VOC提取特定类
import os
import shutil
ann_filepath = r'E:\xunleidownload\VOCdevkit\VOC2012\Annotations/'
img_filepath = r'E:\xunleidownload\VOCdevkit\VOC2012\JPEGImages/'
img_savepath = r'E:\xunleidownload\VOCdevkit\cars\JPEGImages/'
ann_savepath = r'E:\xunleidownload\VOCdevkit\cars\Annotations/'
if not os.path.exists(img_savepath):
os.mkdir(img_savepath)
if not os.path.exists(ann_savepath):
os.mkdir(ann_savepath)
names = locals()
classes = ['bicycle','bus', 'car','motorbike']
for file in os.listdir(ann_filepath):
print(file)
fp = open(ann_filepath + '\\' + file)
ann_savefile = ann_savepath + file
fp_w = open(ann_savefile, 'w')
lines = fp.readlines()
ind_start = []
ind_end = []
lines_id_start = lines[:]
lines_id_end = lines[:]
classes1 = '\t\tbicycle \n'
classes2 = '\t\tmotorbike \n'
classes3 = '\t\tbus \n'
classes4 = '\t\tcar \n'
classes5 = '\t\tperson \n'
# 在xml中找到object块，并将其记录下来
while "\t\n" in lines_id_start:
a = lines_id_start.index("\t\n")
ind_start.append(a)
lines_id_start[a] = "delete"
while "\t \n" in lines_id_end:
b = lines_id_end.index("\t \n")
ind_end.append(b)
lines_id_end[b] = "delete"
# names中存放所有的object块
i = 0
for k in range(0, len(ind_start)):
names['block%d' % k] = []
for j in range(0, len(classes)):
if classes[j] in lines[ind_start[i] + 1]:
a = ind_start[i]
for o in range(ind_end[i] - ind_start[i] + 1):
names['block%d' % k].append(lines[a + o])
break
i += 1
# print(names['block%d' % k])
# xml头
string_start = lines[0:ind_start[0]]
# xml尾
string_end = [lines[len(lines) - 1]]
# 在给定的类中搜索，若存在则，写入object块信息
a = 0
for k in range(0, len(ind_start)):
if classes1 in names['block%d' % k]:
a += 1
string_start += names['block%d' % k]
if classes2 in names['block%d' % k]:
a += 1
string_start += names['block%d' % k]
if classes3 in names['block%d' % k]:
a += 1
string_start += names['block%d' % k]
if classes4 in names['block%d' % k]:
a += 1
string_start += names['block%d' % k]
if classes5 in names['block%d' % k]:
a += 1
string_start += names['block%d' % k]
string_start += string_end
for c in range(0, len(string_start)):
fp_w.write(string_start[c])
fp_w.close()
# 如果没有我们寻找的模块，则删除此xml，有的话拷贝图片
if a == 0:
os.remove(ann_savepath + file)
else:
name_img = img_filepath + os.path.splitext(file)[0] + ".jpg"
shutil.copy(name_img, img_savepath)
fp.close()
COCO提取特定类
from pycocotools.coco import COCO
import os
import shutil
from tqdm import tqdm
import skimage.io as io
import matplotlib.pyplot as plt
import cv2
from PIL import Image, ImageDraw
#the path you want to save your results for coco to voc
savepath=r"G:\py_file\OIDv4_ToolKit-master\OID001\new/"
img_dir=savepath+'JPEGImages/'
anno_dir=savepath+'Annotations/'
# datasets_list=['train2014', 'val2014']
# datasets_list=['train2014']
datasets_list=['train2017']
# classes_names = ['parking meter']
# classes_names = ["person", "bicycle", "car", "motorcycle","bus"]
# classes_names = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
# "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
# "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
# "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
# "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
# "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
# "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
# "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
# "hair drier", "toothbrush"]
# classes_names = ["person", "bicycle", "car", "motorcycle","bus", "truck", "boat",
# "traffic light","stop sign", "parking meter",
# "cat", "dog","umbrella", "suitcase", "baseball bat",
# "bottle", "wine glass", "cup","bowl",
# "banana", "apple","orange", "broccoli", "carrot", "hot dog", "cake",
# "chair", "couch","bench","potted plant", "bed", "dining table", "tv", "laptop","cell phone",
# "microwave","refrigerator", "book", "vase", "teddy bear"]
classes_names = [ "apple","banana", "broccoli","carrot","knife","orange","teddy bear","toothbrush","umbrella"]
# classes_names = ['truck']
#Store annotations and train2014/val2014/... in this folder
dataDir= r'E:\xunleidownload\COCO/'
headstr = """\
VOC
%s
My Database
COCO
flickr
NULL
%d
%d
%d
0
"""
objstr = """\
%s
Unspecified
0
0
%d
%d
%d
%d
"""
tailstr = '''\
'''
#if the dir is not exists,make it,else delete it
def mkr(path):
if os.path.exists(path):
shutil.rmtree(path)
os.mkdir(path)
else:
os.mkdir(path)
mkr(img_dir)
mkr(anno_dir)
def id2name(coco):
classes=dict()
for cls in coco.dataset['categories']:
classes[cls['id']]=cls['name']
return classes
def write_xml(anno_path,head, objs, tail):
f = open(anno_path, "w")
f.write(head)
for obj in objs:
f.write(objstr%(obj[0],obj[1],obj[2],obj[3],obj[4]))
f.write(tail)
def save_annotations_and_imgs(coco,dataset,filename,objs):
#eg:COCO_train2014_000000196610.jpg-->COCO_train2014_000000196610.xml
anno_path=anno_dir+filename[:-3]+'xml'
img_path=dataDir+dataset+'/'+filename
# print(img_path)
dst_imgpath=img_dir+filename
print(img_path,'aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa')
img=cv2.imread(img_path)
# print(img)
if (img.shape[2] == 1):
print(filename + " not a RGB image")
return
shutil.copy(img_path, dst_imgpath)
head=headstr % (filename, img.shape[1], img.shape[0], img.shape[2])
tail = tailstr
write_xml(anno_path,head, objs, tail)
def showimg(coco,dataset,img,classes,cls_id,show=True):
global dataDir
I=Image.open('%s/%s/%s'%(dataDir,dataset,img['file_name']))
#Get the annotated information by ID
annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None)
# print(annIds)
anns = coco.loadAnns(annIds)
# print(anns)
# coco.showAnns(anns)
objs = []
for ann in anns:
class_name=classes[ann['category_id']]
if class_name in classes_names:
print(class_name)
if 'bbox' in ann:
bbox=ann['bbox']
xmin = int(bbox[0])
ymin = int(bbox[1])
xmax = int(bbox[2] + bbox[0])
ymax = int(bbox[3] + bbox[1])
obj = [class_name, xmin, ymin, xmax, ymax]
objs.append(obj)
draw = ImageDraw.Draw(I)
draw.rectangle([xmin, ymin, xmax, ymax])
if show:
plt.figure()
plt.axis('off')
plt.imshow(I)
plt.show()
return objs
for dataset in datasets_list:
#./COCO/annotations/instances_train2014.json
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataset)
#COCO API for initializing annotated data
coco = COCO(annFile)
'''
When the COCO object is created, the following information will be output:
loading annotations into memory...
Done (t=0.81s)
creating index...
index created!
So far, the JSON script has been parsed and the images are associated with the corresponding annotated data.
'''
#show all classes in coco
classes = id2name(coco)
print(classes)
#[1, 2, 3, 4, 6, 8]
classes_ids = coco.getCatIds(catNms=classes_names)
print(classes_ids)
# exit()
for cls in classes_names:
#Get ID number of this class
cls_id=coco.getCatIds(catNms=[cls])
img_ids=coco.getImgIds(catIds=cls_id)
print(cls,len(img_ids))
# imgIds=img_ids[0:10]
for imgId in tqdm(img_ids):
img = coco.loadImgs(imgId)[0]
filename = img['file_name']
# print(filename)
objs=showimg(coco, dataset, img, classes,classes_ids,show=False)
print(objs)
save_annotations_and_imgs(coco, dataset, filename, objs)
