 130
CHN083846_0076.jpg 2 0.99764097 293 227 363 295
CHN083846_0076.jpg 2 0.9971411 217 198 289 255
CHN083846_0076.jpg 2 0.9971004 548 27 585 72
CHN083846_0076.jpg 2 0.99674976 282 171 358 243
转换需要的代码convertxt-zidian.py：
f = open('test_list.txt', 'r')
result = {}
for line in f.readlines():
line = line.strip() # 去除首尾空格
if not len(line):
continue
result[line.split(' ')[0]] = line.split(' ')[1]
input_file = open(r'/home/henry/Files/URPC2018/常用pythoncodes/result3.txt',"r").read();
for key,value in result.items():
input_file=input_file.replace(key,value);
print(input_file)
with open('text_trans.txt','w+') as filetwo:
filetwo.writelines(input_file)
test_list.txt保存在当前路径下，与convertxt-zidian.py放在一起，result.txt放在/home/henry/Files/URPC2018/常用pythoncodes/result3.txt，生成的文件为text_trans.txt，内容如下：
1 2 0.99687284 311 234 389 304
1 2 0.9967654 379 105 464 184
1 2 0.99383944 394 219 465 294
1 2 0.993507 366 99 416 157
1 2 0.98956084 498 164 571 238
1 2 0.9875843 491 370 584 474
1 2 0.98697644 373 178 430 232
1 2 0.9807468 316 201 383 250
2 2 0.99795675 325 120 404 198
2 2 0.9977519 228 219 305 288
2 2 0.9969998 309 111 359 156
2 2 0.99581474 427 193 495 270
2 2 0.9956038 304 229 374 302
2 2 0.9946083 543 405 585 457
2 2 0.9940837 372 394 466 479
2 2 0.9934238 295 183 360 240
2 2 0.99061626 233 188 307 249
2 2 0.9718845 552 86 585 130
- 图片格式转换将* .png转换为* .jpg
henry@henry-Rev-1-0:~/Files/URPC2018/UPRC2018UnderWaterDetection/enhanced0815/B6
2 (复件)$ for i in *.png;do convert ${i} ${i%png}jpg;done
henry@henry-Rev-1-0:~/Files/URPC2018/UPRC2018UnderWaterDetection/enhanced0815/B6
2 (复件)$ rm -rf *.png
- 图片批量修改格式.sh文件，将当前目录中* .png图片转换为*.jpg图片，并删除.png图片
for i in *.png;do convert ${i} ${i%bmp}jpg;done
rm -rf *.png
- comm命令进行文本编辑操作
多文本排序
sort A.txt -o A.txt; sort B.txt -o B.txt
两文本比较并输出至2.txt
comm train.txt test.txt>2.txt
参考资料comm比较两个文件的异同
- diff命令进行文本内容比较操作
文件1中有，文件2中没有输出到_1_not_in_2.txt,文件2中有
diff -u a.txt b.txt|grep '^-' |grep -v '^---' > '_1_not_in_2.txt'
文件1中没有的输出到_2_not_in_1.txt
diff -u a.txt b.txt|grep '^+' |grep -v '^+++' > '_2_not_in_1.txt'
文件1和文件2都是每行一串字符，要选出相同的行输出到same.txt
diff -u a.txt b.txt|grep '^ ' > same.txt
- python批量修改xml属性
修改xml文件，将其filename部分与自身*.xml对应
#coding=utf-8
import os
import os.path
import xml.dom.minidom
path="./7"
files=os.listdir(path) #得到文件夹下所有文件名称
s=[]
for xmlFile in files:
#遍历文件夹
portion = os.path.splitext(xmlFile)
if not os.path.isdir(xmlFile):
#判断是否是文件夹,不是文件夹才打开
# print (xmlFile)
#xml文件读取操作
#将获取的xml文件名送入到dom解析
dom=xml.dom.minidom.parse(os.path.join(path,xmlFile))
###最核心的部分os.path.join(path,xmlFile),路径拼接,输入的是具体路径
root=dom.documentElement
name=root.getElementsByTagName('frame')
#pose=root.getElementsByTagName('pose')
#重命名class name
for i in range(len(name)):
# print (name[i].firstChild.data)
print(xmlFile)
if portion[1] ==".xml":
newname = portion[0]+".jpg"
print(newname)
name[i].firstChild.data=newname
print (name[i].firstChild.data)
#保存修改到xml文件中
with open(os.path.join(path,xmlFile),'w',encoding='UTF-8') as fh:
dom.writexml(fh)
print('修改filename OK!')
参考资料python批量修改xml属性
- python批量修改xml的bounding box数值，修改为图片镜像翻转之后的包围框坐标。
# coding:utf-8
import cv2
import math
import numpy as np
import xml.etree.ElementTree as ET
import os
xmlpath = './5801xml/'
imgpath = './imgs/'
rotated_imgpath = './rotatedimg/'
rotated_xmlpath = './rotatedxml/'
for i in os.listdir(xmlpath):
a, b = os.path.splitext(i)
print(str(i))
tree = ET.parse(xmlpath + a + '.xml')
root = tree.getroot()
for chi in root.iter('size'):
width=int(chi.find('width').text)
for box in root.iter('bndbox'):
xmin = int(box.find('xmin').text)
ymin = int(box.find('ymin').text)
xmax = int(box.find('xmax').text)
ymax = int(box.find('ymax').text)
box.find('xmin').text = str(width-xmax)
box.find('ymin').text = str(ymin)
box.find('xmax').text = str(width-xmin)
box.find('ymax').text = str(ymax)
tree.write(rotated_xmlpath + a + '.xml')
print(str(a) + '.xml has been rotated for '+'°')
- 脚本批量修改图片大小尺寸和翻转操作
修改图片大小，将当前文件夹中图片原地修改为256x256
set -e # or use "set -o errexit" to quit on error.
set -x # or use "set -o xtrace" to print the statement before you execute it.
FILES=*.jpg
for f in $FILES
do
echo "$f"
convert $f -resize 256x256! $f
done
在当前文件夹中对图片镜像翻转
set -e # or use "set -o errexit" to quit on error.
set -x # or use "set -o xtrace" to print the statement before you execute it.
FILES=*.jpg
for f in $FILES
do
echo "$f"
convert $f -flop $f
done
- 读取图像的尺寸大小
import cv2
import os
dirfile = './copy'
filenames = os.listdir(dirfile)
filenames.sort()
f = open('image_shape1.txt','a+')
for filename in filenames:
path = dirfile+'/'+filename
print(dirfile + '/' + filename)
img = cv2.imread(path) # read image.jpg from dirfile
# img = cv2.cv.LoadImage(path)
size = img.shape
size_output = str(size)
print(size)
f.writelines(filename + ' '+ size_output+'\n')
f.close()
- 由csv制作xml文件
import os
from utilscsv import *
from lxml.etree import Element, SubElement, tostring
from xml.dom.minidom import parseString
import cv2
countnum = 0
def save_xml(image_name, bbox_class, save_dir='./VOC2007/Annotations', width=1609, height=500, channel=3):
global countnum
path = './JPEGImages/'+ image_name + '.jpg'
img = cv2.imread(path) # read image.jpg from dirfile
size = img.shape
width = size[1]
height = size[0]
channel = size[2]
node_root = Element('annotation')
node_folder = SubElement(node_root, 'folder')
node_folder.text = 'JPEGImages'
node_filename = SubElement(node_root, 'filename')
node_filename.text = image_name + '.jpg'
node_size = SubElement(node_root, 'size')
node_width = SubElement(node_size, 'width')
node_width.text = '%s' % width
node_height = SubElement(node_size, 'height')
node_height.text = '%s' % height
node_depth = SubElement(node_size, 'depth')
node_depth.text = '%s' % channel
print("bbox_class: ",bbox_class)
# for i in range(len(bbox_class)):
if int(bbox_class[0]) <6 or abs(int(bbox_class[0])-width)<6: # x coordiante near boundary
if int(bbox_class[1]) <6 or abs(int(bbox_class[1])-height)<6: # y coordiante near boundary
print("x near bbox_class[1]: ",int(bbox_class[1]))
# left is minimum
if int(bbox_class[0]) ==1:
left = int(bbox_class[0])
else:
left = int(bbox_class[0])-1
top = int(bbox_class[1])-1
# right is maxmium
if int(bbox_class[0]) == width:
right = int(bbox_class[0])
else:
right = int(bbox_class[0])+1
bottom = int(bbox_class[1]) + 1
else: # y coordiante away from boundary
print("x near y away bbox_class[0]: ", int(bbox_class[0]))
left = int(bbox_class[0]) - 1
top = int(bbox_class[1]) - 1
right = int(bbox_class[0]) + 1
bottom = int(bbox_class[1]) + 1
elif int(bbox_class[1]) <6 or abs(int(bbox_class[1])-height)<6: # y coordiante near boundary
print("y near bbox_class[1]: ",int(bbox_class[1]))
left = int(bbox_class[0])-1
top = int(bbox_class[1])-1
right = int(bbox_class[0])+1
bottom = int(bbox_class[1]) + 1
else:
left, top, right, bottom = int(bbox_class[0])-5, int(bbox_class[1])-5, int(bbox_class[0]) + 5, int(bbox_class[1]) + 5
if (left >=1 and left <= width) and (top >=1 and top <= height) and (right >=1 and right <= width) and (bottom >=1 and bottom <= height):
countnum += 1
print("lefttop and rightbottom are in the range!", countnum)
node_object = SubElement(node_root, 'object')
node_name = SubElement(node_object, 'name')
node_name.text = '%s' % bbox_class[2]
node_difficult = SubElement(node_object, 'difficult')
node_difficult.text = '0'
node_bndbox = SubElement(node_object, 'bndbox')
node_xmin = SubElement(node_bndbox, 'xmin')
node_xmin.text = '%s' % left
node_ymin = SubElement(node_bndbox, 'ymin')
node_ymin.text = '%s' % top
node_xmax = SubElement(node_bndbox, 'xmax')
node_xmax.text = '%s' % right
node_ymax = SubElement(node_bndbox, 'ymax')
node_ymax.text = '%s' % bottom
else:
print("There is an error: ",node_filename.text)
file_object = open('log.txt', 'a+')
file_object.writelines("There is an error: "+ node_filename.text + '\t')
file_object.writelines(str(left)+' '+str(top)+' '+str(right)+' '+ str(bottom)+'\n')
file_object.close()
xml = tostring(node_root, pretty_print=True)
dom = parseString(xml)
save_xml = os.path.join(save_dir, node_filename.text.replace('jpg', 'xml'))
with open(save_xml, 'wb') as f:
f.write(xml)
return
def change2xml(label_dict={}):
for image in label_dict.keys():
image_name = os.path.split(image)[-1]
bbox_object = label_dict.get(image, [])
save_xml(image_name, bbox_object)
return
if __name__ == '__main__':
label_dict = read_csv(csv_path=r'./list.csv',
pre_dir=r'./JPEGImages')
change2xml(label_dict)
- 相关配置文件
import csv
import os
def read_csv(csv_path, pre_dir):
label_dict = {}
with open(csv_path, "r") as f:
reader = csv.reader(f)
header = True
for line in reader:
if header:
header = False
continue
image_path = os.path.join(pre_dir, line[0])
bbox_object = []
for i in range(1,4):
bbox_object.append(line[i])
label_dict.setdefault(image_path, bbox_object)
return label_dict
def write_csv(result_dict, out_path='out.csv'):
with open(out_path, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['name', 'coordinate'])
for image in result_dict.keys():
image_name = os.path.split(image)[-1]
bbox = result_dict.get(image, [])
bbox_rs = ';'.join(['_'.join(str(int(id)) for id in i) for i in bbox])
writer.writerow([image_name, bbox_rs])
if __name__ == '__main__':
label_dict = read_csv(csv_path=r'./train_b.csv',
pre_dir=r'/home/matthew/dataset')
write_csv(label_dict)
- 读取csv文件并保存到txt文件中
import csv
import os
def read_csv(csv_path, pre_dir):
label_dict = {}
with open(csv_path, "r") as f:
reader = csv.reader(f)
header = True
for line in reader:
if header:
header = False
continue
image_path = os.path.join(pre_dir, line[0])
bbox_object = []
for i in range(1,4):
bbox_object.append(line[i])
label_dict.setdefault(image_path, bbox_object)
return label_dict
def write_csv(result_dict, out_path='out.csv'):
with open(out_path, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['name', 'coordinate'])
for image in result_dict.keys():
image_name = os.path.split(image)[-1]
bbox = result_dict.get(image, [])
bbox_rs = ';'.join(['_'.join(str(int(id)) for id in i) for i in bbox])
writer.writerow([image_name, bbox_rs])
if __name__ == '__main__':
label_dict = read_csv(csv_path=r'./train_b.csv',
pre_dir=r'/home/matthew/dataset')
write_csv(label_dict)
- 从txt中读取符合要求的文件到指定目录
import os
import shutil
srcimage_dir_path = "./VOC2007_6280/VOC_nova_all/JPEGImages"
srcxml_dir_path = "./VOC2007_6280/VOC_nova_all/Annotations_2class_0401"
imageto_dir_path = "./VOC2007_6280/all_star/copy_star_image/"
xmlto_dir_path = "./VOC2007_6280/all_star/copy_star_xml/"
txt_path = './csv_reader.txt'
key = '_a'
count = 0
if not os.path.exists(imageto_dir_path):
print("to_dir_path not exist,so create the dir")
os.mkdir(imageto_dir_path)
if not os.path.exists(xmlto_dir_path):
print("to_dir_path not exist,so create the dir")
os.mkdir(xmlto_dir_path)
# if os.path.exists(src_dir_path):
# print("src_dir_path exitst")
fr = open(txt_path)
stringClass = [line.strip().split('\t') for line in fr.readlines()]
# print("stringClass: ",stringClass)
for i in range(len(stringClass)):
if stringClass[i][3] == 'newtarget' or stringClass[i][3] == 'isstar' or stringClass[i][3] == 'asteroid' or stringClass[i][3] == 'isnova' or stringClass[i][3] == 'known':
image_name = stringClass[i][0] + '.jpg'
xml_name = stringClass[i][0] + '.xml'
count +=1
print(image_name,' ',count)
shutil.copy(srcimage_dir_path+'/'+image_name,imageto_dir_path+image_name)
shutil.copy(srcxml_dir_path + '/' + xml_name, xmlto_dir_path + xml_name)
- 对图像进行镜像翻转操作，扩充训练集同时，增加正样本数量
import cv2
import copy
import os
"""
#水平镜像可按公式
#I = i
#J = N - j + 1
#垂直镜像可按公式
#I = M - i + 1
#J = j
#对角镜像可按公式
#I = M - i + 1
#J = N - j + 1
"""
def mirror_imgs(imgs_path, save_path):
for name in os.listdir(imgs_path):
print(name)
image = cv2.imread(os.path.join(imgs_path, name), 1);
height = image.shape[0]
width = image.shape[1]
# channels = image.shape[2]
iLR = copy.deepcopy(image) # 获得一个和原始图像相同的图像，注意这里要使用深度复制
for i in range(height):
for j in range(width):
iLR[i, width - 1 - j] = image[i, j]
# cv2.imshow('image', image)
# cv2.imshow('iLR', iLR)
save_name = name[:-4]+'_zym'+'.jpg'
cv2.imwrite(os.path.join(save_path, save_name), iLR,
[int(cv2.IMWRITE_JPEG_QUALITY), 100]) # 保存图片
# cv2.waitKey(0)
# cv2.destroyAllWindows()
def horizontal_mirror_imgs(imgs_path, save_path):
for name in os.listdir(imgs_path):
print(name)
image = cv2.imread(os.path.join(imgs_path, name), 1);
height = image.shape[0]
width = image.shape[1]
# channels = image.shape[2]
iLR = copy.deepcopy(image) # 获得一个和原始图像相同的图像，注意这里要使用深度复制
for i in range(height):
for j in range(width):
iLR[i, width - 1 - j] = image[i, j]
# cv2.imshow('image', image)
# cv2.imshow('iLR', iLR)
save_name = name[:-4]+'_zym'+'.jpg'
cv2.imwrite(os.path.join(save_path, save_name), iLR,
[int(cv2.IMWRITE_JPEG_QUALITY), 100]) # 保存图片
# cv2.waitKey(0)
# cv2.destroyAllWindows()
def vertical_mirror_imgs(imgs_path, save_path):
for name in os.listdir(imgs_path):
print(name)
image = cv2.imread(os.path.join(imgs_path, name), 1);
height = image.shape[0]
width = image.shape[1]
# channels = image.shape[2]
iLR = copy.deepcopy(image) # 获得一个和原始图像相同的图像，注意这里要使用深度复制
for i in range(height):
for j in range(width):
iLR[height - 1 - i, j] = image[i, j]
# cv2.imshow('image', image)
# cv2.imshow('iLR', iLR)
save_name = name[:-4]+'_sxm'+'.jpg'
cv2.imwrite(os.path.join(save_path, save_name), iLR,
[int(cv2.IMWRITE_JPEG_QUALITY), 100]) # 保存图片
# cv2.waitKey(0)
# cv2.destroyAllWindows()
imgs_path = '/home/henry/Files/FutureAi/VOC2007_6280/all_star/copy_star_image'
save_path = "/home/henry/Files/FutureAi/VOC2007_6280/all_star/copy_star_image_mirror"
if not os.path.exists(save_path):
os.makedirs(save_path)
#mirror_imgs(imgs_path, save_path)
horizontal_mirror_imgs(imgs_path,save_path)
# vertical_mirror_imgs
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