Day #238 - Working with coco dataset

coco - common objects in context

Installation Steps https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md

Packages

pip install Cython

pip install contextlib2

pip install matplotlib

pip install pycocotools

pip install scikit-image

pip install --upgrade scikit-image

Demo Code (Minor Changes)

	#https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoDemo.ipynb
	#Windows
	#Python 3+ Environment
	import matplotlib.pyplot as plt
	from pycocotools.coco import COCO
	from pycocotools.cocoeval import COCOeval
	import numpy as np
	from skimage import io
	anntype = ['segm','bbox','keypoints']
	anntype = anntype[1]
	prefix = 'person_keypoints' if anntype =='keypoints' else 'instances'
	print('Demo for %s'%anntype)
	#Download Annotations from http://images.cocodataset.org/annotations/annotations_trainval2014.zip
	#Download to location C:\datadir
	#Initialize coco ground truth API
	datadir = r'C:\datadir\annotations_trainval2014'
	datatype = 'val2014'
	annfile = '%s/annotations/%s_%s.json'%(datadir,prefix,datatype)
	coco = COCO(annfile)
	#display categories
	cats = coco.loadCats(coco.getCatIds())
	nms = [cat['name'] for cat in cats]
	print('COCO categories: \n{}\n'.format(' '.join(nms)))
	nms = [cat['supercategory'] for cat in cats]
	print('COCO super categories: \n{}\n'.format(' '.join(nms)))
	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]
	I = io.imread(img['coco_url'])
	plt.axis('off')
	plt.imshow(I)
	plt.show()
	plt.imshow(I)
	plt.axis('off')
	annids = coco.getAnnIds(imgIds=img['id'],catIds=catids,iscrowd=None)
	anns = coco.loadAnns(annids)
	coco.showAnns(anns)
	plt.show()

view raw cocodataset.py hosted with ❤ by GitHub

Happy Mastering DL!!!