DOTA数据集分割，并将txt转为xml
作者：互联网
文章目录
DOTA数据集简介
DOTA数据集包含2806张航空图像，尺寸大约从800x800到4000x4000不等，包含15个类别共计188282个实例。其标注方式为四点确定的任意形状和方向的四边形（区别于传统的对边平行bbox）。类别分别为：plane, ship, storage tank, baseball dia- mond, tennis court, swimming pool, ground track field, har- bor, bridge, large vehicle, small vehicle, helicopter, round- about, soccer ball field , basketball court。
可以看出DOTA数据集里的照片，有的尺寸非常大，而且照片大小也统一，因此我们在进行目标检测网络训练时需要对照片进行分割，下面为分割代码：
照片分割
import cv2
import os
def tianchong_you(img):
size = img.shape
# if size[0]>=608 and size[1]<608:
# 这里的大小可以自己设定，但是尽量是32的倍数,并且与txt分割保持一致
constant = cv2.copyMakeBorder(img, 0, 0, 0, 608 - size[1], cv2.BORDER_CONSTANT,
value = (107, 113, 115)) # 填充值为数据集均值
# else:
# print('图像不符合要求')
# return 0
return constant
def tianchong_xia(img):
size = img.shape
# if size[0]<608 and size[1]>=608:
constant = cv2.copyMakeBorder(img, 0, 608 - size[0], 0, 0, cv2.BORDER_CONSTANT, value = (107, 113, 115))
# else:
# print('图像不符合要求')
# return 0
return constant
def tianchong_xy(img):
size = img.shape
# if size[0]<608 and size[1]<608:
constant = cv2.copyMakeBorder(img, 0, 608 - size[0], 0, 608 - size[1], cv2.BORDER_CONSTANT,
value = (107, 113, 115))
# else:
# print('图像不符合要求')
# return 0
return constant
def caijian(path, path_out, size_w = 608, size_h = 608, step = 576): # 重叠度为32
ims_list = os.listdir(path)
# print(ims_list)
count = 0
for im_list in ims_list:
number = 0
name = im_list.split('.')[0] # 去处“.tiff后缀”
img = cv2.imread(ims_path + im_list)
size = img.shape
if size[0] >= 608 and size[1] >= 608:
count = count + 1
for h in range(0, size[0] - 1, step):
star_h = h
for w in range(0, size[1] - 1, step):
star_w = w
end_h = star_h + size_h
if end_h > size[0]:
star_h = size[0] - size_h
end_h = star_h + size_h
end_w = star_w + size_w
if end_w > size[1]:
star_w = size[1] - size_w
end_w = star_w + size_w
cropped = img[star_h:end_h, star_w:end_w]
name_img = name + '_' + str(star_h) + '_' + str(star_w) # 用起始坐标来命名切割得到的图像，为的是方便后续标签数据抓取
cv2.imwrite('{}/{}.jpg'.format(path_out, name_img), cropped)
number = number + 1
if size[0] >= 608 and size[1] < 608:
print('图片{}需要在右面补齐'.format(name))
count = count + 1
img0 = tianchong_you(img)
for h in range(0, size[0] - 1, step):
star_h = h
star_w = 0
end_h = star_h + size_h
if end_h > size[0]:
star_h = size[0] - size_h
end_h = star_h + size_h
end_w = star_w + size_w
cropped = img0[star_h:end_h, star_w:end_w]
name_img = name + '_' + str(star_h) + '_' + str(star_w)
cv2.imwrite('{}/{}.jpg'.format(path_out, name_img), cropped)
number = number + 1
if size[0] < 608 and size[1] >= 608:
count = count + 1
print('图片{}需要在下面补齐'.format(name))
img0 = tianchong_xia(img)
for w in range(0, size[1] - 1, step):
star_h = 0
star_w = w
end_w = star_w + size_w
if end_w > size[1]:
star_w = size[1] - size_w
end_w = star_w + size_w
end_h = star_h + size_h
cropped = img0[star_h:end_h, star_w:end_w]
name_img = name + '_' + str(star_h) + '_' + str(star_w)
cv2.imwrite('{}/{}.jpg'.format(path_out, name_img), cropped)
number = number + 1
if size[0] < 608 and size[1] < 608:
count = count + 1
print('图片{}需要在下面和右面补齐'.format(name))
img0 = tianchong_xy(img)
cropped = img0[0:608, 0:608]
name_img = name + '_' + '0' + '_' + '0'
cv2.imwrite('{}/{}.jpg'.format(path_out, name_img), cropped) # 注意修改照片格式
number = number + 1
print('图片{}切割成{}张'.format(name, number))
print('共完成{}张图片'.format(count))
if __name__ == '__main__':
ims_path = 'G:/cj/JPEGImages1/' # 图像数据集的路径
# txt_path = 'G:/cj/Annotations/'
path = 'G:/cj/JPEGImages/' # 切割得到的数据集存放路径
caijian(ims_path, path, size_w = 608, size_h = 608, step = 576)
txt分割
import cv2
import os
category_set = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field',
'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',
'basketball-court', 'storage-tank', 'soccer-ball-field',
'roundabout', 'harbor', 'swimming-pool', 'helicopter']
def tqtxt(path, path_txt, path_out, size_h = 608, size_w = 608):
ims_list = os.listdir(path)
for im_list in ims_list:
name_list = []
name = im_list.split('.')[0]
name_list = name.split('_')
if len(name_list) < 2:
continue
h = int(name_list[1])
w = int(name_list[2])
txtpath = path_txt + name_list[0] + '.txt'
txt_outpath = path_out + name + '.txt'
f = open(txt_outpath, 'a')
with open(txtpath, 'r') as f_in: # 打开txt文件
i = 0
lines = f_in.readlines()
# print(len(lines))
# splitlines = [x.strip().split(' ') for x in lines] #根据空格分割
for line in lines:
if i in [0, 1]:
f.write(line) # txt前两行直接复制过去
i = i + 1
continue
splitline = line.split(' ')
label = splitline[8]
kunnan = splitline[9]
if label not in category_set: # 只书写指定的类别
continue
x1 = int(float(splitline[0]))
y1 = int(float(splitline[1]))
x2 = int(float(splitline[2]))
y2 = int(float(splitline[3]))
x3 = int(float(splitline[4]))
y3 = int(float(splitline[5]))
x4 = int(float(splitline[6]))
y4 = int(float(splitline[7]))
if w <= x1 <= w + size_w and w <= x2 <= w + size_w and w <= x3 <= w + size_w and w <= x4 <= w + size_w and h <= y1 <= h + size_h and h <= y2 <= h + size_h and h <= y3 <= h + size_h and h <= y4 <= h + size_h:
f.write('{} {} {} {} {} {} {} {} {} {}'.format(float(x1 - w), float(y1 - h), float(x2 - w),
float(y2 - h), float(x3 - w), float(y3 - h),
float(x4 - w), float(y4 - h), label, kunnan))
print('转换成功')
f.close()
if __name__ == '__main__':
ims_path = 'G:/cj/JPEGImages/' # 图像数据集的路径
txt_path = 'G:/cj/Annotations1/' # 原数据集标签文件
path = 'G:/cj/Annotations2/' # 切割后数据集的标签文件存放路径
tqtxt(ims_path, txt_path, path, size_h = 608, size_w = 608)
txt转xml
import os
from xml.dom.minidom import Document
from xml.dom.minidom import parse
import xml.dom.minidom
import numpy as np
import csv
import cv2
import string
def WriterXMLFiles(filename, path, box_list, label_list, w, h, d):
# dict_box[filename]=json_dict[filename]
doc = xml.dom.minidom.Document()
root = doc.createElement('annotation')
doc.appendChild(root)
foldername = doc.createElement("folder")
foldername.appendChild(doc.createTextNode("JPEGImages"))
root.appendChild(foldername)
nodeFilename = doc.createElement('filename')
nodeFilename.appendChild(doc.createTextNode(filename))
root.appendChild(nodeFilename)
pathname = doc.createElement("path")
pathname.appendChild(doc.createTextNode("xxxx"))
root.appendChild(pathname)
sourcename = doc.createElement("source")
databasename = doc.createElement("database")
databasename.appendChild(doc.createTextNode("Unknown"))
sourcename.appendChild(databasename)
annotationname = doc.createElement("annotation")
annotationname.appendChild(doc.createTextNode("xxx"))
sourcename.appendChild(annotationname)
imagename = doc.createElement("image")
imagename.appendChild(doc.createTextNode("xxx"))
sourcename.appendChild(imagename)
flickridname = doc.createElement("flickrid")
flickridname.appendChild(doc.createTextNode("0"))
sourcename.appendChild(flickridname)
root.appendChild(sourcename)
nodesize = doc.createElement('size')
nodewidth = doc.createElement('width')
nodewidth.appendChild(doc.createTextNode(str(w)))
nodesize.appendChild(nodewidth)
nodeheight = doc.createElement('height')
nodeheight.appendChild(doc.createTextNode(str(h)))
nodesize.appendChild(nodeheight)
nodedepth = doc.createElement('depth')
nodedepth.appendChild(doc.createTextNode(str(d)))
nodesize.appendChild(nodedepth)
root.appendChild(nodesize)
segname = doc.createElement("segmented")
segname.appendChild(doc.createTextNode("0"))
root.appendChild(segname)
for (box, label) in zip(box_list, label_list):
nodeobject = doc.createElement('object')
nodename = doc.createElement('name')
nodename.appendChild(doc.createTextNode(str(label)))
nodeobject.appendChild(nodename)
nodebndbox = doc.createElement('bndbox')
nodex1 = doc.createElement('x1')
nodex1.appendChild(doc.createTextNode(str(box[0])))
nodebndbox.appendChild(nodex1)
nodey1 = doc.createElement('y1')
nodey1.appendChild(doc.createTextNode(str(box[1])))
nodebndbox.appendChild(nodey1)
nodex2 = doc.createElement('x2')
nodex2.appendChild(doc.createTextNode(str(box[2])))
nodebndbox.appendChild(nodex2)
nodey2 = doc.createElement('y2')
nodey2.appendChild(doc.createTextNode(str(box[3])))
nodebndbox.appendChild(nodey2)
nodex3 = doc.createElement('x3')
nodex3.appendChild(doc.createTextNode(str(box[4])))
nodebndbox.appendChild(nodex3)
nodey3 = doc.createElement('y3')
nodey3.appendChild(doc.createTextNode(str(box[5])))
nodebndbox.appendChild(nodey3)
nodex4 = doc.createElement('x4')
nodex4.appendChild(doc.createTextNode(str(box[6])))
nodebndbox.appendChild(nodex4)
nodey4 = doc.createElement('y4')
nodey4.appendChild(doc.createTextNode(str(box[7])))
nodebndbox.appendChild(nodey4)
# ang = doc.createElement('angle')
# ang.appendChild(doc.createTextNode(str(angle)))
# nodebndbox.appendChild(ang)
nodeobject.appendChild(nodebndbox)
root.appendChild(nodeobject)
fp = open(path + filename, 'w')
doc.writexml(fp, indent = '\n')
fp.close()
def load_annoataion(p):
'''
load annotation from the text file
:param p:
:return:
'''
text_polys = []
text_tags = []
if not os.path.exists(p):
return np.array(text_polys, dtype = np.float32)
with open(p, 'r') as f:
for line in f.readlines()[2:]:
label = 'text'
# strip BOM. \ufeff for python3, \xef\xbb\bf for python2
# line = [i.strip('\ufeff').strip('\xef\xbb\xbf') for i in line]
# print(line)
x1, y1, x2, y2, x3, y3, x4, y4, label = line.split(' ')[0:9]
# print(label)
text_polys.append([x1, y1, x2, y2, x3, y3, x4, y4])
text_tags.append(label)
return np.array(text_polys, dtype = np.float), np.array(text_tags, dtype = np.str)
txt_path = 'G:/cj/Annotations2/'
xml_path = 'G:/cj/Annotations/'
img_path = 'G:/cj/JPEGImages/'
print(os.path.exists(txt_path))
txts = os.listdir(txt_path)
for count, t in enumerate(txts):
boxes, labels = load_annoataion(os.path.join(txt_path, t))
xml_name = t.replace('.txt', '.xml')
img_name = t.replace('.txt', '.jpg') # 注意修改照片格式
print(img_name)
img = cv2.imread(os.path.join(img_path, img_name))
print(xml_name, xml_path, boxes, labels)
h, w, d = img.shape
print(xml_name, xml_path, boxes, labels, w, h, d)
WriterXMLFiles(xml_name, xml_path, boxes, labels, w, h, d)
if count % 1000 == 0:
print(count)
标签：xml,appendChild,star,name,doc,DOTA,path,txt,size 来源： https://blog.csdn.net/weixin_51296032/article/details/121179852