提取COCO数据集中特定的类—vehicle 4类
- 1 安装pycocotools
- 2 下载COCO数据集
- 3 提取特定的类别
- 4 多类标签合并
1 安装pycocotools
pycocotools github地址
pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
2 下载COCO数据集
COCO官网下载2017 Train images，2017 Val images
文件目录结构如下
data/
├── COCO
│ ├── annotations_trainval2017
│ │ ├── annotations
│ │ │ ├── instances_train2017.json
│ │ │ ├── captions_val2017.json
│ ├── train2017
├── getVehicleFromCOCO.py
├── coco_car
├── coco_bus
├── coco_truck
├── coco_train
3 提取特定的类别
创建coco_car、coco_bus、coco_truck、coco_train四个文件夹，依次修改savepath为以上文件夹名称，classes_names 修改为car、bus、truck、train，分别将四类车辆存到对应的文件夹。
提取特定的类别getVehicleFromCOCO.py代码如下：
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="coco_car" #保存提取类的路径
img_dir=savepath+'images/'
anno_dir=savepath+'Annotations/'
datasets_list=['train2017']
classes_names = ['car'] #要提取类的名字
dataDir= 'COCO/' #原coco数据集
headstr = """\
<annotation>
<folder>VOC</folder>
<filename>%s</filename>
<source>
<database>My Database</database>
<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 = """\
<object>
<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>
</object>
"""
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_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
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_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']))
#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)
#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)
4 多类标签合并
创建以下四个文件夹，将coco_car中的图像拷贝到JPEGImages，标注文件拷贝到Annotations。
执行以下脚本生成ImageSets/train.txt
import sys
import os
folder = "JPEGImages"
voc_train_txt = "ImageSets/train.txt"
file_voc = open(voc_train_txt, 'w',encoding="utf-8")
file_tree = os.walk(folder)
for path, _, files in file_tree:
for file in files:
name, ext = os.path.splitext(file)
file_voc.write(name+'\n')
执行以下脚本将Annotations中的xml文件转换成txt，存储在labels文件夹中。
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets=[('2024', 'train')]
classes = ["car","bus","truck","train"]
def convert(size, box):
dw = 1./size[0]
dh = 1./size[1]
x = (box[0] + box[1])/2.0
y = (box[2] + box[3])/2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_annotation(image_id):
in_file = open('Annotations/%s.xml'%(image_id),"r",encoding="utf-8")
#以追加的方式生成txt，这样4类中的重复图像的标签就会合并
out_file = open('labels/%s.txt'%(image_id), 'a',encoding="utf-8")
print( "imd id: %s " %(image_id))
tree=ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
if obj.find('difficult'):
difficult = obj.find('difficult').text
else:
difficult = 0
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1:
print( "%s has wrong label: %s " %(in_file, cls)) # ignore difficult value
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
bb = convert((w,h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
in_file.close()
out_file.close()
wd = getcwd()
for year, image_set in sets:
if not os.path.exists('labels/'):
os.makedirs('labels/')
image_ids = open('ImageSets/%s.txt'%(image_set)).read().strip().split()
#print(image_ids)
for image_id in image_ids:
convert_annotation(image_id)
#image_ids.close()
再将coco_bus、coco_truck、coco_train按照coco_car相同的方式生成标签，最终不同类别中重复图像的不同类标签就会合并，再将图像拷贝到同一文件夹去重即可。
参考文章：
(1) python提取COCO，VOC数据集中特定的类
(2) philferriere/cocoapi
