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COCO API

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 http://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.

Categories

pycocotools

load and display image 

%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)

dataDir='..'
dataType='val2017'
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)

# initialize COCO api for instance annotations
coco=COCO(annFile)
## output:
## loading annotations into memory...
## Done (t=0.81s)
## creating index...
## index created!

# 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)))
## output:
## 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

nms = set([cat['supercategory'] for cat in cats])
print('COCO supercategories: \n{}'.format(' '.join(nms)))
## output:
## COCO supercategories: 
## outdoor food indoor appliance sports person animal vehicle furniture accessory electronic kitchen

# 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()

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)

load and display keypoints annotations 

# initialize COCO api for person keypoints annotations
annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir,dataType)
coco_kps=COCO(annFile)

## output:
## 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)

load and display caption annotations 

# initialize COCO api for caption annotations
annFile = '{}/annotations/captions_{}.json'.format(dataDir,dataType)
coco_caps=COCO(annFile)

## output:
## 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()

## output:
## 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.

See also

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