import matplotlib

 import numpy as np

 import skimage.io as io

 import matplotlib.pyplot as plt

 import pylab

 from pycocotools.coco import COCO

 pylab.rcParams['figure.figsize'] = (8.0, 10.0)  # image pixel

 # if skimage.io declares before pycocotools.coco, the backend will be chosen as Qt5Agg

 # if pycocotools.coco declares before skimage.io, the backend will be chosen as Agg

 print(matplotlib.get_backend())

 dataDir = r'G:\MSCOCO 2017\annotations_trainval2017'

 dataType = r'val2017'

 annFile = r'{}\annotations\instances_{}.json'.format(dataDir, dataType)

 # initialize COCO api for instance annotations

 coco = COCO(annotation_file=annFile)    # COCO api class that loads COCO annotation file and prepare data structures.

 # loading annotations into memory...

 # Done (t=0.80s)

 # creating index...

 # index created!

 # print(coco.info())  # Print information about the annotation file.

 # description: COCO 2017 Dataset

 # url: http://cocodataset.org

 # version: 1.0

 # year: 2017

 # contributor: COCO Consortium

 # date_created: 2017/09/01

 # None

 # display COCO categories and supercategories

 # coco.loadCats(self, ids=[]): Load cats with the specified ids.

 cats = coco.loadCats(coco.getCatIds())

 # print(cats)

 # [{'supercategory': 'person', 'id': 1, 'name': 'person'},

 #  {'supercategory': 'vehicle', 'id': 2, 'name': 'bicycle'},

 #  {'supercategory': 'vehicle', 'id': 3, 'name': 'car'},

 #  {...}, {...}, {...}, ... ...,

 #  {'supercategory': 'indoor', 'id': 90, 'name': 'toothbrush'}]

 nms = [cat['name'] for cat in cats]

 # print('COCO categories: \n{}\n'.format(' '.join(nms)))

 # person bicycle car motorcycle airplane ... hair drier toothbrush

 nms = set([cat['supercategory'] for cat in cats])

 # print('COCO supercategories: \n{}'.format(' '.join(nms)))

 # vehicle person kitchen electronic appliance indoor accessory animal food furniture outdoor sports

 # get all images containing given categories, select one at random

 catIds = coco.getCatIds(catNms=['person', 'dog', 'skateboard'])

 # print(catIds)   # [1, 18, 41]

 imgIds = coco.getImgIds(catIds=catIds)      # param catIds (int array): get images with all given categories

 # imgIds = coco.getImgIds(imgIds=[324158])    # param imgIds (int array): get images for given ids

 index = np.random.randint(0, len(imgIds))

 # print('randomly selected imgId: %d' % index)

 img = coco.loadImgs(imgIds[index])[0]   # Load images with specified ids.

 # print(img)

 # if index=2, print:

 # {'license': 2, 'file_name': '000000279278.jpg', 'coco_url': 'http://images.cocodataset.org/val2017/000000279278.jpg',

 # 'height': 429, 'width': 640, 'date_captured': '2013-11-15 01:07:24',

 # 'flickr_url': 'http://farm7.staticflickr.com/6101/6275412942_f8dc734c3f_z.jpg', 'id': 279278}

 # load and display image

 # i = io.imread('%s/image/%s/%s' % (dataDir, dataType, img['file_name']))

 # use url to load image

 # print(img['coco_url'])  # http://images.cocodataset.org/val2017/000000279278.jpg

 i = io.imread(img['coco_url'])

 plt.axis('off')

 plt.imshow(i)

 # print(plt.imshow(i))    # AxesImage(100, 110; 620x770)

 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)

 # initialize COCO api for person keypoints annotations

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

 coco_kps = COCO(annFile)

 # 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)

 # initialize COCO api for caption annotations

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

 coco_caps = COCO(annFile)

 # 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()