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VOC.py
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VOC.py
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"""
File:VOC.py
"""
import sys
import os
import xml.etree.ElementTree as ET
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import random
import shutil
import VOCOperationLibrary as vol
class VOC(object):
def __init__(self, dataset_anno, dataset_img=None, num_class=None, datasetdir=None):
if os.path.exists(dataset_anno) == False:
raise FileNotFoundError
self.dataset = datasetdir
self.dataset_anno = dataset_anno
self.dataset_img = dataset_img
self.num_class = num_class
self.dirname = os.path.dirname(self.dataset_anno)
self.listanno = self._listanno()
def _listanno(self, annodir=None):
"""return the list of all above of annotation file"""
if annodir == None:
annodir = self.dataset_anno
return os.listdir(annodir)
def _lowextension(self, imgdir=None):
return
def _listimg(self, imgdir=None):
"""return the list of all above of image file"""
if self.dataset_img == None:
if imgdir == None:
print("you should give a image path of dataset in creating VOC class!")
raise FileNotFoundError
else:
return os.listdir(imgdir)
else:
return os.listdir(self.dataset_img)
def _ParseAnnos(self, annodir=None):
"""
return the information of all above of annotation in this dataset_anno,
format: a list of dictionary, include file name, annotation, size
([{'file', 'info', 'size'}])
annotation is a list, [cls, xmin, ymin, xmax, ymax]
size if a tuple, (weight, height)
"""
annos = []
if annodir == None:
annodir = self.dataset_anno
annolist = self.listanno
else:
annolist = self._listanno(annodir)
for annofile in annolist:
if annofile[-4:] != ".xml":
continue
annotation = vol._parseannotation(os.path.join(annodir, annofile))
annos.append({'file': annofile, 'info': annotation[0], 'size': annotation[1]})
return annos
def _DelAnnotations(self, delclass, annodir=None):
"""
Delete specific cls
Precondition:delclass-a list of what's annotaion name you want to delete
"""
if delclass == None:
return
if annodir== None:
annodir = self.dataset_anno
annolist = self._listanno(annodir)
for annofile in annolist:
vol._deletesinglefile(os.path.join(annodir, annofile), delclass)
def _ChangeAnnotation(self, oldcls, newcls, annodir=None):
"""
Change class name.
Precondition:
oldcls:old class name,string
newcls:new class name,string
annodir:annotation file direction, if it is None,use self.dataset_dir(init value)
"""
if annodir == None:
annodir = self.dataset_anno
annolist = self._listanno(annodir)
for annofile in annolist:
vol._changeone(os.path.join(annodir,annofile), oldcls, newcls)
def _Crop(self, imgdir, cropdir, annos=None):
"""
To crop all the box region of object in dataset
"""
if annos == None:
annos = self._ParseAnnos()
total = len(annos)
for num, annotation in enumerate(annos):
annofile = annotation['file']
if os.path.exists(imgdir+annofile[:-4]+'.jpg') == False:
raise FileNotFoundError
pil_im = Image.open(imgdir+annofile[:-4]+'.jpg')
for i, obj in enumerate(annotation['info']):
obj_class = obj[0]
obj_box = tuple(obj[1:5])
if os.path.exists(cropdir+obj_class) == False:
os.mkdir(cropdir+obj_class)
region = pil_im.crop(obj_box)
pil_region = Image.fromarray(np.uint8(region))
pil_region.save(os.path.join(cropdir+obj_class,
annofile[:-4]+'_'+str(i)+'.jpg'))
process = int(num*100 / total)
s1 = "\r%d%%[%s%s]"%(process,"*"*process," "*(100-process))
s2 = "\r%d%%[%s]"%(100,"*"*100)
sys.stdout.write(s1)
sys.stdout.flush()
sys.stdout.write(s2)
sys.stdout.flush()
print('')
print("crop is completed!")
def _Countobject(self, annofile=None):
"""
Count the label numbers of every class, and print it
Precondition: annofile-the direction of xml file
"""
if annofile == None:
annofile = self.dataset_anno
annoparse = self._ParseAnnos(annofile)
count = {}
for anno in annoparse:
for obj in anno['info']:
if obj[0] in count:
count[obj[0]] +=1
else:
count[obj[0]] = 1
for c in count.items():
print("{}: {}".format(c[0], c[1]))
return count
def _DisplayDirectObjec(self):
"""
To display what's box you want to display.
"""
imglist = self._listimg()
print("input what object you want display, space between numbers")
parseannos = self._ParseAnnos()
for i, annos in enumerate(parseannos):
print("file name: {0}".format(annos['file'][:-4]))
if annos['info'] == []:
print("This image don't have annotation, so programme step it and go on!")
continue
for j, objs in enumerate(annos['info']):
print('''({}): cls={}, \
box=[{:0>4d}, {:0>4d}, {:0>4d}, {:0>4d}]'''.format(
j, objs[0], objs[1], objs[2], objs[3], objs[4]
))
inputstr = input()
numbers = [int(x) for x in inputstr.split(' ')]
self._displayone(annos['info'], annos['file'], numbers)
def _displayone(self, objs, annofile, nums):
"""
display the annotation's box of one image
Precondition: objs-the box information
annofile-annotation file name
nums-the object number of annotation which you want display
"""
im = Image.open(self.dataset_img + annofile[:-4] + '.jpg')
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for i, obj in enumerate(objs):
if i in nums:
bbox = obj[1:]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=3.5)
)
ax.text(bbox[0], bbox[1] - 2,
'{:s}'.format(obj[0]),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
plt.axis('off')
plt.tight_layout()
plt.draw()
plt.show()
def _Mergeannotation(self, newdataset, olddataset=None):
"""
Merge two dataset anntation information.
Precondition:
newdataset:one dataset annotation path
olddataset:one dataset annotation path
and save in this path
"""
if olddataset == None:
olddataset = self.dataset_anno
annolist1 = os.listdir(olddataset)
annolist2 = os.listdir(newdataset)
for anno in annolist2:
if anno in annolist1:
print(anno)
vol._mergeone(olddataset+anno, newdataset+anno)
else:
shutil.copy(newdataset+anno, olddataset+anno)
def _Resize(self, newsize, annodir=None, imgdir=None):
"""
Resize the dataset, include resize all the image into newsize,
and correct the annotation information.
Precondition:
newsize:the newsize of image
annodir:annotation direction
imgdir:image direction
"""
if annodir == None:
annodir = self.dataset_anno
if imgdir == None:
imgdir = self.dataset_img
if imgdir == None:
print('Resize operation need a image direction!')
return
annolist = self._listanno(annodir)
imglist = self._listimg(imgdir)
annos = self._ParseAnnos(annodir)
total = len(annolist)
for num, f in enumerate(annolist):
anno_path = os.path.join(annodir, f)
img_path = os.path.join(imgdir, f)[:-4] + '.jpg'
img = Image.open(img_path)
img = img.resize(newsize)
img.save(img_path, 'jpeg', quality=95)
img.close()
vol._changeone(anno_path, None, None, newsize)
process = int(num*100 / total)
s1 = "\r%d%%[%s%s]"%(process,"*"*process," "*(100-process))
s2 = "\r%d%%[%s]"%(100,"*"*100)
sys.stdout.write(s1)
sys.stdout.flush()
sys.stdout.write(s2)
sys.stdout.flush()
print('')
print('Resize is complete!')
def _Splitdataset(self, traintxt, savedir, annodir=None, imgdir=None):
"""
Split the dataset into train set and test set, according the train.txt.
Precondition:
traintxt:train.txt which include the train set file name
savedir:save direction
annodir:dataset annotation direction
imgdir:dataset image direction
Result:
make four direction, trainAnnotations(storage train set's xml file)
trainJPEGImages(storage train set's image file)
testAnnotations(storage test set's xml file)
testJPEGImages(storage test set's image file)
"""
if annodir == None:
annodir = self.dataset_anno
if imgdir == None:
if self.dataset_img == None:
print("Please give the path of image!")
else:
imgdir = self.dataset_img
annolist = self._listanno(annodir)
f = open(traintxt, 'r')
trainlist = f.readlines()
f.close()
train_xml_path = os.path.join(savedir, 'trainAnnotations')
trian_img_path = os.path.join(savedir, 'trainJPEGImages')
test_xml_path = os.path.join(savedir, 'testAnnotations')
test_img_path = os.path.join(savedir, 'testJPEGImages')
if os.path.exists(train_xml_path) == False:
os.mkdir(train_xml_path)
if os.path.exists(trian_img_path) == False:
os.mkdir(trian_img_path)
if os.path.exists(test_xml_path) == False:
os.mkdir(train_xml_path)
if os.path.exists(test_img_path) == False:
os.mkdir(test_img_path)
for i in range(len(trainlist)):
trainlist[i] = trainlist[i].replace('\n', '')
annolist.remove(trainlist[i])
testlist = annolist
self._Copy(trainlist, annodir, imgdir, train_xml_path, trian_img_path)
self._Copy(trainlist, annodir, imgdir, test_xml_path, test_img_path)
def _Copy(self, xml_list, from_xml_path, from_img_path, save_xml_dir, save_img_dir):
"""
Copy the xml file and image file from dataset to save_xml_dir and save_img_dir.
Precondition:
xml_list:a list of xml file name
from_xml_path:original xml direction
from_img_path:original image direction
save_xml_dir:to save xml direction
save_img_dir:to save image direction
"""
for i, xml in enumerate(xml_list):
shutil.copyfile(os.path.join(from_xml_path, xml),
os.path.join(save_xml_dir, xml))
shutil.copyfile(os.path.join(from_img_path, xml)[:-4] + '.jpg',
os.path.join(save_img_dir, xml)[:-4] + '.jpg')
def _Find(self, cls, annodir=None):
"""
Find files of the direction class object.
Return a list of files name.
Precondition:
cls: a list of class, example:['dog', 'cat']
annodir: the xml files direction
"""
if annodir == None:
annodir = self.dataset_anno
annolist = self._listanno(annodir)
xml_files = []
for anno in annolist:
xml = vol._find_one(os.path.join(annodir, anno), cls)
if xml != None:
xml_files.append(xml)
return xml_files
def _FindandCopy(self, cls, save_xml_path, save_img_path, annodir=None, imgdir=None):
"""
Find files of the direction class object and copy them.
Precondition:
xml_list:a list of xml file name
annodir:the dataset xml files direction
imgdir:the dataset image files direction
save_xml_dir:to save xml direction
save_img_dir:to save image direction
"""
if imgdir == None:
imgdir = self.dataset_img
if imgdir == None:
print('Copy operation need a image direction!')
return
if annodir == None:
annodir = self.dataset_anno
xml_files = self._Find(cls, annodir)
print(xml_files)
self._Copy(xml_files, annodir, imgdir, save_xml_path, save_img_path)
v = VOC('F:/数据集/20190122输电线路主要缺陷优化数据集/Annotations/',
'F:/数据集/20190122输电线路主要缺陷优化数据集/JPEGImages/')
#print(v._ParseAnnos())
#v._Crop('F:/数据集/JPEGImages/', 'F:/数据集/crops/')
#v._DelAnnotations(['123', '234'])
#v._DisplayDirectObjec()
#size = (512, 512)
#v._Resize(size)
#v._Mergeannotation('C:/Users/91279/Desktop/xml/', 'F:/xml/')
#v._DelAnnotations(['123'])
#cls = ['shockproof hammer deformation', 'shockproof hammer intersection', 'grading ring damage', 'shielded ring corrosion']
#v._FindandCopy(cls, 'F:/数据集/20190122输电线路主要缺陷优化数据集/aaaa/', 'F:/数据集/20190122输电线路主要缺陷优化数据集/bbbb/')