将coco数据集转换为voc格式
coco数据集是微软发布的用于目标检测的数据集，难度很大，另外一个问题就是标注是json格式，windows下notepad++都打不开，这无疑加大了查看的难度，下面我们就将其转换为标准的voc格式，以便训练ssd网络等。
其中CK5cats种设置我们要提取的类别
from pycocotools.coco import COCO
import skimage.io as io
import matplotlib.pyplot as plt
import pylab,os,cv2,shutil
from lxml import etree, objectify
from tqdm import tqdm
import random
from PIL import Image
pylab.rcParams['figure.figsize'] = (8.0, 10.0)
dataDir='..'
CK5cats=['chair','dining table','person']
CKdir="../"
CKimg_dir=CKdir+"/"+"images"
CKanno_dir=CKdir+"/"+"Annotations"
def mkr(dir):
if not os.path.exists(dir):
os.makedirs(dir)
def showimg(coco,dataType,img,CK5Ids):
global dataDir
I = io.imread('%s/%s/%s' % (dataDir, dataType, img['file_name']))
plt.imshow(I)
plt.axis('off')
annIds = coco.getAnnIds(imgIds=img['id'], catIds=CK5Ids, iscrowd=None)
anns = coco.loadAnns(annIds)
coco.showAnns(anns)
plt.show()
def save_annotations(dataType,filename,objs):
annopath=CKanno_dir+"/"+filename[:-3]+"xml"
img_path=dataDir+"/"+dataType+"/"+filename
dst_path=CKimg_dir+"/"+filename
img=cv2.imread(img_path)
im=Image.open(img_path)
if im.mode!="RGB":
print(filename+" not a RGB image")
im.close()
return
im.close()
shutil.copy(img_path, dst_path)
E = objectify.ElementMaker(annotate=False)
anno_tree = E.annotation(
E.folder('1'),
E.filename(filename),
E.source(
E.database('CKdemo'),
E.annotation('VOC'),
E.image('CK')
),
E.size(
E.width(img.shape[1]),
E.height(img.shape[0]),
E.depth(img.shape[2])
),
E.segmented(0)
)
for obj in objs:
E2 = objectify.ElementMaker(annotate=False)
anno_tree2 = E2.object(
E.name(obj[0]),
E.pose(),
E.truncated("0"),
E.difficult(0),
E.bndbox(
E.xmin(obj[2]),
E.ymin(obj[3]),
E.xmax(obj[4]),
E.ymax(obj[5])
)
)
anno_tree.append(anno_tree2)
etree.ElementTree(anno_tree).write(annopath, pretty_print=True)
def showbycv(coco,dataType,img,classes,CK5Ids):
global dataDir
filename= img['file_name']
filepath='%s/%s/%s' % (dataDir, dataType,filename)
I = cv2.imread(filepath)
annIds = coco.getAnnIds(imgIds=img['id'], catIds=CK5Ids, iscrowd=None)
anns = coco.loadAnns(annIds)
objs=[]
for ann in anns:
name=classes[ann['category_id']]
if name in CK5cats:
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=[name,1.0,xmin,ymin,xmax,ymax]
objs.append(obj)
cv2.rectangle(I, (xmin,ymin),(xmax,ymax),(255,0,0))
cv2.putText(I,name,(xmin,ymin),3,1,(0,0,255))
save_annotations(dataType,filename,objs)
cv2.imshow("img",I)
cv2.waitKey(1)
def catid2name(coco):
classes=dict()
for cat in coco.dataset['categories']:
classes[cat['id']]=cat['name']
#print(str(cat['id'])+":"+cat['name'])
return classes
def get_CK5():
mkr(CKimg_dir)
mkr(CKanno_dir)
dataTypes=['train2014','val2014']
for dataType in dataTypes:
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataType)
coco = COCO(annFile)
CK5Ids = coco.getCatIds(catNms=CK5cats)
classes=catid2name(coco)
for srccat in CK5cats:
print(dataType + ":" + srccat)
catIds = coco.getCatIds(catNms=[srccat])
imgIds = coco.getImgIds(catIds=catIds)
#imgIds=imgIds[0:100]
for imgId in tqdm(imgIds):
img=coco.loadImgs(imgId)[0]
showbycv(coco,dataType,img,classes,CK5Ids)
#showimg(coco,dataType,img,CK5Ids)
#split train and test for training
def split_traintest(trainratio=0.7,valratio=0.2,testratio=0.1):
dataset_dir=CKdir
files=os.listdir(CKimg_dir)
trains=[]
vals=[]
trainvals=[]
tests=[]
random.shuffle(files)
for i in range(len(files)):
filepath=CKimg_dir+"/"+files[i][:-3]+"jpg"
if(i