1.COCO数据集介绍
COCO的 全称是Common Objects in COntext，是微软团队提供的一个可以用来进行图像识别的数据集。MS COCO数据集中的图像分为训练、验证和测试集。COCO通过在Flickr上搜索80个对象类别和各种场景类型来收集图像，其使用了亚马逊的Mechanical Turk（AMT）。
其网址：cocodataset.org/
COCO通过大量使用Amazon Mechanical Turk来收集数据。COCO数据集现在有3种标注类型：object instances（目标实例）, object keypoints（目标上的关键点）, 和image captions（看图说话），使用JSON文件存储。
2.COCO读取
其英文说明如下：
COCO API - cocodataset.org/
COCO is a large image dataset designed for object detection, segmentation, person keypoints detection, stuff segmentation, and caption generation. This package provides Matlab, Python, and Lua APIs that assists in loading, parsing, and visualizing the annotations in COCO. Please visit cocodataset.org/ for more information on COCO, including for the data, paper, and tutorials. The exact format of the annotations is also described on the COCO website. The Matlab and Python APIs are complete, the Lua API provides only basic functionality.
In addition to this API, please download both the COCO images and annotations in order to run the demos and use the API. Both are available on the project website. -Please download, unzip, and place the images in: coco/images/ -Please download and place the annotations in: coco/annotations/ For substantially more details on the API please see cocodataset.org/#download.
After downloading the images and annotations, run the Matlab, Python, or Lua demos for example usage.
To install:
- For Matlab, add coco/MatlabApi to the Matlab path (OSX/Linux binaries provided)
- For Python, run "make" under coco/PythonAPI
- For Lua, run “luarocks make LuaAPI/rocks/coco-scm-1.rockspec” under coco/
目前提供3种读取方式。
3.python读取COCO示例
3.1 导入必要库
%matplotlib inline from pycocotools.coco import COCO import numpy as np import skimage.io as io import matplotlib.pyplot as plt import pylab pylab.rcParams['figure.figsize'] = (8.0, 10.0)
3.2 读取annotations
dataDir='..' dataType='val2017' annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)
# initialize COCO api for instance annotations coco=COCO(annFile)
loading annotations into memory... Done (t=0.81s) creating index... index created!
3.3 读取分类
# display COCO categories and supercategories cats = coco.loadCats(coco.getCatIds()) nms=[cat['name'] for cat in cats] print('COCO categories: \n{}\n'.format(' '.join(nms))) nms = set([cat['supercategory'] for cat in cats]) print('COCO supercategories: \n{}'.format(' '.join(nms)))
COCO categories: 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 COCO supercategories: outdoor food indoor appliance sports person animal vehicle furniture accessory electronic kitchen
3.4 读取并显示图片
# get all images containing given categories, select one at random catIds = coco.getCatIds(catNms=['person','dog','skateboard']); imgIds = coco.getImgIds(catIds=catIds ); imgIds = coco.getImgIds(imgIds = [324158]) img = coco.loadImgs(imgIds[np.random.randint(0,len(imgIds))])[0]
# load and display image # I = io.imread('%s/images/%s/%s'%(dataDir,dataType,img['file_name'])) # use url to load image I = io.imread(img['coco_url']) plt.axis('off') plt.imshow(I) plt.show()
3.5 显示分隔
# load and display instance annotations plt.imshow(I); plt.axis('off') annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None) anns = coco.loadAnns(annIds) coco.showAnns(anns)
3.6 关键点检测
# initialize COCO api for person keypoints annotations annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir,dataType) coco_kps=COCO(annFile)
loading annotations into memory... Done (t=0.58s) creating index... index created!
# load and display keypoints annotations plt.imshow(I); plt.axis('off') ax = plt.gca() annIds = coco_kps.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None) anns = coco_kps.loadAnns(annIds) coco_kps.showAnns(anns)
3.7 标题信息
# initialize COCO api for caption annotations annFile = '{}/annotations/captions_{}.json'.format(dataDir,dataType) coco_caps=COCO(annFile)
loading annotations into memory... Done (t=0.13s) creating index... index created!
# load and display caption annotations annIds = coco_caps.getAnnIds(imgIds=img['id']); anns = coco_caps.loadAnns(annIds) coco_caps.showAnns(anns) plt.imshow(I); plt.axis('off'); plt.show()
A man is skate boarding down a path and a dog is running by his side. A man on a skateboard with a dog outside. A person riding a skate board with a dog following beside. This man is riding a skateboard behind a dog. A man walking his dog on a quiet country road.
