github:https://github.com/zcc720/COCO2VOC.git
接上篇VOC数据集提取自己需要的类，这次我们依然从coco数据集中提取我们想要的类，并转为voc格式，用于目标检测。
一、去官网下载数据集
二、安装coco-PythonAPI
linux用户：
pip install cython
git clone https://github.com/cocodataset/cocoapi.git
cd coco/PythonAPI
make
windows用户：
pip install cython
git clone https://github.com/cocodataset/cocoapi.git
cd coco/PythonAPI
python setup.py build_ext --inplace
三、get自己想要的类，制作成voc文件
COCO数据集目标检测中有90类：
classes:
{1: \'person\', 2: \'bicycle\', 3: \'car\', 4: \'motorcycle\', 5: \'airplane\', 6: \'bus\', 7: \'train\', 8: \'truck\', 9: \'boat\', 10: \'traffic light\', 11: \'fire hydrant\', 13: \'stop sign\', 14: \'parking meter\', 15: \'bench\', 16: \'bird\', 17: \'cat\', 18: \'dog\', 19: \'horse\', 20: \'sheep\', 21: \'cow\', 22: \'elephant\', 23: \'bear\', 24: \'zebra\', 25: \'giraffe\', 27: \'backpack\', 28: \'umbrella\', 31: \'handbag\', 32: \'tie\', 33: \'suitcase\', 34: \'frisbee\', 35: \'skis\', 36: \'snowboard\', 37: \'sports ball\', 38: \'kite\', 39: \' ball bat\', 40: \' ball glove\', 41: \'skateboard\', 42: \'surfboard\', 43: \'tennis racket\', 44: \'bottle\', 46: \'wine glass\', 47: \'cup\', 48: \'fork\', 49: \'knife\', 50: \'spoon\', 51: \'bowl\', 52: \'banana\', 53: \'apple\', 54: \'sandwich\', 55: \'orange\', 56: \'broccoli\', 57: \'carrot\', 58: \'hot dog\',59: \'pizza\', 60: \'donut\', 61: \'cake\', 62: \'chair\', 63: \'couch\', 64: \'potted plant\', 65: \'bed\', 67: \'dining table\', 70: \'toilet\', 72: \'tv\', 73: \'laptop\', 74: \'mouse\', 75: \'remote\', 76: \'keyboard\',77: \'cell phone\', 78: \'microwave\', 79: \'oven\', 80: \'toaster\', 81: \'sink\', 82: \'refrigerator\', 84: \'book\', 85: \'clock\', 86: \'vase\', 87: \'scissors\', 88: \'teddy bear\', 89: \'hair drier\', 90: \'toothbrush\'}
想要的类
classes_names = [\'car\', \'bicycle\', \'person\', \'motorcycle\', \'bus\', \'truck\']
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=\"E:/datasets/COCO/result/\"
img_dir=savepath+\'images/\'
anno_dir=savepath+\'Annotations/\'
# datasets_list=[\'train2014\', \'val2014\']
datasets_list=[\'train2017\']
classes_names = [\'car\', \'bicycle\', \'person\', \'motorcycle\', \'bus\', \'truck\']
#Store annotations and train2014/val2014/... in this folder
dataDir= \'E:/datasets/COCO/\'
headstr = \"\"\"\\
<annotation>
<folder>VOC</folder>
<filename>%s</filename>
<source>
<data >My Data </data >
<annotation>COCO</annotation>
<image>flickr</image>
<flickrid>NULL</flickrid>
</source>
<owner>
<flickrid>NULL</flickrid>
<name>company</name>
</owner>
<size>
<width>%d</width>
<height>%d</height>
<depth>%d</depth>
</size>
<segmented>0</segmented>
\"\"\"
objstr = \"\"\"\\
< >
<name>%s</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>%d</xmin>
<ymin>%d</ymin>
<xmax>%d</xmax>
<ymax>%d</ymax>
</bndbox>
</ >
\"\"\"
tailstr = \'\'\'\\
</annotation>
\'\'\'
#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_ (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.
anno_path=anno_dir+filename[:-3]+\' \'
img_path=dataDir+dataset+\'/\'+filename
print(img_path)
dst_imgpath=img_dir+filename
img=cv2.imread(img_path)
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_ (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\']))
#通过id，得到注释的信息
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)
\'\'\'
COCO 对象创建完毕后会输出如下信息:
loading annotations into memory...
Done (t=0.81s)
creating index...
index created!
至此, json 脚本解析完毕, 并且将图片和对应的标注数据关联起来.
\'\'\'
#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)
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)
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