本文主要是介绍保存coco dataset注释为单一文件，并逐一显示所有图片的mask，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
大意：
官方的例子只显示 一张图片，我需要逐一显示，并且官方的那个JSON文件太大了，我把注释文件分开存储，每张图片一个注释文件，另行保存在一个叫coco的文件夹中，
#
# windows version cocoapi
# https://github.com/philferriere/cocoapi
#
#
from pycocotools.coco import COCO
import numpy as np
import skimage.io as io
import json
import osimport matplotlib as mpl
mpl.use('TkAgg')
import pylab
import matplotlib.rcsetup as rcsetup
pylab.rcParams['figure.figsize'] = (8.0, 10.0)#dataDir='..'
#dataType='val2017'
#dataDir='F:/BigData/msCoco2014'
#dataType='val2014'dataDir='F:/BigData/msCoco2017'
dataType='val2017'
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)# initialize COCO api for instance annotations
coco=COCO(annFile)# display COCO categories and supercategories
catIds = coco.getCatIds()
cats = coco.loadCats(catIds)
#print the names out
nms=[cat['name'] for cat in cats]
print('COCO categories: \n{}\n'.format(' '.join(nms)))
#print the supercat out
nms = set([cat['supercategory'] for cat in cats])
print('COCO supercategories: \n{}'.format(' '.join(nms)))# recursively display all images and its masks
imgIds = coco.getImgIds()
for id in imgIds: annIds = coco.getAnnIds([id], catIds=catIds, iscrowd=None)anns = coco.loadAnns(annIds)imgIds = coco.getImgIds(imgIds = [id])img = coco.loadImgs(imgIds[0])[0]file_name_ext=img['file_name'](filename,extension) = os.path.splitext(file_name_ext)file_path = "coco/" + filename + ".json"data = {"annotations":anns}with open(file_path, 'w') as result_file:json.dump(data, result_file)I = io.imread('%s/%s/%s'%(dataDir,dataType,img['file_name']))mpl.pyplot.imshow(I)mpl.pyplot.axis('off')coco.showAnns(anns)顺带再提一下coco数据集中各个参数的解释吧，一般的参数望文即可生义，只需要注意的是iscrowd，这个值为0 即表示polygon,注意，单个的对象（iscrowd=0)可能需要多个polygon来表示，比如某个对象在图像中被挡住了一部分。而iscrowd=1时，segmentation使用的就是RLE格式。
具体样式和解释参数官方文档：https://cocodataset.org/#format-data
如果是自己定义的数据集，采用coco数据格式的话，各个id到底有什么用也是需要注意的地方。
{"type": "instances","images": [{"file_name": "0.jpg","height": 600,"width": 800,"id": 0 ----> image_id}],"categories": [{"supercategory": "none", ----> supercategory can be anything"name": "date","id": 0 ----> category_id},{"supercategory": "none","name": "hazelnut","id": 2},{"supercategory": "none","name": "fig","id": 1}],"annotations": [{"id": 1, ----> annotation id (each annotation has a unique id)"bbox": [100,116,140,170],"image_id": 0,"segmentation": [],"ignore": 0,"area": 23800,"iscrowd": 0,"category_id": 0},{"id": 2,"bbox": [321,320,142,102],"image_id": 0,"segmentation": [],"ignore": 0,"area": 14484,"iscrowd": 0,"category_id": 0}]
}
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